xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

9ec7841a173dc4c19d7dac5f98e4c9ddedd5460c

157

py

Python

glimix_core/_util/_array.py

Horta/limix-inference

1ba102fc544f8d307412d361b574da9d4c166f8e

[ "MIT" ]

7

2019-06-10T12:27:25.000Z

2021-07-23T16:36:04.000Z

glimix_core/_util/_array.py

Horta/limix-inference

1ba102fc544f8d307412d361b574da9d4c166f8e

[ "MIT" ]

12

2017-05-28T10:59:31.000Z

2021-05-17T20:11:00.000Z

glimix_core/_util/_array.py

Horta/limix-inference

1ba102fc544f8d307412d361b574da9d4c166f8e

[ "MIT" ]

5

2017-08-27T20:13:45.000Z

2022-02-14T06:33:14.000Z

from numpy import reshape def vec(x): return reshape(x, (-1,) + x.shape[2:], order="F") def unvec(x, shape): return reshape(x, shape, order="F")

15.7

53

0.611465

26

157

3.692308

0.538462

0.1875

0.291667

0

0.015873

0.197452

157

9

54

17.444444

0.746032

0

0.012739

0

1

0.4

false

0

0.2

0.4

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

9edc4b896c4673af8ba61e91bf9ac87a555fe75f

272

py

Python

tests/bitwiseOperations/__init__.py

mgorzkowski/abn

3a9ac6fb0cfe9d497b6d8f26373d2af3b6ff9860

[ "MIT" ]

4

2018-04-24T15:25:55.000Z

2022-03-08T15:01:07.000Z

tests/bitwiseOperations/__init__.py

mgorzkowski/abn

3a9ac6fb0cfe9d497b6d8f26373d2af3b6ff9860

[ "MIT" ]

2

2021-05-04T19:44:28.000Z

2021-05-05T11:51:15.000Z

tests/bitwiseOperations/__init__.py

mgorzkowski/abn

3a9ac6fb0cfe9d497b6d8f26373d2af3b6ff9860

[ "MIT" ]

null

from . import nand_tests from . import and_tests from . import nor_tests from . import not_tests from . import or_tests from . import xor_tests from . import rotate_left_tests from . import rotate_right_tests from . import shift_left_tests from . import shift_right_tests

24.727273

32

0.816176

44

272

4.727273

0.295455

0.480769

0.649038

0.201923

0

0.147059

272

10

33

27.2

0.896552

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

9efe36b7df749158058e0d954855a509a9ce6a8b

7,057

py

Python

tests/library/test_ceph_volume_simple_activate.py

u-kosmonaft-u/ceph-ansible

14c472707c165f77def05826b22885480af3e8f9

[ "Apache-2.0" ]

1,570

2015-01-03T08:38:22.000Z

2022-03-31T09:24:37.000Z

tests/library/test_ceph_volume_simple_activate.py

u-kosmonaft-u/ceph-ansible

14c472707c165f77def05826b22885480af3e8f9

[ "Apache-2.0" ]

4,964

2015-01-05T10:41:44.000Z

2022-03-31T07:59:49.000Z

tests/library/test_ceph_volume_simple_activate.py

u-kosmonaft-u/ceph-ansible

14c472707c165f77def05826b22885480af3e8f9

[ "Apache-2.0" ]

1,231

2015-01-04T11:48:16.000Z

2022-03-31T12:15:28.000Z

from mock.mock import patch import os import pytest import ca_test_common import ceph_volume_simple_activate fake_cluster = 'ceph' fake_container_binary = 'podman' fake_container_image = 'quay.ceph.io/ceph/daemon:latest' fake_id = '42' fake_uuid = '0c4a7eca-0c2a-4c12-beff-08a80f064c52' fake_path = '/etc/ceph/osd/{}-{}.json'.format(fake_id, fake_uuid) class TestCephVolumeSimpleActivateModule(object): @patch('ansible.module_utils.basic.AnsibleModule.exit_json') def test_with_check_mode(self, m_exit_json): ca_test_common.set_module_args({ 'osd_id': fake_id, 'osd_fsid': fake_uuid, '_ansible_check_mode': True }) m_exit_json.side_effect = ca_test_common.exit_json with pytest.raises(ca_test_common.AnsibleExitJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert not result['changed'] assert result['cmd'] == ['ceph-volume', '--cluster', fake_cluster, 'simple', 'activate', fake_id, fake_uuid] assert result['rc'] == 0 assert not result['stdout'] assert not result['stderr'] @patch('ansible.module_utils.basic.AnsibleModule.exit_json') @patch('ansible.module_utils.basic.AnsibleModule.run_command') def test_with_failure(self, m_run_command, m_exit_json): ca_test_common.set_module_args({ 'osd_id': fake_id, 'osd_fsid': fake_uuid }) m_exit_json.side_effect = ca_test_common.exit_json stdout = '' stderr = 'error' rc = 2 m_run_command.return_value = rc, stdout, stderr with pytest.raises(ca_test_common.AnsibleExitJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert result['changed'] assert result['cmd'] == ['ceph-volume', '--cluster', fake_cluster, 'simple', 'activate', fake_id, fake_uuid] assert result['rc'] == rc assert result['stderr'] == stderr @patch('ansible.module_utils.basic.AnsibleModule.exit_json') @patch('ansible.module_utils.basic.AnsibleModule.run_command') def test_activate_all_osds(self, m_run_command, m_exit_json): ca_test_common.set_module_args({ 'osd_all': True }) m_exit_json.side_effect = ca_test_common.exit_json stdout = '' stderr = '' rc = 0 m_run_command.return_value = rc, stdout, stderr with pytest.raises(ca_test_common.AnsibleExitJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert result['changed'] assert result['cmd'] == ['ceph-volume', '--cluster', fake_cluster, 'simple', 'activate', '--all'] assert result['rc'] == rc assert result['stderr'] == stderr assert result['stdout'] == stdout @patch.object(os.path, 'exists', return_value=True) @patch('ansible.module_utils.basic.AnsibleModule.exit_json') @patch('ansible.module_utils.basic.AnsibleModule.run_command') def test_activate_path_exists(self, m_run_command, m_exit_json, m_os_path): ca_test_common.set_module_args({ 'path': fake_path }) m_exit_json.side_effect = ca_test_common.exit_json stdout = '' stderr = '' rc = 0 m_run_command.return_value = rc, stdout, stderr with pytest.raises(ca_test_common.AnsibleExitJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert result['changed'] assert result['cmd'] == ['ceph-volume', '--cluster', fake_cluster, 'simple', 'activate', '--file', fake_path] assert result['rc'] == rc assert result['stderr'] == stderr assert result['stdout'] == stdout @patch.object(os.path, 'exists', return_value=False) @patch('ansible.module_utils.basic.AnsibleModule.fail_json') def test_activate_path_not_exists(self, m_fail_json, m_os_path): ca_test_common.set_module_args({ 'path': fake_path }) m_fail_json.side_effect = ca_test_common.fail_json with pytest.raises(ca_test_common.AnsibleFailJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert result['msg'] == '{} does not exist'.format(fake_path) assert result['rc'] == 1 @patch('ansible.module_utils.basic.AnsibleModule.exit_json') @patch('ansible.module_utils.basic.AnsibleModule.run_command') def test_activate_without_systemd(self, m_run_command, m_exit_json): ca_test_common.set_module_args({ 'osd_id': fake_id, 'osd_fsid': fake_uuid, 'systemd': False }) m_exit_json.side_effect = ca_test_common.exit_json stdout = '' stderr = '' rc = 0 m_run_command.return_value = rc, stdout, stderr with pytest.raises(ca_test_common.AnsibleExitJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert result['changed'] assert result['cmd'] == ['ceph-volume', '--cluster', fake_cluster, 'simple', 'activate', fake_id, fake_uuid, '--no-systemd'] assert result['rc'] == rc assert result['stderr'] == stderr assert result['stdout'] == stdout @patch.dict(os.environ, {'CEPH_CONTAINER_BINARY': fake_container_binary}) @patch.dict(os.environ, {'CEPH_CONTAINER_IMAGE': fake_container_image}) @patch('ansible.module_utils.basic.AnsibleModule.exit_json') @patch('ansible.module_utils.basic.AnsibleModule.run_command') def test_activate_with_container(self, m_run_command, m_exit_json): ca_test_common.set_module_args({ 'osd_id': fake_id, 'osd_fsid': fake_uuid, }) m_exit_json.side_effect = ca_test_common.exit_json stdout = '' stderr = '' rc = 0 m_run_command.return_value = rc, stdout, stderr with pytest.raises(ca_test_common.AnsibleExitJson) as result: ceph_volume_simple_activate.main() result = result.value.args[0] assert result['changed'] assert result['cmd'] == [fake_container_binary, 'run', '--rm', '--privileged', '--ipc=host', '--net=host', '-v', '/etc/ceph:/etc/ceph:z', '-v', '/var/lib/ceph/:/var/lib/ceph/:z', '-v', '/var/log/ceph/:/var/log/ceph/:z', '-v', '/run/lvm/:/run/lvm/', '-v', '/run/lock/lvm/:/run/lock/lvm/', '--entrypoint=ceph-volume', fake_container_image, '--cluster', fake_cluster, 'simple', 'activate', fake_id, fake_uuid] assert result['rc'] == rc assert result['stderr'] == stderr assert result['stdout'] == stdout

