xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

f8ef348d72a2c59ebee4e58d2d54391c314c7c4c

49

py

Python

tasks/__init__.py

narphorium/mesh-transformer-jax

76cda3c2440e5993d697d04650ea6429f8574c83

[ "Apache-2.0" ]

4,045

2021-03-14T06:09:01.000Z

2022-03-31T16:24:44.000Z

tasks/__init__.py

narphorium/mesh-transformer-jax

76cda3c2440e5993d697d04650ea6429f8574c83

[ "Apache-2.0" ]

201

2021-03-19T16:08:36.000Z

2022-03-28T01:55:55.000Z

tasks/__init__.py

narphorium/mesh-transformer-jax

76cda3c2440e5993d697d04650ea6429f8574c83

[ "Apache-2.0" ]

520

2021-04-25T23:53:31.000Z

2022-03-31T14:35:09.000Z

from tasks.eval_harness import EvalHarnessAdaptor

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Python

tests/test_ops/test_roiaware_pool3d.py

BIGWangYuDong/mmcv

c46deb0576edaff5cd5a7d384c617478c7a73a70

[ "Apache-2.0" ]

null

tests/test_ops/test_roiaware_pool3d.py

BIGWangYuDong/mmcv

c46deb0576edaff5cd5a7d384c617478c7a73a70

[ "Apache-2.0" ]

null

tests/test_ops/test_roiaware_pool3d.py

BIGWangYuDong/mmcv

c46deb0576edaff5cd5a7d384c617478c7a73a70

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. import numpy as np import pytest import torch from mmcv.ops import (RoIAwarePool3d, points_in_boxes_all, points_in_boxes_cpu, points_in_boxes_part) @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') def test_RoIAwarePool3d(): roiaware_pool3d_max = RoIAwarePool3d( out_size=4, max_pts_per_voxel=128, mode='max') roiaware_pool3d_avg = RoIAwarePool3d( out_size=4, max_pts_per_voxel=128, mode='avg') rois = torch.tensor( [[1.0, 2.0, 3.0, 5.0, 4.0, 6.0, -0.3 - np.pi / 2], [-10.0, 23.0, 16.0, 20.0, 10.0, 20.0, -0.5 - np.pi / 2]], dtype=torch.float32).cuda( ) # boxes (m, 7) with bottom center in lidar coordinate pts = torch.tensor( [[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6], [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3], [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9], [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]], dtype=torch.float32).cuda() # points (n, 3) in lidar coordinate pts_feature = pts.clone() pooled_features_max = roiaware_pool3d_max( rois=rois, pts=pts, pts_feature=pts_feature) assert pooled_features_max.shape == torch.Size([2, 4, 4, 4, 3]) assert torch.allclose(pooled_features_max.sum(), torch.tensor(51.100).cuda(), 1e-3) pooled_features_avg = roiaware_pool3d_avg( rois=rois, pts=pts, pts_feature=pts_feature) assert pooled_features_avg.shape == torch.Size([2, 4, 4, 4, 3]) assert torch.allclose(pooled_features_avg.sum(), torch.tensor(49.750).cuda(), 1e-3) @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') def test_points_in_boxes_part(): boxes = torch.tensor( [[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3]], [[-10.0, 23.0, 16.0, 10, 20, 20, 0.5]]], dtype=torch.float32).cuda( ) # boxes (b, t, 7) with bottom center in lidar coordinate pts = torch.tensor( [[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6], [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3], [4.7, 3.5, -12.2]], [[3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9], [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4], [6, 4, 9]]], dtype=torch.float32).cuda() # points (b, m, 3) in lidar coordinate point_indices = points_in_boxes_part(points=pts, boxes=boxes) expected_point_indices = torch.tensor( [[0, 0, 0, 0, 0, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1]], dtype=torch.int32).cuda() assert point_indices.shape == torch.Size([2, 8]) assert (point_indices == expected_point_indices).all() boxes = torch.tensor([[[0.0, 0.0, 0.0, 1.0, 20.0, 1.0, 0.523598]]], dtype=torch.float32).cuda() # 30 degrees pts = torch.tensor( [[[4, 6.928, 0], [6.928, 4, 0], [4, -6.928, 0], [6.928, -4, 0], [-4, 6.928, 0], [-6.928, 4, 0], [-4, -6.928, 0], [-6.928, -4, 0]]], dtype=torch.float32).cuda() point_indices = points_in_boxes_part(points=pts, boxes=boxes) expected_point_indices = torch.tensor([[-1, -1, 0, -1, 0, -1, -1, -1]], dtype=torch.int32).cuda() assert (point_indices == expected_point_indices).all() def test_points_in_boxes_cpu(): boxes = torch.tensor( [[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3], [-10.0, 23.0, 16.0, 10, 20, 20, 0.5]]], dtype=torch.float32 ) # boxes (m, 7) with bottom center in lidar coordinate pts = torch.tensor( [[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6], [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3], [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [ -16, -18, 9 ], [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]]], dtype=torch.float32) # points (n, 3) in lidar coordinate point_indices = points_in_boxes_cpu(points=pts, boxes=boxes) expected_point_indices = torch.tensor( [[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]], dtype=torch.int32) assert point_indices.shape == torch.Size([1, 15, 2]) assert (point_indices == expected_point_indices).all() boxes = torch.tensor([[[0.0, 0.0, 0.0, 1.0, 20.0, 1.0, 0.523598]]], dtype=torch.float32) # 30 degrees pts = torch.tensor( [[[4, 6.928, 0], [6.928, 4, 0], [4, -6.928, 0], [6.928, -4, 0], [-4, 6.928, 0], [-6.928, 4, 0], [-4, -6.928, 0], [-6.928, -4, 0]]], dtype=torch.float32) point_indices = points_in_boxes_cpu(points=pts, boxes=boxes) expected_point_indices = torch.tensor( [[[0], [0], [1], [0], [1], [0], [0], [0]]], dtype=torch.int32) assert (point_indices == expected_point_indices).all() @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') def test_points_in_boxes_all(): boxes = torch.tensor( [[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3], [-10.0, 23.0, 16.0, 10, 20, 20, 0.5]]], dtype=torch.float32).cuda( ) # boxes (m, 7) with bottom center in lidar coordinate pts = torch.tensor( [[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6], [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3], [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [ -16, -18, 9 ], [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]]], dtype=torch.float32).cuda() # points (n, 3) in lidar coordinate point_indices = points_in_boxes_all(points=pts, boxes=boxes) expected_point_indices = torch.tensor( [[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]], dtype=torch.int32).cuda() assert point_indices.shape == torch.Size([1, 15, 2]) assert (point_indices == expected_point_indices).all() if torch.cuda.device_count() > 1: pts = pts.to('cuda:1') boxes = boxes.to('cuda:1') expected_point_indices = expected_point_indices.to('cuda:1') point_indices = points_in_boxes_all(points=pts, boxes=boxes) assert point_indices.shape == torch.Size([1, 15, 2]) assert (point_indices == expected_point_indices).all()

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py

Python

tests/__init__.py

SabaunT/bot-motivator

6b80b8d47f9ed0d071195c1f312a419093665994

[ "MIT" ]

null

tests/__init__.py

SabaunT/bot-motivator

6b80b8d47f9ed0d071195c1f312a419093665994

[ "MIT" ]

4

2019-12-15T13:39:22.000Z

2020-02-20T14:07:55.000Z

tests/__init__.py

SabaunT/bot-motivator

6b80b8d47f9ed0d071195c1f312a419093665994

[ "MIT" ]

2

2019-12-12T22:13:35.000Z

2020-03-08T18:37:06.000Z

import sys sys.path.append('/media/sabaun/4C71BE7650587C7D/documents/bot-motivator/app')

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Python

tests/test_user_remove_association.py

ScilifelabDataCentre/DDS_WEB

8799cf51bef456fb843758b2e94462766e2b3319

[ "BSD-3-Clause" ]

3

2021-06-18T09:38:28.000Z

2022-02-28T19:37:54.000Z

tests/test_user_remove_association.py

ScilifelabDataCentre/DDS_WEB

8799cf51bef456fb843758b2e94462766e2b3319

[ "BSD-3-Clause" ]

610

2021-05-12T08:33:31.000Z

2022-03-31T14:55:05.000Z

tests/test_user_remove_association.py

ScilifelabDataCentre/DDS_WEB

8799cf51bef456fb843758b2e94462766e2b3319

[ "BSD-3-Clause" ]

12

2021-05-19T10:33:45.000Z

2022-03-16T10:23:27.000Z

# Installed import json import http import copy # Own import tests from tests.test_project_creation import proj_data_with_existing_users def test_remove_user_from_project(client, boto3_session): """Remove an associated user from a project""" response = client.post( tests.DDSEndpoint.PROJECT_CREATE, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(proj_data_with_existing_users), content_type="application/json", ) assert response.status_code == http.HTTPStatus.OK project_id = response.json.get("project_id") email = proj_data_with_existing_users["users_to_add"][0]["email"] rem_user = {"email": email, "project": project_id} response = client.post( tests.DDSEndpoint.REMOVE_USER_FROM_PROJ, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(rem_user), content_type="application/json", ) assert response.status_code == http.HTTPStatus.OK assert ( f"User with email {email} no longer associated with {project_id}." in response.json["message"] ) def test_remove_not_associated_user_from_project(client, boto3_session): """Try to remove a user that exists in db but is not associated to a project""" proj_data = copy.deepcopy(proj_data_with_existing_users) proj_data["users_to_add"].pop(1) response = client.post( tests.DDSEndpoint.PROJECT_CREATE, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(proj_data), content_type="application/json", ) assert response.status_code == http.HTTPStatus.OK project_id = response.json.get("project_id") email = proj_data_with_existing_users["users_to_add"][1]["email"] rem_user = {"email": email, "project": project_id} response = client.post( tests.DDSEndpoint.REMOVE_USER_FROM_PROJ, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(rem_user), content_type="application/json", ) assert response.status_code == http.HTTPStatus.OK assert "User already not associated with this project" in response.json["message"] def test_remove_nonexistent_user_from_project(client, boto3_session): """Try to remove an nonexistent user from a project""" response = client.post( tests.DDSEndpoint.PROJECT_CREATE, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(proj_data_with_existing_users), content_type="application/json", ) assert response.status_code == http.HTTPStatus.OK project_id = response.json.get("project_id") email = "nonexistent@testmail.com" rem_user = {"email": email, "project": project_id} response = client.post( tests.DDSEndpoint.REMOVE_USER_FROM_PROJ, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(rem_user), content_type="application/json", ) assert response.status_code == http.HTTPStatus.BAD_REQUEST assert f"{email} already not associated with this project" in response.json["message"] def test_remove_existing_user_from_nonexistent_proj(client, boto3_session): """Try to an existing user from a nonexistent project""" project_id = "nonexistent001" email = proj_data_with_existing_users["users_to_add"][0]["email"] rem_user = {"email": email, "project": project_id} response = client.post( tests.DDSEndpoint.REMOVE_USER_FROM_PROJ, headers=tests.UserAuth(tests.USER_CREDENTIALS["unituser"]).token(client), data=json.dumps(rem_user), content_type="application/json", ) assert response.status_code == http.HTTPStatus.BAD_REQUEST assert "The specified project does not exist" in response.json["message"]

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py

Python

project/proj2_baseball/src/RobotControl.py

robot-tutorial/robotics_tutorial

12affebfe6cb3810cc1e8fde4c674ed077b926a5

[ "MIT" ]

null

project/proj2_baseball/src/RobotControl.py

robot-tutorial/robotics_tutorial

12affebfe6cb3810cc1e8fde4c674ed077b926a5

[ "MIT" ]

null

project/proj2_baseball/src/RobotControl.py

robot-tutorial/robotics_tutorial

12affebfe6cb3810cc1e8fde4c674ed077b926a5

[ "MIT" ]

1

2020-04-23T14:11:00.000Z

import pybullet as p import numpy as np import Jacobian def load(): # work in the following section to load your robot robotName = 'HextechCatcher.urdf' # robotPath = os.path.join('project', 'proj2_baseball', 'rsc', robotName) robotInitPos = [0.0, 0.0, 1.1] robotInitOrn = p.getQuaternionFromEuler([0, 0, 0]) robotId = p.loadURDF("../rsc/robotarm/urdf/robotarm.urdf", robotInitPos, robotInitOrn, useFixedBase=1) basePos = [-0.3, -0.3, 0.3, 0] return robotId, basePos def generateTraj(robotId, ballPos, targetPos): # work in this section, generate your tarjectory as a second order list # e.g. traj = [[j_1(t1), j_2(t1), j_3(t1)], [j_1(t2), j_2(t2), j_3(t2)], [j_1(t3), j_2(t3), j_3(t3)], ...] # robotId is the Unique body index for your robot # ballPos is a list for the baseball position, like [x, y, z] # targetPos is a list for the target position, like [x, y, z] # do not use the inverse kinematics function of pybullet!!!!!! pi = 3.14159265358979323846264338327950288419716939937510 traj = [] # numJoints = p.getNumJoints(robotId) Ball_x = ballPos[0] Ball_y = ballPos[1] # calculate the polar angle of ball if True: if Ball_y >= 0: if Ball_x == 0: Ball_theta = pi / 2 elif Ball_x < 0: Ball_theta = np.arctan(Ball_y / Ball_x) + pi else: Ball_theta = np.arctan(Ball_y / Ball_x) else: if Ball_x == 0: Ball_theta = 3 * pi / 2 elif Ball_x < 0: Ball_theta = np.arctan(Ball_y / Ball_x) + pi else: Ball_theta = np.arctan(Ball_y / Ball_x) + 2 * pi # set an set of initial angle in case of singularity theta = [Ball_theta + pi / 2, 0.0, 3 * pi / 4, 3 * pi / 4, pi / 2, 0] for i in range(480): traj.append([theta[0] * i / 480, theta[1] * i / 480, theta[2] * i / 480, 0, theta[3] * i / 480, theta[4] * i / 480, theta[5] * i / 480, 0.0, 0.0]) # set position step here step = 240 # move the catcher towards beyond the ball if True: theta1 = theta[0] theta2 = theta[1] theta3 = theta[2] theta5 = theta[3] theta6 = theta[4] theta7 = theta[5] WRA = np.array([[np.cos(theta1), -np.sin(theta1), 0], [np.sin(theta1), np.cos(theta1), 0], [0, 0, 1]]) WPA = np.array([[0], [0], [215.2]]) WtA = np.concatenate((WRA, WPA), axis=1) WTA = np.concatenate((WtA, [[0, 0, 0, 1]]), axis=0) ARB = np.array([[1, 0, 0], [0, np.cos(theta2), -np.sin(theta2)], [0, np.sin(theta2), np.cos(theta2)]]) APB = np.array([[162.4], [0], [0]]) AtB = np.concatenate((ARB, APB), axis=1) ATB = np.concatenate((AtB, [[0, 0, 0, 1]]), axis=0) BRC = np.array([[1, 0, 0], [0, np.cos(theta3), -np.sin(theta3)], [0, np.sin(theta3), np.cos(theta3)]]) BPC = np.array([[-162.4], [0], [351]]) BtC = np.concatenate((BRC, BPC), axis=1) BTC = np.concatenate((BtC, [[0, 0, 0, 1]]), axis=0) CRD = np.array([[1, 0, 0], [0, np.cos(theta5), -np.sin(theta5)], [0, np.sin(theta5), np.cos(theta5)]]) CPD = np.array([[0], [0], [351.2]]) CtD = np.concatenate((CRD, CPD), axis=1) CTD = np.concatenate((CtD, [[0, 0, 0, 1]]), axis=0) DRE = np.array([[np.cos(theta6), -np.sin(theta6), 0], [np.sin(theta6), np.cos(theta6), 0], [0, 0, 1]]) DPE = np.array([[162.4], [0], [0]]) DtE = np.concatenate((DRE, DPE), axis=1) DTE = np.concatenate((DtE, [[0, 0, 0, 1]]), axis=0) ERF = np.array([[1, 0, 0], [0, np.cos(theta7), -np.sin(theta7)], [0, np.sin(theta7), np.cos(theta7)]]) EPF = np.array([[0], [0], [162.4]]) EtF = np.concatenate((ERF, EPF), axis=1) ETF = np.concatenate((EtF, [[0, 0, 0, 1]]), axis=0) PO = np.array([[0], [0], [0], [1]]) pOF = np.dot(WTA, np.dot(ATB, np.dot(BTC, np.dot(CTD, np.dot(DTE, np.dot(ETF, PO)))))) + [[0.0], [0.0], [1100.0], [0.0]] CurrentX = pOF[0][0] / 1000 print('CurrentX: ') print(CurrentX) print('ballPos: ') print(ballPos[0]) CurrentY = pOF[1][0] / 1000 for i in range(step): delta_p = np.array([[(ballPos[0] - CurrentX) * 1000 / step], [(ballPos[1] - CurrentY) * 1000 / step], [0.0 * 1000 / step], [0.0 / 240.], [0.0 / 240.], [0.0 / 240.]]) Ja = Jacobian.jacobian(theta[0], theta[1], theta[2], theta[3], theta[4], theta[5]) Ja = np.array(Ja, dtype='float') Jainv = np.linalg.inv(Ja) delta_theta = np.dot(Jainv, delta_p) theta = [theta[0] + delta_theta[0][0], theta[1] + delta_theta[1][0], theta[2] + delta_theta[2][0], theta[3] + delta_theta[3][0], theta[4] + delta_theta[4][0], theta[5] + delta_theta[5][0] ] traj.append([theta[0], theta[1], theta[2], 0, theta[3], theta[4], theta[5], 0.0, 0.0]) if True: theta1 = theta[0] theta2 = theta[1] theta3 = theta[2] theta5 = theta[3] theta6 = theta[4] theta7 = theta[5] WRA = np.array([[np.cos(theta1), -np.sin(theta1), 0], [np.sin(theta1), np.cos(theta1), 0], [0, 0, 1]]) WPA = np.array([[0], [0], [215.2]]) WtA = np.concatenate((WRA, WPA), axis=1) WTA = np.concatenate((WtA, [[0, 0, 0, 1]]), axis=0) ARB = np.array([[1, 0, 0], [0, np.cos(theta2), -np.sin(theta2)], [0, np.sin(theta2), np.cos(theta2)]]) APB = np.array([[162.4], [0], [0]]) AtB = np.concatenate((ARB, APB), axis=1) ATB = np.concatenate((AtB, [[0, 0, 0, 1]]), axis=0) BRC = np.array([[1, 0, 0], [0, np.cos(theta3), -np.sin(theta3)], [0, np.sin(theta3), np.cos(theta3)]]) BPC = np.array([[-162.4], [0], [351]]) BtC = np.concatenate((BRC, BPC), axis=1) BTC = np.concatenate((BtC, [[0, 0, 0, 1]]), axis=0) CRD = np.array([[1, 0, 0], [0, np.cos(theta5), -np.sin(theta5)], [0, np.sin(theta5), np.cos(theta5)]]) CPD = np.array([[0], [0], [351.2]]) CtD = np.concatenate((CRD, CPD), axis=1) CTD = np.concatenate((CtD, [[0, 0, 0, 1]]), axis=0) DRE = np.array([[np.cos(theta6), -np.sin(theta6), 0], [np.sin(theta6), np.cos(theta6), 0], [0, 0, 1]]) DPE = np.array([[162.4], [0], [0]]) DtE = np.concatenate((DRE, DPE), axis=1) DTE = np.concatenate((DtE, [[0, 0, 0, 1]]), axis=0) ERF = np.array([[1, 0, 0], [0, np.cos(theta7), -np.sin(theta7)], [0, np.sin(theta7), np.cos(theta7)]]) EPF = np.array([[0], [0], [162.4]]) EtF = np.concatenate((ERF, EPF), axis=1) ETF = np.concatenate((EtF, [[0, 0, 0, 1]]), axis=0) PO = np.array([[0], [0], [0], [1]]) pOF = np.dot(WTA, np.dot(ATB, np.dot(BTC, np.dot(CTD, np.dot(DTE, np.dot(ETF, PO)))))) + [[0.0], [0.0], [1100.0], [0.0]] CurrentX = pOF[0][0] / 1000 print('CurrentX: ') print(CurrentX) print('ballPos: ') print(ballPos[0]) CurrentY = pOF[1][0] / 1000 for i in range(step): delta_p = np.array([[(ballPos[0] - CurrentX) * 1000 / step], [(ballPos[1] - CurrentY) * 1000 / step], [0.0 * 1000 / step], [0.0 / 240.], [0.0 / 240.], [0.0 / 240.]]) Ja = Jacobian.jacobian(theta[0], theta[1], theta[2], theta[3], theta[4], theta[5]) Ja = np.array(Ja, dtype='float') Jainv = np.linalg.inv(Ja) delta_theta = np.dot(Jainv, delta_p) theta = [theta[0] + delta_theta[0][0], theta[1] + delta_theta[1][0], theta[2] + delta_theta[2][0], theta[3] + delta_theta[3][0], theta[4] + delta_theta[4][0], theta[5] + delta_theta[5][0] ] traj.append([theta[0], theta[1], theta[2], 0, theta[3], theta[4], theta[5], 0.0, 0.0]) # move down along the z axis towards the ball if True: theta1 = theta[0] theta2 = theta[1] theta3 = theta[2] theta5 = theta[3] theta6 = theta[4] theta7 = theta[5] WRA = np.array([[np.cos(theta1), -np.sin(theta1), 0], [np.sin(theta1), np.cos(theta1), 0], [0, 0, 1]]) WPA = np.array([[0], [0], [215.2]]) WtA = np.concatenate((WRA, WPA), axis=1) WTA = np.concatenate((WtA, [[0, 0, 0, 1]]), axis=0) ARB = np.array([[1, 0, 0], [0, np.cos(theta2), -np.sin(theta2)], [0, np.sin(theta2), np.cos(theta2)]]) APB = np.array([[162.4], [0], [0]]) AtB = np.concatenate((ARB, APB), axis=1) ATB = np.concatenate((AtB, [[0, 0, 0, 1]]), axis=0) BRC = np.array([[1, 0, 0], [0, np.cos(theta3), -np.sin(theta3)], [0, np.sin(theta3), np.cos(theta3)]]) BPC = np.array([[-162.4], [0], [351]]) BtC = np.concatenate((BRC, BPC), axis=1) BTC = np.concatenate((BtC, [[0, 0, 0, 1]]), axis=0) CRD = np.array([[1, 0, 0], [0, np.cos(theta5), -np.sin(theta5)], [0, np.sin(theta5), np.cos(theta5)]]) CPD = np.array([[0], [0], [351.2]]) CtD = np.concatenate((CRD, CPD), axis=1) CTD = np.concatenate((CtD, [[0, 0, 0, 1]]), axis=0) DRE = np.array([[np.cos(theta6), -np.sin(theta6), 0], [np.sin(theta6), np.cos(theta6), 0], [0, 0, 1]]) DPE = np.array([[162.4], [0], [0]]) DtE = np.concatenate((DRE, DPE), axis=1) DTE = np.concatenate((DtE, [[0, 0, 0, 1]]), axis=0) ERF = np.array([[1, 0, 0], [0, np.cos(theta7), -np.sin(theta7)], [0, np.sin(theta7), np.cos(theta7)]]) EPF = np.array([[0], [0], [162.4]]) EtF = np.concatenate((ERF, EPF), axis=1) ETF = np.concatenate((EtF, [[0, 0, 0, 1]]), axis=0) PO = np.array([[0], [0], [0], [1]]) pOF = np.dot(WTA, np.dot(ATB, np.dot(BTC, np.dot(CTD, np.dot(DTE, np.dot(ETF, PO)))))) + [[0.0], [0.0], [1100.0], [0.0]] CurrentX = pOF[0][0] / 1000 print('CurrentX: ') print(CurrentX) print('ballPos: ') print(ballPos[0]) CurrentY = pOF[1][0] / 1000 CurrentZ = pOF[2][0] / 1000 for i in range(step): delta_p = np.array([[(ballPos[0] - CurrentX) * 1000 / step], [(ballPos[1] - CurrentY) * 1000 / step], [(1.24 - CurrentZ) * 1000 / step], [0.0 / 240.], [0.0 / 240.], [0.0 / 240.]]) Ja = Jacobian.jacobian(theta[0], theta[1], theta[2], theta[3], theta[4], theta[5]) Ja = np.array(Ja, dtype='float') Jainv = np.linalg.inv(Ja) delta_theta = np.dot(Jainv, delta_p) theta = [theta[0] + delta_theta[0][0], theta[1] + delta_theta[1][0], theta[2] + delta_theta[2][0], theta[3] + delta_theta[3][0], theta[4] + delta_theta[4][0], theta[5] + delta_theta[5][0] ] traj.append([theta[0], theta[1], theta[2], 0, theta[3], theta[4], theta[5], 0.0, 0.0]) # grasp the ball AnglePara = 0.65 for i in range(100): traj.append([theta[0], theta[1], theta[2], 0, theta[3], theta[4], theta[5], pi / 2 * AnglePara, pi / 2 * AnglePara]) for i in range(1000): delta_p = np.array([[0], [0], [100 / 240], [0.0 / 240.], [0.0 / 240.], [0.0 / 240.]]) Ja = Jacobian.jacobian(theta[0], theta[1], theta[2], theta[3], theta[4], theta[5]) Ja = np.array(Ja, dtype='float') Jainv = np.linalg.inv(Ja) delta_theta = np.dot(Jainv, delta_p) theta = [theta[0] + delta_theta[0][0], theta[1] + delta_theta[1][0], theta[2] + delta_theta[2][0], theta[3] + delta_theta[3][0], theta[4] + delta_theta[4][0], theta[5] + delta_theta[5][0] ] traj.append([theta[0], theta[1], theta[2], 0, theta[3], theta[4], theta[5], pi / 2 * AnglePara, pi / 2 * AnglePara]) return traj def addDebugItems(robotId): # add any debug Items you like p.addUserDebugLine([0, 0, 0], [1, 0, 0], lineColorRGB=[ 0.5, 0.5, 0.5], parentObjectUniqueId=robotId, parentLinkIndex=6) pass

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5d754a6d621ae31faaddae4c3d9bcea8f1fa9210

98

py

Python

tests/test_initialize.py

bos-lab/piret

23ecdf80331d72612bb2f48ab3207117673c373d

[ "BSD-3-Clause" ]

2

2017-05-04T05:25:43.000Z

2017-08-02T19:20:53.000Z

tests/test_initialize.py

bos-lab/piret

23ecdf80331d72612bb2f48ab3207117673c373d

[ "BSD-3-Clause" ]

7

2018-01-24T16:39:47.000Z

2018-04-11T16:49:42.000Z

tests/test_initialize.py

bos-lab/piret

23ecdf80331d72612bb2f48ab3207117673c373d

[ "BSD-3-Clause" ]

4

2017-08-02T19:20:57.000Z

2018-01-10T00:31:31.000Z

#! /usr/bin/env python from piret import initialize def test_initialize(): assert initialize

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6

537e7aaa2404beb498874165d39ce1998b7e2c9f

174

py

Python

gallery/email_info.py

glpinto10/multimedia-gallery

95e33ac85f872442c6de193fdf8bf58e57833085

[ "MIT" ]

1

2020-12-15T02:39:41.000Z

gallery/email_info.py

cinemafactory2/multimedia-gallery

af5a05a1bdecb7d5c7a20c06f371a3e5c542f076

[ "MIT" ]

1

2019-02-09T23:37:56.000Z

gallery/email_info.py

cinemafactory2/multimedia-gallery

af5a05a1bdecb7d5c7a20c06f371a3e5c542f076

[ "MIT" ]

2

2019-02-02T21:51:00.000Z

2019-02-10T00:15:39.000Z

#Info email EMAIL_USE_TLS = True EMAIL_HOST = 'smtp.gmail.com' EMAIL_HOST_USER = 'info.multimedia.gallery@gmail.com' EMAIL_HOST_PASSWORD = 'tVxT/_yk4&3fYG..' EMAIL_PORT = 587

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539e44c3fe26e6378a9099ddcc68a69bb61154e4

41

py

Python

test/run/t521.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

test/run/t521.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

test/run/t521.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

x = (1,2,3) print hash(x), type(hash(x))

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0.085714

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6

53b2f3a444d1fdda965256906160179199bd07c9

242

py

Python

torchinfo/__init__.py

kgarg8/torchinfo

2d472402906b2da1a95fff249f2a2d89e5175fa1

[ "MIT" ]

null

torchinfo/__init__.py

kgarg8/torchinfo

2d472402906b2da1a95fff249f2a2d89e5175fa1

[ "MIT" ]

null

torchinfo/__init__.py

kgarg8/torchinfo

2d472402906b2da1a95fff249f2a2d89e5175fa1

[ "MIT" ]

null

""" torchinfo """ from .formatting import ALL_COLUMN_SETTINGS, ALL_ROW_SETTINGS from .model_statistics import ModelStatistics from .torchinfo import summary __all__ = ("ModelStatistics", "summary", "ALL_COLUMN_SETTINGS", "ALL_ROW_SETTINGS")

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1

0

6

07029a1251dbe0b229b46aa6ab66a36fbc3baeba

4,190

py

Python

tests/cupy_tests/padding_tests/test_pad.py

wkentaro/cupy

1d072d0b3cb2780c0874201c0222d46fa8e7797d

[ "BSD-3-Clause" ]

1

2020-11-24T03:44:35.000Z

tests/cupy_tests/padding_tests/test_pad.py

wkentaro/cupy

1d072d0b3cb2780c0874201c0222d46fa8e7797d

[ "BSD-3-Clause" ]

null

tests/cupy_tests/padding_tests/test_pad.py

wkentaro/cupy

1d072d0b3cb2780c0874201c0222d46fa8e7797d

[ "BSD-3-Clause" ]

1

2020-11-24T03:44:35.000Z

import unittest import warnings import numpy from cupy import testing @testing.parameterize( {'array': numpy.arange(6).reshape([2, 3]), 'pad_width': 1, 'mode': 'constant'}, {'array': numpy.arange(6).reshape([2, 3]), 'pad_width': [1, 2], 'mode': 'constant'}, {'array': numpy.arange(6).reshape([2, 3]), 'pad_width': [[1, 2], [3, 4]], 'mode': 'constant'}, ) @testing.gpu class TestPadDefault(unittest.TestCase): _multiprocess_can_split_ = True @testing.for_all_dtypes(no_bool=True) @testing.numpy_cupy_array_equal() def test_pad_default(self, xp, dtype): array = xp.array(self.array, dtype=dtype) # Older version of NumPy(<1.12) can emit ComplexWarning def f(): return xp.pad(array, self.pad_width, mode=self.mode) if xp is numpy: with warnings.catch_warnings(): warnings.simplefilter('ignore', numpy.ComplexWarning) return f() else: return f() @testing.parameterize( {'array': numpy.arange(6).reshape([2, 3]), 'pad_width': 1, 'mode': 'constant', 'constant_values': 3}, {'array': numpy.arange(6).reshape([2, 3]), 'pad_width': [1, 2], 'mode': 'constant', 'constant_values': [3, 4]}, {'array': numpy.arange(6).reshape([2, 3]), 'pad_width': [[1, 2], [3, 4]], 'mode': 'constant', 'constant_values': [[3, 4], [5, 6]]}, ) @testing.gpu # Old numpy does not work with multi-dimensional constant_values @testing.with_requires('numpy>=1.11.1') class TestPad(unittest.TestCase): _multiprocess_can_split_ = True @testing.for_all_dtypes(no_bool=True) @testing.numpy_cupy_array_equal() def test_pad(self, xp, dtype): array = xp.array(self.array, dtype=dtype) # Older version of NumPy(<1.12) can emit ComplexWarning def f(): return xp.pad(array, self.pad_width, mode=self.mode, constant_values=self.constant_values) if xp is numpy: with warnings.catch_warnings(): warnings.simplefilter('ignore', numpy.ComplexWarning) return f() else: return f() @testing.gpu class TestPadNumpybug(unittest.TestCase): _multiprocess_can_split_ = True @testing.with_requires('numpy>=1.11.2') @testing.for_all_dtypes(no_bool=True, no_complex=True) @testing.numpy_cupy_array_equal() def test_pad_highdim_default(self, xp, dtype): array = xp.arange(6, dtype=dtype).reshape([2, 3]) pad_width = [[1, 2], [3, 4]] constant_values = [[1, 2], [3, 4]] a = xp.pad(array, pad_width, mode='constant', constant_values=constant_values) return a @testing.parameterize( {'array': [], 'pad_width': 1, 'mode': 'constant', 'constant_values': 3}, {'array': 1, 'pad_width': 1, 'mode': 'constant', 'constant_values': 3}, {'array': [0, 1, 2, 3], 'pad_width': 1, 'mode': 'constant', 'constant_values': 3}, {'array': [0, 1, 2, 3], 'pad_width': [1, 2], 'mode': 'constant', 'constant_values': 3}, ) @testing.gpu class TestPadSpecial(unittest.TestCase): _multiprocess_can_split_ = True @testing.numpy_cupy_array_equal() def test_pad_special(self, xp): a = xp.pad(self.array, self.pad_width, mode=self.mode, constant_values=self.constant_values) return a @testing.parameterize( {'array': [0, 1, 2, 3], 'pad_width': [-1, 1], 'mode': 'constant', 'constant_values': 3}, {'array': [0, 1, 2, 3], 'pad_width': [], 'mode': 'constant', 'constant_values': 3}, {'array': [0, 1, 2, 3], 'pad_width': [[3, 4], [5, 6]], 'mode': 'constant', 'constant_values': 3}, {'array': [0, 1, 2, 3], 'pad_width': [1], 'mode': 'constant', 'notallowedkeyword': 3}, ) @testing.gpu @testing.with_requires('numpy>=1.11.1') # Old numpy fails differently class TestPadFailure(unittest.TestCase): _multiprocess_can_split_ = True @testing.numpy_cupy_raises() def test_pad_failure(self, xp): a = xp.pad(self.array, self.pad_width, mode=self.mode, constant_values=self.constant_values) return a

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null

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6

ab2046fe69a24cd2c8cc223b956b951c8b80b773

39

py

Python

evaml/logging/__init__.py

hillolkallol/evaML

dec7b2b97e25fa0c7c2df8356952417cf8f7051b

[ "MIT" ]

null

evaml/logging/__init__.py

hillolkallol/evaML

dec7b2b97e25fa0c7c2df8356952417cf8f7051b

[ "MIT" ]

3

2021-03-29T20:46:58.000Z

2021-03-29T21:00:07.000Z

evaml/logging/__init__.py

hillolkallol/evaML

dec7b2b97e25fa0c7c2df8356952417cf8f7051b

[ "MIT" ]

null

from evaml.logging.logger import logger

39

0.871795

6

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null

0

1

0

1

0

1

0

6

ab26858bdcd04218b32fd4fecbc7e5a2407c871b

463

py

Python

orchestra/contrib/orchestration/signals.py

RubenPX/django-orchestra

5ab4779e1ae12ec99569d682601b7810587ed381

[ "Unlicense" ]

68

2015-02-09T10:28:44.000Z

2022-03-12T11:08:36.000Z

orchestra/contrib/orchestration/signals.py

RubenPX/django-orchestra

5ab4779e1ae12ec99569d682601b7810587ed381

[ "Unlicense" ]

17

2015-05-01T18:10:03.000Z

2021-03-19T21:52:55.000Z

orchestra/contrib/orchestration/signals.py

RubenPX/django-orchestra

5ab4779e1ae12ec99569d682601b7810587ed381

[ "Unlicense" ]

29

2015-03-31T04:51:03.000Z

2022-02-17T02:58:50.000Z

import django.dispatch pre_action = django.dispatch.Signal(providing_args=['backend', 'instance', 'action']) post_action = django.dispatch.Signal(providing_args=['backend', 'instance', 'action']) pre_prepare = django.dispatch.Signal(providing_args=['backend']) post_prepare = django.dispatch.Signal(providing_args=['backend']) pre_commit = django.dispatch.Signal(providing_args=['backend']) post_commit = django.dispatch.Signal(providing_args=['backend'])

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463

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null

1

0

null

0

6

ab4924b7f24f20882e6ea4056cba01142573c5f1

17,949

py

Python

pyscf/pbc/dft/test/test_multigrid.py

willwheelera/pyscf

1de7f6fb8403bb0769a05eade2c2e7aa4f8a160e

[ "Apache-2.0" ]

null

pyscf/pbc/dft/test/test_multigrid.py

willwheelera/pyscf

1de7f6fb8403bb0769a05eade2c2e7aa4f8a160e

[ "Apache-2.0" ]

null

pyscf/pbc/dft/test/test_multigrid.py

willwheelera/pyscf

1de7f6fb8403bb0769a05eade2c2e7aa4f8a160e

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright 2014-2018 The PySCF Developers. 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. # # Author: Qiming Sun <osirpt.sun@gmail.com> # import unittest import numpy from pyscf import lib from pyscf.pbc import gto, scf, dft, df from pyscf.pbc import tools from pyscf.pbc.dft import gen_grid from pyscf.pbc.dft import multigrid multigrid.R_RATIO_SUBLOOP = 0.6 def setUpModule(): global cell_orth, cell_nonorth, cell_he, mydf global kpts, nao, dm, dm1, vj_uks_orth, he_nao, dm_he numpy.random.seed(2) cell_orth = gto.M( verbose = 7, output = '/dev/null', a = numpy.eye(3)*3.5668, atom = '''C 0. 0. 0. C 1.8 1.8 1.8 ''', basis = 'gth-dzv', pseudo = 'gth-pade', precision = 1e-9, mesh = [48] * 3, ) cell_nonorth = gto.M( a = numpy.eye(3)*3.5668 + numpy.random.random((3,3)), atom = '''C 0. 0. 0. C 0.8917 0.8917 0.8917''', basis = 'gth-dzv', pseudo = 'gth-pade', precision = 1e-9, mesh = [44,43,42], ) cell_he = gto.M(atom='He 0 0 0', basis=[[0, ( 1, 1, .1), (.5, .1, 1)], [1, (.8, 1)]], unit='B', precision = 1e-9, mesh=[18]*3, a=numpy.eye(3)*5) kptsa = numpy.random.random((2,3)) kpts = kptsa.copy() kpts[1] = -kpts[0] nao = cell_orth.nao_nr() dm = numpy.random.random((len(kpts),nao,nao)) * .2 dm1 = dm + numpy.eye(nao) dm = dm1 + dm1.transpose(0,2,1) mydf = df.FFTDF(cell_orth) vj_uks_orth = mydf.get_jk(dm1, with_k=False)[0] he_nao = cell_he.nao dm_he = numpy.random.random((len(kpts), he_nao, he_nao)) dm_he = dm_he + dm_he.transpose(0,2,1) dm_he = dm_he * .2 + numpy.eye(he_nao) def tearDownModule(): global cell_orth, cell_nonorth, cell_he, mydf del cell_orth, cell_nonorth, cell_he, mydf class KnownValues(unittest.TestCase): def test_orth_get_pp(self): ref = df.FFTDF(cell_orth).get_pp() out = multigrid.MultiGridFFTDF(cell_orth).get_pp() self.assertAlmostEqual(abs(ref-out).max(), 0, 9) def test_nonorth_get_pp(self): ref = df.FFTDF(cell_nonorth).get_pp() out = multigrid.MultiGridFFTDF(cell_nonorth).get_pp() self.assertAlmostEqual(abs(ref-out).max(), 0, 9) def test_orth_get_nuc_kpts(self): ref = df.FFTDF(cell_orth).get_nuc(kpts) out = multigrid.MultiGridFFTDF(cell_orth).get_nuc(kpts) self.assertAlmostEqual(abs(ref-out).max(), 0, 8) def test_orth_get_j_kpts(self): ref = df.FFTDF(cell_orth).get_jk(dm, kpts=kpts, with_k=False)[0] out = multigrid.MultiGridFFTDF(cell_orth).get_jk(dm, kpts=kpts)[0] self.assertAlmostEqual(abs(ref-out).max(), 0, 9) # mydf = multigrid.MultiGridFFTDF(cell_orth) # self.assertRaises(ValueError, mydf.get_jk, dm1, hermi=0, kpts=kpts, with_k=False) def test_nonorth_get_j_kpts(self): ref = df.FFTDF(cell_nonorth).get_jk(dm, kpts=kpts, with_k=False)[0] out = multigrid.MultiGridFFTDF(cell_nonorth, kpts=kpts).get_jk(dm)[0] self.assertAlmostEqual(abs(ref-out).max(), 0, 9) def test_nonorth_get_j(self): ref = df.FFTDF(cell_nonorth).get_jk(dm[0], with_k=False)[0] out = multigrid.MultiGridFFTDF(cell_nonorth).get_jk(dm)[0] self.assertAlmostEqual(abs(ref-out).max(), 0, 9) def test_orth_rks_lda_kpts(self): xc = 'lda,' mydf = df.FFTDF(cell_orth) ni = dft.numint.KNumInt() n, exc0, ref = ni.nr_rks(cell_orth, mydf.grids, xc, dm, 0, kpts=kpts) mydf = multigrid.MultiGridFFTDF(cell_orth) n, exc1, vxc = multigrid.nr_rks(mydf, xc, dm, kpts=kpts) self.assertAlmostEqual(float(abs(ref-vxc).max()), 0, 9) self.assertAlmostEqual(abs(exc0-exc1).max(), 0, 8) def test_multigrid_kuks(self): mf = dft.KUKS(cell_he) mf.xc = 'lda,' ref = mf.get_veff(cell_he, numpy.array((dm_he,dm_he)), kpts=kpts) out = multigrid.multigrid(mf).get_veff(cell_he, (dm_he,dm_he), kpts=kpts) self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9) self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9) self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 9) def test_multigrid_krks(self): mf = dft.KRKS(cell_he) mf.xc = 'lda,' ref = mf.get_veff(cell_he, dm_he, kpts=kpts) out = multigrid.multigrid(mf).get_veff(cell_he, dm_he, kpts=kpts) self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9) self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9) self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 9) def test_multigrid_kroks(self): mf = dft.KROKS(cell_he) mf.xc = 'lda,' nao = cell_he.nao mo = dm_he mo_occ = numpy.ones((2,nao)) dm1 = numpy.einsum('kpi,ki,kqi->kpq', mo, mo_occ, mo) dm1 = lib.tag_array(numpy.array([dm1,dm1]), mo_coeff=mo, mo_occ=mo_occ*2) ref = mf.get_veff(cell_he, dm1, kpts=kpts) out = multigrid.multigrid(mf).get_veff(cell_he, dm1, kpts=kpts) self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9) self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9) self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 9) def test_multigrid_uks(self): mf = dft.UKS(cell_he) mf.xc = 'lda,' ref = mf.get_veff(cell_he, numpy.array((dm_he[0],dm_he[0]))) out = multigrid.multigrid(mf).get_veff(cell_he, (dm_he[0], dm_he[0])) self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9) self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9) self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 9) def test_multigrid_rks(self): mf = dft.RKS(cell_he) mf.xc = 'lda,' ref = mf.get_veff(cell_he, dm_he[0]) out = multigrid.multigrid(mf).get_veff(cell_he, dm_he[0]) self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9) self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9) self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 9) def test_multigrid_roks(self): mf = dft.ROKS(cell_he) mf.xc = 'lda,' mo = dm_he[0] nao = cell_he.nao mo_occ = numpy.ones(nao) dm1 = numpy.einsum('pi,i,qi->pq', mo, mo_occ, mo) dm1 = lib.tag_array(numpy.array([dm1,dm1]), mo_coeff=mo, mo_occ=mo_occ*2) ref = mf.get_veff(cell_he, dm1) out = multigrid.multigrid(mf).get_veff(cell_he, dm1) self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9) self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9) self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 8) def test_orth_rks_gga_kpts(self): xc = 'b88,' mydf = df.FFTDF(cell_orth) ni = dft.numint.KNumInt() n, exc0, ref = ni.nr_rks(cell_orth, mydf.grids, xc, dm, hermi=1, kpts=kpts) ref += mydf.get_jk(dm, hermi=1, with_k=False, kpts=kpts)[0] mydf = multigrid.MultiGridFFTDF(cell_orth) n, exc1, vxc = multigrid.nr_rks(mydf, xc, dm, hermi=1, kpts=kpts, with_j=True) self.assertAlmostEqual(float(abs(ref-vxc).max()), 0, 9) self.assertAlmostEqual(abs(exc0-exc1).max(), 0, 8) def test_orth_uks_lda_hermi0(self): xc = 'lda,' mydf = df.FFTDF(cell_orth) ni = dft.numint.NumInt() n, exc0, ref = ni.nr_uks(cell_orth, mydf.grids, xc, dm1, 0) ref += vj_uks_orth[0] + vj_uks_orth[1] mydf = multigrid.MultiGridFFTDF(cell_orth) n, exc1, vxc = multigrid.nr_uks(mydf, xc, dm1, hermi=0, with_j=True) self.assertAlmostEqual(float(abs(ref-vxc).max()), 0, 8) self.assertAlmostEqual(abs(exc0-exc1).max(), 0, 7) def test_orth_uks_gga_hermi0(self): xc = 'b88,' mydf = df.FFTDF(cell_orth) ni = dft.numint.NumInt() n, exc0, ref = ni.nr_uks(cell_orth, mydf.grids, xc, dm1, 0) ref += vj_uks_orth[0] + vj_uks_orth[1] mydf = multigrid.MultiGridFFTDF(cell_orth) n, exc1, vxc = multigrid.nr_uks(mydf, xc, dm1, hermi=0, with_j=True) self.assertAlmostEqual(float(abs(ref-vxc).max()), 0, 8) self.assertAlmostEqual(abs(exc0-exc1).max(), 0, 8) def test_eval_rhoG_orth_kpts(self): numpy.random.seed(9) dm = numpy.random.random(dm1.shape) + numpy.random.random(dm1.shape) * 1j mydf = multigrid.MultiGridFFTDF(cell_orth) rhoG = multigrid._eval_rhoG(mydf, dm, hermi=0, kpts=kpts, deriv=0, rhog_high_order=True) self.assertTrue(rhoG.dtype == numpy.complex128) mydf = df.FFTDF(cell_orth) ni = dft.numint.KNumInt() ao_kpts = ni.eval_ao(cell_orth, mydf.grids.coords, kpts, deriv=0) ref = ni.eval_rho(cell_orth, ao_kpts, dm, hermi=0, xctype='LDA') rhoR = tools.ifft(rhoG[0], cell_orth.mesh).real rhoR *= numpy.prod(cell_orth.mesh)/cell_orth.vol self.assertAlmostEqual(abs(rhoR-ref).max(), 0, 8) def test_eval_rhoG_orth_gga(self): mydf = multigrid.MultiGridFFTDF(cell_orth) rhoG = multigrid._eval_rhoG(mydf, dm, hermi=1, kpts=kpts, deriv=1, rhog_high_order=True) mydf = df.FFTDF(cell_orth) ni = dft.numint.KNumInt() ao_kpts = ni.eval_ao(cell_orth, mydf.grids.coords, kpts, deriv=1) ref = ni.eval_rho(cell_orth, ao_kpts, dm, xctype='GGA') rhoR = tools.ifft(rhoG[0], cell_orth.mesh).real rhoR *= numpy.prod(cell_orth.mesh)/cell_orth.vol self.assertAlmostEqual(abs(rhoR-ref).max(), 0, 8) def test_eval_rhoG_nonorth_gga(self): mydf = multigrid.MultiGridFFTDF(cell_nonorth) rhoG = multigrid._eval_rhoG(mydf, dm, hermi=1, kpts=kpts, deriv=1, rhog_high_order=True) mydf = df.FFTDF(cell_nonorth) ni = dft.numint.KNumInt() ao_kpts = ni.eval_ao(cell_nonorth, mydf.grids.coords, kpts, deriv=1) ref = ni.eval_rho(cell_nonorth, ao_kpts, dm, xctype='GGA') rhoR = tools.ifft(rhoG[0], cell_nonorth.mesh).real rhoR *= numpy.prod(cell_nonorth.mesh)/cell_nonorth.vol self.assertAlmostEqual(abs(rhoR-ref).max(), 0, 7) def test_gen_rhf_response(self): numpy.random.seed(9) dm1 = numpy.random.random(dm_he.shape) dm1 = dm1 + dm1.transpose(0,2,1) dm1[1] = dm1[0] mydf = df.FFTDF(cell_he) ni = dft.numint.KNumInt() mf = dft.KRKS(cell_he) mf.with_df = multigrid.MultiGridFFTDF(cell_he) mf.kpts = kpts mf.xc = 'lda,' ref = dft.numint.nr_rks_fxc(ni, cell_he, mydf.grids, mf.xc, dm_he, dm1, kpts=kpts) vj = mydf.get_jk(dm1, with_k=False, kpts=kpts)[0] ref += vj v = multigrid._gen_rhf_response(mf, dm_he)(dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 9) mf.xc = 'b88,' ref = dft.numint.nr_rks_fxc(ni, cell_he, mydf.grids, mf.xc, dm_he, dm1, kpts=kpts) ref += vj v = multigrid._gen_rhf_response(mf, dm_he, hermi=1)(dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 6) def test_nr_rks_fxc(self): numpy.random.seed(9) dm1 = numpy.random.random(dm_he.shape) dm1 = dm1 + dm1.transpose(0,2,1) mydf = df.FFTDF(cell_he) ni = dft.numint.NumInt() mg_df = multigrid.MultiGridFFTDF(cell_he) xc = 'lda,' ref = dft.numint.nr_rks_fxc(ni, cell_he, mydf.grids, xc, dm_he[0], dm1) v = multigrid.nr_rks_fxc(mg_df, xc, dm_he[0], dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 9) xc = 'b88,' ref = dft.numint.nr_rks_fxc(ni, cell_he, mydf.grids, xc, dm_he, dm1) v = multigrid.nr_rks_fxc(mg_df, xc, dm_he, dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 6) def test_nr_rks_fxc_st(self): numpy.random.seed(9) dm1 = numpy.random.random(dm_he.shape) dm1 = dm1 + dm1.transpose(0,2,1) dm1[1] = dm1[0] mydf = df.FFTDF(cell_he) ni = dft.numint.KNumInt() mg_df = multigrid.MultiGridFFTDF(cell_he) mf = dft.KRKS(cell_he) mf.with_df = mg_df mf.kpts = kpts xc = 'lda,' ref = dft.numint.nr_rks_fxc_st(ni, cell_he, mydf.grids, xc, dm_he, dm1, singlet=True, kpts=kpts) v = multigrid.nr_rks_fxc_st(mg_df, xc, dm_he, dm1, singlet=True, kpts=kpts) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 9) mf.xc = 'b88,' ref = dft.numint.nr_rks_fxc_st(ni, cell_he, mydf.grids, mf.xc, dm_he, dm1, singlet=True, kpts=kpts) ref += mydf.get_jk(dm1, with_k=False, kpts=kpts)[0] v = multigrid._gen_rhf_response(mf, dm_he, singlet=True, hermi=1)(dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 6) mf.xc = 'lda,' ref = dft.numint.nr_rks_fxc_st(ni, cell_he, mydf.grids, mf.xc, dm_he, dm1, singlet=False, kpts=kpts) v = multigrid._gen_rhf_response(mf, dm_he, singlet=False, hermi=1)(dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 9) xc = 'b88,' ref = dft.numint.nr_rks_fxc_st(ni, cell_he, mydf.grids, xc, dm_he, dm1, singlet=False, kpts=kpts) v = multigrid.nr_rks_fxc_st(mg_df, xc, dm_he, dm1, singlet=False, kpts=kpts) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 6) def test_gen_uhf_response(self): numpy.random.seed(9) dm1 = numpy.random.random(dm_he.shape) dm1 = dm1 + dm1.transpose(0,2,1) mydf = df.FFTDF(cell_he) ni = dft.numint.NumInt() mf = dft.UKS(cell_he) mf.with_df = multigrid.MultiGridFFTDF(cell_he) mf.xc = 'lda,' ref = dft.numint.nr_uks_fxc(ni, cell_he, mydf.grids, mf.xc, dm_he, dm1) vj = mydf.get_jk(dm1, with_k=False)[0] ref += vj[0] + vj[1] v = multigrid._gen_uhf_response(mf, dm_he, with_j=True)(dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 9) mf.xc = 'b88,' ref = dft.numint.nr_uks_fxc(ni, cell_he, mydf.grids, mf.xc, dm_he, dm1) ref += vj[0] + vj[1] v = multigrid._gen_uhf_response(mf, dm_he, with_j=True)(dm1) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 7) def test_nr_uks_fxc(self): numpy.random.seed(9) dm1 = numpy.random.random(dm_he.shape) dm1 = dm1 + dm1.transpose(0,2,1) mydf = df.FFTDF(cell_he) ni = dft.numint.KNumInt() mg_df = multigrid.MultiGridFFTDF(cell_he) xc = 'lda,' ref = dft.numint.nr_uks_fxc(ni, cell_he, mydf.grids, xc, (dm_he, dm_he), (dm1, dm1), kpts=kpts) v = multigrid.nr_uks_fxc(mg_df, xc, (dm_he, dm_he), (dm1, dm1), kpts=kpts) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 9) xc = 'b88,' ref = dft.numint.nr_uks_fxc(ni, cell_he, mydf.grids, xc, (dm_he, dm_he), (dm1, dm1), kpts=kpts) v = multigrid.nr_uks_fxc(mg_df, xc, (dm_he, dm_he), (dm1, dm1), kpts=kpts) self.assertEqual(ref.dtype, v.dtype) self.assertEqual(ref.shape, v.shape) self.assertAlmostEqual(abs(v-ref).max(), 0, 8) def test_rcut_vs_ke_cut(self): xc = 'lda,' with lib.temporary_env(multigrid, TASKS_TYPE='rcut'): mg_df = multigrid.MultiGridFFTDF(cell_orth) n1, exc1, v1 = multigrid.nr_rks(mg_df, xc, dm1, kpts=kpts) self.assertEqual(len(mg_df.tasks), 3) with lib.temporary_env(multigrid, TASKS_TYPE='ke_cut'): mg_df = multigrid.MultiGridFFTDF(cell_orth) n2, exc2, v2 = multigrid.nr_rks(mg_df, xc, dm1, kpts=kpts) self.assertEqual(len(mg_df.tasks), 6) self.assertAlmostEqual(n1, n2, 8) self.assertAlmostEqual(exc1, exc2, 8) self.assertAlmostEqual(abs(v1-v2).max(), 0, 8) if __name__ == '__main__': print("Full Tests for multigrid") unittest.main()

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ab63864ff2de6af656ead09d70797f9b27c5569c

33

py

Python

tnp/__init__.py

daico007/TNP

1877bc86f5a40f1503022491f5dd9495d23332ad

[ "MIT" ]

null

tnp/__init__.py

daico007/TNP

1877bc86f5a40f1503022491f5dd9495d23332ad

[ "MIT" ]

null

tnp/__init__.py

daico007/TNP

1877bc86f5a40f1503022491f5dd9495d23332ad

[ "MIT" ]

null

from .silicatnp import SilicaTNP

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db5170b93a7516bfe1e27c019fa0a5bb53c9bcac

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py

Python

var/spack/repos/builtin/packages/nano/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/nano/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

8

2021-11-09T20:28:40.000Z

2022-03-15T03:26:33.000Z

var/spack/repos/builtin/packages/nano/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-08T20:37:20.000Z

2019-03-31T15:19:26.000Z

# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack.package import * class Nano(AutotoolsPackage): """Tiny little text editor""" homepage = "https://www.nano-editor.org" url = "https://www.nano-editor.org/dist/v6/nano-6.3.tar.xz" list_url = "https://www.nano-editor.org/dist/" list_depth = 1 # 6.x version('6.3', sha256='eb532da4985672730b500f685dbaab885a466d08fbbf7415832b95805e6f8687') version('6.2', sha256='2bca1804bead6aaf4ad791f756e4749bb55ed860eec105a97fba864bc6a77cb3') version('6.1', sha256='3d57ec893fbfded12665b7f0d563d74431fc43abeaccacedea23b66af704db40') version('6.0', sha256='93ac8cb68b4ad10e0aaeb80a2dd15c5bb89eb665a4844f7ad01c67efcb169ea2') # 5.x version('5.9', sha256='757db8cda4bb2873599e47783af463e3b547a627b0cabb30ea7bf71fb4c24937') version('5.8', sha256='e43b63db2f78336e2aa123e8d015dbabc1720a15361714bfd4b1bb4e5e87768c') version('5.7', sha256='d4b181cc2ec11def3711b4649e34f2be7a668e70ab506860514031d069cccafa') version('5.6', sha256='fce183e4a7034d07d219c79aa2f579005d1fd49f156db6e50f53543a87637a32') version('5.5', sha256='390b81bf9b41ff736db997aede4d1f60b4453fbd75a519a4ddb645f6fd687e4a') version('5.4', sha256='fe993408b22286355809ce48ebecc4444d19af8203ed4959d269969112ed86e9') version('5.3', sha256='c5c1cbcf622d9a96b6030d66409ed12b204e8bc01ef5e6554ebbe6fb1d734352') version('5.2', sha256='32c2da43e1ae9a5e43437d8c6e1ec0388af870c7762c479e5bffb5f292bda7e1') version('5.1', sha256='9efc46f341404d60095d16fc4f0419fc84b6e4eaeaf6ebce605d0465d92a6ee6') version('5.0', sha256='7c0d94be69cd066f20df2868a2da02f7b1d416ce8d47c0850a8bd270897caa36') # 4.x version('4.9', sha256='0e399729d105cb1a587b4140db5cf1b23215a0886a42b215efa98137164233a6') version('4.8', sha256='c348f61c68ab1d573b308398212a09cd68c60fbee20f01a5bd4b50071a258e63') version('4.7', sha256='58c0e197de5339ca3cad3ef42b65626d612ddb0b270e730f02e6ab3785c736f5') version('4.6', sha256='9bac3a4153774fd921dd3eb291986d43985466b081165b5ac5262b37b79628e9') version('4.5', sha256='ded5c38f5ecd9de2b624e0db8013a375c169d3fbbd49575967b868847df8f533') version('4.4', sha256='2af222e0354848ffaa3af31b5cd0a77917e9cb7742cd073d762f3c32f0f582c7') version('4.3', sha256='00d3ad1a287a85b4bf83e5f06cedd0a9f880413682bebd52b4b1e2af8cfc0d81') version('4.2', sha256='1143defce62e391b241252ffdb6e5c1ded56cfe26d46ee81b796abe0ccc45df9') version('4.1', sha256='86bde596a038d6fde619b49d785c0ebf0b3eaa7001a39dbe9316bd5392d221d0') version('4.0', sha256='1e2fcfea35784624a7d86785768b772d58bb3995d1aec9176a27a113b1e9bac3') # 3.x version('3.2', sha256='d12773af3589994b2e4982c5792b07c6240da5b86c5aef2103ab13b401fe6349') version('3.1', sha256='14c02ca40a5bc61c580ce2f9cb7f9fc72d5ccc9da17ad044f78f6fb3fdb7719e') version('3.0', sha256='e0a5bca354514e64762c987c200a8758b05e7bcced3b00b3e48ea0a2d383c8a0') # 2.9.x version('2.9.8', sha256='c2deac31ba4d3fd27a42fafcc47ccf499296cc69a422bbecab63f2933ea85488') version('2.9.7', sha256='b64ab017305b1777e97b5b9b07b31db8aeebfc3e8719f61e8da1cf3866d344bd') version('2.9.6', sha256='a373507ebb4e9307a8202fbc19b5d29718025c8ec773669349211c362545d4c6') version('2.9.5', sha256='7b8d181cb57f42fa86a380bb9ad46abab859b60383607f731b65a9077f4b4e19') version('2.9.4', sha256='2cf9726e735f5c962af63d27c2faaead5936e45adec983659fb9e4af88ffa35a') version('2.9.3', sha256='7783bcfd4b2d5dc0bf64d4bd07b1a19e7ba3c91da881a4249772a36b972d4012') version('2.9.2', sha256='4eccb7451b5729ce8abae8f9a5679f32e41ae58df73ea86b850ec45b10a83d55') version('2.9.1', sha256='6316d52d0d26af3e79a13dcb4db1c7a4aeac61b37fd9381e801a4189a2ecba7c') version('2.9.0', sha256='d2d30c39caef53aba1ec1b4baff4186d4496f35d2411b0848242a5f2e27e129e') # 2.8.x version('2.8.7', sha256='fbe31746958698d73c6726ee48ad8b0612697157961a2e9aaa83b4aa53d1165a') version('2.8.6', sha256='9a46962a3ae59db922467a095217ed23280b42d80640f932f3a59ba2a6a85776') version('2.8.5', sha256='cb43bf11990b2839446229b0c21ed7abef67c2df861f250cc874553ca27d89c2') version('2.8.4', sha256='c7cf264f0f3e4af43ecdbc4ec72c3b1e831c69a1a5f6512d5b0c109e6bac7b11') version('2.8.3', sha256='62b8e55b934091edbb41e948eac3d6769cc13d18b837c38faf7226c0820b0f55') version('2.8.2', sha256='023e8a7b38b2420d5476d7b2b4d8524d7de55c0853b4dc0b02e4a4adf7ecb9e0') version('2.8.1', sha256='e935a8bb373345c833dff3a304c6d392775d206b94c802d9285ae80ac6b66d0b') version('2.8.0', sha256='15c1bcf4d8888d3b56f68a0b75871cc349b81a9094f8a42d73010ffc26c85dc2') # 2.7.x version('2.7.5', sha256='a64d24e6bc4fc448376d038f9a755af77f8e748c9051b6f45bf85e783a7e67e4') version('2.7.4', sha256='752170643039e2c95a433de357f0c70a8c4c4c561a90a7e7259a63e225b659b9') version('2.7.3', sha256='d926ef5060d23bafec75dab9328bb9b9df9a08e58c56b9061d686f5698770bfc') version('2.7.2', sha256='77016f73c686857ce8a3ec217832febb6e636122762d47ce3c6cbef6f7e390c2') version('2.7.1', sha256='df5cbe69831d7394c0a32fb27373ab313335ea4dc586d6f4be4081eb1de857cd') version('2.7.0', sha256='f86af39514ae74e20bef3c29cd01d1090a9aca772a70e9c9f9e70c3d14b39521') # 2.6.x version('2.6.3', sha256='69ecbfbaa845800f43c27d6190ca87d277f3278f81e9c55ee569181b572b7519') version('2.6.2', sha256='22f79cc635458e0c0d110d211576f1edc03b112a62d73b914826a46547a6ac27') version('2.6.1', sha256='45721fa6d6128068895ad71a6967ff7398d11b064b3f888e5073c97a2b6e9a81') depends_on('ncurses') def url_for_version(self, version): url = "https://www.nano-editor.org/dist/v{0}/nano-{1}.tar.xz" subdir = version.up_to(2) major = version.up_to(1) if int(str(major)) > 2: subdir = major return url.format(subdir, version)

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6

db6e003633a91ae4f78a2d376693bdd0a4fcfe63

19,070

py

Python

layers/equivariant_linear.py

JiaHe-yogurt/GNN

6b6dbc362591b4521e0b437d17ab09c1c879aa75

[ "Apache-2.0" ]

null

layers/equivariant_linear.py

JiaHe-yogurt/GNN

6b6dbc362591b4521e0b437d17ab09c1c879aa75

[ "Apache-2.0" ]

null

layers/equivariant_linear.py

JiaHe-yogurt/GNN

6b6dbc362591b4521e0b437d17ab09c1c879aa75

[ "Apache-2.0" ]

null

import tensorflow.compat.v1 as tf import numpy as np def equi_2_to_2(name, input_depth, output_depth, inputs, normalization='inf', normalization_val=1.0): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m x m tensor :return: output: N x S x m x m tensor ''' basis_dimension = 15 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) #coeffs_values = tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[3]) # extract dimension ops_out = ops_2_to_2(inputs, m, normalization=normalization) ops_out = tf.stack(ops_out, axis=2) output = tf.einsum('dsb,ndbij->nsij', coeffs, ops_out) # N x S x m x m # bias diag_bias = tf.get_variable('diag_bias', initializer=tf.zeros([1, output_depth, 1, 1], dtype=tf.float32)) all_bias = tf.get_variable('all_bias', initializer=tf.zeros([1, output_depth, 1, 1], dtype=tf.float32)) mat_diag_bias = tf.multiply(tf.expand_dims(tf.expand_dims(tf.eye(tf.to_int32(tf.shape(inputs)[3])), 0), 0), diag_bias) output = output + all_bias + mat_diag_bias return output def equi_2_to_1(name, input_depth, output_depth, inputs, normalization='inf', normalization_val=1.0): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m x m tensor :return: output: N x S x m tensor ''' basis_dimension = 5 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) #coeffs_values = tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[3]) # extract dimension ops_out = ops_2_to_1(inputs, m, normalization=normalization) ops_out = tf.stack(ops_out, axis=2) # N x D x B x m output = tf.einsum('dsb,ndbi->nsi', coeffs, ops_out) # N x S x m # bias bias = tf.get_variable('bias', initializer=tf.zeros([1, output_depth, 1], dtype=tf.float32)) output = output + bias return output def equi_1_to_2(name, input_depth, output_depth, inputs, normalization='inf', normalization_val=1.0): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m tensor :return: output: N x S x m x m tensor ''' basis_dimension = 5 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) #coeffs_values = tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[2]) # extract dimension ops_out = ops_1_to_2(inputs, m, normalization=normalization) ops_out = tf.stack(ops_out, axis=2) # N x D x B x m x m output = tf.einsum('dsb,ndbij->nsij', coeffs, ops_out) # N x S x m x m # bias bias = tf.get_variable('bias', initializer=tf.zeros([1, output_depth, 1, 1], dtype=tf.float32)) output = output + bias return output def equi_1_to_1(name, input_depth, output_depth, inputs, normalization='inf', normalization_val=1.0): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m tensor :return: output: N x S x m tensor ''' basis_dimension = 2 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) #coeffs_values = tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[2]) # extract dimension ops_out = ops_1_to_1(inputs, m, normalization=normalization) ops_out = tf.stack(ops_out, axis=2) # N x D x B x m output = tf.einsum('dsb,ndbi->nsi', coeffs, ops_out) # N x S x m # bias bias = tf.get_variable('bias', initializer=tf.zeros([1, output_depth, 1], dtype=tf.float32)) output = output + bias return output def equi_basic(name, input_depth, output_depth, inputs): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m x m tensor :return: output: N x S x m x m tensor ''' basis_dimension = 4 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) #coeffs_values = tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[3]) # extract dimension float_dim = tf.to_float(m) # apply ops ops_out = [] # w1 - identity ops_out.append(inputs) # w2 - sum cols sum_of_cols = tf.divide(tf.reduce_sum(inputs, axis=2), float_dim) # N x D x m ops_out.append(tf.tile(tf.expand_dims(sum_of_cols, axis=2), [1, 1, m, 1])) # N x D x m x m # w3 - sum rows sum_of_rows = tf.divide(tf.reduce_sum(inputs, axis=3), float_dim) # N x D x m ops_out.append(tf.tile(tf.expand_dims(sum_of_rows, axis=3), [1, 1, 1, m])) # N x D x m x m # w4 - sum all sum_all = tf.divide(tf.reduce_sum(sum_of_rows, axis=2), tf.square(float_dim)) # N x D ops_out.append(tf.tile(tf.expand_dims(tf.expand_dims(sum_all, axis=2), axis=3), [1, 1, m, m])) # N x D x m x m ops_out = tf.stack(ops_out, axis=2) output = tf.einsum('dsb,ndbij->nsij', coeffs, ops_out) # N x S x m x m # bias bias = tf.get_variable('bias', initializer=tf.zeros([1, output_depth, 1, 1], dtype=tf.float32)) output = output + bias return output def ops_2_to_2(inputs, dim, normalization='inf', normalization_val=1.0): # N x D x m x m diag_part = tf.matrix_diag_part(inputs) # N x D x m sum_diag_part = tf.reduce_sum(diag_part, axis=2, keepdims=True) # N x D x 1 sum_of_rows = tf.reduce_sum(inputs, axis=3) # N x D x m sum_of_cols = tf.reduce_sum(inputs, axis=2) # N x D x m sum_all = tf.reduce_sum(sum_of_rows, axis=2) # N x D # op1 - (1234) - extract diag op1 = tf.matrix_diag(diag_part) # N x D x m x m # op2 - (1234) + (12)(34) - place sum of diag on diag op2 = tf.matrix_diag(tf.tile(sum_diag_part, [1, 1, dim])) # N x D x m x m # op3 - (1234) + (123)(4) - place sum of row i on diag ii op3 = tf.matrix_diag(sum_of_rows) # N x D x m x m # op4 - (1234) + (124)(3) - place sum of col i on diag ii op4 = tf.matrix_diag(sum_of_cols) # N x D x m x m # op5 - (1234) + (124)(3) + (123)(4) + (12)(34) + (12)(3)(4) - place sum of all entries on diag op5 = tf.matrix_diag(tf.tile(tf.expand_dims(sum_all, axis=2), [1, 1, dim])) # N x D x m x m # op6 - (14)(23) + (13)(24) + (24)(1)(3) + (124)(3) + (1234) - place sum of col i on row i op6 = tf.tile(tf.expand_dims(sum_of_cols, axis=3), [1, 1, 1, dim]) # N x D x m x m # op7 - (14)(23) + (23)(1)(4) + (234)(1) + (123)(4) + (1234) - place sum of row i on row i op7 = tf.tile(tf.expand_dims(sum_of_rows, axis=3), [1, 1, 1, dim]) # N x D x m x m # op8 - (14)(2)(3) + (134)(2) + (14)(23) + (124)(3) + (1234) - place sum of col i on col i op8 = tf.tile(tf.expand_dims(sum_of_cols, axis=2), [1, 1, dim, 1]) # N x D x m x m # op9 - (13)(24) + (13)(2)(4) + (134)(2) + (123)(4) + (1234) - place sum of row i on col i op9 = tf.tile(tf.expand_dims(sum_of_rows, axis=2), [1, 1, dim, 1]) # N x D x m x m # op10 - (1234) + (14)(23) - identity op10 = inputs # N x D x m x m # op11 - (1234) + (13)(24) - transpose op11 = tf.transpose(inputs, [0, 1, 3, 2]) # N x D x m x m # op12 - (1234) + (234)(1) - place ii element in row i op12 = tf.tile(tf.expand_dims(diag_part, axis=3), [1, 1, 1, dim]) # N x D x m x m # op13 - (1234) + (134)(2) - place ii element in col i op13 = tf.tile(tf.expand_dims(diag_part, axis=2), [1, 1, dim, 1]) # N x D x m x m # op14 - (34)(1)(2) + (234)(1) + (134)(2) + (1234) + (12)(34) - place sum of diag in all entries op14 = tf.tile(tf.expand_dims(sum_diag_part, axis=3), [1, 1, dim, dim]) # N x D x m x m # op15 - sum of all ops - place sum of all entries in all entries op15 = tf.tile(tf.expand_dims(tf.expand_dims(sum_all, axis=2), axis=3), [1, 1, dim, dim]) # N x D x m x m if normalization is not None: float_dim = tf.to_float(dim) if normalization is 'inf': op2 = tf.divide(op2, float_dim) op3 = tf.divide(op3, float_dim) op4 = tf.divide(op4, float_dim) op5 = tf.divide(op5, float_dim**2) op6 = tf.divide(op6, float_dim) op7 = tf.divide(op7, float_dim) op8 = tf.divide(op8, float_dim) op9 = tf.divide(op9, float_dim) op14 = tf.divide(op14, float_dim) op15 = tf.divide(op15, float_dim**2) return [op1, op2, op3, op4, op5, op6, op7, op8, op9, op10, op11, op12, op13, op14, op15] def ops_2_to_1(inputs, dim, normalization='inf', normalization_val=1.0): # N x D x m x m diag_part = tf.matrix_diag_part(inputs) # N x D x m sum_diag_part = tf.reduce_sum(diag_part, axis=2, keepdims=True) # N x D x 1 sum_of_rows = tf.reduce_sum(inputs, axis=3) # N x D x m sum_of_cols = tf.reduce_sum(inputs, axis=2) # N x D x m sum_all = tf.reduce_sum(inputs, axis=(2, 3)) # N x D # op1 - (123) - extract diag op1 = diag_part # N x D x m # op2 - (123) + (12)(3) - tile sum of diag part op2 = tf.tile(sum_diag_part, [1, 1, dim]) # N x D x m # op3 - (123) + (13)(2) - place sum of row i in element i op3 = sum_of_rows # N x D x m # op4 - (123) + (23)(1) - place sum of col i in element i op4 = sum_of_cols # N x D x m # op5 - (1)(2)(3) + (123) + (12)(3) + (13)(2) + (23)(1) - tile sum of all entries op5 = tf.tile(tf.expand_dims(sum_all, axis=2), [1, 1, dim]) # N x D x m if normalization is not None: float_dim = tf.to_float(dim) if normalization is 'inf': op2 = tf.divide(op2, float_dim) op3 = tf.divide(op3, float_dim) op4 = tf.divide(op4, float_dim) op5 = tf.divide(op5, float_dim ** 2) return [op1, op2, op3, op4, op5] def ops_1_to_2(inputs, dim, normalization='inf', normalization_val=1.0): # N x D x m sum_all = tf.reduce_sum(inputs, axis=2, keepdims=True) # N x D x 1 # op1 - (123) - place on diag op1 = tf.matrix_diag(inputs) # N x D x m x m # op2 - (123) + (12)(3) - tile sum on diag op2 = tf.matrix_diag(tf.tile(sum_all, [1, 1, dim])) # N x D x m x m # op3 - (123) + (13)(2) - tile element i in row i op3 = tf.tile(tf.expand_dims(inputs, axis=2), [1, 1, dim, 1]) # N x D x m x m # op4 - (123) + (23)(1) - tile element i in col i op4 = tf.tile(tf.expand_dims(inputs, axis=3), [1, 1, 1, dim]) # N x D x m x m # op5 - (1)(2)(3) + (123) + (12)(3) + (13)(2) + (23)(1) - tile sum of all entries op5 = tf.tile(tf.expand_dims(sum_all, axis=3), [1, 1, dim, dim]) # N x D x m x m if normalization is not None: float_dim = tf.to_float(dim) if normalization is 'inf': op2 = tf.divide(op2, float_dim) op5 = tf.divide(op5, float_dim) return [op1, op2, op3, op4, op5] def ops_1_to_1(inputs, dim, normalization='inf', normalization_val=1.0): # N x D x m sum_all = tf.reduce_sum(inputs, axis=2, keepdims=True) # N x D x 1 # op1 - (12) - identity op1 = inputs # N x D x m # op2 - (1)(2) - tile sum of all op2 = tf.tile(sum_all, [1, 1, dim]) # N x D x m if normalization is not None: float_dim = tf.to_float(dim) if normalization is 'inf': op2 = tf.divide(op2, float_dim) return [op1, op2] def ops_3_to_1(inputs, dim, normalization='inf', normalization_val=1.0): # N x D x m x m x m diag = tf.matrix_diag_part(inputs) sum_of_cols = tf.reduce_sum(inputs, axis=3) sum_of_rows = tf.reduce_sum(inputs, axis=4) op1 = tf.reduce_sum(tf.matrix_diag_part(diag), axis=2) op2 = tf.reduce_sum(tf.reduce_sum(diag, axis=2), axis=2) op3 = tf.reduce_sum(tf.matrix_diag_part(sum_of_cols), axis=2) op4 = tf.reduce_sum(tf.matrix_diag_part(sum_of_rows), axis=2) op5 = tf.reduce_sum(tf.reduce_sum(sum_of_cols, axis=2), axis=2) if normalization is not None: float_dim = tf.to_float(dim) if normalization is 'inf': op1 = tf.divide(op1, float_dim) op2 = tf.divide(op2, float_dim) op3 = tf.divide(op3, float_dim) op4 = tf.divide(op4, float_dim) op5 = tf.divide(op5, float_dim) return [op1, op2, op3, op4, op5] # return [ op5] def equi_3_to_1(name, input_depth, output_depth, inputs, normalization='inf', normalization_val=1.0): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m x m tensor :return: output: N x S tensor ''' basis_dimension = 5 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[2]) # extract dimension ops_out = ops_3_to_1(inputs, m, normalization=normalization) # ops_out = tf.concat(ops_out, axis=1) # output = tf.einsum('sdb,nb->nd', coeffs, ops_out) # N x D ops_out = tf.stack(ops_out, axis=2) # N x D x B x m output = tf.einsum('sdb,nsb->nd', coeffs, ops_out) # N x D # bias bias = tf.get_variable('bias', initializer=tf.zeros([1, output_depth], dtype=tf.float32)) output = output + bias return output def ops_4_to_1(inputs, dim, normalization='inf', normalization_val=1.0): # N x m x m x m x m diag = tf.matrix_diag_part(inputs) slice_pointwise_sum = tf.reduce_sum(inputs, axis=3) sum_vertical_slice = tf.reduce_sum(slice_pointwise_sum, axis=2) sum_of_col = tf.reduce_sum(inputs, axis=4) sum_of_row = tf.reduce_sum(inputs, axis=5) sum_horizotal_slice = tf.reduce_sum(sum_of_row, axis=4) trans1 = tf.transpose(inputs, [0, 1, 2, 4, 3, 5]) trans2 = tf.transpose(inputs, [0, 1, 2, 5, 4, 3]) # sum all elements op1 = tf.reduce_sum(tf.reduce_sum(sum_horizotal_slice, axis=2), axis=2) # sum column slice # op2 = tf.reduce_sum(tf.matrix_diag_part(sum_vertical_slice), axis=2) # sum ith column of ith slice for all tensor # op3 = tf.reduce_sum(tf.reduce_sum( tf.matrix_diag_part(sum_of_col), axis=2), axis=2) # sum ith row of ith slice for all tensor # op4 = tf.reduce_sum(tf.reduce_sum( tf.matrix_diag_part(sum_of_row),axis=2),axis=2) # sum ith slice for ith tensor # op5 = tf.reduce_sum(tf.matrix_diag_part(sum_horizotal_slice),axis=2) # sum of ith row of all slice for every tensor # op6 = tf.reduce_sum(tf.matrix_diag_part( tf.reduce_sum(slice_pointwise_sum , axis=4)),axis=2) # sum of all diagonal # op7 = tf.reduce_sum( tf.reduce_sum( tf.reduce_sum(diag, axis=2), axis=2), axis=2) # (iiii) # op8 = tf.reduce_sum(tf.matrix_diag_part(tf.matrix_diag_part(diag)), axis=2) # (iiii) + (iijj) # op9 = tf.reduce_sum(tf.matrix_diag_part(tf.reduce_sum(diag, axis=4)),axis=2) # (iiii) + (ijij) # op10 = tf.reduce_sum(tf.matrix_diag_part(tf.matrix_diag_part(sum_of_col)), axis=2) # (iiii) + (iiij) # op11 = tf.reduce_sum(tf.matrix_diag_part(tf.matrix_diag_part(sum_of_row)), axis=2) # (iiii) + (ijii) # op12 = tf.reduce_sum(tf.matrix_diag_part((tf.matrix_diag_part(slice_pointwise_sum))), axis=2) # (iiii) + (ijjj) ## op13 = tf.reduce_sum(tf.reduce_sum(tf.matrix_diag_part(diag),axis=2),axis=2) # (iiii) + (ijij) op14 = tf.reduce_sum(tf.matrix_diag_part(tf.reduce_sum(tf.matrix_diag_part(trans1), axis=4)), axis=2) # (iiii) + (ijji) # op15 = tf.reduce_sum(tf.matrix_diag_part(tf.reduce_sum(tf.matrix_diag_part(trans2), axis=4)),axis=2) # return [op1, op2, op3, op4, op5, op6, op7, op8, op9, op10, op11, op12, op13, op14, op15] return [op1] def equi_4_to_1(name, input_depth, output_depth, inputs, normalization='inf', normalization_val=1.0): ''' :param name: name of layer :param input_depth: D :param output_depth: S :param inputs: N x D x m x m x m tensor :return: output: N x S tensor ''' basis_dimension = 1 with tf.variable_scope(name, reuse=tf.AUTO_REUSE) as scope: # initialization values for variables coeffs_values = tf.multiply(tf.random_normal([input_depth, output_depth, basis_dimension], dtype=tf.float32), tf.sqrt(2. / tf.to_float(input_depth + output_depth))) # define variables coeffs = tf.get_variable('coeffs', initializer=coeffs_values) m = tf.to_int32(tf.shape(inputs)[2]) # extract dimension ops_out = ops_4_to_1(inputs, m, normalization=normalization) ops_out = tf.concat(ops_out, axis=1) output = tf.einsum('sdb,nb->nd', coeffs, ops_out) # N x D # bias bias = tf.get_variable('bias', initializer=tf.zeros([1, output_depth], dtype=tf.float32)) output = output + bias return output

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6

db76c034e675ea1b2db91f7069f2c7c7df97b830

93

py

Python

utils/models.py

HackTJ/live

2e29176da78e2a2834e1004c7390d4a74c324142

[ "MIT" ]

2

2021-03-11T22:50:23.000Z

2021-05-13T14:52:25.000Z

utils/models.py

HackTJ/live

2e29176da78e2a2834e1004c7390d4a74c324142

[ "MIT" ]

78

2020-08-01T20:06:38.000Z

2022-03-30T23:34:02.000Z

utils/models.py

HackTJ/live

2e29176da78e2a2834e1004c7390d4a74c324142

[ "MIT" ]

null

from django.contrib.auth.models import AbstractUser class LiveUser(AbstractUser): pass

15.5

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py

Python

experiments/data_structure/__init__.py

shruti-bt/data-structure-python

0729f486f516ce05acdd92b28b108f43b67f656f

[ "MIT" ]

1

2022-01-10T17:17:35.000Z

experiments/data_structure/__init__.py

shruti-bt/data-structure-python

0729f486f516ce05acdd92b28b108f43b67f656f

[ "MIT" ]

null

experiments/data_structure/__init__.py

shruti-bt/data-structure-python

0729f486f516ce05acdd92b28b108f43b67f656f

[ "MIT" ]

null

from ._stack import Stack

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Python

pybind/slxos/v17r_2_00/interface/ethernet/qos/rx_queue/multicast/queue_size/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/interface/ethernet/qos/rx_queue/multicast/queue_size/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/interface/ethernet/qos/rx_queue/multicast/queue_size/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class queue_size(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-interface - based on the path /interface/ethernet/qos/rx-queue/multicast/queue-size. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__traffic_class','__min_queue_size','__max_queue_size',) _yang_name = 'queue-size' _rest_name = 'queue-size' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__traffic_class = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), restriction_dict={'range': [u'0 .. 3']}), is_leaf=True, yang_name="traffic-class", rest_name="traffic-class", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Traffic class to configure multicast queue size', u'cli-full-no': None, u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='uint8', is_config=True) self.__max_queue_size = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 2048']}), is_leaf=True, yang_name="max-queue-size", rest_name="max-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure maximum queue size'}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='max-queue-size-type', is_config=True) self.__min_queue_size = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 1024']}), is_leaf=True, yang_name="min-queue-size", rest_name="min-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure minimum queue size', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='min-queue-size-type', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'interface', u'ethernet', u'qos', u'rx-queue', u'multicast', u'queue-size'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'Ethernet', u'qos', u'rx-queue', u'multicast', u'queue-size'] def _get_traffic_class(self): """ Getter method for traffic_class, mapped from YANG variable /interface/ethernet/qos/rx_queue/multicast/queue_size/traffic_class (uint8) """ return self.__traffic_class def _set_traffic_class(self, v, load=False): """ Setter method for traffic_class, mapped from YANG variable /interface/ethernet/qos/rx_queue/multicast/queue_size/traffic_class (uint8) If this variable is read-only (config: false) in the source YANG file, then _set_traffic_class is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_traffic_class() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), restriction_dict={'range': [u'0 .. 3']}), is_leaf=True, yang_name="traffic-class", rest_name="traffic-class", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Traffic class to configure multicast queue size', u'cli-full-no': None, u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='uint8', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """traffic_class must be of a type compatible with uint8""", 'defined-type': "uint8", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), restriction_dict={'range': [u'0 .. 3']}), is_leaf=True, yang_name="traffic-class", rest_name="traffic-class", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Traffic class to configure multicast queue size', u'cli-full-no': None, u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='uint8', is_config=True)""", }) self.__traffic_class = t if hasattr(self, '_set'): self._set() def _unset_traffic_class(self): self.__traffic_class = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), restriction_dict={'range': [u'0 .. 3']}), is_leaf=True, yang_name="traffic-class", rest_name="traffic-class", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Traffic class to configure multicast queue size', u'cli-full-no': None, u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='uint8', is_config=True) def _get_min_queue_size(self): """ Getter method for min_queue_size, mapped from YANG variable /interface/ethernet/qos/rx_queue/multicast/queue_size/min_queue_size (min-queue-size-type) """ return self.__min_queue_size def _set_min_queue_size(self, v, load=False): """ Setter method for min_queue_size, mapped from YANG variable /interface/ethernet/qos/rx_queue/multicast/queue_size/min_queue_size (min-queue-size-type) If this variable is read-only (config: false) in the source YANG file, then _set_min_queue_size is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_min_queue_size() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 1024']}), is_leaf=True, yang_name="min-queue-size", rest_name="min-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure minimum queue size', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='min-queue-size-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """min_queue_size must be of a type compatible with min-queue-size-type""", 'defined-type': "brocade-qos-mls:min-queue-size-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 1024']}), is_leaf=True, yang_name="min-queue-size", rest_name="min-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure minimum queue size', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='min-queue-size-type', is_config=True)""", }) self.__min_queue_size = t if hasattr(self, '_set'): self._set() def _unset_min_queue_size(self): self.__min_queue_size = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 1024']}), is_leaf=True, yang_name="min-queue-size", rest_name="min-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure minimum queue size', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='min-queue-size-type', is_config=True) def _get_max_queue_size(self): """ Getter method for max_queue_size, mapped from YANG variable /interface/ethernet/qos/rx_queue/multicast/queue_size/max_queue_size (max-queue-size-type) """ return self.__max_queue_size def _set_max_queue_size(self, v, load=False): """ Setter method for max_queue_size, mapped from YANG variable /interface/ethernet/qos/rx_queue/multicast/queue_size/max_queue_size (max-queue-size-type) If this variable is read-only (config: false) in the source YANG file, then _set_max_queue_size is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_max_queue_size() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 2048']}), is_leaf=True, yang_name="max-queue-size", rest_name="max-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure maximum queue size'}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='max-queue-size-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """max_queue_size must be of a type compatible with max-queue-size-type""", 'defined-type': "brocade-qos-mls:max-queue-size-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 2048']}), is_leaf=True, yang_name="max-queue-size", rest_name="max-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure maximum queue size'}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='max-queue-size-type', is_config=True)""", }) self.__max_queue_size = t if hasattr(self, '_set'): self._set() def _unset_max_queue_size(self): self.__max_queue_size = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'0 .. 2048']}), is_leaf=True, yang_name="max-queue-size", rest_name="max-queue-size", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure maximum queue size'}}, namespace='urn:brocade.com:mgmt:brocade-qos-mls', defining_module='brocade-qos-mls', yang_type='max-queue-size-type', is_config=True) traffic_class = __builtin__.property(_get_traffic_class, _set_traffic_class) min_queue_size = __builtin__.property(_get_min_queue_size, _set_min_queue_size) max_queue_size = __builtin__.property(_get_max_queue_size, _set_max_queue_size) _pyangbind_elements = {'traffic_class': traffic_class, 'min_queue_size': min_queue_size, 'max_queue_size': max_queue_size, }

72.489899

655

0.730788

2,029

14,353

4.923115

0.096599

0.086495

0.043248

0.037241

0.806087

0.769847

0.748924

0.743918

0.728702

0.712484

0

0.01487

0.128545

14,353

197

656

72.857868

0.783738

0.129938

0

0.378788

0

0.022727

0.357125

0.133344

0

1

0.090909

false

0

0.060606

0

0.280303

0

null

0

1

0

null

0

6

dbd801795470214342b240e3b88738a800ef6744

196

py

Python

ADVANCED_MODULE/05_Functions_Advanced/LAB/02_Person_Info.py

sleepychild/SoftUni_SE

ae94488befb6de8b74ffdcb14ed6470739a67786

[ "MIT" ]

null

ADVANCED_MODULE/05_Functions_Advanced/LAB/02_Person_Info.py

sleepychild/SoftUni_SE

ae94488befb6de8b74ffdcb14ed6470739a67786

[ "MIT" ]

1

2022-01-15T10:33:56.000Z

ADVANCED_MODULE/05_Functions_Advanced/LAB/02_Person_Info.py

sleepychild/SoftUni_SE

ae94488befb6de8b74ffdcb14ed6470739a67786

[ "MIT" ]

null

def get_info(**kwargs) -> str: return f"This is {kwargs['name']} from {kwargs['town']} and he is {kwargs['age']} years old" print(get_info(**{"name": "George", "town": "Sofia", "age": 20}))

32.666667

96

0.602041

30

196

3.866667

0.7

0.12069

0

0.011905

0.142857

196

5

97

39.2

0.678571

0

0.333333

0.530612

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true

0

0.333333

0.666667

0.333333

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0

1

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1

0

null

0

1

0

1

0

6

9167966ac67f118af122eae97b67691eb0e749ba

28,531

py

Python

src/bayes_tec/likelihoods.py

Joshuaalbert/bayes_tec

655c4ec29427c7bb0616d5752c34207714a0151c

[ "Apache-2.0" ]

null

src/bayes_tec/likelihoods.py

Joshuaalbert/bayes_tec

655c4ec29427c7bb0616d5752c34207714a0151c

[ "Apache-2.0" ]

null

src/bayes_tec/likelihoods.py

Joshuaalbert/bayes_tec

655c4ec29427c7bb0616d5752c34207714a0151c

[ "Apache-2.0" ]

null

import gpflow as gp import numpy as np import tensorflow as tf from gpflow import params_as_tensors from gpflow import transforms from gpflow.params import Parameter from gpflow.likelihoods import Likelihood from gpflow import settings from gpflow.quadrature import ndiagquad, ndiag_mc, mvnquad from gpflow import logdensities float_type = settings.float_type try: @tf.RegisterGradient('WrapGrad') def _wrap_grad(op,grad): phi = op.inputs[0] return tf.ones_like(phi)*grad def wrap(phi): out = tf.atan2(tf.sin(phi),tf.cos(phi)) with tf.get_default_graph().gradient_override_map({'Identity': 'WrapGrad'}): return tf.identity(out) except: pass#already defined class WrappedPhaseGaussianEncoded(Likelihood): def __init__(self, tec_scale=0.01, num_gauss_hermite_points=20, variance=1.0, name=None): super().__init__(name=name) self.variance = Parameter( variance, transform=transforms.positive, dtype=settings.float_type) self.tec_scale = tec_scale self.num_gauss_hermite_points = num_gauss_hermite_points self.freq = tf.convert_to_tensor(freq,dtype=settings.float_type,name='test_freq') # frequency the phase is calculated at for the predictive distribution self.tec_conversion = tf.convert_to_tensor(tec_scale * -8.4480e9,dtype=settings.float_type,name='tec_conversion') # rad Hz/ tecu self.tec2phase = tf.convert_to_tensor(self.tec_conversion / self.freq,dtype=settings.float_type,name='tec2phase') @params_as_tensors def logp(self, F, Y, freqs, **kwargs): """The log-likelihood function.""" assert freqs is not None #freqs = Y[:,-1:] #Y = Y[:,:self.num_latent] # N,1 tec2phase = self.tec_conversion/freqs phase = F*tec2phase dphase = wrap(phase) - wrap(Y) # Ito theorem arg = tf.stack([-0.5*tf.square(dphase + 2*np.pi*k)/self.variance - 0.5 * tf.log((2*np.pi) * self.variance) \ for k in range(-2,3,1)],axis=-1) return tf.reduce_logsumexp(arg,axis=-1) @params_as_tensors def conditional_mean(self, F, eval_freq=None): # pylint: disable=R0201 """The mean of the likelihood conditioned on latent.""" eval_freq = self.freq if eval_freq is None else eval_freq tec2phase = self.tec_conversion/eval_freq phase = F*tec2phase return phase @params_as_tensors def conditional_variance(self, F): return tf.fill(tf.shape(F),tf.cast(self.variance,gp.settings.float_type)) def predict_mean_and_var(self, Fmu, Fvar, **kwargs): r""" Given a Normal distribution for the latent function, return the mean of Y if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean \int\int y p(y|f)q(f) df dy and the predictive variance \int\int y^2 p(y|f)q(f) df dy - [ \int\int y^2 p(y|f)q(f) df dy ]^2 Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (e.g. Gaussian) will implement specific cases. """ integrand2 = lambda *X, **kwargs: self.conditional_variance(*X, **kwargs) + tf.square(self.conditional_mean(*X, **kwargs)) E_y, E_y2 = ndiagquad([self.conditional_mean, integrand2], self.num_gauss_hermite_points, Fmu, Fvar, **kwargs) V_y = E_y2 - tf.square(E_y) return E_y, V_y def predict_density(self, Fmu, Fvar, Y, **kwargs): r""" Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y. i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive density \log \int p(y=Y|f)q(f) df Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, logspace=True, Y=Y, **kwargs) def variational_expectations(self, Fmu, Fvar, Y, **kwargs): r""" Compute the expected log density of the data, given a Gaussian distribution for the function values. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes \int (\log p(y|f)) q(f) df. Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, Y=Y, **kwargs) class WrappedPhaseGaussianMulti(Likelihood): """This is an efficient version of the encoded likelihood.""" def __init__(self, tec_scale=0.001, freqs=None, num_gauss_hermite_points=20, variance=1.0, name=None): super().__init__(name=name) self.variance = Parameter( variance, transform=transforms.positive, dtype=settings.float_type) self.tec_scale = tec_scale self.num_gauss_hermite_points = num_gauss_hermite_points self.Nf = len(freqs) self.freqs = tf.convert_to_tensor(freqs,dtype=settings.float_type,name='freqs') # freqs of data self.tec_conversion = tf.convert_to_tensor(tec_scale * -8.4480e9,dtype=settings.float_type,name='tec_conversion') # rad Hz/ tecu # Nf self.tec2phase = self.tec_conversion / self.freqs @params_as_tensors def logp(self, F, **kwargs): """The log-likelihood function.""" #..., Nf Y = tf.stack([kwargs["Y_{}".format(i)] for i in range(self.Nf)],axis=2) phase = F[..., None]*self.tec2phase dphase = wrap(phase) - wrap(Y) # Ito theorem arg = tf.stack([-0.5*(tf.square(dphase + 2*np.pi*k)/self.variance + tf.cast(tf.log(2*np.pi), settings.float_type) + tf.log(self.variance)) \ for k in range(-2,3,1)],axis=0) if kwargs.get("W_0") is not None: W = tf.stack([kwargs["W_{}".format(i)] for i in range(self.Nf)],axis=2) return tf.reduce_mean(W*tf.reduce_logsumexp(arg,axis=0), axis=-1) else: return tf.reduce_mean(tf.reduce_logsumexp(arg,axis=0),axis=-1) @params_as_tensors def conditional_mean(self, F): # pylint: disable=R0201 """The mean of the likelihood conditioned on latent.""" # ..., Nf phase = F[..., None]*self.tec2phase return phase @params_as_tensors def conditional_variance(self, F): return tf.fill(tf.concat([tf.shape(F),tf.shape(self.freqs)],axis=0), tf.cast(self.variance,gp.settings.float_type)) def predict_mean_and_var(self, Fmu, Fvar, **kwargs): r""" Given a Normal distribution for the latent function, return the mean of Y if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean \int\int y p(y|f)q(f) df dy and the predictive variance \int\int y^2 p(y|f)q(f) df dy - [ \int\int y^2 p(y|f)q(f) df dy ]^2 Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (e.g. Gaussian) will implement specific cases. """ integrand2 = lambda *X, **kwargs: self.conditional_variance(*X, **kwargs) + tf.square(self.conditional_mean(*X, **kwargs)) E_y, E_y2 = ndiagquad([self.conditional_mean, integrand2], self.num_gauss_hermite_points, Fmu, Fvar, **kwargs) V_y = E_y2 - tf.square(E_y) return E_y, V_y def predict_density(self, Fmu, Fvar, Y, **kwargs): r""" Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y. i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive density \log \int p(y=Y|f)q(f) df Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ Y_burst = {"Y_{}".format(i): Y[:,:,i] for i in range(self.Nf)} return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, logspace=True, **Y_burst, **kwargs) def variational_expectations(self, Fmu, Fvar, Y, weights, **kwargs): r""" Compute the expected log density of the data, given a Gaussian distribution for the function values. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes \int (\log p(y|f)) q(f) df. Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ Y_burst = {"Y_{}".format(i): Y[:,:,i] for i in range(self.Nf)} weights_burst = {"W_{}".format(i): weights[:,:,i] for i in range(self.Nf)} return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, **Y_burst, **weights_burst, **kwargs) class ItohGaussianEncodedHetero(Likelihood): """This is an efficient version of the encoded likelihood.""" def __init__(self, tec_scale=0.005, num_gauss_hermite_points=20, num_mc_samples=1, variance=1.0, name=None): super().__init__(name=name) self.variance = Parameter( variance, transform=transforms.positive, dtype=settings.float_type) self.tec_scale = tec_scale self.num_gauss_hermite_points = num_gauss_hermite_points self.num_mc_samples = num_mc_samples self.tec_conversion = tf.convert_to_tensor(tec_scale * -8.4480e9,dtype=settings.float_type, name='tec_conversion') # rad Hz/ tecu @params_as_tensors def logp(self, F, Y, Y_var, freq, **kwargs): """ The log-likelihood function. F is ..., P Y is ..., P Y_var ..., P freq ..., P Returns: tensor ..., P """ #..., Nf phase = self.tec_conversion * (F / freq) dphase = wrap(wrap(phase) - wrap(Y)) # Ito theorem log_prob = tf.distributions.Normal(dphase, tf.sqrt(Y_var)).log_prob(tf.zeros_like(F))#..., P return log_prob @params_as_tensors def conditional_mean(self, F, freq, **kwargs): # pylint: disable=R0201 """The mean of the likelihood conditioned on latent.""" # ..., Nf phase = self.tec_conversion * (F/freq) return phase @params_as_tensors def conditional_variance(self, Y_var, **kwargs): return Y_var + self.variance def predict_mean_and_var(self, Fmu, Fvar, Y_var, freq): r""" Given a Normal distribution for the latent function, return the mean of Y if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean \int\int y p(y|f)q(f) df dy and the predictive variance \int\int y^2 p(y|f)q(f) df dy - [ \int\int y^2 p(y|f)q(f) df dy ]^2 Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (e.g. Gaussian) will implement specific cases. """ integrand2 = lambda *X, **kwargs: self.conditional_variance(*X, **kwargs) + tf.square(self.conditional_mean(*X, **kwargs)) E_y, E_y2 = ndiagquad([self.conditional_mean, integrand2], self.num_gauss_hermite_points, Fmu, Fvar, Y_var=Y_var, freq=freq) V_y = E_y2 - tf.square(E_y) return E_y, V_y def predict_density(self, Fmu, Fvar, Y, Y_var, freq): r""" Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y. i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive density \log \int p(y=Y|f)q(f) df Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, logspace=True, Y=Y, Y_var=Y_var, freq=freq) def variational_expectations(self, Fmu, Fvar, Y, Y_var, freq, mc=False, mvn=False): r""" Compute the expected log density of the data, given a Gaussian distribution for the function values. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes \int (\log p(y|f)) q(f) df. Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ if mvn: assert len(Fvar.shape) == 3 if not mvn: if not mc: return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) else: return ndiag_mc(self.logp, self.num_mc_samples , Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) else: if not mc: raise ValueError("Too slow to do this") else: return mvn_mc(self.logp, self.num_mc_samples , Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) class WrappedPhaseGaussianEncodedHetero(Likelihood): """This is an efficient version of the encoded likelihood.""" def __init__(self, tec_scale=0.005, num_gauss_hermite_points=20, num_mc_samples=1, variance=1.0, K=2, use_mc=False, name=None): super().__init__(name=name) self.K = K self.use_mc = use_mc self.variance = Parameter( variance, transform=transforms.positive, dtype=settings.float_type) self.tec_scale = tec_scale self.num_gauss_hermite_points = num_gauss_hermite_points self.num_mc_samples = num_mc_samples self.tec_conversion = tf.convert_to_tensor(tec_scale * -8.4480e9,dtype=settings.float_type, name='tec_conversion') # rad Hz/ tecu @params_as_tensors def logp(self, F, Y, Y_var, freq, **kwargs): """ The log-likelihood function. F is ..., P Y is ..., P Y_var ..., P freq ..., P Returns: tensor ..., P """ #..., Nf phase = self.tec_conversion * (F / freq) # dphase = wrap(phase) - wrap(Y) # Ito theorem log_prob = tf.stack([tf.distributions.Normal(phase + tf.convert_to_tensor(k*2*np.pi,float_type), tf.sqrt(Y_var)).log_prob(Y) for k in range(-self.K,self.K+1,1)], axis=0) log_prob = tf.reduce_logsumexp(log_prob, axis=0) #..., P return log_prob @params_as_tensors def conditional_mean(self, F, freq, **kwargs): # pylint: disable=R0201 """The mean of the likelihood conditioned on latent.""" # ..., Nf phase = self.tec_conversion * (F/freq) return phase @params_as_tensors def conditional_variance(self, Y_var, **kwargs): return Y_var + self.variance def predict_mean_and_var(self, Fmu, Fvar, Y_var, freq): r""" Given a Normal distribution for the latent function, return the mean of Y if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean \int\int y p(y|f)q(f) df dy and the predictive variance \int\int y^2 p(y|f)q(f) df dy - [ \int\int y^2 p(y|f)q(f) df dy ]^2 Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (e.g. Gaussian) will implement specific cases. """ integrand2 = lambda *X, **kwargs: self.conditional_variance(*X, **kwargs) + tf.square(self.conditional_mean(*X, **kwargs)) E_y, E_y2 = ndiagquad([self.conditional_mean, integrand2], self.num_gauss_hermite_points, Fmu, Fvar, Y_var=Y_var, freq=freq) V_y = E_y2 - tf.square(E_y) return E_y, V_y def predict_density(self, Fmu, Fvar, Y, Y_var, freq): r""" Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y. i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive density \log \int p(y=Y|f)q(f) df Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, logspace=True, Y=Y, Y_var=Y_var, freq=freq) def variational_expectations(self, Fmu, Fvar, Y, Y_var, freq): r""" Compute the expected log density of the data, given a Gaussian distribution for the function values. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes \int (\log p(y|f)) q(f) df. Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ if self.use_mc: return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) return ndiag_mc(self.logp, self.num_mc_samples , Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) class GaussianTecHetero(Likelihood): def __init__(self, tec_scale=0.005, **kwargs): super().__init__(**kwargs) self.tec_scale = tf.convert_to_tensor(tec_scale, dtype=float_type) @params_as_tensors def logp(self, F, Y, Y_var): tec = F*self.tec_scale return logdensities.gaussian(Y, tec, Y_var) @params_as_tensors def predict_mean_and_var(self, Fmu, Fvar, Y_var): return tf.identity(Fmu)*self.tec_scale, Fvar*self.tec_scale**2 + Y_var @params_as_tensors def predict_density(self, Fmu, Fvar, Y, Y_var): return logdensities.gaussian(Y, Fmu*self.tec_scale, Fvar*self.tec_scale**2 + Y_var) @params_as_tensors def variational_expectations(self, Fmu, Fvar, Y, Y_var): return -0.5 * np.log(2 * np.pi) - 0.5 * tf.log(Y_var) \ - 0.5 * (tf.square(Y - Fmu*self.tec_scale) + Fvar*self.tec_scale**2) / Y_var class WrappedPhaseGaussianEncodedHeteroDirectionalOutliers(Likelihood): """This is an efficient version of the encoded likelihood.""" def __init__(self, tec_scale=0.005, num_gauss_hermite_points=20, num_mc_samples=1, variance=1.0, K=2, directional_var_matrix=None, name=None): super().__init__(name=name) self.K = K self.variance = Parameter( variance, transform=transforms.positive, dtype=settings.float_type) assert directionla_var_matrix is not None self.directional_var_matrix = Parameter(directional_var_matrix, transform=transforms.positive, dtype=settings.float_type) self.tec_scale = tec_scale self.num_gauss_hermite_points = num_gauss_hermite_points self.num_mc_samples = num_mc_samples self.tec_conversion = tf.convert_to_tensor(tec_scale * -8.4480e9,dtype=settings.float_type, name='tec_conversion') # rad Hz/ tecu @params_as_tensors def logp(self, F, Y, Y_var, freq, dir_idx, **kwargs): """ The log-likelihood function. F is ..., P Y is ..., P Y_var ..., P freq ..., P Returns: tensor ..., P """ #..., Nf phase = wrap(self.tec_conversion * (F / freq)) # dphase = wrap(phase) - wrap(Y) # Ito theorem dir_var = tf.gather(self.directional_var_matrix, dir_idx) log_prob = tf.stack([tf.distributions.Normal(phase + tf.convert_to_tensor(k*2*np.pi,float_type), tf.sqrt(Y_var)).log_prob(wrap(Y)) for k in range(-self.K,self.K+1,1)], axis=0) log_prob = tf.reduce_logsumexp(log_prob, axis=0) #..., P return log_prob @params_as_tensors def conditional_mean(self, F, freq, **kwargs): # pylint: disable=R0201 """The mean of the likelihood conditioned on latent.""" # ..., Nf phase = self.tec_conversion * (F/freq) return phase @params_as_tensors def conditional_variance(self, Y_var, **kwargs): return Y_var + self.variance def predict_mean_and_var(self, Fmu, Fvar, Y_var, freq): r""" Given a Normal distribution for the latent function, return the mean of Y if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean \int\int y p(y|f)q(f) df dy and the predictive variance \int\int y^2 p(y|f)q(f) df dy - [ \int\int y^2 p(y|f)q(f) df dy ]^2 Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (e.g. Gaussian) will implement specific cases. """ integrand2 = lambda *X, **kwargs: self.conditional_variance(*X, **kwargs) + tf.square(self.conditional_mean(*X, **kwargs)) E_y, E_y2 = ndiagquad([self.conditional_mean, integrand2], self.num_gauss_hermite_points, Fmu, Fvar, Y_var=Y_var, freq=freq) V_y = E_y2 - tf.square(E_y) return E_y, V_y def predict_density(self, Fmu, Fvar, Y, Y_var, freq): r""" Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y. i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive density \log \int p(y=Y|f)q(f) df Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, logspace=True, Y=Y, Y_var=Y_var, freq=freq) def variational_expectations(self, Fmu, Fvar, Y, Y_var, freq, mc=False, mvn=False): r""" Compute the expected log density of the data, given a Gaussian distribution for the function values. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes \int (\log p(y|f)) q(f) df. Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ if mvn: assert len(Fvar.shape) == 3 if not mvn: if not mc: return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) else: return ndiag_mc(self.logp, self.num_mc_samples , Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) else: if not mc: raise ValueError("Too slow to do this") else: return mvn_mc(self.logp, self.num_mc_samples , Fmu, Fvar, Y=Y, Y_var=Y_var, freq=freq) class ComplexHarmonicPhaseOnlyGaussianEncodedHetero(Likelihood): """This is an efficient version of the encoded likelihood.""" def __init__(self, tec_scale=0.005, variance=1.0, analytic = False, name=None): super().__init__(name=name) self.variance = Parameter( variance, transform=transforms.positive, dtype=settings.float_type) self.tec_scale = tec_scale self.tec_conversion = tf.convert_to_tensor(tec_scale * -8.4480e9,dtype=settings.float_type, name='tec_conversion') # rad Hz/ tecu self.analytic = analytic @params_as_tensors def logp(self, F, Y, Y_var, freq, **kwargs): """ The log-likelihood function. F is ..., P Y is ..., P Y_var ..., P freq ..., P Returns: tensor ..., P """ #..., Nf phase = self.tec_conversion * (F / freq) ### might need to use with I(1./Y_var) kappa = 1./Y_var log_prob = kappa * tf.cos(phase - Y) - np.log(2*np.pi) - kappa - tf.log(tf.math.bessel_i0e(kappa)) return log_prob @params_as_tensors def conditional_mean(self, F, freq, **kwargs): # pylint: disable=R0201 """The mean of the likelihood conditioned on latent.""" # ..., Nf phase = self.tec_conversion * (F/freq) return phase @params_as_tensors def conditional_variance(self, Y_var, **kwargs): return Y_var + self.variance def predict_mean_and_var(self, Fmu, Fvar, Y_var, freq): r""" Given a Normal distribution for the latent function, return the mean of Y if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean \int\int y p(y|f)q(f) df dy and the predictive variance \int\int y^2 p(y|f)q(f) df dy - [ \int\int y^2 p(y|f)q(f) df dy ]^2 Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (e.g. Gaussian) will implement specific cases. """ integrand2 = lambda *X, **kwargs: self.conditional_variance(*X, **kwargs) + tf.square(self.conditional_mean(*X, **kwargs)) E_y, E_y2 = ndiagquad([self.conditional_mean, integrand2], self.num_gauss_hermite_points, Fmu, Fvar, Y_var=Y_var, freq=freq) V_y = E_y2 - tf.square(E_y) return E_y, V_y def predict_density(self, Fmu, Fvar, Y, Y_var, freq): r""" Given a Normal distribution for the latent function, and a datum Y, compute the log predictive density of Y. i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive density \log \int p(y=Y|f)q(f) df Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ return ndiagquad(self.logp, self.num_gauss_hermite_points, Fmu, Fvar, logspace=True, Y=Y, Y_var=Y_var, freq=freq) def variational_expectations(self, Fmu, Fvar, Y, Y_var, freq): r""" Compute the expected log density of the data, given a Gaussian distribution for the function values. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes \int (\log p(y|f)) q(f) df. Here, we implement a default Gauss-Hermite quadrature routine, but some likelihoods (Gaussian, Poisson) will implement specific cases. """ kappa = 1./Y_var A = self.tec_conversion / freq norm = np.log(2*np.pi) + kappa + tf.log(tf.math.bessel_i0e(kappa)) #..., Nf # phase = self.tec_conversion * (Fmu / freq) return kappa * tf.cos(A*Fmu - Y) * tf.exp(-A**2 * Fvar / 2.) - norm

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Python

fireant/tests/dataset/operations/test_share.py

mikeengland/fireant

63c12728c11f1fb252265459f8b8f384d20414b9

[ "Apache-2.0" ]

122

2016-08-05T13:34:52.000Z

2022-03-15T13:21:13.000Z

fireant/tests/dataset/operations/test_share.py

mikeengland/fireant

63c12728c11f1fb252265459f8b8f384d20414b9

[ "Apache-2.0" ]

321

2016-08-10T08:48:15.000Z

2021-07-28T13:08:18.000Z

fireant/tests/dataset/operations/test_share.py

mikeengland/fireant

63c12728c11f1fb252265459f8b8f384d20414b9

[ "Apache-2.0" ]

27

2016-08-10T08:11:08.000Z

2021-08-23T08:14:37.000Z

from unittest import TestCase from unittest.mock import MagicMock import numpy as np import pandas as pd import pandas.testing from fireant import Field, Share from fireant.dataset.references import Reference, WeekOverWeek from fireant.tests.dataset.mocks import ( dimx0_metricx1_df, dimx1_str_df, dimx1_str_totals_df, dimx2_date_str_df, dimx2_date_str_totals_df, dimx2_date_str_totalsx2_df, mock_dataset, ) from fireant.utils import alias_selector class ShareTests(TestCase): def test_apply_to_zero_dims(self): share = Share(mock_dataset.fields.votes) result = share.apply(dimx0_metricx1_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series([100.0], name=f_metric_key) pandas.testing.assert_series_equal(expected, result) def test_apply_to_one_dim_over_first(self): share = Share(mock_dataset.fields.votes, over=mock_dataset.fields.political_party) result = share.apply(dimx1_str_totals_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series([48.8487, 0.9638, 50.1873, 100.0], name=f_metric_key, index=dimx1_str_totals_df.index) pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_apply_to_one_dim_over_none(self): share = Share(mock_dataset.fields.votes) result = share.apply(dimx1_str_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series([100.0] * 3, name=f_metric_key, index=dimx1_str_df.index) pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_apply_to_two_dims_over_first(self): share = Share(mock_dataset.fields.votes, over=mock_dataset.fields.timestamp) result = share.apply(dimx2_date_str_totalsx2_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) metric_series = dimx2_date_str_totalsx2_df[f_metric_key] expected = 100 * metric_series / metric_series.iloc[-1] pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_apply_to_two_dims_over_second(self): share = Share(mock_dataset.fields.votes, over=mock_dataset.fields.political_party) result = share.apply(dimx2_date_str_totals_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series( [ 49.79, 7.07, 43.12, 100.0, 49.78, 50.21, 100.0, 48.83, 51.16, 100.0, 55.42, 44.57, 100.0, 60.39, 39.60, 100.0, 26.60, 73.39, 100.0, ], name=f_metric_key, index=dimx2_date_str_totals_df.index, ) pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_apply_to_two_dims_over_second_all_totals(self): share = Share(mock_dataset.fields.votes, over=mock_dataset.fields.political_party) result = share.apply(dimx2_date_str_totalsx2_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series( [ 49.79, 7.07, 43.12, 100.0, 49.78, 50.21, 100.0, 48.83, 51.16, 100.0, 55.42, 44.57, 100.0, 60.39, 39.60, 100.0, 26.60, 73.39, 100.0, 100.0, ], name=f_metric_key, index=dimx2_date_str_totalsx2_df.index, ) pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_apply_to_two_dims_over_second_with_one_row_per_group(self): raw_df = dimx2_date_str_totals_df.iloc[[0, 3, 4, 6]] share = Share(mock_dataset.fields.votes, over=mock_dataset.fields.political_party) result = share.apply(raw_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series([49.79, 100.0, 49.78, 100.0], name=f_metric_key, index=raw_df.index) pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_apply_to_two_dims_over_none(self): share = Share(mock_dataset.fields.votes) result = share.apply(dimx2_date_str_df, None) f_metric_key = alias_selector(mock_dataset.fields.votes.alias) expected = pd.Series([100.0] * 13, name=f_metric_key, index=dimx2_date_str_df.index) pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3) def test_share_for_references_with_delta_percent(self): dataset = MagicMock() dataset.table._table_name = "table" value_field = Field("value", None) over_field = Field("dim-over", None) share = Share(value_field, over_field) reference = Reference(value_field, WeekOverWeek, delta=True, delta_percent=True) df = pd.DataFrame.from_dict( { "$value_wow": [10, 15, 20, 5, 50], "$value": [12, 16, 14, 8, 50], "$share(value,dim-over)": [24, 32, 28, 16, 100], "$dim-over": ["A", "B", "C", "D", "~~totals"], } ).set_index('$dim-over') result = share.apply(df, reference) np.testing.assert_array_equal(([20.0, 6 + (2 / 3), -30.0, 60, 0]), result.values) def test_share_for_references_with_delta(self): dataset = MagicMock() dataset.table._table_name = "table" value_field = Field("value", None) over_field = Field("dim-over", None) share = Share(value_field, over_field) reference = Reference(value_field, WeekOverWeek, delta=True, delta_percent=False) df = pd.DataFrame.from_dict( { "$value_wow": [10, 15, 20, 5, 50], "$value": [12, 16, 14, 8, 50], "$share(value,dim-over)": [24, 32, 28, 16, 100], "$dim-over": ["A", "B", "C", "D", "~~totals"], } ).set_index('$dim-over') result = share.apply(df, reference) np.testing.assert_array_equal(([4, 2, -12, 6, 0]), result.values)

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0.751419

0.743853

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0.072439

0.292172

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null

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null

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6

91b0cdf6068a00244732f06fa195518867211586

41

py

Python

call_seq_browser/TextEdit/__init__.py

ya790206/call_seq_browser

3f52fe3cc340af8e454f57d04e3dec17168a29bd

[ "Apache-2.0" ]

3

2016-02-26T10:46:47.000Z

2016-06-02T03:14:30.000Z

call_seq/TextEdit/__init__.py

ya790206/call_seq

ee6e0022e1731ce0c72e4101100b6f2a94812b15

[ "Apache-2.0" ]

null

call_seq/TextEdit/__init__.py

ya790206/call_seq

ee6e0022e1731ce0c72e4101100b6f2a94812b15

[ "Apache-2.0" ]

1

2018-12-09T04:35:34.000Z

from .rich import RichTextEdit as Editor

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40

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35,070

py

Python

tf_verify/deeppoly_nodes.py

yugeshk/eran

30c6c92e4f2de1fa9da4dd445701385cbfe0fe57

[ "Apache-2.0" ]

4

2021-05-19T17:35:30.000Z

2021-08-17T04:03:21.000Z

tf_verify/deeppoly_nodes.py

verivital/eran

f4cbc73422cda19316b6312bb288b0e46848510c

[ "Apache-2.0" ]

null

tf_verify/deeppoly_nodes.py

verivital/eran

f4cbc73422cda19316b6312bb288b0e46848510c

[ "Apache-2.0" ]

1

2021-01-20T14:35:53.000Z

''' @author: Adrian Hoffmann ''' import numpy as np from config import config, Device if config.device == Device.CPU: from fppoly import * else: from fppoly_gpu import * from elina_interval import * from elina_abstract0 import * from elina_manager import * from krelu import encode_krelu_cons from ai_milp import * from functools import reduce def calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer = False, destroy=True, use_krelu = False): layerno = nn.calc_layerno() bounds = box_for_layer(man, element, layerno) num_neurons = get_num_neurons_in_layer(man, element, layerno) itv = [bounds[i] for i in range(num_neurons)] lbi = [x.contents.inf.contents.val.dbl for x in itv] ubi = [x.contents.sup.contents.val.dbl for x in itv] if is_refine_layer: nlb.append(lbi) nub.append(ubi) if use_krelu: encode_krelu_cons(nn, man, element, 0, layerno, num_neurons, lbi, ubi, relu_groups, False, 'refinepoly') if destroy: elina_interval_array_free(bounds,num_neurons) return lbi, ubi return layerno, bounds, num_neurons, lbi, ubi def add_input_output_information_deeppoly(self, input_names, output_name, output_shape): """ sets for an object the three fields: - self.output_length - self.input_names - self.output_name which will mainly be used by the Optimizer, but can also be used by the Nodes itself Arguments --------- self : Object will be a DeepzonoNode, but could be any object input_names : iterable iterable of strings, each one being the name of another Deepzono-Node output_name : str name of self output_shape : iterable iterable of ints with the shape of the output of this node Return ------ None """ self.output_length = reduce((lambda x, y: x*y), output_shape) self.input_names = input_names self.output_name = output_name class DeeppolyInput: def __init__(self, specLB, specUB, input_names, output_name, output_shape, lexpr_weights=None, lexpr_cst=None, lexpr_dim=None, uexpr_weights=None, uexpr_cst=None, uexpr_dim=None, expr_size=0): """ Arguments --------- specLB : numpy.ndarray 1D array with the lower bound of the input spec specUB : numpy.ndarray 1D array with the upper bound of the input spec lexpr_weights: numpy.ndarray ndarray of doubles with coefficients of lower polyhedral expressions lexpr_cst: numpy.ndarray ndarray of doubles with the constants of lower polyhedral expressions lexpr_dim: numpy.ndarray ndarray of unsigned int with the indexes of pixels from the original image for the lower polyhedral expressions uexpr_weights: numpy.ndarray ndarray of doubles with coefficients of upper polyhedral expressions uexpr_cst: numpy.ndarray ndarray of doubles with the constants of upper polyhedral expressions uexpr_dim: numpy.ndarray ndarray of unsigned int with the indexes of pixels from the original image for the upper polyhedral expressions expr_size: numpy.ndarray unsigned int with the sizes of polyhedral expressions """ self.specLB = np.ascontiguousarray(specLB, dtype=np.double) self.specUB = np.ascontiguousarray(specUB, dtype=np.double) if lexpr_weights is not None: self.lexpr_weights = np.ascontiguousarray(lexpr_weights, dtype=np.double) else: self.lexpr_weights = None if lexpr_cst is not None: self.lexpr_cst = np.ascontiguousarray(lexpr_cst, dtype=np.double) else: self.lexpr_cst = None if lexpr_dim is not None: self.lexpr_dim = np.ascontiguousarray(lexpr_dim, dtype=np.uintp) else: self.lexpr_dim = None if uexpr_weights is not None: self.uexpr_weights = np.ascontiguousarray(uexpr_weights, dtype=np.double) else: self.uexpr_weights = None if uexpr_cst is not None: self.uexpr_cst = np.ascontiguousarray(uexpr_cst, dtype=np.double) else: self.uexpr_cst = None if uexpr_dim is not None: self.uexpr_dim = np.ascontiguousarray(lexpr_dim, dtype=np.uintp) else: self.uexpr_dim = None self.expr_size = expr_size add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, man): """ creates an abstract element from the input spec Arguments --------- man : ElinaManagerPtr inside this manager the abstract element will be created Return ------ output : ElinaAbstract0Ptr new abstract element representing the element specified by self.specLB and self.specUB """ if self.expr_size == 0: return fppoly_from_network_input(man, 0, len(self.specLB), self.specLB, self.specUB) else: return fppoly_from_network_input_poly(man, 0, len(self.specLB), self.specLB, self.specUB, self.lexpr_weights, self.lexpr_cst, self.lexpr_dim, self.uexpr_weights, self.uexpr_cst, self.uexpr_dim, self.expr_size) class DeeppolyNode: """ Parent class for all the classes that implement fully connected layers """ def __init__(self, weights, bias, input_names, output_name, output_shape): """ Arguments --------- weights : numpy.ndarray matrix of the fully connected layer (must be 2D) bias : numpy.ndarray bias of the fully connected layer """ self.weights = np.ascontiguousarray(weights, dtype=np.double) self.bias = np.ascontiguousarray(bias, dtype=np.double) add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def get_arguments(self): """ facilitates putting together all the arguments for the transformers in the child classes Return ------ output : tuple the four entries are pointers to the rows of the matrix, the bias, the length of the output, and the length of the input """ xpp = self.get_xpp() return xpp, self.bias, self.weights.shape[0], self.weights.shape[1], self.predecessors def get_xpp(self): """ helper function to get pointers to the rows of self.weights. Return ------ output : numpy.ndarray pointers to the rows of the matrix """ return (self.weights.__array_interface__['data'][0]+ np.arange(self.weights.shape[0])*self.weights.strides[0]).astype(np.uintp) class DeeppolyReluNodeFirst(DeeppolyNode): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for the first layer of a neural network, if that first layer is fully connected with relu Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_first_relu_layer(man, element, *self.get_arguments()) calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True, use_krelu=refine) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolySigmoidNodeFirst(DeeppolyNode): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for the first layer of a neural network, if that first layer is fully connected with sigmoid Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_first_sigmoid_layer(man, element, *self.get_arguments()) if testing: lb, ub = calc_bounds(man, element, nn, nlb, nub, relu_groups) nn.ffn_counter+=1 if testing: return element, lb, ub return element class DeeppolyTanhNodeFirst(DeeppolyNode): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for the first layer of a neural network, if that first layer is fully connected with tanh Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_first_tanh_layer(man, element, *self.get_arguments()) if testing: lb, ub = calc_bounds(man, element, nn, nlb, nub, relu_groups) nn.ffn_counter+=1 if testing: return element, lb, ub return element class DeeppolyReluNodeIntermediate(DeeppolyNode): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for any intermediate fully connected layer with relu Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_intermediate_relu_layer(man, element, *self.get_arguments(), use_default_heuristic) layerno, bounds, num_neurons, lbi, ubi = calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True, destroy = False) candidate_vars = [i for i, (l, u) in enumerate(zip(lbi, ubi)) if l<0 and u>0] #print("lbi ", timeout_milp, "ubi ", timeout_lp) if refine: if layerno <= 1: use_milp = config.use_milp else: use_milp = 0 if use_milp: timeout = timeout_milp else: timeout = timeout_lp if nn.is_ffn(): resl, resu, indices = get_bounds_for_layer_with_milp(nn, nn.specLB, nn.specUB, layerno, layerno, num_neurons, nlb, nub, relu_groups, use_milp, candidate_vars, timeout) for j in indices: update_bounds_for_neuron(man,element,layerno,j,resl[j],resu[j]) nlb[-1] = resl nub[-1] = resu encode_krelu_cons(nn, man, element, 0, layerno, num_neurons, lbi, ubi, relu_groups, False, 'refinepoly') elina_interval_array_free(bounds,num_neurons) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolySigmoidNodeIntermediate(DeeppolyNode): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for any intermediate fully connected layer with sigmoid Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_intermediate_sigmoid_layer(man, element, *self.get_arguments(), use_default_heuristic) if testing or refine: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyTanhNodeIntermediate(DeeppolyNode): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for any intermediate fully connected layer with tanh Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_intermediate_tanh_layer(man, element, *self.get_arguments(), use_default_heuristic) if testing or refine: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyReluNodeLast(DeeppolyNode): def __init__(self, weights, bias, relu_present, input_names, output_name, output_shape): """ Arguments --------- weights : numpy.ndarray matrix of the fully connected layer (must be 2D) bias : numpy.ndarray bias of the fully connected layer relu_present : bool whether this layer has relu or not """ DeeppolyNode.__init__(self, weights, bias, input_names, output_name, output_shape) self.relu_present = relu_present def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for a fully connected layer if it's the last layer in the network Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_last_relu_layer(man, element, *self.get_arguments(), self.relu_present, use_default_heuristic) layerno, bounds, num_neurons, lbi, ubi = calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True, destroy=False) candidate_vars = [i for i, (l, u) in enumerate(zip(lbi, ubi)) if l<0 and u>0] if(refine): if layerno<=1: use_milp = 1 else: use_milp = 0 if use_milp: timeout = timeout_milp else: timeout = timeout_lp if nn.is_ffn(): resl, resu, indices = get_bounds_for_layer_with_milp(nn, nn.specLB, nn.specUB, layerno, layerno, num_neurons, nlb, nub, relu_groups, use_milp, candidate_vars, timeout) for j in indices: update_bounds_for_neuron(man,element,layerno,j,resl[j],resu[j]) #print("resl ", resl, "resu ", resu) nlb[-1] = resl nub[-1] = resu encode_krelu_cons(nn, man, element, 0, layerno, num_neurons, lbi, ubi, relu_groups, False, 'refinepoly') elina_interval_array_free(bounds,num_neurons) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolySigmoidNodeLast(DeeppolyNode): def __init__(self, weights, bias, sigmoid_present, input_names, output_name, output_shape): """ Arguments --------- weights : numpy.ndarray matrix of the fully connected layer (must be 2D) bias : numpy.ndarray bias of the fully connected layer relu_present : bool whether this layer has sigmoid or not """ DeeppolySigmoidNode.__init__(self, weights, bias, input_names, output_name, output_shape) self.sigmoid_present = sigmoid_present def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for a fully connected layer if it's the last layer in the network Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_last_sigmoid_layer(man, element, *self.get_arguments(), self.sigmoid_present, use_default_heuristic) if testing: lb, ub = calc_bounds(man, element, nn, nlb, nub) nn.ffn_counter+=1 if testing: return element, lb, ub return element class DeeppolyTanhNodeLast(DeeppolyNode): def __init__(self, weights, bias, tanh_present, input_names, output_name, output_shape): """ Arguments --------- weights : numpy.ndarray matrix of the fully connected layer (must be 2D) bias : numpy.ndarray bias of the fully connected layer relu_present : bool whether this layer has relu or not """ DeeppolyTanhNode.__init__(self, weights, bias, input_names, output_name, output_shape) self.tanh_present = tanh_present def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for a fully connected layer if it's the last layer in the network Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ ffn_handle_last_tanh_layer(man, element, *self.get_arguments(), self.tanh_present, use_default_heuristic) if testing: lb, ub = calc_bounds(man, element, nn, nlb, nub) nn.ffn_counter+=1 if testing: return element, lb, ub return element class DeeppolyConv2dNodeIntermediate: def __init__(self, filters, strides, pad_top, pad_left, bias, image_shape, input_names, output_name, output_shape, has_relu): """ collects the information needed for the conv_handle_intermediate_relu_layer transformer and brings it into the required shape Arguments --------- filters : numpy.ndarray the actual 4D filter of the convolutional layer strides : numpy.ndarray 1D with to elements, stride in height and width direction bias : numpy.ndarray the bias of the layer image_shape : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer """ self.image_shape = np.ascontiguousarray(image_shape, dtype=np.uintp) self.filters = np.ascontiguousarray(filters, dtype=np.double) self.strides = np.ascontiguousarray(strides, dtype=np.uintp) self.bias = np.ascontiguousarray(bias, dtype=np.double) self.out_size = (c_size_t * 3)(output_shape[1], output_shape[2], output_shape[3]) self.pad_top = pad_top self.pad_left = pad_left self.has_relu = has_relu add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def get_arguments(self): """ facilitates putting together all the arguments for the transformers in the child classes Return ------ output : tuple the 5 entries are: 1. the filter (numpy.ndarray) 2. the bias (numpy.ndarray) 3. the image_shape (numpy.ndarray) 4. length of a side of the square kernel (int) 5. number of filters (int) """ filter_size = (c_size_t * 2) (self.filters.shape[0], self.filters.shape[1]) numfilters = self.filters.shape[3] strides = (c_size_t * 2)(self.strides[0], self.strides[1]) return self.filters, self.bias, self.image_shape, filter_size, numfilters, strides, self.out_size, self.pad_top, self.pad_left, True, self.predecessors def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for a convolutional layer, if that layer is an intermediate of the network Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ if(self.has_relu): conv_handle_intermediate_relu_layer(man, element, *self.get_arguments(), use_default_heuristic) else: conv_handle_intermediate_affine_layer(man, element, *self.get_arguments(), use_default_heuristic) layerno, bounds, num_neurons, lbi, ubi = calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True, destroy=False) candidate_vars = [i for i, (l, u) in enumerate(zip(lbi, ubi)) if l<0 and u>0] if(refine): use_milp = config.use_milp if use_milp: timeout = timeout_milp else: timeout = timeout_lp #numconvslayers = sum('Conv2D' in l for l in nn.layertypes) #if numconvslayers-nn.conv_counter <= 1: if nn.is_ffn(): resl, resu, indices = get_bounds_for_layer_with_milp(nn, nn.specLB, nn.specUB, num_neurons, nlb, nub, relu_groups, use_milp, candidate_vars, timeout) nlb[-1] = resl nub[-1] = resu for j in indices: update_bounds_for_neuron(man,element,layerno,j,resl[j],resu[j]) encode_krelu_cons(nn, man, element, 0, layerno, num_neurons, lbi, ubi, relu_groups, False, 'refinepoly') elina_interval_array_free(bounds,num_neurons) nn.conv_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyConv2dNodeFirst(DeeppolyConv2dNodeIntermediate): def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for a convolutional layer, if that layer is the first of the network Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ conv_handle_first_layer(man, element, *self.get_arguments()) calc_bounds(man, element, nn, nlb, nub, relu_groups, use_krelu=refine, is_refine_layer=True) nn.conv_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyMaxpool: def __init__(self, image_shape, window_size, strides, input_names, output_name, output_shape): """ collects the information needed for the handle_maxpool_layer transformer and brings it into the required shape Arguments --------- input_shape : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer window_size : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the window's size in these directions strides : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the stride in these directions """ self.image_shape = np.ascontiguousarray(image_shape, dtype=np.uintp) self.window_size = np.ascontiguousarray([window_size[0], window_size[1], 1], dtype=np.uintp) add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): """ transformer for a maxpool layer, this can't be the first layer of a network Arguments --------- man : ElinaManagerPtr man to which element belongs element : ElinaAbstract0Ptr abstract element onto which the transformer gets applied Return ------ output : ElinaAbstract0Ptr abstract element after the transformer """ handle_maxpool_layer(man, element, self.window_size, self.image_shape, self.predecessors) if refine or testing: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.maxpool_counter += 1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyResadd: def __init__(self, input_names, output_name, output_shape, has_relu): """ Arguments --------- input_names : iterable iterable with the names of the two nodes you want to add output_name : str name of this node's output output_shape : iterable iterable of ints with the shape of the output of this node """ self.has_relu = has_relu add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): if(self.has_relu): handle_residual_relu_layer(man,element,self.output_length,self.predecessors,use_default_heuristic) else: handle_residual_affine_layer(man,element,self.output_length,self.predecessors,use_default_heuristic) calc_bounds(man, element, nn, nlb, nub, relu_groups, use_krelu=refine, is_refine_layer=True) # print("Residual ", nn.layertypes[layerno],layerno) nn.residual_counter += 1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyGather: def __init__(self, indexes, input_names, output_name, output_shape): """ collects the information needed for the handle_gather_layer transformer and brings it into the required shape Arguments --------- indexes : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer window_size : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the window's size in these directions strides : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the stride in these directions """ self.indexes = np.ascontiguousarray(indexes, dtype=np.uintp) add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): handle_gather_layer(man, element, self.indexes) return element class DeeppolySubNodeFirst: def __init__(self, bias, is_minuend, input_names, output_name, output_shape): """ collects the information needed for the handle_gather_layer transformer and brings it into the required shape Arguments --------- indexes : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer window_size : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the window's size in these directions strides : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the stride in these directions """ self.bias = np.ascontiguousarray(bias, dtype=np.float64) self.is_minuend = is_minuend add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): ffn_handle_first_sub_layer(man, element, self.bias, self.is_minuend, len(self.bias.reshape(-1)), self.predecessors) if refine or testing: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolySubNodeIntermediate: def __init__(self, bias, is_minuend, input_names, output_name, output_shape): """ collects the information needed for the handle_gather_layer transformer and brings it into the required shape Arguments --------- indexes : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer window_size : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the window's size in these directions strides : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the stride in these directions """ self.bias = np.ascontiguousarray(bias.reshape(-1), dtype=np.float64) self.is_minuend = is_minuend add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): layerno = nn.calc_layerno() num_neurons = get_num_neurons_in_layer(man, element, layerno) ffn_handle_intermediate_sub_layer(man, element, self.bias, self.is_minuend, num_neurons, self.predecessors, use_default_heuristic) if refine or testing: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyMulNodeFirst: def __init__(self, bias, input_names, output_name, output_shape): """ collects the information needed for the handle_gather_layer transformer and brings it into the required shape Arguments --------- indexes : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer window_size : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the window's size in these directions strides : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the stride in these directions """ self.bias = np.ascontiguousarray(bias, dtype=np.float64) add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): ffn_handle_first_mul_layer(man, element, self.bias, len(self.bias.reshape(-1)), self.predecessors) if refine or testing: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element class DeeppolyMulNodeIntermediate: def __init__(self, bias, input_names, output_name, output_shape): """ collects the information needed for the handle_gather_layer transformer and brings it into the required shape Arguments --------- indexes : numpy.ndarray 1D array of ints with 3 entries [height, width, channels] representing the shape of the of the image that is passed to the conv-layer window_size : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the window's size in these directions strides : numpy.ndarray 1D array of ints with 2 entries [height, width] representing the stride in these directions """ self.bias = np.ascontiguousarray(bias.reshape(-1), dtype=np.float64) add_input_output_information_deeppoly(self, input_names, output_name, output_shape) def transformer(self, nn, man, element, nlb, nub, relu_groups, refine, timeout_lp, timeout_milp, use_default_heuristic, testing): ffn_handle_intermediate_mul_layer(man, element, self.bias, len(self.bias.reshape(-1)), self.predecessors, use_default_heuristic) if refine or testing: calc_bounds(man, element, nn, nlb, nub, relu_groups, is_refine_layer=True) nn.ffn_counter+=1 if testing: return element, nlb[-1], nub[-1] return element

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0.030968

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0.737988

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0

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853

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false

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0.320588

0

null

0

1

0

null

0

6

37e5545312950baed8e45d929ea6e8a99b2a4b2d

27

py

Python

raymarching/__init__.py

NamorNiradnug/raymarching

49cbe47b0a0616e6f3cb0c0eb1d3bcf50eb22fff

[ "MIT" ]

null

raymarching/__init__.py

NamorNiradnug/raymarching

49cbe47b0a0616e6f3cb0c0eb1d3bcf50eb22fff

[ "MIT" ]

null

raymarching/__init__.py

NamorNiradnug/raymarching

49cbe47b0a0616e6f3cb0c0eb1d3bcf50eb22fff

[ "MIT" ]

null

from .raymarching import *

13.5

26

0.777778

3

27

7

1

0

0.148148

27

1

27

0.913043

0

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null

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1

0

null

0

1

0

1

0

1

0

6

37f50bfff26a9d21699607a2917c38e52f099bf6

33

py

Python

vrpc/__init__.py

pablitovicente/vrpc_260_debug

6ffffcaefae261772fd71c642d5d6ef349440b2d

[ "MIT" ]

null

vrpc/__init__.py

pablitovicente/vrpc_260_debug

6ffffcaefae261772fd71c642d5d6ef349440b2d

[ "MIT" ]

null

vrpc/__init__.py

pablitovicente/vrpc_260_debug

6ffffcaefae261772fd71c642d5d6ef349440b2d

[ "MIT" ]

null

from .VrpcLocal import VrpcLocal

16.5

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0.848485

4

33

7

0.75

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0

null

0

1

0

1

0

1

0

6

727a8db4db591fb6ca953bdbdadf6777fd3c6e4f

1,776

py

Python

quadpy/hexahedron/helpers.py

gdmcbain/quadpy

c083d500027d7c1b2187ae06ff2b7fbdd360ccc7

[ "MIT" ]

1

2019-01-02T19:04:42.000Z

quadpy/hexahedron/helpers.py

gdmcbain/quadpy

c083d500027d7c1b2187ae06ff2b7fbdd360ccc7

[ "MIT" ]

null

quadpy/hexahedron/helpers.py

gdmcbain/quadpy

c083d500027d7c1b2187ae06ff2b7fbdd360ccc7

[ "MIT" ]

null

# -*- coding: utf-8 -*- # import numpy def z(): return numpy.array([[0, 0, 0]]) def fs_r00(a): return numpy.array( [[+a, 0, 0], [0, +a, 0], [0, 0, +a], [-a, 0, 0], [0, -a, 0], [0, 0, -a]] ) def fs_rr0(a): return numpy.array( [ [+a, +a, 0], [+a, 0, +a], [0, +a, +a], [+a, -a, 0], [+a, 0, -a], [0, +a, -a], [-a, +a, 0], [-a, 0, +a], [0, -a, +a], [-a, -a, 0], [-a, 0, -a], [0, -a, -a], ] ) def fs_rrs(a, b): return numpy.array( [ [+a, +a, +b], [+a, +b, +a], [+b, +a, +a], [+a, -a, +b], [+a, +b, -a], [+b, +a, -a], [-a, +a, +b], [-a, +b, +a], [+b, -a, +a], [-a, -a, +b], [-a, +b, -a], [+b, -a, -a], [+a, +a, -b], [+a, -b, +a], [-b, +a, +a], [+a, -a, -b], [+a, -b, -a], [-b, +a, -a], [-a, +a, -b], [-a, -b, +a], [-b, -a, +a], [-a, -a, -b], [-a, -b, -a], [-b, -a, -a], ] ) def rss_pm(r, s): return numpy.array( [ [+r, +s, +s], [+s, +r, +s], [+s, +s, +r], [-r, -s, -s], [-s, -r, -s], [-s, -s, -r], ] ) def pm_rrr(a): return numpy.array( [ [+a, +a, +a], [-a, +a, +a], [+a, -a, +a], [-a, -a, +a], [+a, +a, -a], [-a, +a, -a], [+a, -a, -a], [-a, -a, -a], ] )

19.304348

80

0.193694

218

1,776

1.555046

0.09633

0.342183

0.371681

0.365782

0.693215

0.59587

0.495575

0.389381

0

0.037304

0.532095

1,776

91

81

19.516484

0.370638

0.011824

0

0.064935

0

1

0.077922

false

0

0.012987

0.077922

0.168831

0

1

null

1

0

1

0

1

0

null

0

6

7287be5aae461d6a67ddf9b30b8b3cc1d7b986c0

24

py

Python

foil/data/datasets/__init__.py

nbrgr/Foil-VLBert

f6a1b54a87affa91a7362216e8c7598e30d45ae5

[ "MIT" ]

null

foil/data/datasets/__init__.py

nbrgr/Foil-VLBert

f6a1b54a87affa91a7362216e8c7598e30d45ae5

[ "MIT" ]

null

foil/data/datasets/__init__.py

nbrgr/Foil-VLBert

f6a1b54a87affa91a7362216e8c7598e30d45ae5

[ "MIT" ]

null

from .foil import Foil

8

22

0.75

4

24

4.5

0.75

0

0.208333

24

2

23

12

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

72e85908a10f2b7bee28f8f0e12051d7e5b8f76b

19

py

Python

frameworks/Python/api_hour/yocto_http/hello/endpoints/__init__.py

xsoheilalizadeh/FrameworkBenchmarks

855527008f7488e4fd508d1e72dfa9953874a2c6

[ "BSD-3-Clause" ]

5,300

2015-01-02T08:04:20.000Z

2022-03-31T10:08:33.000Z

frameworks/Python/api_hour/yocto_http/hello/endpoints/__init__.py

xsoheilalizadeh/FrameworkBenchmarks

855527008f7488e4fd508d1e72dfa9953874a2c6

[ "BSD-3-Clause" ]

3,075

2015-01-01T05:11:45.000Z

2022-03-31T23:56:33.000Z

frameworks/Python/api_hour/yocto_http/hello/endpoints/__init__.py

xsoheilalizadeh/FrameworkBenchmarks

855527008f7488e4fd508d1e72dfa9953874a2c6

[ "BSD-3-Clause" ]