40.325714

132

0.621794

857

7,057

4.816803

0.123687

0.081395

0.063953

0.06686

0.803537

0.796269

0.766231

0.753634

0.741764

0

0.006254

0.252232

7,057

174

133

40.557471

0.776009

0

0.66

0

0.205186

0.121581

0

0.206667

1

0.046667

false

0

0.033333

0

0.086667

0

null

0

1

0

null

0

6

73455aa40d8fdaf8fad425f0bc60becf47571215

4,387

py

Python

tests/test_infection.py

chinapnr/covid-19-data

409fa260c16e09b7ef820435c5086207bb5e40ef

[ "MIT" ]

3

2020-05-27T01:21:50.000Z

2020-08-20T07:54:42.000Z

tests/test_infection.py

chinapnr/covid-19-data

409fa260c16e09b7ef820435c5086207bb5e40ef

[ "MIT" ]

24

2020-03-26T10:45:34.000Z

2020-04-06T06:13:50.000Z

tests/test_infection.py

chinapnr/covid-19-data

409fa260c16e09b7ef820435c5086207bb5e40ef

[ "MIT" ]

null

import json import pytest @pytest.mark.usefixtures('client', 'headers') class TestInfection: def test_infection_region_tc01(self, client, headers): # db has data BETWEEN 2020-03-22 2020-03-24 region = 'China' payload = { 'region': region, 'start_date': '2020-03-22', 'end_date': '2020-03-24', 'include_hmt': 'false' } response = client.get('/infection/region', params=payload, headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data def test_infection_region_tc02(self, client, headers): # db has no data BETWEEN 2020-03-25 2020-03-26 region = 'China' payload = { 'region': region, 'start_date': '2020-03-25', 'end_date': '2020-03-26', 'include_hmt': 'false' } response = client.get('/infection/region', params=payload, headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data def test_infection_region_tc03(self, client, headers): # db has data BETWEEN 2020-03-22 2020-03-24 # look up detail region = 'China' payload = { 'region': region, 'start_date': '2020-03-22', 'end_date': '2020-03-24', 'include_hmt': 'true' } response = client.get('/infection/region', params=payload, headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data def test_infection_region_tc04(self, client, headers): # db has data BETWEEN 2020-03-22 2020-03-24 # look up detail region = 'China' payload = { 'region': region, 'start_date': '2020-03-22', # 'end_date': '2020-03-24', 'include_hmt': 'true' } response = client.get('/infection/region', params=payload, headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data def test_infection_region_tc05(self, client, headers): # db has data BETWEEN 2020-03-22 2020-03-24 # look up detail region = 'China' payload = { 'region': region, 'start_date': '2020-01-22', # 'end_date': '2020-03-24', 'include_hmt': 'true' } response = client.get('/infection/region', params=payload, headers=headers) assert response.status_code == 400 print("response: ", response.text) response_data = json.loads(response.text)['code'] assert response_data == "30018" def test_infection_region_detail(self, client, headers): region = 'China' payload = { 'region': region, 'start_date': '2020-03-22', 'end_date': '2020-03-24', 'include_hmt': 'true' } response = client.get('/infection/region/detail', params=payload, headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data @pytest.mark.skip def test_infection_area(self, client, headers): region = 'China' area = 'Chongqing' payload = { 'region': region, 'area': area, 'start_date': '2020-03-22', 'end_date': '2020-03-24' } response = client.get('/infection/area', params=payload, headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data def test_infection_global(self, client, headers): response = client.get('/infection/global', headers=headers) assert response.status_code == 200 print("response: ", response.text) response_data = json.loads(response.text)['data'] assert response_data

35.096

90

0.581947

487

4,387

5.106776

0.12115

0.055489

0.052272

0.083635

0.844391

0.817451

0.799357

0

0.074372

0.291999

4,387

124

91

35.379032

0.726336

0.070435

0

0.683168

0

0.167937

0.005901

0

0.158416

1

0.079208

false

0

0.019802

0

0.108911

0.079208

0

null

0

1

0

null

0

6

b40913984e0d9a08276edd74c8a43fc4a6017a70

9,921

py

Python

utils.py

sWizad/HashNeRF-pytorch

e8fe9b4879fc6ef3cdfa8fd3d268a92c4fa0d910

[ "MIT" ]

null

utils.py

sWizad/HashNeRF-pytorch

e8fe9b4879fc6ef3cdfa8fd3d268a92c4fa0d910

[ "MIT" ]

null

utils.py

sWizad/HashNeRF-pytorch

e8fe9b4879fc6ef3cdfa8fd3d268a92c4fa0d910

[ "MIT" ]