2,151

2015-01-02T14:16:09.000Z

2022-03-30T00:15:26.000Z

from . import world

19

0.789474

3

19

5

1

0

0.157895

19

1

19

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

72fbbf5336a69e038b317edbf5d3b989493f3ca6

28

py

Python

lcm/__init__.py

bashirk/lcmfinda

da57e9127367cdd2b24fbb351dc478b2318a2882

[ "MIT" ]

null

lcm/__init__.py

bashirk/lcmfinda

da57e9127367cdd2b24fbb351dc478b2318a2882

[ "MIT" ]

null

lcm/__init__.py

bashirk/lcmfinda

da57e9127367cdd2b24fbb351dc478b2318a2882

[ "MIT" ]

null

from lcm.lcm import cal_lcm

14

27

0.821429

6

28

3.666667

0.666667

0

0.142857

28

1

28

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f4214cfb5642facc7a9c6ae222a0074549016e89

28

py

Python

conduit/data/datasets/audio/__init__.py

DavidHurst/palbolts

72f9ca3f82499b532f14d0e797426e1b425d3efe

[ "MIT" ]

2

2021-07-15T20:36:25.000Z

2021-08-04T15:53:50.000Z

conduit/data/datasets/audio/__init__.py

DavidHurst/palbolts

72f9ca3f82499b532f14d0e797426e1b425d3efe

[ "MIT" ]

18

2021-09-07T13:50:10.000Z

2021-12-06T19:02:23.000Z

conduit/data/datasets/audio/__init__.py

predictive-analytics-lab/pal-bolts

5f1932f351f2e551276b47dfeda7888772d99895

[ "MIT" ]

1

2022-03-24T03:52:44.000Z

from .ecoacoustics import *

14

27

0.785714

3

28

7.333333

1

0

0.142857

28

1

28

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f473190f9141668ad046402b61fb1a081fb9f63e

27

py

Python

examples/verlet_chain/__init__.py

abbasegbeyemi/pyqtgraph

6aeafce477d1d7eebb9d2fe824d4c5573ef9ceed

[ "MIT" ]

150

2018-03-27T16:45:37.000Z

2022-03-30T03:47:56.000Z

examples/verlet_chain/__init__.py

abbasegbeyemi/pyqtgraph

6aeafce477d1d7eebb9d2fe824d4c5573ef9ceed

[ "MIT" ]

67

2019-11-30T14:45:05.000Z

2022-03-14T20:26:06.000Z

examples/verlet_chain/__init__.py

abbasegbeyemi/pyqtgraph

6aeafce477d1d7eebb9d2fe824d4c5573ef9ceed

[ "MIT" ]

40

2018-04-06T19:42:21.000Z

2022-01-11T00:34:17.000Z

from .chain import ChainSim

27

0.851852

4

27

5.75

1

0

0.111111

27

1

27

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

be420617d1f6a9d4325f318fcad5cbb304689b60

136

py

Python

ex9.py

nguyennam9696/Learn_Python_The_Hard_Way

402ffad8d8dc80f0c1f541d8e3d69980268bb559

[ "MIT" ]

null

ex9.py

nguyennam9696/Learn_Python_The_Hard_Way

402ffad8d8dc80f0c1f541d8e3d69980268bb559

[ "MIT" ]

null

ex9.py

nguyennam9696/Learn_Python_The_Hard_Way

402ffad8d8dc80f0c1f541d8e3d69980268bb559

[ "MIT" ]

null

print "I am 6\'2\"" print "I am \\ a \\ cat." print "\tTabbed" while True: for i in ["/", "-", "|", "\\", "|"]: print "%s\r" % i,

19.428571

38

0.419118

21

136

2.714286

0.666667

0.210526

0.280702

0

0.019417

0.242647

136

7

39

19.428571

0.533981

0

0.328467

0

null

0

null

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

be6bcaa5e7bf3d656bbeef0d053d9e75c2dd3e67

6,391

py

Python

projects/vdk-core/tests/vdk/internal/builtin_plugins/connection/test_managed_cursor.py

vmware/versatile-data-kit

c4e10324a4f3203c58079cb18203880f68053f15

[ "Apache-2.0" ]

100

2021-10-04T09:32:04.000Z

2022-03-30T11:23:53.000Z

projects/vdk-core/tests/vdk/internal/builtin_plugins/connection/test_managed_cursor.py

vmware/versatile-data-kit

c4e10324a4f3203c58079cb18203880f68053f15

[ "Apache-2.0" ]

208

2021-10-04T16:56:40.000Z

2022-03-31T10:41:44.000Z

projects/vdk-core/tests/vdk/internal/builtin_plugins/connection/test_managed_cursor.py

vmware/versatile-data-kit

c4e10324a4f3203c58079cb18203880f68053f15

[ "Apache-2.0" ]

14

2021-10-11T14:15:13.000Z

2022-03-11T13:39:17.000Z

# Copyright 2021 VMware, Inc. # SPDX-License-Identifier: Apache-2.0 import logging from unittest.mock import call import pytest from vdk.internal.builtin_plugins.connection.decoration_cursor import DecorationCursor from vdk.internal.builtin_plugins.connection.recovery_cursor import RecoveryCursor from vdk.plugin.test_utils.util_funcs import populate_mock_managed_cursor _query = "select 1" def test_validation__query_valid__execute(): ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() mock_managed_cursor.execute(_query) mock_connection_hook_spec.db_connection_validate_operation.assert_called_once_with( operation=_query, parameters=None ) mock_native_cursor.execute.assert_called_once() def test_validation__query_nonvalid__execute(): ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() mock_connection_hook_spec.db_connection_validate_operation.side_effect = Exception( "Validation exception" ) with pytest.raises(Exception) as e: mock_managed_cursor.execute(_query) assert "Validation exception" == e.value.args[0] mock_native_cursor.execute.assert_not_called() def test_decoration__success__execute(): ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() def mock_decorate(decoration_cursor: DecorationCursor): managed_operation = decoration_cursor.get_managed_operation() managed_operation.set_operation( f"decorated {managed_operation.get_operation()}" ) mock_connection_hook_spec.db_connection_decorate_operation.side_effect = ( mock_decorate ) mock_managed_cursor.execute(_query) mock_connection_hook_spec.db_connection_decorate_operation.assert_called_once() calls = [call(f"decorated {_query}")] mock_native_cursor.execute.assert_has_calls(calls) def test_decoration__failure__execute(): ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() mock_connection_hook_spec.db_connection_decorate_operation.side_effect = Exception( "Decoration exception" ) with pytest.raises(Exception) as e: mock_managed_cursor.execute(_query) assert True == mock_connection_hook_spec.db_connection_decorate_operation.called assert "Decoration exception" == e.value.args[0] mock_native_cursor.execute.assert_not_called() def test_recovery__success__execute(): ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() def mock_decorate(decoration_cursor: DecorationCursor): managed_operation = decoration_cursor.get_managed_operation() managed_operation.set_operation( f"decorated {managed_operation.get_operation()}" ) def mock_recover(recovery_cursor: RecoveryCursor): recovery_cursor.execute("recovery") recovery_cursor.retry_operation() assert recovery_cursor.get_retries() == 1 mock_connection_hook_spec.db_connection_decorate_operation.side_effect = ( mock_decorate ) mock_connection_hook_spec.db_connection_recover_operation.side_effect = mock_recover exception = Exception() mock_native_cursor.execute.side_effect = [exception, None, None] mock_managed_cursor.execute(_query) mock_connection_hook_spec.db_connection_recover_operation.assert_called_once() calls = [ call(f"decorated {_query}"), call(f"decorated recovery"), call(f"decorated {_query}"), ] mock_native_cursor.execute.assert_has_calls(calls) def test_recovery__failure__execute(): ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() def mock_decorate(decoration_cursor: DecorationCursor): managed_operation = decoration_cursor.get_managed_operation() managed_operation.set_operation( f"decorated {managed_operation.get_operation()}" ) def mock_recover(recovery_cursor: RecoveryCursor): raise Exception("Could not handle execution exception") mock_connection_hook_spec.db_connection_decorate_operation.side_effect = ( mock_decorate ) mock_connection_hook_spec.db_connection_recover_operation.side_effect = mock_recover exception = Exception() mock_native_cursor.execute.side_effect = exception with pytest.raises(Exception) as e: mock_managed_cursor.execute(_query) assert "Could not handle execution exception" == e.value.args[0] mock_connection_hook_spec.db_connection_recover_operation.assert_called_once() mock_native_cursor.execute.assert_called_once() def test_query_timing_successful_query(caplog): caplog.set_level(logging.INFO) ( _, mock_managed_cursor, _, _, _, ) = populate_mock_managed_cursor() mock_managed_cursor.execute(_query) assert "Query duration 00h:00m:" in str(caplog.records) def test_query_timing_recovered_query(caplog): caplog.set_level(logging.INFO) ( mock_native_cursor, mock_managed_cursor, _, _, _, ) = populate_mock_managed_cursor() mock_native_cursor.execute.side_effect = [Exception("Mock exception")] mock_managed_cursor.execute(_query) assert "Recovered query duration 00h:00m:" in str(caplog.records) def test_query_timing_failed_query(caplog): caplog.set_level(logging.INFO) ( mock_native_cursor, mock_managed_cursor, _, _, mock_connection_hook_spec, ) = populate_mock_managed_cursor() exception = Exception("Mock exception") mock_native_cursor.execute.side_effect = [exception] mock_connection_hook_spec.db_connection_recover_operation.side_effect = [exception] with pytest.raises(Exception): mock_managed_cursor.execute(_query) assert "Failed query duration 00h:00m:" in str(caplog.records)

30.004695

88

0.724613

718

6,391

5.940111

0.129526

0.072216

0.111606

0.103165

0.842673

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0.807503

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be7f0d131224cf60a95fdd0a0b5d6aaf6b19a1a0

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py

Python

seapy/model/__init__.py

hadfieldnz/seapy

88c2747dbcf85fa9d1da3509e4c510c53016680b

[ "MIT" ]

null

seapy/model/__init__.py

hadfieldnz/seapy

88c2747dbcf85fa9d1da3509e4c510c53016680b

[ "MIT" ]

null

seapy/model/__init__.py

hadfieldnz/seapy

88c2747dbcf85fa9d1da3509e4c510c53016680b

[ "MIT" ]

null

""" State Estimation and Analysis for PYthon Module for working with oceanographic data and models Copyright (c)2020 University of Hawaii under the MIT-License. Import classes include: - :class:`~seapy.model.grid` Imported functions include: - :func:`~seapy.model.lib.bvf` - :func:`~seapy.model.lib.density` - :func:`~seapy.model.hycom.load_history` - :func:`~seapy.model.soda.load_history` - :func:`~seapy.model.lib.pressure` - :func:`~seapy.model.lib.rho2u` - :func:`~seapy.model.lib.rho2v` - :func:`~seapy.model.lib.sound` - :func:`~seapy.model.lib.u2rho` - :func:`~seapy.model.lib.v2rho` - :func:`~seapy.model.lib.v2rho` - :func:`~seapy.model.lib.w` """ from .grid import grid, asgrid from .lib import * from .hycom import * from .soda import *

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py

Python

src/graph_transpiler/webdnn/backend/fallback/kernels/broadcast.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

1

2018-07-26T13:52:21.000Z

src/graph_transpiler/webdnn/backend/fallback/kernels/broadcast.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

null

src/graph_transpiler/webdnn/backend/fallback/kernels/broadcast.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

null

from webdnn.backend.fallback.kernels.elementwise import register_elementwise_kernel from webdnn.graph.operators.broadcast import Broadcast register_elementwise_kernel(Broadcast, "y = x0;")

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1

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0

6

bead51cd0b282b2bbcd757acb45eec77e088b316

41

py

Python

onto/query/__init__.py

billyrrr/onto

72733d36a2583ae4758f7cf33a5229b79773702b

[ "MIT" ]

1

2020-10-04T10:01:45.000Z

onto/query/__init__.py

billyrrr/onto

72733d36a2583ae4758f7cf33a5229b79773702b

[ "MIT" ]

null

onto/query/__init__.py

billyrrr/onto

72733d36a2583ae4758f7cf33a5229b79773702b

[ "MIT" ]

null

from .transaction import run_transaction

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6

fe3ab7746a46a0f797f9c0d364948d9c7fdc855f

144

py

Python

magical_sqlserver/__init__.py

brennoflavio/magical-sqlserver

6dc7cb3df8341f8234f18d36fd13b637a4ffc948

[ "MIT" ]

3

2018-12-27T14:15:47.000Z

2021-05-02T10:23:07.000Z

magical_sqlserver/__init__.py

brennoflavio/magical-sqlserver

6dc7cb3df8341f8234f18d36fd13b637a4ffc948

[ "MIT" ]

null

magical_sqlserver/__init__.py

brennoflavio/magical-sqlserver

6dc7cb3df8341f8234f18d36fd13b637a4ffc948

[ "MIT" ]

null

# flake8: noqa from magical_sqlserver.api import SQLServer from magical_sqlserver.decorators import provide_session name = "magical_sqlserver"

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fe3dcbfc658822e6f3be554c63956da9069f93f2

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py

Python

poll/reddit.py

nath/rcfbpoll

364cb734f97b33b42fa72efb797d9783d391d79a

[ "0BSD" ]

null

poll/reddit.py

nath/rcfbpoll

364cb734f97b33b42fa72efb797d9783d391d79a

[ "0BSD" ]

null

poll/reddit.py

nath/rcfbpoll

364cb734f97b33b42fa72efb797d9783d391d79a

[ "0BSD" ]

null

import praw from .models import User, UserRole def message_voters(username, access_token, title, body): pass

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6

fe444035e2f464ae94435151e7a0a349481c1be4

96

py

Python

venv/lib/python3.8/site-packages/rope/base/history.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/rope/base/history.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/rope/base/history.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/fe/13/d5/dcf4b396fe8c506578795049b7eca94b89333bd904e26977206a8902f2

96

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null

1

0

1

0

6

fe49eeedf618e37861c472469d1004711cf6b2c8

2,969

py

Python

tests/test_logistic_regression.py

shotahorii/ml-from-scratch

10fe8c9d5811bfcb9ee303aba2087524574681e6

[ "MIT" ]

3

2021-03-21T21:16:42.000Z

2021-06-27T03:20:04.000Z

tests/test_logistic_regression.py

shotahorii/ml-from-scratch

10fe8c9d5811bfcb9ee303aba2087524574681e6

[ "MIT" ]

null

tests/test_logistic_regression.py

shotahorii/ml-from-scratch

10fe8c9d5811bfcb9ee303aba2087524574681e6

[ "MIT" ]

null

import numpy as np import pandas as pd from sklearn.datasets import load_iris, load_breast_cancer from sklearn.linear_model import LogisticRegression as LogisticRegression_skl import sys sys.path.append('../') from bareml.machinelearning.supervised import LogisticRegression from bareml.machinelearning.utils.model_selection import KFold, StratifiedKFold def test_binary_classification(): data = load_breast_cancer() X = data.data y = data.target clf_skl = LogisticRegression_skl() clf_bareml = LogisticRegression() skl_scores = [] bareml_scores = [] kf = KFold() for train_idx, test_idx in kf.split(X,y): X_train, X_test = X[train_idx], X[test_idx] y_train, y_test = y[train_idx], y[test_idx] clf_skl.fit(X_train, y_train) clf_bareml.fit(X_train, y_train) skl_scores.append(clf_skl.score(X_test, y_test)) bareml_scores.append(clf_bareml.score(X_test, y_test)['accuracy']) skl_score = np.array(skl_scores).mean() bareml_score = np.array(bareml_scores).mean() # accuracy difference from sklearn's LogisticRegression is less than 5% assert skl_score - bareml_score < 0.05 def test_multi_classification(): data = load_iris() X = data.data y = data.target clf_skl = LogisticRegression_skl() clf_bareml = LogisticRegression() skl_scores = [] bareml_scores = [] kf = StratifiedKFold() for train_idx, test_idx in kf.split(X,y): X_train, X_test = X[train_idx], X[test_idx] y_train, y_test = y[train_idx], y[test_idx] clf_skl.fit(X_train, y_train) clf_bareml.fit(X_train, y_train) skl_scores.append(clf_skl.score(X_test, y_test)) bareml_scores.append(clf_bareml.score(X_test, y_test)['accuracy']) skl_score = np.array(skl_scores).mean() bareml_score = np.array(bareml_scores).mean() # accuracy difference from sklearn's LogisticRegression is less than 5% assert skl_score - bareml_score < 0.05 def test_multi_classification_onehot(): data = load_iris() X = data.data y = data.target clf_skl = LogisticRegression_skl() clf_bareml = LogisticRegression() skl_scores = [] bareml_scores = [] kf = StratifiedKFold() for train_idx, test_idx in kf.split(X,y): X_train, X_test = X[train_idx], X[test_idx] y_train, y_test = y[train_idx], y[test_idx] y_train_onehot = pd.get_dummies(y_train).values y_test_onehot = pd.get_dummies(y_test).values clf_skl.fit(X_train, y_train) clf_bareml.fit(X_train, y_train_onehot) skl_scores.append(clf_skl.score(X_test, y_test)) bareml_scores.append(clf_bareml.score(X_test, y_test_onehot)['accuracy']) skl_score = np.array(skl_scores).mean() bareml_score = np.array(bareml_scores).mean() # accuracy difference from sklearn's LogisticRegression is less than 5% assert skl_score - bareml_score < 0.05

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fe91b2aa940491c23131e880511f2d458bec70d7

46

py

Python

panoptes/analysis/admin.py

oberlin/panoptes

67d451ea4ffc58c23b5f347bfa5609fa7f853b45

[ "BSD-3-Clause" ]

2

2017-07-24T05:11:59.000Z

2017-08-27T19:17:42.000Z

panoptes/analysis/admin.py

oberlin/panoptes

67d451ea4ffc58c23b5f347bfa5609fa7f853b45

[ "BSD-3-Clause" ]

null

panoptes/analysis/admin.py

oberlin/panoptes

67d451ea4ffc58c23b5f347bfa5609fa7f853b45

[ "BSD-3-Clause" ]

null

import panoptes.analysis.panels.events.admin

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1

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6

feabcece44b356ca3c8fa42d9d51eb38f916ba3c

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py

Python

adapters/actuators/overhead_display/hc595/slow_cycling_through.py

andycavatorta/thirtybirds3.0

d2987c29af48f879bddb8e12fc42549fefb084cf

[ "MIT" ]

2

2020-05-13T02:53:02.000Z

2021-03-21T05:54:53.000Z

adapters/gpio/hc595/slow_cycling_through.py

andycavatorta/thirtybirds3.0

d2987c29af48f879bddb8e12fc42549fefb084cf

[ "MIT" ]

null

adapters/gpio/hc595/slow_cycling_through.py

andycavatorta/thirtybirds3.0

d2987c29af48f879bddb8e12fc42549fefb084cf

[ "MIT" ]

1

2021-05-06T18:42:41.000Z

#!/usr/bin/env python import time import math import HC595_shift_reg as shifter reg = shifter.HC595() # attraction mode seq = [ [ 1, 0, 0, 0, 0, ], [ 0, 1, 0, 0, 0, ], [ 0, 0, 1, 0, 0, ], [ 0, 0, 0, 1, 0, ], [ 0, 0, 0, 0, 1 ] ] """ # score seq = [ [ 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 ] ] seq = [ [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ] ] seq = [ [ 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0 ], [ 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1 ], [ 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 ], [ 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0 ] ] seq = [ [ 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0 ], [ 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1 ], [ 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1 ] ] seq = [ [ 1, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 1, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 1, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 1, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 1, 0, 0, 0 ] ] """ #seq = [ 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1 ] seq_step = 0 val = [ 0 ] lfo = 3.141596 period = 0.1 period = 1.1 # this is put inside a try block so it can clean up # the output enable. very important to protect relays from # being left on!!!! try: while True: print() ontime =2.09 offtime = 2.0 val[ 0 ] = 0; for trk in range( 0, 5 ): if seq[ trk ][ seq_step ] == 1: val[ 0 ] = val[ 0 ] + ( 1 << trk ) #print( ontime, offtime ) reg.write( val ) print( val ) time.sleep( ontime ) val[ 0 ] = 0x00 reg.write( val ) print( val ) time.sleep( offtime ) seq_step = seq_step + 1 if seq_step >= 5: seq_step = 0 except KeyboardInterrupt: print( "You've exited the program." ) finally: print( "cleaning up GPIO now." ) reg.disable_Output_Enable()

28.028571

109

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1

0

1

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null

0

6

feb2f8aa617eeae9bfa99eb3bb351a7a8bd60965

308

py

Python

allennlp_semparse/predictors/__init__.py

pdasigi/allennlp-semparse

843c9e5a4d15f449c8f11e6c08940d3de3e2a8c7

[ "Apache-2.0" ]

null

allennlp_semparse/predictors/__init__.py

pdasigi/allennlp-semparse

843c9e5a4d15f449c8f11e6c08940d3de3e2a8c7

[ "Apache-2.0" ]

null

allennlp_semparse/predictors/__init__.py

pdasigi/allennlp-semparse

843c9e5a4d15f449c8f11e6c08940d3de3e2a8c7

[ "Apache-2.0" ]

null

from allennlp_semparse.predictors.atis_parser import AtisParserPredictor from allennlp_semparse.predictors.nlvr_parser import NlvrParserPredictor from allennlp_semparse.predictors.quarel_parser import QuarelParserPredictor from allennlp_semparse.predictors.wikitables_parser import WikiTablesParserPredictor

61.6

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6

22870aca78e0ec5d773d4b6f4ead3a2952c7f983

126,032

py

Python

koku/masu/database/ocp_report_db_accessor.py

bsquizz/koku

386dd6ca4a4fd1b50790a929acc81d2dc245a91c

[ "Apache-2.0" ]

null

koku/masu/database/ocp_report_db_accessor.py

bsquizz/koku

386dd6ca4a4fd1b50790a929acc81d2dc245a91c

[ "Apache-2.0" ]

null

koku/masu/database/ocp_report_db_accessor.py

bsquizz/koku

386dd6ca4a4fd1b50790a929acc81d2dc245a91c

[ "Apache-2.0" ]

null

# # Copyright 2021 Red Hat Inc. # SPDX-License-Identifier: Apache-2.0 # """Database accessor for OCP report data.""" import copy import datetime import json import logging import os import pkgutil import uuid from decimal import Decimal import pytz from dateutil.parser import parse from dateutil.rrule import MONTHLY from dateutil.rrule import rrule from django.conf import settings from django.db import connection from django.db.models import DecimalField from django.db.models import ExpressionWrapper from django.db.models import F from django.db.models import Sum from django.db.models import Value from django.db.models.functions import Coalesce from jinjasql import JinjaSql from tenant_schemas.utils import schema_context from trino.exceptions import TrinoExternalError import koku.presto_database as kpdb from api.metrics import constants as metric_constants from api.utils import DateHelper from koku.database import JSONBBuildObject from koku.database import SQLScriptAtomicExecutorMixin from masu.config import Config from masu.database import AWS_CUR_TABLE_MAP from masu.database import OCP_REPORT_TABLE_MAP from masu.database.report_db_accessor_base import ReportDBAccessorBase from masu.util.common import month_date_range_tuple from reporting.models import OCP_ON_ALL_PERSPECTIVES from reporting.provider.aws.models import PRESTO_LINE_ITEM_DAILY_TABLE as AWS_PRESTO_LINE_ITEM_DAILY_TABLE from reporting.provider.azure.models import PRESTO_LINE_ITEM_DAILY_TABLE as AZURE_PRESTO_LINE_ITEM_DAILY_TABLE from reporting.provider.ocp.models import OCPCluster from reporting.provider.ocp.models import OCPNode from reporting.provider.ocp.models import OCPProject from reporting.provider.ocp.models import OCPPVC from reporting.provider.ocp.models import OCPUsageLineItemDailySummary from reporting.provider.ocp.models import OCPUsageReport from reporting.provider.ocp.models import OCPUsageReportPeriod from reporting.provider.ocp.models import PRESTO_LINE_ITEM_TABLE_DAILY_MAP from reporting.provider.ocp.models import UI_SUMMARY_TABLES LOG = logging.getLogger(__name__) def create_filter(data_source, start_date, end_date, cluster_id): """Create filter with data source, start and end dates.""" filters = {"data_source": data_source} if start_date: filters["usage_start__gte"] = start_date if isinstance(start_date, datetime.date) else start_date.date() if end_date: filters["usage_start__lte"] = end_date if isinstance(end_date, datetime.date) else end_date.date() if cluster_id: filters["cluster_id"] = cluster_id return filters class OCPReportDBAccessor(SQLScriptAtomicExecutorMixin, ReportDBAccessorBase): """Class to interact with customer reporting tables.""" # Empty string will put a path seperator on the end OCP_ON_ALL_SQL_PATH = os.path.join("sql", "openshift", "all", "") def __init__(self, schema): """Establish the database connection. Args: schema (str): The customer schema to associate with """ super().__init__(schema) self._datetime_format = Config.OCP_DATETIME_STR_FORMAT self.jinja_sql = JinjaSql() self.date_helper = DateHelper() self._table_map = OCP_REPORT_TABLE_MAP self._aws_table_map = AWS_CUR_TABLE_MAP @property def line_item_daily_summary_table(self): return OCPUsageLineItemDailySummary def get_current_usage_report(self): """Get the most recent usage report object.""" table_name = self._table_map["report"] with schema_context(self.schema): return self._get_db_obj_query(table_name).order_by("-interval_start").first() def get_current_usage_period(self): """Get the most recent usage report period object.""" table_name = self._table_map["report_period"] with schema_context(self.schema): return self._get_db_obj_query(table_name).order_by("-report_period_start").first() def get_usage_periods_by_date(self, start_date): """Return all report period entries for the specified start date.""" table_name = self._table_map["report_period"] with schema_context(self.schema): return self._get_db_obj_query(table_name).filter(report_period_start=start_date).all() def get_usage_period_by_dates_and_cluster(self, start_date, end_date, cluster_id): """Return all report period entries for the specified start date.""" table_name = self._table_map["report_period"] with schema_context(self.schema): return ( self._get_db_obj_query(table_name) .filter(report_period_start=start_date, report_period_end=end_date, cluster_id=cluster_id) .first() ) def get_usage_period_on_or_before_date(self, date, provider_uuid=None): """Get the usage report period objects before provided date.""" table_name = self._table_map["report_period"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) if provider_uuid: usage_period_query = base_query.filter(report_period_start__lte=date, provider_id=provider_uuid) else: usage_period_query = base_query.filter(report_period_start__lte=date) return usage_period_query def get_usage_period_query_by_provider(self, provider_uuid): """Return all report periods for the specified provider.""" table_name = self._table_map["report_period"] with schema_context(self.schema): return self._get_db_obj_query(table_name).filter(provider_id=provider_uuid) def report_periods_for_provider_uuid(self, provider_uuid, start_date=None): """Return all report periods for provider_uuid on date.""" report_periods = self.get_usage_period_query_by_provider(provider_uuid) with schema_context(self.schema): if start_date: if isinstance(start_date, str): start_date = parse(start_date) report_date = start_date.replace(day=1) report_periods = report_periods.filter(report_period_start=report_date).first() return report_periods def get_lineitem_query_for_reportid(self, query_report_id): """Get the usage report line item for a report id query.""" table_name = self._table_map["line_item"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) line_item_query = base_query.filter(report_id=query_report_id) return line_item_query def get_daily_usage_query_for_clusterid(self, cluster_identifier): """Get the usage report daily item for a cluster id query.""" table_name = self._table_map["line_item_daily"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) daily_usage_query = base_query.filter(cluster_id=cluster_identifier) return daily_usage_query def get_summary_usage_query_for_clusterid(self, cluster_identifier): """Get the usage report summary for a cluster id query.""" table_name = self._table_map["line_item_daily_summary"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) summary_usage_query = base_query.filter(cluster_id=cluster_identifier) return summary_usage_query def get_item_query_report_period_id(self, report_period_id): """Get the usage report line item for a report id query.""" table_name = self._table_map["line_item"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) line_item_query = base_query.filter(report_period_id=report_period_id) return line_item_query def get_storage_item_query_report_period_id(self, report_period_id): """Get the storage report line item for a report id query.""" table_name = self._table_map["storage_line_item"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) line_item_query = base_query.filter(report_period_id=report_period_id) return line_item_query def get_daily_storage_item_query_cluster_id(self, cluster_identifier): """Get the daily storage report line item for a cluster id query.""" table_name = self._table_map["storage_line_item_daily"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) daily_item_query = base_query.filter(cluster_id=cluster_identifier) return daily_item_query def get_storage_summary_query_cluster_id(self, cluster_identifier): """Get the storage report summary for a cluster id query.""" table_name = self._table_map["line_item_daily_summary"] filters = {"cluster_id": cluster_identifier, "data_source": "Storage"} with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) daily_item_query = base_query.filter(**filters) return daily_item_query def get_node_label_item_query_report_period_id(self, report_period_id): """Get the node label report line item for a report id query.""" table_name = self._table_map["node_label_line_item"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) line_item_query = base_query.filter(report_period_id=report_period_id) return line_item_query def get_ocp_aws_summary_query_for_cluster_id(self, cluster_identifier): """Get the OCP-on-AWS report summary item for a given cluster id query.""" table_name = self._aws_table_map["ocp_on_aws_daily_summary"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) summary_item_query = base_query.filter(cluster_id=cluster_identifier) return summary_item_query def get_ocp_aws_project_summary_query_for_cluster_id(self, cluster_identifier): """Get the OCP-on-AWS report project summary item for a given cluster id query.""" table_name = self._aws_table_map["ocp_on_aws_project_daily_summary"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) summary_item_query = base_query.filter(cluster_id=cluster_identifier) return summary_item_query def get_report_query_report_period_id(self, report_period_id): """Get the usage report line item for a report id query.""" table_name = self._table_map["report"] with schema_context(self.schema): base_query = self._get_db_obj_query(table_name) usage_report_query = base_query.filter(report_period_id=report_period_id) return usage_report_query def get_report_periods(self): """Get all usage period objects.""" periods = [] with schema_context(self.schema): periods = OCPUsageReportPeriod.objects.values("id", "cluster_id", "report_period_start", "provider_id") return_value = {(p["cluster_id"], p["report_period_start"], p["provider_id"]): p["id"] for p in periods} return return_value def get_reports(self): """Make a mapping of reports by time.""" with schema_context(self.schema): reports = OCPUsageReport.objects.all() return { (entry.report_period_id, entry.interval_start.strftime(self._datetime_format)): entry.id for entry in reports } def get_pod_usage_cpu_core_hours(self, start_date, end_date, cluster_id=None): """Make a mapping of cpu pod usage hours.""" table = OCPUsageLineItemDailySummary filters = create_filter("Pod", start_date, end_date, cluster_id) with schema_context(self.schema): reports = self._get_reports(table, filters) return {entry.uuid: entry.pod_usage_cpu_core_hours for entry in reports} def _get_reports(self, table, filters=None): """Return requested reports from given table. Args: table (Django models.Model object): The table to query against filters (dict): Columns to filter the query on Returns: (QuerySet): Django queryset of objects queried on """ with schema_context(self.schema): if filters: reports = self._get_db_obj_query(table).filter(**filters).all() else: reports = self._get_db_obj_query(table).all() return reports def get_pod_request_cpu_core_hours(self, start_date, end_date, cluster_id=None): """Make a mapping of cpu pod request hours.""" table = OCPUsageLineItemDailySummary filters = create_filter("Pod", start_date, end_date, cluster_id) with schema_context(self.schema): reports = self._get_reports(table, filters) return {entry.uuid: entry.pod_request_cpu_core_hours for entry in reports} def get_pod_usage_memory_gigabyte_hours(self, start_date, end_date, cluster_id=None): """Make a mapping of memory_usage hours.""" table = OCPUsageLineItemDailySummary filters = create_filter("Pod", start_date, end_date, cluster_id) with schema_context(self.schema): reports = self._get_reports(table, filters) return {entry.uuid: entry.pod_usage_memory_gigabyte_hours for entry in reports} def get_pod_request_memory_gigabyte_hours(self, start_date, end_date, cluster_id=None): """Make a mapping of memory_request_hours.""" table = OCPUsageLineItemDailySummary filters = create_filter("Pod", start_date, end_date, cluster_id) with schema_context(self.schema): reports = self._get_reports(table, filters) return {entry.uuid: entry.pod_request_memory_gigabyte_hours for entry in reports} def get_persistentvolumeclaim_usage_gigabyte_months(self, start_date, end_date, cluster_id=None): """Make a mapping of persistentvolumeclaim_usage_gigabyte_months.""" table = OCPUsageLineItemDailySummary filters = create_filter("Storage", start_date, end_date, cluster_id) with schema_context(self.schema): reports = self._get_reports(table, filters) return {entry.uuid: entry.persistentvolumeclaim_usage_gigabyte_months for entry in reports} def get_volume_request_storage_gigabyte_months(self, start_date, end_date, cluster_id=None): """Make a mapping of volume_request_storage_gigabyte_months.""" table = OCPUsageLineItemDailySummary filters = create_filter("Storage", start_date, end_date, cluster_id) with schema_context(self.schema): reports = self._get_reports(table, filters) return {entry.uuid: entry.volume_request_storage_gigabyte_months for entry in reports} def populate_line_item_daily_table(self, start_date, end_date, cluster_id): """Populate the daily aggregate of line items table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. cluster_id (String) Cluster Identifier Returns (None) """ # Cast start_date and end_date into date object instead of string if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() table_name = self._table_map["line_item_daily"] daily_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpusagelineitem_daily.sql") daily_sql = daily_sql.decode("utf-8") daily_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "start_date": start_date, "end_date": end_date, "cluster_id": cluster_id, "schema": self.schema, } daily_sql, daily_sql_params = self.jinja_sql.prepare_query(daily_sql, daily_sql_params) self._execute_raw_sql_query(table_name, daily_sql, start_date, end_date, bind_params=list(daily_sql_params)) def populate_ui_summary_tables(self, start_date, end_date, source_uuid, tables=UI_SUMMARY_TABLES): """Populate our UI summary tables (formerly materialized views).""" for table_name in tables: summary_sql = pkgutil.get_data("masu.database", f"sql/openshift/{table_name}.sql") summary_sql = summary_sql.decode("utf-8") summary_sql_params = { "start_date": start_date, "end_date": end_date, "schema": self.schema, "source_uuid": source_uuid, } summary_sql, summary_sql_params = self.jinja_sql.prepare_query(summary_sql, summary_sql_params) self._execute_raw_sql_query( table_name, summary_sql, start_date, end_date, bind_params=list(summary_sql_params) ) def update_line_item_daily_summary_with_enabled_tags(self, start_date, end_date, report_period_ids): """Populate the enabled tag key table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. bill_ids (list) A list of bill IDs. Returns (None) """ table_name = self._table_map["line_item_daily_summary"] summary_sql = pkgutil.get_data( "masu.database", "sql/reporting_ocpusagelineitem_daily_summary_update_enabled_tags.sql" ) summary_sql = summary_sql.decode("utf-8") summary_sql_params = { "start_date": start_date, "end_date": end_date, "report_period_ids": report_period_ids, "schema": self.schema, } summary_sql, summary_sql_params = self.jinja_sql.prepare_query(summary_sql, summary_sql_params) self._execute_raw_sql_query( table_name, summary_sql, start_date, end_date, bind_params=list(summary_sql_params) ) def get_ocp_infrastructure_map(self, start_date, end_date, **kwargs): """Get the OCP on infrastructure map. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. Returns (None) """ # kwargs here allows us to optionally pass in a provider UUID based on # the provider type this is run for ocp_provider_uuid = kwargs.get("ocp_provider_uuid") aws_provider_uuid = kwargs.get("aws_provider_uuid") azure_provider_uuid = kwargs.get("azure_provider_uuid") # In case someone passes this function a string instead of the date object like we asked... # Cast the string into a date object, end_date into date object instead of string if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() infra_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpinfrastructure_provider_map.sql") infra_sql = infra_sql.decode("utf-8") infra_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "start_date": start_date, "end_date": end_date, "schema": self.schema, "aws_provider_uuid": aws_provider_uuid, "ocp_provider_uuid": ocp_provider_uuid, "azure_provider_uuid": azure_provider_uuid, } infra_sql, infra_sql_params = self.jinja_sql.prepare_query(infra_sql, infra_sql_params) with connection.cursor() as cursor: cursor.db.set_schema(self.schema) cursor.execute(infra_sql, list(infra_sql_params)) results = cursor.fetchall() db_results = {} for entry in results: # This dictionary is keyed on an OpenShift provider UUID # and the tuple contains # (Infrastructure Provider UUID, Infrastructure Provider Type) db_results[entry[0]] = (entry[1], entry[2]) return db_results def get_ocp_infrastructure_map_trino(self, start_date, end_date, **kwargs): """Get the OCP on infrastructure map. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. Returns (None) """ # kwargs here allows us to optionally pass in a provider UUID based on # the provider type this is run for ocp_provider_uuid = kwargs.get("ocp_provider_uuid") aws_provider_uuid = kwargs.get("aws_provider_uuid") azure_provider_uuid = kwargs.get("azure_provider_uuid") if not self.table_exists_trino(PRESTO_LINE_ITEM_TABLE_DAILY_MAP.get("pod_usage")): return {} if aws_provider_uuid and not self.table_exists_trino(AWS_PRESTO_LINE_ITEM_DAILY_TABLE): return {} if azure_provider_uuid and not self.table_exists_trino(AZURE_PRESTO_LINE_ITEM_DAILY_TABLE): return {} if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() infra_sql = pkgutil.get_data("masu.database", "presto_sql/reporting_ocpinfrastructure_provider_map.sql") infra_sql = infra_sql.decode("utf-8") infra_sql_params = { "start_date": start_date, "end_date": end_date, "year": start_date.strftime("%Y"), "month": start_date.strftime("%m"), "schema": self.schema, "aws_provider_uuid": aws_provider_uuid, "ocp_provider_uuid": ocp_provider_uuid, "azure_provider_uuid": azure_provider_uuid, } infra_sql, infra_sql_params = self.jinja_sql.prepare_query(infra_sql, infra_sql_params) results = self._execute_presto_raw_sql_query(self.schema, infra_sql, bind_params=infra_sql_params) db_results = {} for entry in results: # This dictionary is keyed on an OpenShift provider UUID # and the tuple contains # (Infrastructure Provider UUID, Infrastructure Provider Type) db_results[entry[0]] = (entry[1], entry[2]) return db_results def populate_storage_line_item_daily_table(self, start_date, end_date, cluster_id): """Populate the daily storage aggregate of line items table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. cluster_id (String) Cluster Identifier Returns (None) """ # Cast string to date object if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() table_name = self._table_map["storage_line_item_daily"] daily_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpstoragelineitem_daily.sql") daily_sql = daily_sql.decode("utf-8") daily_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "start_date": start_date, "end_date": end_date, "cluster_id": cluster_id, "schema": self.schema, } daily_sql, daily_sql_params = self.jinja_sql.prepare_query(daily_sql, daily_sql_params) self._execute_raw_sql_query(table_name, daily_sql, start_date, end_date, bind_params=list(daily_sql_params)) def populate_pod_charge(self, cpu_temp_table, mem_temp_table): """Populate the memory and cpu charge on daily summary table. Args: cpu_temp_table (String) Name of cpu charge temp table mem_temp_table (String) Name of mem charge temp table Returns (None) """ table_name = self._table_map["line_item_daily_summary"] daily_charge_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpusagelineitem_daily_pod_charge.sql") charge_line_sql = daily_charge_sql.decode("utf-8") charge_line_sql_params = {"cpu_temp": cpu_temp_table, "mem_temp": mem_temp_table, "schema": self.schema} charge_line_sql, charge_line_sql_params = self.jinja_sql.prepare_query(charge_line_sql, charge_line_sql_params) self._execute_raw_sql_query(table_name, charge_line_sql, bind_params=list(charge_line_sql_params)) def populate_storage_charge(self, temp_table_name): """Populate the storage charge into the daily summary table. Args: storage_charge (Float) Storage charge. Returns (None) """ table_name = self._table_map["line_item_daily_summary"] daily_charge_sql = pkgutil.get_data("masu.database", "sql/reporting_ocp_storage_charge.sql") charge_line_sql = daily_charge_sql.decode("utf-8") charge_line_sql_params = {"temp_table": temp_table_name, "schema": self.schema} charge_line_sql, charge_line_sql_params = self.jinja_sql.prepare_query(charge_line_sql, charge_line_sql_params) self._execute_raw_sql_query(table_name, charge_line_sql, bind_params=list(charge_line_sql_params)) def populate_line_item_daily_summary_table(self, start_date, end_date, cluster_id, source): """Populate the daily aggregate of line items table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. cluster_id (String) Cluster Identifier source (String) Source UUID Returns (None) """ # Cast start_date to date if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() table_name = self._table_map["line_item_daily_summary"] summary_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpusagelineitem_daily_summary.sql") summary_sql = summary_sql.decode("utf-8") summary_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "start_date": start_date, "end_date": end_date, "cluster_id": cluster_id, "schema": self.schema, "source_uuid": source, } summary_sql, summary_sql_params = self.jinja_sql.prepare_query(summary_sql, summary_sql_params) self._execute_raw_sql_query( table_name, summary_sql, start_date, end_date, bind_params=list(summary_sql_params) ) def populate_storage_line_item_daily_summary_table(self, start_date, end_date, cluster_id, source): """Populate the daily aggregate of storage line items table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. cluster_id (String) Cluster Identifier source (String) Source UUID Returns (None) """ # Cast start_date and end_date to date object, if they aren't already if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() table_name = self._table_map["line_item_daily_summary"] summary_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpstoragelineitem_daily_summary.sql") summary_sql = summary_sql.decode("utf-8") summary_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "start_date": start_date, "end_date": end_date, "cluster_id": cluster_id, "schema": self.schema, "source_uuid": source, } summary_sql, summary_sql_params = self.jinja_sql.prepare_query(summary_sql, summary_sql_params) self._execute_raw_sql_query(table_name, summary_sql, start_date, end_date, list(summary_sql_params)) def delete_ocp_hive_partition_by_day(self, days, source, year, month): """Deletes partitions individually for each day in days list.""" table = self._table_map["line_item_daily_summary"] retries = settings.HIVE_PARTITION_DELETE_RETRIES if self.table_exists_trino(table): LOG.info( "Deleting partitions for the following: \n\tSchema: %s " "\n\tOCP Source: %s \n\tTable: %s \n\tYear-Month: %s-%s \n\tDays: %s", self.schema, source, table, year, month, days, ) for day in days: for i in range(retries): try: sql = f""" DELETE FROM hive.{self.schema}.{table} WHERE source = '{source}' AND year = '{year}' AND (month = replace(ltrim(replace('{month}', '0', ' ')),' ', '0') OR month = '{month}') AND day = '{day}' """ self._execute_presto_raw_sql_query(self.schema, sql) break except TrinoExternalError as err: if err.error_name == "HIVE_METASTORE_ERROR" and i < (retries - 1): continue else: raise err def populate_line_item_daily_summary_table_presto( self, start_date, end_date, report_period_id, cluster_id, cluster_alias, source ): """Populate the daily aggregate of line items table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. report_period_id (int) : report period for which we are processing cluster_id (str) : Cluster Identifier cluster_alias (str) : Cluster alias source (UUID) : provider uuid Returns (None) """ # Cast start_date to date if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() days = DateHelper().list_days(start_date, end_date) days_str = "','".join([str(day.day) for day in days]) days_list = [str(day.day) for day in days] year = start_date.strftime("%Y") month = start_date.strftime("%m") self.delete_ocp_hive_partition_by_day(days_list, source, year, month) tmpl_summary_sql = pkgutil.get_data("masu.database", "presto_sql/reporting_ocpusagelineitem_daily_summary.sql") tmpl_summary_sql = tmpl_summary_sql.decode("utf-8") summary_sql_params = { "uuid": str(source).replace("-", "_"), "start_date": start_date, "end_date": end_date, "report_period_id": report_period_id, "cluster_id": cluster_id, "cluster_alias": cluster_alias, "schema": self.schema, "source": str(source), "year": year, "month": month, "days": days_str, } LOG.info("PRESTO OCP: Connect") presto_conn = kpdb.connect(schema=self.schema) try: LOG.info("PRESTO OCP: executing SQL buffer for OCP usage processing") kpdb.executescript( presto_conn, tmpl_summary_sql, params=summary_sql_params, preprocessor=self.jinja_sql.prepare_query ) except Exception as e: LOG.error(f"PRESTO OCP ERROR : {e}") try: presto_conn.rollback() except RuntimeError: # If presto has not started a transaction, it will throw # a RuntimeError that we just want to ignore. pass raise e else: LOG.info("PRESTO OCP: Commit actions") presto_conn.commit() finally: LOG.info("PRESTO OCP: Close connection") presto_conn.close() def populate_pod_label_summary_table_presto(self, report_period_ids, start_date, end_date, source): """ Populate label usage summary tables Args: report_period_ids (list(int)) : List of report_period_ids for processing start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. source (UUID) : provider uuid Returns (None) """ # Cast start_date to date if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() agg_sql = pkgutil.get_data("masu.database", "presto_sql/reporting_ocp_usage_label_summary.sql") agg_sql = agg_sql.decode("utf-8") agg_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "schema": self.schema, "report_period_ids": tuple(report_period_ids), "start_date": start_date, "end_date": end_date, "source": str(source), "year": start_date.strftime("%Y"), "month": start_date.strftime("%m"), } LOG.info("PRESTO OCP: Connect") presto_conn = kpdb.connect(schema=self.schema) try: LOG.info("PRESTO OCP: executing SQL buffer for OCP tag/label processing") kpdb.executescript(presto_conn, agg_sql, params=agg_sql_params, preprocessor=self.jinja_sql.prepare_query) except Exception as e: LOG.error(f"PRESTO OCP ERROR : {e}") try: presto_conn.rollback() except RuntimeError: # If presto has not started a transaction, it will throw # a RuntimeError that we just want to ignore. pass raise e else: LOG.info("PRESTO OCP: Commit actions") presto_conn.commit() finally: LOG.info("PRESTO OCP: Close connection") presto_conn.close() def get_cost_summary_for_clusterid(self, cluster_identifier): """Get the cost summary for a cluster id query.""" table_name = self._table_map["cost_summary"] base_query = self._get_db_obj_query(table_name) cost_summary_query = base_query.filter(cluster_id=cluster_identifier) return cost_summary_query def populate_pod_label_summary_table(self, report_period_ids, start_date, end_date): """Populate the line item aggregated totals data table.""" table_name = self._table_map["pod_label_summary"] agg_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpusagepodlabel_summary.sql") agg_sql = agg_sql.decode("utf-8") agg_sql_params = { "schema": self.schema, "report_period_ids": report_period_ids, "start_date": start_date, "end_date": end_date, } agg_sql, agg_sql_params = self.jinja_sql.prepare_query(agg_sql, agg_sql_params) self._execute_raw_sql_query(table_name, agg_sql, bind_params=list(agg_sql_params)) def populate_volume_label_summary_table(self, report_period_ids, start_date, end_date): """Populate the OCP volume label summary table.""" table_name = self._table_map["volume_label_summary"] agg_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpstoragevolumelabel_summary.sql") agg_sql = agg_sql.decode("utf-8") agg_sql_params = { "schema": self.schema, "report_period_ids": report_period_ids, "start_date": start_date, "end_date": end_date, } agg_sql, agg_sql_params = self.jinja_sql.prepare_query(agg_sql, agg_sql_params) self._execute_raw_sql_query(table_name, agg_sql, bind_params=list(agg_sql_params)) def populate_markup_cost(self, markup, start_date, end_date, cluster_id): """Set markup cost for OCP including infrastructure cost markup.""" with schema_context(self.schema): OCPUsageLineItemDailySummary.objects.filter( cluster_id=cluster_id, usage_start__gte=start_date, usage_start__lte=end_date ).update( infrastructure_markup_cost=( (Coalesce(F("infrastructure_raw_cost"), Value(0, output_field=DecimalField()))) * markup ), infrastructure_project_markup_cost=( (Coalesce(F("infrastructure_project_raw_cost"), Value(0, output_field=DecimalField()))) * markup ), ) def get_distinct_nodes(self, start_date, end_date, cluster_id): """Return a list of nodes for a cluster between given dates.""" with schema_context(self.schema): unique_nodes = ( OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lt=end_date, cluster_id=cluster_id, node__isnull=False ) .values_list("node") .distinct() ) return [node[0] for node in unique_nodes] def get_distinct_pvcs(self, start_date, end_date, cluster_id): """Return a list of tuples of (PVC, node) for a cluster between given dates.""" with schema_context(self.schema): unique_pvcs = ( OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lt=end_date, cluster_id=cluster_id, persistentvolumeclaim__isnull=False, namespace__isnull=False, ) .values_list("persistentvolumeclaim", "node", "namespace") .distinct() ) return [(pvc[0], pvc[1], pvc[2]) for pvc in unique_pvcs] def generate_monthly_cost_json_object(self, distribution, distributed_cost): """Generates the default monthly cost dict.""" default_cost = Decimal(0) if not isinstance(distributed_cost, Decimal) and distributed_cost: distributed_cost = Decimal(distributed_cost) if not distributed_cost: distributed_cost = default_cost cost_mapping = {distribution: distributed_cost} return JSONBBuildObject( Value(metric_constants.CPU_DISTRIBUTION), cost_mapping.get(metric_constants.CPU_DISTRIBUTION, default_cost), Value(metric_constants.MEMORY_DISTRIBUTION), cost_mapping.get(metric_constants.MEMORY_DISTRIBUTION, default_cost), Value(metric_constants.PVC_DISTRIBUTION), cost_mapping.get(metric_constants.PVC_DISTRIBUTION, default_cost), ) def populate_monthly_cost( self, cost_type, rate_type, rate, start_date, end_date, cluster_id, cluster_alias, distribution, provider_uuid ): """ Populate the monthly cost of a customer. There are three types of monthly rates Node, Cluster & PVC. args: cost_type (str): Contains the type of monthly cost. ex: "Node" rate_type(str): Contains the metric name. ex: "node_cost_per_month" rate (decimal): Contains the rate amount ex: 100.0 node_cost (Decimal): The node cost per month start_date (datetime, str): The start_date to calculate monthly_cost. end_date (datetime, str): The end_date to calculate monthly_cost. cluster_id (str): The id of the cluster cluster_alias: The name of the cluster distribution: Choice of monthly distribution ex. memory """ if isinstance(start_date, str): start_date = parse(start_date).date() if isinstance(end_date, str): end_date = parse(end_date).date() # usage_start, usage_end are date types first_month = datetime.datetime(*start_date.replace(day=1).timetuple()[:3]).replace(tzinfo=pytz.UTC) end_date = datetime.datetime(*end_date.timetuple()[:3]).replace(hour=23, minute=59, second=59, tzinfo=pytz.UTC) # Calculate monthly cost for each month from start date to end date for curr_month in rrule(freq=MONTHLY, until=end_date, dtstart=first_month): first_curr_month, first_next_month = month_date_range_tuple(curr_month) LOG.info("Populating monthly cost from %s to %s.", first_curr_month, first_next_month) if cost_type == "Node": if rate is None: self.remove_monthly_cost(first_curr_month, first_next_month, cluster_id, cost_type) else: self.upsert_monthly_node_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate, distribution, provider_uuid, ) elif cost_type == "Cluster": if rate is None: self.remove_monthly_cost(first_curr_month, first_next_month, cluster_id, cost_type) else: # start_date, end_date, cluster_id, cluster_alias, rate_type, cluster_cost self.upsert_monthly_cluster_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate, distribution, provider_uuid, ) elif cost_type == "PVC": if rate is None: self.remove_monthly_cost(first_curr_month, first_next_month, cluster_id, cost_type) else: self.upsert_monthly_pvc_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate, provider_uuid ) def populate_monthly_tag_cost( self, cost_type, rate_type, rate_dict, start_date, end_date, cluster_id, cluster_alias, distribution, provider_uuid, ): """ Populate the monthly cost of a customer based on tag rates. Right now this is just the node/month cost. Calculated from tag value cost * number_unique_nodes for each tag key value pair that is found on a line item with that node. """ if isinstance(start_date, str): start_date = parse(start_date).date() if isinstance(end_date, str): end_date = parse(end_date).date() # usage_start, usage_end are date types first_month = datetime.datetime(*start_date.replace(day=1).timetuple()[:3]).replace(tzinfo=pytz.UTC) end_date = datetime.datetime(*end_date.timetuple()[:3]).replace(hour=23, minute=59, second=59, tzinfo=pytz.UTC) # Calculate monthly cost for each month from start date to end date for each tag key:value pair in the rate for curr_month in rrule(freq=MONTHLY, until=end_date, dtstart=first_month): first_curr_month, first_next_month = month_date_range_tuple(curr_month) LOG.info("Populating monthly tag based cost from %s to %s.", first_curr_month, first_next_month) if cost_type == "Node": self.tag_upsert_monthly_node_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid, ) elif cost_type == "Cluster": self.tag_upsert_monthly_cluster_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid, ) elif cost_type == "PVC": self.tag_upsert_monthly_pvc_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate_dict, provider_uuid ) def populate_monthly_tag_default_cost( self, cost_type, rate_type, rate_dict, start_date, end_date, cluster_id, cluster_alias, distribution, provider_uuid, ): """ Populate the monthly default cost of a customer based on tag rates. Right now this is just the node/month cost. Calculated from tag value cost * number_unique_nodes for each tag key value pair that is found on a line item with that node. """ if isinstance(start_date, str): start_date = parse(start_date).date() if isinstance(end_date, str): end_date = parse(end_date).date() # usage_start, usage_end are date types first_month = datetime.datetime(*start_date.replace(day=1).timetuple()[:3]).replace(tzinfo=pytz.UTC) end_date = datetime.datetime(*end_date.timetuple()[:3]).replace(hour=23, minute=59, second=59, tzinfo=pytz.UTC) # Calculate monthly cost for each month from start date to end date for each tag key:value pair in the rate for curr_month in rrule(freq=MONTHLY, until=end_date, dtstart=first_month): first_curr_month, first_next_month = month_date_range_tuple(curr_month) LOG.info("Populating monthly tag based default cost from %s to %s.", first_curr_month, first_next_month) if cost_type == "Node": self.tag_upsert_monthly_default_node_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid, ) elif cost_type == "Cluster": self.tag_upsert_monthly_default_cluster_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid, ) elif cost_type == "PVC": self.tag_upsert_monthly_default_pvc_cost_line_item( first_curr_month, first_next_month, cluster_id, cluster_alias, rate_type, rate_dict, provider_uuid ) def get_node_to_project_distribution(self, start_date, end_date, cluster_id, node_cost): """Returns a list of dictionaries containing the distributed cost. args: start_date (datetime, str): The start_date to calculate monthly_cost. end_date (datetime, str): The end_date to calculate monthly_cost. cluster_id (str): The id of the cluster cluster_cost (dec): The flat cost of the cluster Node to Project Distribution: - Node to project distribution is based on a per node scenario - (node_cost) / (number of projects) Return nested dictionaries: - ex {'master_3': {'namespaces': ['openshift', 'kube-system'], 'distributed_cost': Decimal('500.0000000000')} """ with schema_context(self.schema): distributed_project_list = ( OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lt=end_date, cluster_id=cluster_id ) .filter(namespace__isnull=False) .filter(node__isnull=False) .values("namespace", "node") .distinct() ) node_mappings = {} for project in distributed_project_list: node_value = project.get("node") namespace_value = project.get("namespace") node_map = node_mappings.get(node_value) if node_map: namespaces = copy.deepcopy(node_map.get("namespaces", [])) namespaces.append(namespace_value) node_map["namespaces"] = namespaces node_map["distributed_cost"] = Decimal(node_cost) / Decimal(len(namespaces)) node_mappings[node_value] = node_map else: initial_map = {"namespaces": [namespace_value], "distributed_cost": Decimal(node_cost)} node_mappings[node_value] = initial_map return node_mappings def upsert_monthly_node_cost_line_item( self, start_date, end_date, cluster_id, cluster_alias, rate_type, node_cost, distribution, provider_uuid ): """Update or insert daily summary line item for node cost.""" unique_nodes = self.get_distinct_nodes(start_date, end_date, cluster_id) report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) project_distrib_map = self.get_node_to_project_distribution(start_date, end_date, cluster_id, node_cost) with schema_context(self.schema): for node in unique_nodes: line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=node, data_source="Pod", namespace__isnull=True, ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=node, data_source="Pod", source_uuid=provider_uuid, ) monthly_cost = self.generate_monthly_cost_json_object(distribution, node_cost) if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug("Node (%s) has a monthly infrastructure cost of %s.", node, node_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug("Node (%s) has a monthly supplemenarty cost of %s.", node, node_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() # How are we gonna handle distributing the node cost to the projects. project_nodes = project_distrib_map.keys() for project_node in project_nodes: for namespace in project_distrib_map[project_node]["namespaces"]: distributed_cost = project_distrib_map[project_node]["distributed_cost"] project_line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=project_node, namespace=namespace, data_source="Pod", ).first() if not project_line_item: project_line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=project_node, namespace=namespace, data_source="Pod", source_uuid=provider_uuid, ) monthly_cost = self.generate_monthly_cost_json_object(distribution, distributed_cost) log_statement = ( f"Distributing Node Cost to Project:\n" f" node ({project_node}) cost: {node_cost} \n" f" project ({namespace}) distributed cost: {distributed_cost}\n" f" distribution type: {distribution}\n" ) if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: project_line_item.infrastructure_project_monthly_cost = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: project_line_item.supplementary_project_monthly_cost = monthly_cost project_line_item.save() LOG.debug(log_statement) def tag_upsert_monthly_node_cost_line_item( # noqa: C901 self, start_date, end_date, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid ): """ Update or insert daily summary line item for node cost. It checks to see if a line item exists for each node that contains the tag key:value pair, if it does then the price is added to the monthly cost. """ unique_nodes = self.get_distinct_nodes(start_date, end_date, cluster_id) report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) with schema_context(self.schema): for node in unique_nodes: if rate_dict is not None: for tag_key in rate_dict: tag_values = rate_dict.get(tag_key) for value_name, rate_value in tag_values.items(): # this makes sure that there is an entry for that node # that contains the specified key_value pair item_check = OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lte=end_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, node=node, pod_labels__contains={tag_key: value_name}, ).first() if item_check: line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=node, data_source="Pod", ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=node, data_source="Pod", source_uuid=provider_uuid, ) node_cost = rate_value if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug("Node (%s) has a monthly infrastructure cost of %s.", node, rate_value) if line_item.infrastructure_monthly_cost_json: node_cost = ( line_item.infrastructure_monthly_cost_json.get(distribution, 0) + rate_value ) monthly_cost = self.generate_monthly_cost_json_object(distribution, node_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug("Node (%s) has a monthly supplemenarty cost of %s.", node, rate_value) if line_item.supplementary_monthly_cost_json: node_cost = ( line_item.supplementary_monthly_cost_json.get(distribution, 0) + rate_value ) monthly_cost = self.generate_monthly_cost_json_object(distribution, node_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() def tag_upsert_monthly_default_node_cost_line_item( # noqa: C901 self, start_date, end_date, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid ): """ Update or insert daily summary line item for node cost. It checks to see if a line item exists for each node that contains the tag key:value pair, if it does then the price is added to the monthly cost. """ unique_nodes = self.get_distinct_nodes(start_date, end_date, cluster_id) report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) with schema_context(self.schema): for node in unique_nodes: if rate_dict is not None: for tag_key in rate_dict: tag_values = rate_dict.get(tag_key) tag_default = tag_values.get("default_value") values_to_skip = tag_values.get("defined_keys") item_check = OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lte=end_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, node=node, pod_labels__has_key=tag_key, ) for value in values_to_skip: item_check = item_check.exclude(pod_labels__contains={tag_key: value}) # this won't run if there are no matching items and item_check will continue to be # filtered until there are no items left while item_check: # get the first value for our tag key and exclude it from the queryset for the next check # will remove values until there are none left tag_key_value = item_check.first().pod_labels.get(tag_key) item_check = item_check.exclude(pod_labels__contains={tag_key: tag_key_value}) line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=node, data_source="Pod", ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Node", node=node, data_source="Pod", source_uuid=provider_uuid, ) node_cost = tag_default if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.info( "Node (%s) has a default monthly infrastructure cost of %s.", node, tag_default ) if line_item.infrastructure_monthly_cost_json: node_cost = ( line_item.infrastructure_monthly_cost_json.get(distribution, 0) + tag_default ) monthly_cost = self.generate_monthly_cost_json_object(distribution, node_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.info( "Node (%s) has a default monthly supplemenarty cost of %s.", node, tag_default ) if line_item.supplementary_monthly_cost_json: node_cost = ( line_item.supplementary_monthly_cost_json.get(distribution, 0) + tag_default ) monthly_cost = self.generate_monthly_cost_json_object(distribution, node_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() def tag_upsert_monthly_default_pvc_cost_line_item( # noqa: C901 self, start_date, end_date, cluster_id, cluster_alias, rate_type, rate_dict, provider_uuid ): """ Update or insert daily summary line item for node cost. It checks to see if a line item exists for each node that contains the tag key:value pair, if it does then the price is added to the monthly cost. """ distribution = metric_constants.PVC_DISTRIBUTION unique_pvcs = self.get_distinct_pvcs(start_date, end_date, cluster_id) report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) with schema_context(self.schema): for pvc, node, namespace in unique_pvcs: if rate_dict is not None: for tag_key in rate_dict: tag_values = rate_dict.get(tag_key) tag_default = tag_values.get("default_value") values_to_skip = tag_values.get("defined_keys") item_check = OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lte=end_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, persistentvolumeclaim=pvc, node=node, volume_labels__has_key=tag_key, namespace=namespace, ) for value in values_to_skip: item_check = item_check.exclude(volume_labels__contains={tag_key: value}) # this won't run if there are no matching items and item_check will continue to be # filtered until there are no items left while item_check: # get the first value for our tag key and exclude it from the queryset for the next check # will remove values until there are none left tag_key_value = item_check.first().volume_labels.get(tag_key) item_check = item_check.exclude(volume_labels__contains={tag_key: tag_key_value}) line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, data_source="Storage", namespace=namespace, ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, data_source="Storage", namespace=namespace, source_uuid=provider_uuid, ) pvc_cost = tag_default if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.info("PVC (%s) has a default monthly infrastructure cost of %s.", pvc, tag_default) if line_item.infrastructure_monthly_cost_json: pvc_cost = ( line_item.infrastructure_monthly_cost_json.get(distribution, 0) + tag_default ) monthly_cost = self.generate_monthly_cost_json_object(distribution, pvc_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.info("PVC (%s) has a default monthly supplemenarty cost of %s.", pvc, tag_default) if line_item.supplementary_monthly_cost_json: pvc_cost = ( line_item.supplementary_monthly_cost_json.get(distribution, 0) + tag_default ) monthly_cost = self.generate_monthly_cost_json_object(distribution, pvc_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() def get_cluster_to_node_distribution(self, start_date, end_date, cluster_id, distribution, cluster_cost): """Returns a list of dictionaries containing the distributed cost. args: start_date (datetime, str): The start_date to calculate monthly_cost. end_date (datetime, str): The end_date to calculate monthly_cost. cluster_id (str): The id of the cluster cluster_cost (dec): The flat cost of the cluster distribution: Choice of monthly distribution ex. (memory or cpu) Memory Distribution: - (node memory capacity/cluster memory capacity) x cluster_cost CPU Distribution: - (node cpu capacity/cluster cpu capacity) x cluster_cost Return list of dictionaries: ex [{"node":"aws_compute2", "distributed_cost": 285.71}] """ node_column = "node_capacity_cpu_core_hours" cluster_column = "cluster_capacity_cpu_core_hours" if "memory" in distribution: node_column = "node_capacity_memory_gigabyte_hours" cluster_column = "cluster_capacity_memory_gigabyte_hours" with schema_context(self.schema): distributed_node_list = ( OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lt=end_date, cluster_id=cluster_id ) .values("node") .annotate( distributed_cost=ExpressionWrapper( Sum(node_column) / Sum(cluster_column) * cluster_cost, output_field=DecimalField() ) ) ) # TIP: For debugging add these to the annotation # node_hours=Sum(node_column), # cluster_hours=Sum(cluster_column), # node_to_cluster_ratio=Sum(node_column)/Sum(cluster_column) return distributed_node_list def get_cluster_to_project_distribution(self, start_date, end_date, cluster_id, distribution, cluster_cost): """Returns a list of dictionaries containing the distributed cost. args: start_date (datetime, str): The start_date to calculate monthly_cost. end_date (datetime, str): The end_date to calculate monthly_cost. cluster_id (str): The id of the cluster cluster_cost (dec): The flat cost of the cluster distribution: Choice of monthly distribution ex. (memory or cpu) Project Distribution: - Project distribution is a rolling window estimate of month to date. - (project_usage / cluster_usage) x cluster_cost Return list of dictionaries: - ex [{'namespace': 'openshift', 'distributed_cost': Decimal('71.84')} """ usage_column = "pod_usage_cpu_core_hours" if "memory" in distribution: usage_column = "pod_usage_memory_gigabyte_hours" with schema_context(self.schema): cluster_hours = ( OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lt=end_date, cluster_id=cluster_id ).aggregate(cluster_hours=Sum(usage_column)) ).get("cluster_hours") distributed_project_list = ( OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lt=end_date, cluster_id=cluster_id ) .filter(namespace__isnull=False) .values("namespace") .annotate( distributed_cost=ExpressionWrapper( Sum(usage_column) / cluster_hours * cluster_cost, output_field=DecimalField() ) ) ) return distributed_project_list def upsert_monthly_cluster_cost_line_item( self, start_date, end_date, cluster_id, cluster_alias, rate_type, cluster_cost, distribution, provider_uuid ): """ Update or insert a daily summary line item for cluster cost. args: start_date (datetime, str): The start_date to calculate monthly_cost. end_date (datetime, str): The end_date to calculate monthly_cost. cluster_id (str): The id of the cluster cluster_alias: The name of the cluster cost_type (str): Contains the type of monthly cost. ex: "Node" rate_type (str): Contains the metric name. ex: "node_cost_per_month" cluster_cost (dec): The flat cost of the cluster distribution: Choice of monthly distribution ex. (memory or cpu) """ report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) distribution_list = self.get_cluster_to_node_distribution( start_date, end_date, cluster_id, distribution, cluster_cost ) if report_period: with schema_context(self.schema): # NOTE: I implemented a logic change here, now instead of one entry per cluster cost # We now have multiple cluster cost entries for each node. LOG.debug("Cluster (%s) has a monthly cost of %s.", cluster_id, cluster_cost) LOG.debug("Distributing the cluster cost to nodes using %s distribution.", distribution) for node_dikt in distribution_list: node = node_dikt.get("node") distributed_cost = node_dikt.get("distributed_cost", Decimal(0)) line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", node=node, data_source="Pod", namespace__isnull=True, ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", node=node, data_source="Pod", source_uuid=provider_uuid, ) monthly_cost = self.generate_monthly_cost_json_object(distribution, distributed_cost) log_statement = ( f"Distributing Cluster Cost to Nodes:\n" f" cluster ({cluster_id}) cost: {cluster_cost} \n" f" node ({node}) distributed cost: {distributed_cost}\n" f" distribution type: {distribution}\n" ) if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: line_item.supplementary_monthly_cost_json = monthly_cost LOG.debug(log_statement) line_item.save() # Project Distribution project_distribution_list = self.get_cluster_to_project_distribution( start_date, end_date, cluster_id, distribution, cluster_cost ) with schema_context(self.schema): for project_dikt in project_distribution_list: namespace = project_dikt.get("namespace") distributed_cost = project_dikt.get("distributed_cost", Decimal(0)) project_line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", namespace=namespace, data_source="Pod", ).first() if not project_line_item: project_line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", namespace=namespace, data_source="Pod", source_uuid=provider_uuid, ) monthly_cost = self.generate_monthly_cost_json_object(distribution, distributed_cost) log_statement = ( f"Distributing Cluster Cost to Project:\n" f" cluster ({cluster_id}) cost: {cluster_cost} \n" f" project ({namespace}) distributed cost: {distributed_cost}\n" f" distribution type: {distribution}\n" ) if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: project_line_item.infrastructure_project_monthly_cost = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: project_line_item.supplementary_project_monthly_cost = monthly_cost project_line_item.save() LOG.debug(log_statement) def tag_upsert_monthly_pvc_cost_line_item( # noqa: C901 self, start_date, end_date, cluster_id, cluster_alias, rate_type, rate_dict, provider_uuid ): """ Update or insert daily summary line item for PVC cost. It checks to see if a line item exists for each PVC that contains the tag key:value pair, if it does then the price is added to the monthly cost. """ distribution = metric_constants.PVC_DISTRIBUTION unique_pvcs = self.get_distinct_pvcs(start_date, end_date, cluster_id) report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) with schema_context(self.schema): for pvc, node, namespace in unique_pvcs: if rate_dict is not None: for tag_key in rate_dict: tag_values = rate_dict.get(tag_key) for value_name, rate_value in tag_values.items(): item_check = OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lte=end_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, persistentvolumeclaim=pvc, node=node, volume_labels__contains={tag_key: value_name}, namespace=namespace, ).first() if item_check: line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, data_source="Storage", namespace=namespace, ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, data_source="Storage", namespace=namespace, source_uuid=provider_uuid, ) pvc_cost = rate_value if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug("PVC (%s) has a monthly infrastructure cost of %s.", pvc, rate_value) if line_item.infrastructure_monthly_cost_json: pvc_cost = ( line_item.infrastructure_monthly_cost_json.get(distribution, 0) + rate_value ) monthly_cost = self.generate_monthly_cost_json_object(distribution, pvc_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug("PVC (%s) has a monthly supplemenarty cost of %s.", pvc, rate_value) if line_item.supplementary_monthly_cost_json: pvc_cost = ( line_item.supplementary_monthly_cost_json.get(distribution, 0) + rate_value ) monthly_cost = self.generate_monthly_cost_json_object(distribution, pvc_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() def upsert_monthly_pvc_cost_line_item( self, start_date, end_date, cluster_id, cluster_alias, rate_type, pvc_cost, provider_uuid ): """Update or insert daily summary line item for pvc cost.""" unique_pvcs = self.get_distinct_pvcs(start_date, end_date, cluster_id) report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) with schema_context(self.schema): for pvc, node, namespace in unique_pvcs: line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, data_source="Storage", namespace=namespace, infrastructure_project_monthly_cost__isnull=True, supplementary_project_monthly_cost__isnull=True, ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, data_source="Storage", namespace=namespace, source_uuid=provider_uuid, ) monthly_cost = self.generate_monthly_cost_json_object(metric_constants.PVC_DISTRIBUTION, pvc_cost) if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug("PVC (%s) has a monthly infrastructure cost of %s.", pvc, pvc_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug("PVC (%s) has a monthly supplemenarty cost of %s.", pvc, pvc_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() # PVC to project Distribution project_line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, namespace=namespace, data_source="Storage", infrastructure_monthly_cost_json__isnull=True, supplementary_monthly_cost_json__isnull=True, ).first() if not project_line_item: project_line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="PVC", persistentvolumeclaim=pvc, node=node, namespace=namespace, data_source="Storage", source_uuid=provider_uuid, ) monthly_cost = self.generate_monthly_cost_json_object(metric_constants.PVC_DISTRIBUTION, pvc_cost) if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug("PVC (%s) has a monthly project infrastructure cost of %s.", pvc, pvc_cost) project_line_item.infrastructure_project_monthly_cost = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug("PVC (%s) has a monthly project supplemenarty cost of %s.", pvc, pvc_cost) project_line_item.supplementary_project_monthly_cost = monthly_cost project_line_item.save() def tag_upsert_monthly_cluster_cost_line_item( # noqa: C901 self, start_date, end_date, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid ): """ Update or insert a daily summary line item for cluster cost based on tag rates. It checks to see if a line item exists for the cluster that contains the tag key:value pair, if it does then the price is added to the monthly cost. """ report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) if report_period: with schema_context(self.schema): if rate_dict is not None: for tag_key in rate_dict: tag_values = rate_dict.get(tag_key) for value_name, rate_value in tag_values.items(): # this makes sure that there is an entry for that node # that contains the specified key_value pair item_check = line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lte=end_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, pod_labels__contains={tag_key: value_name}, ).first() if item_check: line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", data_source="Pod", ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", data_source="Pod", source_uuid=provider_uuid, ) cluster_cost = rate_value if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug( "Cluster (%s) has a monthly infrastructure cost of %s from tag rates.", cluster_id, rate_value, ) if line_item.infrastructure_monthly_cost_json: cluster_cost = ( line_item.infrastructure_monthly_cost_json.get(distribution, 0) + rate_value ) monthly_cost = self.generate_monthly_cost_json_object(distribution, cluster_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug( "Cluster (%s) has a monthly supplemenarty cost of %s from tag rates.", cluster_id, rate_value, ) if line_item.supplementary_monthly_cost_json: cluster_cost = ( line_item.supplementary_monthly_cost_json.get(distribution, 0) + rate_value ) monthly_cost = self.generate_monthly_cost_json_object(distribution, cluster_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() def tag_upsert_monthly_default_cluster_cost_line_item( # noqa: C901 self, start_date, end_date, cluster_id, cluster_alias, rate_type, rate_dict, distribution, provider_uuid ): """ Update or insert daily summary line item for cluster cost. It checks to see if a line item exists for each cluster that contains the tag key:value pair, if it does then the price is added to the monthly cost. """ report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) with schema_context(self.schema): if rate_dict is not None: for tag_key in rate_dict: tag_values = rate_dict.get(tag_key) tag_default = tag_values.get("default_value") values_to_skip = tag_values.get("defined_keys") item_check = OCPUsageLineItemDailySummary.objects.filter( usage_start__gte=start_date, usage_start__lte=end_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, pod_labels__has_key=tag_key, ) for value in values_to_skip: item_check = item_check.exclude(pod_labels__contains={tag_key: value}) # this won't run if there are no matching items and item_check will continue to be # filtered until there are no items left while item_check: # get the first value for our tag key and exclude it from the queryset for the next check # will remove values until there are none left tag_key_value = item_check.first().pod_labels.get(tag_key) item_check = item_check.exclude(pod_labels__contains={tag_key: tag_key_value}) line_item = OCPUsageLineItemDailySummary.objects.filter( usage_start=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", data_source="Pod", ).first() if not line_item: line_item = OCPUsageLineItemDailySummary( uuid=uuid.uuid4(), usage_start=start_date, usage_end=start_date, report_period=report_period, cluster_id=cluster_id, cluster_alias=cluster_alias, monthly_cost_type="Cluster", data_source="Pod", source_uuid=provider_uuid, ) cluster_cost = tag_default if rate_type == metric_constants.INFRASTRUCTURE_COST_TYPE: LOG.debug( "Cluster (%s) has a default monthly infrastructure cost of %s.", cluster_id, tag_default, ) if line_item.infrastructure_monthly_cost_json: cluster_cost = ( line_item.infrastructure_monthly_cost_json.get(distribution, 0) + tag_default ) monthly_cost = self.generate_monthly_cost_json_object(distribution, cluster_cost) line_item.infrastructure_monthly_cost_json = monthly_cost elif rate_type == metric_constants.SUPPLEMENTARY_COST_TYPE: LOG.debug( "Cluster (%s) has a default monthly supplemenarty cost of %s.", cluster_id, tag_default ) if line_item.supplementary_monthly_cost_json: cluster_cost = ( line_item.supplementary_monthly_cost_json.get(distribution, 0) + tag_default ) monthly_cost = self.generate_monthly_cost_json_object(distribution, cluster_cost) line_item.supplementary_monthly_cost_json = monthly_cost line_item.save() def remove_monthly_cost(self, start_date, end_date, cluster_id, cost_type): """Delete all monthly costs of a specific type over a date range.""" report_period = self.get_usage_period_by_dates_and_cluster(start_date, end_date, cluster_id) filters = { "usage_start": start_date.date(), "report_period": report_period, "cluster_id": cluster_id, "monthly_cost_type": cost_type, } for rate_type, __ in metric_constants.COST_TYPE_CHOICES: cost_filters = [ f"{rate_type.lower()}_monthly_cost__isnull", f"{rate_type.lower()}_monthly_cost_json__isnull", f"{rate_type.lower()}_project_monthly_cost__isnull", ] for cost_filter in cost_filters: filters.update({cost_filter: False}) LOG.info( "Removing %s %s monthly costs \n\tfor %s \n\tfrom %s - %s.", cost_type, rate_type, cluster_id, start_date, end_date, ) with schema_context(self.schema): OCPUsageLineItemDailySummary.objects.filter(**filters).all().delete() filters.pop(cost_filter) def populate_node_label_line_item_daily_table(self, start_date, end_date, cluster_id): """Populate the daily node label aggregate of line items table. Args: start_date (datetime.date) The date to start populating the table. end_date (datetime.date) The date to end on. cluster_id (String) Cluster Identifier Returns (None) """ # Cast string to date object if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() table_name = self._table_map["node_label_line_item_daily"] daily_sql = pkgutil.get_data("masu.database", "sql/reporting_ocpnodelabellineitem_daily.sql") daily_sql = daily_sql.decode("utf-8") daily_sql_params = { "uuid": str(uuid.uuid4()).replace("-", "_"), "start_date": start_date, "end_date": end_date, "cluster_id": cluster_id, "schema": self.schema, } daily_sql, daily_sql_params = self.jinja_sql.prepare_query(daily_sql, daily_sql_params) self._execute_raw_sql_query(table_name, daily_sql, start_date, end_date, bind_params=list(daily_sql_params)) def populate_usage_costs(self, infrastructure_rates, supplementary_rates, start_date, end_date, cluster_id): """Update the reporting_ocpusagelineitem_daily_summary table with usage costs.""" # Cast start_date and end_date to date object, if they aren't already if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() OCPUsageLineItemDailySummary.objects.filter( cluster_id=cluster_id, usage_start__gte=start_date, usage_start__lte=end_date ).update( infrastructure_usage_cost=JSONBBuildObject( Value("cpu"), Coalesce( Value(infrastructure_rates.get("cpu_core_usage_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_usage_cpu_core_hours"), Value(0), output_field=DecimalField()) + Value(infrastructure_rates.get("cpu_core_request_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_request_cpu_core_hours"), Value(0), output_field=DecimalField()) + Value( infrastructure_rates.get("cpu_core_effective_usage_per_hour", 0), output_field=DecimalField() ) * Coalesce(F("pod_effective_usage_cpu_core_hours"), Value(0), output_field=DecimalField()), 0, output_field=DecimalField(), ), Value("memory"), Coalesce( Value(infrastructure_rates.get("memory_gb_usage_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_usage_memory_gigabyte_hours"), Value(0), output_field=DecimalField()) + Value(infrastructure_rates.get("memory_gb_request_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_request_memory_gigabyte_hours"), Value(0), output_field=DecimalField()) + Value( infrastructure_rates.get("memory_gb_effective_usage_per_hour", 0), output_field=DecimalField() ) * Coalesce(F("pod_effective_usage_memory_gigabyte_hours"), Value(0), output_field=DecimalField()), 0, output_field=DecimalField(), ), Value("storage"), Coalesce( Value(infrastructure_rates.get("storage_gb_usage_per_month", 0), output_field=DecimalField()) * Coalesce(F("persistentvolumeclaim_usage_gigabyte_months"), Value(0), output_field=DecimalField()) + Value(infrastructure_rates.get("storage_gb_request_per_month", 0), output_field=DecimalField()) * Coalesce(F("volume_request_storage_gigabyte_months"), Value(0), output_field=DecimalField()), 0, output_field=DecimalField(), ), ), supplementary_usage_cost=JSONBBuildObject( Value("cpu"), Coalesce( Value(supplementary_rates.get("cpu_core_usage_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_usage_cpu_core_hours"), Value(0), output_field=DecimalField()) + Value(supplementary_rates.get("cpu_core_request_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_request_cpu_core_hours"), Value(0), output_field=DecimalField()) + Value( supplementary_rates.get("cpu_core_effective_usage_per_hour", 0), output_field=DecimalField() ) * Coalesce(F("pod_effective_usage_cpu_core_hours"), Value(0), output_field=DecimalField()), 0, output_field=DecimalField(), ), Value("memory"), Coalesce( Value(supplementary_rates.get("memory_gb_usage_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_usage_memory_gigabyte_hours"), Value(0), output_field=DecimalField()) + Value(supplementary_rates.get("memory_gb_request_per_hour", 0), output_field=DecimalField()) * Coalesce(F("pod_request_memory_gigabyte_hours"), Value(0), output_field=DecimalField()) + Value( supplementary_rates.get("memory_gb_effective_usage_per_hour", 0), output_field=DecimalField() ) * Coalesce(F("pod_effective_usage_memory_gigabyte_hours"), Value(0), output_field=DecimalField()), 0, output_field=DecimalField(), ), Value("storage"), Coalesce( Value(supplementary_rates.get("storage_gb_usage_per_month", 0), output_field=DecimalField()) * Coalesce(F("persistentvolumeclaim_usage_gigabyte_months"), Value(0), output_field=DecimalField()) + Value(supplementary_rates.get("storage_gb_request_per_month", 0), output_field=DecimalField()) * Coalesce(F("volume_request_storage_gigabyte_months"), Value(0), output_field=DecimalField()), 0, output_field=DecimalField(), ), ), ) def populate_tag_usage_costs( # noqa: C901 self, infrastructure_rates, supplementary_rates, start_date, end_date, cluster_id ): """ Update the reporting_ocpusagelineitem_daily_summary table with usage costs based on tag rates. Due to the way the tag_keys are stored it loops through all of the tag keys to filter and update costs. The data structure for infrastructure and supplementary rates are a dictionary that include the metric name, the tag key, the tag value names, and the tag value, for example: {'cpu_core_usage_per_hour': { 'app': { 'far': '0.2000000000', 'manager': '100.0000000000', 'walk': '5.0000000000' } } } """ # defines the usage type for each metric metric_usage_type_map = { "cpu_core_usage_per_hour": "cpu", "cpu_core_request_per_hour": "cpu", "cpu_core_effective_usage_per_hour": "cpu", "memory_gb_usage_per_hour": "memory", "memory_gb_request_per_hour": "memory", "memory_gb_effective_usage_per_hour": "memory", "storage_gb_usage_per_month": "storage", "storage_gb_request_per_month": "storage", } # define the rates so the loop can operate on both rate types rate_types = [ {"rates": infrastructure_rates, "sql_file": "sql/infrastructure_tag_rates.sql"}, {"rates": supplementary_rates, "sql_file": "sql/supplementary_tag_rates.sql"}, ] # Cast start_date and end_date to date object, if they aren't already if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() # updates costs from tags for rate_type in rate_types: rate = rate_type.get("rates") sql_file = rate_type.get("sql_file") for metric in rate: tags = rate.get(metric, {}) usage_type = metric_usage_type_map.get(metric) if usage_type == "storage": labels_field = "volume_labels" else: labels_field = "pod_labels" table_name = self._table_map["line_item_daily_summary"] for tag_key in tags: tag_vals = tags.get(tag_key, {}) value_names = list(tag_vals.keys()) for val_name in value_names: rate_value = tag_vals[val_name] key_value_pair = json.dumps({tag_key: val_name}) tag_rates_sql = pkgutil.get_data("masu.database", sql_file) tag_rates_sql = tag_rates_sql.decode("utf-8") tag_rates_sql_params = { "start_date": start_date, "end_date": end_date, "rate": rate_value, "cluster_id": cluster_id, "schema": self.schema, "usage_type": usage_type, "metric": metric, "k_v_pair": key_value_pair, "labels_field": labels_field, } tag_rates_sql, tag_rates_sql_params = self.jinja_sql.prepare_query( tag_rates_sql, tag_rates_sql_params ) msg = f"Running populate_tag_usage_costs SQL with params: {tag_rates_sql_params}" LOG.info(msg) self._execute_raw_sql_query( table_name, tag_rates_sql, start_date, end_date, bind_params=list(tag_rates_sql_params) ) def populate_tag_usage_default_costs( # noqa: C901 self, infrastructure_rates, supplementary_rates, start_date, end_date, cluster_id ): """ Update the reporting_ocpusagelineitem_daily_summary table with usage costs based on tag rates. The data structure for infrastructure and supplementary rates are a dictionary that includes the metric, the tag key, the default value, and the values for that key that have rates defined and do not need the default applied, for example: { 'cpu_core_usage_per_hour': { 'app': { 'default_value': '100.0000000000', 'defined_keys': ['far', 'manager', 'walk'] } } } """ # defines the usage type for each metric metric_usage_type_map = { "cpu_core_usage_per_hour": "cpu", "cpu_core_request_per_hour": "cpu", "cpu_core_effective_usage_per_hour": "cpu", "memory_gb_usage_per_hour": "memory", "memory_gb_request_per_hour": "memory", "memory_gb_effective_usage_per_hour": "memory", "storage_gb_usage_per_month": "storage", "storage_gb_request_per_month": "storage", } # define the rates so the loop can operate on both rate types rate_types = [ {"rates": infrastructure_rates, "sql_file": "sql/default_infrastructure_tag_rates.sql"}, {"rates": supplementary_rates, "sql_file": "sql/default_supplementary_tag_rates.sql"}, ] # Cast start_date and end_date to date object, if they aren't already if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date() end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date() if isinstance(start_date, datetime.datetime): start_date = start_date.date() end_date = end_date.date() # updates costs from tags for rate_type in rate_types: rate = rate_type.get("rates") sql_file = rate_type.get("sql_file") for metric in rate: tags = rate.get(metric, {}) usage_type = metric_usage_type_map.get(metric) if usage_type == "storage": labels_field = "volume_labels" else: labels_field = "pod_labels" table_name = self._table_map["line_item_daily_summary"] for tag_key in tags: key_value_pair = [] tag_vals = tags.get(tag_key) rate_value = tag_vals.get("default_value", 0) if rate_value == 0: continue value_names = tag_vals.get("defined_keys", []) for value_to_skip in value_names: key_value_pair.append(json.dumps({tag_key: value_to_skip})) json.dumps(key_value_pair) tag_rates_sql = pkgutil.get_data("masu.database", sql_file) tag_rates_sql = tag_rates_sql.decode("utf-8") tag_rates_sql_params = { "start_date": start_date, "end_date": end_date, "rate": rate_value, "cluster_id": cluster_id, "schema": self.schema, "usage_type": usage_type, "metric": metric, "tag_key": tag_key, "k_v_pair": key_value_pair, "labels_field": labels_field, } tag_rates_sql, tag_rates_sql_params = self.jinja_sql.prepare_query( tag_rates_sql, tag_rates_sql_params ) msg = f"Running populate_tag_usage_default_costs SQL with params: {tag_rates_sql_params}" LOG.info(msg) self._execute_raw_sql_query( table_name, tag_rates_sql, start_date, end_date, bind_params=list(tag_rates_sql_params) ) def populate_openshift_cluster_information_tables(self, provider, cluster_id, cluster_alias, start_date, end_date): """Populate the cluster, node, PVC, and project tables for the cluster.""" cluster = self.populate_cluster_table(provider, cluster_id, cluster_alias) nodes = self.get_nodes_presto(str(provider.uuid), start_date, end_date) pvcs = self.get_pvcs_presto(str(provider.uuid), start_date, end_date) projects = self.get_projects_presto(str(provider.uuid), start_date, end_date) # pvcs = self.match_node_to_pvc(pvcs, projects) self.populate_node_table(cluster, nodes) self.populate_pvc_table(cluster, pvcs) self.populate_project_table(cluster, projects) def populate_cluster_table(self, provider, cluster_id, cluster_alias): """Get or create an entry in the OCP cluster table.""" with schema_context(self.schema): cluster, created = OCPCluster.objects.get_or_create( cluster_id=cluster_id, cluster_alias=cluster_id, provider=provider ) if created: msg = f"Add entry in reporting_ocp_clusters for {cluster_id}/{cluster_alias}" LOG.info(msg) return cluster def populate_node_table(self, cluster, nodes): """Get or create an entry in the OCP cluster table.""" LOG.info("Populating reporting_ocp_nodes table.") with schema_context(self.schema): for node in nodes: OCPNode.objects.get_or_create( node=node[0], resource_id=node[1], node_capacity_cpu_cores=node[2], cluster=cluster ) def populate_pvc_table(self, cluster, pvcs): """Get or create an entry in the OCP cluster table.""" LOG.info("Populating reporting_ocp_pvcs table.") with schema_context(self.schema): for pvc in pvcs: OCPPVC.objects.get_or_create(persistent_volume=pvc[0], persistent_volume_claim=pvc[1], cluster=cluster) def populate_project_table(self, cluster, projects): """Get or create an entry in the OCP cluster table.""" LOG.info("Populating reporting_ocp_projects table.") with schema_context(self.schema): for project in projects: OCPProject.objects.get_or_create(project=project, cluster=cluster) def get_nodes_presto(self, source_uuid, start_date, end_date): """Get the nodes from an OpenShift cluster.""" sql = f""" SELECT node, resource_id, max(node_capacity_cpu_cores) as node_capacity_cpu_cores FROM hive.{self.schema}.openshift_pod_usage_line_items_daily as ocp WHERE ocp.source = '{source_uuid}' AND ocp.year = '{start_date.strftime("%Y")}' AND ocp.month = '{start_date.strftime("%m")}' AND ocp.interval_start >= TIMESTAMP '{start_date}' AND ocp.interval_start < date_add('day', 1, TIMESTAMP '{end_date}') GROUP BY node, resource_id """ nodes = self._execute_presto_raw_sql_query(self.schema, sql) return nodes def get_pvcs_presto(self, source_uuid, start_date, end_date): """Get the nodes from an OpenShift cluster.""" sql = f""" SELECT distinct persistentvolume, persistentvolumeclaim FROM hive.{self.schema}.openshift_storage_usage_line_items_daily as ocp WHERE ocp.source = '{source_uuid}' AND ocp.year = '{start_date.strftime("%Y")}' AND ocp.month = '{start_date.strftime("%m")}' AND ocp.interval_start >= TIMESTAMP '{start_date}' AND ocp.interval_start < date_add('day', 1, TIMESTAMP '{end_date}') """ pvcs = self._execute_presto_raw_sql_query(self.schema, sql) return pvcs def get_projects_presto(self, source_uuid, start_date, end_date): """Get the nodes from an OpenShift cluster.""" sql = f""" SELECT distinct namespace FROM hive.{self.schema}.openshift_pod_usage_line_items_daily as ocp WHERE ocp.source = '{source_uuid}' AND ocp.year = '{start_date.strftime("%Y")}' AND ocp.month = '{start_date.strftime("%m")}' AND ocp.interval_start >= TIMESTAMP '{start_date}' AND ocp.interval_start < date_add('day', 1, TIMESTAMP '{end_date}') """ projects = self._execute_presto_raw_sql_query(self.schema, sql) return [project[0] for project in projects] def get_cluster_for_provider(self, provider_uuid): """Return the cluster entry for a provider UUID.""" with schema_context(self.schema): cluster = OCPCluster.objects.filter(provider_id=provider_uuid).first() return cluster def get_nodes_for_cluster(self, cluster_id): """Get all nodes for an OCP cluster.""" with schema_context(self.schema): nodes = OCPNode.objects.filter(cluster_id=cluster_id).values_list("node", "resource_id") nodes = [(node[0], node[1]) for node in nodes] return nodes def get_pvcs_for_cluster(self, cluster_id): """Get all nodes for an OCP cluster.""" with schema_context(self.schema): pvcs = OCPPVC.objects.filter(cluster_id=cluster_id).values_list( "persistent_volume", "persistent_volume_claim" ) pvcs = [(pvc[0], pvc[1]) for pvc in pvcs] return pvcs def get_projects_for_cluster(self, cluster_id): """Get all nodes for an OCP cluster.""" with schema_context(self.schema): projects = OCPProject.objects.filter(cluster_id=cluster_id).values_list("project") projects = [project[0] for project in projects] return projects def get_openshift_topology_for_provider(self, provider_uuid): """Return a dictionary with Cluster topology.""" cluster = self.get_cluster_for_provider(provider_uuid) topology = {"cluster_id": cluster.cluster_id, "cluster_alias": cluster.cluster_alias} node_tuples = self.get_nodes_for_cluster(cluster.uuid) pvc_tuples = self.get_pvcs_for_cluster(cluster.uuid) topology["nodes"] = [node[0] for node in node_tuples] topology["resource_ids"] = [node[1] for node in node_tuples] topology["persistent_volumes"] = [pvc[0] for pvc in pvc_tuples] topology["persistent_volume_claims"] = [pvc[1] for pvc in pvc_tuples] topology["projects"] = self.get_projects_for_cluster(cluster.uuid) return topology def delete_infrastructure_raw_cost_from_daily_summary(self, provider_uuid, report_period_id, start_date, end_date): table_name = OCP_REPORT_TABLE_MAP["line_item_daily_summary"] msg = f"Removing infrastructure_raw_cost for {provider_uuid} from {start_date} to {end_date}." LOG.info(msg) sql = f""" DELETE FROM {self.schema}.reporting_ocpusagelineitem_daily_summary WHERE usage_start >= '{start_date}'::date AND usage_start <= '{end_date}'::date AND report_period_id = {report_period_id} AND infrastructure_raw_cost IS NOT NULL AND infrastructure_raw_cost != 0 """ self._execute_raw_sql_query(table_name, sql, start_date, end_date) def populate_ocp_on_all_project_daily_summary(self, platform, sql_params): LOG.info(f"Populating {platform.upper()} records for ocpallcostlineitem_project_daily_summary") script_file_name = f"reporting_ocpallcostlineitem_project_daily_summary_{platform.lower()}.sql" script_file_path = f"{self.OCP_ON_ALL_SQL_PATH}{script_file_name}" self._execute_processing_script("masu.database", script_file_path, sql_params) def populate_ocp_on_all_daily_summary(self, platform, sql_params): LOG.info(f"Populating {platform.upper()} records for ocpallcostlineitem_daily_summary") script_file_name = f"reporting_ocpallcostlineitem_daily_summary_{platform.lower()}.sql" script_file_path = f"{self.OCP_ON_ALL_SQL_PATH}{script_file_name}" self._execute_processing_script("masu.database", script_file_path, sql_params) def populate_ocp_on_all_ui_summary_tables(self, sql_params): for perspective in OCP_ON_ALL_PERSPECTIVES: LOG.info(f"Populating {perspective._meta.db_table} data using {sql_params}") script_file_path = f"{self.OCP_ON_ALL_SQL_PATH}{perspective._meta.db_table}.sql" self._execute_processing_script("masu.database", script_file_path, sql_params) def get_max_min_timestamp_from_parquet(self, source_uuid, start_date, end_date): """Get the max and min timestamps for parquet data given a date range""" sql = f""" SELECT min(interval_start) as min_timestamp, max(interval_start) as max_timestamp FROM hive.{self.schema}.openshift_pod_usage_line_items_daily as ocp WHERE ocp.source = '{source_uuid}' AND ocp.year = '{start_date.strftime("%Y")}' AND ocp.month = '{start_date.strftime("%m")}' AND ocp.interval_start >= TIMESTAMP '{start_date}' AND ocp.interval_start < date_add('day', 1, TIMESTAMP '{end_date}') """ timestamps = self._execute_presto_raw_sql_query(self.schema, sql) max, min = timestamps[0] return parse(max), parse(min)

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null

0

6

22c7cab92448f7fbdb5216234b5cbd6796657787

222

py

Python

tests/conftest.py

JLSteenwyk/ClipKIT

b2d6033e638a78acc36942f9f420d5d3bc0e09ad

[ "MIT" ]

28

2020-06-11T14:06:15.000Z

2022-03-14T04:32:12.000Z

tests/conftest.py

JLSteenwyk/ClipKIT

b2d6033e638a78acc36942f9f420d5d3bc0e09ad

[ "MIT" ]

10

2020-09-14T13:59:13.000Z

2022-02-25T17:17:01.000Z

tests/conftest.py

JLSteenwyk/ClipKIT

b2d6033e638a78acc36942f9f420d5d3bc0e09ad

[ "MIT" ]

1

2020-12-15T07:25:09.000Z

# global fixtures can go here import pytest def pytest_configure(config): config.addinivalue_line("markers", "integration: mark as integration test") config.addinivalue_line("markers", "slow: mark as slow test")

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py

Python

battleforcastile/utils/get_match_by_id.py

battleforcastile/battleforcastile

65223fcb56ecc550f1a7c7b70beadff22c866d85

[ "MIT" ]

null

battleforcastile/utils/get_match_by_id.py

battleforcastile/battleforcastile

65223fcb56ecc550f1a7c7b70beadff22c866d85

[ "MIT" ]

1

2021-08-21T10:16:03.000Z

battleforcastile/utils/get_match_by_id.py

battleforcastile/battleforcastile

65223fcb56ecc550f1a7c7b70beadff22c866d85

[ "MIT" ]

null

import requests from battleforcastile.constants import BATTLEFORCASTILE_BACKEND_URL def get_match_by_id(match_id: int): url = f'{BATTLEFORCASTILE_BACKEND_URL}/matches/{match_id}/' return requests.get(url)

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Python

src/python/nimbusml/tests/test_syntax.py

montehoover/NimbusML

f6be39ce9359786976429bab0ccd837e849b4ba5

[ "MIT" ]

134

2018-11-01T22:15:24.000Z

2019-05-04T11:30:08.000Z

src/python/nimbusml/tests/test_syntax.py

montehoover/NimbusML

f6be39ce9359786976429bab0ccd837e849b4ba5

[ "MIT" ]

226

2019-05-07T19:00:44.000Z

2021-01-06T07:59:48.000Z

src/python/nimbusml/tests/test_syntax.py

montehoover/NimbusML

f6be39ce9359786976429bab0ccd837e849b4ba5

[ "MIT" ]

43

2019-05-15T20:19:42.000Z

2022-03-30T10:26:07.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # -------------------------------------------------------------------------------------------- import unittest import pandas import six from nimbusml import Pipeline from nimbusml.feature_extraction.categorical import OneHotVectorizer, \ OneHotHashVectorizer from nimbusml.feature_extraction.text import NGramFeaturizer from nimbusml.feature_selection import MutualInformationSelector from nimbusml.internal.entrypoints._ngramextractor_ngram import n_gram from nimbusml.internal.utils.data_roles import Role from nimbusml.linear_model import FastLinearBinaryClassifier from nimbusml.preprocessing.normalization import LogMeanVarianceScaler from nimbusml.preprocessing.schema import ColumnConcatenator as Concat, \ ColumnDropper as Drop # from sklearn.pipeline import Pipeline if six.PY2: pass else: pass class TestSyntax(unittest.TestCase): def test_syntax1(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer(), FastLinearBinaryClassifier(maximum_number_of_iterations=1) ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax2(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << 'education', OneHotVectorizer(max_num_terms=2) << 'workclass', FastLinearBinaryClassifier(maximum_number_of_iterations=1) ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax2_lt(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << 'education', OneHotVectorizer(max_num_terms=2) << 'workclass', FastLinearBinaryClassifier(maximum_number_of_iterations=1) ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax3(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'edu1': 'education'}, OneHotHashVectorizer() << 'education', OneHotVectorizer(max_num_terms=2) << 'workclass', # Currently the learner does not use edu1 # unless it is specified explicitely so nimbusml # does not do what the syntax implicetely tells. # We need to modify either the bridge to look into # every available column at one step. FastLinearBinaryClassifier(maximum_number_of_iterations=1) ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax4(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'edu1': 'education'}, OneHotHashVectorizer() << {'edu2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'wki': 'workclass'}, Concat() << {'Inputs': ['edu1', 'edu2', 'wki']}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'Inputs' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax4_2(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'edu1': 'education'}, OneHotHashVectorizer() << {'edu2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'wki': 'workclass'}, Concat() << {'Inputs': ['edu1', 'edu2', 'wki']}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'Inputs' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax4_dict(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'edu1': 'education'}, OneHotHashVectorizer() << {'edu2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'wki': 'workclass'}, Concat() << {'Inputs': ['edu1', 'edu2', 'wki']}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'Inputs' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax4_columns(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer(columns={'edu1': 'education'}), OneHotHashVectorizer(columns={'edu2': 'education'}), OneHotVectorizer(max_num_terms=2, columns={'wki': 'workclass'}), Concat(columns={'Inputs': ['edu1', 'edu2', 'wki']}), FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'Inputs' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) @unittest.skip( "skip until we have a proper way to catch exception raised by nimbusml") def test_syntax4_fail(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'edu1': 'education'}, OneHotHashVectorizer() << {'edu2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'wki': 'workclass'}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << ['edu1', 'edu2', 'wki'] ]) try: exp.fit(X, y, verbose=0) assert False except RuntimeError as e: assert "ConcatTransform() << {'Input': ['edu1', 'edu2', 'wki']}" \ in str(e) @unittest.skip( "skip until we have a proper way to catch exception raised by nimbusml") def test_syntax4_fail2(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'edu1': 'education'}, OneHotHashVectorizer() << {'edu2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'wki': 'workclass'}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << ['edu1', 'edu4', 'wki'] ]) try: exp.fit(X, y, verbose=0) raise AssertionError("The test should not reach this line.") except Exception as e: assert "Feature column 'edu4' not found" in str(e) def test_syntax5(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'f1': 'education'}, OneHotHashVectorizer() << {'f2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'}, Concat() << {'Features': ['f%d' % i for i in range(1, 4)]}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'Features' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) @unittest.skip("regular expression not yet implemented") def test_syntax5_regular_expression(self): # REVIEW: not implemented yet # The best would be to handle regular expression inside nimbusml. # It could be handled in entrypoint.py just before calling nimbusml. # It can be handled inside Pipeline if it is aware of # the input schema. df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'f1': 'education'}, OneHotHashVectorizer() << {'f2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'}, Concat() << {'Features': 'f[0-9]+'}, FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'Features' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax6(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'f1': 'education'}, OneHotHashVectorizer() << {'f2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'}, Concat() << {'Features': ['f%d' % i for i in range(1, 4)]}, Drop() << ['education', 'workclass', 'f1', 'f2', 'f3'], FastLinearBinaryClassifier(maximum_number_of_iterations=1) << ['Features'] ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax6_not_features(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'f1': 'education'}, OneHotHashVectorizer() << {'f2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'}, Concat() << {'FeaturesCustom': ['f%d' % i for i in range(1, 4)]}, Drop() << ['education', 'workclass', 'f1', 'f2', 'f3'], FastLinearBinaryClassifier(maximum_number_of_iterations=1) << 'FeaturesCustom' ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) @unittest.skip(reason="what should be the default behavior") def test_syntax6_change_role(self): # REVIEW: the pipeline drops all columns but one --> # nimbusml still thinks the Features are eduction, workclass # and does not automatically detects that the only remaining # columns should play that role # (maybe because the label column is here too even though # the only remaining column without a role is Features). df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'f1': 'education'}, OneHotHashVectorizer() << {'f2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'}, Concat() << {'Features': ['f%d' % i for i in range(1, 4)]}, Drop() << ['education', 'workclass', 'f1', 'f2', 'f3'], FastLinearBinaryClassifier(maximum_number_of_iterations=1) << ['Features'] ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) @unittest.skip("regular expression not yet implemented") def test_syntax6_regular_expression(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) y = df['y'] exp = Pipeline([ OneHotVectorizer() << {'f1': 'education'}, OneHotHashVectorizer() << {'f2': 'education'}, OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'}, Concat() << {'Features': ['f%d' % i for i in range(1, 4)]}, Drop() << '~Features', FastLinearBinaryClassifier(maximum_number_of_iterations=1) ]) exp.fit(X, y, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax9_slots_label(self): train_reviews = pandas.DataFrame( data=dict( review=[ "This is great", "I hate it", "Love it", "Do not like it", "Really like it", "I hate it", "I like it a lot", "I kind of hate it", "I do like it", "I really hate it", "It is very good", "I hate it a bunch", "I love it a bunch", "I hate it", "I like it very much", "I hate it very much.", "I really do love it", "I really do hate it", "Love it!", "Hate it!", "I love it", "I hate it", "I love it", "I hate it", "I love it"], like=[ True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True])) X = train_reviews.loc[:, train_reviews.columns != 'like'] y = train_reviews[['like']] transform_1 = NGramFeaturizer(word_feature_extractor=n_gram()) transform_2 = MutualInformationSelector() exp = Pipeline([transform_1, transform_2]) res = exp.fit_transform(X, y, verbose=0) assert res is not None # Scikit compatibility (Compose transforms inside Scikit Pipeline). # In this scenario, we do not provide {input, output} arguments transform_1 = NGramFeaturizer(word_feature_extractor=n_gram()) transform_2 = MutualInformationSelector(slots_in_output=2) pipe = Pipeline([transform_1, transform_2]) res = pipe.fit_transform(X, y, verbose=0) assert res is not None def test_syntax10_multi_output1(self): in_df = pandas.DataFrame( data=dict( Sepal_Length=[ 2.5, 1, 2.1, 1.0], Sepal_Width=[ .75, .9, .8, .76], Petal_Length=[ 0, 2.5, 2.6, 2.4], Species=[ "setosa", "viginica", "setosa", 'versicolor'])) # generate two new Columns - Petal_Normed and Sepal_Normed normed = LogMeanVarianceScaler() << { 'Petal_Normed': 'Petal_Length', 'Sepal_Normed': 'Sepal_Width'} out_df = normed.fit_transform(in_df, verbose=0) self.assertEqual(sorted(list(out_df.columns)), ['Petal_Length', 'Petal_Normed', 'Sepal_Length', 'Sepal_Normed', 'Sepal_Width', 'Species']) def test_syntax10_multi_output2(self): in_df = pandas.DataFrame( data=dict( Sepal_Length=[ 2.5, 1, 2.1, 1.0], Sepal_Width=[ .75, .9, .8, .76], Petal_Length=[ 0, 2.5, 2.6, 2.4], Species=[ "setosa", "viginica", "setosa", 'versicolor'])) # generate two new Columns - Petal_Normed and Sepal_Normed newcols = { 'Petal_Normed': 'Petal_Length', 'Sepal_Normed': 'Sepal_Width'} normed = LogMeanVarianceScaler() << newcols out_df = normed.fit_transform(in_df, verbose=0) self.assertEqual(sorted(list(out_df.columns)), ['Petal_Length', 'Petal_Normed', 'Sepal_Length', 'Sepal_Normed', 'Sepal_Width', 'Species']) def test_syntax11_learner(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) exp = Pipeline( [ OneHotVectorizer() << { 'edu1': 'education'}, OneHotHashVectorizer() << { 'edu2': 'education'}, FastLinearBinaryClassifier( maximum_number_of_iterations=1) << { 'Features': ['edu1', 'edu2'], Role.Label: 'y'}]) exp.fit(df, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) def test_syntax11_append_insert(self): df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'], workclass=['X', 'X', 'Y', 'Y', 'Y'], y=[1, 0, 1, 0, 0])) X = df.drop('y', axis=1) exp = Pipeline() exp.append( ("OneHotHashVectorizer", OneHotHashVectorizer() << { 'edu2': 'education'})) exp.insert(0, OneHotVectorizer() << {'edu1': 'education'}) exp.append( FastLinearBinaryClassifier( maximum_number_of_iterations=1) << { 'Features': [ 'edu1', 'edu2'], Role.Label: 'y'}) exp.append(OneHotHashVectorizer() << {'edu2': 'education'}) del exp[-1] assert len(exp) == 3 exp.fit(df, verbose=0) prediction = exp.predict(X) assert isinstance(prediction, pandas.DataFrame) assert sorted(list(prediction.columns)) == [ 'PredictedLabel', 'Probability', 'Score'] assert prediction.shape == (5, 3) try: exp.append(OneHotHashVectorizer() << {'edu2': 'education'}) except RuntimeError as e: assert "Model is fitted and cannot be modified" in str(e) try: exp.insert(0, OneHotHashVectorizer() << {'edu2': 'education'}) except RuntimeError as e: assert "Model is fitted and cannot be modified" in str(e) try: del exp[0] except RuntimeError as e: assert "Model is fitted and cannot be modified" in str(e) obj = exp[1][1] assert obj.__class__.__name__ == "OneHotHashVectorizer" obj = exp[1][1] assert obj.__class__.__name__ == "OneHotHashVectorizer" res = exp['OneHotHashVectorizer'] assert len(res) == 1 graph = exp.graph_ assert len(graph.nodes) >= len(exp) if __name__ == "__main__": unittest.main()

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6

a3f3fd516c4c0f4c175e6506a9bdc8cdbcfb2670

114,906

py

Python

google/cloud/contact_center_insights_v1/services/contact_center_insights/async_client.py

googleapis/python-contact-center-insights

3eb4794a0c25a090f0f3a0e0e5f7fd74eb7c356e

[ "Apache-2.0" ]

4

2021-08-15T04:55:44.000Z

2022-02-01T09:19:57.000Z

google/cloud/contact_center_insights_v1/services/contact_center_insights/async_client.py

googleapis/python-contact-center-insights

3eb4794a0c25a090f0f3a0e0e5f7fd74eb7c356e

[ "Apache-2.0" ]

53

2021-07-16T11:02:44.000Z

2022-03-07T16:39:20.000Z

google/cloud/contact_center_insights_v1/services/contact_center_insights/async_client.py

googleapis/python-contact-center-insights

3eb4794a0c25a090f0f3a0e0e5f7fd74eb7c356e

[ "Apache-2.0" ]

5

2021-07-15T18:17:53.000Z

2022-01-29T08:09:16.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # 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. # from collections import OrderedDict import functools import re from typing import Dict, Sequence, Tuple, Type, Union import pkg_resources from google.api_core.client_options import ClientOptions from google.api_core import exceptions as core_exceptions from google.api_core import gapic_v1 from google.api_core import retry as retries from google.auth import credentials as ga_credentials # type: ignore from google.oauth2 import service_account # type: ignore try: OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.Retry, object] # type: ignore from google.api_core import operation # type: ignore from google.api_core import operation_async # type: ignore from google.cloud.contact_center_insights_v1.services.contact_center_insights import ( pagers, ) from google.cloud.contact_center_insights_v1.types import contact_center_insights from google.cloud.contact_center_insights_v1.types import resources from google.protobuf import duration_pb2 # type: ignore from google.protobuf import empty_pb2 # type: ignore from google.protobuf import field_mask_pb2 # type: ignore from google.protobuf import timestamp_pb2 # type: ignore from .transports.base import ContactCenterInsightsTransport, DEFAULT_CLIENT_INFO from .transports.grpc_asyncio import ContactCenterInsightsGrpcAsyncIOTransport from .client import ContactCenterInsightsClient class ContactCenterInsightsAsyncClient: """An API that lets users analyze and explore their business conversation data. """ _client: ContactCenterInsightsClient DEFAULT_ENDPOINT = ContactCenterInsightsClient.DEFAULT_ENDPOINT DEFAULT_MTLS_ENDPOINT = ContactCenterInsightsClient.DEFAULT_MTLS_ENDPOINT analysis_path = staticmethod(ContactCenterInsightsClient.analysis_path) parse_analysis_path = staticmethod(ContactCenterInsightsClient.parse_analysis_path) conversation_path = staticmethod(ContactCenterInsightsClient.conversation_path) parse_conversation_path = staticmethod( ContactCenterInsightsClient.parse_conversation_path ) issue_path = staticmethod(ContactCenterInsightsClient.issue_path) parse_issue_path = staticmethod(ContactCenterInsightsClient.parse_issue_path) issue_model_path = staticmethod(ContactCenterInsightsClient.issue_model_path) parse_issue_model_path = staticmethod( ContactCenterInsightsClient.parse_issue_model_path ) participant_path = staticmethod(ContactCenterInsightsClient.participant_path) parse_participant_path = staticmethod( ContactCenterInsightsClient.parse_participant_path ) phrase_matcher_path = staticmethod(ContactCenterInsightsClient.phrase_matcher_path) parse_phrase_matcher_path = staticmethod( ContactCenterInsightsClient.parse_phrase_matcher_path ) settings_path = staticmethod(ContactCenterInsightsClient.settings_path) parse_settings_path = staticmethod(ContactCenterInsightsClient.parse_settings_path) view_path = staticmethod(ContactCenterInsightsClient.view_path) parse_view_path = staticmethod(ContactCenterInsightsClient.parse_view_path) common_billing_account_path = staticmethod( ContactCenterInsightsClient.common_billing_account_path ) parse_common_billing_account_path = staticmethod( ContactCenterInsightsClient.parse_common_billing_account_path ) common_folder_path = staticmethod(ContactCenterInsightsClient.common_folder_path) parse_common_folder_path = staticmethod( ContactCenterInsightsClient.parse_common_folder_path ) common_organization_path = staticmethod( ContactCenterInsightsClient.common_organization_path ) parse_common_organization_path = staticmethod( ContactCenterInsightsClient.parse_common_organization_path ) common_project_path = staticmethod(ContactCenterInsightsClient.common_project_path) parse_common_project_path = staticmethod( ContactCenterInsightsClient.parse_common_project_path ) common_location_path = staticmethod( ContactCenterInsightsClient.common_location_path ) parse_common_location_path = staticmethod( ContactCenterInsightsClient.parse_common_location_path ) @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: ContactCenterInsightsAsyncClient: The constructed client. """ return ContactCenterInsightsClient.from_service_account_info.__func__(ContactCenterInsightsAsyncClient, info, *args, **kwargs) # type: ignore @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: ContactCenterInsightsAsyncClient: The constructed client. """ return ContactCenterInsightsClient.from_service_account_file.__func__(ContactCenterInsightsAsyncClient, filename, *args, **kwargs) # type: ignore from_service_account_json = from_service_account_file @property def transport(self) -> ContactCenterInsightsTransport: """Returns the transport used by the client instance. Returns: ContactCenterInsightsTransport: The transport used by the client instance. """ return self._client.transport get_transport_class = functools.partial( type(ContactCenterInsightsClient).get_transport_class, type(ContactCenterInsightsClient), ) def __init__( self, *, credentials: ga_credentials.Credentials = None, transport: Union[str, ContactCenterInsightsTransport] = "grpc_asyncio", client_options: ClientOptions = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the contact center insights client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Union[str, ~.ContactCenterInsightsTransport]): The transport to use. If set to None, a transport is chosen automatically. client_options (ClientOptions): Custom options for the client. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. Raises: google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport creation failed for any reason. """ self._client = ContactCenterInsightsClient( credentials=credentials, transport=transport, client_options=client_options, client_info=client_info, ) async def create_conversation( self, request: Union[contact_center_insights.CreateConversationRequest, dict] = None, *, parent: str = None, conversation: resources.Conversation = None, conversation_id: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Conversation: r"""Creates a conversation. Args: request (Union[google.cloud.contact_center_insights_v1.types.CreateConversationRequest, dict]): The request object. Request to create a conversation. parent (:class:`str`): Required. The parent resource of the conversation. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. conversation (:class:`google.cloud.contact_center_insights_v1.types.Conversation`): Required. The conversation resource to create. This corresponds to the ``conversation`` field on the ``request`` instance; if ``request`` is provided, this should not be set. conversation_id (:class:`str`): A unique ID for the new conversation. This ID will become the final component of the conversation's resource name. If no ID is specified, a server-generated ID will be used. This value should be 4-64 characters and must match the regular expression ``^[a-z0-9-]{4,64}$``. Valid characters are ``[a-z][0-9]-`` This corresponds to the ``conversation_id`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Conversation: The conversation resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, conversation, conversation_id]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CreateConversationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if conversation is not None: request.conversation = conversation if conversation_id is not None: request.conversation_id = conversation_id # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_conversation, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def update_conversation( self, request: Union[contact_center_insights.UpdateConversationRequest, dict] = None, *, conversation: resources.Conversation = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Conversation: r"""Updates a conversation. Args: request (Union[google.cloud.contact_center_insights_v1.types.UpdateConversationRequest, dict]): The request object. The request to update a conversation. conversation (:class:`google.cloud.contact_center_insights_v1.types.Conversation`): Required. The new values for the conversation. This corresponds to the ``conversation`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): The list of fields to be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Conversation: The conversation resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([conversation, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UpdateConversationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if conversation is not None: request.conversation = conversation if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_conversation, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("conversation.name", request.conversation.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def get_conversation( self, request: Union[contact_center_insights.GetConversationRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Conversation: r"""Gets a conversation. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetConversationRequest, dict]): The request object. The request to get a conversation. name (:class:`str`): Required. The name of the conversation to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Conversation: The conversation resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetConversationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_conversation, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def list_conversations( self, request: Union[contact_center_insights.ListConversationsRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListConversationsAsyncPager: r"""Lists conversations. Args: request (Union[google.cloud.contact_center_insights_v1.types.ListConversationsRequest, dict]): The request object. Request to list conversations. parent (:class:`str`): Required. The parent resource of the conversation. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.services.contact_center_insights.pagers.ListConversationsAsyncPager: The response of listing conversations. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ListConversationsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_conversations, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListConversationsAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def delete_conversation( self, request: Union[contact_center_insights.DeleteConversationRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> None: r"""Deletes a conversation. Args: request (Union[google.cloud.contact_center_insights_v1.types.DeleteConversationRequest, dict]): The request object. The request to delete a conversation. name (:class:`str`): Required. The name of the conversation to delete. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.DeleteConversationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_conversation, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) async def create_analysis( self, request: Union[contact_center_insights.CreateAnalysisRequest, dict] = None, *, parent: str = None, analysis: resources.Analysis = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Creates an analysis. The long running operation is done when the analysis has completed. Args: request (Union[google.cloud.contact_center_insights_v1.types.CreateAnalysisRequest, dict]): The request object. The request to create an analysis. parent (:class:`str`): Required. The parent resource of the analysis. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. analysis (:class:`google.cloud.contact_center_insights_v1.types.Analysis`): Required. The analysis to create. This corresponds to the ``analysis`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.contact_center_insights_v1.types.Analysis` The analysis resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, analysis]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CreateAnalysisRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if analysis is not None: request.analysis = analysis # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_analysis, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, resources.Analysis, metadata_type=contact_center_insights.CreateAnalysisOperationMetadata, ) # Done; return the response. return response async def get_analysis( self, request: Union[contact_center_insights.GetAnalysisRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Analysis: r"""Gets an analysis. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetAnalysisRequest, dict]): The request object. The request to get an analysis. name (:class:`str`): Required. The name of the analysis to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Analysis: The analysis resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetAnalysisRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_analysis, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def list_analyses( self, request: Union[contact_center_insights.ListAnalysesRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListAnalysesAsyncPager: r"""Lists analyses. Args: request (Union[google.cloud.contact_center_insights_v1.types.ListAnalysesRequest, dict]): The request object. The request to list analyses. parent (:class:`str`): Required. The parent resource of the analyses. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.services.contact_center_insights.pagers.ListAnalysesAsyncPager: The response to list analyses. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ListAnalysesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_analyses, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListAnalysesAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def delete_analysis( self, request: Union[contact_center_insights.DeleteAnalysisRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> None: r"""Deletes an analysis. Args: request (Union[google.cloud.contact_center_insights_v1.types.DeleteAnalysisRequest, dict]): The request object. The request to delete an analysis. name (:class:`str`): Required. The name of the analysis to delete. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.DeleteAnalysisRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_analysis, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) async def export_insights_data( self, request: Union[contact_center_insights.ExportInsightsDataRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Export insights data to a destination defined in the request body. Args: request (Union[google.cloud.contact_center_insights_v1.types.ExportInsightsDataRequest, dict]): The request object. The request to export insights. parent (:class:`str`): Required. The parent resource to export data from. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.contact_center_insights_v1.types.ExportInsightsDataResponse` Response for an export insights operation. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ExportInsightsDataRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.export_insights_data, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, contact_center_insights.ExportInsightsDataResponse, metadata_type=contact_center_insights.ExportInsightsDataMetadata, ) # Done; return the response. return response async def create_issue_model( self, request: Union[contact_center_insights.CreateIssueModelRequest, dict] = None, *, parent: str = None, issue_model: resources.IssueModel = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Creates an issue model. Args: request (Union[google.cloud.contact_center_insights_v1.types.CreateIssueModelRequest, dict]): The request object. The request to create an issue model. parent (:class:`str`): Required. The parent resource of the issue model. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. issue_model (:class:`google.cloud.contact_center_insights_v1.types.IssueModel`): Required. The issue model to create. This corresponds to the ``issue_model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.contact_center_insights_v1.types.IssueModel` The issue model resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, issue_model]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CreateIssueModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if issue_model is not None: request.issue_model = issue_model # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_issue_model, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, resources.IssueModel, metadata_type=contact_center_insights.CreateIssueModelMetadata, ) # Done; return the response. return response async def update_issue_model( self, request: Union[contact_center_insights.UpdateIssueModelRequest, dict] = None, *, issue_model: resources.IssueModel = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.IssueModel: r"""Updates an issue model. Args: request (Union[google.cloud.contact_center_insights_v1.types.UpdateIssueModelRequest, dict]): The request object. The request to update an issue model. issue_model (:class:`google.cloud.contact_center_insights_v1.types.IssueModel`): Required. The new values for the issue model. This corresponds to the ``issue_model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): The list of fields to be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.IssueModel: The issue model resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([issue_model, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UpdateIssueModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if issue_model is not None: request.issue_model = issue_model if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_issue_model, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("issue_model.name", request.issue_model.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def get_issue_model( self, request: Union[contact_center_insights.GetIssueModelRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.IssueModel: r"""Gets an issue model. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetIssueModelRequest, dict]): The request object. The request to get an issue model. name (:class:`str`): Required. The name of the issue model to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.IssueModel: The issue model resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetIssueModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_issue_model, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def list_issue_models( self, request: Union[contact_center_insights.ListIssueModelsRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> contact_center_insights.ListIssueModelsResponse: r"""Lists issue models. Args: request (Union[google.cloud.contact_center_insights_v1.types.ListIssueModelsRequest, dict]): The request object. Request to list issue models. parent (:class:`str`): Required. The parent resource of the issue model. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.ListIssueModelsResponse: The response of listing issue models. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ListIssueModelsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_issue_models, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def delete_issue_model( self, request: Union[contact_center_insights.DeleteIssueModelRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Deletes an issue model. Args: request (Union[google.cloud.contact_center_insights_v1.types.DeleteIssueModelRequest, dict]): The request object. The request to delete an issue model. name (:class:`str`): Required. The name of the issue model to delete. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.DeleteIssueModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_issue_model, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, empty_pb2.Empty, metadata_type=contact_center_insights.DeleteIssueModelMetadata, ) # Done; return the response. return response async def deploy_issue_model( self, request: Union[contact_center_insights.DeployIssueModelRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Deploys an issue model. Returns an error if a model is already deployed. An issue model can only be used in analysis after it has been deployed. Args: request (Union[google.cloud.contact_center_insights_v1.types.DeployIssueModelRequest, dict]): The request object. The request to deploy an issue model. name (:class:`str`): Required. The issue model to deploy. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.contact_center_insights_v1.types.DeployIssueModelResponse` The response to deploy an issue model. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.DeployIssueModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.deploy_issue_model, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, contact_center_insights.DeployIssueModelResponse, metadata_type=contact_center_insights.DeployIssueModelMetadata, ) # Done; return the response. return response async def undeploy_issue_model( self, request: Union[contact_center_insights.UndeployIssueModelRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Undeploys an issue model. An issue model can not be used in analysis after it has been undeployed. Args: request (Union[google.cloud.contact_center_insights_v1.types.UndeployIssueModelRequest, dict]): The request object. The request to undeploy an issue model. name (:class:`str`): Required. The issue model to undeploy. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.contact_center_insights_v1.types.UndeployIssueModelResponse` The response to undeploy an issue model. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UndeployIssueModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.undeploy_issue_model, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, contact_center_insights.UndeployIssueModelResponse, metadata_type=contact_center_insights.UndeployIssueModelMetadata, ) # Done; return the response. return response async def get_issue( self, request: Union[contact_center_insights.GetIssueRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Issue: r"""Gets an issue. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetIssueRequest, dict]): The request object. The request to get an issue. name (:class:`str`): Required. The name of the issue to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Issue: The issue resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetIssueRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_issue, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def list_issues( self, request: Union[contact_center_insights.ListIssuesRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> contact_center_insights.ListIssuesResponse: r"""Lists issues. Args: request (Union[google.cloud.contact_center_insights_v1.types.ListIssuesRequest, dict]): The request object. Request to list issues. parent (:class:`str`): Required. The parent resource of the issue. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.ListIssuesResponse: The response of listing issues. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ListIssuesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_issues, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def update_issue( self, request: Union[contact_center_insights.UpdateIssueRequest, dict] = None, *, issue: resources.Issue = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Issue: r"""Updates an issue. Args: request (Union[google.cloud.contact_center_insights_v1.types.UpdateIssueRequest, dict]): The request object. The request to update an issue. issue (:class:`google.cloud.contact_center_insights_v1.types.Issue`): Required. The new values for the issue. This corresponds to the ``issue`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): The list of fields to be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Issue: The issue resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([issue, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UpdateIssueRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if issue is not None: request.issue = issue if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_issue, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("issue.name", request.issue.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def calculate_issue_model_stats( self, request: Union[ contact_center_insights.CalculateIssueModelStatsRequest, dict ] = None, *, issue_model: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> contact_center_insights.CalculateIssueModelStatsResponse: r"""Gets an issue model's statistics. Args: request (Union[google.cloud.contact_center_insights_v1.types.CalculateIssueModelStatsRequest, dict]): The request object. Request to get statistics of an issue model. issue_model (:class:`str`): Required. The resource name of the issue model to query against. This corresponds to the ``issue_model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.CalculateIssueModelStatsResponse: Response of querying an issue model's statistics. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([issue_model]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CalculateIssueModelStatsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if issue_model is not None: request.issue_model = issue_model # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.calculate_issue_model_stats, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("issue_model", request.issue_model),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def create_phrase_matcher( self, request: Union[contact_center_insights.CreatePhraseMatcherRequest, dict] = None, *, parent: str = None, phrase_matcher: resources.PhraseMatcher = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.PhraseMatcher: r"""Creates a phrase matcher. Args: request (Union[google.cloud.contact_center_insights_v1.types.CreatePhraseMatcherRequest, dict]): The request object. Request to create a phrase matcher. parent (:class:`str`): Required. The parent resource of the phrase matcher. Required. The location to create a phrase matcher for. Format: ``projects/<Project ID>/locations/<Location ID>`` or ``projects/<Project Number>/locations/<Location ID>`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. phrase_matcher (:class:`google.cloud.contact_center_insights_v1.types.PhraseMatcher`): Required. The phrase matcher resource to create. This corresponds to the ``phrase_matcher`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.PhraseMatcher: The phrase matcher resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, phrase_matcher]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CreatePhraseMatcherRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if phrase_matcher is not None: request.phrase_matcher = phrase_matcher # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_phrase_matcher, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def get_phrase_matcher( self, request: Union[contact_center_insights.GetPhraseMatcherRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.PhraseMatcher: r"""Gets a phrase matcher. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetPhraseMatcherRequest, dict]): The request object. The request to get a a phrase matcher. name (:class:`str`): Required. The name of the phrase matcher to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.PhraseMatcher: The phrase matcher resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetPhraseMatcherRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_phrase_matcher, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def list_phrase_matchers( self, request: Union[contact_center_insights.ListPhraseMatchersRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListPhraseMatchersAsyncPager: r"""Lists phrase matchers. Args: request (Union[google.cloud.contact_center_insights_v1.types.ListPhraseMatchersRequest, dict]): The request object. Request to list phrase matchers. parent (:class:`str`): Required. The parent resource of the phrase matcher. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.services.contact_center_insights.pagers.ListPhraseMatchersAsyncPager: The response of listing phrase matchers. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ListPhraseMatchersRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_phrase_matchers, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListPhraseMatchersAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def delete_phrase_matcher( self, request: Union[contact_center_insights.DeletePhraseMatcherRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> None: r"""Deletes a phrase matcher. Args: request (Union[google.cloud.contact_center_insights_v1.types.DeletePhraseMatcherRequest, dict]): The request object. The request to delete a phrase matcher. name (:class:`str`): Required. The name of the phrase matcher to delete. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.DeletePhraseMatcherRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_phrase_matcher, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) async def update_phrase_matcher( self, request: Union[contact_center_insights.UpdatePhraseMatcherRequest, dict] = None, *, phrase_matcher: resources.PhraseMatcher = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.PhraseMatcher: r"""Updates a phrase matcher. Args: request (Union[google.cloud.contact_center_insights_v1.types.UpdatePhraseMatcherRequest, dict]): The request object. The request to update a phrase matcher. phrase_matcher (:class:`google.cloud.contact_center_insights_v1.types.PhraseMatcher`): Required. The new values for the phrase matcher. This corresponds to the ``phrase_matcher`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): The list of fields to be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.PhraseMatcher: The phrase matcher resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([phrase_matcher, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UpdatePhraseMatcherRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if phrase_matcher is not None: request.phrase_matcher = phrase_matcher if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_phrase_matcher, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("phrase_matcher.name", request.phrase_matcher.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def calculate_stats( self, request: Union[contact_center_insights.CalculateStatsRequest, dict] = None, *, location: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> contact_center_insights.CalculateStatsResponse: r"""Gets conversation statistics. Args: request (Union[google.cloud.contact_center_insights_v1.types.CalculateStatsRequest, dict]): The request object. The request for calculating conversation statistics. location (:class:`str`): Required. The location of the conversations. This corresponds to the ``location`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.CalculateStatsResponse: The response for calculating conversation statistics. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([location]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CalculateStatsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if location is not None: request.location = location # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.calculate_stats, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("location", request.location),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def get_settings( self, request: Union[contact_center_insights.GetSettingsRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Settings: r"""Gets project-level settings. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetSettingsRequest, dict]): The request object. The request to get project-level settings. name (:class:`str`): Required. The name of the settings resource to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Settings: The settings resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetSettingsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_settings, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def update_settings( self, request: Union[contact_center_insights.UpdateSettingsRequest, dict] = None, *, settings: resources.Settings = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.Settings: r"""Updates project-level settings. Args: request (Union[google.cloud.contact_center_insights_v1.types.UpdateSettingsRequest, dict]): The request object. The request to update project-level settings. settings (:class:`google.cloud.contact_center_insights_v1.types.Settings`): Required. The new settings values. This corresponds to the ``settings`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): Required. The list of fields to be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.Settings: The settings resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([settings, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UpdateSettingsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if settings is not None: request.settings = settings if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_settings, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("settings.name", request.settings.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def create_view( self, request: Union[contact_center_insights.CreateViewRequest, dict] = None, *, parent: str = None, view: resources.View = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.View: r"""Creates a view. Args: request (Union[google.cloud.contact_center_insights_v1.types.CreateViewRequest, dict]): The request object. The request to create a view. parent (:class:`str`): Required. The parent resource of the view. Required. The location to create a view for. Format: ``projects/<Project ID>/locations/<Location ID>`` or ``projects/<Project Number>/locations/<Location ID>`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. view (:class:`google.cloud.contact_center_insights_v1.types.View`): Required. The view resource to create. This corresponds to the ``view`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.View: The View resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, view]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.CreateViewRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if view is not None: request.view = view # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_view, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def get_view( self, request: Union[contact_center_insights.GetViewRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.View: r"""Gets a view. Args: request (Union[google.cloud.contact_center_insights_v1.types.GetViewRequest, dict]): The request object. The request to get a view. name (:class:`str`): Required. The name of the view to get. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.View: The View resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.GetViewRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_view, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def list_views( self, request: Union[contact_center_insights.ListViewsRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListViewsAsyncPager: r"""Lists views. Args: request (Union[google.cloud.contact_center_insights_v1.types.ListViewsRequest, dict]): The request object. The request to list views. parent (:class:`str`): Required. The parent resource of the views. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.services.contact_center_insights.pagers.ListViewsAsyncPager: The response of listing views. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.ListViewsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_views, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListViewsAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def update_view( self, request: Union[contact_center_insights.UpdateViewRequest, dict] = None, *, view: resources.View = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> resources.View: r"""Updates a view. Args: request (Union[google.cloud.contact_center_insights_v1.types.UpdateViewRequest, dict]): The request object. The request to update a view. view (:class:`google.cloud.contact_center_insights_v1.types.View`): Required. The new view. This corresponds to the ``view`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): The list of fields to be updated. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.contact_center_insights_v1.types.View: The View resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([view, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.UpdateViewRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if view is not None: request.view = view if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_view, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("view.name", request.view.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def delete_view( self, request: Union[contact_center_insights.DeleteViewRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> None: r"""Deletes a view. Args: request (Union[google.cloud.contact_center_insights_v1.types.DeleteViewRequest, dict]): The request object. The request to delete a view. name (:class:`str`): Required. The name of the view to delete. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = contact_center_insights.DeleteViewRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_view, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) async def __aenter__(self): return self async def __aexit__(self, exc_type, exc, tb): await self.transport.close() try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution( "google-cloud-contact-center-insights", ).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() __all__ = ("ContactCenterInsightsAsyncClient",)