null

import json import numpy as np import pdb import torch from ray_utils import get_rays, get_ray_directions, get_ndc_rays BOX_OFFSETS = torch.tensor([[[i,j,k] for i in [0, 1] for j in [0, 1] for k in [0, 1]]], device='cuda') SQR_OFFSETS = torch.tensor([[[i,j] for i in [0, 1] for j in [0, 1] ]], device='cuda') def hash(coords, log2_hashmap_size): ''' coords: 3D coordinates. B x 3 log2T: logarithm of T w.r.t 2 ''' x, y, z = coords[..., 0], coords[..., 1], coords[..., 2] return torch.tensor((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663 ^ z*83492791) #return ((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663 ^ z*83492791) def hash2d(coords, log2_hashmap_size): ''' coords: 2D coordinates. B x 3 log2T: logarithm of T w.r.t 2 ''' x, y = coords[..., 0], coords[..., 1] return torch.tensor((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663) def xy2index(xy,resolution): return xy[...,0]+xy[...,1]*resolution def get_bbox3d_for_blenderobj(camera_transforms, H, W, near=2.0, far=6.0): camera_angle_x = float(camera_transforms['camera_angle_x']) focal = 0.5*W/np.tan(0.5 * camera_angle_x) # ray directions in camera coordinates directions = get_ray_directions(H, W, focal) min_bound = [100, 100, 100] max_bound = [-100, -100, -100] points = [] for frame in camera_transforms["frames"]: c2w = torch.FloatTensor(frame["transform_matrix"]) rays_o, rays_d = get_rays(directions, c2w) def find_min_max(pt): for i in range(3): if(min_bound[i] > pt[i]): min_bound[i] = pt[i] if(max_bound[i] < pt[i]): max_bound[i] = pt[i] return for i in [0, W-1, H*W-W, H*W-1]: min_point = rays_o[i] + near*rays_d[i] max_point = rays_o[i] + far*rays_d[i] points += [min_point, max_point] find_min_max(min_point) find_min_max(max_point) return (torch.tensor(min_bound)-torch.tensor([1.0,1.0,1.0]), torch.tensor(max_bound)+torch.tensor([1.0,1.0,1.0])) def get_bbox3d_for_llff(poses, hwf, near=0.0, far=1.0): H, W, focal = hwf H, W = int(H), int(W) # ray directions in camera coordinates directions = get_ray_directions(H, W, focal) min_bound = [100, 100, 100] max_bound = [-100, -100, -100] points = [] poses = torch.FloatTensor(poses) for pose in poses: rays_o, rays_d = get_rays(directions, pose) rays_o, rays_d = get_ndc_rays(H, W, focal, 1.0, rays_o, rays_d) def find_min_max(pt): for i in range(3): if(min_bound[i] > pt[i]): min_bound[i] = pt[i] if(max_bound[i] < pt[i]): max_bound[i] = pt[i] return for i in [0, W-1, H*W-W, H*W-1]: min_point = rays_o[i] + near*rays_d[i] max_point = rays_o[i] + far*rays_d[i] points += [min_point, max_point] find_min_max(min_point) find_min_max(max_point) return (torch.tensor(min_bound)-torch.tensor([0.1,0.1,0.0001]), torch.tensor(max_bound)+torch.tensor([0.1,0.1,0.0001])) def get_voxel_vertices(xyz, bounding_box, resolution, log2_hashmap_size): ''' xyz: 3D coordinates of samples. B x 3 bounding_box: min and max x,y,z coordinates of object bbox resolution: number of voxels per axis ''' box_min, box_max = bounding_box if not torch.all(xyz <= box_max) or not torch.all(xyz >= box_min): # print("ALERT: some points are outside bounding box. Clipping them!") pdb.set_trace() xyz = torch.clamp(xyz, min=box_min, max=box_max) grid_size = (box_max-box_min)/resolution bottom_left_idx = torch.floor((xyz-box_min)/grid_size).int() voxel_min_vertex = bottom_left_idx*grid_size + box_min voxel_max_vertex = voxel_min_vertex + torch.tensor([1.0,1.0,1.0])*grid_size # hashed_voxel_indices = [] # B x 8 ... 000,001,010,011,100,101,110,111 # for i in [0, 1]: # for j in [0, 1]: # for k in [0, 1]: # vertex_idx = bottom_left_idx + torch.tensor([i,j,k]) # # vertex = bottom_left + torch.tensor([i,j,k])*grid_size # hashed_voxel_indices.append(hash(vertex_idx, log2_hashmap_size)) voxel_indices = bottom_left_idx.unsqueeze(1) + BOX_OFFSETS hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size) return voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices def get_plane_vertices_old(xyz, bounding_box, resolution, log2_hashmap_size): ''' xyz: 3D coordinates of samples. B x 3 bounding_box: min and max x,y,z coordinates of object bbox resolution: number of voxels per axis ''' def box2plane(input): in_xy = input[:,:2]#.unsqueeze(1) in_xz = input[:,::2]#.unsqueeze(1) in_yz = input[:,-2:]#.unsqueeze(1) return [in_xy,in_xz,in_yz] box_min, box_max = bounding_box if not torch.all(xyz <= box_max) or not torch.all(xyz >= box_min): # print("ALERT: some points are outside bounding box. Clipping them!") pdb.set_trace() xyz = torch.clamp(xyz, min=box_min, max=box_max) grid_size = (box_max-box_min)/resolution bottom_left_idx = torch.floor((xyz-box_min)/grid_size).int() #(B, 3) voxel_min_vertex = bottom_left_idx*grid_size + box_min voxel_max_vertex = voxel_min_vertex + torch.tensor([1.0,1.0,1.0])*grid_size # hashed_voxel_indices = [] # B x 8 ... 000,001,010,011,100,101,110,111 # for i in [0, 1]: # for j in [0, 1]: # for k in [0, 1]: # vertex_idx = bottom_left_idx + torch.tensor([i,j,k]) # # vertex = bottom_left + torch.tensor([i,j,k])*grid_size # hashed_voxel_indices.append(hash(vertex_idx, log2_hashmap_size)) #voxel_indices = bottom_left_idx.unsqueeze(1) + BOX_OFFSETS #(B, 8, 3) #hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size) #(B, 8) voxel_indices_xy = bottom_left_idx[:,:2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2) voxel_indices_xz = bottom_left_idx[:,::2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2) voxel_indices_yz = bottom_left_idx[:,-2:].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2) hashed_voxel_indices_xy = hash2d(voxel_indices_xy, log2_hashmap_size) #(B, 4) hashed_voxel_indices_xz = hash2d(voxel_indices_xz, log2_hashmap_size) #(B, 4) hashed_voxel_indices_yz = hash2d(voxel_indices_yz, log2_hashmap_size) #(B, 4) hashed_voxel_indices = [hashed_voxel_indices_xy, hashed_voxel_indices_xz, hashed_voxel_indices_yz] voxel_min_vertex = box2plane(voxel_min_vertex) voxel_max_vertex = box2plane(voxel_max_vertex) #pdb.set_trace() return voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices def get_plane_vertices(xyz, bounding_box, resolution, log2_hashmap_size): ''' xyz: 3D coordinates of samples. B x 3 bounding_box: min and max x,y,z coordinates of object bbox resolution: number of voxels per axis ''' def box2plane(input): in_xy = input[:,:2]#.unsqueeze(1) in_xz = input[:,::2]#.unsqueeze(1) in_yz = input[:,-2:]#.unsqueeze(1) return [in_xy,in_xz,in_yz] box_min, box_max = bounding_box if not torch.all(xyz <= box_max) or not torch.all(xyz >= box_min): # print("ALERT: some points are outside bounding box. Clipping them!") pdb.set_trace() xyz = torch.clamp(xyz, min=box_min, max=box_max) grid_size = (box_max-box_min)/resolution bottom_left_idx = torch.floor((xyz-box_min)/grid_size).int() #(B, 3) voxel_min_vertex = bottom_left_idx*grid_size + box_min voxel_max_vertex = voxel_min_vertex + torch.tensor([1.0,1.0,1.0])*grid_size # hashed_voxel_indices = [] # B x 8 ... 000,001,010,011,100,101,110,111 # for i in [0, 1]: # for j in [0, 1]: # for k in [0, 1]: # vertex_idx = bottom_left_idx + torch.tensor([i,j,k]) # # vertex = bottom_left + torch.tensor([i,j,k])*grid_size # hashed_voxel_indices.append(hash(vertex_idx, log2_hashmap_size)) #voxel_indices = bottom_left_idx.unsqueeze(1) + BOX_OFFSETS #(B, 8, 3) #hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size) #(B, 8) voxel_indices_xy = bottom_left_idx[:,:2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2) voxel_indices_xz = bottom_left_idx[:,::2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2) voxel_indices_yz = bottom_left_idx[:,-2:].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2) #hashed_voxel_indices_xy = hash2d(voxel_indices_xy, log2_hashmap_size) #(B, 4) #hashed_voxel_indices_xz = hash2d(voxel_indices_xz, log2_hashmap_size) #(B, 4) #hashed_voxel_indices_yz = hash2d(voxel_indices_yz, log2_hashmap_size) #(B, 4) hashed_voxel_indices_xy = xy2index(voxel_indices_xy,resolution) #(B, 4) hashed_voxel_indices_xz = xy2index(voxel_indices_xz,resolution) #(B, 4) hashed_voxel_indices_yz = xy2index(voxel_indices_yz,resolution) #(B, 4) #print(hashed_voxel_indices_yz.shape) #pdb.set_trace() hashed_voxel_indices = [hashed_voxel_indices_xy, hashed_voxel_indices_xz, hashed_voxel_indices_yz] voxel_min_vertex = box2plane(voxel_min_vertex) voxel_max_vertex = box2plane(voxel_max_vertex) return voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices if __name__=="__main__": with open("data/nerf_synthetic/chair/transforms_train.json", "r") as f: camera_transforms = json.load(f) bounding_box = get_bbox3d_for_blenderobj(camera_transforms, 800, 800)