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null

0

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null

0

6

4334de44baf022ae30ecaa322a2da3016fc21820

70

py

Python

code/optimizers/__init__.py

OnlinePredictorTS/AOLForTimeSeries

ba2cd6aae7f367c6af879d0a4e58870050c00d04

[ "Apache-2.0" ]

null

code/optimizers/__init__.py

OnlinePredictorTS/AOLForTimeSeries

ba2cd6aae7f367c6af879d0a4e58870050c00d04

[ "Apache-2.0" ]

null

code/optimizers/__init__.py

OnlinePredictorTS/AOLForTimeSeries

ba2cd6aae7f367c6af879d0a4e58870050c00d04

[ "Apache-2.0" ]

null

# utils init file import optimizers.RealOGD import optimizers.RealONS

17.5

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6

4a4b1e4a510b3df83a4c1daf938eb73ae8003137

66,446

py

Python

scripts/calendar_view_gui/utils/batch_utils.py

voicagi/cbm

c076e272f34a93a2e7dcc7de3c9685bd5c86c27a

[ "BSD-3-Clause" ]

null

scripts/calendar_view_gui/utils/batch_utils.py

voicagi/cbm

c076e272f34a93a2e7dcc7de3c9685bd5c86c27a

[ "BSD-3-Clause" ]

null

scripts/calendar_view_gui/utils/batch_utils.py

voicagi/cbm

c076e272f34a93a2e7dcc7de3c9685bd5c86c27a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # This file is part of CbM (https://github.com/ec-jrc/cbm). # Author : Csaba Wirnhardt # Credits : GTCAP Team # Copyright : 2021 European Commission, Joint Research Centre # License : 3-Clause BSD import time import geopandas import download_utils, extract_utils, plot_utils from glob import glob import os import lut from osgeo import ogr import datetime import collections import warnings import calendar import numpy import pandas as pd import rasterio from rasterio.enums import Resampling def select_parcel(vector_file_name, parcel_id_column, parcel_id, logfile): fout = open(logfile, 'a') start = time.time() parcels = geopandas.read_file(vector_file_name) parcel = parcels[parcels[parcel_id_column]==parcel_id] print(f"Parcel selected in: {time.time() - start} seconds") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.select_parcel:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() return parcel def run_get_scl_imagettes(parcel, parcel_id, crop, out_tif_folder_base, search_window_start_date, search_window_end_date, search_split_days, raw_chips_by_location_url, username, password, chipsize, url_base, lon, lat, logfile ): fout = open(logfile, 'a') start = time.time() # get the list of SCL imagettes for the parcel in a given date range chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # lon, lat = download_utils.get_centroid_of_parcel(parcel) date_ranges = download_utils.split_date_range(search_window_start_date, search_window_end_date, search_split_days) for date_range in date_ranges: start_date = date_range[0] end_date = date_range[1] print("Getting SCL imagettes from" , start_date, "to", end_date) was_error_1 = True was_error_2 = True while was_error_1: locurl, list_of_scl_imagettes, was_error_1 = download_utils.get_scl_imagettes(raw_chips_by_location_url, lon, lat, start_date, end_date, username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_scl_imagettes(url_base, list_of_scl_imagettes, out_tif_folder, username, password) print(f"Got list of SCL imagettes and downloaded in: {time.time() - start} seconds") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_get_scl_imagettes:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() def run_get_scl_imagettes_l1c(parcel, parcel_id, crop, out_tif_folder_base, search_window_start_date, search_window_end_date, search_split_days, raw_chips_by_location_url, username, password, chipsize, url_base, lon, lat, logfile ): fout = open(logfile, 'a') start = time.time() # get the list of SCL imagettes for the parcel in a given date range chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # lon, lat = download_utils.get_centroid_of_parcel(parcel) date_ranges = download_utils.split_date_range(search_window_start_date, search_window_end_date, search_split_days) for date_range in date_ranges: start_date = date_range[0] end_date = date_range[1] print("Getting SCL imagettes from" , start_date, "to", end_date) was_error_1 = True was_error_2 = True while was_error_1: locurl, list_of_scl_imagettes, was_error_1 = download_utils.get_scl_imagettes_l1c(raw_chips_by_location_url, lon, lat, start_date, end_date, username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_scl_imagettes(url_base, list_of_scl_imagettes, out_tif_folder, username, password) print(f"Got list of SCL imagettes and downloaded in: {time.time() - start} seconds") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_get_scl_imagettes:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() def create_list_of_tiles_to_be_downloaded(parcel, parcel_id, crop, out_tif_folder_base, cloud_categories, logfile): # create the list of tiles to be downloaded warnings.simplefilter(action='ignore', category=FutureWarning) fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # get downloaded SCL tile tifs and see if they are cloudfree downloaded_scl_files_pattern = out_tif_folder + "/*/*.SCL.tif" downloaded_scl_files = glob(downloaded_scl_files_pattern) tiles_to_download = [] for downloaded_scl_file in downloaded_scl_files: is_tile_cloudy = download_utils.is_tile_cloudy_geopandas(downloaded_scl_file, parcel, cloud_categories) if not is_tile_cloudy: tile_scl_name = os.path.basename(downloaded_scl_file) tile_name = tile_scl_name.split(".")[0] tiles_to_download.append(tile_name) print(f"List of tiles to be downloaded created in {time.time() - start} seconds") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.create_list_of_tiles_to_be_downloaded:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() return tiles_to_download def create_list_of_tiles_to_be_downloaded_l1c(parcel, parcel_id, crop, out_tif_folder_base, cloud_categories, logfile): print('valami') # create the list of tiles to be downloaded warnings.simplefilter(action='ignore', category=FutureWarning) fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # get downloaded SCL tile tifs and see if they are cloudfree downloaded_scl_files_pattern = out_tif_folder + "/*/*.B08.tif" downloaded_scl_files = glob(downloaded_scl_files_pattern) print(downloaded_scl_files_pattern) tiles_to_download = [] for downloaded_scl_file in downloaded_scl_files: # is_tile_cloudy = download_utils.is_tile_cloudy_geopandas(downloaded_scl_file, parcel, cloud_categories) # if not is_tile_cloudy: tile_scl_name = os.path.basename(downloaded_scl_file) tile_name = tile_scl_name.split(".")[0] tiles_to_download.append(tile_name) print(f"List of tiles to be downloaded created in {time.time() - start} seconds") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.create_list_of_tiles_to_be_downloaded_l1c:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() return tiles_to_download def run_get_and_download_band_imagettes(max_number_of_tiles_per_request, tiles_to_download, raw_chips_batch_url, lon, lat, bands, username, password, chipsize, url_base, parcel_id, crop, out_tif_folder_base, logfile): # run the batch chip extract query with the JSON input as POST # and get the response which contains the download folder of the extracted chips # and download the cloudfree band imagettes fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # max_number_of_tiles_per_request = 12 number_of_full_requests = len(tiles_to_download)//max_number_of_tiles_per_request if number_of_full_requests == 0: number_of_full_requests = 1 for request in range(0,number_of_full_requests): list_of_band_imagettes = {} request_end_index = max_number_of_tiles_per_request*(request+1) request_start_index = request_end_index - max_number_of_tiles_per_request print("request number:", request) tiles_to_download_subset = tiles_to_download[request_start_index:request_end_index] was_error_1 = True was_error_2 = True while was_error_1: # print("Requesting band imagettes for tiles: ") # print(tiles_to_download_subset) list_of_band_imagettes, was_error_1 = download_utils.get_band_imagettes(raw_chips_batch_url, lon, lat, tiles_to_download_subset, bands, username, password, chipsize ) while was_error_2: was_error_2 = download_utils.download_band_imagettes(url_base, list_of_band_imagettes, out_tif_folder, username, password) # print("*******************************************") # print(list_of_band_imagettes) # print("*******************************************") last_request_end_index = len(tiles_to_download) + 1 last_request_start_index = request_end_index print("last bunch") was_error_1 = True was_error_2 = True while was_error_1: list_of_band_imagettes, was_error_1 = download_utils.get_band_imagettes(raw_chips_batch_url, lon, lat, tiles_to_download[last_request_start_index:last_request_end_index], bands, username, password, chipsize ) while was_error_2: was_error_2 = download_utils.download_band_imagettes(url_base, list_of_band_imagettes, out_tif_folder, username, password) # print("*******************************************") # print(list_of_band_imagettes) # print("*******************************************") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_get_and_download_band_imagettes:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"Got list of cloudfree bands and downloaded images: {time.time() - start} seconds") def run_get_and_download_band_imagettes_l1c(max_number_of_tiles_per_request, tiles_to_download, raw_chips_batch_url, lon, lat, bands, username, password, chipsize, url_base, parcel_id, crop, out_tif_folder_base, logfile): # run the batch chip extract query with the JSON input as POST # and get the response which contains the download folder of the extracted chips # and download the cloudfree band imagettes fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # max_number_of_tiles_per_request = 12 number_of_full_requests = len(tiles_to_download)//max_number_of_tiles_per_request if number_of_full_requests == 0: number_of_full_requests = 1 for request in range(0,number_of_full_requests): list_of_band_imagettes = {} request_end_index = max_number_of_tiles_per_request*(request+1) request_start_index = request_end_index - max_number_of_tiles_per_request print("request number:", request) tiles_to_download_subset = tiles_to_download[request_start_index:request_end_index] was_error_1 = True was_error_2 = True while was_error_1: # print("Requesting band imagettes for tiles: ") # print(tiles_to_download_subset) list_of_band_imagettes, was_error_1 = download_utils.get_band_imagettes_l1c(raw_chips_batch_url, lon, lat, tiles_to_download_subset, bands, username, password, chipsize ) while was_error_2: was_error_2 = download_utils.download_band_imagettes(url_base, list_of_band_imagettes, out_tif_folder, username, password) # print("*******************************************") # print(list_of_band_imagettes) # print("*******************************************") last_request_end_index = len(tiles_to_download) + 1 last_request_start_index = request_end_index print("last bunch") was_error_1 = True was_error_2 = True while was_error_1: list_of_band_imagettes, was_error_1 = download_utils.get_band_imagettes_l1c(raw_chips_batch_url, lon, lat, tiles_to_download[last_request_start_index:last_request_end_index], bands, username, password, chipsize ) while was_error_2: was_error_2 = download_utils.download_band_imagettes(url_base, list_of_band_imagettes, out_tif_folder, username, password) # print("*******************************************") # print(list_of_band_imagettes) # print("*******************************************") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_get_and_download_band_imagettes_l1c:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"Got list of cloudfree bands and downloaded images: {time.time() - start} seconds") def run_merge_bands(parcel_id, crop, out_tif_folder_base, logfile): # look around in the date folders where the bands were downloade and merge bands # B08, B11, B04 for each tile where these bands were downloaded and the bands were # not yet merged fout = open(logfile, 'a') start = time.time() download_utils.merge_bands(parcel_id, crop, out_tif_folder_base) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_merge_bands:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"Merging cloudfree bands images in {time.time() - start} seconds") def run_merge_4_bands(parcel_id, crop, out_tif_folder_base): # look around in the date folders where the bands were downloade and merge bands # B08, B11, B04 for each tile where these bands were downloaded and the bands were # not yet merged start = time.time() download_utils.merge_4_bands(parcel_id, crop, out_tif_folder_base) print(f"Merging 4 bands images in {time.time() - start} seconds") def run_lut_stretch(parcel_id, crop, out_tif_folder_base, left_percent, right_percent, lut_txt_file, logfile): # lut stretch fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder lut_bands=[1,2,3] merge_folder = out_tif_folder + "_merged" merge_lut_folder = out_tif_folder + "_merged_lut_magic" # merge_lut_folder = out_tif_folder + "_merged_lut_dynamic" if not os.path.exists(merge_lut_folder): os.makedirs(merge_lut_folder) merged_files_pattern = merge_folder + "/*.tif" merged_files = glob(merged_files_pattern) for merged_file in merged_files: # print(merged_file) merged_file_base = os.path.basename(merged_file) merged_file_path = os.path.dirname(merged_file) tile_name = merged_file_base.split(".")[0] #get acquisition date from tile name acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(tile_name) output = merge_lut_folder + "/" + tile_name + ".tif" # here again: if the lut stretched image is already created we do not create it again if os.path.isfile(output): # we already created the lut stretched image for this date for this parcel so we skip it print(tile_name + " already created") else: print("LUT stretching tile: ", tile_name, end="") lut.writeMinMaxToFile(merged_file, acq_date, lut_bands, left_percent, right_percent, lut_txt_file, tile_name) lut.lutStretchMagicLut(merged_file, output, lut_bands ) # lut.lutStretch(merged_file, output, left_percent, right_percent, lut_bands ) print("...done") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_lut_stretch:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"LUT stretch: {time.time() - start} seconds") def run_lut_stretch_dynamic(parcel_id, crop, out_tif_folder_base, left_percent, right_percent, lut_txt_file, logfile): # lut stretch fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder lut_bands=[1,2,3] merge_folder = out_tif_folder + "_merged" # merge_lut_folder = out_tif_folder + "_merged_lut_magic" merge_lut_folder = out_tif_folder + "_merged_lut_dynamic" if not os.path.exists(merge_lut_folder): os.makedirs(merge_lut_folder) merged_files_pattern = merge_folder + "/*.tif" merged_files = glob(merged_files_pattern) for merged_file in merged_files: # print(merged_file) merged_file_base = os.path.basename(merged_file) merged_file_path = os.path.dirname(merged_file) tile_name = merged_file_base.split(".")[0] #get acquisition date from tile name acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(tile_name) output = merge_lut_folder + "/" + tile_name + ".tif" # here again: if the lut stretched image is already created we do not create it again if os.path.isfile(output): # we already created the lut stretched image for this date for this parcel so we skip it print(tile_name + " already created") else: print("LUT stretching tile: ", tile_name, end="") lut.writeMinMaxToFile(merged_file, acq_date, lut_bands, left_percent, right_percent, lut_txt_file, tile_name) lut.lutStretchMagicLut(merged_file, output, lut_bands ) # lut.lutStretch(merged_file, output, left_percent, right_percent, lut_bands ) print("...done") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_lut_stretch_dynamic:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"LUT stretch: {time.time() - start} seconds") def get_merged_lutstretched_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base, logfile): fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder merge_lut_folder = out_tif_folder + "_merged_lut_magic" # merge_lut_folder = out_tif_folder + "_merged_lut_dynamic" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates = [] for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates.append(acq_date) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.get_merged_lutstretched_files_and_acquisition_dates:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() return acq_dates, merged_lut_files def get_merged_lutstretched_files_and_acquisition_dates_dynamic(parcel_id, crop, out_tif_folder_base, logfile): fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # merge_lut_folder = out_tif_folder + "_merged_lut_magic" merge_lut_folder = out_tif_folder + "_merged_lut_dynamic" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates = [] for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates.append(acq_date) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.get_merged_lutstretched_files_and_acquisition_dates_dynamic:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() return acq_dates, merged_lut_files def get_merged_ndvi_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder merge_lut_folder = out_tif_folder + "_merged_ndvi" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates = [] for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates.append(acq_date) return acq_dates, merged_lut_files def get_merged_ndwi_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder merge_lut_folder = out_tif_folder + "_merged_ndwi" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates = [] for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates.append(acq_date) return acq_dates, merged_lut_files def get_merged_tif_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder merge_lut_folder = out_tif_folder + "_merged" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates = [] for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates.append(acq_date) return acq_dates, merged_lut_files def get_merged_tif_files_and_acquisition_dates_in_dict(parcel_id, crop, out_tif_folder_base): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder merge_lut_folder = out_tif_folder + "_merged" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates_tif_files_dict = {} for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates_tif_files_dict[acq_date]=merged_lut_file return collections.OrderedDict(sorted(acq_dates_tif_files_dict.items())) def get_index_files_and_acquisition_dates_in_dict(parcel_id, crop, out_tif_folder_base, index_name): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder index_folder = out_tif_folder + "_merged_" + index_name index_files_pattern = index_folder + "/*.tif" index_files = glob(index_files_pattern) acq_dates_tif_files_dict = {} for index_file in index_files: index_file_base = os.path.basename(index_file) index_file_path = os.path.dirname(index_file) tile_name = index_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates_tif_files_dict[acq_date]=index_file return collections.OrderedDict(sorted(acq_dates_tif_files_dict.items())) def get_acq_dates_band_names_tif_files_list(parcel_id, crop, out_tif_folder_base): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder band_tif_files_pattern = out_tif_folder + "/????-??-??/*.B??.tif" # S2A_MSIL2A_20180326T103021_N0207_R108_T32TMR_20180326T155240.B11.tif band_tif_files = glob(band_tif_files_pattern) # print(band_tif_files) acq_dates_band_names_tif_files_list = [] for band_tif_file in band_tif_files: acq_dates_band_names_tif_files = [] band_tif_file_base = os.path.basename(band_tif_file) band_tif_file_path = os.path.dirname(band_tif_file) tile_name = band_tif_file_base.split(".")[0] band_name = band_tif_file_base.split(".")[1] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates_band_names_tif_files.append(band_tif_file_path) acq_dates_band_names_tif_files.append(tile_name) acq_dates_band_names_tif_files.append(band_name) acq_dates_band_names_tif_files.append(acq_date) acq_dates_band_names_tif_files_list.append(acq_dates_band_names_tif_files) return acq_dates_band_names_tif_files_list def run_ndvi_creation(parcel_id, crop, out_tif_folder_base, logfile): fout = open(logfile, 'a') start = time.time() # create ndvi image chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder lut_bands=[1,2,3] merge_folder = out_tif_folder + "_merged" merge_ndvi_folder = out_tif_folder + "_merged_ndvi" if not os.path.exists(merge_ndvi_folder): os.makedirs(merge_ndvi_folder) merged_files_pattern = merge_folder + "/*.tif" merged_files = glob(merged_files_pattern) for merged_file in merged_files: # print(merged_file) merged_file_base = os.path.basename(merged_file) merged_file_path = os.path.dirname(merged_file) tile_name = merged_file_base.split(".")[0] #get acquisition date from tile name acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(tile_name) output = merge_ndvi_folder + "/" + tile_name + ".tif" # here again: if the ndvi image image is already created we do not create it again if os.path.isfile(output): # we already created the ndvi image for this date for this parcel so we skip it print(tile_name + " ndvi already created") else: print("Creating NDVI for tile: ", tile_name, end="") extract_utils.calculate_ndvi(merged_file, output) print("...done") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_ndvi_creation:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"NDVI created in: {time.time() - start} seconds") def run_ndwi_creation(parcel_id, crop, out_tif_folder_base, logfile): fout = open(logfile, 'a') start = time.time() # create ndwi image chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder lut_bands=[1,2,3] merge_folder = out_tif_folder + "_merged" merge_ndwi_folder = out_tif_folder + "_merged_ndwi" if not os.path.exists(merge_ndwi_folder): os.makedirs(merge_ndwi_folder) merged_files_pattern = merge_folder + "/*.tif" merged_files = glob(merged_files_pattern) for merged_file in merged_files: # print(merged_file) merged_file_base = os.path.basename(merged_file) merged_file_path = os.path.dirname(merged_file) tile_name = merged_file_base.split(".")[0] #get acquisition date from tile name acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(tile_name) output = merge_ndwi_folder + "/" + tile_name + ".tif" # here again: if the ndwi image image is already created we do not create it again if os.path.isfile(output): # we already created the ndwi image for this date for this parcel so we skip it print(tile_name + " ndwi already created") else: print("Creating NDWI for tile: ", tile_name, end="") extract_utils.calculate_ndwi(merged_file, output) print("...done") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_ndwi_creation:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"NDWI created in: {time.time() - start} seconds") def run_bare_soil_index_creation(parcel_id, crop, out_tif_folder_base, logfile): fout = open(logfile, 'a') start = time.time() # create ndvi image chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder lut_bands=[1,2,3] merge_folder = out_tif_folder + "_merged" merge_ndvi_folder = out_tif_folder + "_merged_ndvi" if not os.path.exists(merge_ndvi_folder): os.makedirs(merge_ndvi_folder) merged_files_pattern = merge_folder + "/*.tif" merged_files = glob(merged_files_pattern) for merged_file in merged_files: # print(merged_file) merged_file_base = os.path.basename(merged_file) merged_file_path = os.path.dirname(merged_file) tile_name = merged_file_base.split(".")[0] #get acquisition date from tile name acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(tile_name) output = merge_ndvi_folder + "/" + tile_name + ".tif" # here again: if the ndvi image image is already created we do not create it again if os.path.isfile(output): # we already created the ndvi image for this date for this parcel so we skip it print(tile_name + " ndvi already created") else: print("Creating NDVI for tile: ", tile_name, end="") extract_utils.calculate_baresoil_index_image(merged_file, output) print("...done") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_ndvi_creation:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"NDVI created in: {time.time() - start} seconds") def calculate_ndvi_statistics(parcel_id, crop, out_tif_folder_base, tiles_to_download, parcel, vector_file_name, parcel_id_column, logfile): fout = open(logfile, 'a') start = time.time() acq_dates, merged_ndvi_files = get_merged_ndvi_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base) chip_folder = str(parcel_id) + '_' + crop output_ndvi_folder = out_tif_folder_base + "/ndvi" output_ndvi_csv_file = output_ndvi_folder + "/" + chip_folder + "_ndvi.csv" if not os.path.exists(output_ndvi_folder): os.makedirs(output_ndvi_folder) first_line ="Field_ID,acq_date,ndvi_mean,ndvi_count,ndvi_std" print(first_line, file=open(output_ndvi_csv_file, "w")) for merged_ndvi_file in merged_ndvi_files: merged_ndvi_file_base = os.path.basename(merged_ndvi_file) merged_ndvi_file_path = os.path.dirname(merged_ndvi_file) tile_name = merged_ndvi_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(merged_ndvi_file) ndvi_mean, ndvi_count, ndvi_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(merged_ndvi_file, parcel) # print(parcel_id, acq_date, ndvi_mean, ndvi_count, ndvi_std, sep=',') print(parcel_id, acq_date, ndvi_mean, ndvi_count, ndvi_std, sep=',', file=open(output_ndvi_csv_file, "a")) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.calculate_ndvi_statistics:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"NDVI stats read in: {time.time() - start} seconds") def calculate_ndwi_statistics(parcel_id, crop, out_tif_folder_base, tiles_to_download, parcel, vector_file_name, parcel_id_column, logfile): fout = open(logfile, 'a') start = time.time() acq_dates, merged_ndwi_files = get_merged_ndwi_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base) chip_folder = str(parcel_id) + '_' + crop output_ndwi_folder = out_tif_folder_base + "/ndwi" output_ndwi_csv_file = output_ndwi_folder + "/" + chip_folder + "_ndwi.csv" if not os.path.exists(output_ndwi_folder): os.makedirs(output_ndwi_folder) first_line ="Field_ID,acq_date,ndwi_mean,ndwi_count,ndwi_std" print(first_line, file=open(output_ndwi_csv_file, "w")) for merged_ndwi_file in merged_ndwi_files: merged_ndwi_file_base = os.path.basename(merged_ndwi_file) merged_ndwi_file_path = os.path.dirname(merged_ndwi_file) tile_name = merged_ndwi_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(merged_ndwi_file) ndwi_mean, ndwi_count, ndwi_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(merged_ndwi_file, parcel) # print(parcel_id, acq_date, ndwi_mean, ndwi_count, ndwi_std, sep=',') print(parcel_id, acq_date, ndwi_mean, ndwi_count, ndwi_std, sep=',', file=open(output_ndwi_csv_file, "a")) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.calculate_ndwi_statistics:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"ndwi stats read in: {time.time() - start} seconds") def calculate_bs_statistics(parcel_id, crop, out_tif_folder_base, parcel, logfile, polarisation, orbit_orientation): fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop output_s1_bs_folder = out_tif_folder_base + "/s1_bs" output_s1_bs_csv_file = output_s1_bs_folder + "/" + chip_folder + "_s1bs_" + polarisation + "_" + orbit_orientation + ".csv" acquisition_dates_and_s1_bs_files_dict = plot_utils.get_acquisition_dates_and_s1_bs_files_dict(out_tif_folder_base + "/" + chip_folder + "_s1_bs", polarisation, orbit_orientation) if not os.path.exists(output_s1_bs_folder): os.makedirs(output_s1_bs_folder) first_line ="Field_ID,acq_date,bs_mean,bs_count,bs_std" print(first_line, file=open(output_s1_bs_csv_file, "w")) for acq_date, s1_bs_file in acquisition_dates_and_s1_bs_files_dict.items(): bs_mean, bs_count, bs_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel_bs(s1_bs_file, parcel) if bs_mean != None: # print(parcel_id, acq_date, bs_mean, bs_count, bs_std, sep=',') print(parcel_id, acq_date, bs_mean, bs_count, bs_std, sep=',', file=open(output_s1_bs_csv_file, "a")) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.calculate_bs_statistics:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print("S1 BS_" + polarisation + "_" + orbit_orientation + f" stats read in: {time.time() - start} seconds") def calculate_coh6_statistics(parcel_id, crop, out_tif_folder_base, parcel, logfile, polarisation, orbit_orientation): fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop output_s1_coh6_folder = out_tif_folder_base + "/s1_coh6" output_s1_coh6_csv_file = output_s1_coh6_folder + "/" + chip_folder + "_s1coh6_" + polarisation + "_" + orbit_orientation + ".csv" acquisition_dates_and_s1_coh6_files_dict = plot_utils.get_acquisition_dates_and_s1_bs_files_dict(out_tif_folder_base + "/" + chip_folder + "_s1_coh6", polarisation, orbit_orientation) if not os.path.exists(output_s1_coh6_folder): os.makedirs(output_s1_coh6_folder) first_line ="Field_ID,acq_date,coh6_mean,coh6_count,coh6_std" print(first_line, file=open(output_s1_coh6_csv_file, "w")) for acq_date, s1_coh6_file in acquisition_dates_and_s1_coh6_files_dict.items(): coh6_mean, coh6_count, coh6_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel_bs(s1_coh6_file, parcel) if coh6_mean != None: # print(parcel_id, acq_date, coh6_mean, coh6_count, coh6_std, sep=',') print(parcel_id, acq_date, coh6_mean, coh6_count, coh6_std, sep=',', file=open(output_s1_coh6_csv_file, "a")) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.calculate_coh6_statistics:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print("S1 COH6 " + polarisation + " " + orbit_orientation + f" stats read in: {time.time() - start} seconds") def calculate_index_statistics(parcel_id, crop, out_tif_folder_base, parcel, logfile, index_name): fout = open(logfile, 'a') start = time.time() acquisition_dates_and_index_files_dict = get_index_files_and_acquisition_dates_in_dict(parcel_id, crop, out_tif_folder_base, index_name) chip_folder = str(parcel_id) + '_' + crop output_index_folder = out_tif_folder_base + "/" + index_name output_index_csv_file = output_index_folder + "/" + chip_folder + "_" + index_name + ".csv" if not os.path.exists(output_index_folder): os.makedirs(output_index_folder) first_line ="Field_ID,acq_date,mean,count,std" print(first_line, file=open(output_index_csv_file, "w")) for acq_date, index_file in acquisition_dates_and_index_files_dict.items(): mean, count, std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(index_file, parcel) print(parcel_id, acq_date, mean, count, std, sep=',', file=open(output_index_csv_file, "a")) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.calculate_index_statistics:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"Index stats read in: {time.time() - start} seconds") def get_all_parcel_ids_from_parcel_shape(parcel_shp, parcel_id_column, crop_name_column): ds=ogr.Open(parcel_shp) lyr=ds.GetLayer() parcel_id_crop_list = [] for feat in lyr: parcel_id = feat.GetField(parcel_id_column) crop_name = feat.GetField(crop_name_column) if crop_name is None: crop_name = "" parcel_id_crop_list.append((parcel_id,crop_name.replace(" ", "_"))) parcel_id_crop_list = sorted(parcel_id_crop_list, key=getKey) return parcel_id_crop_list def getKey(item): return item[0] def does_ndvi_csv_exist(parcel_id, crop, out_tif_folder_base): chip_folder = str(parcel_id) + '_' + crop output_ndvi_folder = out_tif_folder_base + "/ndvi" output_ndvi_csv_file = output_ndvi_folder + "/" + chip_folder + "_ndvi.csv" if os.path.isfile(output_ndvi_csv_file): return True else: return False def does_ndvi_graph_exist(parcel_id, out_tif_folder_base): output_ndvi_graph_folder = out_tif_folder_base + "/ndvi_graphs" output_ndvi_graph_file = output_ndvi_graph_folder + "/parcel_id_" + str(parcel_id) + "_NDVI.jpg" if os.path.isfile(output_ndvi_graph_file): return True else: return False def run_get_and_download_s1_bs_imagettes(raw_chips_s1_batch_url, out_s1_bs_folder, search_window_start_date, search_window_end_date, lon, lat, username, password, chipsize, url_base, logfile): # list_of_s1_bs_imagettes, was_error_1 = download_utils.get_s1_bs_imagettes(raw_chips_s1_batch_url, lon, lat, start_date, end_date, username, password, chipsize) # download_utils.download_s1_bs_imagettes(url_base, list_of_s1_bs_imagettes, out_s1_bs_folder, username, password) # run the batch chip extract query with the JSON input as POST # and get the response which contains the download folder of the extracted chips # and download the s1 backscatter imagettes fout = open(logfile, 'a') start = time.time() # we get and download the s1 bs images by month # search_window_start_date, search_window_end_date # search_window_start_date = "2019-11-15" # search_window_end_date = "2020-09-15" dt_search_window_start_date = plot_utils.get_date_from_string(search_window_start_date) dt_search_window_end_date = plot_utils.get_date_from_string(search_window_end_date) # print(last_day_of_month(dt_search_window_start_date)) # print(add_one_month(dt_search_window_start_date)) act_start_date = dt_search_window_start_date while act_start_date < dt_search_window_end_date: act_end_date = last_day_of_month(act_start_date) if act_start_date == dt_search_window_start_date: was_error_1 = True was_error_2 = True while was_error_1: list_of_s1_bs_imagettes, was_error_1 = download_utils.get_s1_bs_imagettes(raw_chips_s1_batch_url, lon, lat, str(act_start_date), str(act_end_date), username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_s1_bs_imagettes(url_base, list_of_s1_bs_imagettes, out_s1_bs_folder, username, password) elif act_end_date > dt_search_window_end_date: act_end_date = dt_search_window_end_date was_error_1 = True was_error_2 = True while was_error_1: list_of_s1_bs_imagettes, was_error_1 = download_utils.get_s1_bs_imagettes(raw_chips_s1_batch_url, lon, lat, str(act_start_date), str(act_end_date), username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_s1_bs_imagettes(url_base, list_of_s1_bs_imagettes, out_s1_bs_folder, username, password) else: was_error_1 = True was_error_2 = True while was_error_1: list_of_s1_bs_imagettes, was_error_1 = download_utils.get_s1_bs_imagettes(raw_chips_s1_batch_url, lon, lat, str(act_start_date), str(act_end_date), username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_s1_bs_imagettes(url_base, list_of_s1_bs_imagettes, out_s1_bs_folder, username, password) act_start_date = add_one_month(act_start_date) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t\tbatch_utils.run_get_and_download_s1_bs_imagettes:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"Got list of S1 BS downloaded images: {time.time() - start} seconds") def run_get_and_download_s1_coh6_imagettes(raw_chips_s1_batch_url, out_s1_coh6_folder, search_window_start_date, search_window_end_date, lon, lat, username, password, chipsize, url_base, logfile): # list_of_s1_bs_imagettes, was_error_1 = download_utils.get_s1_bs_imagettes(raw_chips_s1_batch_url, lon, lat, start_date, end_date, username, password, chipsize) # download_utils.download_s1_bs_imagettes(url_base, list_of_s1_bs_imagettes, out_s1_bs_folder, username, password) # run the batch chip extract query with the JSON input as POST # and get the response which contains the download folder of the extracted chips # and download the s1 backscatter imagettes fout = open(logfile, 'a') start = time.time() # we get and download the s1 bs images by month # search_window_start_date, search_window_end_date # search_window_start_date = "2019-11-15" # search_window_end_date = "2020-09-15" dt_search_window_start_date = plot_utils.get_date_from_string(search_window_start_date) dt_search_window_end_date = plot_utils.get_date_from_string(search_window_end_date) # print(last_day_of_month(dt_search_window_start_date)) # print(add_one_month(dt_search_window_start_date)) act_start_date = dt_search_window_start_date while act_start_date < dt_search_window_end_date: act_end_date = last_day_of_month(act_start_date) if act_start_date == dt_search_window_start_date: was_error_1 = True was_error_2 = True while was_error_1: list_of_s1_coh6_imagettes, was_error_1 = download_utils.get_s1_coh6_imagettes(raw_chips_s1_batch_url, lon, lat, str(act_start_date), str(act_end_date), username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_s1_coh6_imagettes(url_base, list_of_s1_coh6_imagettes, out_s1_coh6_folder, username, password) elif act_end_date > dt_search_window_end_date: act_end_date = dt_search_window_end_date was_error_1 = True was_error_2 = True while was_error_1: list_of_s1_coh6_imagettes, was_error_1 = download_utils.get_s1_coh6_imagettes(raw_chips_s1_batch_url, lon, lat, str(act_start_date), str(act_end_date), username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_s1_coh6_imagettes(url_base, list_of_s1_coh6_imagettes, out_s1_coh6_folder, username, password) else: was_error_1 = True was_error_2 = True while was_error_1: list_of_s1_coh6_imagettes, was_error_1 = download_utils.get_s1_coh6_imagettes(raw_chips_s1_batch_url, lon, lat, str(act_start_date), str(act_end_date), username, password, chipsize) while was_error_2: was_error_2 = download_utils.download_s1_coh6_imagettes(url_base, list_of_s1_coh6_imagettes, out_s1_coh6_folder, username, password) act_start_date = add_one_month(act_start_date) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t\tbatch_utils.run_get_and_download_s1_coh6_imagettes:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"Got list of S1 COH6 and downloaded images: {time.time() - start} seconds") def run_rescale_s1_bs_images(out_s1_bs_folder, out_s1_bs_folder_rescale): # we take all the downloaded s1 bs images for the given parcel and rescale them to uint16 if not os.path.exists(out_s1_bs_folder_rescale): os.makedirs(out_s1_bs_folder_rescale) raw_files_pattern = out_s1_bs_folder + "/*.tif" raw_files = glob(raw_files_pattern) for raw_file in raw_files: raw_file_base = os.path.basename(raw_file) actdate = raw_file_base.split(".")[0] # print(tile_name) output = out_s1_bs_folder_rescale + "/" + actdate + ".tif" download_utils.rescale_s1_bs_image(raw_file, output) def run_lut_stretch_one_band_s1_bs(out_s1_bs_folder_rescale, out_s1_bs_folder_rescale_lut, s1_bs_left_percent, s1_bs_right_percent): # we take all the downloaded s1 bs images for the given parcel and rescale them to uint16 if not os.path.exists(out_s1_bs_folder_rescale_lut): os.makedirs(out_s1_bs_folder_rescale_lut) rescaled_files_pattern = out_s1_bs_folder_rescale + "/*.tif" rescaled_files = glob(rescaled_files_pattern) for rescaled_file in rescaled_files: rescaled_file_base = os.path.basename(rescaled_file) actdate = rescaled_file_base.split(".")[0] print(actdate) output = out_s1_bs_folder_rescale_lut + "/" + actdate + ".tif" lut.lut_stretch_one_band_s1_bs(rescaled_file, output, s1_bs_left_percent, s1_bs_right_percent) def add_one_month(orig_date): # advance year and month by one month new_year = orig_date.year new_month = orig_date.month + 1 # note: in datetime.date, months go from 1 to 12 if new_month > 12: new_year += 1 new_month -= 12 last_day_of_month = calendar.monthrange(new_year, new_month)[1] new_day = min(orig_date.day, last_day_of_month) return orig_date.replace(year=new_year, month=new_month, day=new_day) def last_day_of_month(any_day): next_month = any_day.replace(day=28) + datetime.timedelta(days=4) # this will never fail return next_month - datetime.timedelta(days=next_month.day) def run_lut_stretch_dynamic(parcel_id, crop, out_tif_folder_base, left_percent, right_percent, lut_txt_file, logfile): # lut stretch fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder lut_bands=[1,2,3] merge_folder = out_tif_folder + "_merged" # merge_lut_folder = out_tif_folder + "_merged_lut_magic" merge_lut_folder = out_tif_folder + "_merged_lut_dynamic" if not os.path.exists(merge_lut_folder): os.makedirs(merge_lut_folder) merged_files_pattern = merge_folder + "/*.tif" merged_files = glob(merged_files_pattern) for merged_file in merged_files: # print(merged_file) merged_file_base = os.path.basename(merged_file) merged_file_path = os.path.dirname(merged_file) tile_name = merged_file_base.split(".")[0] #get acquisition date from tile name acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) # print(tile_name) output = merge_lut_folder + "/" + tile_name + ".tif" # here again: if the lut stretched image is already created we do not create it again if os.path.isfile(output): # we already created the lut stretched image for this date for this parcel so we skip it print(tile_name + " already created") else: print("LUT stretching tile: ", tile_name, end="") lut.writeMinMaxToFile(merged_file, acq_date, lut_bands, left_percent, right_percent, lut_txt_file, tile_name) # lut.lutStretchMagicLut(merged_file, output, lut_bands ) lut.lutStretch(merged_file, output, left_percent, right_percent, lut_bands ) print("...done") print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.run_lut_stretch_dynamic:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() print(f"LUT stretch dynamic: {time.time() - start} seconds") def get_merged_dynamically_lutstretched_files_and_acquisition_dates(parcel_id, crop, out_tif_folder_base, logfile): fout = open(logfile, 'a') start = time.time() chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder # merge_lut_folder = out_tif_folder + "_merged_lut_magic" merge_lut_folder = out_tif_folder + "_merged_lut_dynamic" merged_lut_files_pattern = merge_lut_folder + "/*.tif" merged_lut_files = glob(merged_lut_files_pattern) acq_dates = [] for merged_lut_file in merged_lut_files: merged_lut_file_base = os.path.basename(merged_lut_file) merged_lut_file_path = os.path.dirname(merged_lut_file) tile_name = merged_lut_file_base.split(".")[0] acq_date = download_utils.get_acquisition_date_from_tile_name(tile_name) acq_dates.append(acq_date) print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "\t", parcel_id, "\tbatch_utils.get_merged_dynamically_lutstretched_files_and_acquisition_dates:\t", "{0:.3f}".format(time.time() - start), file=fout) fout.close() return acq_dates, merged_lut_files def calculate_band_statistics_orig(parcel_id, crop, out_tif_folder_base, parcel): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder downloaded_band04_files_pattern = out_tif_folder + "/*/*.B04.tif" downloaded_band04_files = glob(downloaded_band04_files_pattern) band_stats_folder = out_tif_folder_base + "/band_stats" if not os.path.exists(band_stats_folder): os.makedirs(band_stats_folder) band_stats_file_b04 = band_stats_folder + "/" + str(parcel_id) + "_b04.csv" band_stats_file_b08 = band_stats_folder + "/" + str(parcel_id) + "_b08.csv" band_stats_file_b11 = band_stats_folder + "/" + str(parcel_id) + "_b11.csv" first_line ="Field_ID,acq_date,band_mean,band_count,band_std" print(first_line, file=open(band_stats_file_b04, "w")) print(first_line, file=open(band_stats_file_b08, "w")) print(first_line, file=open(band_stats_file_b11, "w")) for downloaded_band04_file in downloaded_band04_files: band04_file_base = os.path.basename(downloaded_band04_file) band_file_path = os.path.dirname(downloaded_band04_file) tile_name = band04_file_base.split(".")[0] #get acquisition date from tile name acq_date_full = tile_name.split("_")[2] acq_date = acq_date_full[0:4] + "-" + acq_date_full[4:6] + "-" + acq_date_full[6:8] # check if the other bands are also available for this tile if os.path.isfile(band_file_path + "/" + tile_name + ".B08.tif") and \ os.path.isfile(band_file_path + "/" + tile_name + ".B11.tif"): band04 = band_file_path + "/" + tile_name + ".B04.tif" band08 = band_file_path + "/" + tile_name + ".B08.tif" band11 = band_file_path + "/" + tile_name + ".B11.tif" band04_mean, band04_count, band04_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band04, parcel) if band04_mean != None: print(parcel_id, acq_date, band04_mean, band04_count, band04_std, sep=',', file=open(band_stats_file_b04, "a")) band08_mean, band08_count, band08_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band08, parcel) if band08_mean != None: print(parcel_id, acq_date, band08_mean, band08_count, band08_std, sep=',', file=open(band_stats_file_b08, "a")) band11_mean, band11_count, band11_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band11, parcel) if band11_mean != None: print(parcel_id, acq_date, band11_mean, band11_count, band11_std, sep=',', file=open(band_stats_file_b11, "a")) def calculate_band_statistics(parcel_id, crop, out_tif_folder_base, parcel): chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder downloaded_band04_files_pattern = out_tif_folder + "/*/*.B04.tif" downloaded_band04_files = glob(downloaded_band04_files_pattern) if len(downloaded_band04_files)>0: print("Calculating band statistics for parcel:" + str(parcel_id)) else: return band_stats_folder = out_tif_folder_base + "/band_stats" if not os.path.exists(band_stats_folder): os.makedirs(band_stats_folder) band_stats_file = band_stats_folder + "/" + str(parcel_id) + ".csv" first_date = True for downloaded_band04_file in downloaded_band04_files: band04_file_base = os.path.basename(downloaded_band04_file) band_file_path = os.path.dirname(downloaded_band04_file) tile_name = band04_file_base.split(".")[0] #get acquisition date from tile name acq_date_full = tile_name.split("_")[2] acq_date = acq_date_full[0:4] + "-" + acq_date_full[4:6] + "-" + acq_date_full[6:8] if first_date: print(acq_date , "is the first date") first_date = False # check what other bands are available for this tile band_files_available = glob(band_file_path + "/" + tile_name + ".B??.tif") band_names=[] for band_file_available in band_files_available: band_name = os.path.basename(band_file_available).split(".")[1] band_names.append(band_name) #now we can get the band statistisc for the firs date and write it to csv #with header row! first_line ="Field_ID,acq_date," for band_name in band_names[0:-1]: first_line+=band_name.lower() + "_mean," first_line+=band_name.lower() + "_count," first_line+=band_name.lower() + "_std," first_line+=band_names[-1].lower() + "_mean," first_line+=band_names[-1].lower() + "_count," first_line+=band_names[-1].lower() + "_std" print(first_line, file=open(band_stats_file, "w")) stats_line = str(parcel_id) + "," + str(acq_date) + "," for band_name in band_names[0:-1]: band_tif = band_file_path + "/" + tile_name + "." + band_name + ".tif" band_mean, band_count, band_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band_tif, parcel) stats_line += "{:.3f}".format(band_mean) + "," stats_line += "{:.0f}".format(band_count) + "," stats_line += "{:.3f}".format(band_std) + "," band_tif = band_file_path + "/" + tile_name + "." + band_names[-1] + ".tif" band_mean, band_count, band_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band_tif, parcel) stats_line += "{:.3f}".format(band_mean) + "," stats_line += "{:.0f}".format(band_count) + "," stats_line += "{:.3f}".format(band_std) print(stats_line, file=open(band_stats_file, "a")) else: #we assume the same bands are available for all the dates as for the first one print(acq_date, "is NOT the first date") stats_line = str(parcel_id) + "," + str(acq_date) + "," for band_name in band_names[0:-1]: band_tif = band_file_path + "/" + tile_name + "." + band_name + ".tif" band_mean, band_count, band_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band_tif, parcel) stats_line += "{:.3f}".format(band_mean) + "," stats_line += "{:.0f}".format(band_count) + "," stats_line += "{:.3f}".format(band_std) + "," band_tif = band_file_path + "/" + tile_name + "." + band_names[-1] + ".tif" band_mean, band_count, band_std = extract_utils.extract_stats_for_one_parcel_geopandas_presel(band_tif, parcel) stats_line += "{:.3f}".format(band_mean) + "," stats_line += "{:.0f}".format(band_count) + "," stats_line += "{:.3f}".format(band_std) print(stats_line, file=open(band_stats_file, "a")) def create_index_images(parcel_id, crop, out_tif_folder_base, acq_dates_band_names_tif_files_list, index_name): # create bare soil index image # https://giscrack.com/list-of-spectral-indices-for-sentinel-and-landsat/ chip_folder = str(parcel_id) + '_' + crop out_tif_folder = out_tif_folder_base + "/" + chip_folder if index_name == "bare_soil_index": create_bare_soil_index_images(acq_dates_band_names_tif_files_list, index_name, out_tif_folder) else: print(index_name + " index calculation is not defined yet") def create_bare_soil_index_images(acq_dates_band_names_tif_files_list, index_name, out_tif_folder): df = pd.DataFrame(acq_dates_band_names_tif_files_list, columns = ['band_file_path', 'tile_name', 'band','acq_date']) bands_needed_for_this_index = ['B02', 'B04', 'B08', 'B11'] # create index image bare_soil_index_folder = out_tif_folder + "_merged_" + index_name if not os.path.exists(bare_soil_index_folder): os.makedirs(bare_soil_index_folder) # first create a list of unique dates where there is at least one band image acq_dates = df.acq_date.unique() for acq_date in acq_dates: # select corresponding band files current_bands = df[df['acq_date'] == acq_date] bands_present_for_this_date = current_bands['band'].tolist() all_bands_available = all(elem in bands_present_for_this_date for elem in bands_needed_for_this_index) if all_bands_available: # print("All bands available, we can calculate the given index") # we put together given band filenames for rasterio processing full_path = current_bands['band_file_path'].iloc[0] tile_name = current_bands['tile_name'].iloc[0] band02_tif = full_path + "/" + tile_name + ".B02.tif" band04_tif = full_path + "/" + tile_name + ".B04.tif" band08_tif = full_path + "/" + tile_name + ".B08.tif" band11_tif = full_path + "/" + tile_name + ".B11.tif" with rasterio.open(band02_tif) as src02: band02 = src02.read(1) with rasterio.open(band04_tif) as src04: band04 = src04.read(1) with rasterio.open(band08_tif) as src08: band08 = src08.read(1) with rasterio.open(band11_tif) as src11: band11 = src11.read(1, out_shape=( src11.count, src08.height, src08.width ), resampling=Resampling.bilinear ) # scale image transform transform = src11.transform * src11.transform.scale( (src11.width / band11.shape[-1]), (src11.height / band11.shape[-2]) ) # Allow division by zero numpy.seterr(divide='ignore', invalid='ignore') bsi_file = bare_soil_index_folder + "/" + tile_name + ".tif" # here again: if the bsi image image is already created we do not create it again if os.path.isfile(bsi_file): # we already created the ndvi image for this date for this parcel so we skip it print(tile_name + " bare soil index image already created") else: print("Creating bare soil index image for tile: ", tile_name, end="") # Formula of BSI = ((Red+SWIR) – (NIR+Blue)) / ((Red+SWIR) + (NIR+Blue)) # BSI (Sentinel 2) = ((B11 + B4) – (B8 + B2)) / ((B11 + B4) + (B8 + B2)) # Calculate BSI bsi = ((band11.astype(float) + band04.astype(float)) - (band08.astype(float) + band02.astype(float))) / \ ((band11 + band04) + (band08 + band02)) # bsi = (band11.astype(float) / band08) # Set spatial characteristics of the output object to mirror the input kwargs = src08.meta kwargs.update( dtype=rasterio.float32, count = 1) # Create the file with rasterio.open(bsi_file, 'w', **kwargs) as dst: dst.write_band(1, bsi.astype(rasterio.float32)) print("...done") else: print("We do not have all bands for this index")

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0.046735

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0.869924

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0.81134

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0

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0.241429

66,446

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0.624309

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false

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0.110497

0

null

0

1

0

null

0

6

4a5c79724f4734739c573dc503e5b42e30ff7fcc

118

py

Python

src/main/python/twitter/thermos/config/bin/config_repl.py

isomer/incubator-aurora

5f54d4de25413bb18acec16120eb18f3e08c6bf0

[ "Apache-2.0" ]

null

src/main/python/twitter/thermos/config/bin/config_repl.py

isomer/incubator-aurora

5f54d4de25413bb18acec16120eb18f3e08c6bf0

[ "Apache-2.0" ]

null

src/main/python/twitter/thermos/config/bin/config_repl.py

isomer/incubator-aurora

5f54d4de25413bb18acec16120eb18f3e08c6bf0

[ "Apache-2.0" ]

null

from twitter.thermos.config.schema import * from code import interact interact('Thermos Config REPL', local=locals())

29.5

47

0.79661

16

118

5.875

0.6875

0.276596

0

0.101695

118

3

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true

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null

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1

0

1

0

1

0

6

4a8116df307fa5a86aaf035f0108479b462ef5b2

147

py

Python

src/kraetos/v1/helpers/google.py

sinhadotabhinav/kraetos-v1

adc4f37c2648968a9981331135ff366351c97bac

[ "MIT" ]

1

2021-05-08T10:11:15.000Z

src/kraetos/v1/helpers/google.py

sinhadotabhinav/kraetos-v1

adc4f37c2648968a9981331135ff366351c97bac

[ "MIT" ]

null

src/kraetos/v1/helpers/google.py

sinhadotabhinav/kraetos-v1

adc4f37c2648968a9981331135ff366351c97bac

[ "MIT" ]

null

from googlesearch import search # Make Google search query def googleSearch(query): return search(query, num_results=3, lang="en", proxy=None)

29.4

62

0.768707

21

147

5.333333

0.761905

0.196429

0

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147

5

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29.4

0.874016

0.163265

0

0.016393

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

4ab09df92507adca7347bc083fc49366215ba098

39

py

Python

examples/math.sin/ex1.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/math.sin/ex1.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/math.sin/ex1.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

import math print(math.sin(math.pi/2))

13

26

0.74359

8

39

3.625

0.75

0

0.027778

0.076923

39

2

27

19.5

0.777778

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

436109567fd3b0c93424d845f5cf31e29db79275

219

py

Python

src/gpt2_output.py

yulonglin/gpt2

c86239940bfb9fc34a3ea5df3c6aa16e9c57887e

[ "MIT" ]

null

src/gpt2_output.py

yulonglin/gpt2

c86239940bfb9fc34a3ea5df3c6aa16e9c57887e

[ "MIT" ]

null

src/gpt2_output.py

yulonglin/gpt2

c86239940bfb9fc34a3ea5df3c6aa16e9c57887e

[ "MIT" ]

null

from dataclasses import dataclass from torchtyping import TensorType @dataclass class GPT2Output: logits: TensorType["batch_size", "vocab_size"] final_encoding: TensorType["batch_size", "hidden_size"]

24.333333

60

0.757991

24

219

6.708333

0.625

0.186335

0.236025

0

0.005435

0.159817

219

8

61

27.375

0.869565

0

0.194313

0

1

0

true

0

0.333333

0

0.833333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

437cfe29ec099e567cf181e0ddd700c491ad3877

141

py

Python

mysite/polls/views.py

smurve/cbrokerage

9c0ada2981d60ab04a4a2120f40f9ebc4a38befc

[ "Apache-2.0" ]

null

mysite/polls/views.py

smurve/cbrokerage

9c0ada2981d60ab04a4a2120f40f9ebc4a38befc

[ "Apache-2.0" ]

3

2021-03-19T03:06:40.000Z

2022-02-10T13:35:19.000Z

mysite/polls/views.py

smurve/cbrokerage

9c0ada2981d60ab04a4a2120f40f9ebc4a38befc

[ "Apache-2.0" ]

null

from django.http import HttpResponse # Create your views here. def index(request): return HttpResponse("Hello Wolfie. Your at polls.")

20.142857

55

0.751773

19

141

5.578947

0.894737

0

0.163121

141

6

56

23.5

0.898305

0.163121

0

0.241379

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

438d54eb3fa1824dcf1f422f8efdd923564112c3

34,394

py

Python

posthog/api/test/test_feature_flag.py

ld-rale/posthog

0fa5b18b2e940cf5cdbe8afc733eb7e3cd4ae810

[ "MIT" ]

null

posthog/api/test/test_feature_flag.py

ld-rale/posthog

0fa5b18b2e940cf5cdbe8afc733eb7e3cd4ae810

[ "MIT" ]

null

posthog/api/test/test_feature_flag.py

ld-rale/posthog

0fa5b18b2e940cf5cdbe8afc733eb7e3cd4ae810

[ "MIT" ]

null

import json from unittest.mock import patch from rest_framework import status from posthog.models import FeatureFlag, GroupTypeMapping, User from posthog.models.cohort import Cohort from posthog.models.feature_flag import FeatureFlagOverride from posthog.test.base import APIBaseTest class TestFeatureFlag(APIBaseTest): feature_flag: FeatureFlag = None # type: ignore @classmethod def setUpTestData(cls): super().setUpTestData() cls.feature_flag = FeatureFlag.objects.create(team=cls.team, created_by=cls.user, key="red_button") def test_cant_create_flag_with_duplicate_key(self): count = FeatureFlag.objects.count() # Make sure the endpoint works with and without the trailing slash response = self.client.post( f"/api/projects/{self.team.id}/feature_flags", {"name": "Beta feature", "key": "red_button"} ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( response.json(), { "type": "validation_error", "code": "unique", "detail": "There is already a feature flag with this key.", "attr": "key", }, ) self.assertEqual(FeatureFlag.objects.count(), count) def test_cant_update_flag_with_duplicate_key(self): another_feature_flag = FeatureFlag.objects.create( team=self.team, rollout_percentage=50, name="some feature", key="some-feature", created_by=self.user, ) response = self.client.patch( f"/api/projects/{self.team.id}/feature_flags/{another_feature_flag.pk}", {"name": "Beta feature", "key": "red_button"}, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( response.json(), { "type": "validation_error", "code": "unique", "detail": "There is already a feature flag with this key.", "attr": "key", }, ) another_feature_flag.refresh_from_db() self.assertEqual(another_feature_flag.key, "some-feature") # Try updating the existing one response = self.client.patch( f"/api/projects/{self.team.id}/feature_flags/{self.feature_flag.id}/", {"name": "Beta feature 3", "key": "red_button"}, ) self.assertEqual(response.status_code, 200) self.feature_flag.refresh_from_db() self.assertEqual(self.feature_flag.name, "Beta feature 3") def test_is_simple_flag(self): feature_flag = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", data={"name": "Beta feature", "key": "beta-feature", "filters": {"groups": [{"rollout_percentage": 65,}]},}, format="json", ).json() self.assertTrue(feature_flag["is_simple_flag"]) self.assertEqual(feature_flag["rollout_percentage"], 65) def test_is_not_simple_flag(self): feature_flag = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", data={ "name": "Beta feature", "key": "beta-feature", "filters": { "groups": [ { "rollout_percentage": 65, "properties": [ {"key": "email", "type": "person", "value": "@posthog.com", "operator": "icontains",}, ], } ] }, }, format="json", ).json() self.assertFalse(feature_flag["is_simple_flag"]) @patch("posthog.api.feature_flag.report_user_action") def test_is_simple_flag_groups(self, mock_capture): feature_flag = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", data={ "name": "Beta feature", "key": "beta-feature", "filters": {"aggregation_group_type_index": 0, "groups": [{"rollout_percentage": 65,}]}, }, format="json", ).json() self.assertFalse(feature_flag["is_simple_flag"]) # Assert analytics are sent instance = FeatureFlag.objects.get(id=feature_flag["id"]) mock_capture.assert_called_once_with( self.user, "feature flag created", { "groups_count": 1, "has_variants": False, "variants_count": 0, "has_rollout_percentage": True, "has_filters": False, "filter_count": 0, "created_at": instance.created_at, "aggregating_by_groups": True, }, ) @patch("posthog.api.feature_flag.report_user_action") def test_create_feature_flag(self, mock_capture): response = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", {"name": "Alpha feature", "key": "alpha-feature", "filters": {"groups": [{"rollout_percentage": 50}]}}, format="json", ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) instance = FeatureFlag.objects.get(id=response.json()["id"]) self.assertEqual(instance.key, "alpha-feature") # Assert analytics are sent mock_capture.assert_called_once_with( self.user, "feature flag created", { "groups_count": 1, "has_variants": False, "variants_count": 0, "has_rollout_percentage": True, "has_filters": False, "filter_count": 0, "created_at": instance.created_at, "aggregating_by_groups": False, }, ) @patch("posthog.api.feature_flag.report_user_action") def test_create_minimal_feature_flag(self, mock_capture): response = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", {"key": "omega-feature"}, format="json" ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.json()["key"], "omega-feature") self.assertEqual(response.json()["name"], "") instance = FeatureFlag.objects.get(id=response.json()["id"]) self.assertEqual(instance.key, "omega-feature") self.assertEqual(instance.name, "") # Assert analytics are sent mock_capture.assert_called_once_with( self.user, "feature flag created", { "groups_count": 1, # 1 is always created by default "has_variants": False, "variants_count": 0, "has_rollout_percentage": False, "has_filters": False, "filter_count": 0, "created_at": instance.created_at, "aggregating_by_groups": False, }, ) @patch("posthog.api.feature_flag.report_user_action") def test_create_multivariate_feature_flag(self, mock_capture): response = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", { "name": "Multivariate feature", "key": "multivariate-feature", "filters": { "groups": [{"properties": [], "rollout_percentage": None}], "multivariate": { "variants": [ {"key": "first-variant", "name": "First Variant", "rollout_percentage": 50}, {"key": "second-variant", "name": "Second Variant", "rollout_percentage": 25}, {"key": "third-variant", "name": "Third Variant", "rollout_percentage": 25}, ], }, }, }, format="json", ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) instance = FeatureFlag.objects.get(id=response.json()["id"]) self.assertEqual(instance.key, "multivariate-feature") # Assert analytics are sent mock_capture.assert_called_once_with( self.user, "feature flag created", { "groups_count": 1, "has_variants": True, "variants_count": 3, "has_filters": False, "has_rollout_percentage": False, "filter_count": 0, "created_at": instance.created_at, "aggregating_by_groups": False, }, ) def test_cant_create_multivariate_feature_flag_with_variant_rollout_lt_100(self): response = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", { "name": "Multivariate feature", "key": "multivariate-feature", "filters": { "groups": [{"properties": [], "rollout_percentage": None}], "multivariate": { "variants": [ {"key": "first-variant", "name": "First Variant", "rollout_percentage": 50}, {"key": "second-variant", "name": "Second Variant", "rollout_percentage": 25}, {"key": "third-variant", "name": "Third Variant", "rollout_percentage": 0}, ], }, }, }, format="json", ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json().get("type"), "validation_error") self.assertEqual( response.json().get("detail"), "Invalid variant definitions: Variant rollout percentages must sum to 100." ) def test_cant_create_multivariate_feature_flag_with_variant_rollout_gt_100(self): response = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", { "name": "Multivariate feature", "key": "multivariate-feature", "filters": { "groups": [{"properties": [], "rollout_percentage": None}], "multivariate": { "variants": [ {"key": "first-variant", "name": "First Variant", "rollout_percentage": 50}, {"key": "second-variant", "name": "Second Variant", "rollout_percentage": 25}, {"key": "third-variant", "name": "Third Variant", "rollout_percentage": 50}, ], }, }, }, format="json", ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json().get("type"), "validation_error") self.assertEqual( response.json().get("detail"), "Invalid variant definitions: Variant rollout percentages must sum to 100." ) def test_cant_create_feature_flag_without_key(self): count = FeatureFlag.objects.count() response = self.client.post(f"/api/projects/{self.team.id}/feature_flags/", format="json") self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( response.json(), {"type": "validation_error", "code": "required", "detail": "This field is required.", "attr": "key"}, ) self.assertEqual(FeatureFlag.objects.count(), count) @patch("posthog.api.feature_flag.report_user_action") def test_updating_feature_flag(self, mock_capture): instance = self.feature_flag response = self.client.patch( f"/api/projects/{self.team.id}/feature_flags/{instance.pk}", { "name": "Updated name", "filters": { "groups": [ { "rollout_percentage": 65, "properties": [ {"key": "email", "type": "person", "value": "@posthog.com", "operator": "icontains",}, ], } ] }, }, format="json", ) self.assertEqual(response.status_code, status.HTTP_200_OK) instance.refresh_from_db() self.assertEqual(instance.name, "Updated name") self.assertEqual(instance.conditions[0]["rollout_percentage"], 65) # Assert analytics are sent mock_capture.assert_called_once_with( self.user, "feature flag updated", { "groups_count": 1, "has_variants": False, "variants_count": 0, "has_rollout_percentage": True, "has_filters": True, "filter_count": 1, "created_at": instance.created_at, "aggregating_by_groups": False, }, ) def test_deleting_feature_flag(self): new_user = User.objects.create_and_join(self.organization, "new_annotations@posthog.com", None) instance = FeatureFlag.objects.create(team=self.team, created_by=self.user) self.client.force_login(new_user) with patch("posthog.mixins.report_user_action") as mock_capture: response = self.client.delete(f"/api/projects/{self.team.id}/feature_flags/{instance.pk}/") self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(FeatureFlag.objects.filter(pk=instance.pk).exists()) # Assert analytics are sent (notice the event is sent on the user that executed the deletion, not the creator) mock_capture.assert_called_once_with( new_user, "feature flag deleted", { "groups_count": 1, "has_variants": False, "variants_count": 0, "has_rollout_percentage": False, "has_filters": False, "filter_count": 0, "created_at": instance.created_at, "aggregating_by_groups": False, }, ) @patch("posthog.api.feature_flag.report_user_action") def test_cannot_delete_feature_flag_on_another_team(self, mock_capture): _, other_team, other_user = User.objects.bootstrap("Test", "team2@posthog.com", None) self.client.force_login(other_user) response = self.client.delete(f"/api/projects/{other_team.id}/feature_flags/{self.feature_flag.pk}/") self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) self.assertTrue(FeatureFlag.objects.filter(pk=self.feature_flag.pk).exists()) mock_capture.assert_not_called() def test_get_flags_with_specified_token(self): _, _, user = User.objects.bootstrap("Test", "team2@posthog.com", None) self.client.force_login(user) assert user.team is not None assert self.team is not None self.assertNotEqual(user.team.id, self.team.id) response_team_1 = self.client.get(f"/api/projects/@current/feature_flags") response_team_1_token = self.client.get(f"/api/projects/@current/feature_flags?token={user.team.api_token}") response_team_2 = self.client.get(f"/api/projects/@current/feature_flags?token={self.team.api_token}") self.assertEqual(response_team_1.json(), response_team_1_token.json()) self.assertNotEqual(response_team_1.json(), response_team_2.json()) response_invalid_token = self.client.get(f"/api/projects/@current/feature_flags?token=invalid") self.assertEqual(response_invalid_token.status_code, 401) def test_creating_a_feature_flag_with_same_team_and_key_after_deleting(self): FeatureFlag.objects.create(team=self.team, created_by=self.user, key="alpha-feature", deleted=True) response = self.client.post( f"/api/projects/{self.team.id}/feature_flags/", {"name": "Alpha feature", "key": "alpha-feature"} ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) instance = FeatureFlag.objects.get(id=response.json()["id"]) self.assertEqual(instance.key, "alpha-feature") def test_updating_a_feature_flag_with_same_team_and_key_of_a_deleted_one(self): FeatureFlag.objects.create(team=self.team, created_by=self.user, key="alpha-feature", deleted=True) instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") response = self.client.patch( f"/api/projects/{self.team.id}/feature_flags/{instance.pk}", {"key": "alpha-feature",}, format="json", ) self.assertEqual(response.status_code, status.HTTP_200_OK) instance.refresh_from_db() self.assertEqual(instance.key, "alpha-feature") @patch("posthog.api.feature_flag.report_user_action") def test_my_flags(self, mock_capture): self.client.post( f"/api/projects/{self.team.id}/feature_flags/", { "name": "Alpha feature", "key": "alpha-feature", "filters": { "groups": [{"rollout_percentage": 20}], "multivariate": { "variants": [ {"key": "first-variant", "name": "First Variant", "rollout_percentage": 50}, {"key": "second-variant", "name": "Second Variant", "rollout_percentage": 25}, {"key": "third-variant", "name": "Third Variant", "rollout_percentage": 25}, ], }, }, }, format="json", ) # # alpha-feature is set for "distinct_id" distinct_id_user = User.objects.create_and_join(self.organization, "distinct_id_user@posthog.com", None) distinct_id_user.distinct_id = "distinct_id" distinct_id_user.save() self.client.force_login(distinct_id_user) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data), 2) first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "alpha-feature") self.assertEqual(first_flag["value_for_user_without_override"], "third-variant") self.assertEqual(first_flag["override"], None) second_flag = response_data[1] self.assertEqual(second_flag["feature_flag"]["key"], "red_button") self.assertEqual(second_flag["value_for_user_without_override"], True) self.assertEqual(second_flag["override"], None) # alpha-feature is not set for "distinct_id_0" distinct_id_0_user = User.objects.create_and_join(self.organization, "distinct_id_0_user@posthog.com", None) distinct_id_0_user.distinct_id = "distinct_id_0" distinct_id_0_user.save() self.client.force_login(distinct_id_0_user) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data), 2) first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "alpha-feature") self.assertEqual(first_flag["value_for_user_without_override"], False) self.assertEqual(first_flag["override"], None) @patch("posthoganalytics.capture") def test_my_flags_groups(self, mock_capture): self.client.post( f"/api/projects/{self.team.id}/feature_flags/", { "name": "groups flag", "key": "groups-flag", "filters": {"aggregation_group_type_index": 0, "groups": [{"rollout_percentage": 100,}]}, }, format="json", ) GroupTypeMapping.objects.create(team=self.team, group_type="organization", group_type_index=0) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) groups_flag = response.json()[0] self.assertEqual(groups_flag["feature_flag"]["key"], "groups-flag") self.assertEqual(groups_flag["value_for_user_without_override"], False) response = self.client.get( f"/api/projects/{self.team.id}/feature_flags/my_flags", data={"groups": json.dumps({"organization": "7"})} ) groups_flag = response.json()[0] self.assertEqual(groups_flag["feature_flag"]["key"], "groups-flag") self.assertEqual(groups_flag["value_for_user_without_override"], True) def test_create_override(self): # Boolean override value feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance.id, "override_value": True}, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertIsNotNone( FeatureFlagOverride.objects.get( team=self.team, user=self.user, feature_flag=feature_flag_instance, override_value=True ) ) # String override value feature_flag_instance_2 = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature-2") response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance_2.id, "override_value": "hey-hey"}, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertIsNotNone( FeatureFlagOverride.objects.get( team=self.team, user=self.user, feature_flag=feature_flag_instance_2, override_value="hey-hey" ) ) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "beta-feature-2") self.assertEqual(first_flag["override"]["override_value"], "hey-hey") second_flag = response_data[1] self.assertEqual(second_flag["feature_flag"]["key"], "beta-feature") self.assertEqual(second_flag["override"]["override_value"], True) third_flag = response_data[2] self.assertEqual(third_flag["feature_flag"]["key"], "red_button") self.assertEqual(third_flag["override"], None) def test_update_override(self): # Create an override and, and make sure the my_flags response shows it feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance.id, "override_value": "hey-hey"}, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "beta-feature") self.assertEqual(first_flag["override"]["override_value"], "hey-hey") # Update the override, and make sure the my_flags response reflects the update response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance.id, "override_value": "new-override"}, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "beta-feature") self.assertEqual(first_flag["override"]["override_value"], "new-override") # Ensure only 1 override exists in the DB for the feature_flag/user combo self.assertEqual( FeatureFlagOverride.objects.filter(user=self.user, feature_flag=feature_flag_instance).count(), 1 ) def test_delete_override(self): # Create an override and, and make sure the my_flags response shows it feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance.id, "override_value": "hey-hey"}, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "beta-feature") self.assertEqual(first_flag["override"]["override_value"], "hey-hey") # Delete the override, and make sure the my_flags response reflects the update existing_override_id = first_flag["override"]["id"] response = self.client.delete(f"/api/projects/@current/feature_flag_overrides/{existing_override_id}",) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) response = self.client.get(f"/api/projects/{self.team.id}/feature_flags/my_flags") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() first_flag = response_data[0] self.assertEqual(first_flag["feature_flag"]["key"], "beta-feature") self.assertEqual(first_flag["override"], None) def test_create_override_with_invalid_override(self): feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance.id, "override_value": {"key": "a dict"}}, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_override_for_feature_flag_in_another_team(self): feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") _, _, team_2_user = User.objects.bootstrap("Test", "team2@posthog.com", None) self.client.force_login(team_2_user) response = self.client.post( "/api/projects/@current/feature_flag_overrides/my_overrides", {"feature_flag": feature_flag_instance.id, "override_value": True}, ) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_delete_another_users_override(self): feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") feature_flag_override = FeatureFlagOverride.objects.create( team=self.team, user=self.user, feature_flag=feature_flag_instance, override_value=True ) feature_flag_override_id = feature_flag_override.id _, _, user_2 = User.objects.bootstrap(self.organization.name, "user2@posthog.com", None) self.client.force_login(user_2) response = self.client.delete(f"/api/projects/@current/feature_flag_overrides/{feature_flag_override_id}",) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_standard_viewset_endpoints_are_not_available(self): feature_flag_instance = FeatureFlag.objects.create(team=self.team, created_by=self.user, key="beta-feature") feature_flag_override = FeatureFlagOverride.objects.create( team=self.team, user=self.user, feature_flag=feature_flag_instance, override_value=True ) feature_flag_override_id = feature_flag_override.id response = self.client.put( f"/api/projects/@current/feature_flag_overrides/{feature_flag_override_id}", {"feature_flag": feature_flag_instance.id, "override_value": True}, ) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) response = self.client.patch( f"/api/projects/@current/feature_flag_overrides/{feature_flag_override_id}", {"feature_flag": feature_flag_instance.id, "override_value": True}, ) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) response = self.client.get(f"/api/projects/@current/feature_flag_overrides/{feature_flag_override_id}") self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) response = self.client.get(f"/api/projects/@current/feature_flag_overrides/") self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) response = self.client.post( f"/api/projects/@current/feature_flag_overrides/", {"feature_flag": feature_flag_instance.id, "override_value": True}, ) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_validation_person_properties(self): person_request = self._create_flag_with_properties( "person-flag", [{"key": "email", "type": "person", "value": "@posthog.com", "operator": "icontains",},] ) self.assertEqual(person_request.status_code, status.HTTP_201_CREATED) cohort_request = self._create_flag_with_properties( "cohort-flag", [{"key": "id", "type": "cohort", "value": 5},] ) self.assertEqual(cohort_request.status_code, status.HTTP_201_CREATED) event_request = self._create_flag_with_properties("illegal-event-flag", [{"key": "id", "value": 5},]) self.assertEqual(event_request.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( event_request.json(), { "type": "validation_error", "code": "invalid_input", "detail": "Filters are not valid (can only use person and cohort properties)", "attr": "filters", }, ) groups_request = self._create_flag_with_properties( "illegal-groups-flag", [{"key": "industry", "value": "finance", "type": "group", "group_type_index": 0}] ) self.assertEqual(groups_request.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( groups_request.json(), { "type": "validation_error", "code": "invalid_input", "detail": "Filters are not valid (can only use person and cohort properties)", "attr": "filters", }, ) @patch("posthog.tasks.calculate_cohort.calculate_cohort_ch.delay") def test_cohort_is_calculated(self, calculate_cohort_ch): cohort = Cohort.objects.create( team=self.team, groups=[{"properties": {"$some_prop": "something", "$another_prop": "something"}}], name="cohort1", ) cohort_request = self._create_flag_with_properties( "cohort-flag", [{"key": "id", "type": "cohort", "value": cohort.pk},] ) self.assertEqual(cohort_request.status_code, status.HTTP_201_CREATED) self.assertEqual(calculate_cohort_ch.call_count, 1) def test_validation_group_properties(self): groups_request = self._create_flag_with_properties( "groups-flag", [{"key": "industry", "value": "finance", "type": "group", "group_type_index": 0}], aggregation_group_type_index=0, ) self.assertEqual(groups_request.status_code, status.HTTP_201_CREATED) illegal_groups_request = self._create_flag_with_properties( "illegal-groups-flag", [{"key": "industry", "value": "finance", "type": "group", "group_type_index": 0}], aggregation_group_type_index=3, ) self.assertEqual(illegal_groups_request.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( illegal_groups_request.json(), { "type": "validation_error", "code": "invalid_input", "detail": "Filters are not valid (can only use group properties)", "attr": "filters", }, ) person_request = self._create_flag_with_properties( "person-flag", [{"key": "email", "type": "person", "value": "@posthog.com", "operator": "icontains",},], aggregation_group_type_index=0, ) self.assertEqual(person_request.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( person_request.json(), { "type": "validation_error", "code": "invalid_input", "detail": "Filters are not valid (can only use group properties)", "attr": "filters", }, ) def _create_flag_with_properties(self, name, properties, **kwargs): return self.client.post( f"/api/projects/{self.team.id}/feature_flags/", data={"name": name, "key": name, "filters": {**kwargs, "groups": [{"properties": properties,}],},}, format="json", )

46.042838

120

0.610368

3,755

34,394

5.327297

0.067377

0.067636

0.054039

0.042991

0.837333

0.813187

0.791892

0.770146

0.739602

0.727604

0

0.010659

0.263534

34,394

746

121

46.104558

0.779076

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0.537152

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0.253239

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0.063467

0

null

0

1

0

null

0

6

43adeb0b315f3844b15bd4d49be46779cb1ebb32

94

py

Python

interfaces/python/test/trainers_test.py

awf/ELL

25c94a1422efc41d5560db11b136f9d8f957ad41

[ "MIT" ]

2,094

2016-09-28T05:55:24.000Z

2019-05-04T19:06:36.000Z

interfaces/python/test/trainers_test.py

awesomemachinelearning/ELL

cb897e3aec148a1e9bd648012b5f53ab9d0dd20c

[ "MIT" ]

213

2017-06-30T12:53:40.000Z

2019-05-03T06:35:38.000Z

interfaces/python/test/trainers_test.py

awesomemachinelearning/ELL

cb897e3aec148a1e9bd648012b5f53ab9d0dd20c

[ "MIT" ]

301

2017-03-24T08:40:00.000Z

2019-05-02T21:22:28.000Z

import ell_helper import ell def test(): print("trainers_test.test -- TBD") return 0

13.428571

38

0.680851

14

94

4.428571

0.714286

0.290323

0

0.013514

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94

6

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0.824324

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null

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0

1

0

1

0

6

43af954e8ed556cd8c31fb9944438d1bc9067fbb

556

py

Python

templates/fastApiService/zarubaServiceName/helpers/transport/__init__.py

state-alchemists/zaruba

2c689c920df3589168ec81664b92110021892464

[ "Apache-2.0" ]

39

2020-03-13T19:41:11.000Z

2022-02-14T02:01:00.000Z

templates/fastApiService/zarubaServiceName/helpers/transport/__init__.py

state-alchemists/zaruba

2c689c920df3589168ec81664b92110021892464

[ "Apache-2.0" ]

5

2020-08-01T08:55:48.000Z

2022-02-10T00:55:39.000Z

templates/fastApiService/zarubaServiceName/helpers/transport/__init__.py

state-alchemists/zaruba

2c689c920df3589168ec81664b92110021892464

[ "Apache-2.0" ]

4

2020-11-10T20:45:12.000Z

2021-03-18T06:18:55.000Z

from typing import Mapping from helpers.transport.interface import MessageBus, RPC from helpers.transport.rmq_connection import get_rmq_connection_parameters from helpers.transport.rmq_mb import RMQMessageBus from helpers.transport.rmq_rpc import RMQRPC from helpers.transport.rmq_config import RMQEventMap from helpers.transport.kafka_mb import KafkaMessageBus, get_kafka_connection_parameters from helpers.transport.kafka_config import KafkaEventMap from helpers.transport.local_mb import LocalMessageBus from helpers.transport.local_rpc import LocalRPC

50.545455

87

0.888489

74

556

6.486486

0.324324

0.20625

0.375

0.191667

0.166667

0

0.07554

556

10

88

55.6

0.933852

0

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true

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null

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0

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0

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43c5dd76e089ab74fecdf1df9f255393f4550f8e

167

py

Python

tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/utils/__init__.py

hito0512/Vitis-AI

996459fb96cb077ed2f7e789d515893b1cccbc95

[ "Apache-2.0" ]

1

2022-02-17T22:13:23.000Z

tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/utils/__init__.py

hito0512/Vitis-AI

996459fb96cb077ed2f7e789d515893b1cccbc95

[ "Apache-2.0" ]

null

tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/utils/__init__.py

hito0512/Vitis-AI

996459fb96cb077ed2f7e789d515893b1cccbc95

[ "Apache-2.0" ]

null

from .torch_op_attr import * from .nndct2torch_op_map import * from .op_register import * from .torch_const import * from .tensor_util import * from .schema import *

20.875

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0.778443

25

167

4.92

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0.406504

0

0.007042

0.149701

167

7

34

23.857143

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0

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true

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1

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null

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1

0

1

0

1

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6

78dad85a74c50279420c37bee23b73f7afcccdae

67

py

Python

library_api_sematics/controllers/__init__.py

sematicshood/addons_sematics

a9e1871938d12b595730122b55f538d300a6255f

[ "MIT" ]

null

library_api_sematics/controllers/__init__.py

sematicshood/addons_sematics

a9e1871938d12b595730122b55f538d300a6255f

[ "MIT" ]

null

library_api_sematics/controllers/__init__.py

sematicshood/addons_sematics

a9e1871938d12b595730122b55f538d300a6255f

[ "MIT" ]

null

# -*- coding: utf-8 -*- from . import bitly from . import midtrans

16.75

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67

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0.465116

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0.777778

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1

0

null

0

1

0

1

0

1

0

6

78f0c5ac88aa898cce0abb13733698822d935083

43

py

Python

byol_pytorch/__init__.py

TariqAHassan/byol-pytorch

7be5b87b7dfd41eec8a1b1c2d44b0211a30673da

[ "MIT" ]

1,230

2020-06-17T01:05:21.000Z

2022-03-30T10:21:04.000Z

byol_pytorch/__init__.py

TariqAHassan/byol-pytorch

7be5b87b7dfd41eec8a1b1c2d44b0211a30673da

[ "MIT" ]

74

2020-06-17T10:12:14.000Z

2022-03-30T06:19:15.000Z

byol_pytorch/__init__.py

TariqAHassan/byol-pytorch

7be5b87b7dfd41eec8a1b1c2d44b0211a30673da

[ "MIT" ]

193

2020-06-17T08:11:52.000Z

2022-03-31T21:10:49.000Z

from byol_pytorch.byol_pytorch import BYOL

21.5

42

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0

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null

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0

1

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6

78fe2ff8c32f6631cebc50746f0a9700e79cfe7e

17,699

py

Python

tests/test_rules.py

DrackThor/artifactory-cleanup

7fe154e1822fd6449c7dc896c0d9904f61adbc86

[ "MIT" ]