40.493878

123

0.627961

1,528

9,921

3.793194

0.102094

0.10559

0.093168

0.008282

0.8706

0.848516

0.819531

0.810214

0.791063

0

0.053681

0.241407

9,921

244

124

40.659836

0.71645

0.270235

0

0.654135

0

0.014158

0.006654

0

1

0.090226

false

0

0.037594

0.007519

0.218045

0

null

0

1

0

null

0

6

b40c87bef3a1437769ac688f07452b9daed5f901

189

py

Python

src/base/admin.py

dhavall13/Decode

8b9cbec72ade727d62edb90c3a38152e0285fe90

[ "MIT" ]

null

src/base/admin.py

dhavall13/Decode

8b9cbec72ade727d62edb90c3a38152e0285fe90

[ "MIT" ]

null

src/base/admin.py

dhavall13/Decode

8b9cbec72ade727d62edb90c3a38152e0285fe90

[ "MIT" ]

null

from django.contrib import admin from .models import Room, Topic, Message, User admin.site.register(Room) admin.site.register(Topic) admin.site.register(Message) admin.site.register(User)

23.625

46

0.804233

28

189

5.428571

0.428571

0.236842

0.447368

0

0.084656

189

7

47

27

0.878613

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b414e74ae421f14965c6e966091b96bde22167db

8,249

py

Python

orca/topology/infra/k8s/__init__.py

filwie/orca

84cfd53d309d85f7a7fb8649ba4abc8c2df9feac

[ "Apache-2.0" ]

null

orca/topology/infra/k8s/__init__.py

filwie/orca

84cfd53d309d85f7a7fb8649ba4abc8c2df9feac

[ "Apache-2.0" ]

null

orca/topology/infra/k8s/__init__.py

filwie/orca

84cfd53d309d85f7a7fb8649ba4abc8c2df9feac

[ "Apache-2.0" ]

null

# Copyright 2020 OpenRCA Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from orca.topology import bundle from orca.topology.infra.istio import linker as istio_linker from orca.topology.infra.k8s import cluster, linker, probe def get_probes(): return [ bundle.ProbeBundle( probe=probe.PodPullProbe, linkers=[ linker.PodToServiceLinker, linker.PodToReplicaSetLinker, linker.PodToStatefulSetLinker, linker.PodToDaemonSetLinker, linker.PodToNodeLinker, linker.ConfigMapToPodLinker, linker.SecretToPodLinker, linker.PersistentVolumeClaimToPodLinker ] ), bundle.ProbeBundle( probe=probe.PodPushProbe, linkers=[ linker.PodToServiceLinker, linker.PodToReplicaSetLinker, linker.PodToStatefulSetLinker, linker.PodToDaemonSetLinker, linker.PodToNodeLinker, linker.ConfigMapToPodLinker, linker.SecretToPodLinker, linker.PersistentVolumeClaimToPodLinker ] ), bundle.ProbeBundle( probe=probe.ServicePullProbe, linkers=[ linker.PodToServiceLinker, linker.EndpointsToServiceLinker, istio_linker.VirtualServiceToServiceLinker, istio_linker.DestinationRuleToServiceLinker, linker.IngressToServiceLinker ] ), bundle.ProbeBundle( probe=probe.ServicePushProbe, linkers=[ linker.PodToServiceLinker, linker.EndpointsToServiceLinker, istio_linker.VirtualServiceToServiceLinker, istio_linker.DestinationRuleToServiceLinker, linker.IngressToServiceLinker ] ), bundle.ProbeBundle( probe=probe.EndpointsPullProbe, linkers=[ linker.EndpointsToServiceLinker ] ), bundle.ProbeBundle( probe=probe.EndpointsPushProbe, linkers=[ linker.EndpointsToServiceLinker ] ), bundle.ProbeBundle( probe=probe.DeploymentPullProbe, linkers=[ linker.DeploymentToHorizontalPodAutoscalerLinker, linker.ReplicaSetToDeploymentLinker ] ), bundle.ProbeBundle( probe=probe.DeploymentPushProbe, linkers=[ linker.DeploymentToHorizontalPodAutoscalerLinker, linker.ReplicaSetToDeploymentLinker ] ), bundle.ProbeBundle( probe=probe.ReplicaSetPullProbe, linkers=[ linker.PodToReplicaSetLinker, linker.ReplicaSetToDeploymentLinker, linker.ReplicaSetToHorizontalPodAutoscalerLinker ] ), bundle.ProbeBundle( probe=probe.ReplicaSetPushProbe, linkers=[ linker.PodToReplicaSetLinker, linker.ReplicaSetToDeploymentLinker, linker.ReplicaSetToHorizontalPodAutoscalerLinker ] ), bundle.ProbeBundle( probe=probe.DaemonSetPullProbe, linkers=[ linker.PodToDaemonSetLinker ] ), bundle.ProbeBundle( probe=probe.DaemonSetPushProbe, linkers=[ linker.PodToDaemonSetLinker ] ), bundle.ProbeBundle( probe=probe.StatefulSetPullProbe, linkers=[ linker.PodToStatefulSetLinker, linker.StatefulSetToHorizontalPodAutoscalerLinker ] ), bundle.ProbeBundle( probe=probe.StatefulSetPushProbe, linkers=[ linker.PodToStatefulSetLinker, linker.StatefulSetToHorizontalPodAutoscalerLinker ] ), bundle.ProbeBundle( probe=probe.ConfigMapPullProbe, linkers=[ linker.ConfigMapToPodLinker ] ), bundle.ProbeBundle( probe=probe.ConfigMapPushProbe, linkers=[ linker.ConfigMapToPodLinker ] ), bundle.ProbeBundle( probe=probe.SecretPullProbe, linkers=[ linker.SecretToPodLinker ] ), bundle.ProbeBundle( probe=probe.SecretPushProbe, linkers=[ linker.SecretToPodLinker ] ), bundle.ProbeBundle( probe=probe.StorageClassPullProbe, linkers=[ linker.PersistentVolumeToStorageClassLinker ] ), bundle.ProbeBundle( probe=probe.StorageClassPushProbe, linkers=[ linker.PersistentVolumeToStorageClassLinker ] ), bundle.ProbeBundle( probe=probe.PersistentVolumePullProbe, linkers=[ linker.PersistentVolumeToStorageClassLinker, linker.PersistentVolumeToPersistentVolumeClaimLinker ] ), bundle.ProbeBundle( probe=probe.PersistentVolumePushProbe, linkers=[ linker.PersistentVolumeToStorageClassLinker, linker.PersistentVolumeToPersistentVolumeClaimLinker ] ), bundle.ProbeBundle( probe=probe.PersistentVolumeClaimPullProbe, linkers=[ linker.PersistentVolumeToPersistentVolumeClaimLinker, linker.PersistentVolumeClaimToPodLinker ] ), bundle.ProbeBundle( probe=probe.PersistentVolumeClaimPushProbe, linkers=[ linker.PersistentVolumeToPersistentVolumeClaimLinker, linker.PersistentVolumeClaimToPodLinker ] ), bundle.ProbeBundle( probe=probe.HorizontalPodAutoscalerPullProbe, linkers=[ linker.DeploymentToHorizontalPodAutoscalerLinker, linker.ReplicaSetToHorizontalPodAutoscalerLinker, linker.StatefulSetToHorizontalPodAutoscalerLinker ] ), bundle.ProbeBundle( probe=probe.HorizontalPodAutoscalerPushProbe, linkers=[ linker.DeploymentToHorizontalPodAutoscalerLinker, linker.ReplicaSetToHorizontalPodAutoscalerLinker, linker.StatefulSetToHorizontalPodAutoscalerLinker ] ), bundle.ProbeBundle( probe=probe.NodePullProbe, linkers=[ linker.PodToNodeLinker, linker.NodeToClusterLinker ] ), bundle.ProbeBundle( probe=probe.NodePushProbe, linkers=[ linker.PodToNodeLinker, linker.NodeToClusterLinker ] ), bundle.ProbeBundle( probe=probe.IngressPullProbe, linkers=[ linker.IngressToServiceLinker ] ), bundle.ProbeBundle( probe=probe.IngressPushProbe, linkers=[ linker.IngressToServiceLinker ] ), bundle.ProbeBundle( probe=cluster.ClusterProbe, linkers=[ linker.NodeToClusterLinker ] ) ]

29.566308

74

0.562856

450

8,249

10.304444

0.288889

0.113651

0.147078

0.174682

0.730429

0.704766

0.56804

0.328661

0

0.00176

0.380046

8,249

278

75

29.672662

0.904967

0.066917

0

0.702586

0

1

0.00431

true

0

0.012931

0.00431

0.021552

0

1

null

0

1

0

1

0

1

0

null

0

1

0

6

b44ff9fd50fe2d54276ded1d327434e0e7c23eab

282

py

Python

tests/test_classes/users.py

dialogs/python-bot-sdk

737152e5ef8406af0b22600ef7cefa78da9035e8

[ "Apache-2.0" ]

9

2019-01-22T09:59:12.000Z

2021-05-08T10:59:00.000Z

tests/test_classes/users.py

dialogs/python-bot-sdk

737152e5ef8406af0b22600ef7cefa78da9035e8

[ "Apache-2.0" ]

29

2018-10-08T17:10:49.000Z

2021-04-28T18:46:30.000Z

tests/test_classes/users.py

dialogs/python-bot-sdk

737152e5ef8406af0b22600ef7cefa78da9035e8

[ "Apache-2.0" ]

8

2019-01-22T09:49:32.000Z

2022-01-26T18:55:52.000Z

from dialog_api.users_pb2 import RequestLoadFullUsers, ResponseLoadFullUsers, FullUser class Users: def LoadFullUsers(self, request: RequestLoadFullUsers) -> ResponseLoadFullUsers: return ResponseLoadFullUsers(full_users=[FullUser(id=1, contact_info=[], about=None)])

40.285714

94

0.797872

28

282

7.892857

0.785714

0.371041

0

0.008

0.113475

282

6

95

47

0.876

0

1

0.25

false

0

0.25

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b45b6c7e93b004510cd39ca579e1ae1a135f82e4

30

py

Python

SVDD/__init__.py

SolidusAbi/SVDD-Python

ce2b834bf31cfdbbbebc08c8a1bac8c37b081d0e

[ "MIT" ]

null

SVDD/__init__.py

SolidusAbi/SVDD-Python

ce2b834bf31cfdbbbebc08c8a1bac8c37b081d0e

[ "MIT" ]

null

SVDD/__init__.py

SolidusAbi/SVDD-Python

ce2b834bf31cfdbbbebc08c8a1bac8c37b081d0e

[ "MIT" ]

null

from .BaseSVDD import BaseSVDD

30

0.866667

4

30

6.5

0.75

0

0.1

30

1

30

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b4620451d250c59f2d44e900d7695fc1a4e00f84

258

py

Python

python/hayate/store/actions.py

tao12345666333/Talk-Is-Cheap

7b2c5959828b6d8bbbad8144b9b97f9b77c6b34c

[ "MIT" ]

4

2016-04-14T02:11:35.000Z

2019-05-30T10:18:41.000Z

python/hayate/store/actions.py

tao12345666333/Talk-Is-Cheap

7b2c5959828b6d8bbbad8144b9b97f9b77c6b34c

[ "MIT" ]

8

2016-07-21T16:02:17.000Z

2021-09-23T02:49:34.000Z

python/hayate/store/actions.py

tao12345666333/Talk-Is-Cheap

7b2c5959828b6d8bbbad8144b9b97f9b77c6b34c

[ "MIT" ]

2

2017-02-17T05:02:02.000Z

2017-11-08T12:22:09.000Z

from turbo.flux import Mutation, register, dispatch, register_dispatch import mutation_types @register_dispatch('user', mutation_types.INCREASE) def increase(rank): pass def decrease(rank): return dispatch('user', mutation_types.DECREASE, rank)