1

2022-03-22T06:54:36.000Z

tests/test_rules.py

DrackThor/artifactory-cleanup

7fe154e1822fd6449c7dc896c0d9904f61adbc86

[ "MIT" ]

null

tests/test_rules.py

DrackThor/artifactory-cleanup

7fe154e1822fd6449c7dc896c0d9904f61adbc86

[ "MIT" ]

null

from artifactory_cleanup import rules import custom_rules from policy import RULES def test_repo_rules(): for repo_rules in RULES: assert isinstance(repo_rules.name, str) def test_keep_latest_n_version(): rule = rules.keep_latest_nupkg_n_version(2) result = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.108", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.113", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.109-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.111-Feature", }, }, ] result_expexted = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.108", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.109-Feature", }, }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_version_with_tar_gz(): rule = rules.keep_latest_nupkg_n_version(1) result = [ { "name": ".tar.gz", }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.113", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.111-Feature", }, }, ] result_expexted = [ { "name": ".tar.gz", }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110-Feature", }, }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_version_one(): rule = rules.keep_latest_nupkg_n_version(1) result = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.113", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.111-Feature", }, }, ] result_expexted = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110-Feature", }, }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_version_empty(): rule = rules.keep_latest_nupkg_n_version(2) result = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.113", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.110-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.111-Feature", }, }, ] result_expexted = [] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_version_patch(): rule = rules.keep_latest_nupkg_n_version(2) result = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.2.109-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.1.111-Feature", }, }, { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.1.110-Feature", }, }, ] result_to_delete = [ { "name": ".nupkg", "properties": { "nuget.id": "Package", "nuget.version": "16.0.1.110-Feature", }, }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_to_delete def test_keep_latest_n_file(): rule = rules.keep_latest_n_file(2) result = [ {"path": 1, "name": 1}, {"path": 1, "name": 2}, {"path": 1, "name": 3}, {"path": 1, "name": 4}, {"path": 1, "name": 5}, ] result_to_delete = [ {"path": 1, "name": 1}, {"path": 1, "name": 2}, {"path": 1, "name": 3}, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_to_delete def test_keep_latest_n_file_empty(): rule = rules.keep_latest_n_file(10) result = [ {"path": 1, "name": 1}, {"path": 1, "name": 2}, {"path": 1, "name": 3}, {"path": 1, "name": 4}, {"path": 1, "name": 5}, ] result_to_delete = [] result_after_filter = rule.filter_result(result) assert result_after_filter == result_to_delete def test_keep_latest_n_file_in_folder(): rule = rules.keep_latest_n_file_in_folder(2) result = [ {"path": 1, "name": 1}, {"path": 1, "name": 2}, {"path": 1, "name": 3}, {"path": 1, "name": 4}, {"path": 1, "name": 5}, {"path": 2, "name": 1}, {"path": 2, "name": 2}, {"path": 2, "name": 3}, {"path": 2, "name": 4}, {"path": 2, "name": 5}, {"path": 3, "name": 1}, {"path": 3, "name": 2}, {"path": 3, "name": 3}, ] result_to_delete = [ {"path": 1, "name": 1}, {"path": 1, "name": 2}, {"path": 1, "name": 3}, {"path": 2, "name": 1}, {"path": 2, "name": 2}, {"path": 2, "name": 3}, {"path": 3, "name": 1}, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_to_delete def test_keep_latest_n_file_in_folder_empty(): rule = rules.keep_latest_n_file_in_folder(100) result = [ {"path": 1, "name": 1}, {"path": 1, "name": 2}, {"path": 1, "name": 3}, {"path": 1, "name": 4}, {"path": 1, "name": 5}, {"path": 2, "name": 1}, {"path": 2, "name": 2}, {"path": 2, "name": 3}, {"path": 2, "name": 4}, {"path": 2, "name": 5}, {"path": 3, "name": 1}, {"path": 3, "name": 2}, {"path": 3, "name": 3}, ] result_to_delete = [] result_after_filter = rule.filter_result(result) assert result_after_filter == result_to_delete def test_keep_latest_version_n_file_in_folder(): rule = rules.keep_latest_version_n_file_in_folder(1) result = [ { "name": "name.1.2.100.tar.gz", "path": "repo/folder", }, { "name": "name.1.2.200.tar.gz", "path": "repo/folder", }, { "name": "new_name_1.2.3.101.tar.gz", "path": "repo/folder", }, { "name": "new_name_1.2.4.100.tar.gz", "path": "repo/folder", }, ] result_expexted = [ { "name": "name.1.2.100.tar.gz", "path": "repo/folder", }, { "name": "new_name_1.2.3.101.tar.gz", "path": "repo/folder", }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_delete_if_image_not_contained_in_properties(): rule = rules.delete_docker_image_if_not_contained_in_properties( "docker-repo", "test_docker." ) result = [ {"properties": {"test_docker.test1": "tag1"}}, {"properties": {"test_docker.test2": "tag2"}}, ] result_expexted = { "test1": {"tag1": True}, "test2": {"tag2": True}, } assert rule.get_properties_dict(result) == result_expexted def test_delete_images_older_than_n_days(): rule = rules.delete_docker_images_older_than(days=10) rule._collect_docker_size = lambda x: x result = [ {"path": "repo/image/tag", "name": "manifest.json"}, {"path": "repo/image/tag1", "name": "manifest.json"}, {"path": "repo/image/tag2", "name": "manifest.json"}, ] result_expexted = [ {"path": "repo/image", "name": "tag"}, {"path": "repo/image", "name": "tag1"}, {"path": "repo/image", "name": "tag2"}, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_file_in_folder_by_version(): rule = custom_rules.keep_latest_cross_package_n_version(2) result = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", }, { "name": "package-name.0.50.90.tar.gz", "path": "package-name/develop/0.50.90/other/folder/inside", }, { "name": "package-name.0.50.201.tar.gz", "path": "package-name/master/0.50.201/other/folder/inside", }, { "name": "package-name.0.50.94.tar.gz", "path": "package-name/master/0.50.94/other/folder/inside", }, { "name": "package-name.0.51.104.tar.gz", "path": "package-name/develop/0.51.104/other/folder/inside", }, { "name": "package-name.0.51.105.tar.gz", "path": "package-name/release/0.51.105/other/folder/inside", }, ] result_expexted = [ { "name": "package-name.0.50.94.tar.gz", "path": "package-name/master/0.50.94/other/folder/inside", }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_file_in_folder_by_version_does_not_suit_check_for_major_minor(): rule = custom_rules.keep_latest_cross_package_n_version(2) # версия артефакта, которая не подходит по количеству цифр не удаляется. В result: 0.50.1.02 result = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", }, { "name": "package-name.0.50.101.tar.gz", "path": "package-name/develop/0.50.101/other/folder/inside", }, { "name": "package-name.0.50.1.02.tar.gz", "path": "package-name/master/0.50.1.02/other/folder/inside", }, { "name": "package-name.0.50.103.tar.gz", "path": "package-name/master/0.50.103/other/folder/inside", }, { "name": "package-name.0.50.104.tar.gz", "path": "package-name/develop/0.50.104/other/folder/inside", }, { "name": "package-name.0.50.105.tar.gz", "path": "package-name/master/0.50.105/other/folder/inside", }, ] result_expexted = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_keep_latest_n_file_in_folder_by_version_multiple_versions_in_path(): rule = custom_rules.keep_latest_cross_package_n_version(1) # Если в пути есть несколько версий, то артефакт не удаляем. # Скорее всего ветку так назвали или ошибочно в пути появилась версия дважды. В result: /0.50/0.50.103/ result = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", }, { "name": "package-name.0.50.101.tar.gz", "path": "package-name/develop/0.50.101/other/folder/inside", }, { "name": "package-name.0.50.102.tar.gz", "path": "package-name/0.50/0.50.102/other/folder/inside", }, { "name": "package-name.0.50.103.tar.gz", "path": "package-name/0.50/0.50.103/other/folder/inside", }, { "name": "package-name.0.50.104.tar.gz", "path": "package-name/master/0.50.104/other/folder/inside", }, ] result_expexted = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", }, { "name": "package-name.0.50.102.tar.gz", "path": "package-name/0.50/0.50.102/other/folder/inside", }, ] result_after_filter = rule.filter_result(result) assert result_after_filter == result_expexted def test_delete_files_that_do_not_exist_in_other_repository(): rule = custom_rules.delete_files_that_do_not_exist_in_other_repository( "other_repository", "property" ) result = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", "properties": {"property": "95117"}, }, { "name": "package-name.0.50.101.tar.gz", "path": "package-name/master/0.50.101/other/folder/inside", "properties": {"property": "95118"}, }, { "name": "package-name.0.50.102.tar.gz", "path": "package-name/master/0.50.102/other/folder/inside", "properties": {"property": "95119"}, }, { "name": "package-name.0.50.103.tar.gz", "path": "package-name/master/0.50.103/other/folder/inside", }, ] artifacts_in_other_repo = [ { "name": "package-name.0.50.100.tar.gz", "path": "package-name/master/0.50.100/other/folder/inside", "properties": {"property": "95117"}, }, { "name": "package-name.0.50.101.tar.gz", "path": "package-name/master/0.50.101/other/folder/inside", "properties": {"property": "95118"}, }, { "name": "package-name.0.50.102.tar.gz", "path": "package-name/master/0.50.102/other/folder/inside", }, ] result_expexted = [ { "name": "package-name.0.50.102.tar.gz", "path": "package-name/master/0.50.102/other/folder/inside", "properties": {"property": "95119"}, }, ] result_after_filter = rule.remove_artifacts_from_result_artifact_if_property_exists_in_other_repository( result, artifacts_in_other_repo ) assert result_after_filter == result_expexted def test_docker_values(): rule = rules.delete_docker_image_if_not_contained_in_properties_value( "docker-repo", "test_docker." ) result = [ {"properties": {"test_docker.test1": "value1"}}, {"properties": {"test_docker.test2": "value2"}}, {"properties": {"no_test_docker.test3": "value3"}}, {"no_properties": {"test_key4": "value4"}}, ] expected_set = {"value1", "value2"} test_set = rule.get_properties_values(result) assert test_set == expected_set

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Python

library_simulator/__init__.py

harmsm/library_simulator

26092cde0f9f89659b210f6829e01799ac77e555

[ "Unlicense" ]

null

library_simulator/__init__.py

harmsm/library_simulator

26092cde0f9f89659b210f6829e01799ac77e555

[ "Unlicense" ]

null

library_simulator/__init__.py

harmsm/library_simulator

26092cde0f9f89659b210f6829e01799ac77e555

[ "Unlicense" ]

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2019-06-03T21:28:05.000Z

from .simulator import LibrarySimulator

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Python

tests/unit/amlb/utils/serialization/test_serializers.py

PGijsbers/automlbenchmark

6e0296b097455caf18d754e79a2bd85e85d01548

[ "MIT" ]

282

2018-09-19T09:45:46.000Z

2022-03-30T04:05:51.000Z

tests/unit/amlb/utils/serialization/test_serializers.py

PGijsbers/automlbenchmark

6e0296b097455caf18d754e79a2bd85e85d01548

[ "MIT" ]

267

2018-11-02T11:43:11.000Z

2022-03-31T08:58:16.000Z

tests/unit/amlb/utils/serialization/test_serializers.py

PGijsbers/automlbenchmark

6e0296b097455caf18d754e79a2bd85e85d01548

[ "MIT" ]

104

2018-10-17T19:32:36.000Z

2022-03-19T22:47:59.000Z

import os import pytest from amlb.utils.core import Namespace as ns from amlb.utils.serialization import is_sparse, serialize_data, deserialize_data @pytest.mark.use_disk def test_serialize_list_json(tmpdir): li = [[1, 2.2, None, 3, 4.4, 'foo', True], ['bar', False, 2/3]] dest = os.path.join(tmpdir, "my_list") path = serialize_data(li, dest, config=ns(fallback_serializer='json')) assert path == f"{dest}.json" reloaded = deserialize_data(path) assert isinstance(reloaded, list) assert li == reloaded @pytest.mark.use_disk def test_serialize_list_pickle(tmpdir): li = [[1, 2.2, None, 3, 4.4, 'foo', True], ['bar', False, 2/3]] dest = os.path.join(tmpdir, "my_list") path = serialize_data(li, dest, config=ns(fallback_serializer='pickle')) assert path == f"{dest}.pkl" reloaded = deserialize_data(path) assert isinstance(reloaded, list) assert li == reloaded @pytest.mark.use_disk def test_serialize_dict_json(tmpdir): di = dict( first=[1, 2.2, None, 3, 4.4, 'foo', True], second=['bar', False, 2/3] ) dest = os.path.join(tmpdir, "my_dict") path = serialize_data(di, dest, config=ns(fallback_serializer='json')) assert path == f"{dest}.json" reloaded = deserialize_data(path) assert isinstance(reloaded, dict) assert di == reloaded @pytest.mark.use_disk def test_serialize_dict_pickle(tmpdir): di = dict( first=[1, 2.2, None, 3, 4.4, 'foo', True], second=['bar', False, 2/3] ) dest = os.path.join(tmpdir, "my_dict") path = serialize_data(di, dest, config=ns(fallback_serializer='pickle')) assert path == f"{dest}.pkl" reloaded = deserialize_data(path) assert isinstance(reloaded, dict) assert di == reloaded @pytest.mark.use_disk def test_serialize_numpy_array(tmpdir): import numpy as np arr = np.array([1, 2.2, np.nan, 3, 4.4]) dest = os.path.join(tmpdir, "my_np_arr") path = serialize_data(arr, dest) assert path == f"{dest}.npy" reloaded = deserialize_data(path) assert isinstance(reloaded, np.ndarray) assert np.array_equal(arr, reloaded, equal_nan=True) @pytest.mark.use_disk def test_serialize_pandas_series(tmpdir): import pandas as pd ser = pd.Series([1, 2.2, pd.NA, 3, 4.4]) dest = os.path.join(tmpdir, "my_pd_ser") path = serialize_data(ser, dest) assert path == f"{dest}.pd" reloaded = deserialize_data(path) assert isinstance(reloaded, pd.Series) assert ser.compare(reloaded).empty @pytest.mark.use_disk def test_serialize_pandas_dataframes(tmpdir): import pandas as pd df = pd.DataFrame(dict( first=[1, 2.2, pd.NA, 3, 4.4], second=['a', 'b', 'c', 'a', 'b'] )) dest = os.path.join(tmpdir, "my_pd_df") path = serialize_data(df, dest) assert path == f"{dest}.pd" reloaded = deserialize_data(path) assert isinstance(reloaded, pd.DataFrame) assert df.compare(reloaded).empty @pytest.mark.use_disk def test_serialize_sparse_matrix(tmpdir): import scipy.sparse as sp import numpy as np arr = np.array([[0, 0, 0, 3.3], [4.4, 0, 0, 0], [0, np.nan, 0, 0]]) nans = np.count_nonzero(np.isnan(arr)) mat = sp.csc_matrix(arr) assert sp.issparse(mat) dest = os.path.join(tmpdir, "my_sparse_mat") path = serialize_data(mat, dest) assert path == f"{dest}.spy.npz" reloaded = deserialize_data(path, config=ns(sparse_matrix_deserialized_format=None)) assert isinstance(reloaded, sp.spmatrix) assert (mat != reloaded).nnz == nans assert np.array_equal(mat.toarray(), reloaded.toarray(), equal_nan=True) @pytest.mark.use_disk def test_serialize_sparse_matrix_reload_as_dense(tmpdir): import scipy.sparse as sp import numpy as np arr = np.array([[0, 0, 0, 3.3], [4.4, 0, 0, 0], [0, np.nan, 0, 0]]) mat = sp.csc_matrix(arr) assert sp.issparse(mat) dest = os.path.join(tmpdir, "my_sparse_mat") path = serialize_data(mat, dest) assert path == f"{dest}.spy.npz" reloaded = deserialize_data(path, config=ns(sparse_matrix_deserialized_format='dense')) assert not sp.issparse(reloaded) assert isinstance(reloaded, np.matrix) assert np.array_equal(mat.toarray(), np.asarray(reloaded), equal_nan=True) @pytest.mark.use_disk def test_serialize_sparse_matrix_reload_as_array(tmpdir): import scipy.sparse as sp import numpy as np arr = np.array([[0, 0, 0, 3.3], [4.4, 0, 0, 0], [0, np.nan, 0, 0]]) mat = sp.csc_matrix(arr) assert sp.issparse(mat) dest = os.path.join(tmpdir, "my_sparse_mat") path = serialize_data(mat, dest) assert path == f"{dest}.spy.npz" reloaded = deserialize_data(path, config=ns(sparse_matrix_deserialized_format='array')) assert isinstance(reloaded, np.ndarray) assert np.array_equal(mat.toarray(), reloaded, equal_nan=True) @pytest.mark.use_disk def test_serialize_sparse_dataframe(tmpdir): import pandas as pd ser_config = ns(pandas_serializer='pickle', sparse_dataframe_deserialized_format=None) dfs = pd.DataFrame(dict( first=[0, 0, 0, 3.3], second=[4.4, 0, 0, 0], third=[0, pd.NA, 0, 0], )).astype('Sparse') assert is_sparse(dfs) dest = os.path.join(tmpdir, "my_sparse_df") path = serialize_data(dfs, dest, config=ser_config) assert path == f"{dest}.pd" reloaded = deserialize_data(path, config=ser_config) assert isinstance(reloaded, pd.DataFrame) assert is_sparse(reloaded) assert dfs.compare(reloaded).empty @pytest.mark.use_disk def test_serialize_pandas_dataframe_reload_as_dense(tmpdir): import pandas as pd ser_config = ns(pandas_serializer='pickle', sparse_dataframe_deserialized_format='dense') dfs = pd.DataFrame(dict( first=[0, 0, 0, 3.3], second=[4.4, 0, 0, 0], third=[0, pd.NA, 0, 0], # fourth=[None, None, 'a', None] )).astype('Sparse') assert is_sparse(dfs) dest = os.path.join(tmpdir, "my_sparse_df") path = serialize_data(dfs, dest, config=ser_config) assert path == f"{dest}.pd" reloaded = deserialize_data(path, config=ser_config) assert isinstance(reloaded, pd.DataFrame) assert not is_sparse(reloaded) assert dfs.compare(reloaded).empty @pytest.mark.use_disk def test_serialize_pandas_dataframe_reload_as_array(tmpdir): import numpy as np import pandas as pd ser_config = ns(pandas_serializer='pickle', sparse_dataframe_deserialized_format='array') dfs = pd.DataFrame(dict( first=[0, 0, 0, 3.3], second=[4.4, 0, 0, 0], third=[0, pd.NA, 0, 0], # fourth=[None, None, 'a', None] )).astype('Sparse') assert is_sparse(dfs) dest = os.path.join(tmpdir, "my_sparse_df") path = serialize_data(dfs, dest, config=ser_config) assert path == f"{dest}.pd" reloaded = deserialize_data(path, config=ser_config) assert isinstance(reloaded, np.ndarray) assert np.array_equal(dfs.to_numpy(), np.asarray(reloaded), equal_nan=True) @pytest.mark.use_disk def test_serialize_sparse_numerical_dataframe_to_parquet(tmpdir): import pandas as pd ser_config = ns(pandas_serializer='parquet', sparse_dataframe_deserialized_format=None) dfs = pd.DataFrame(dict( first=[0, 0, 0, 3.3], second=[4.4, 0, 0, 0], third=[0, pd.NA, 0, 0], )).astype('Sparse') assert is_sparse(dfs) dest = os.path.join(tmpdir, "my_sparse_df") path = serialize_data(dfs, dest, config=ser_config) assert path == f"{dest}.sparse.pd" reloaded = deserialize_data(path, config=ser_config) assert isinstance(reloaded, pd.DataFrame) assert is_sparse(reloaded) assert dfs.compare(reloaded).empty @pytest.mark.use_disk def test_serialize_mixed_dataframe_to_parquet(tmpdir): import pandas as pd ser_config = ns(pandas_serializer='parquet', sparse_dataframe_deserialized_format=None) dfm = pd.DataFrame(dict( first=pd.arrays.SparseArray([0, 0, 0, 3.3]), second=pd.arrays.SparseArray([4.4, 0, 0, 0], dtype=pd.SparseDtype(float, 0)), third=pd.arrays.SparseArray([0, pd.NA, 0, 0]), fourth=[None, None, 'a', None] )) assert is_sparse(dfm) dest = os.path.join(tmpdir, "my_mixed_df") path = serialize_data(dfm, dest, config=ser_config) assert path == f"{dest}.sparse.pd" reloaded = deserialize_data(path, config=ser_config) assert isinstance(reloaded, pd.DataFrame) assert is_sparse(reloaded) assert dfm.compare(reloaded).empty

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Python

pretrained_mol_sim/molecule_optimization/get_final_results.py

allisontam/graph_coattention

5edc98615d5526269452b519bb13512ec5e952da

[ "MIT" ]

null

pretrained_mol_sim/molecule_optimization/get_final_results.py

allisontam/graph_coattention

5edc98615d5526269452b519bb13512ec5e952da

[ "MIT" ]

null

pretrained_mol_sim/molecule_optimization/get_final_results.py

allisontam/graph_coattention

5edc98615d5526269452b519bb13512ec5e952da

[ "MIT" ]

null

import pickle import gzip # We define the functions used to load and save objects def save_object(obj, filename): """ Function that saves an object to a file using pickle """ result = pickle.dumps(obj) with gzip.GzipFile(filename, 'wb') as dest: dest.write(result) dest.close() def load_object(filename): """ Function that loads an object from a file using pickle """ with gzip.GzipFile(filename, 'rb') as source: result = source.read() ret = pickle.loads(result) source.close() return ret # We compute the average statistics for the grammar autoencoder import numpy as np n_simulations = 10 iteration = 5 results_grammar = np.zeros((n_simulations, 3)) for j in range(1, n_simulations + 1): best_value = 1e10 n_valid = 0 max_value = 0 for i in range(iteration): smiles = load_object('simulation{}/grammar/results/valid_smiles{}.dat'.format(j, i)) scores = load_object('simulation{}/grammar/results/scores{}.dat'.format(j, i)) n_valid += len([ x for x in smiles if x is not None ]) if min(scores) < best_value: best_value = min(scores) if max(scores) > max_value: max_value = max(scores) import numpy as np sum_values = 0 count_values = 0 for i in range(iteration): scores = np.array(load_object('simulation{}/grammar/results/scores{}.dat'.format(j, i))) sum_values += np.sum(scores[ scores < max_value ]) count_values += len(scores[ scores < max_value ]) # fraction of valid smiles results_grammar[ j - 1, 0 ] = 1.0 * n_valid / (iteration * 50) # Best value results_grammar[ j - 1, 1 ] = best_value # Average value = results_grammar[ j - 1, 2 ] = 1.0 * sum_values / count_values print("Results Grammar VAE (fraction valid, best, average)):") print("Mean:", np.mean(results_grammar, 0)[ 0 ], -np.mean(results_grammar, 0)[ 1 ], -np.mean(results_grammar, 0)[ 2 ]) print("Std:", np.std(results_grammar, 0) / np.sqrt(iteration)) print("First:", -np.min(results_grammar[ : , 1 ])) best_score = np.min(results_grammar[ : , 1 ]) results_grammar[ results_grammar[ : , 1 ] == best_score , 1 ] = 1e10 print("Second:", -np.min(results_grammar[ : , 1 ])) second_best_score = np.min(results_grammar[ : , 1 ]) results_grammar[ results_grammar[ : , 1 ] == second_best_score, 1 ] = 1e10 print("Third:", -np.min(results_grammar[ : , 1 ])) third_best_score = np.min(results_grammar[ : , 1 ]) from rdkit.Chem import MolFromSmiles, MolToSmiles from rdkit.Chem import Draw from rdkit.Chem import Descriptors mols = [] for j in range(1, n_simulations + 1): for i in range(iteration): smiles = np.array(load_object('simulation{}/grammar/results/valid_smiles{}.dat'.format(j, i))) scores = np.array(load_object('simulation{}/grammar/results/scores{}.dat'.format(j, i))) if np.any(scores == best_score): smile = smiles[ scores == best_score ] smile = np.array(smile).astype('str')[ 0 ] print("First:", smile) mol = MolFromSmiles(smile) mols.append(mol) best_score = 1e10 if np.any(scores == second_best_score): smile = smiles[ scores == second_best_score ] smile = np.array(smile).astype('str')[ 0 ] print("Second:", smile) mol = MolFromSmiles(smile) mols.append(mol) second_best_score = 1e10 if np.any(scores == third_best_score): smile = smiles[ scores == third_best_score ] smile = np.array(smile).astype('str')[ 0 ] print("Third:", smile) mol = MolFromSmiles(smile) mols.append(mol) third_best_score = 1e10 img = Draw.MolsToGridImage(mols, molsPerRow = len(mols), subImgSize=(300, 300), useSVG=True) with open("molecule_images/best_grammar_molecule.svg", "w") as text_file: text_file.write(img) results_character = np.zeros((n_simulations, 3)) for j in range(1, n_simulations + 1): best_value = 1e10 n_valid = 0 max_value = 0 for i in range(iteration): smiles = load_object('simulation{}/character/results/valid_smiles{}.dat'.format(j, i)) scores = load_object('simulation{}/character/results/scores{}.dat'.format(j, i)) n_valid += len([ x for x in smiles if x is not None ]) if min(scores) < best_value: best_value = min(scores) if max(scores) > max_value: max_value = max(scores) import numpy as np sum_values = 0 count_values = 0 for i in range(iteration): scores = np.array(load_object('simulation{}/character/results/scores{}.dat'.format(j, i))) sum_values += np.sum(scores[ scores < max_value ]) count_values += len(scores[ scores < max_value ]) # fraction of valid smiles results_character[ j - 1, 0 ] = 1.0 * n_valid / (iteration * 50) # Best value results_character[ j - 1, 1 ] = best_value # Average value = results_character[ j - 1, 2 ] = 1.0 * sum_values / count_values print("Results Character VAE (fraction valid, best, average)):") print("Mean:", np.mean(results_character, 0)[ 0 ], -np.mean(results_character, 0)[ 1 ], -np.mean(results_character, 0)[ 2 ]) print("Std:", np.std(results_character, 0) / np.sqrt(iteration)) print("First:", -np.min(results_character[ : , 1 ])) best_score = np.min(results_character[ : , 1 ]) results_character[ results_character[ : , 1 ] == best_score , 1 ] = 1e10 print("Second:", -np.min(results_character[ : , 1 ])) second_best_score = np.min(results_character[ : , 1 ]) results_character[ results_character[ : , 1 ] == second_best_score, 1 ] = 1e10 print("Third:", -np.min(results_character[ : , 1 ])) third_best_score = np.min(results_character[ : , 1 ]) # We print the best smile found the character autoencoder mols = [] for j in range(1, n_simulations + 1): for i in range(iteration): smiles = np.array(load_object('simulation{}/character/results/valid_smiles{}.dat'.format(j, i))) scores = np.array(load_object('simulation{}/character/results/scores{}.dat'.format(j, i))) if np.any(scores == best_score): smile = smiles[ scores == best_score ] smile = np.array(smile).astype('str')[ 0 ] print("First:", smile) mol = MolFromSmiles(smile) mols.append(mol) best_score = 1e10 if np.any(scores == second_best_score): smile = smiles[ scores == second_best_score ] smile = np.array(smile).astype('str')[ 0 ] print("Second:", smile) mol = MolFromSmiles(smile) mols.append(mol) second_best_score = 1e10 if np.any(scores == third_best_score): smile = smiles[ scores == third_best_score ] smile = np.array(smile).astype('str')[ 0 ] print("Third:", smile) mol = MolFromSmiles(smile) mols.append(mol) third_best_score = 1e10 img = Draw.MolsToGridImage(mols, molsPerRow = len(mols), subImgSize=(300, 300), useSVG=True) with open("molecule_images/best_character_molecule.svg", "w") as text_file: text_file.write(img)

34.72381

124

0.625206

978

7,292

4.507157

0.126789

0.057169

0.032668

0.024955

0.842559

0.803539

0.799456

0.769964

0.755898

0.738657

0

0.023981

0.23944

7,292

209

125

34.889952

0.770826

0.052935

0

0.622378

0

0.109816

0.076901

0

1

0.013986

false

0

0.055944

0

0.076923

0.125874

0

null

0

1

0

null

0

6

608c48677c96388db33737cc0a430016c00fbb8d

1,000

py

Python

Phase_3/ds-k-nearest_neighbors-main/src/euclid.py

VaneezaAhmad/ds-east-042621-lectures

334f98bb4bd4f8020055e95994764b1587a809c0

[ "MIT" ]

1

2021-08-12T21:48:21.000Z

Phase_3/ds-k-nearest_neighbors-main/src/euclid.py

VaneezaAhmad/ds-east-042621-lectures

334f98bb4bd4f8020055e95994764b1587a809c0

[ "MIT" ]

null

Phase_3/ds-k-nearest_neighbors-main/src/euclid.py

VaneezaAhmad/ds-east-042621-lectures

334f98bb4bd4f8020055e95994764b1587a809c0

[ "MIT" ]

20

2021-04-27T19:27:58.000Z

2021-06-16T15:08:50.000Z

import pandas as pd import numpy as np def euclid(train_X, val_X): """ :param train_X: one record from the training set (type series or dataframe including target (survived)) :param val_X: one record from the validation set series or dataframe include target (survived) :return: """ diff = train_X - val_X # Remove survived column diff = diff.iloc[:, :-1] dist = np.sqrt((diff ** 2).sum(axis=1)) return dist def manhattan(train_X, val_X): """ :param train_X: one record from the training set (type series or dataframe including target (survived)) :param val_X: one record from the validation set series or dataframe include target (survived) :return: the Manhattan distance between train_X and val_X """ diff = train_X - val_X # Remove survived column diff = diff.iloc[:, :-1] dist = np.sqrt((np.abs(diff)).sum(axis=1)) return dist

27.027027

74

0.618

138

1,000

4.376812

0.311594

0.069536

0.059603

0.066225

0.824503

0.764901

0

0.007052

0.291

1,000

36

75

27.777778

0.844852

0.575

0

0.5

0

1

0.166667

false

0

0.166667

0

0.5

0

null

0

1

0

null

0

6

60b8a20d80d44a1cb041da6833d2028e2e163f6b

5,038

py

Python

posthog/test/test_entity_model.py

alx-a/posthog

a76959bb2a7640ca8cf367a4d3a0e4ca67f65a5e

[ "MIT" ]

null

posthog/test/test_entity_model.py

alx-a/posthog

a76959bb2a7640ca8cf367a4d3a0e4ca67f65a5e

[ "MIT" ]

null

posthog/test/test_entity_model.py

alx-a/posthog

a76959bb2a7640ca8cf367a4d3a0e4ca67f65a5e

[ "MIT" ]

null

from django.test import TestCase from posthog.models.entity import TREND_FILTER_TYPE_ACTIONS, TREND_FILTER_TYPE_EVENTS, Entity class TestEntity(TestCase): def test_equality_with_ids(self): entity1 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_ACTIONS}) entity2 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_ACTIONS}) self.assertTrue(entity1.equals(entity2)) entity2 = Entity({"id": "e2", "type": TREND_FILTER_TYPE_ACTIONS}) self.assertFalse(entity1.equals(entity2)) def test_equality_with_type(self): entity1 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_EVENTS}) entity2 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_EVENTS}) self.assertTrue(entity1.equals(entity2)) entity1 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_EVENTS}) entity2 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_ACTIONS}) self.assertFalse(entity1.equals(entity2)) def test_equality_with_simple_properties(self): entity1 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "email", "value": "test@posthog.com", "type": "person"}, {"key": "current_url", "value": "test@posthog.com", "type": "element"}, ], } ) entity2 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "current_url", "value": "test@posthog.com", "type": "element"}, {"key": "email", "value": "test@posthog.com", "type": "person"}, ], } ) self.assertTrue(entity1.equals(entity2)) entity2 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "current$url", "value": "test@posthog.com", "type": "element"}, {"key": "email", "value": "test@posthog.com", "type": "person"}, ], } ) self.assertFalse(entity1.equals(entity2)) def test_equality_with_complex_operator_properties(self): entity1 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "count", "operator": "lt", "value": 12, "type": "element"}, {"key": "email", "operator": "in", "value": ["a, b"], "type": "person"}, {"key": "selector", "value": [".btn"], "operator": "exact", "type": "element"}, {"key": "test_prop", "value": 1.2, "operator": "gt"}, ], } ) entity2 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "test_prop", "value": 1.20, "operator": "gt"}, {"key": "count", "operator": "lt", "value": 12, "type": "element"}, {"key": "selector", "value": [".btn"], "operator": "exact", "type": "element"}, {"key": "email", "operator": "in", "value": ["a, b"], "type": "person"}, ], } ) self.assertTrue(entity1.equals(entity2)) # playing with decimals entity2 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "test_prop", "value": 1.200, "operator": "gt"}, {"key": "count", "operator": "lt", "value": 12, "type": "element"}, {"key": "selector", "value": [".btn"], "operator": "exact", "type": "element"}, {"key": "email", "operator": "in", "value": ["a, b"], "type": "person"}, ], } ) self.assertTrue(entity1.equals(entity2)) entity2 = Entity( { "id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [ {"key": "test_prop", "value": 1.2001, "operator": "gt"}, {"key": "count", "operator": "lt", "value": 12, "type": "element"}, {"key": "selector", "value": [".btn"], "operator": "exact", "type": "element"}, {"key": "email", "operator": "in", "value": ["a, b"], "type": "person"}, ], } ) self.assertFalse(entity1.equals(entity2)) def test_equality_with_old_style_and_new_style_properties(self): entity1 = Entity({"id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": {"key": "value"}}) entity2 = Entity( {"id": "e1", "type": TREND_FILTER_TYPE_EVENTS, "properties": [{"key": "key", "value": "value"},]} ) self.assertTrue(entity1.equals(entity2))

38.166667

109

0.470623

455

5,038

5.028571

0.136264

0.086538

0.118007

0.132867

0.888986

0.853147

0.815559

0.746941

0

0.022451

0.345772

5,038

131

110

38.458015

0.671723

0.004168

0

0.626168

0

0.212363

0

0.093458

1

0.046729

false

0

0.018692

0

0.074766

0

null

0

1

0

null

0

6

60c7aa21b3becb1e4a441128f6d43ba3e4e7eaa3

49,109

py

Python

tests/test_services.py

gustavofonseca/multiverse

1d98f9374a92cdce3c198518d6f70a010f5abc67

[ "BSD-2-Clause" ]

6

2018-12-05T15:52:13.000Z

2019-04-18T14:14:32.000Z

tests/test_services.py

gustavofonseca/multiverse

1d98f9374a92cdce3c198518d6f70a010f5abc67

[ "BSD-2-Clause" ]

117

2018-09-03T21:13:30.000Z

2019-09-26T19:16:24.000Z

tests/test_services.py

gustavofonseca/multiverse

1d98f9374a92cdce3c198518d6f70a010f5abc67

[ "BSD-2-Clause" ]

9

2018-12-05T14:01:30.000Z

2019-07-04T17:34:08.000Z

import os import unittest from unittest import mock import datetime import random from bson.objectid import ObjectId from documentstore import services, exceptions, domain from . import apptesting def make_services(): session = apptesting.Session() return services.get_handlers(lambda: session, subscribers=[]), session class CommandTestMixin: SUBSCRIBERS_EVENTS = [subscriber[0] for subscriber in services.DEFAULT_SUBSCRIBERS] def test_command_interface(self): self.assertIsNotNone(self.command) self.assertTrue(callable(self.command)) class CreateDocumentsBundleTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("create_documents_bundle") self.event = services.Events.DOCUMENTSBUNDLE_CREATED def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_success(self): self.assertIsNone(self.command(id="xpto")) def test_command_with_documents_success(self): self.assertIsNone( self.command(id="xpto", docs=[{"id": "/document/1"}, {"id": "/document/2"}]) ) def test_command_with_metadata_success(self): self.assertIsNone( self.command( id="xpto", metadata={"publication_year": "2018", "volume": "2"} ) ) def test_command_raises_exception_if_already_exists(self): self.command(id="xpto") self.assertRaises(exceptions.AlreadyExists, self.command, id="xpto") def test_command_notify_event(self): with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="xpto", docs=[{"id": "/document/1"}]) mock_notify.assert_called_once_with( self.event, { "id": "xpto", "docs": [{"id": "/document/1"}], "metadata": None, "instance": mock.ANY, }, ) class FetchDocumentsBundleTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("fetch_documents_bundle") datetime_patcher = mock.patch.object( domain, "datetime", mock.Mock(wraps=datetime.datetime) ) mocked_datetime = datetime_patcher.start() mocked_datetime.utcnow.return_value = datetime.datetime( 2018, 8, 5, 22, 33, 49, 795151 ) self.addCleanup(datetime_patcher.stop) def test_command_raises_exception_if_does_not_exist(self): self.assertRaises(exceptions.DoesNotExist, self.command, id="xpto") def test_command_success(self): self.services["create_documents_bundle"](id="xpto") result = self.command(id="xpto") self.assertEqual(result["id"], "xpto") def test_command_with_documents_success(self): self.services["create_documents_bundle"]( id="xpto", docs=[{"id": "/document/1"}, {"id": "/document/2"}] ) result = self.command(id="xpto") self.assertEqual( result["items"], [{"id": "/document/1"}, {"id": "/document/2"}] ) def test_command_with_metadata_success(self): self.services["create_documents_bundle"]( id="xpto", metadata={"publication_year": "2018", "volume": "2"} ) result = self.command(id="xpto") self.assertEqual( result["metadata"], {"publication_year": "2018", "volume": "2"} ) def test_command_with_unexpected_metadata(self): self.services["create_documents_bundle"]( id="xpto", metadata={"publication_year": "2018", "volume": "2", "unknown": "0"}, ) result = self.command(id="xpto") self.assertEqual( result["metadata"], {"publication_year": "2018", "volume": "2"} ) class UpdateDocumentsBundleTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("update_documents_bundle_metadata") self.event = services.Events.DOCUMENTSBUNDLE_METATADA_UPDATED datetime_patcher = mock.patch.object( domain, "datetime", mock.Mock(wraps=datetime.datetime) ) mocked_datetime = datetime_patcher.start() mocked_datetime.utcnow.side_effect = lambda: ( datetime.datetime(2018, 8, 5, 22, 33, 49, random.randint(1, 1000000)) ) self.addCleanup(datetime_patcher.stop) def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_does_not_exist(self): self.assertRaises(exceptions.DoesNotExist, self.command, id="xpto", metadata={}) def test_command_success(self): self.services["create_documents_bundle"]( id="xpto", metadata={"publication_year": "2018", "volume": "2"} ) self.command(id="xpto", metadata={"publication_year": "2019"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual( result["metadata"], {"publication_year": "2019", "volume": "2"} ) def test_command_with_unexpected_metadata(self): self.services["create_documents_bundle"]( id="xpto", metadata={"publication_year": "2018", "volume": "2"} ) self.command(id="xpto", metadata={"unknown": "0"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual( result["metadata"], {"publication_year": "2018", "volume": "2"} ) def test_command_remove_metadata(self): """ Por ora, a maneira de remover um metadado é através da atribuição de uma string vazia para o mesmo. Note que este procedimento não removerá o metadado do manifesto. """ self.services["create_documents_bundle"]( id="xpto", metadata={"publication_year": "2018", "volume": "2"} ) self.command(id="xpto", metadata={"volume": ""}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual(result["metadata"], {"publication_year": "2018", "volume": ""}) def test_command_notify_event(self): self.services["create_documents_bundle"]( id="xpto", metadata={"publication_year": "2018", "volume": "2"} ) with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="xpto", metadata={"publication_year": "2019"}) mock_notify.assert_called_once_with( self.event, { "id": "xpto", "metadata": {"publication_year": "2019"}, "instance": mock.ANY, }, ) class AddDocumentToDocumentsBundleTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("add_document_to_documents_bundle") self.event = services.Events.DOCUMENT_ADDED_TO_DOCUMENTSBUNDLE def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="xpto", doc="/document/1" ) def test_command_success(self): self.services["create_documents_bundle"](id="xpto") self.command(id="xpto", doc={"id": "/document/1"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual(result["items"], [{"id": "/document/1"}]) self.command(id="xpto", doc={"id": "/document/2"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual( result["items"], [{"id": "/document/1"}, {"id": "/document/2"}] ) def test_command_raises_exception_if_already_exists(self): self.services["create_documents_bundle"]( id="xpto", docs=[{"id": "/document/1"}] ) self.assertRaises( exceptions.AlreadyExists, self.command, id="xpto", doc={"id": "/document/1"} ) def test_command_notify_event(self): self.services["create_documents_bundle"](id="xpto") with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="xpto", doc={"id": "/document/1"}) mock_notify.assert_called_once_with( self.event, {"id": "xpto", "doc": {"id": "/document/1"}, "instance": mock.ANY}, ) class InsertDocumentToDocumentsBundleTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("insert_document_to_documents_bundle") self.event = services.Events.DOCUMENT_INSERTED_TO_DOCUMENTSBUNDLE def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="xpto", index=0, doc={"id": "/document/1"}, ) def test_command_success(self): self.services["create_documents_bundle"](id="xpto") self.command(id="xpto", index=1, doc={"id": "/document/1"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual(result["items"], [{"id": "/document/1"}]) self.command(id="xpto", index=0, doc={"id": "/document/2"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual( result["items"], [{"id": "/document/2"}, {"id": "/document/1"}] ) self.command(id="xpto", index=10, doc={"id": "/document/3"}) result = self.services["fetch_documents_bundle"](id="xpto") self.assertEqual( result["items"], [{"id": "/document/2"}, {"id": "/document/1"}, {"id": "/document/3"}], ) def test_command_raises_exception_if_already_exists(self): self.services["create_documents_bundle"]( id="xpto", docs=[{"id": "/document/1"}, {"id": "/document/2"}] ) self.assertRaises( exceptions.AlreadyExists, self.command, id="xpto", index=0, doc={"id": "/document/1"}, ) self.assertRaises( exceptions.AlreadyExists, self.command, id="xpto", index=1, doc={"id": "/document/1"}, ) def test_command_notify_event(self): self.services["create_documents_bundle"](id="xpto") with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="xpto", index=10, doc={"id": "/document/3"}) mock_notify.assert_called_once_with( self.event, { "id": "xpto", "doc": {"id": "/document/3"}, "index": 10, "instance": mock.ANY, }, ) class UpdateDocumentInDocumentsBundleTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("update_documents_in_documents_bundle") self.event = services.Events.ISSUE_DOCUMENTS_UPDATED create_documents_bundle_command = self.services.get("create_documents_bundle") create_documents_bundle_command(id="issue-example-id") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_raises_does_not_exists_if_journal_not_found(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="not-found-issue", docs=[] ) def test_issues_list_should_be_updated(self): with mock.patch.object(self.session.documents_bundles, "fetch") as mock_fetch: DocumentsBundleStub = mock.Mock(spec=domain.DocumentsBundle) DocumentsBundleStub.documents = [{"id": "a"}, {"id": "b"}, {"id": "c"}] DocumentsBundleStub.add_document = mock.Mock() DocumentsBundleStub.remove_document = mock.Mock() mock_fetch.return_value = DocumentsBundleStub self.command(id="issue-example-id", docs=["d"]) DocumentsBundleStub.remove_document.assert_has_calls( [mock.call("a"), mock.call("b"), mock.call("c")] ) DocumentsBundleStub.add_document.assert_called_once_with("d") def test_raises_already_exists_if_duplicated_are_in_list(self): self.assertRaises( exceptions.AlreadyExists, self.command, id="issue-example-id", docs=[{"id": "a"}, {"id": "a"}, {"id": "b"}, {"id": "a"}, {"id": "b"}], ) def test_should_call_update_issue(self): with mock.patch.object(self.session.documents_bundles, "update") as mock_update: self.command(id="issue-example-id", docs=[{"id": "a"}]) mock_update.assert_called_once() def test_should_empty_bundle_document(self): with mock.patch.object(self.session.documents_bundles, "fetch") as mock_fetch: DocumentsBundleStub = mock.Mock(spec=domain.DocumentsBundle) DocumentsBundleStub.documents = [{"id": "a"}] DocumentsBundleStub.add_document = mock.Mock() DocumentsBundleStub.remove_document = mock.Mock() mock_fetch.return_value = DocumentsBundleStub self.command(id="issue-example-id", docs=[]) DocumentsBundleStub.remove_document.assert_has_calls([mock.call("a")]) DocumentsBundleStub.add_document.assert_not_called() def test_command_notify_event(self): with mock.patch.object(self.session.documents_bundles, "fetch") as mock_fetch: DocumentsBundleStub = mock.Mock(spec=domain.DocumentsBundle) DocumentsBundleStub.documents = [] mock_fetch.return_value = DocumentsBundleStub with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="issue-example-id", docs=[{"id": "a"}]) mock_notify.assert_called_once_with( self.event, { "instance": DocumentsBundleStub, "id": "issue-example-id", "docs": [{"id": "a"}], }, ) class CreateJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("create_journal") self.event = services.Events.JOURNAL_CREATED def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_success(self): self.assertIsNone(self.command(id="xpto")) def test_command_with_metadata_success(self): self.assertIsNone( self.command( id="xpto", metadata={ "title": "Journal Title", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, ], }, ) ) def test_command_raises_exception_if_already_exists(self): self.command(id="xpto") self.assertRaises(exceptions.AlreadyExists, self.command, id="xpto") def test_command_notify_event(self): with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="jxpto") mock_notify.assert_called_once_with( self.event, {"id": "jxpto", "instance": mock.ANY, "metadata": None} ) class AddIssueToJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("add_issue_to_journal") self.event = services.Events.ISSUE_ADDED_TO_JOURNAL create_journal_command = self.services.get("create_journal") create_journal_command(id="0034-8910-rsp") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_calls_add_issue(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.add_issue = mock.Mock() mock_fetch.return_value = JournalStub self.command(id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"}) JournalStub.add_issue.assert_called_once_with({"id": "0034-8910-rsp-48-2"}) def test_command_update_journals(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.add_issue = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session.journals, "update") as mock_update: self.command(id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"}) mock_update.assert_called_once_with(JournalStub) def test_command_success(self): self.assertIsNone( self.command(id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"}) ) def test_command_raises_exception_if_journal_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="0101-8910-csp", issue="0101-8910-csp-48-2", ) def test_command_raises_exception_if_issue_already_exists(self): self.command(id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"}) self.assertRaises( exceptions.AlreadyExists, self.command, id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"}, ) def test_command_notify_event(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.insert_issue = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"}) mock_notify.assert_called_once_with( self.event, { "instance": JournalStub, "id": "0034-8910-rsp", "issue": {"id": "0034-8910-rsp-48-2"}, }, ) class InsertIssueToJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("insert_issue_to_journal") self.event = services.Events.ISSUE_INSERTED_TO_JOURNAL create_journal_command = self.services.get("create_journal") create_journal_command(id="0034-8910-rsp") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_journal_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="0101-8910-csp", index=0, issue={"id": "0101-8910-csp-48-2"}, ) def test_command_calls_insert_issue(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.insert_issue = mock.Mock() mock_fetch.return_value = JournalStub self.command( id="0034-8910-rsp", index=0, issue={"id": "0034-8910-rsp-48-2"} ) JournalStub.insert_issue.assert_called_once_with( 0, {"id": "0034-8910-rsp-48-2"} ) def test_command_update_journals(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.insert_issue = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session.journals, "update") as mock_update: self.command( id="0034-8910-rsp", index=0, issue={"id": "0034-8910-rsp-48-2"} ) mock_update.assert_called_once_with(JournalStub) def test_command_success(self): self.assertIsNone( self.command( id="0034-8910-rsp", index=0, issue={"id": "0034-8910-rsp-48-2"} ) ) self.assertIsNone( self.command( id="0034-8910-rsp", index=10, issue={"id": "0034-8910-rsp-48-3"} ) ) self.assertIsNone( self.command( id="0034-8910-rsp", index=-1, issue={"id": "0034-8910-rsp-48-4"} ) ) def test_command_raises_exception_if_issue_already_exists(self): self.command(id="0034-8910-rsp", index=0, issue={"id": "0034-8910-rsp-48-2"}) self.assertRaises( exceptions.AlreadyExists, self.command, id="0034-8910-rsp", index=0, issue={"id": "0034-8910-rsp-48-2"}, ) self.assertRaises( exceptions.AlreadyExists, self.command, id="0034-8910-rsp", index=5, issue={"id": "0034-8910-rsp-48-2"}, ) def test_command_notify_event(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.insert_issue = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session, "notify") as mock_notify: self.command( id="0034-8910-rsp", index=0, issue={"id": "0034-8910-rsp-48-2"} ) mock_notify.assert_called_once_with( self.event, { "instance": JournalStub, "id": "0034-8910-rsp", "index": 0, "issue": {"id": "0034-8910-rsp-48-2"}, }, ) class RemoveIssueFromJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("remove_issue_from_journal") self.event = services.Events.ISSUE_REMOVED_FROM_JOURNAL create_journal_command = self.services.get("create_journal") create_journal_command(id="0034-8910-rsp") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_journal_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="0101-8910-csp", issue="0101-8910-csp-48-2", ) def test_command_calls_remove_issue(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.remove_issue = mock.Mock() mock_fetch.return_value = JournalStub self.command(id="0034-8910-rsp", issue="0034-8910-rsp-48-2") JournalStub.remove_issue.assert_called_once_with("0034-8910-rsp-48-2") def test_command_update_journals(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.remove_issue = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session.journals, "update") as mock_update: self.command(id="0034-8910-rsp", issue="0034-8910-rsp-48-2") mock_update.assert_called_once_with(JournalStub) def test_command_success(self): self.services.get("add_issue_to_journal")( id="0034-8910-rsp", issue={"id": "0034-8910-rsp-48-2"} ) self.assertIsNone(self.command(id="0034-8910-rsp", issue="0034-8910-rsp-48-2")) def test_command_raises_exception_if_issue_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="0034-8910-rsp", issue="0034-8910-rsp-48-2", ) def test_command_notify_event(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.remove_issue = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="0034-8910-rsp", issue="0034-8910-rsp-48-2") mock_notify.assert_called_once_with( self.event, { "instance": JournalStub, "id": "0034-8910-rsp", "issue": "0034-8910-rsp-48-2", }, ) class UpdateIssuesInJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("update_issues_in_journal") self.event = services.Events.JOURNAL_ISSUES_UPDATED create_journal_command = self.services.get("create_journal") create_journal_command(id="journal-example-id") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_raises_does_not_exists_if_journal_not_found(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="not-found-journal", issues=[] ) def test_issues_list_should_be_updated(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.issues = [{"id": "a"}, {"id": "b"}, {"id": "c"}] JournalStub.add_issue = mock.Mock() JournalStub.remove_issue = mock.Mock() mock_fetch.return_value = JournalStub self.command(id="journal-example-id", issues=["d"]) JournalStub.remove_issue.assert_has_calls( [mock.call("a"), mock.call("b"), mock.call("c")] ) JournalStub.add_issue.assert_called_once_with("d") def test_raises_already_exists_if_duplicated_are_in_list(self): self.assertRaises( exceptions.AlreadyExists, self.command, id="journal-example-id", issues=[{"id": "a"}, {"id": "a"}, {"id": "b"}, {"id": "a"}, {"id": "b"}], ) def test_should_call_update_journal(self): with mock.patch.object(self.session.journals, "update") as mock_update: self.command(id="journal-example-id", issues=[{"id": "a"}]) mock_update.assert_called_once() def test_should_empty_journal_issues(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.issues = [{"id": "a"}] JournalStub.add_issue = mock.Mock() JournalStub.remove_issue = mock.Mock() mock_fetch.return_value = JournalStub self.command(id="journal-example-id", issues=[]) JournalStub.remove_issue.assert_has_calls([mock.call("a")]) JournalStub.add_issue.assert_not_called() def test_command_notify_event(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.issues = [] mock_fetch.return_value = JournalStub with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="journal-example-id", issues=[{"id": "a"}]) mock_notify.assert_called_once_with( self.event, { "instance": JournalStub, "id": "journal-example-id", "issues": [{"id": "a"}], }, ) class SetAheadOfPrintBundleToJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("set_ahead_of_print_bundle_to_journal") self.event = services.Events.AHEAD_OF_PRINT_BUNDLE_SET_TO_JOURNAL create_journal_command = self.services.get("create_journal") create_journal_command(id="0034-8910-rsp") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_journal_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="0101-8910-csp", aop="0101-8910-csp-aop", ) def test_command_calls_ahead_of_print_bundle(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.ahead_of_print_bundle = mock.Mock() mock_fetch.return_value = JournalStub self.command(id="0034-8910-rsp", aop="0034-8910-rsp-aop") self.assertEqual(JournalStub.ahead_of_print_bundle, "0034-8910-rsp-aop") def test_command_update_journals(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.ahead_of_print_bundle = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session.journals, "update") as mock_update: self.command(id="0034-8910-rsp", aop="0034-8910-rsp-aop") mock_update.assert_called_once_with(JournalStub) def test_command_success(self): self.assertIsNone(self.command(id="0034-8910-rsp", aop="0034-8910-rsp-aop")) def test_command_notify_event(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.ahead_of_print_bundle = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="0034-8910-rsp", aop="0034-8910-rsp-aop") mock_notify.assert_called_once_with( self.event, { "instance": JournalStub, "id": "0034-8910-rsp", "aop": "0034-8910-rsp-aop", }, ) class RemoveAheadOfPrintBundleFromJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("remove_ahead_of_print_bundle_from_journal") self.event = services.Events.AHEAD_OF_PRINT_BUNDLE_REMOVED_FROM_JOURNAL create_journal_command = self.services.get("create_journal") create_journal_command(id="0034-8910-rsp") def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_journal_does_not_exist(self): self.assertRaises(exceptions.DoesNotExist, self.command, id="0101-8910-csp") def test_command_calls_remove_ahead_of_print(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.remove_ahead_of_print_bundle = mock.Mock() mock_fetch.return_value = JournalStub self.command(id="0034-8910-rsp") JournalStub.remove_ahead_of_print_bundle.assert_called_once_with() def test_command_update_journals(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.remove_ahead_of_print_bundle = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session.journals, "update") as mock_update: self.command(id="0034-8910-rsp") mock_update.assert_called_once_with(JournalStub) def test_command_raises_exception_if_ahead_of_print_does_not_exist(self): self.assertRaises(exceptions.DoesNotExist, self.command, id="0034-8910-rsp") def test_command_notify_event(self): with mock.patch.object(self.session.journals, "fetch") as mock_fetch: JournalStub = mock.Mock(spec=domain.Journal) JournalStub.remove_ahead_of_print_bundle = mock.Mock() mock_fetch.return_value = JournalStub with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="0034-8910-rsp") mock_notify.assert_called_once_with( self.event, {"instance": JournalStub, "id": "0034-8910-rsp"} ) class FetchJournalTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("fetch_journal") create_journal_command = self.services.get("create_journal") create_journal_command(id="1678-4596-cr-49-02") def test_should_raise_does_not_exists_exception(self): self.assertRaises( exceptions.DoesNotExist, self.command, id="1678-4596-cr-49-03" ) def test_should_return_a_journal(self): self.assertIsNotNone(self.command(id="1678-4596-cr-49-02")) def test_should_require_an_id(self): self.assertRaises(TypeError, self.command) class UpdateJornalMetadataTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services.get("update_journal_metadata") self.event = services.Events.JOURNAL_METATADA_UPDATED self.services["create_journal"]( id="1678-4596-cr", metadata={ "title": "Journal Title", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, ], }, ) def test_event(self): self.assertIn(self.event, self.SUBSCRIBERS_EVENTS) def test_command_raises_exception_if_does_not_exist(self): self.session.journals.fetch = mock.Mock(side_effect=exceptions.DoesNotExist) self.assertRaises( exceptions.DoesNotExist, self.command, id="1678-4596-cr", metadata={} ) def test_command_success(self): self.command( id="1678-4596-cr", metadata={ "title": "Journal New Title", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, {"language": "es", "value": "Misión de la Revista"}, ], }, ) result = self.services["fetch_journal"](id="1678-4596-cr") self.assertEqual( result["metadata"], { "title": "Journal New Title", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, {"language": "es", "value": "Misión de la Revista"}, ], }, ) def test_command_with_unexpected_metadata(self): self.command( id="1678-4596-cr", metadata={ "unknown": "0", "title": "Journal New Title", "title_iso": "Title ISO", }, ) result = self.services["fetch_journal"](id="1678-4596-cr") self.assertEqual( result["metadata"], { "title": "Journal New Title", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, ], "title_iso": "Title ISO", }, ) def test_command_remove_metadata(self): """ Por ora, a maneira de remover um metadado é através da atribuição de uma string vazia para o mesmo. Note que este procedimento não removerá o metadado do manifesto. """ self.command(id="1678-4596-cr", metadata={"title": ""}) result = self.services["fetch_journal"](id="1678-4596-cr") self.assertEqual( result["metadata"], { "title": "", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, ], }, ) def test_command_notify_event(self): metadata = { "title": "Journal New Title", "mission": [ {"language": "pt", "value": "Missão do Periódico"}, {"language": "en", "value": "Journal Mission"}, {"language": "es", "value": "Misión de la Revista"}, ], } with mock.patch.object(self.session, "notify") as mock_notify: self.command(id="1678-4596-cr", metadata=metadata) mock_notify.assert_called_once_with( self.event, {"id": "1678-4596-cr", "metadata": metadata, "instance": mock.ANY}, ) class RegisterRenditionVersionTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services["register_rendition_version"] self.event = services.Events.RENDITION_VERSION_REGISTERED self.document = domain.Document(manifest=apptesting.manifest_data_fixture()) self.session.documents.add(self.document) def test_register_rendition_version_returns_none(self): self.assertIsNone( self.command( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) ) def test_register_duplicated_rendition_version_raises_error(self): self.command( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) self.assertRaises( exceptions.VersionAlreadySet, self.command, self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) def test_register_new_rendition_version(self): """Qualquer diferença em qualquer campo é suficiente para que seja considerada uma nova versão válida. """ self.command( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) self.assertIsNone( self.command( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt-v2.pdf", "application/pdf", "pt", 23456, ) ) def test_command_notify_event(self): with mock.patch.object(self.session, "notify") as mock_notify: self.command( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) mock_notify.assert_called_once_with( self.event, { "instance": mock.ANY, "id": self.document.id(), "filename": "0034-8910-rsp-48-2-0275-pt.pdf", "data_url": "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "mimetype": "application/pdf", "lang": "pt", "size_bytes": 23456, }, ) class FetchDocumentRenditionsTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services["fetch_document_renditions"] self.document = domain.Document(manifest=apptesting.manifest_data_fixture()) self.session.documents.add(self.document) def test_fetch_rendition(self): self.services["register_rendition_version"]( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) renditions = self.command(self.document.id()) self.assertEqual(len(renditions), 1) self.assertEqual( renditions[0]["url"], "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", ) def test_fetch_latest_version(self): self.services["register_rendition_version"]( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) self.services["register_rendition_version"]( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/8ca9f9c1397cc/0035-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 234567, ) renditions = self.command(self.document.id()) self.assertEqual(len(renditions), 1) self.assertEqual( renditions[0]["url"], "/rawfiles/8ca9f9c1397cc/0035-8910-rsp-48-2-0275-pt.pdf", ) def test_fetch_version_at(self): self.services["register_rendition_version"]( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 23456, ) now = services.utcnow()[:-8] + "Z" # em segundos datetime_patcher = mock.patch.object( domain, "datetime", mock.Mock(wraps=datetime.datetime) ) mocked_datetime = datetime_patcher.start() # faz com que o timestamp da próxima versão seja do próximo ano mocked_datetime.utcnow.return_value = datetime.datetime( datetime.date.today().year + 1, 8, 5, 22, 34, 49, 795151 ) self.addCleanup(datetime_patcher.stop) self.services["register_rendition_version"]( self.document.id(), "0034-8910-rsp-48-2-0275-pt.pdf", "/rawfiles/8ca9f9c1397cc/0035-8910-rsp-48-2-0275-pt.pdf", "application/pdf", "pt", 234567, ) renditions = self.command(self.document.id(), version_at=now) self.assertEqual(len(renditions), 1) self.assertEqual( renditions[0]["url"], "/rawfiles/7ca9f9b2687cb/0034-8910-rsp-48-2-0275-pt.pdf", ) class DeleteDocumentTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services["delete_document"] self.document = domain.Document(manifest=apptesting.manifest_data_fixture()) self.session.documents.add(self.document) self.event = services.Events.DOCUMENT_DELETED def test_delete_document_returns_none(self): self.assertIsNone(self.command(self.document.id())) def test_raises_when_document_does_not_exist(self): self.assertRaises( exceptions.DoesNotExist, self.command, "inexistent-document-id" ) def test_command_notify_event(self): with mock.patch.object(self.session, "notify") as mock_notify: self.command(self.document.id()) mock_notify.assert_called_once_with( self.event, {"instance": mock.ANY, "id": self.document.id()} ) class FetchChangeTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.change_id = str(ObjectId()) self.session.changes.add( { "_id": self.change_id, "timestamp": "2018-08-05T23:08:50.331687Z", "entity": "Document", "id": "S0034-89102014000200347", "content_gz": '{"hello": "world"}', "content_type": "application/json", } ) self.command = self.services.get("fetch_change") def test_should_raise_does_not_exists_exception(self): self.assertRaises(exceptions.DoesNotExist, self.command, id="missing-change") def test_should_return_a_change(self): self.assertIsNotNone(self.command(id=self.change_id)) def test_should_require_an_id(self): self.assertRaises(TypeError, self.command) class RegisterDocumentVersionTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.manifest = { "id": "0034-8910-rsp-48-2-0347", "versions": [ { "data": "https://url.to/0034-8910-rsp-48-2-0347.xml", "assets": { "0034-8910-rsp-48-2-0347-gf01": [ [ "2018-08-05T23:03:44.971230Z", "http://www.scielo.br/img/revistas/rsp/v48n2/0034-8910-rsp-48-2-0347-gf01.jpg", ], ], }, "renditions": [], }, ], } self.doc = domain.Document(manifest=self.manifest) self.session.documents.add(self.doc) self.command = self.services["register_document_version"] def test_swollows_VersionAlreadySet_exception_for_assets(self): with mock.patch("documentstore.domain.requests.get") as mock_request: with open( os.path.join( os.path.dirname(os.path.abspath(__file__)), "0034-8910-rsp-48-2-0347.xml", ) ) as fixture: mock_request.return_value.content = fixture.read().encode("utf-8") assets = self.doc.version()["assets"] self.assertIsNone( self.command( id=self.doc.id(), data_url="https://url.to.new/0034-8910-rsp-48-2-0347.xml", assets=assets, ) ) class FetchDocumentFrontTest(CommandTestMixin, unittest.TestCase): def setUp(self): self.services, self.session = make_services() self.command = self.services["sanitize_document_front"] with open( os.path.join( os.path.dirname(os.path.abspath(__file__)), "0034-8910-rsp-48-2-0347.xml", ), "rb" ) as fixture: self.data = fixture.read() def test_call_returns_display_format(self): expected = { 'article_title': { "en": """Proposal for a telehealth concept in the translational research model""", "pt": """Proposta conceitual de telessaúde no modelo da pesquisa translacional""", } } result = self.command(self.data) self.assertEqual(expected, result['display_format'])

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0.80613

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null

0

1

0

null

0

6

60d27bc7cc5914cc9c25adb19669b05a9e543193

7,879

py

Python

tests/test_fill.py

mgesteiro/pyubx2

02fd8fa2863b88ed2d746b5800717a1b6b213181

[ "BSD-3-Clause" ]

null

tests/test_fill.py

mgesteiro/pyubx2

02fd8fa2863b88ed2d746b5800717a1b6b213181

[ "BSD-3-Clause" ]

null

tests/test_fill.py

mgesteiro/pyubx2

02fd8fa2863b88ed2d746b5800717a1b6b213181

[ "BSD-3-Clause" ]

null

''' Created on 21 Oct 2020 Fill method tests for pyubx2.UBXMessage @author: semuadmin ''' # pylint: disable=line-too-long, invalid-name, missing-docstring, no-member import unittest from pyubx2 import UBXMessage, SET, POLL class FillTest(unittest.TestCase): def setUp(self): self.maxDiff = None def tearDown(self): pass def testFill_CFGMSG(self): # test POLL constructor fill, format 1 EXPECTED_RESULT = "<UBX(CFG-MSG, msgClass=NMEA-Standard, msgID=VTG)>" res = UBXMessage('CFG', 'CFG-MSG', POLL, msgClass=240, msgID=5) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGMSG2(self): # test POLL constructor fill, format 2 EXPECTED_RESULT = "<UBX(CFG-MSG, msgClass=NMEA-Standard, msgID=VTG)>" res = UBXMessage(b'\x06', b'\x01', POLL, msgClass=240, msgID=5) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGMSG3(self): # test POLL constructor fill, format 3 EXPECTED_RESULT = "<UBX(CFG-MSG, msgClass=NMEA-Standard, msgID=VTG)>" res = UBXMessage(6, 1, POLL, msgClass=240, msgID=5) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGMSG4(self): # test SET constructor fill EXPECTED_RESULT = "<UBX(CFG-MSG, msgClass=NMEA-Standard, msgID=GLL, rateDDC=0, rateUART1=1, rateUART2=0, rateUSB=1, rateSPI=0, reserved=0)>" res = UBXMessage('CFG', 'CFG-MSG', SET, msgClass=240, msgID=1, rateUART1=1, rateUSB=1) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGNMEA(self): # test SET constructor fill, set all values EXPECTED_RESULT = "<UBX(CFG-NMEA, filter=b'E', nmeaVersion=4.0, numSV=4, flags=b'\\x14', gnssToFilter=b'\\x00\\x00\\x00\\x00', svNumbering=0, mainTalkerId=0, gsvTalkerId=0, version=0, bdsTalkerId=b'\\x00\\x00', reserved1=0)>" res = UBXMessage('CFG', 'CFG-NMEA', SET, filter=b'\x45', nmeaVersion=64, numSV=4, flags=b'\x14') self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGNMEA2(self): # test SET constructor fill, set some values, default others EXPECTED_RESULT = "<UBX(CFG-NMEA, filter=b'\\x00', nmeaVersion=2.3, numSV=1, flags=b'\\x00', gnssToFilter=b'\\x00\\x00\\x00\\x00', svNumbering=0, mainTalkerId=0, gsvTalkerId=0, version=0, bdsTalkerId=b'\\x00\\x00', reserved1=0)>" res = UBXMessage('CFG', 'CFG-NMEA', SET, nmeaVersion=35, numSV=1) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGNMEAPARSE(self): # check that raw payload is correctly populated and parses back to original message EXPECTED_RESULT = "<UBX(CFG-NMEA, filter=b'\\x00', nmeaVersion=2.3, numSV=1, flags=b'\\x00', gnssToFilter=b'\\x00\\x00\\x00\\x00', svNumbering=0, mainTalkerId=0, gsvTalkerId=0, version=0, bdsTalkerId=b'\\x00\\x00', reserved1=0)>" res = UBXMessage('CFG', 'CFG-NMEA', SET, nmeaVersion=35, numSV=1) res2 = UBXMessage.parse(res.serialize()) self.assertEqual(str(res2), EXPECTED_RESULT) def testFill_CFGNMEAPOLL(self): # test POLL constructor, no payload EXPECTED_RESULT = "<UBX(CFG-NMEA)>" res = UBXMessage('CFG', 'CFG-NMEA', POLL) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGNMEAPOLL2(self): # test POLL constructor, no payload EXPECTED_RESULT = "<UBX(CFG-NMEA)>" res = UBXMessage('CFG', 'CFG-NMEA', POLL) res2 = UBXMessage.parse(res.serialize()) self.assertEqual(str(res2), EXPECTED_RESULT) def testFill_CFGDOSC(self): # multiple repeats in group EXPECTED_RESULT = "<UBX(CFG-DOSC, version=23, numOsc=2, reserved1=0, oscId_01=4, reserved2_01=0, flags_01=b'\\x00\\x00', freq_01=22, phaseOffset_01=0, withTemp_01=0, withAge_01=0, timeToTemp_01=0, reserved3_01=0, gainVco_01=0, gainUncertainty_01=0, reserved4_01=0, oscId_02=7, reserved2_02=0, flags_02=b'\\x00\\x00', freq_02=44, phaseOffset_02=0, withTemp_02=0, withAge_02=0, timeToTemp_02=0, reserved3_02=0, gainVco_02=0, gainUncertainty_02=0, reserved4_02=0)>" res = UBXMessage('CFG', 'CFG-DOSC', SET, version=23, numOsc=2, oscId_01=4, freq_01=22, oscId_02=7, freq_02=44) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGDOSC1(self): # single repeat in group EXPECTED_RESULT = "<UBX(CFG-DOSC, version=37, numOsc=1, reserved1=0, oscId_01=8, reserved2_01=0, flags_01=b'\\x00\\x00', freq_01=53, phaseOffset_01=26, withTemp_01=0, withAge_01=0, timeToTemp_01=0, reserved3_01=0, gainVco_01=4, gainUncertainty_01=123, reserved4_01=0)>" res = UBXMessage('CFG', 'CFG-DOSC', SET, version=37, numOsc=1, oscId_01=8, freq_01=53, phaseOffset_01=26, gainVco_01=4, gainUncertainty_01=123) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGDOSCPARSE(self): # check that raw payload is correctly populated and parses back to original message EXPECTED_RESULT = "<UBX(CFG-DOSC, version=37, numOsc=1, reserved1=0, oscId_01=8, reserved2_01=0, flags_01=b'\\x00\\x00', freq_01=53, phaseOffset_01=26, withTemp_01=0, withAge_01=0, timeToTemp_01=0, reserved3_01=0, gainVco_01=4, gainUncertainty_01=123, reserved4_01=0)>" res = UBXMessage('CFG', 'CFG-DOSC', SET, version=37, numOsc=1, oscId_01=8, freq_01=53, phaseOffset_01=26, gainVco_01=4, gainUncertainty_01=123) res2 = UBXMessage.parse(res.serialize()) self.assertEqual(str(res2), EXPECTED_RESULT) def testFill_CFGDOSC2(self): # empty group EXPECTED_RESULT = "<UBX(CFG-DOSC, version=37, numOsc=0, reserved1=0)>" res = UBXMessage('CFG', 'CFG-DOSC', SET, version=37, numOsc=0) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGDAT(self): # floating point attribute, single and double precision EXPECTED_RESULT = "<UBX(CFG-DAT, datumNum=4, datumName=b'WGS-84', majA=4321.123456789128, flat=-2964.00469836, dX=-1.2345678, dY=27.40654, dZ=0.0, rotX=0.0, rotY=0.0, rotZ=0.0, scale=0.0)>" res = UBXMessage('CFG', 'CFG-DAT', SET, datumNum=4, datumName=b'WGS-84', majA=4321.123456789128, flat=-2964.00469836, dX=-1.2345678, dY=27.40654) self.assertEqual(str(res), EXPECTED_RESULT) def testFill_CFGDATPARSE(self): # check that raw payload is correctly populated and parses back to original message EXPECTED_RESULT = "<UBX(CFG-DAT, datumNum=4, datumName=b'WGS-84', majA=4321.123456789128, flat=-2964.00469836, dX=-1.2345677614212036, dY=27.406539916992188, dZ=0.0, rotX=0.0, rotY=0.0, rotZ=0.0, scale=0.0)>" res = UBXMessage('CFG', 'CFG-DAT', SET, datumNum=4, datumName=b'WGS-84', majA=4321.123456789128, flat=-2964.00469836, dX=-1.2345678, dY=27.40654) res2 = UBXMessage.parse(res.serialize()) self.assertEqual(str(res2), EXPECTED_RESULT) def testFill_CFGDATPARSE2(self): # check that raw payload is correctly populated and parses back to original message EXPECTED_RESULT = "<UBX(CFG-DAT, datumNum=4, datumName=b'WGS-84', majA=0.0, flat=0.0, dX=-1.2345677614212036, dY=27.406539916992188, dZ=0.0, rotX=0.0, rotY=0.0, rotZ=0.0, scale=0.0)>" res = UBXMessage('CFG', 'CFG-DAT', SET, datumNum=4, datumName=b'WGS-84', dX=-1.2345678, dY=27.40654) res2 = UBXMessage.parse(res.serialize()) self.assertEqual(str(res2), EXPECTED_RESULT) def testEVAL(self): # test eval of repr res = UBXMessage('CFG', 'CFG-MSG', POLL, msgClass=240, msgID=5) reseval = eval(repr(res)) assert type(reseval) is UBXMessage def testEVAL2(self): # test eval of repr res = UBXMessage('CFG', 'CFG-MSG', SET, msgClass=240, msgID=5, rateUART1=1, rateUSB=1) reseval = eval(repr(res)) assert type(reseval) is UBXMessage if __name__ == "__main__": # import sys;sys.argv = ['', 'Test.testName'] unittest.main()