19.846154

70

0.77907

32

258

6.125

0.46875

0.244898

0.204082

0.255102

0

0.127907

258

12

71

21.5

0.871111

0

0.031008

0

1

0.285714

false

0.142857

0.285714

0.142857

0.714286

0

null

1

0

null

0

1

0

1

0

1

0

6

b46ebc3b01df0741b7690606a0b55aac51c6693f

237

py

Python

wagtail/wagtailadmin/blocks.py

patphongs/wagtail

32555f7a1c599c139e0f26c22907c9612af2e015

[ "BSD-3-Clause" ]

3

2016-08-17T13:56:36.000Z

2019-04-23T19:59:25.000Z

wagtail/wagtailadmin/blocks.py

patphongs/wagtail

32555f7a1c599c139e0f26c22907c9612af2e015

[ "BSD-3-Clause" ]

11

2016-08-05T15:43:06.000Z

2016-12-16T13:32:23.000Z

wagtail/wagtailadmin/blocks.py

patphongs/wagtail

32555f7a1c599c139e0f26c22907c9612af2e015

[ "BSD-3-Clause" ]

2

2017-08-08T01:39:02.000Z

2018-05-06T06:16:10.000Z

from __future__ import absolute_import, unicode_literals import warnings from wagtail.wagtailcore.blocks import * # noqa warnings.warn("wagtail.wagtailadmin.blocks has moved to wagtail.wagtailcore.blocks", UserWarning, stacklevel=2)

29.625

111

0.827004

29

237

6.551724

0.655172

0.189474

0.252632

0

0.004695

0.101266

237

7

112

33.857143

0.887324

0.016878

0

0.290043

0.229437

0

1

0

true

0

0.75

0

0.75

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

81e7bcf77b3d24a119c0b31470b009787721b442

15,921

py

Python

pipeline/tests/engine/core/data/test_api.py

wkma/bk-sops

8fb5609c0c4495c28d588fbafa9d9f5f2976929b

[ "Apache-2.0" ]

2

2021-07-28T01:48:31.000Z

2021-11-17T11:02:26.000Z

pipeline/tests/engine/core/data/test_api.py

wkma/bk-sops

8fb5609c0c4495c28d588fbafa9d9f5f2976929b

[ "Apache-2.0" ]

null

pipeline/tests/engine/core/data/test_api.py

wkma/bk-sops

8fb5609c0c4495c28d588fbafa9d9f5f2976929b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import sys from django.test import TestCase from django.utils.module_loading import import_string from pipeline.tests.mock import * # noqa from pipeline.tests.mock_settings import * # noqa class EngineDataAPITestCase(TestCase): @classmethod def setUpClass(cls): cls.mock_settings = MagicMock() cls.settings_patch = patch(ENGINE_DATA_API_SETTINGS, cls.mock_settings) cls.import_backend_patch = patch(ENGINE_DATA_API_IMPORT_BACKEND, MagicMock()) cls.settings_patch.start() cls.import_backend_patch.start() cls.api = import_string("pipeline.engine.core.data.api") cls.write_methods = ["set_object", "del_object", "expire_cache"] cls.read_methods = ["get_object", "cache_for"] cls.method_params = { "set_object": ["key", "obj"], "del_object": ["key"], "expire_cache": ["key", "obj", "expires"], "cache_for": ["key"], "get_object": ["key"], } @classmethod def tearDownClass(cls): cls.settings_patch.stop() cls.import_backend_patch.stop() def setUp(self): self.backend = MagicMock() self.candidate_backend = MagicMock() self.mock_settings.PIPELINE_DATA_BACKEND_AUTO_EXPIRE = False def test_write__without_candidate(self): for method in self.write_methods: with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, None): getattr(self.api, method)(*self.method_params[method]) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_not_called() sys.stdout.write( "{} pass test_write__without_candidate test\n".format(method) ) def test_write__without_candiate_raise_err(self): for method in self.write_methods: setattr(self.backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, None): self.assertRaises( Exception, getattr(self.api, method), *self.method_params[method] ) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_not_called() sys.stdout.write( "{} pass test_write__without_candiate_raise_err test\n".format(method) ) def test_write__with_candidate(self): for method in self.write_methods: with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): getattr(self.api, method)(*self.method_params[method]) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write("{} pass test_write__with_candidate test\n".format(method)) def test_write__with_candidate_main_raise_err(self): for method in self.write_methods: setattr(self.backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): getattr(self.api, method)(*self.method_params[method]) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_write__with_candidate_main_raise_err test\n".format( method ) ) def test_write__with_candidate_raise_err(self): for method in self.write_methods: setattr(self.candidate_backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): getattr(self.api, method)(*self.method_params[method]) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_write__with_candidate_raise_err test\n".format(method) ) def test_write__with_candidate_both_raise_err(self): for method in self.write_methods: setattr(self.backend, method, MagicMock(side_effect=Exception)) setattr(self.candidate_backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): self.assertRaises( Exception, getattr(self.api, method), *self.method_params[method] ) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_write__with_candidate_both_raise_err test\n".format( method ) ) def test_write__with_auto_expire(self): self.mock_settings.PIPELINE_DATA_BACKEND_AUTO_EXPIRE = True self.mock_settings.PIPELINE_DATA_BACKEND_AUTO_EXPIRE_SECONDS = 30 for method in self.write_methods: with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): getattr(self.api, method)(*self.method_params[method]) if method == "set_object": getattr(self.backend, "expire_cache").assert_called_once_with( *self.method_params[method], expires=30 ) self.backend.expire_cache.reset_mock() else: getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_write__with_candidate_both_raise_err test\n".format( method ) ) def test_read__without_candidate(self): for method in self.read_methods: with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, None): data = getattr(self.api, method)(*self.method_params[method]) self.assertIsNotNone(data) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_not_called() sys.stdout.write( "{} pass test_read__without_candidate test\n".format(method) ) def test_read__without_candidate_raise_err(self): for method in self.read_methods: setattr(self.backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, None): self.assertRaises( Exception, getattr(self.api, method), *self.method_params[method] ) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_not_called() sys.stdout.write( "{} pass test_read__without_candidate_raise_err test\n".format(method) ) def test_read__with_candidate_not_use(self): for method in self.read_methods: with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): data = getattr(self.api, method)(*self.method_params[method]) self.assertIsNotNone(data) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_not_called() sys.stdout.write( "{} pass test_read__with_candidate_not_use test\n".format(method) ) def test_read__with_candidate_use(self): for method in self.read_methods: setattr(self.backend, method, MagicMock(return_value=None)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): data = getattr(self.api, method)(*self.method_params[method]) self.assertIsNotNone(data) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_read__with_candidate_use test\n".format(method) ) def test_read__with_candidate_err(self): for method in self.read_methods: setattr(self.backend, method, MagicMock(return_value=None)) setattr(self.candidate_backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): data = getattr(self.api, method)(*self.method_params[method]) self.assertIsNone(data) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_read__with_candidate_err test\n".format(method) ) def test_read__with_candidate_main_raise_err(self): for method in self.read_methods: setattr(self.backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): data = getattr(self.api, method)(*self.method_params[method]) self.assertIsNotNone(data) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_read__with_candidate_main_raise_err test\n".format(method) ) def test_read__with_candidate_both_raise_err(self): for method in self.read_methods: setattr(self.backend, method, MagicMock(side_effect=Exception)) setattr(self.candidate_backend, method, MagicMock(side_effect=Exception)) with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): self.assertRaises( Exception, getattr(self.api, method), *self.method_params[method] ) getattr(self.backend, method).assert_called_once_with( *self.method_params[method] ) getattr(self.candidate_backend, method).assert_called_once_with( *self.method_params[method] ) sys.stdout.write( "{} pass test_read__with_candidate_both_raise_err test\n".format(method) ) def test_set_schedule_data(self): with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): self.api.set_schedule_data("key", "data") self.backend.set_object.assert_called_once_with( "key_schedule_parent_data", "data" ) self.candidate_backend.set_object.assert_called_once_with( "key_schedule_parent_data", "data" ) def test_delete_parent_data(self): with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): self.api.delete_parent_data("key") self.backend.del_object.assert_called_once_with( "key_schedule_parent_data" ) self.candidate_backend.del_object.assert_called_once_with( "key_schedule_parent_data" ) def test_get_schedule_parent_data(self): with patch(ENGINE_DATA_API_BACKEND, self.backend): with patch(ENGINE_DATA_API_CANDIDATE_BACKEND, self.candidate_backend): data = self.api.get_schedule_parent_data("key") self.assertIsNotNone(data) self.backend.get_object.assert_called_once_with( "key_schedule_parent_data" ) self.candidate_backend.get_object.assert_not_called()

43.381471

115

0.592174

1,672

15,921

5.297847

0.098086

0.093926

0.068639

0.094378

0.817227

0.812034

0.80876

0.80797

0.781553

0.771054

0

0.001402

0.328183

15,921

366

116

43.5

0.826758

0.045977

0

0.573883

0

0.067264

0.042032

0

0.154639

1

0.068729

false

0.04811

0.030928

0

0.103093

0

null

0

1

0

null

0

6

c30a8241bc4eb176e2d35bfc53ddbf79b7ca685f

77

py

Python

test/settings/test_kafka_consumer_config.py

DebasishMaji/PI

e293982cae8f8755d28d7b3de22966dc74759b90

[ "Apache-2.0" ]

null

test/settings/test_kafka_consumer_config.py

DebasishMaji/PI

e293982cae8f8755d28d7b3de22966dc74759b90

[ "Apache-2.0" ]

null

test/settings/test_kafka_consumer_config.py

DebasishMaji/PI

e293982cae8f8755d28d7b3de22966dc74759b90

[ "Apache-2.0" ]

null

import unittest class TestKafkaConsumerConfig(unittest.TestCase): pass

12.833333

49

0.805195

7

77

8.857143

0.857143

0

0.142857

77

5

50

15.4

0.939394

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

c30ea52dd60b15b77f690236c9544837627ac0f7

7,684

py

Python

Pycraft/StartupAnimation.py

demirdogukan/InsiderPycraft

5567107326fbd222a7df6aabf4ab265e0a157636

[ "MIT" ]

22

2021-03-25T17:47:45.000Z

2022-03-29T01:56:12.000Z

Pycraft/StartupAnimation.py

demirdogukan/InsiderPycraft

5567107326fbd222a7df6aabf4ab265e0a157636

[ "MIT" ]

1

2021-12-22T16:12:59.000Z

Pycraft/StartupAnimation.py

demirdogukan/InsiderPycraft

5567107326fbd222a7df6aabf4ab265e0a157636

[ "MIT" ]

3

2021-09-05T14:10:05.000Z

2022-01-10T12:57:34.000Z

if not __name__ == "__main__": print("Started <Pycraft_StartupAnimation>") class GenerateStartupScreen: def __init__(self): pass def Start(self): try: self.Display.fill(self.BackgroundCol) self.mod_Pygame__.display.flip() self.mod_Pygame__.display.set_caption(f"Pycraft: v{self.version}: Welcome") PresentsFont = self.mod_Pygame__.font.Font(self.mod_OS__.path.join(self.base_folder, ("Fonts\\Book Antiqua.ttf")), 35) PycraftFont = self.mod_Pygame__.font.Font(self.mod_OS__.path.join(self.base_folder, ("Fonts\\Book Antiqua.ttf")), 60) NameFont = self.mod_Pygame__.font.Font(self.mod_OS__.path.join(self.base_folder, ("Fonts\\Book Antiqua.ttf")), 45) NameText = NameFont.render("Tom Jebbo", True, self.FontCol) NameTextWidth = NameText.get_width() NameTextHeight = NameText.get_height() PresentsText = PresentsFont.render("presents", True, self.FontCol) PycraftText = PycraftFont.render("Pycraft", True, self.FontCol) PycraftTextWidth = PycraftText.get_width() PycraftTextHeight = PycraftText.get_height() iteration = 0 clock = self.mod_Pygame__.time.Clock() if self.RunFullStartup == True: while iteration <= (60*3): self.realWidth, self.realHeight = self.mod_Pygame__.display.get_window_size() self.Display.fill(self.BackgroundCol) self.Display.blit(NameText, ((self.realWidth-NameTextWidth)/2, (self.realHeight-NameTextHeight)/2)) iteration += 1 if self.realWidth < 1280: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, 1280, self.SavedHeight) if self.realHeight < 720: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, self.SavedWidth, 720) self.mod_Pygame__.display.flip() clock.tick(60) for event in self.mod_Pygame__.event.get(): if event.type == self.mod_Pygame__.QUIT: self.Stop_Thread_Event.set() self.Thread_StartLongThread.join() self.Thread_AdaptiveMode.join() self.Thread_StartLongThread.join() self.mod_Pygame__.quit() self.mod_Sys__.exit("Thanks for playing") quit() iteration = 0 while iteration <= (60*2): self.realWidth, self.realHeight = self.mod_Pygame__.display.get_window_size() self.Display.fill(self.BackgroundCol) self.Display.blit(NameText, ((self.realWidth-NameTextWidth)/2, (self.realHeight-NameTextHeight)/2)) self.Display.blit(PresentsText, ((((self.realWidth-NameTextWidth)/2)+120), ((self.realHeight-NameTextHeight)/2)+30)) iteration += 1 if self.realWidth < 1280: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, 1280, self.SavedHeight) if self.realHeight < 720: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, self.SavedWidth, 720) self.mod_Pygame__.display.flip() clock.tick(60) for event in self.mod_Pygame__.event.get(): if event.type == self.mod_Pygame__.QUIT: self.Stop_Thread_Event.set() self.Thread_StartLongThread.join() self.Thread_AdaptiveMode.join() self.Thread_StartLongThread.join() self.mod_Pygame__.quit() self.mod_Sys__.exit("Thanks for playing") quit() iteration = 0 while iteration <= (60*3): self.realWidth, self.realHeight = self.mod_Pygame__.display.get_window_size() self.Display.fill(self.BackgroundCol) self.Display.blit(PycraftText, ((self.realWidth-PycraftTextWidth)/2, (self.realHeight-PycraftTextHeight)/2)) iteration += 1 if self.realWidth < 1280: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, 1280, self.SavedHeight) if self.realHeight < 720: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, self.SavedWidth, 720) self.mod_Pygame__.display.flip() clock.tick(60) for event in self.mod_Pygame__.event.get(): if event.type == self.mod_Pygame__.QUIT: self.Stop_Thread_Event.set() self.Thread_StartLongThread.join() self.Thread_AdaptiveMode.join() self.Thread_StartLongThread.join() self.mod_Pygame__.quit() self.mod_Sys__.exit("Thanks for playing") quit() y = 0 while True: self.realWidth, self.realHeight = self.mod_Pygame__.display.get_window_size() self.Display.fill(self.BackgroundCol) self.Display.blit(PycraftText, ((self.realWidth-PycraftTextWidth)/2, ((self.realHeight-PycraftTextHeight)/2)-y)) y += 2 if self.realWidth < 1280: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, 1280, self.SavedHeight) if self.realHeight < 720: self.mod_DisplayUtils__.DisplayUtils.GenerateMinDisplay(self, self.SavedWidth, 720) self.mod_Pygame__.display.flip() clock.tick(60) for event in self.mod_Pygame__.event.get(): if event.type == self.mod_Pygame__.QUIT: self.Stop_Thread_Event.set() self.Thread_StartLongThread.join() self.Thread_AdaptiveMode.join() self.Thread_StartLongThread.join() self.mod_Pygame__.quit() self.mod_Sys__.exit("Thanks for playing") quit() if ((self.realHeight-PycraftTextHeight)/2)-y <= 0: self.RunFullStartup = False return None except Exception as Message: self.RunFullStartup = False return Message else: print("You need to run this as part of Pycraft") import tkinter as tk from tkinter import messagebox root = tk.Tk() root.withdraw() messagebox.showerror("Startup Fail", "You need to run this as part of Pycraft, please run the 'main.py' file") quit()