64.581967

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0.678639

1,135

7,879

4.607048

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0.052018

0.061197

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7,879

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false

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0

null

0

1

0

1

0

null

0

1

0

6

60e29c5b15948891a63018e89f81ccd1d0f84b90

88

py

Python

saopy/owlssc/__init__.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

2

2016-11-03T14:57:45.000Z

2019-05-13T13:21:08.000Z

saopy/owlssc/__init__.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

null

saopy/owlssc/__init__.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

1

2020-07-23T11:27:15.000Z

import saopy.model from saopy.model import owlssc___ServiceCategory as ServiceCategory

22

67

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11

88

6.727273

0.636364

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0.102273

88

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true

0

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null

1

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null

0

1

0

1

0

1

0

6

880bad88a3f34b8e4e3ccb80279c4c4c188539a5

141

py

Python

commondata/__init__.py

softwaresaved/policy_common_data

2c20a5929a9509a323269af5fb815a8273e64516

[ "BSD-3-Clause" ]

null

commondata/__init__.py

softwaresaved/policy_common_data

2c20a5929a9509a323269af5fb815a8273e64516

[ "BSD-3-Clause" ]

null

commondata/__init__.py

softwaresaved/policy_common_data

2c20a5929a9509a323269af5fb815a8273e64516

[ "BSD-3-Clause" ]

null

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) DATA_DIR = os.path.abspath(os.path.join(BASE_DIR, 'data'))

28.2

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25

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0.181818

0.30303

0.323232

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141

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0

1

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false

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0.333333

0

0.333333

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null

1

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1

0

null

0

1

0

6

71664c0de31b805d12b7f53141d6708136fe4d5d

32

py

Python

app/views/api/users/__init__.py

dandye/DjanGoat

72beb30afe3ddd5b31ce74a5d3b9da61d2c5df1d

[ "MIT" ]

65

2017-08-18T15:12:03.000Z

2021-08-14T16:50:07.000Z

app/views/api/users/__init__.py

dandye/DjanGoat

72beb30afe3ddd5b31ce74a5d3b9da61d2c5df1d

[ "MIT" ]

83

2017-11-28T21:45:20.000Z

2021-11-02T18:52:52.000Z

app/views/api/users/__init__.py

dandye/DjanGoat

72beb30afe3ddd5b31ce74a5d3b9da61d2c5df1d

[ "MIT" ]

71

2017-08-17T14:58:01.000Z

2022-02-02T17:09:49.000Z

import app.views.api.users.urls

16

31

0.8125

6

32

4.333333

1

0

0.0625

32

1

32

0.866667

0

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true

0

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0

1

0

1

0

null

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0

null

0

1

0

1

0

1

0

6

71b36a5752301a4fab1f956f44b1cf0805f7207e

20,533

py

Python

tests/test_Models.py

xin-huang/dadi-cli

d403e9dced19c3a71dc134a8993ad0ceba592c51

[ "Apache-2.0" ]

5

2021-12-07T23:27:40.000Z

2022-03-15T08:59:33.000Z

tests/test_Models.py

xin-huang/dadi-cli

d403e9dced19c3a71dc134a8993ad0ceba592c51

[ "Apache-2.0" ]

2

2022-01-15T09:27:12.000Z

2022-03-25T16:08:52.000Z

tests/test_Models.py

xin-huang/dadi-cli

d403e9dced19c3a71dc134a8993ad0ceba592c51

[ "Apache-2.0" ]

5

2021-03-31T19:22:23.000Z

2021-12-07T18:24:59.000Z

import dadi import dadi.DFE as DFE import pytest from src.Models import get_dadi_model_func, get_dadi_model_params, print_available_models, print_model_details def test_get_dadi_model_func(): #Selection with a gamma shared between populations assert get_dadi_model_func('IM', withSelection=True, single_gamma=True) == DFE.DemogSelModels.IM_single_gamma assert get_dadi_model_func('IM_pre', withSelection=True, single_gamma=True) == DFE.DemogSelModels.IM_pre_single_gamma assert get_dadi_model_func('split_mig', withSelection=True, single_gamma=True) == DFE.DemogSelModels.split_mig_single_gamma assert get_dadi_model_func('split_asym_mig', withSelection=True, single_gamma=True) == DFE.DemogSelModels.split_asym_mig_single_gamma #Selection with independant gammas assert get_dadi_model_func('IM', withSelection=True, single_gamma=False) == DFE.DemogSelModels.IM assert get_dadi_model_func('IM_pre', withSelection=True, single_gamma=False) == DFE.DemogSelModels.IM_pre assert get_dadi_model_func('split_mig', withSelection=True, single_gamma=False) == DFE.DemogSelModels.split_mig assert get_dadi_model_func('split_asym_mig', withSelection=True, single_gamma=False) == DFE.DemogSelModels.split_asym_mig assert get_dadi_model_func('equil', withSelection=True, single_gamma=False) == DFE.DemogSelModels.equil assert get_dadi_model_func('two_epoch', withSelection=True, single_gamma=False) == DFE.DemogSelModels.two_epoch assert get_dadi_model_func('three_epoch', withSelection=True, single_gamma=False) == DFE.DemogSelModels.three_epoch #1D demographic models assert get_dadi_model_func('bottlegrowth_1d', withSelection=False, single_gamma=False) == dadi.Demographics1D.bottlegrowth assert get_dadi_model_func('growth', withSelection=False, single_gamma=False) == dadi.Demographics1D.growth assert get_dadi_model_func('snm_1d', withSelection=False, single_gamma=False) == dadi.Demographics1D.snm assert get_dadi_model_func('three_epoch', withSelection=False, single_gamma=False) == dadi.Demographics1D.three_epoch assert get_dadi_model_func('two_epoch', withSelection=False, single_gamma=False) == dadi.Demographics1D.two_epoch #2D demographic models assert get_dadi_model_func('bottlegrowth_2d', withSelection=False, single_gamma=False) == dadi.Demographics2D.bottlegrowth assert get_dadi_model_func('bottlegrowth_split', withSelection=False, single_gamma=False) == dadi.Demographics2D.bottlegrowth_split assert get_dadi_model_func('bottlegrowth_split_mig', withSelection=False, single_gamma=False) == dadi.Demographics2D.bottlegrowth_split_mig assert get_dadi_model_func('IM', withSelection=False, single_gamma=False) == dadi.Demographics2D.IM assert get_dadi_model_func('IM_pre', withSelection=False, single_gamma=False) == dadi.Demographics2D.IM_pre assert get_dadi_model_func('split_mig', withSelection=False, single_gamma=False) == dadi.Demographics2D.split_mig assert get_dadi_model_func('split_asym_mig', withSelection=False, single_gamma=False) == dadi.Demographics2D.split_asym_mig assert get_dadi_model_func('snm_2d', withSelection=False, single_gamma=False) == dadi.Demographics2D.snm #Cover error message with pytest.raises(Exception) as e_info: get_dadi_model_func('haha', withSelection=False, single_gamma=False) with pytest.raises(Exception) as e_info: get_dadi_model_func('haha', withSelection=True, single_gamma=False) with pytest.raises(Exception) as e_info: get_dadi_model_func('haha', withSelection=True, single_gamma=True) def test_get_dadi_model_params(): #1D demographic models assert get_dadi_model_params('bottlegrowth_1d') == ['nuB', 'nuF', 'T'] assert get_dadi_model_params('growth') == ['nu', 'T'] assert get_dadi_model_params('snm_1d') == [] assert get_dadi_model_params('three_epoch') == ['nuB', 'nuF', 'TB', 'TF'] assert get_dadi_model_params('two_epoch') == ['nu', 'T'] #2D demographic models assert get_dadi_model_params('bottlegrowth_2d') == ['nuB', 'nuF', 'T'] assert get_dadi_model_params('bottlegrowth_split') == ['nuB', 'nuF', 'T', 'Ts'] assert get_dadi_model_params('bottlegrowth_split_mig') == ['nuB', 'nuF', 'm', 'T', 'Ts'] assert get_dadi_model_params('IM') == ['s', 'nu1', 'nu2', 'T', 'm12', 'm21'] assert get_dadi_model_params('IM_pre') == ['nuPre', 'TPre', 's', 'nu1', 'nu2', 'T', 'm12', 'm21'] assert get_dadi_model_params('split_mig') == ['nu1', 'nu2', 'T', 'm'] assert get_dadi_model_params('split_asym_mig') == ['nu1', 'nu2', 'T', 'm12', 'm21'] assert get_dadi_model_params('snm_2d') == [] #Cover error message with pytest.raises(Exception) as e_info: get_dadi_model_params('haha') def test_print_available_models(capfd): print_available_models() out, err = capfd.readouterr() assert out == 'Available 1D demographic models:\n' + '- bottlegrowth_1d\n' + '- growth\n' + '- snm_1d\n' + '- three_epoch\n' + '- two_epoch\n\n' + 'Available 2D demographic models:\n' + '- bottlegrowth_2d\n' + '- bottlegrowth_split\n' + '- bottlegrowth_split_mig\n' + '- IM\n' + '- IM_pre\n' + '- split_mig\n' + '- split_asym_mig\n' + '- split_delay_mig\n' + '- snm_2d\n\n' + 'Available demographic models with selection:\n' + '- equil\n' + '- equil_X\n' + '- IM_sel\n' + '- IM_sel_single_gamma\n' + '- IM_pre_sel\n' + '- IM_pre_sel_single_gamma\n' + '- split_mig_sel\n' + '- split_mig_sel_single_gamma\n' + '- split_asym_mig_sel\n' + '- split_asym_mig_sel_single_gamma\n' + '- two_epoch_sel\n' + '- three_epoch_sel\n' def test_print_model_details(capfd): print_model_details('bottlegrowth_1d') out, err = capfd.readouterr() exp_out = '- bottlegrowth_1d:\n' + ''' Instantanous size change followed by exponential growth. Only one population in this model. params = [nuB,nuF,T] nuB: Ratio of population size after instantanous change to ancient population size (in units of Na) nuF: Ratio of contemporary to ancient population size (in units of Na) T: Time in the past at which instantaneous change happened and growth began (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('growth') out, err = capfd.readouterr() exp_out = '- growth:\n' + ''' Exponential growth beginning some time ago. Only one population in this model. params = [nu,T] nu: Ratio of contemporary to ancient population size (in units of Na) T: Time in the past at which growth began (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('snm_1d') out, err = capfd.readouterr() exp_out = '- snm_1d:\n' + ''' Standard neutral model. Only one population in this model. ''' + '\n' assert out == exp_out print_model_details('three_epoch') out, err = capfd.readouterr() exp_out = '- three_epoch:\n' + ''' Two instantaneous size changes some time ago. Only one population in this model. params = [nuB,nuF,TB,TF] nuB: Ratio of bottleneck population size to ancient pop size (in units of Na) nuF: Ratio of contemporary to ancient pop size (in units of Na) TB: Length of bottleneck (in units of 2*Na generations) TF: Time since bottleneck recovery (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('two_epoch') out, err = capfd.readouterr() exp_out = '- two_epoch:\n' + ''' One instantaneous size change some time ago. Only one population in this model. params = [nu,T] nu: Ratio of contemporary to ancient population size (in units of Na) T: Time in the past at which size change happened (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('bottlegrowth_2d') out, err = capfd.readouterr() exp_out = '- bottlegrowth_2d:\n' + ''' Instantanous size change followed by exponential growth with no population split. Two populations in this model. params = [nuB,nuF,T] nuB: Ratio of population size after instantanous change to ancient population size (in units of Na) nuF: Ratio of contempoary to ancient population size (in units of Na) T: Time in the past at which instantaneous change happened and growth began (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('bottlegrowth_split') out, err = capfd.readouterr() exp_out = '- bottlegrowth_split:\n' + ''' Instantanous size change followed by exponential growth then split without migration. Two populations in this model. params = [nuB,nuF,T,Ts] nuB: Ratio of population size after instantanous change to ancient population size (in units of Na) nuF: Ratio of contempoary to ancient population size (in units of Na) T: Time in the past at which instantaneous change happened and growth began (in units of 2*Na generations) Ts: Time in the past at which the two populations split (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('bottlegrowth_split_mig') out, err = capfd.readouterr() exp_out = '- bottlegrowth_split_mig:\n' + ''' Instantanous size change followed by exponential growth then split with symmetric migration. Two populations in this model. params = [nuB,nuF,m,T,Ts] nuB: Ratio of population size after instantanous change to ancient population size (in units of Na) nuF: Ratio of contempoary to ancient population size (in units of Na) m: Migration rate between the two populations (2*Na*m) T: Time in the past at which instantaneous change happened and growth began (in units of 2*Na generations) Ts: Time in the past at which the two populations split (in units of 2*Na generations) ''' + '\n' assert out == exp_out print_model_details('IM') out, err = capfd.readouterr() exp_out = '- IM:\n' + ''' Isolation-with-migration model with exponential pop growth. Two populations in this model. params = [s,nu1,nu2,T,m12,m21] s: Size of pop 1 after split (Pop 2 has size 1-s) nu1: Final size of pop 1 (in units of Na) nu2: Final size of pop 2 (in units of Na) T: Time in the past of split (in units of 2*Na generations) m12: Migration from pop 2 to pop 1 (2*Na*m12) m21: Migration from pop 1 to pop 2 (2*Na*m21) ''' + '\n' assert out == exp_out print_model_details('IM_pre') out, err = capfd.readouterr() exp_out = '- IM_pre:\n' + ''' Isolation-with-migration model with exponential pop growth and a size change prior to split. Two populations in this model. params = [nuPre,TPre,s,nu1,nu2,T,m12,m21] nuPre: Size after first size change (in units of Na) TPre: Time before split of first size change (in units of 2*Na generations) s: Fraction of nuPre that goes to pop1 (Pop 2 has size nuPre*(1-s)) nu1: Final size of pop 1 (in units of Na) nu2: Final size of pop 2 (in units of Na) T: Time in the past of split (in units of 2*Na generations) m12: Migration from pop 2 to pop 1 (2*Na*m12) m21: Migration from pop 1 to pop 2 (2*Na*m21) ''' + '\n' assert out == exp_out print_model_details('split_mig') out, err = capfd.readouterr() exp_out = '- split_mig:\n' + ''' Split into two populations of specifed size, with symmetric migration. Two populations in this model. params = [nu1,nu2,T,m] nu1: Size of population 1 after split (in units of Na) nu2: Size of population 2 after split (in units of Na) T: Time in the past of split (in units of 2*Na generations) m: Migration rate between populations (2*Na*m) ''' + '\n' assert out == exp_out print_model_details('split_asym_mig') out, err = capfd.readouterr() exp_out = '- split_asym_mig:\n' + ''' Split into two populations of specifed size, with asymmetric migration . Two populations in this model. params = [nu1,nu2,T,m12,m21] nu1: Size of population 1 after split (in units of Na) nu2: Size of population 2 after split (in units of Na) T: Time in the past of split (in units of 2*Na generations) m12: Migration from pop 2 to pop 1 (2*Na*m12) m21: Migration from pop 1 to pop 2 (2*Na*m21) ''' + '\n' assert out == exp_out print_model_details('snm_2d') out, err = capfd.readouterr() exp_out = '- snm_2d:\n' + ''' Standard neutral model, populations never diverge. Two populations in this model. ''' + '\n' assert out == exp_out print_model_details('equil') out, err = capfd.readouterr() exp_out = '- equil:\n' + ''' Equilibrium demography, plus selection. Only one population in this model. params: [gamma] gamma: Population-scaled selection coefficient ''' + '\n' assert out == exp_out print_model_details('equil_X') out, err = capfd.readouterr() exp_out = '- equil_X:\n' + ''' Equilibrium demography in chromosome X, plus selection. Only one population in this model. params: [gamma] gamma: Population-scaled selection coefficient ''' + '\n' assert out == exp_out print_model_details('IM_sel') out, err = capfd.readouterr() exp_out = '- IM_sel:\n' + ''' Isolation-with-migration model with exponential pop growth and selection. Two populations in this model. params: [s,nu1,nu2,T,m12,m21,gamma1,gamma2] s: Fraction of nuPre that goes to pop1 (Pop 2 has size Na*(1-s)) nu1: Final size of pop 1 (in units of Na) nu2: Final size of pop 2 (in units of Na) T: Time in the past of split (in units of 2*Na generations) m12: Migration from pop 2 to pop 1 (2*Na*m12) m21: Migration from pop 1 to pop 2 (2*Na*m21) gamma1: Population-scaled selection coefficient in pop 1 *and* the ancestral population gamma2: Population-scaled selection coefficient in pop 2 ''' + '\n' assert out == exp_out print_model_details('IM_sel_single_gamma') out, err = capfd.readouterr() exp_out = '- IM_sel_single_gamma:\n' + ''' IM model with selection assumed to be equal in all populations. Two populations in this model. See IM_sel for argument definitions, but only a single gamma in params. ''' + '\n' assert out == exp_out print_model_details('IM_pre_sel') out, err = capfd.readouterr() exp_out = '- IM_pre_sel:\n' + ''' Isolation-with-migration model with exponential pop growth, a size change prior to split, and selection. Two populations in this model. params: [nuPre,TPre,s,nu1,nu2,T,m12,m21,gamma1,gamma2] nuPre: Size after first size change (in units of Na) TPre: Time before split of first size change (in units of 2*Na generations) s: Fraction of nuPre that goes to pop1 (Pop 2 has size nuPre*(1-s)) nu1: Final size of pop 1 (in units of Na) nu2: Final size of pop 2 (in units of Na) T: Time in the past of split (in units of 2*Na generations) m12: Migration from pop 2 to pop 1 (2*Na*m12) m21: Migration from pop 1 to pop 2 (2*Na*m21) gamma1: Population-scaled selection coefficient in pop 1 *and* the ancestral population gamma2: Population-scaled selection coefficient in pop 2 ''' + '\n' assert out == exp_out print_model_details('IM_pre_sel_single_gamma') out, err = capfd.readouterr() exp_out = '- IM_pre_sel_single_gamma:\n' + ''' IM_pre model with selection assumed to be equal in all populations. Two populations in this model. See IM_pre_sel for argument definitions, but only a single gamma in params. ''' + '\n' assert out == exp_out print_model_details('split_mig_sel') out, err = capfd.readouterr() exp_out = '- split_mig_sel:\n' + ''' Instantaneous split into two populations of specified size, with symmetric migration. Two populations in this model. params = [nu1,nu2,T,m] nu1: Size of population 1 after split (in units of Na) nu2: Size of population 2 after split (in units of Na) T: Time in the past of split (in units of 2*Na generations) m: Migration rate between populations (2*Na*m) gamma1: Population-scaled selection coefficient in pop 1 *and* the ancestral population gamma2: Population-scaled selection coefficient in pop 2 ''' + '\n' assert out == exp_out print_model_details('split_mig_sel_single_gamma') out, err = capfd.readouterr() exp_out = '- split_mig_sel_single_gamma:\n' + ''' split_mig model with selection assumed to be equal in all populations. Two populations in this model. See split_mig_sel for argument definitions, but only a single gamma in params. ''' + '\n' assert out == exp_out print_model_details('split_asym_mig_sel') out, err = capfd.readouterr() exp_out = '- split_asym_mig_sel:\n' + ''' Instantaneous split into two populations of specified size, with asymmetric migration. Two populations in this model. params = [nu1,nu2,T,m12,m21] nu1: Size of population 1 after split (in units of Na) nu2: Size of population 2 after split (in units of Na) T: Time in the past of split (in units of 2*Na generations) m12: Migration rate from population 2 to population 1 (2*Na*m12) m21: Migration rate from population 1 to population 2 (2*Na*m21) gamma1: Population-scaled selection coefficient in pop 1 *and* the ancestral population gamma2: Population-scaled selection coefficient in pop 2 ''' + '\n' assert out == exp_out print_model_details('split_asym_mig_sel_single_gamma') out, err = capfd.readouterr() exp_out = '- split_asym_mig_sel_single_gamma:\n' + ''' split_asym_mig model with selection assumed to be equal in all populations. Two populations in this model. See split_asym_mig_sel for argument definitions, but only a single gamma in params. ''' + '\n' assert out == exp_out print_model_details('two_epoch_sel') out, err = capfd.readouterr() exp_out = '- two_epoch_sel:\n' + ''' One instantaneous population size change, plus selection. Only one population in this model. params: [nu,T,gamma] nu: Final population size (in units of Na) T: Time of size changei (in units of 2*Na generations) gamma: Population-scaled selection coefficient ''' + '\n' assert out == exp_out print_model_details('three_epoch_sel') out, err = capfd.readouterr() exp_out = '- three_epoch_sel:\n' + ''' Two instantaneous size changes some time ago, plus selection. Only one population in this model. params = [nuB,nuF,TB,TF,gamma] nuB: Ratio of bottleneck population size to ancient pop size (in units of Na) nuF: Ratio of contemporary to ancient pop size (in units of Na) TB: Length of bottleneck (in units of 2*Na generations) TF: Time since bottleneck recovery (in units of 2*Na generations) gamma: Population-scaled selection coefficient ''' + '\n' assert out == exp_out with pytest.raises(Exception) as e_info: print_model_details('mixture')

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71c710af7bd067012d5a03d00b4c1e65d2abcfc2

6,709

py

Python

tests/integration/commands/test_set.py

real-digital/esque

0b779fc308ce8bce45c1903f36c33664b2e832e7

[ "MIT" ]

29

2019-05-10T21:12:38.000Z

2021-08-24T08:09:49.000Z

tests/integration/commands/test_set.py

real-digital/esque

0b779fc308ce8bce45c1903f36c33664b2e832e7

[ "MIT" ]

103

2019-05-17T07:21:41.000Z

2021-12-02T08:29:00.000Z

tests/integration/commands/test_set.py

real-digital/esque

0b779fc308ce8bce45c1903f36c33664b2e832e7

[ "MIT" ]

2

2019-05-28T06:45:14.000Z

2019-11-21T00:33:15.000Z

import pendulum import pytest from confluent_kafka.cimpl import Producer as ConfluenceProducer from confluent_kafka.cimpl import TopicPartition from esque.cli.commands import esque from esque.controller.consumergroup_controller import ConsumerGroupController from tests.utils import produce_text_test_messages @pytest.mark.integration def test_set_offsets_offset_to_absolute_value( topic: str, interactive_cli_runner, producer: ConfluenceProducer, consumer_group: str, consumergroup_controller: ConsumerGroupController, ): produce_text_test_messages(producer=producer, topic_name=topic, amount=10) consumergroup_controller.commit_offsets(consumer_group, [TopicPartition(topic=topic, partition=0, offset=10)]) consumergroup_desc_before = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) interactive_cli_runner.invoke( esque, args=["set", "offsets", consumer_group, "--topic-name", topic, "--offset-to-value", "1"], input="y\n", catch_exceptions=False, ) # Check assertions: consumergroup_desc_after = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) assert consumergroup_desc_before["offsets"][topic][0]["consumer_offset"] == 10 assert consumergroup_desc_after["offsets"][topic][0]["consumer_offset"] == 1 @pytest.mark.integration def test_set_offsets_offset_to_delta( topic: str, interactive_cli_runner, producer: ConfluenceProducer, consumer_group: str, consumergroup_controller: ConsumerGroupController, ): produce_text_test_messages(producer=producer, topic_name=topic, amount=10) consumergroup_controller.commit_offsets(consumer_group, [TopicPartition(topic=topic, partition=0, offset=10)]) consumergroup_desc_before = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) interactive_cli_runner.invoke( esque, args=["set", "offsets", consumer_group, "--topic-name", topic, "--offset-by-delta", "-2"], input="y\n", catch_exceptions=False, ) # Check assertions: consumergroup_desc_after = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) assert consumergroup_desc_before["offsets"][topic][0]["consumer_offset"] == 10 assert consumergroup_desc_after["offsets"][topic][0]["consumer_offset"] == 8 @pytest.mark.integration def test_set_offsets_offset_to_delta_all_topics( topic: str, interactive_cli_runner, producer: ConfluenceProducer, consumer_group: str, consumergroup_controller: ConsumerGroupController, ): produce_text_test_messages(producer=producer, topic_name=topic, amount=10) consumergroup_controller.commit_offsets(consumer_group, [TopicPartition(topic=topic, partition=0, offset=10)]) consumergroup_desc_before = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) interactive_cli_runner.invoke( esque, args=["set", "offsets", consumer_group, "--offset-by-delta", "-2"], input="y\n", catch_exceptions=False ) # Check assertions: consumergroup_desc_after = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) assert consumergroup_desc_before["offsets"][topic][0]["consumer_offset"] == 10 assert consumergroup_desc_after["offsets"][topic][0]["consumer_offset"] == 8 @pytest.mark.integration def test_set_offsets_offset_from_group( topic: str, interactive_cli_runner, producer: ConfluenceProducer, consumer_group: str, target_consumer_group: str, consumergroup_controller: ConsumerGroupController, ): produce_text_test_messages(producer=producer, topic_name=topic, amount=10) consumergroup_controller.commit_offsets(consumer_group, [TopicPartition(topic=topic, partition=0, offset=10)]) consumergroup_desc_before = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) interactive_cli_runner.invoke( esque, args=["set", "offsets", consumer_group, "--offset-by-delta", "-2"], input="y\n", catch_exceptions=False ) consumergroup_desc_after = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) # create a new consumer in a separate group and consume just one message consumergroup_controller.commit_offsets( target_consumer_group, [TopicPartition(topic=topic, partition=0, offset=1)] ) interactive_cli_runner.invoke( esque, args=["set", "offsets", target_consumer_group, "--offset-from-group", consumer_group], input="y\n", catch_exceptions=False, ) consumergroup_desc_target = consumergroup_controller.get_consumer_group( consumer_id=target_consumer_group ).describe(partitions=True) assert consumergroup_desc_before["offsets"][topic][0]["consumer_offset"] == 10 assert consumergroup_desc_after["offsets"][topic][0]["consumer_offset"] == 8 assert consumergroup_desc_target["offsets"][topic][0]["consumer_offset"] == 8 @pytest.mark.integration def test_set_offsets_offset_to_timestamp_value( topic: str, interactive_cli_runner, producer: ConfluenceProducer, consumer_group: str, consumergroup_controller: ConsumerGroupController, ): messages = produce_text_test_messages(producer=producer, topic_name=topic, amount=10) consumergroup_controller.commit_offsets(consumer_group, [TopicPartition(topic=topic, partition=0, offset=10)]) consumergroup_desc_before = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) fifth_message = messages[4] timestamp = fifth_message.timestamp dt = pendulum.from_timestamp(round(timestamp / 1000) - 1) interactive_cli_runner.invoke( esque, args=[ "set", "offsets", consumer_group, "--topic-name", topic, "--offset-to-timestamp", dt.format("YYYY-MM-DDTHH:mm:ss"), ], input="y\n", catch_exceptions=False, ) # Check assertions: consumergroup_desc_after = consumergroup_controller.get_consumer_group(consumer_id=consumer_group).describe( partitions=True ) assert consumergroup_desc_before["offsets"][topic][0]["consumer_offset"] == 10 assert consumergroup_desc_after["offsets"][topic][0]["consumer_offset"] == 4

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71d262f35c243d34bf28b4d9faec2f9d4b3dc6f3

247

py

Python

libqtile/core/base.py

daroczig/qtile

75528dd304f48e6a3a945b9029c40131129d2e7e

[ "MIT" ]

1

2019-06-18T07:44:04.000Z

libqtile/core/base.py

daroczig/qtile

75528dd304f48e6a3a945b9029c40131129d2e7e

[ "MIT" ]

22

2019-02-23T23:56:05.000Z

2019-09-04T21:35:24.000Z

libqtile/core/base.py

daroczig/qtile

75528dd304f48e6a3a945b9029c40131129d2e7e

[ "MIT" ]

4

2019-02-22T23:26:00.000Z

2022-01-03T17:46:54.000Z

from abc import ABCMeta, abstractmethod import typing class Core(metaclass=ABCMeta): @abstractmethod def get_keys(self) -> typing.List[str]: pass @abstractmethod def get_modifiers(self) -> typing.List[str]: pass

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141

py

Python

src/www/api/query.py

SuLab/bioreel

1c701316a338b16d12c11903e40866e945abb8d1

[ "Apache-2.0" ]

32

2015-10-23T19:47:09.000Z

2019-11-16T01:28:26.000Z

src/www/api/query.py

SuLab/bioreel

1c701316a338b16d12c11903e40866e945abb8d1

[ "Apache-2.0" ]

12

2015-10-27T20:20:41.000Z

2017-04-04T21:35:46.000Z

src/www/api/query.py

SuLab/bioreel

1c701316a338b16d12c11903e40866e945abb8d1

[ "Apache-2.0" ]

15

2015-10-15T20:46:50.000Z

2021-07-12T19:17:49.000Z

# -*- coding: utf-8 -*- from biothings.www.api.es.query import ESQuery class ESQuery(ESQuery): # Add app specific queries here pass

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e099930567f761c4be80e5e0de1694f146ac73ef

550

py

Python

__init__.py

NWChemEx-Project/PythonProjectorEmbedding

a1c3346f65382de3493d690b4075930b34c96862

[ "Apache-2.0" ]

null

__init__.py

NWChemEx-Project/PythonProjectorEmbedding

a1c3346f65382de3493d690b4075930b34c96862

[ "Apache-2.0" ]

null

__init__.py

NWChemEx-Project/PythonProjectorEmbedding

a1c3346f65382de3493d690b4075930b34c96862

[ "Apache-2.0" ]

null

""" __init__.py """ from projectorEmbedding.embed_utils import make_dm from projectorEmbedding.embed_utils import flatten_basis from projectorEmbedding.embed_utils import purify from projectorEmbedding.embed_utils import screen_aos from projectorEmbedding.embed_utils import truncate_basis from projectorEmbedding.embed_partition import mulliken_partition from projectorEmbedding.embed_partition import occupancy_partition from projectorEmbedding.embed_partition import spade_partition from projectorEmbedding.embed_proc import embedding_procedure

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1

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6

e0a7639e28fef5a8793742c26a1a9e2498e954d1

11,841

py

Python

onnx_chainer/functions/math.py

ir5/onnx-chainer

c4e4a900c612b3528df9ef7535b7f94c7eda2f8a

[ "MIT" ]

null

onnx_chainer/functions/math.py

ir5/onnx-chainer

c4e4a900c612b3528df9ef7535b7f94c7eda2f8a

[ "MIT" ]

null

onnx_chainer/functions/math.py

ir5/onnx-chainer

c4e4a900c612b3528df9ef7535b7f94c7eda2f8a

[ "MIT" ]

null

import numpy as np from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE from onnx_chainer.functions.opset_version import support from onnx_chainer import onnx_helper @support((1, 6, 7)) def convert_Add(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Add', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Add', input_names, output_names), @support((1, 6, 7)) def convert_AddConstant( func, opset_version, input_names, output_names, context): value_name = context.add_const( np.array(func.value, dtype=func.inputs[0].dtype), 'value') input_names.append(value_name) if opset_version == 1: return onnx_helper.make_node( 'Add', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Add', input_names, output_names), @support((1, 6, 7)) def convert_Sub(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Sub', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Sub', input_names, output_names), @support((1, 6, 7)) def convert_SubFromConstant( func, opset_version, input_names, output_names, context): value_name = context.add_const( np.array(func.value, dtype=func.inputs[0].dtype), 'value') input_names[:0] = [value_name] if opset_version == 1: return onnx_helper.make_node( 'Sub', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Sub', input_names, output_names), @support((1, 6, 7)) def convert_Mul(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Mul', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Mul', input_names, output_names), @support((1, 6, 7)) def convert_MulConstant( func, opset_version, input_names, output_names, context): value_name = context.add_const( np.array(func.value, dtype=func.inputs[0].dtype), 'value') input_names.append(value_name) if opset_version == 1: return onnx_helper.make_node( 'Mul', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Mul', input_names, output_names), @support((1, 6)) def convert_Neg(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Neg', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6: return onnx_helper.make_node('Neg', input_names, output_names), @support((1, 6, 7)) def convert_Div(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Div', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Div', input_names, output_names), @support((1, 6, 7)) def convert_DivFromConstant( func, opset_version, input_names, output_names, context): value_name = context.add_const( np.array(func.value, dtype=func.inputs[0].dtype), 'value') input_names[:0] = [value_name] if opset_version == 1: return onnx_helper.make_node( 'Div', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node('Div', input_names, output_names), @support((1, 6)) def convert_Absolute(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Abs', input_names, output_names, consumed_inputs=[1]), elif opset_version == 6: return onnx_helper.make_node('Abs', input_names, output_names), @support((1, 7)) def convert_PowVarConst( func, opset_version, input_names, output_names, context): value_name = context.add_const( np.array(func.value, dtype=func.inputs[0].dtype), 'value') input_names.append(value_name) if opset_version == 1 or opset_version == 7: return onnx_helper.make_node('Pow', input_names, output_names), @support((1, 6)) def convert_Clip(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Clip', input_names, output_names, max=func.x_max, min=func.x_min, consumed_inputs=[1] ), elif opset_version == 6: return onnx_helper.make_node( 'Clip', input_names, output_names, max=func.x_max, min=func.x_min, ), @support((1, 6)) def convert_Exp(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Exp', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6: return onnx_helper.make_node('Exp', input_names, output_names), def convert_Identity(func, opset_version, input_names, output_names, context): return onnx_helper.make_node('Identity', input_names, output_names), def convert_MatMul(func, opset_version, input_names, output_names, context): ndim_a = len(func.inputs[0].shape) ndim_b = len(func.inputs[1].shape) gb = onnx_helper.GraphBuilder() if ndim_a > 1 and func.transa: perm = list(range(ndim_a)) perm[-1], perm[-2] = perm[-2], perm[-1] input_names[0] = gb.op('Transpose', [input_names[0]], perm=perm) if ndim_b > 1 and func.transb: perm = list(range(ndim_b)) perm[-1], perm[-2] = perm[-2], perm[-1] input_names[1] = gb.op('Transpose', [input_names[1]], perm=perm) gb.op('MatMul', input_names) return gb.nodes(output_names) @support((1, 6, 8)) def convert_Maximum(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Max', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 8: return onnx_helper.make_node('Max', input_names, output_names), @support((1, 6, 8)) def convert_Minimum(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Min', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 8: return onnx_helper.make_node('Min', input_names, output_names), @support((1, 6)) def convert_Sqrt(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Sqrt', input_names, output_names, consumed_inputs=[1, 1]), elif opset_version == 6: return onnx_helper.make_node('Sqrt', input_names, output_names), def convert_RsqrtGPU(func, opset_version, input_names, output_names, context): gb = onnx_helper.GraphBuilder() sqrt_out = gb.op('Sqrt', input_names) gb.op('Reciprocal', [sqrt_out]) return gb.nodes(output_names) def convert_LogSumExp(func, opset_version, input_names, output_names, context): # Use keepdims=False by default # since the chainer does not support keepdims option kwargs = {'keepdims': False} if hasattr(func, 'keepdims'): kwargs['keepdims'] = func.keepdims if func.axis is not None: kwargs['axes'] = func.axis return onnx_helper.make_node( 'ReduceLogSumExp', input_names, output_names, **kwargs), def convert_Max(func, opset_version, input_names, output_names, context): kwargs = {'keepdims': func.keepdims} if func.axis is not None: kwargs['axes'] = func.axis return onnx_helper.make_node( 'ReduceMax', input_names, output_names, **kwargs), def convert_Mean(func, opset_version, input_names, output_names, context): kwargs = {'keepdims': func.keepdims} if func.axis is not None: kwargs['axes'] = func.axis return onnx_helper.make_node( 'ReduceMean', input_names, output_names, **kwargs), def convert_Min(func, opset_version, input_names, output_names, context): kwargs = {'keepdims': func.keepdims} if func.axis is not None: kwargs['axes'] = func.axis return onnx_helper.make_node( 'ReduceMin', input_names, output_names, **kwargs), def convert_Prod(func, opset_version, input_names, output_names, context): kwargs = {'keepdims': func.keepdims} if func.axis is not None: kwargs['axes'] = func.axis return onnx_helper.make_node( 'ReduceProd', input_names, output_names, **kwargs), def convert_Sum(func, opset_version, input_names, output_names, context): kwargs = {'keepdims': func.keepdims} if func.axis is not None: kwargs['axes'] = func.axis return onnx_helper.make_node( 'ReduceSum', input_names, output_names, **kwargs), @support((1, 6, 7)) def convert_LinearInterpolate( func, opset_version, input_names, output_names, context): typ = func.inputs[0].dtype if isinstance( func.inputs[0].dtype, np.dtype) else np.dtype(func.inputs[0].dtype) one_name = context.add_const(np.array(1, dtype=typ), 'one') kwargs = {'consumed_inputs': [1, 1]} if opset_version == 1 else {} kwargs2 = {} if opset_version >= 7 else {'broadcast': 1} gb = onnx_helper.GraphBuilder() p, x, y = input_names n1 = gb.op('Sub', [one_name, p], **kwargs, **kwargs2) n2 = gb.op('Mul', [p, x], **kwargs) n3 = gb.op('Mul', [n1, y], **kwargs) gb.op_output_named('Add', [n2, n3], output_names, **kwargs) return gb.nodes() @support((1, 6, 7)) def convert_Square(func, opset_version, input_names, output_names, context): if opset_version == 1: return onnx_helper.make_node( 'Mul', [input_names[0], input_names[0]], output_names, consumed_inputs=[1, 1]), elif opset_version == 6 or opset_version == 7: return onnx_helper.make_node( 'Mul', [input_names[0], input_names[0]], output_names), @support((8,)) def convert_BroadcastTo( func, opset_version, input_names, output_names, context): shape_name = context.add_const(np.array(func._shape), 'shape') input_names.append(shape_name) return onnx_helper.make_node('Expand', input_names, output_names), def _argminmax_nodes(op_name, func, input_names, output_names, context): gb = onnx_helper.GraphBuilder() target_input_names = input_names axis = func.axis if axis is None: shape_name = context.add_const(np.array([-1]), 'shape') input_names.append(shape_name) target_input_names = [gb.op('Reshape', input_names)] axis = 0 out = gb.op(op_name, target_input_names, axis=axis, keepdims=0) # Chainer's ArgMax always return value as int32 # Cast spec is changed from opset6, this logic does not support ~opset5 gb.op('Cast', [out], to=NP_TYPE_TO_TENSOR_TYPE[np.dtype('int32')]) return gb.nodes(output_names) @support((6,)) def convert_ArgMax(func, opset_version, input_names, output_names, context): return _argminmax_nodes('ArgMax', func, input_names, output_names, context) @support((6,)) def convert_ArgMin(func, opset_version, input_names, output_names, context): return _argminmax_nodes('ArgMin', func, input_names, output_names, context)

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null

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1

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1

0

null

0

6

e0eab40ce8550a4b66719f827842d29b129472c0

38

py

Python

test_calc_fail.py

piersto/pilot_project

1d3aad28f1d9f77ba95de9adc8a0702701ab6c12

[ "Apache-2.0" ]

null

test_calc_fail.py

piersto/pilot_project

1d3aad28f1d9f77ba95de9adc8a0702701ab6c12

[ "Apache-2.0" ]

null

test_calc_fail.py

piersto/pilot_project

1d3aad28f1d9f77ba95de9adc8a0702701ab6c12

[ "Apache-2.0" ]

null

def test_add(): assert (1+2 == 4)

12.666667

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0

1

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1

0

6

e0f520d4f53ec84746fea23a298cc88e355e4dcf

25,429

py

Python

tests/wallet/cat_wallet/test_trades.py

bolshoytoster/chia-blockchain

1086fb255601de931dc771caa7327f4df5c87ace

[ "Apache-2.0" ]

2

2022-02-03T01:22:32.000Z

2022-02-03T01:44:43.000Z

tests/wallet/cat_wallet/test_trades.py

jacobdmn/chia-blockchain

29fad42e0c42654ec123e414966b1aa29789f384

[ "Apache-2.0" ]

null

tests/wallet/cat_wallet/test_trades.py

jacobdmn/chia-blockchain

29fad42e0c42654ec123e414966b1aa29789f384

[ "Apache-2.0" ]

null

import asyncio from secrets import token_bytes from typing import List import pytest from chia.full_node.mempool_manager import MempoolManager from chia.simulator.simulator_protocol import FarmNewBlockProtocol from chia.types.peer_info import PeerInfo from chia.util.ints import uint16, uint64 from chia.wallet.cat_wallet.cat_wallet import CATWallet from chia.wallet.trading.offer import Offer from chia.wallet.trading.trade_status import TradeStatus from chia.wallet.transaction_record import TransactionRecord from tests.setup_nodes import setup_simulators_and_wallets from tests.time_out_assert import time_out_assert async def tx_in_pool(mempool: MempoolManager, tx_id): tx = mempool.get_spendbundle(tx_id) if tx is None: return False return True @pytest.fixture(scope="module") def event_loop(): loop = asyncio.get_event_loop() yield loop @pytest.fixture(scope="function") async def two_wallet_nodes(): async for _ in setup_simulators_and_wallets(1, 2, {}): yield _ buffer_blocks = 4 @pytest.fixture(scope="function") async def wallets_prefarm(two_wallet_nodes, trusted): """ Sets up the node with 10 blocks, and returns a payer and payee wallet. """ farm_blocks = 10 buffer = 4 full_nodes, wallets = two_wallet_nodes full_node_api = full_nodes[0] full_node_server = full_node_api.server wallet_node_0, wallet_server_0 = wallets[0] wallet_node_1, wallet_server_1 = wallets[1] wallet_0 = wallet_node_0.wallet_state_manager.main_wallet wallet_1 = wallet_node_1.wallet_state_manager.main_wallet ph0 = await wallet_0.get_new_puzzlehash() ph1 = await wallet_1.get_new_puzzlehash() if trusted: wallet_node_0.config["trusted_peers"] = {full_node_server.node_id.hex(): full_node_server.node_id.hex()} wallet_node_1.config["trusted_peers"] = {full_node_server.node_id.hex(): full_node_server.node_id.hex()} else: wallet_node_0.config["trusted_peers"] = {} wallet_node_1.config["trusted_peers"] = {} await wallet_server_0.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) await wallet_server_1.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) for i in range(0, farm_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph0)) for i in range(0, farm_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph1)) for i in range(0, buffer): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) return wallet_node_0, wallet_node_1, full_node_api @pytest.mark.parametrize( "trusted", [True, False], ) class TestCATTrades: @pytest.mark.asyncio async def test_cat_trades(self, wallets_prefarm): wallet_node_maker, wallet_node_taker, full_node = wallets_prefarm wallet_maker = wallet_node_maker.wallet_state_manager.main_wallet wallet_taker = wallet_node_taker.wallet_state_manager.main_wallet # Create two new CATs, one in each wallet async with wallet_node_maker.wallet_state_manager.lock: cat_wallet_maker: CATWallet = await CATWallet.create_new_cat_wallet( wallet_node_maker.wallet_state_manager, wallet_maker, {"identifier": "genesis_by_id"}, uint64(100) ) await asyncio.sleep(1) async with wallet_node_taker.wallet_state_manager.lock: new_cat_wallet_taker: CATWallet = await CATWallet.create_new_cat_wallet( wallet_node_taker.wallet_state_manager, wallet_taker, {"identifier": "genesis_by_id"}, uint64(100) ) await asyncio.sleep(1) for i in range(1, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, 100) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, 100) await time_out_assert(15, new_cat_wallet_taker.get_confirmed_balance, 100) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, 100) # Add the taker's CAT to the maker's wallet assert cat_wallet_maker.cat_info.my_tail is not None assert new_cat_wallet_taker.cat_info.my_tail is not None new_cat_wallet_maker: CATWallet = await CATWallet.create_wallet_for_cat( wallet_node_maker.wallet_state_manager, wallet_maker, new_cat_wallet_taker.get_asset_id() ) # Create the trade parameters MAKER_CHIA_BALANCE = 20 * 1000000000000 - 100 TAKER_CHIA_BALANCE = 20 * 1000000000000 - 100 await time_out_assert(25, wallet_maker.get_confirmed_balance, MAKER_CHIA_BALANCE) await time_out_assert(25, wallet_taker.get_unconfirmed_balance, TAKER_CHIA_BALANCE) MAKER_CAT_BALANCE = 100 MAKER_NEW_CAT_BALANCE = 0 TAKER_CAT_BALANCE = 0 TAKER_NEW_CAT_BALANCE = 100 chia_for_cat = { wallet_maker.id(): -1, new_cat_wallet_maker.id(): 2, # This is the CAT that the taker made } cat_for_chia = { wallet_maker.id(): 3, cat_wallet_maker.id(): -4, # The taker has no knowledge of this CAT yet } cat_for_cat = { cat_wallet_maker.id(): -5, new_cat_wallet_maker.id(): 6, } chia_for_multiple_cat = { wallet_maker.id(): -7, cat_wallet_maker.id(): 8, new_cat_wallet_maker.id(): 9, } multiple_cat_for_chia = { wallet_maker.id(): 10, cat_wallet_maker.id(): -11, new_cat_wallet_maker.id(): -12, } chia_and_cat_for_cat = { wallet_maker.id(): -13, cat_wallet_maker.id(): -14, new_cat_wallet_maker.id(): 15, } trade_manager_maker = wallet_node_maker.wallet_state_manager.trade_manager trade_manager_taker = wallet_node_taker.wallet_state_manager.trade_manager # Execute all of the trades # chia_for_cat success, trade_make, error = await trade_manager_maker.create_offer_for_ids(chia_for_cat, fee=uint64(1)) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None success, trade_take, error = await trade_manager_taker.respond_to_offer( Offer.from_bytes(trade_make.offer), fee=uint64(1) ) await asyncio.sleep(1) assert error is None assert success is True assert trade_take is not None MAKER_CHIA_BALANCE -= 2 # -1 and -1 for fee MAKER_NEW_CAT_BALANCE += 2 TAKER_CHIA_BALANCE += 0 # +1 and -1 for fee TAKER_NEW_CAT_BALANCE -= 2 await time_out_assert(15, wallet_taker.get_unconfirmed_balance, TAKER_CHIA_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) for i in range(0, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, wallet_maker.get_confirmed_balance, MAKER_CHIA_BALANCE) await time_out_assert(15, wallet_maker.get_unconfirmed_balance, MAKER_CHIA_BALANCE) await time_out_assert(15, new_cat_wallet_maker.get_confirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_maker.get_unconfirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, wallet_taker.get_confirmed_balance, TAKER_CHIA_BALANCE) await time_out_assert(15, wallet_taker.get_unconfirmed_balance, TAKER_CHIA_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_confirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) async def get_trade_and_status(trade_manager, trade) -> TradeStatus: trade_rec = await trade_manager.get_trade_by_id(trade.trade_id) return TradeStatus(trade_rec.status) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_maker, trade_make) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_taker, trade_take) maker_txs = await wallet_node_maker.wallet_state_manager.tx_store.get_transactions_by_trade_id( trade_make.trade_id ) taker_txs = await wallet_node_taker.wallet_state_manager.tx_store.get_transactions_by_trade_id( trade_take.trade_id ) assert len(maker_txs) == 1 # The other side will show up as a regular incoming transaction assert len(taker_txs) == 3 # One for each: the outgoing CAT, the incoming chia, and the outgoing chia fee # cat_for_chia success, trade_make, error = await trade_manager_maker.create_offer_for_ids(cat_for_chia) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is None assert success is True assert trade_take is not None MAKER_CAT_BALANCE -= 4 MAKER_CHIA_BALANCE += 3 TAKER_CAT_BALANCE += 4 TAKER_CHIA_BALANCE -= 3 cat_wallet_taker: CATWallet = await wallet_node_taker.wallet_state_manager.get_wallet_for_asset_id( cat_wallet_maker.get_asset_id() ) await time_out_assert(15, wallet_taker.get_unconfirmed_balance, TAKER_CHIA_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) for i in range(0, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, wallet_maker.get_confirmed_balance, MAKER_CHIA_BALANCE) await time_out_assert(15, wallet_maker.get_unconfirmed_balance, MAKER_CHIA_BALANCE) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, wallet_taker.get_confirmed_balance, TAKER_CHIA_BALANCE) await time_out_assert(15, wallet_taker.get_unconfirmed_balance, TAKER_CHIA_BALANCE) await time_out_assert(15, cat_wallet_taker.get_confirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_maker, trade_make) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_taker, trade_take) maker_txs = await wallet_node_maker.wallet_state_manager.tx_store.get_transactions_by_trade_id( trade_make.trade_id ) taker_txs = await wallet_node_taker.wallet_state_manager.tx_store.get_transactions_by_trade_id( trade_take.trade_id ) assert len(maker_txs) == 1 # The other side will show up as a regular incoming transaction assert len(taker_txs) == 2 # One for each: the outgoing chia, the incoming CAT # cat_for_cat success, trade_make, error = await trade_manager_maker.create_offer_for_ids(cat_for_cat) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is None assert success is True assert trade_take is not None MAKER_CAT_BALANCE -= 5 MAKER_NEW_CAT_BALANCE += 6 TAKER_CAT_BALANCE += 5 TAKER_NEW_CAT_BALANCE -= 6 await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) for i in range(0, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, new_cat_wallet_maker.get_confirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_maker.get_unconfirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_confirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_confirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_maker, trade_make) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_taker, trade_take) # chia_for_multiple_cat success, trade_make, error = await trade_manager_maker.create_offer_for_ids(chia_for_multiple_cat) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is None assert success is True assert trade_take is not None MAKER_CHIA_BALANCE -= 7 MAKER_CAT_BALANCE += 8 MAKER_NEW_CAT_BALANCE += 9 TAKER_CHIA_BALANCE += 7 TAKER_CAT_BALANCE -= 8 TAKER_NEW_CAT_BALANCE -= 9 await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) for i in range(0, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, new_cat_wallet_maker.get_confirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_maker.get_unconfirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_confirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_confirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_maker, trade_make) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_taker, trade_take) # multiple_cat_for_chia success, trade_make, error = await trade_manager_maker.create_offer_for_ids(multiple_cat_for_chia) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is None assert success is True assert trade_take is not None MAKER_CAT_BALANCE -= 11 MAKER_NEW_CAT_BALANCE -= 12 MAKER_CHIA_BALANCE += 10 TAKER_CAT_BALANCE += 11 TAKER_NEW_CAT_BALANCE += 12 TAKER_CHIA_BALANCE -= 10 await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) for i in range(0, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, new_cat_wallet_maker.get_confirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_maker.get_unconfirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_confirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_confirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_maker, trade_make) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_taker, trade_take) # chia_and_cat_for_cat success, trade_make, error = await trade_manager_maker.create_offer_for_ids(chia_and_cat_for_cat) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is None assert success is True assert trade_take is not None MAKER_CHIA_BALANCE -= 13 MAKER_CAT_BALANCE -= 14 MAKER_NEW_CAT_BALANCE += 15 TAKER_CHIA_BALANCE += 13 TAKER_CAT_BALANCE += 14 TAKER_NEW_CAT_BALANCE -= 15 await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) for i in range(0, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, new_cat_wallet_maker.get_confirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_maker.get_unconfirmed_balance, MAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_confirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, new_cat_wallet_taker.get_unconfirmed_balance, TAKER_NEW_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_confirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, cat_wallet_taker.get_unconfirmed_balance, TAKER_CAT_BALANCE) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_maker, trade_make) await time_out_assert(15, get_trade_and_status, TradeStatus.CONFIRMED, trade_manager_taker, trade_take) @pytest.mark.asyncio async def test_trade_cancellation(self, wallets_prefarm): wallet_node_maker, wallet_node_taker, full_node = wallets_prefarm wallet_maker = wallet_node_maker.wallet_state_manager.main_wallet wallet_taker = wallet_node_taker.wallet_state_manager.main_wallet async with wallet_node_maker.wallet_state_manager.lock: cat_wallet_maker: CATWallet = await CATWallet.create_new_cat_wallet( wallet_node_maker.wallet_state_manager, wallet_maker, {"identifier": "genesis_by_id"}, uint64(100) ) tx_queue: List[TransactionRecord] = await wallet_node_maker.wallet_state_manager.tx_store.get_not_sent() await time_out_assert( 15, tx_in_pool, True, full_node.full_node.mempool_manager, tx_queue[0].spend_bundle.name() ) for i in range(1, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, 100) await time_out_assert(15, cat_wallet_maker.get_unconfirmed_balance, 100) MAKER_CHIA_BALANCE = 20 * 1000000000000 - 100 MAKER_CAT_BALANCE = 100 TAKER_CHIA_BALANCE = 20 * 1000000000000 await time_out_assert(15, wallet_maker.get_confirmed_balance, MAKER_CHIA_BALANCE) cat_for_chia = { wallet_maker.id(): 1, cat_wallet_maker.id(): -2, } chia_for_cat = { wallet_maker.id(): -3, cat_wallet_maker.id(): 4, } trade_manager_maker = wallet_node_maker.wallet_state_manager.trade_manager trade_manager_taker = wallet_node_taker.wallet_state_manager.trade_manager async def get_trade_and_status(trade_manager, trade) -> TradeStatus: trade_rec = await trade_manager.get_trade_by_id(trade.trade_id) return TradeStatus(trade_rec.status) success, trade_make, error = await trade_manager_maker.create_offer_for_ids(cat_for_chia) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None await trade_manager_maker.cancel_pending_offer(trade_make.trade_id) await time_out_assert(15, get_trade_and_status, TradeStatus.CANCELLED, trade_manager_maker, trade_make) # Due to current mempool rules, trying to force a take out of the mempool with a cancel will not work. # Uncomment this when/if it does # success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) # await asyncio.sleep(1) # assert error is None # assert success is True # assert trade_take is not None # await time_out_assert(15, get_trade_and_status, TradeStatus.PENDING_CONFIRM, trade_manager_taker, trade_take) # await time_out_assert( # 15, # tx_in_pool, # True, # full_node.full_node.mempool_manager, # Offer.from_bytes(trade_take.offer).to_valid_spend().name(), # ) FEE = uint64(2000000000000) txs = await trade_manager_maker.cancel_pending_offer_safely(trade_make.trade_id, fee=FEE) await time_out_assert(15, get_trade_and_status, TradeStatus.PENDING_CANCEL, trade_manager_maker, trade_make) for tx in txs: if tx.spend_bundle is not None: await time_out_assert(15, tx_in_pool, True, full_node.full_node.mempool_manager, tx.spend_bundle.name()) for i in range(1, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, get_trade_and_status, TradeStatus.CANCELLED, trade_manager_maker, trade_make) # await time_out_assert(15, get_trade_and_status, TradeStatus.FAILED, trade_manager_taker, trade_take) await time_out_assert(15, wallet_maker.get_pending_change_balance, 0) await time_out_assert(15, wallet_maker.get_confirmed_balance, MAKER_CHIA_BALANCE - FEE) await time_out_assert(15, cat_wallet_maker.get_confirmed_balance, MAKER_CAT_BALANCE) await time_out_assert(15, wallet_taker.get_confirmed_balance, TAKER_CHIA_BALANCE) success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is not None assert success is False assert trade_take is None # Now we're going to create the other way around for test coverage sake success, trade_make, error = await trade_manager_maker.create_offer_for_ids(chia_for_cat) await asyncio.sleep(1) assert error is None assert success is True assert trade_make is not None # This take should fail since we have no CATs to fulfill it with success, trade_take, error = await trade_manager_taker.respond_to_offer(Offer.from_bytes(trade_make.offer)) await asyncio.sleep(1) assert error is not None assert success is False assert trade_take is None txs = await trade_manager_maker.cancel_pending_offer_safely(trade_make.trade_id, fee=uint64(0)) await time_out_assert(15, get_trade_and_status, TradeStatus.PENDING_CANCEL, trade_manager_maker, trade_make) for tx in txs: if tx.spend_bundle is not None: await time_out_assert(15, tx_in_pool, True, full_node.full_node.mempool_manager, tx.spend_bundle.name()) for i in range(1, buffer_blocks): await full_node.farm_new_transaction_block(FarmNewBlockProtocol(token_bytes())) await time_out_assert(15, get_trade_and_status, TradeStatus.CANCELLED, trade_manager_maker, trade_make)

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src/telemetryserver/__init__.py

arcan1s/telemetry-server

39eca89db557b21ab1315ef4db50d33a7947535b

[ "MIT" ]

null

src/telemetryserver/__init__.py

arcan1s/telemetry-server

39eca89db557b21ab1315ef4db50d33a7947535b

[ "MIT" ]

null

src/telemetryserver/__init__.py

arcan1s/telemetry-server

39eca89db557b21ab1315ef4db50d33a7947535b

[ "MIT" ]

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marvel/modules/events.py

wrap-away/Marvellous

d4312fc91c45df6910d0f5f8b52be2b46cc73a3f

[ "MIT" ]

28

2018-10-27T08:36:29.000Z

2021-11-08T12:55:58.000Z

marvel/modules/events.py

wrap-away/Marvellous

d4312fc91c45df6910d0f5f8b52be2b46cc73a3f

[ "MIT" ]

2

2020-08-31T17:01:35.000Z

2021-07-29T13:46:39.000Z

marvel/modules/events.py

wrap-away/Marvellous

d4312fc91c45df6910d0f5f8b52be2b46cc73a3f

[ "MIT" ]

4

2019-04-08T00:59:13.000Z

2021-12-17T21:55:10.000Z

from marvel.modules.base_module import BaseModule class Events(BaseModule): def __init__(self, requester): """ Events Module. :param requester: Requester """ super().__init__(requester) def all(self, **kwargs): """ This returns data containing all events :param kwargs: dict :return: dict """ data, headers = self.r.request('events', payload=kwargs) return data def get(self, identifier, **kwargs): """ This returns data containing a single event using identifier (id) :param identifier: int :param kwargs: dict :return: dict """ data, headers = self.r.request('events', identifier=identifier, payload=kwargs) return data def characters(self, identifier, **kwargs): """ This returns data containing a single event's characters using identifier (id) :param identifier: int :param kwargs: dict :return: dict """ data, headers = self.r.request('events', identifier=identifier, payload=kwargs, sub_endpoint="characters") return data def comics(self, identifier, **kwargs): """ This returns data containing a single event's comics using identifier (id) :param identifier: int :param kwargs: dict :return: dict """ data, headers = self.r.request('events', identifier=identifier, payload=kwargs, sub_endpoint="comics") return data def creators(self, identifier, **kwargs): """ This returns data containing a single event's creators using identifier (id) :param identifier: int :param kwargs: dict :return: dict """ data, headers = self.r.request('events', identifier=identifier, payload=kwargs, sub_endpoint="creators") return data def series(self, identifier, **kwargs): """ This returns data containing a single event's series using identifier (id) :param identifier: int :param kwargs: dict :return: dict """ data, headers = self.r.request('events', identifier=identifier, payload=kwargs, sub_endpoint="series") return data def stories(self, identifier, **kwargs): """ This returns data containing a single event's stories using identifier (id) :param identifier: int :param kwargs: dict :return: dict """ data, headers = self.r.request('events', identifier=identifier, payload=kwargs, sub_endpoint="stories") return data

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0

6

1c26b1b4cd6d1882c2123dabf2bf1409399320bb

43

py

Python

pynet_ansible/subdir/my.py

joeyb182/pynet_ansible

b8221ebf23937838e0ecf5c71277cb042f408698

[ "Apache-2.0" ]

null

pynet_ansible/subdir/my.py

joeyb182/pynet_ansible

b8221ebf23937838e0ecf5c71277cb042f408698

[ "Apache-2.0" ]

null

pynet_ansible/subdir/my.py

joeyb182/pynet_ansible

b8221ebf23937838e0ecf5c71277cb042f408698

[ "Apache-2.0" ]

null

def print_hello(): print 'hello, world!'

10.75

22

0.674419

6

43

4.666667

0.666667

0.714286

0

0.162791

43

3

23

14.333333

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0

0.309524

0

null

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1

0

1

0

null

0

1

0

1

0

6

1c3714f8b97190de54387c05067462c712019aa2

7,071

py

Python

pyxform/tests_v1/test_support_external_instances.py

PMA-2020/pmaxform3

9d36f97f25cb09f0fb8aafb69370454731ecbbd5

[ "BSD-2-Clause" ]

null

pyxform/tests_v1/test_support_external_instances.py

PMA-2020/pmaxform3

9d36f97f25cb09f0fb8aafb69370454731ecbbd5

[ "BSD-2-Clause" ]

null

pyxform/tests_v1/test_support_external_instances.py

PMA-2020/pmaxform3

9d36f97f25cb09f0fb8aafb69370454731ecbbd5

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- """ Test external instance syntax """ from pyxform.tests_v1.pyxform_test_case import PyxformTestCase class ExternalCSVInstancesTest(PyxformTestCase): def test_external_csv_instances(self): # re: https://github.com/XLSForm/pyxform/issues/30 self.assertPyxformXform( name="ecsv", md=""" | survey | | | | | | type | name | label | | | select_one_from_file cities.csv | city | City | | | select_multiple_from_file neighbourhoods.csv | neighbourhoods | Neighbourhoods | """, # noqa xml__contains=[ """<instance id="cities" src="jr://file-csv/cities.csv"> <root> <item> <name>_</name> <label>_</label> </item> </root> </instance>""", # noqa '<select1 ref="/ecsv/city">', "<itemset nodeset=\"instance('cities')/root/item\">", """<instance id="neighbourhoods" src="jr://file-csv/neighbourhoods.csv"> <root> <item> <name>_</name> <label>_</label> </item> </root> </instance>""", # noqa '<select ref="/ecsv/neighbourhoods">', "<itemset nodeset=\"instance('neighbourhoods')/root/item\">", ], run_odk_validate=True, ) def test_external_csv_instances_w_choice_filter(self): # re: https://github.com/XLSForm/pyxform/issues/30 self.assertPyxformXform( name="ecsv", md=""" | survey | | | | | | type | name | label | choice_filter | | | select_one_from_file cities.csv | city | City | | | | select_multiple_from_file neighbourhoods.csv | neighbourhoods | Neighbourhoods | city=${city} | """, # noqa xml__contains=[ """<instance id="cities" src="jr://file-csv/cities.csv"> <root> <item> <name>_</name> <label>_</label> </item> </root> </instance>""", # noqa '<select1 ref="/ecsv/city">', """<instance id="neighbourhoods" src="jr://file-csv/neighbourhoods.csv"> <root> <item> <name>_</name> <label>_</label> </item> </root> </instance>""", # noqa '<select ref="/ecsv/neighbourhoods">', "<itemset nodeset=\"instance('neighbourhoods')/root/item[city= /ecsv/city ]\">", # noqa ], run_odk_validate=True, ) class ExternalXMLInstancesTest(PyxformTestCase): def test_external_xml_instances(self): # re: https://github.com/XLSForm/pyxform/issues/30 self.assertPyxformXform( name="exml", md=""" | survey | | | | | | type | name | label | | | select_one_from_file cities.xml | city | City | | | select_multiple_from_file neighbourhoods.xml | neighbourhoods | Neighbourhoods | """, # noqa xml__contains=[ """<instance id="cities" src="jr://file/cities.xml"> <root> <item> <name>_</name> <label>_</label> </item> </root> </instance>""", # noqa '<select1 ref="/exml/city">', "<itemset nodeset=\"instance('cities')/root/item\">", """<instance id="neighbourhoods" src="jr://file/neighbourhoods.xml"> <root> <item> <name>_</name> <label>_</label> </item> </root> </instance>""", # noqa '<select ref="/exml/neighbourhoods">', "<itemset nodeset=\"instance('neighbourhoods')/root/item\">", ], run_odk_validate=True, ) class InvalidExternalFileInstancesTest(PyxformTestCase): def test_external_other_extension_instances(self): # re: https://github.com/XLSForm/pyxform/issues/30 self.assertPyxformXform( name="epdf", md=""" | survey | | | | | | type | name | label | | | select_one_from_file cities.pdf | city | City | | | select_multiple_from_file neighbourhoods.pdf | neighbourhoods | Neighbourhoods | """, # noqa errored=True, error_contains=["should be a choices sheet in this xlsform"], ) def test_external_choices_sheet_included_instances(self): # re: https://github.com/XLSForm/pyxform/issues/30 self.assertPyxformXform( name="epdf", md=""" | survey | | | | | | type | name | label | | | select_one_from_file cities.pdf | city | City | | | select_multiple_from_file neighbourhoods.pdf | neighbourhoods | Neighbourhoods | | choices | | | list name | name | label | | | fruits | apple | Apple | """, # noqa errored=True, error__contains=["List name not in choices sheet: cities.pdf"], ) class ExternalCSVInstancesBugsTest(PyxformTestCase): def test_non_existent_itext_reference(self): # re: https://github.com/XLSForm/pyxform/issues/80 self.assertPyxformXform( name="ecsv", md=""" | survey | | | | | | type | name | label | | | select_one_from_file cities.csv | city | City | | | select_multiple_from_file neighbourhoods.csv | neighbourhoods | Neighbourhoods | """, # noqa xml__contains=[ """<itemset nodeset="instance('cities')/root/item"> <value ref="name"/> <label ref="label"/> </itemset>""" ], )

42.089286

119

0.424975

514

7,071

5.663424

0.171206

0.043284

0.031261

0.03504

0.790106

0.745792

0.733425

0.71831

0.704569

0

0.004421

0.45623

7,071

167

120

42.341317

0.752666

0.058125

0

0.63

0

0.605198

0.043825

0

0.06

1

0.06

false

0

0.01

0

0.11

0

null

0

1

0

1

0

null

0

6

1c6b7ce6628ed4c2c6ba2a9fb3019e845e0efb7a

84

py

Python

shiftevent/__init__.py

projectshift/shift-event

ce4b7cf5398dd8108de304e1fb64016b511bacc5

[ "MIT" ]

null

shiftevent/__init__.py

projectshift/shift-event

ce4b7cf5398dd8108de304e1fb64016b511bacc5

[ "MIT" ]

5

2018-07-30T09:46:41.000Z

2018-09-10T10:43:43.000Z

shiftevent/__init__.py

projectshift/shift-event

ce4b7cf5398dd8108de304e1fb64016b511bacc5

[ "MIT" ]

null

from .event_service import EventService from .event import Event from .db import Db

21

39

0.821429

13

84

5.230769

0.461538

0.264706

0

0.142857

84

3

40

28

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

98f9ba4c752a7f9c1271d58b61768db7efcba440

254

py

Python

strings/tests/test_implement_str_str.py

ahcode0919/python-ds-algorithms

0d617b78c50b6c18da40d9fa101438749bfc82e1

[ "MIT" ]

null

strings/tests/test_implement_str_str.py

ahcode0919/python-ds-algorithms

0d617b78c50b6c18da40d9fa101438749bfc82e1

[ "MIT" ]

null

strings/tests/test_implement_str_str.py

ahcode0919/python-ds-algorithms

0d617b78c50b6c18da40d9fa101438749bfc82e1

[ "MIT" ]

3

2020-10-07T20:24:45.000Z

2020-12-16T04:53:19.000Z

from strings.implement_str_str import str_str def test_str_str(): assert str_str('hello', 'll') == 2 assert str_str('test', '') == 0 assert str_str('foo', 'bar') == -1 assert str_str('a', 'aa') == -1 assert str_str('aaa', 'a') == 0

25.4

45

0.590551

41

254

3.414634

0.439024

0.342857

0.428571

0.185714

0

0.024876

0.208661

254

9

46

28.222222

0.671642

0

0.094488

0

0.714286

1

0.142857

true

0

0.142857

0

0.285714

0

null

1

0

1

0

null

0

1

0

1

0

6

c709246d79078a23fde624f1ae90f9aad90b2cf4

1,089

py

Python

test/authentication/test_logout.py

rubelw/auth0_client

51e68239babcf7c40e40491d1aaa3f8547a67f63

[ "MIT" ]

2

2020-10-08T21:42:56.000Z

2021-03-21T08:17:52.000Z

test/authentication/test_logout.py

rubelw/auth0_client

51e68239babcf7c40e40491d1aaa3f8547a67f63

[ "MIT" ]

null

test/authentication/test_logout.py

rubelw/auth0_client

51e68239babcf7c40e40491d1aaa3f8547a67f63

[ "MIT" ]

null

import unittest import mock from auth0_client.v3.authentication.logout import Logout class TestLogout(unittest.TestCase): @mock.patch('auth0_client.v3.authentication.logout.Logout.get') def test_logout(self, mock_get): g = Logout('my.domain.com') g.logout(client_id='cid', return_to='rto') args, kwargs = mock_get.call_args self.assertEqual(args[0], 'https://my.domain.com/v2/logout?client_id=cid&returnTo=rto') self.assertEqual(kwargs['headers'], { 'Content-Type': 'application/json' }) @mock.patch('auth0_client.v3.authentication.logout.Logout.get') def test_federated_logout(self, mock_get): g = Logout('my.domain.com') g.logout(client_id='cid', return_to='rto', federated=True) args, kwargs = mock_get.call_args self.assertEqual(args[0], 'https://my.domain.com/v2/logout?federated&client_id=cid&returnTo=rto') self.assertEqual(kwargs['headers'], { 'Content-Type': 'application/json' })

28.657895

105

0.630854

134

1,089

4.992537

0.30597

0.041854

0.06577

0.121076

0.847534

0.798206

0

0.011919

0.229568

1,089

37

106

29.432432

0.785459

0

0.56

0

0.04

0.30303

0.088154

0

0.16

1

0.08

false

0

0.12

0

0.24

0

null

0

1

0

null

0

6

c725e05e913a7cdcaff62a2f96ebb30f80f29197

840

py

Python

temboo/core/Library/eBay/Finding/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

7

2016-03-07T02:07:21.000Z

2022-01-21T02:22:41.000Z

temboo/core/Library/eBay/Finding/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

null

temboo/core/Library/eBay/Finding/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

8

2016-06-14T06:01:11.000Z

2020-04-22T09:21:44.000Z

from temboo.Library.eBay.Finding.FindCompletedItems import FindCompletedItems, FindCompletedItemsInputSet, FindCompletedItemsResultSet, FindCompletedItemsChoreographyExecution from temboo.Library.eBay.Finding.FindItemsAdvanced import FindItemsAdvanced, FindItemsAdvancedInputSet, FindItemsAdvancedResultSet, FindItemsAdvancedChoreographyExecution from temboo.Library.eBay.Finding.FindItemsByImage import FindItemsByImage, FindItemsByImageInputSet, FindItemsByImageResultSet, FindItemsByImageChoreographyExecution from temboo.Library.eBay.Finding.FindItemsByProduct import FindItemsByProduct, FindItemsByProductInputSet, FindItemsByProductResultSet, FindItemsByProductChoreographyExecution from temboo.Library.eBay.Finding.GetHistograms import GetHistograms, GetHistogramsInputSet, GetHistogramsResultSet, GetHistogramsChoreographyExecution

140

175

0.916667

55

840

14

0.472727

0.064935

0.11039

0.136364

0.181818

0

0.041667

840

5

176

168

0.956522

0

1

0

true

0

1

0

1

0

1

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c74e5adfddaee9185e9b60592ade7fa035c1905b

41

py

Python

kivymd/uix/dropdownitem/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

kivymd/uix/dropdownitem/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

kivymd/uix/dropdownitem/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

from .dropdownitem import MDDropDownItem

20.5

40

0.878049

4

41

9

1

0

0.097561

41

1

41

0.972973

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c752bc69f299b69e947bc51fc332212eb3810b95

20

py

Python

models/__init__.py

kex5n/Vehicles-Dispatch-Simulator

d0cca03fbf56e4b0ceeef8dafc59de105c1d4507

[ "MIT" ]

2

2020-02-08T06:09:37.000Z

2020-02-09T04:11:20.000Z

models/__init__.py

kex5n/Vehicles-Dispatch-Simulator

d0cca03fbf56e4b0ceeef8dafc59de105c1d4507

[ "MIT" ]

null

models/__init__.py

kex5n/Vehicles-Dispatch-Simulator

d0cca03fbf56e4b0ceeef8dafc59de105c1d4507

[ "MIT" ]

null

from .dqn import DQN

20

0.8

4

20

4

0.75

0

0.15

20

1

20

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c75d24fb1ca74abb1d21c56cb9dab5046f360f39

99

py

Python

tests/data/nested_raise_without_from.py

jdufresne/flake8-raise

22415a4ae85a9dbb859cc92252ad5f7252b8fc98

[ "MIT" ]

21

2020-01-19T17:33:07.000Z

2021-10-02T16:53:40.000Z

tests/data/nested_raise_without_from.py

jdufresne/flake8-raise

22415a4ae85a9dbb859cc92252ad5f7252b8fc98

[ "MIT" ]

3

2020-01-20T08:47:49.000Z

2020-01-30T16:39:50.000Z

tests/data/nested_raise_without_from.py

jdufresne/flake8-raise

22415a4ae85a9dbb859cc92252ad5f7252b8fc98

[ "MIT" ]

null

try: pass except ValueError: try: pass except OSError: raise TypeError

12.375

23

0.575758

10

99

5.7

0.7

0.245614

0.45614

0

0.383838

99

7

24

14.142857

0.934426

0

0.571429

0

1

0

true

0.285714

0

1

0

null

1

0

1

0

null

0

1

0

6

c7664fff21f573f38379359f7b3aa4285e079324

32,548

py

Python

tests/views/test_reporting_units.py

ONSdigital/response-operations-ui

1ec70c89e443fdfba620af328a4a13ce67459aa8

[ "MIT" ]

3

2018-03-06T12:33:11.000Z

2021-03-09T09:20:55.000Z

tests/views/test_reporting_units.py

ONSdigital/response-operations-ui

1ec70c89e443fdfba620af328a4a13ce67459aa8

[ "MIT" ]

519

2017-11-30T16:32:24.000Z

2022-03-28T13:37:57.000Z

tests/views/test_reporting_units.py

ONSdigital/response-operations-ui

1ec70c89e443fdfba620af328a4a13ce67459aa8

[ "MIT" ]

2

2020-01-21T20:27:32.000Z

2021-04-11T07:45:16.000Z

import json import os import re from random import randint from unittest import TestCase import requests_mock from config import TestingConfig from response_operations_ui import create_app respondent_party_id = "cd592e0f-8d07-407b-b75d-e01fbdae8233" business_party_id = "b3ba864b-7cbc-4f44-84fe-88dc018a1a4c" ru_ref = "50012345678" collection_exercise_id_1 = "14fb3e68-4dca-46db-bf49-04b84e07e77c" collection_exercise_id_2 = "9af403f8-5fc5-43b1-9fca-afbd9c65da5c" iac_1 = "jkbvyklkwj88" iac_2 = "ljbgg3kgstr4" survey_id = "cb0711c3-0ac8-41d3-ae0e-567e5ea1ef87" case_id = "10b04906-f478-47f9-a985-783400dd8482" CONNECTION_ERROR = "Connection error" url_search_reporting_units = f"{TestingConfig.PARTY_URL}/party-api/v1/businesses/search" get_respondent_by_id_url = f"{TestingConfig.PARTY_URL}/party-api/v1/respondents/id/{respondent_party_id}" url_edit_contact_details = f"{TestingConfig.PARTY_URL}/party-api/v1/respondents/id/{respondent_party_id}" url_post_case_event = f"{TestingConfig.CASE_URL}/cases/{case_id}/events" url_change_enrolment_status = f"{TestingConfig.PARTY_URL}/party-api/v1/respondents/change_enrolment_status" url_change_respondent_status = ( f"{TestingConfig.PARTY_URL}/party-api/v1/respondents/edit-account-status/" f"{respondent_party_id}" ) url_get_business_by_ru_ref = f"{TestingConfig.PARTY_URL}/party-api/v1/businesses/ref/{ru_ref}" url_get_cases_by_business_party_id = f"{TestingConfig.CASE_URL}/cases/partyid/{business_party_id}" url_get_collection_exercise_by_id = f"{TestingConfig.COLLECTION_EXERCISE_URL}/collectionexercises" url_get_business_attributes = f"{TestingConfig.PARTY_URL}/party-api/v1/businesses/id/{business_party_id}/attributes" url_get_survey_by_id = f"{TestingConfig.SURVEY_URL}/surveys/{survey_id}" url_get_respondent_party_by_party_id = f"{TestingConfig.PARTY_URL}/party-api/v1/respondents/id/{respondent_party_id}" url_get_respondent_party_by_list = f"{TestingConfig.PARTY_URL}/party-api/v1/respondents?id={respondent_party_id}" url_get_iac = f"{TestingConfig.IAC_URL}/iacs" url_get_case = f"{TestingConfig.CASE_URL}/cases/{case_id}?iac=true" project_root = os.path.dirname(os.path.dirname(__file__)) with open(f"{project_root}/test_data/reporting_units/respondent.json") as fp: respondent = json.load(fp) with open(f"{project_root}/test_data/reporting_units/respondent_with_pending_email.json") as fp: respondent_with_pending_email = json.load(fp) with open(f"{project_root}/test_data/case/case.json") as fp: case = json.load(fp) with open(f"{project_root}/test_data/party/business_reporting_unit.json") as fp: business_reporting_unit = json.load(fp) with open(f"{project_root}/test_data/case/cases_list.json") as fp: cases_list = json.load(fp) with open(f"{project_root}/test_data/case/cases_list_completed.json") as fp: cases_list_completed = json.load(fp) with open(f"{project_root}/test_data/case/case_groups_list.json") as fp: case_groups = json.load(fp) with open(f"{project_root}/test_data/collection_exercise/collection_exercise.json") as fp: collection_exercise = json.load(fp) with open(f"{project_root}/test_data/collection_exercise/collection_exercise_2.json") as fp: collection_exercise_2 = json.load(fp) with open(f"{project_root}/test_data/party/business_party.json") as fp: business_party = json.load(fp) with open(f"{project_root}/test_data/party/business_attributes.json") as fp: business_attributes = json.load(fp) with open(f"{project_root}/test_data/case/case_group_statuses.json") as fp: case_group_statuses = json.load(fp) with open(f"{project_root}/test_data/survey/single_survey.json") as fp: survey = json.load(fp) with open(f"{project_root}/test_data/party/respondent_party.json") as fp: respondent_party = json.load(fp) with open(f"{project_root}/test_data/party/respondent_party_list.json") as fp: respondent_party_list = json.load(fp) with open(f"{project_root}/test_data/iac/iac.json") as fp: iac = json.load(fp) class TestReportingUnits(TestCase): def setUp(self): self.app = create_app("TestingConfig") self.client = self.app.test_client() @requests_mock.mock() def test_get_reporting_unit(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", json=iac) mock_request.get(f"{url_get_iac}/{iac_2}", json=iac) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Bolts and Ratchets Ltd".encode(), response.data) self.assertIn("50012345678".encode(), response.data) self.assertIn("221 BLOCKS".encode(), response.data) self.assertIn("Not started".encode(), response.data) @requests_mock.mock() def test_get_reporting_unit_party_ru_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, status_code=500) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 1) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_cases_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, status_code=500) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 2) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_cases_404(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, status_code=404) mock_request.get(url_get_business_attributes, json={}) mock_request.get(url_get_respondent_party_by_list, json=[]) response = self.client.get("/reporting-units/50012345678") self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_get_reporting_unit_collection_exercise_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", status_code=500) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", status_code=500) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 3) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_party_id_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, status_code=500) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 5) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_survey_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_survey_by_id, status_code=500) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 5) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_respondent_party_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_respondent_party_by_party_id, status_code=500) response = self.client.get("/reporting-units/50012345678/surveys/BLOCKS", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 5) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_iac_fail(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", status_code=500) response = self.client.get("/reporting-units/50012345678/surveys/BLOCKS", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 7) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_get_reporting_unit_iac_404(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", status_code=404) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Bolts and Ratchets Ltd".encode(), response.data) self.assertIn("50012345678".encode(), response.data) @requests_mock.mock() def test_get_reporting_unit_hides_change_link_when_no_available_statuses(self, mock_request): mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list_completed) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", json=iac) mock_request.get(f"{url_get_iac}/{iac_2}", json=iac) response = self.client.get("/reporting-units/50012345678", follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertNotIn("ChaFnge</a>".encode(), response.data) @requests_mock.mock() def test_search_reporting_units_for_1_business_redirects_and_holds_correct_data(self, mock_request): mock_business_search_response = {"businesses": [{"name": "test", "ruref": "123456"}], "total_business_count": 2} mock_request.get(url_search_reporting_units, json=mock_business_search_response) response = self.client.post("/reporting-units", follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("test".encode(), response.data) self.assertIn("123456".encode(), response.data) @requests_mock.mock() def test_search_reporting_units_fail(self, mock_request): mock_request.get(url_search_reporting_units, status_code=500) response = self.client.post("/reporting-units", follow_redirects=True) request_history = mock_request.request_history self.assertEqual(len(request_history), 1) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_search_reporting_units_show_correct_pagination_data(self, mock_request): mock_business_search_response = TestReportingUnits._build_test_ru_search_response_data(75) mock_request.get(url_search_reporting_units, json=mock_business_search_response) form_data = {"query": ""} response = self.client.post("/reporting-units", data=form_data, follow_redirects=True) self.assertEqual(response.status_code, 200) data = re.sub("<[^<]+?>", "", response.data.decode()) # Strip out html tags from the response data self.assertIn("75 Results found", data) self.assertIn("Displaying 1 - 25 of 75", data) self.assertIn("Page 1 of 3", data) # Validates the page count is correct self.assertIn("Previous 123Next", data) # Validates Pagination controls displayed @requests_mock.mock() def test_search_reporting_units_no_results_displays_correctly(self, mock_request): mock_business_search_response = {"businesses": [], "total_business_count": 0} mock_request.get(url_search_reporting_units, json=mock_business_search_response) form_data = {"query": ""} response = self.client.post("/reporting-units", data=form_data, follow_redirects=True) self.assertEqual(response.status_code, 200) data = re.sub("<[^<]+?>", "", response.data.decode()) # Strip out html tags from the response data self.assertIn("No results found", data) @requests_mock.mock() def test_search_reporting_units_for_specific_name_displays_correctly(self, mock_request): ru_ref_num = "12345678901" # named so as to not clash with outer definition of ru_ref mock_response = {"businesses": [{"name": "SomeName", "ruref": ru_ref_num}], "total_business_count": 1} mock_request.get(url_search_reporting_units, json=mock_response) form_data = {"query": "SomeName"} response = self.client.post("/reporting-units", data=form_data, follow_redirects=True) self.assertEqual(response.status_code, 200) data = response.data.decode() self.assertIn("1 Result found", data) self.assertIn('value="SomeName"', data) # Validates that search term is displayed in text entry box # now validate that the ru is displayed as an href self.assertIn(f'href="/reporting-units/{ru_ref_num}" name="details-link-{ru_ref_num}">{ru_ref_num}', data) @requests_mock.mock() def test_resend_verification_email(self, mock_request): mock_request.get(get_respondent_by_id_url, json=respondent) response = self.client.get(f"reporting-units/resend_verification/50012345678/{respondent_party_id}") self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_resend_verification_email_to_pending_email_address(self, mock_request): mock_request.get(get_respondent_by_id_url, json=respondent_with_pending_email) response = self.client.get(f"reporting-units/resend_verification/50012345678/{respondent_party_id}") self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_change_respondent_status(self, mock_request): mock_request.put(url_change_respondent_status) mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", json=iac) mock_request.get(f"{url_get_iac}/{iac_2}", json=iac) response = self.client.post( f"reporting-units/50012345678/change-respondent-status" f"?respondent_id={respondent_party_id}&change_flag=ACTIVE", follow_redirects=True, ) self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_change_respondent_status_fail(self, mock_request): mock_request.put(url_change_respondent_status, status_code=500) response = self.client.post( f"reporting-units/50012345678/change-respondent-status" f"?respondent_id={respondent_party_id}&change_flag=ACTIVE", follow_redirects=True, ) request_history = mock_request.request_history self.assertEqual(len(request_history), 1) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_confirm_change_respondent_status(self, mock_request): mock_request.get(get_respondent_by_id_url) mock_request.put(url_change_respondent_status) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_party_id, json=respondent_party) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", json=iac) mock_request.get(f"{url_get_iac}/{iac_2}", json=iac) response = self.client.get( f"reporting-units/50012345678/change-respondent-status" f"?party_id={respondent_party_id}&change_flag=ACTIVE&tab=reporting_units", follow_redirects=True, ) self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_get_contact_details(self, mock_request): mock_request.get(get_respondent_by_id_url, json=respondent) response = self.client.get(f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}") self.assertEqual(response.status_code, 200) self.assertIn("Jacky".encode(), response.data) self.assertIn("Turner".encode(), response.data) self.assertIn("0987654321".encode(), response.data) @requests_mock.mock() def test_get_contact_details_fail(self, mock_request): mock_request.get(get_respondent_by_id_url, status_code=500) response = self.client.get( f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}", follow_redirects=True ) request_history = mock_request.request_history self.assertEqual(len(request_history), 1) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_edit_contact_details(self, mock_request): changed_details = { "first_name": "Tom", "last_name": "Smith", "email": "Jacky.Turner@email.com", "telephone": "7971161867", } response = self.mock_for_change_details(changed_details, mock_request) self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_edit_contact_details_email_already_exists(self, mock_request): changed_details = { "first_name": "Tom", "last_name": "Smith", "email": "Jacky.Turner@email.com", "telephone": "7971161859", } mock_request.get(get_respondent_by_id_url, json=respondent) mock_request.put(url_edit_contact_details, status_code=409) response = self.client.post( f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}", data=changed_details, follow_redirects=True, ) self.assertIn("Error - email address already exists".encode(), response.data) @requests_mock.mock() def test_edit_contact_details_404_response(self, mock_request): changed_details = { "first_name": "Tom", "last_name": "Smith", "email": "Jacky.Turner@email.com", "telephone": "7971161859", } mock_request.get(get_respondent_by_id_url, json=respondent) mock_request.put(url_edit_contact_details, status_code=404) response = self.client.post( f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}", data=changed_details, follow_redirects=True, ) self.assertIn(CONNECTION_ERROR.encode(), response.data) @requests_mock.mock() def test_edit_contact_details_500_response(self, mock_request): changed_details = { "first_name": "Tom", "last_name": "Smith", "email": "Jacky.Turner@email.com", "telephone": "7971161867", } mock_request.get(get_respondent_by_id_url, json=respondent) mock_request.put(url_edit_contact_details, status_code=500) response = self.client.post( f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}", data=changed_details, follow_redirects=True, ) self.assertIn(CONNECTION_ERROR.encode(), response.data) @requests_mock.mock() def test_edit_contact_details_error_response(self, mock_request): changed_details = { "first_name": "Tom", "last_name": "Smith", "email": "Jacky.Turner@email.com", "telephone": "7971161867", } mock_request.get(get_respondent_by_id_url, json=respondent) mock_request.put(url_edit_contact_details, status_code=405) response = self.client.post( f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}", data=changed_details, follow_redirects=True, ) self.assertIn(CONNECTION_ERROR.encode(), response.data) @requests_mock.mock() def test_edit_contact_details_last_name_change(self, mock_request): changed_details = { "first_name": "Jacky", "last_name": "Smith", "email": "Jacky.Turner@email.com", "telephone": "7971161859", } response = self.mock_for_change_details(changed_details, mock_request) self.assertEqual(response.status_code, 200) def mock_for_change_details(self, changed_details, mock_request): mock_request.get(get_respondent_by_id_url, json=respondent) mock_request.put(url_edit_contact_details) mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", json=iac) mock_request.get(f"{url_get_iac}/{iac_2}", json=iac) response = self.client.post( f"/reporting-units/50012345678/edit-contact-details/{respondent_party_id}", data=changed_details, follow_redirects=True, ) return response @requests_mock.mock() def test_edit_contact_details_telephone_change(self, mock_request): changed_details = { "first_name": "Jacky", "last_name": "Turner", "email": "Jacky.Turner@email.com", "telephone": "7971161867", } response = self.mock_for_change_details(changed_details, mock_request) self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_edit_contact_details_email_change(self, mock_request): changed_details = { "first_name": "Jacky", "last_name": "Turner", "email": "Jacky.Turner@thisemail.com", "telephone": "7971161859", } response = self.mock_for_change_details(changed_details, mock_request) self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_edit_contact_details_email_change_with_trailing_space(self, mock_request): changed_details = { "first_name": "Jacky", "last_name": "Turner", "email": r"Jacky.Turner@thisemail.com ", "telephone": "7971161859", } response = self.mock_for_change_details(changed_details, mock_request) self.assertEqual(response.status_code, 200) self.assertIsNot(r"Jacky.Turner@thisemail.com ".encode(), response.data) @requests_mock.mock() def test_edit_contact_details_and_email_change(self, mock_request): changed_details = { "first_name": "Jacky", "last_name": "Turner", "email": "Jacky.Turner@thisemail.com", "telephone": "7971161867", } response = self.mock_for_change_details(changed_details, mock_request) self.assertEqual(response.status_code, 200) @requests_mock.mock() def test_reporting_unit_generate_new_code(self, mock_request): mock_request.post(url_post_case_event) mock_request.get(url_get_case, json=case) response = self.client.get( f"/reporting-units/{ru_ref}/new_enrolment_code?case_id={case['id']}&" "survey_name=test_survey_name&trading_as=trading_name&ru_name=test_ru_name", follow_redirects=True, ) self.assertEqual(response.status_code, 200) self.assertIn("jkbvyklkwj88".encode(), response.data) self.assertIn("test_ru_name".encode(), response.data) self.assertIn("trading_name".encode(), response.data) self.assertIn("test_survey_name".encode(), response.data) @requests_mock.mock() def test_reporting_unit_generate_new_code_event_fail(self, mock_request): mock_request.post(url_post_case_event, status_code=500) response = self.client.get( f"/reporting-units/{ru_ref}/new_enrolment_code?case_id={case['id']}", follow_redirects=True ) request_history = mock_request.request_history self.assertEqual(len(request_history), 1) self.assertEqual(response.status_code, 500) @requests_mock.mock() def test_reporting_unit_generate_new_code_case_fail(self, mock_request): mock_request.post(url_post_case_event) mock_request.get(url_get_case, status_code=500) response = self.client.get( f"/reporting-units/{ru_ref}/new_enrolment_code?case_id={case['id']}&" "survey_name=test_survey_name&trading_as=trading_name&ru_name=test_ru_name", follow_redirects=True, ) request_history = mock_request.request_history self.assertEqual(len(request_history), 2) self.assertEqual(response.status_code, 500) def test_disable_enrolment_view(self): response = self.client.get( "/reporting-units/ru_ref/change-enrolment-status" "?survey_id=test_id&survey_name=test_survey_name&respondent_id=test_id" "&respondent_first_name=first_name&respondent_last_name=last_name" "&business_id=test_id" "&trading_as=test_name&change_flag=DISABLED" "&ru_name=test_ru_name&tab=reporting_units" ) self.assertEqual(response.status_code, 200) self.assertIn("test_ru_name".encode(), response.data) self.assertIn("test_name".encode(), response.data) self.assertIn("test_survey_name".encode(), response.data) self.assertIn("first_name".encode(), response.data) self.assertIn("Disable enrolment".encode(), response.data) @requests_mock.mock() def test_disable_enrolment_post(self, mock_request): mock_request.put(url_change_enrolment_status) mock_request.get(url_get_business_by_ru_ref, json=business_reporting_unit) mock_request.get(url_get_cases_by_business_party_id, json=cases_list) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_1}", json=collection_exercise) mock_request.get(f"{url_get_collection_exercise_by_id}/{collection_exercise_id_2}", json=collection_exercise_2) mock_request.get(url_get_business_attributes, json=business_attributes) mock_request.get(url_get_survey_by_id, json=survey) mock_request.get(url_get_respondent_party_by_list, json=respondent_party_list) mock_request.get(f"{url_get_iac}/{iac_1}", json=iac) mock_request.get(f"{url_get_iac}/{iac_2}", json=iac) response = self.client.post( "/reporting-units/50012345678/change-enrolment-status" "?survey_id=test_id&respondent_id=test_id&business_id=test_id&change_flag=DISABLED", follow_redirects=True, ) self.assertEqual(response.status_code, 200) self.assertIn("Bolts and Ratchets Ltd".encode(), response.data) @requests_mock.mock() def test_disable_enrolment_post_fail(self, mock_request): mock_request.put(url_change_enrolment_status, status_code=500) response = self.client.post( "/reporting-units/50012345678/change-enrolment-status" "?survey_id=test_id&respondent_id=test_id&business_id=test_id&change_flag=DISABLED", follow_redirects=True, ) request_history = mock_request.request_history self.assertEqual(len(request_history), 1) self.assertEqual(response.status_code, 500) @staticmethod def _build_test_ru_search_response_data(count): businesses = [{"name": f"{i}_name", "ruref": f"{randint(0, 100000000000)}"} for i in range(count)] return {"businesses": businesses, "total_business_count": count}

48.579104

120

0.728801

4,312

32,548

5.108534

0.05821

0.091883

0.071818

0.050163

0.873025

0.851643

0.839704

0.827447

0.794897

0.778328

0

0.031207

0.165141

32,548

669

121

48.651719

0.77945

0.009985

0

0.605119

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0.19702

0

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0.076782

false

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0.014625

0

0.096892

0

null

0

1

0

null

0

6

4001ae10566099b627db4979f72b2da91561b25b

7,361

py

Python

stackprinter/colorschemes.py

cknd/talkative_tracebacks

02516555ea4f070d15bc39d4b26b448ba686ff17

[ "MIT" ]

1,233

2019-04-23T10:51:05.000Z

2022-03-25T23:50:20.000Z

stackprinter/colorschemes.py

cknd/talkative_tracebacks

02516555ea4f070d15bc39d4b26b448ba686ff17

[ "MIT" ]

40

2019-04-28T21:29:41.000Z

2022-02-18T03:47:32.000Z

stackprinter/colorschemes.py

cknd/talkative_tracebacks

02516555ea4f070d15bc39d4b26b448ba686ff17

[ "MIT" ]

48

2019-04-28T23:25:54.000Z

2022-02-22T20:12:52.000Z

import random __all__ = ['color', 'darkbg', 'darkbg2', 'darkbg3', 'lightbg', 'lightbg2', 'lightbg3'] class ColorScheme(): def __getitem__(self, name): raise NotImplemented def get_random(self): raise NotImplemented class darkbg(ColorScheme): # Hue, Sat, Val, Bold colors = {'exception_type': (0.0, 0.9, 0.6, False), 'exception_msg': (0.0, 0.9, 0.6, True), 'highlight': (0.0, 0., 0.8, True), 'header': (0., 0., 0.3, False), 'lineno': (0., 0.0, 0.1, False), 'arrow_lineno': (0., 0.0, 0.2, True), 'dots': (0., 0.0, 0.6, False), 'source_bold': (0.,0., 0.6, True), 'source_default': (0.,0., 0.7, False), 'source_comment': (0.,0.,0.2, False), 'var_invisible': (0.6, 0.4, 0.4, False) } def __init__(self): self.rng = random.Random() def __getitem__(self, name): return self.colors[name] def get_random(self, seed, highlight): self.rng.seed(seed) hue = self.rng.uniform(0.05,0.7) # if hue < 0: # hue = hue + 1 sat = 1. #1. if highlight else 0.5 val = 0.5 #1. if highlight else 0.3 bold = highlight return hue, sat, val, bold class darkbg2(ColorScheme): # Hue, Sat, Val, Bold colors = {'exception_type': (0., 1., 0.8, True), 'exception_msg': (0., 1., 0.8, True), 'highlight': (0., 0., 1., True), 'header': (0, 0, 0.6, False), 'lineno': (0, 0, 0.2, True), 'arrow_lineno': (0, 0, 0.8, True), 'dots': (0, 0, 0.4, False), 'source_bold': (0.,0.,0.8, True), 'source_default': (0.,0.,0.8, False), 'source_comment': (0.,0.,0.2, False), 'var_invisible': (0.6, 0.4, 0.4, False) } def __init__(self): self.rng = random.Random() def __getitem__(self, name): return self.colors[name] def get_random(self, seed, highlight): self.rng.seed(seed) hue = self.rng.uniform(0.05,0.7) # if hue < 0: # hue = hue + 1 sat = 1. if highlight else 1. val = 0.8 #if highlight else 0.5 bold = highlight return hue, sat, val, bold class darkbg3(ColorScheme): # Hue, Sat, Val, Bold colors = {'exception_type': (0., 1., 0.8, True), 'exception_msg': (0., 1., 0.8, True), 'highlight': (0., 1., 0.8, True), 'header': (0, 0, 0.8, True), 'lineno': (0, 0, 0.2, True), 'arrow_lineno': (0, 0, 0.8, True), 'dots': (0, 0, 0.4, False), 'source_bold': (0.,0.,0.8, True), 'source_default': (0.,0.,0.8, False), 'source_comment': (0.,0.,0.2, False), 'var_invisible': (0.6, 0.4, 0.4, False) } def __init__(self): self.rng = random.Random() def __getitem__(self, name): return self.colors[name] def get_random(self, seed, highlight): self.rng.seed(seed) hue = self.rng.uniform(0.05,0.7) # if hue < 0: # hue = hue + 1 sat = 1. if highlight else 1. val = 0.8 if highlight else 0.5 bold = highlight return hue, sat, val, bold class lightbg(ColorScheme): # Hue, Sat, Val, Bold colors = {'exception_type': (0.0, 1., 0.6, False), 'exception_msg': (0.0, 1., 0.6, True), 'highlight': (0.0, 0, 0., True), 'header': (0, 0, 0.2, False), 'lineno': (0, 0, 0.8, True), 'arrow_lineno': (0, 0, 0.3, True), 'dots': (0, 0, 0.4, False), 'source_bold': (0.,0.,0.2, True), 'source_default': (0.,0.,0.1, False), 'source_comment': (0.,0.,0.6, False), 'var_invisible': (0.6, 0.4, 0.2, False) } def __init__(self): self.rng = random.Random() def __getitem__(self, name): return self.colors[name] def get_random(self, seed, highlight): self.rng.seed(seed) hue = self.rng.uniform(0.05, 0.7) # if hue < 0: # hue = hue + 1 sat = 1. val = 0.5 #0.5 #0.6 if highlight else 0.2 bold = highlight return hue, sat, val, bold class lightbg2(ColorScheme): # Hue, Sat, Val, Bold colors = {'exception_type': (0.0, 1., 0.6, False), 'exception_msg': (0.0, 1., 0.6, True), 'highlight': (0.0, 0, 0., True), 'header': (0, 0, 0.1, False), 'lineno': (0, 0, 0.5, True), 'arrow_lineno': (0, 0, 0.1, True), 'dots': (0, 0, 0.4, False), 'source_bold': (0.,0.,0.1, True), 'source_default': (0.,0.,0., False), 'source_comment': (0.,0.,0.6, False), 'var_invisible': (0.6, 0.4, 0.2, False) } def __init__(self): self.rng = random.Random() def __getitem__(self, name): return self.colors[name] def get_random(self, seed, highlight): self.rng.seed(seed) hue = self.rng.uniform(0.05, 0.7) # if hue < 0: # hue = hue + 1 sat = 1. val = 0.5 bold = True return hue, sat, val, bold class lightbg3(ColorScheme): # Hue, Sat, Val, Bold colors = {'exception_type': (0.0, 1., 0.7, False), 'exception_msg': (0.0, 1., 0.7, True), 'highlight': (0.0, 1., 0.6, True), 'header': (0, 0, 0.1, True), 'lineno': (0, 0, 0.5, True), 'arrow_lineno': (0, 0, 0.1, True), 'dots': (0, 0, 0.4, False), 'source_bold': (0.,0.,0., True), 'source_default': (0.,0.,0., False), 'source_comment': (0.,0.,0.6, False), 'var_invisible': (0.6, 0.4, 0.2, False) } def __init__(self): self.rng = random.Random() def __getitem__(self, name): return self.colors[name] def get_random(self, seed, highlight): self.rng.seed(seed) hue = self.rng.uniform(0.05, 0.7) # if hue < 0: # hue = hue + 1 sat = 1. val = 0.5 bold = True return hue, sat, val, bold color = darkbg2 if __name__ == '__main__': import numpy as np from utils import get_ansi_tpl for hue in np.arange(0,1.05,0.05): print('\n\nhue %.2f\nsat' % hue) for sat in np.arange(0,1.05,0.05): print('%.2f ' % sat, end='') for val in np.arange(0,1.05,0.05): tpl = get_ansi_tpl(hue, sat, val) # number = " (%.1f %.1f %.1f)" % (hue, sat, val) number = ' %.2f' % val print(tpl % number, end='') print(' %.2f' % sat)

29.210317

64

0.440701

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7,361

3.267503

0.079415

0.069076

0.050847

0.049888

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0

0.090013

0.393289

7,361

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0.024242

0

null

0

1

0

null

0

6

403b80c13dd122ac54e744c585147065f5c58cfa

4,678

py

Python

seq2seq/layers.py

anna1995d/signature_verification-1

eb5c9e8486f7e0135e71080f26649b0e438a1a3d

[ "MIT" ]

14

2018-03-01T08:51:39.000Z

2021-03-27T17:41:33.000Z

seq2seq/layers.py

anna1995d/signature_verification-1

eb5c9e8486f7e0135e71080f26649b0e438a1a3d

[ "MIT" ]

3

2019-02-15T06:39:18.000Z

2020-08-10T08:42:07.000Z

seq2seq/layers.py

kahrabian/signature_verification

2a35bb2c7c934bd94104cf9e1fd83e18bd4846ee

[ "MIT" ]

10

2017-10-30T16:59:26.000Z

2021-04-23T01:26:16.000Z

import keras.backend as K from keras import initializers, regularizers, constraints from keras.layers import Layer class AttentionWithContext(Layer): def __init__(self, kernel_regularizer=None, align_regularizer=None, bias_regularizer=None, kernel_constraint=None, align_constraint=None, bias_constraint=None, use_bias=True, **kwargs): self.kernel = None self.bias = None self.align = None self.supports_masking = True self.kernel_initializer = initializers.get('glorot_uniform') self.kernel_regularizer = regularizers.get(kernel_regularizer) self.align_regularizer = regularizers.get(align_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.align_constraint = constraints.get(align_constraint) self.bias_constraint = constraints.get(bias_constraint) self.use_bias = use_bias super(AttentionWithContext, self).__init__(**kwargs) def build(self, input_shape): assert len(input_shape) == 3 self.kernel = self.add_weight( name='kernel', shape=(input_shape[-1], input_shape[-1],), initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint ) if self.use_bias: self.bias = self.add_weight( name='bias', shape=(input_shape[-1],), initializer='zero', regularizer=self.bias_regularizer, constraint=self.bias_constraint ) else: self.bias = None self.align = self.add_weight( name='align', shape=(input_shape[-1],), initializer=self.kernel_initializer, regularizer=self.align_regularizer, constraint=self.align_constraint ) super(AttentionWithContext, self).build(input_shape) def compute_mask(self, inputs, mask=None): return None def call(self, inputs, mask=None): uit = K.tanh(K.dot(inputs, self.kernel) + (self.bias if self.use_bias else 0)) ait = K.sum(uit * self.align, axis=2) if K.backend() == 'tensorflow' else K.dot(uit, self.align) a = K.exp(ait) * (K.cast(mask, K.floatx()) if mask is not None else 1) a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx()) a = K.expand_dims(a) return K.sum(inputs * a, axis=1) def compute_output_shape(self, input_shape): return input_shape[0], input_shape[-1] class Attention(Layer): def __init__(self, kernel_regularizer=None, bias_regularizer=None, kernel_constraint=None, bias_constraint=None, use_bias=True, **kwargs): self.kernel = None self.bias = None self.supports_masking = True self.kernel_initializer = initializers.get('glorot_uniform') self.kernel_regularizer = regularizers.get(kernel_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.use_bias = use_bias super(Attention, self).__init__(**kwargs) def build(self, input_shape): assert len(input_shape) == 3 self.kernel = self.add_weight( name='kernel', shape=(input_shape[-1], input_shape[-1],), initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint ) if self.use_bias: self.bias = self.add_weight( name='bias', shape=(input_shape[-1],), initializer='zero', regularizer=self.bias_regularizer, constraint=self.bias_constraint ) else: self.bias = None super(Attention, self).build(input_shape) def compute_mask(self, inputs, mask=None): return None def call(self, inputs, mask=None): eij = K.tanh(K.dot(inputs, self.kernel) + (self.bias if self.use_bias else 0)) ai = K.exp(eij) * K.expand_dims(K.cast(mask, K.floatx()) if mask is not None else 1) a = ai / K.cast(K.sum(ai, axis=1, keepdims=True) + K.epsilon(), K.floatx()) return K.sum(inputs * a, axis=1) def compute_output_shape(self, input_shape): return input_shape[0], input_shape[-1]

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104

0.617999

549

4,678

5.071038

0.132969

0.075431

0.03556

0.030532

0.82579

0.818966

0.797773

0.74102

0.720187

0

0.006499

0.2764

4,678

132

105

35.439394

0.815953

0

0.679612

0

0.015177

0

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0.097087

false

0

0.029126

0.038835

0.203884

0

null

0

1

0

null

0

6

40898d3ddd8d94cacf42fb634042ca56e7a2d9b5

41,242

py

Python

chainlibpy/generated/tendermint/types/types_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

chainlibpy/generated/tendermint/types/types_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

chainlibpy/generated/tendermint/types/types_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

'Generated protocol buffer code.' from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from ...gogoproto import gogo_pb2 as gogoproto_dot_gogo__pb2 from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 from ...tendermint.crypto import proof_pb2 as tendermint_dot_crypto_dot_proof__pb2 from ...tendermint.version import types_pb2 as tendermint_dot_version_dot_types__pb2 from ...tendermint.types import validator_pb2 as tendermint_dot_types_dot_validator__pb2 DESCRIPTOR = _descriptor.FileDescriptor(name='tendermint/types/types.proto', package='tendermint.types', syntax='proto3', serialized_options=b'Z7github.com/tendermint/tendermint/proto/tendermint/types', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x1ctendermint/types/types.proto\x12\x10tendermint.types\x1a\x14gogoproto/gogo.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x1dtendermint/crypto/proof.proto\x1a\x1etendermint/version/types.proto\x1a tendermint/types/validator.proto",\n\rPartSetHeader\x12\r\n\x05total\x18\x01 \x01(\r\x12\x0c\n\x04hash\x18\x02 \x01(\x0c"S\n\x04Part\x12\r\n\x05index\x18\x01 \x01(\r\x12\r\n\x05bytes\x18\x02 \x01(\x0c\x12-\n\x05proof\x18\x03 \x01(\x0b2\x18.tendermint.crypto.ProofB\x04\xc8\xde\x1f\x00"W\n\x07BlockID\x12\x0c\n\x04hash\x18\x01 \x01(\x0c\x12>\n\x0fpart_set_header\x18\x02 \x01(\x0b2\x1f.tendermint.types.PartSetHeaderB\x04\xc8\xde\x1f\x00"\xb3\x03\n\x06Header\x124\n\x07version\x18\x01 \x01(\x0b2\x1d.tendermint.version.ConsensusB\x04\xc8\xde\x1f\x00\x12\x1d\n\x08chain_id\x18\x02 \x01(\tB\x0b\xe2\xde\x1f\x07ChainID\x12\x0e\n\x06height\x18\x03 \x01(\x03\x122\n\x04time\x18\x04 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\x01(\x0b2\x18.tendermint.crypto.Proof*\xd7\x01\n\x0bBlockIDFlag\x121\n\x15BLOCK_ID_FLAG_UNKNOWN\x10\x00\x1a\x16\x8a\x9d \x12BlockIDFlagUnknown\x12/\n\x14BLOCK_ID_FLAG_ABSENT\x10\x01\x1a\x15\x8a\x9d \x11BlockIDFlagAbsent\x12/\n\x14BLOCK_ID_FLAG_COMMIT\x10\x02\x1a\x15\x8a\x9d \x11BlockIDFlagCommit\x12)\n\x11BLOCK_ID_FLAG_NIL\x10\x03\x1a\x12\x8a\x9d \x0eBlockIDFlagNil\x1a\x08\xa8\xa4\x1e\x01\x88\xa3\x1e\x00*\xd7\x01\n\rSignedMsgType\x12,\n\x17SIGNED_MSG_TYPE_UNKNOWN\x10\x00\x1a\x0f\x8a\x9d \x0bUnknownType\x12,\n\x17SIGNED_MSG_TYPE_PREVOTE\x10\x01\x1a\x0f\x8a\x9d \x0bPrevoteType\x120\n\x19SIGNED_MSG_TYPE_PRECOMMIT\x10\x02\x1a\x11\x8a\x9d \rPrecommitType\x12.\n\x18SIGNED_MSG_TYPE_PROPOSAL\x10 \x1a\x10\x8a\x9d \x0cProposalType\x1a\x08\xa8\xa4\x1e\x01\x88\xa3\x1e\x00B9Z7github.com/tendermint/tendermint/proto/tendermint/typesb\x06proto3', dependencies=[gogoproto_dot_gogo__pb2.DESCRIPTOR, google_dot_protobuf_dot_timestamp__pb2.DESCRIPTOR, tendermint_dot_crypto_dot_proof__pb2.DESCRIPTOR, tendermint_dot_version_dot_types__pb2.DESCRIPTOR, tendermint_dot_types_dot_validator__pb2.DESCRIPTOR]) _BLOCKIDFLAG = _descriptor.EnumDescriptor(name='BlockIDFlag', full_name='tendermint.types.BlockIDFlag', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[_descriptor.EnumValueDescriptor(name='BLOCK_ID_FLAG_UNKNOWN', index=0, number=0, serialized_options=b'\x8a\x9d \x12BlockIDFlagUnknown', type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor(name='BLOCK_ID_FLAG_ABSENT', index=1, number=1, serialized_options=b'\x8a\x9d \x11BlockIDFlagAbsent', type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor(name='BLOCK_ID_FLAG_COMMIT', index=2, number=2, serialized_options=b'\x8a\x9d \x11BlockIDFlagCommit', type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor(name='BLOCK_ID_FLAG_NIL', index=3, number=3, serialized_options=b'\x8a\x9d \x0eBlockIDFlagNil', type=None, create_key=_descriptor._internal_create_key)], containing_type=None, serialized_options=b'\xa8\xa4\x1e\x01\x88\xa3\x1e\x00', serialized_start=2207, serialized_end=2422) _sym_db.RegisterEnumDescriptor(_BLOCKIDFLAG) BlockIDFlag = enum_type_wrapper.EnumTypeWrapper(_BLOCKIDFLAG) _SIGNEDMSGTYPE = _descriptor.EnumDescriptor(name='SignedMsgType', full_name='tendermint.types.SignedMsgType', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[_descriptor.EnumValueDescriptor(name='SIGNED_MSG_TYPE_UNKNOWN', index=0, number=0, serialized_options=b'\x8a\x9d \x0bUnknownType', type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor(name='SIGNED_MSG_TYPE_PREVOTE', index=1, number=1, serialized_options=b'\x8a\x9d \x0bPrevoteType', type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor(name='SIGNED_MSG_TYPE_PRECOMMIT', index=2, number=2, serialized_options=b'\x8a\x9d \rPrecommitType', type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor(name='SIGNED_MSG_TYPE_PROPOSAL', index=3, number=32, serialized_options=b'\x8a\x9d \x0cProposalType', type=None, create_key=_descriptor._internal_create_key)], containing_type=None, serialized_options=b'\xa8\xa4\x1e\x01\x88\xa3\x1e\x00', serialized_start=2425, serialized_end=2640) _sym_db.RegisterEnumDescriptor(_SIGNEDMSGTYPE) SignedMsgType = enum_type_wrapper.EnumTypeWrapper(_SIGNEDMSGTYPE) BLOCK_ID_FLAG_UNKNOWN = 0 BLOCK_ID_FLAG_ABSENT = 1 BLOCK_ID_FLAG_COMMIT = 2 BLOCK_ID_FLAG_NIL = 3 SIGNED_MSG_TYPE_UNKNOWN = 0 SIGNED_MSG_TYPE_PREVOTE = 1 SIGNED_MSG_TYPE_PRECOMMIT = 2 SIGNED_MSG_TYPE_PROPOSAL = 32 _PARTSETHEADER = _descriptor.Descriptor(name='PartSetHeader', full_name='tendermint.types.PartSetHeader', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='total', full_name='tendermint.types.PartSetHeader.total', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='hash', full_name='tendermint.types.PartSetHeader.hash', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=202, serialized_end=246) _PART = _descriptor.Descriptor(name='Part', full_name='tendermint.types.Part', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='index', full_name='tendermint.types.Part.index', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='bytes', full_name='tendermint.types.Part.bytes', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='proof', full_name='tendermint.types.Part.proof', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=248, serialized_end=331) _BLOCKID = _descriptor.Descriptor(name='BlockID', full_name='tendermint.types.BlockID', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='hash', full_name='tendermint.types.BlockID.hash', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='part_set_header', full_name='tendermint.types.BlockID.part_set_header', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=333, serialized_end=420) _HEADER = _descriptor.Descriptor(name='Header', full_name='tendermint.types.Header', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='version', full_name='tendermint.types.Header.version', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='chain_id', full_name='tendermint.types.Header.chain_id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xe2\xde\x1f\x07ChainID', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='height', full_name='tendermint.types.Header.height', index=2, number=3, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='time', full_name='tendermint.types.Header.time', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\x90\xdf\x1f\x01', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='last_block_id', full_name='tendermint.types.Header.last_block_id', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='last_commit_hash', full_name='tendermint.types.Header.last_commit_hash', index=5, number=6, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='data_hash', full_name='tendermint.types.Header.data_hash', index=6, number=7, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='validators_hash', full_name='tendermint.types.Header.validators_hash', index=7, number=8, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='next_validators_hash', full_name='tendermint.types.Header.next_validators_hash', index=8, number=9, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='consensus_hash', full_name='tendermint.types.Header.consensus_hash', index=9, number=10, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='app_hash', full_name='tendermint.types.Header.app_hash', index=10, number=11, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='last_results_hash', full_name='tendermint.types.Header.last_results_hash', index=11, number=12, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='evidence_hash', full_name='tendermint.types.Header.evidence_hash', index=12, number=13, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='proposer_address', full_name='tendermint.types.Header.proposer_address', index=13, number=14, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=423, serialized_end=858) _DATA = _descriptor.Descriptor(name='Data', full_name='tendermint.types.Data', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='txs', full_name='tendermint.types.Data.txs', index=0, number=1, type=12, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=860, serialized_end=879) _VOTE = _descriptor.Descriptor(name='Vote', full_name='tendermint.types.Vote', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='type', full_name='tendermint.types.Vote.type', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='height', full_name='tendermint.types.Vote.height', index=1, number=2, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='round', full_name='tendermint.types.Vote.round', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='block_id', full_name='tendermint.types.Vote.block_id', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\xe2\xde\x1f\x07BlockID', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='timestamp', full_name='tendermint.types.Vote.timestamp', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\x90\xdf\x1f\x01', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='validator_address', full_name='tendermint.types.Vote.validator_address', index=5, number=6, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='validator_index', full_name='tendermint.types.Vote.validator_index', index=6, number=7, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='signature', full_name='tendermint.types.Vote.signature', index=7, number=8, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=882, serialized_end=1156) _COMMIT = _descriptor.Descriptor(name='Commit', full_name='tendermint.types.Commit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='height', full_name='tendermint.types.Commit.height', index=0, number=1, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='round', full_name='tendermint.types.Commit.round', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='block_id', full_name='tendermint.types.Commit.block_id', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\xe2\xde\x1f\x07BlockID', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='signatures', full_name='tendermint.types.Commit.signatures', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1159, serialized_end=1315) _COMMITSIG = _descriptor.Descriptor(name='CommitSig', full_name='tendermint.types.CommitSig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='block_id_flag', full_name='tendermint.types.CommitSig.block_id_flag', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='validator_address', full_name='tendermint.types.CommitSig.validator_address', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='timestamp', full_name='tendermint.types.CommitSig.timestamp', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\x90\xdf\x1f\x01', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='signature', full_name='tendermint.types.CommitSig.signature', index=3, number=4, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1318, serialized_end=1486) _PROPOSAL = _descriptor.Descriptor(name='Proposal', full_name='tendermint.types.Proposal', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='type', full_name='tendermint.types.Proposal.type', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='height', full_name='tendermint.types.Proposal.height', index=1, number=2, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='round', full_name='tendermint.types.Proposal.round', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='pol_round', full_name='tendermint.types.Proposal.pol_round', index=3, number=4, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='block_id', full_name='tendermint.types.Proposal.block_id', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xe2\xde\x1f\x07BlockID\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='timestamp', full_name='tendermint.types.Proposal.timestamp', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\x90\xdf\x1f\x01', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='signature', full_name='tendermint.types.Proposal.signature', index=6, number=7, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1489, serialized_end=1734) _SIGNEDHEADER = _descriptor.Descriptor(name='SignedHeader', full_name='tendermint.types.SignedHeader', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='header', full_name='tendermint.types.SignedHeader.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='commit', full_name='tendermint.types.SignedHeader.commit', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1736, serialized_end=1834) _LIGHTBLOCK = _descriptor.Descriptor(name='LightBlock', full_name='tendermint.types.LightBlock', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='signed_header', full_name='tendermint.types.LightBlock.signed_header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='validator_set', full_name='tendermint.types.LightBlock.validator_set', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1836, serialized_end=1958) _BLOCKMETA = _descriptor.Descriptor(name='BlockMeta', full_name='tendermint.types.BlockMeta', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='block_id', full_name='tendermint.types.BlockMeta.block_id', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xe2\xde\x1f\x07BlockID\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='block_size', full_name='tendermint.types.BlockMeta.block_size', index=1, number=2, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='header', full_name='tendermint.types.BlockMeta.header', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='num_txs', full_name='tendermint.types.BlockMeta.num_txs', index=3, number=4, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1961, serialized_end=2119) _TXPROOF = _descriptor.Descriptor(name='TxProof', full_name='tendermint.types.TxProof', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='root_hash', full_name='tendermint.types.TxProof.root_hash', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='data', full_name='tendermint.types.TxProof.data', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='proof', full_name='tendermint.types.TxProof.proof', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=2121, serialized_end=2204) _PART.fields_by_name['proof'].message_type = tendermint_dot_crypto_dot_proof__pb2._PROOF _BLOCKID.fields_by_name['part_set_header'].message_type = _PARTSETHEADER _HEADER.fields_by_name['version'].message_type = tendermint_dot_version_dot_types__pb2._CONSENSUS _HEADER.fields_by_name['time'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _HEADER.fields_by_name['last_block_id'].message_type = _BLOCKID _VOTE.fields_by_name['type'].enum_type = _SIGNEDMSGTYPE _VOTE.fields_by_name['block_id'].message_type = _BLOCKID _VOTE.fields_by_name['timestamp'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _COMMIT.fields_by_name['block_id'].message_type = _BLOCKID _COMMIT.fields_by_name['signatures'].message_type = _COMMITSIG _COMMITSIG.fields_by_name['block_id_flag'].enum_type = _BLOCKIDFLAG _COMMITSIG.fields_by_name['timestamp'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _PROPOSAL.fields_by_name['type'].enum_type = _SIGNEDMSGTYPE _PROPOSAL.fields_by_name['block_id'].message_type = _BLOCKID _PROPOSAL.fields_by_name['timestamp'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _SIGNEDHEADER.fields_by_name['header'].message_type = _HEADER _SIGNEDHEADER.fields_by_name['commit'].message_type = _COMMIT _LIGHTBLOCK.fields_by_name['signed_header'].message_type = _SIGNEDHEADER _LIGHTBLOCK.fields_by_name['validator_set'].message_type = tendermint_dot_types_dot_validator__pb2._VALIDATORSET _BLOCKMETA.fields_by_name['block_id'].message_type = _BLOCKID _BLOCKMETA.fields_by_name['header'].message_type = _HEADER _TXPROOF.fields_by_name['proof'].message_type = tendermint_dot_crypto_dot_proof__pb2._PROOF DESCRIPTOR.message_types_by_name['PartSetHeader'] = _PARTSETHEADER DESCRIPTOR.message_types_by_name['Part'] = _PART DESCRIPTOR.message_types_by_name['BlockID'] = _BLOCKID DESCRIPTOR.message_types_by_name['Header'] = _HEADER DESCRIPTOR.message_types_by_name['Data'] = _DATA DESCRIPTOR.message_types_by_name['Vote'] = _VOTE DESCRIPTOR.message_types_by_name['Commit'] = _COMMIT DESCRIPTOR.message_types_by_name['CommitSig'] = _COMMITSIG DESCRIPTOR.message_types_by_name['Proposal'] = _PROPOSAL DESCRIPTOR.message_types_by_name['SignedHeader'] = _SIGNEDHEADER DESCRIPTOR.message_types_by_name['LightBlock'] = _LIGHTBLOCK DESCRIPTOR.message_types_by_name['BlockMeta'] = _BLOCKMETA DESCRIPTOR.message_types_by_name['TxProof'] = _TXPROOF DESCRIPTOR.enum_types_by_name['BlockIDFlag'] = _BLOCKIDFLAG DESCRIPTOR.enum_types_by_name['SignedMsgType'] = _SIGNEDMSGTYPE _sym_db.RegisterFileDescriptor(DESCRIPTOR) PartSetHeader = _reflection.GeneratedProtocolMessageType('PartSetHeader', (_message.Message,), {'DESCRIPTOR': _PARTSETHEADER, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(PartSetHeader) Part = _reflection.GeneratedProtocolMessageType('Part', (_message.Message,), {'DESCRIPTOR': _PART, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(Part) BlockID = _reflection.GeneratedProtocolMessageType('BlockID', (_message.Message,), {'DESCRIPTOR': _BLOCKID, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(BlockID) Header = _reflection.GeneratedProtocolMessageType('Header', (_message.Message,), {'DESCRIPTOR': _HEADER, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(Header) Data = _reflection.GeneratedProtocolMessageType('Data', (_message.Message,), {'DESCRIPTOR': _DATA, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(Data) Vote = _reflection.GeneratedProtocolMessageType('Vote', (_message.Message,), {'DESCRIPTOR': _VOTE, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(Vote) Commit = _reflection.GeneratedProtocolMessageType('Commit', (_message.Message,), {'DESCRIPTOR': _COMMIT, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(Commit) CommitSig = _reflection.GeneratedProtocolMessageType('CommitSig', (_message.Message,), {'DESCRIPTOR': _COMMITSIG, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(CommitSig) Proposal = _reflection.GeneratedProtocolMessageType('Proposal', (_message.Message,), {'DESCRIPTOR': _PROPOSAL, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(Proposal) SignedHeader = _reflection.GeneratedProtocolMessageType('SignedHeader', (_message.Message,), {'DESCRIPTOR': _SIGNEDHEADER, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(SignedHeader) LightBlock = _reflection.GeneratedProtocolMessageType('LightBlock', (_message.Message,), {'DESCRIPTOR': _LIGHTBLOCK, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(LightBlock) BlockMeta = _reflection.GeneratedProtocolMessageType('BlockMeta', (_message.Message,), {'DESCRIPTOR': _BLOCKMETA, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(BlockMeta) TxProof = _reflection.GeneratedProtocolMessageType('TxProof', (_message.Message,), {'DESCRIPTOR': _TXPROOF, '__module__': 'tendermint.types.types_pb2'}) _sym_db.RegisterMessage(TxProof) DESCRIPTOR._options = None _BLOCKIDFLAG._options = None _BLOCKIDFLAG.values_by_name['BLOCK_ID_FLAG_UNKNOWN']._options = None _BLOCKIDFLAG.values_by_name['BLOCK_ID_FLAG_ABSENT']._options = None _BLOCKIDFLAG.values_by_name['BLOCK_ID_FLAG_COMMIT']._options = None _BLOCKIDFLAG.values_by_name['BLOCK_ID_FLAG_NIL']._options = None _SIGNEDMSGTYPE._options = None _SIGNEDMSGTYPE.values_by_name['SIGNED_MSG_TYPE_UNKNOWN']._options = None _SIGNEDMSGTYPE.values_by_name['SIGNED_MSG_TYPE_PREVOTE']._options = None _SIGNEDMSGTYPE.values_by_name['SIGNED_MSG_TYPE_PRECOMMIT']._options = None _SIGNEDMSGTYPE.values_by_name['SIGNED_MSG_TYPE_PROPOSAL']._options = None _PART.fields_by_name['proof']._options = None _BLOCKID.fields_by_name['part_set_header']._options = None _HEADER.fields_by_name['version']._options = None _HEADER.fields_by_name['chain_id']._options = None _HEADER.fields_by_name['time']._options = None _HEADER.fields_by_name['last_block_id']._options = None _VOTE.fields_by_name['block_id']._options = None _VOTE.fields_by_name['timestamp']._options = None _COMMIT.fields_by_name['block_id']._options = None _COMMIT.fields_by_name['signatures']._options = None _COMMITSIG.fields_by_name['timestamp']._options = None _PROPOSAL.fields_by_name['block_id']._options = None _PROPOSAL.fields_by_name['timestamp']._options = None _BLOCKMETA.fields_by_name['block_id']._options = None _BLOCKMETA.fields_by_name['header']._options = None

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5,788

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5,932

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0.067458

0.796877

0.722861

0.68476

0.664366

0.63401

0.6099

0

0.043374

0.042384

41,242

131

5,789

314.824427

0.767382

0.000752

0

1

0.738462

0.219151

0.171116

0

1

0

false

0

0.076923

0

0.076923

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null

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6

408adaf01422058a2623a04ce6cfc948ad877247

10,001

py

Python

bindings/python/tests/magnet_uri_test.py

evsh/libtorrent

6be7c5fde24ff3b3942933a18d02592a63f22cc0

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

9

2019-11-05T16:47:12.000Z

2022-03-05T15:21:25.000Z

bindings/python/tests/magnet_uri_test.py

zeule/libtorrent

6be7c5fde24ff3b3942933a18d02592a63f22cc0

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

null

bindings/python/tests/magnet_uri_test.py

zeule/libtorrent

6be7c5fde24ff3b3942933a18d02592a63f22cc0

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

null

import tempfile import unittest import libtorrent as lt from . import lib class ParseMagnetTest(unittest.TestCase): def setUp(self) -> None: self.info_hash_sha1 = lib.get_random_bytes(20).hex() self.info_hash_sha256 = lib.get_random_bytes(32).hex() def test_parse_to_atp(self) -> None: uri = f"magnet:?xt=urn:btih:{self.info_hash_sha1}" atp = lt.parse_magnet_uri(uri) self.assertEqual(str(atp.info_hash).lower(), self.info_hash_sha1) def test_parse_to_atp_error(self) -> None: with self.assertRaises(RuntimeError): lt.parse_magnet_uri("magnet:?") @unittest.skip("https://github.com/arvidn/libtorrent/issues/5992") def test_parse_dict_deprecated(self) -> None: uri = f"magnet:?xt=urn:btih:{self.info_hash_sha1}" with self.assertWarns(DeprecationWarning): lt.parse_magnet_uri_dict(uri) def test_parse_dict_sha1(self) -> None: uri = ( f"magnet:?xt=urn:btih:{self.info_hash_sha1}&" "dn=test.txt&" "tr=http://example.com/tr&" "ws=http://example.com/ws&" "so=0-2,4&" "x.pe=0.1.2.3:4567&" "dht=1.2.3.4:5678" ) params = lt.parse_magnet_uri_dict(uri) self.assertEqual( params, { "dht_nodes": [("1.2.3.4", 5678)], "flags": lt.add_torrent_params_flags_t.default_flags, "info_hash": bytes.fromhex(self.info_hash_sha1), "info_hashes": bytes.fromhex(self.info_hash_sha1), "name": "test.txt", "save_path": "", "storage_mode": lt.storage_mode_t.storage_mode_sparse, "trackers": ["http://example.com/tr"], "url": "", }, ) # The dict is intended to be usable as argument to session.add_torrent() session = lt.session(lib.get_isolated_settings()) with tempfile.TemporaryDirectory() as path: params["save_path"] = path with self.assertWarns(DeprecationWarning): handle = session.add_torrent(params) self.assertEqual(str(handle.info_hashes().v1), self.info_hash_sha1) self.assertEqual(handle.status().name, "test.txt") self.assertEqual( [t["url"] for t in handle.trackers()], ["http://example.com/tr"] ) # self.assertEqual(handle.url_seeds(), ["http://example.com/ws"]) # self.assertEqual(handle.file_priorities(), [4, 4, 4, 0, 4]) # Can't test peers or dht @unittest.skip("need to parse more params") def test_parse_dict_sha1_broken(self) -> None: uri = ( f"magnet:?xt=urn:btih:{self.info_hash_sha1}&" "dn=test.txt&" "tr=http://example.com/tr&" "ws=http://example.com/ws&" "so=0-2,4&" "x.pe=0.1.2.3:4567&" "dht=1.2.3.4:5678" ) params = lt.parse_magnet_uri_dict(uri) self.assertEqual( params, { "dht_nodes": [("1.2.3.4", 5678)], "file_priorities": [4, 4, 4, 0, 4], "flags": lt.add_torrent_params_flags_t.default_flags, "info_hash": bytes.fromhex(self.info_hash_sha1), "info_hashes": bytes.fromhex(self.info_hash_sha1), "name": "test.txt", "save_path": "", "storage_mode": lt.storage_mode_t.storage_mode_sparse, "trackers": ["http://example.com/tr"], "url": "", "url_seeds": ["http://example.com/ws"], }, ) # The dict is intended to be usable as argument to session.add_torrent() session = lt.session(lib.get_isolated_settings()) with tempfile.TemporaryDirectory() as path: params["save_path"] = path handle = session.add_torrent(params) self.assertEqual(str(handle.info_hashes().v1), self.info_hash_sha1) self.assertEqual(handle.name(), "test.txt") self.assertEqual( [t["url"] for t in handle.trackers()], ["http://example.com/tr"] ) self.assertEqual(handle.url_seeds(), ["http://example.com/ws"]) self.assertEqual(handle.file_priorities(), [4, 4, 4, 0, 4]) # Can't test peers or dht def test_parse_dict_sha256(self) -> None: uri = ( f"magnet:?xt=urn:btmh:1220{self.info_hash_sha256}&" "dn=test.txt&" "tr=http://example.com/tr&" "ws=http://example.com/ws&" "so=0-2,4&" "x.pe=0.1.2.3:4567&" "dht=1.2.3.4:5678" ) params = lt.parse_magnet_uri_dict(uri) self.assertEqual( params, { "dht_nodes": [("1.2.3.4", 5678)], "flags": lt.add_torrent_params_flags_t.default_flags, "info_hash": bytes.fromhex(self.info_hash_sha256)[:20], "info_hashes": bytes.fromhex(self.info_hash_sha256), "name": "test.txt", "save_path": "", "storage_mode": lt.storage_mode_t.storage_mode_sparse, "trackers": ["http://example.com/tr"], "url": "", }, ) # The dict is intended to be usable as argument to session.add_torrent() session = lt.session(lib.get_isolated_settings()) with tempfile.TemporaryDirectory() as path: params["save_path"] = path with self.assertWarns(DeprecationWarning): handle = session.add_torrent(params) # self.assertEqual(str(handle.info_hashes().v2), self.info_hash_sha256) self.assertEqual(handle.status().name, "test.txt") self.assertEqual( [t["url"] for t in handle.trackers()], ["http://example.com/tr"] ) # self.assertEqual(handle.url_seeds(), ["http://example.com/ws"]) # self.assertEqual(handle.file_priorities(), [4, 4, 4, 0, 4]) # Can't test peers or dht @unittest.skip("need to parse more params") def test_parse_dict_sha256_broken(self) -> None: uri = ( f"magnet:?xt=urn:btmh:1220{self.info_hash_sha256}&" "dn=test.txt&" "tr=http://example.com/tr&" "ws=http://example.com/ws&" "so=0-2,4&" "x.pe=0.1.2.3:4567&" "dht=1.2.3.4:5678" ) params = lt.parse_magnet_uri_dict(uri) self.assertEqual( params, { "dht_nodes": [("1.2.3.4", 5678)], "file_priorities": [4, 4, 4, 0, 4], "flags": lt.add_torrent_params_flags_t.default_flags, "info_hash": bytes.fromhex(self.info_hash_sha256)[:20], "info_hashes": bytes.fromhex(self.info_hash_sha256), "name": "test.txt", "peers": [("0.1.2.3", 4567)], "save_path": "", "storage_mode": lt.storage_mode_t.storage_mode_sparse, "trackers": ["http://example.com/tr"], "url": "", "url_seeds": "http://example.com/ws", }, ) # The dict is intended to be usable as argument to session.add_torrent() session = lt.session(lib.get_isolated_settings()) with tempfile.TemporaryDirectory() as path: params["save_path"] = path handle = session.add_torrent(params) self.assertEqual( str(handle.info_hashes().v2), # type: ignore self.info_hash_sha256, ) self.assertEqual(handle.name(), "test.txt") self.assertEqual( [t["url"] for t in handle.trackers()], ["http://example.com/tr"] ) self.assertEqual(handle.url_seeds(), ["http://example.com/ws"]) self.assertEqual(handle.file_priorities(), [4, 4, 4, 0, 4]) # Can't test peers or dht def test_parse_dict_error(self) -> None: with self.assertRaises(RuntimeError): lt.parse_magnet_uri_dict("magnet:?") class AddMagnetUriTest(unittest.TestCase): def setUp(self) -> None: self.session = lt.session(lib.get_isolated_settings()) self.dir = tempfile.TemporaryDirectory() self.info_hash_sha1 = lib.get_random_bytes(20).hex() def tearDown(self) -> None: lib.cleanup_with_windows_fix(self.dir, timeout=5) def test_error(self) -> None: with self.assertWarns(DeprecationWarning): with self.assertRaises(RuntimeError): lt.add_magnet_uri(self.session, "magnet:?", {}) def test_add(self) -> None: uri = f"magnet:?xt=urn:btih:{self.info_hash_sha1}" with self.assertWarns(DeprecationWarning): handle = lt.add_magnet_uri(self.session, uri, {"save_path": self.dir.name}) self.assertEqual(str(handle.info_hashes().v1), self.info_hash_sha1) class MakeMagnetUriTest(unittest.TestCase): def setUp(self) -> None: self.info_hash_sha1 = lib.get_random_bytes(20).hex() def test_torrent_info(self) -> None: ti = lt.torrent_info(lt.sha1_hash(bytes.fromhex(self.info_hash_sha1))) uri = lt.make_magnet_uri(ti) self.assertEqual(uri, f"magnet:?xt=urn:btih:{self.info_hash_sha1}") def test_torrent_handle(self) -> None: atp = lt.add_torrent_params() atp.info_hashes = lt.info_hash_t( lt.sha1_hash(bytes.fromhex(self.info_hash_sha1)) ) session = lt.session(lib.get_isolated_settings()) with tempfile.TemporaryDirectory() as path: atp.save_path = path handle = session.add_torrent(atp) uri = lt.make_magnet_uri(handle) self.assertEqual(uri, f"magnet:?xt=urn:btih:{self.info_hash_sha1}")

40.489879

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10,001

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0.103589

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0

0.110577

0

null

0

1

0

null

0

6

40d47771cf4fedd8ab38f717dbc5e79b4675e572

185

py

Python

python/ray/job_submission/__init__.py

mgelbart/ray

4cec2286572e368a4bd64aae467751a384eff62d

[ "Apache-2.0" ]

22

2018-05-08T05:52:34.000Z

2020-04-01T10:09:55.000Z

python/ray/job_submission/__init__.py

mgelbart/ray

4cec2286572e368a4bd64aae467751a384eff62d

[ "Apache-2.0" ]

73

2021-09-25T07:11:39.000Z

2022-03-26T07:10:59.000Z

python/ray/job_submission/__init__.py

mgelbart/ray

4cec2286572e368a4bd64aae467751a384eff62d

[ "Apache-2.0" ]

10

2018-04-27T10:50:59.000Z

2020-02-24T02:41:43.000Z

from ray.dashboard.modules.job.sdk import JobSubmissionClient from ray.dashboard.modules.job.common import JobStatus, JobInfo __all__ = ["JobSubmissionClient", "JobStatus", "JobInfo"]

37

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0.315068

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185

4

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0

1

0

null

0

1

0

1

0

6

9068a9c175055a5f5321007d2df7665ba414cff7

5,534

py

Python

tests/test.py

Mambix/ChaosDuino

b4b95b2f0d47a60d165145ab02af1fa0ac3239aa

[ "MIT" ]

null

tests/test.py

Mambix/ChaosDuino

b4b95b2f0d47a60d165145ab02af1fa0ac3239aa

[ "MIT" ]

null

tests/test.py

Mambix/ChaosDuino

b4b95b2f0d47a60d165145ab02af1fa0ac3239aa

[ "MIT" ]

null

try: from StringIO import StringIO except ImportError: from io import StringIO import unittest import sys import time from serial import Serial PORT = '/dev/ttyACM0' class TestSettings(unittest.TestCase): def setUp(self): self.held, sys.stdout = sys.stdout, StringIO() def test_00_echo(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('AT\r') time.sleep(0.1) response = ser.readline() ser.close() self.assertEqual(response, 'AT\r\n') def test_01_version(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATV\r') time.sleep(0.1) response = ser.readline() ser.close() self.assertEqual(response, 'ChaosDuino v0.1.3 for PCB rev3 running...\r\n') def test_02_LED(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATLR0\r') ser.write('ATLG0\r') ser.write('ATLB0\r') j = 0 for i in range(50): if j & 1 == 1: ser.write('ATLB1\r') if j & 1 == 0: ser.write('ATLB0\r') if j & 2 == 0: ser.write('ATLG1\r') if j % 4 != 0: ser.write('ATLG0\r') if j % 8 == 0: ser.write('ATLR1\r') if j % 8 != 0: ser.write('ATLR0\r') j+=1 time.sleep(0.1) ser.write('ATLR0\r') ser.write('ATLG0\r') ser.write('ATLB0\r') ser.close() def test_03_ATE(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATE?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATE0\r\n') ser.write('ATE1\r') time.sleep(0.1) ser.write('ATE?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATE1\r\n') ser.close() def test_04_ATP(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATP?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATP0\r\n') for i in range(3): ser.write('ATP{}\r'.format(i)) time.sleep(0.1) ser.write('ATP?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATP{}\r\n'.format(i)) ser.write('ATP0\r') time.sleep(0.1) ser.write('ATP?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATP0\r\n') ser.close() def test_05_ATM(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATM?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATM1\r\n') for i in range(8): ser.write('ATM{}\r'.format(i)) time.sleep(0.1) ser.write('ATM?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATM{}\r\n'.format(i)) ser.write('ATM1\r') time.sleep(0.1) ser.write('ATM?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATM1\r\n') ser.close() def test_06_OK(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATOK?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'ATOK0\r\n') ser.close() def test_07_POOL(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('ATPOOL?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, '10000\r\n') ser.close() def test_08_BIP39(self): ser=Serial(PORT, 2000000, timeout=3) time.sleep(0.1) ser.write('BIP39W?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'BIP39W24\r\n') for i in [15, 18, 21, 24]: ser.write('BIP39W{}\r'.format(i)) time.sleep(0.1) ser.write('BIP39W?\r') time.sleep(0.1) response = ser.readline() self.assertEqual(response, 'BIP39W{}\r\n'.format(i)) ser.close() # class TestData(unittest.TestCase): # def setUp(self): # self.held, sys.stdout = sys.stdout, StringIO() # def test_dual_classes(self): # jam1 = Jam({'issue': {'rmamba': 0.5}}) # jam2 = Jam({'issue': {'ledi_mambix': 1.5}}) # self.assertEqual(jam1.jam, {'issue': {'rmamba': 0.5}}) # self.assertFalse(jam1.modified, 'Should not be modified!!!') # self.assertEqual(jam2.jam, {'issue': {'ledi_mambix': 1.5}}) # self.assertFalse(jam2.modified, 'Should NOT be modified!!!') # class TestEntropy(unittest.TestCase): # def setUp(self): # self.held, sys.stdout = sys.stdout, StringIO() # def test_dual_classes(self): # jam1 = Jam({'issue': {'rmamba': 0.5}}) # jam2 = Jam({'issue': {'ledi_mambix': 1.5}}) # self.assertEqual(jam1.jam, {'issue': {'rmamba': 0.5}}) # self.assertFalse(jam1.modified, 'Should not be modified!!!') # self.assertEqual(jam2.jam, {'issue': {'ledi_mambix': 1.5}}) # self.assertFalse(jam2.modified, 'Should NOT be modified!!!')

29.280423

83

0.526744

728

5,534

3.968407

0.151099

0.088612

0.103842

0.114226

0.817238

0.810315

0.762894

0.752163

0.743856

0

0.065274

0.307915

5,534

188

84

29.43617

0.689034

0.185399

0

0.602837

0

0.086472

0

0.099291

1

0.070922

false

0

0.049645

0

0.12766

0

null

0

1

0

null

0

6

907ecfcfb4010949c3cdd2644fb88bf4f0cba483

103

py

Python

tableschema_to_template/__init__.py

mccalluc/tableschema-to-template

6a206fcaf29227491259502f7a7743ca8d2a710a

[ "MIT" ]

5

2020-12-05T18:53:54.000Z

2021-06-07T15:54:44.000Z

tableschema_to_template/__init__.py

mccalluc/tableschema-to-template

6a206fcaf29227491259502f7a7743ca8d2a710a

[ "MIT" ]

13

2020-12-01T19:20:01.000Z

2021-03-07T03:03:04.000Z

tableschema_to_template/__init__.py

mccalluc/tableschema-to-excel-template

6a206fcaf29227491259502f7a7743ca8d2a710a

[ "MIT" ]

2

2021-02-08T15:15:33.000Z

2021-06-07T15:55:49.000Z

# Export from the top level: from tableschema_to_template.create_xlsx import create_xlsx # noqa: F401

34.333333

73

0.815534

16

103

5

0.8125

0.25

0

0.033708

0.135922

103

2

74

51.5

0.865169

0.359223

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

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0

1

0

1

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6