53.361111

141

0.511322

679

7,684

5.533137

0.185567

0.076391

0.089965

0.053234

0.769497

0.755124

0.745542

0.729039

0

0.022628

0.401874

7,684

144

142

53.361111

0.794822

0

0.68595

1

0

0.047865

0.003447

0

1

0.016529

false

0.008264

0.016529

0

0.057851

0.016529

0

null

0

1

0

1

0

null

0

6

c331cb67fa44126ad7899136fc1a363b37ea7fe2

263

py

Python

gdal/swig/python/scripts/gdal2xyz.py

Sokigo-GLS/gdal

595f74bf60dff89fc5df53f9f4c3e40fc835e909

[ "MIT" ]

null

gdal/swig/python/scripts/gdal2xyz.py

Sokigo-GLS/gdal

595f74bf60dff89fc5df53f9f4c3e40fc835e909

[ "MIT" ]

null

gdal/swig/python/scripts/gdal2xyz.py

Sokigo-GLS/gdal

595f74bf60dff89fc5df53f9f4c3e40fc835e909

[ "MIT" ]

null

import sys # import osgeo.utils.gdal2xyz as a convenience to use as a script from osgeo.utils.gdal2xyz import * # noqa from osgeo.utils.gdal2xyz import main from osgeo.gdal import deprecation_warn deprecation_warn('gdal2xyz', 'utils') sys.exit(main(sys.argv))

26.3

65

0.787072

41

263

5

0.463415

0.146341

0.263415

0.214634

0.273171

0

0.017391

0.125475

263

9

66

29.222222

0.873913

0.258555

0

0.067708

0

1

0

true

0

0.666667

0

0.666667

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c37854af006991db33cfa5319fe951302a09dbf2

164

py

Python

segmentation/data/transforms/__init__.py

RajasekharChowdary9/panoptic-deeplab

7645bc1cf51e3ebc85153666f26f8630a407b52b

[ "Apache-2.0" ]

506

2020-06-12T01:07:56.000Z

2022-03-26T00:56:52.000Z

segmentation/data/transforms/__init__.py

RajasekharChowdary9/panoptic-deeplab

7645bc1cf51e3ebc85153666f26f8630a407b52b

[ "Apache-2.0" ]

85

2020-06-12T04:51:31.000Z

2022-03-23T16:19:44.000Z

segmentation/data/transforms/__init__.py

RajasekharChowdary9/panoptic-deeplab

7645bc1cf51e3ebc85153666f26f8630a407b52b

[ "Apache-2.0" ]

102

2020-06-12T06:45:44.000Z

2022-03-22T14:03:04.000Z

from .build import build_transforms from .pre_augmentation_transforms import Resize from .target_transforms import PanopticTargetGenerator, SemanticTargetGenerator

41

79

0.896341

17

164

8.411765

0.588235

0.223776

0

0.079268

164

3

80

54.666667

0.94702

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

5edecbbe347219a2740ccd3534f648ace677fd24

10,232

py

Python

tests/exchanges_tests.py

tomwalton78/Crypto-Exchange-API-Aggregator

c5b1756eac46274cdbe5c4e49db62450a35b70a6

[ "MIT" ]

null

tests/exchanges_tests.py

tomwalton78/Crypto-Exchange-API-Aggregator

c5b1756eac46274cdbe5c4e49db62450a35b70a6

[ "MIT" ]

null

tests/exchanges_tests.py

tomwalton78/Crypto-Exchange-API-Aggregator

c5b1756eac46274cdbe5c4e49db62450a35b70a6

[ "MIT" ]

1

2019-11-16T07:31:00.000Z

import unittest from datetime import datetime import os import sys from api.exchanges.exchange import ExchangeAPICallFailedException from api.exchanges.gdax_exchange import GdaxExchange from api.exchanges.kraken_exchange import KrakenExchange from api.exchanges.bitstamp_exchange import BitstampExchange from api.exchanges.bitfinex_exchange import BitfinexExchange class HiddenPrints: """Class to disable printing for functions run under its scope. Example: with HiddenPrints() print('hello world') Nothing will print, since anything under the scope of HiddenPrints has its printing output suppressed. """ def __enter__(self): """Disable printing on entering 'with HiddenPrints()' scope """ self._original_stdout = sys.stdout sys.stdout = open(os.devnull, 'w') def __exit__(self, exc_type, exc_val, exc_tb): """Re-enable printing on exiting 'with HiddenPrints()' scope """ sys.stdout.close() sys.stdout = self._original_stdout class GdaxExchangeTests(unittest.TestCase): """ Tests that functions within GdaxExchange class perform as intended. """ def test_initialisation_with_valid_market(self): try: g = GdaxExchange('BTC-EUR') pass except KeyError: self.fail( 'Initialising GdaxExchange with BTC-EUR raised KeyError.' ) def test_initialisation_with_invalid_market(self): with self.assertRaises(KeyError): g = GdaxExchange('REDDDDDDDDDD-BLUEEEEEEEEEE') def test_fetch_l1_quote_on_supported_market(self): try: g = GdaxExchange('BTC-EUR') g.fetch_l1_quote() pass except Exception as e: self.fail( 'Fetch l1 quote on supported market failed: {}'.format( str(e) ) ) def test_fetch_l1_quote_on_unsupported_market(self): with self.assertRaises(ExchangeAPICallFailedException): g = GdaxExchange('LTC-GBP') g.fetch_l1_quote() def test_latest_l1_quote_to_csv(self): g = GdaxExchange('BTC-EUR') g.latest_l1_quote = { "best ask size": 0.65333759, "best bid price": 5780.1, "best ask price": 5781.24, "timestamp": datetime.utcnow(), "best bid size": 0.001006 } g.latest_l1_quote_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/gdax_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) def test_fetch_l1_quote_and_write_to_csv(self): g = GdaxExchange('BTC-EUR') with HiddenPrints(): g.fetch_l1_quote_and_write_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/gdax_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) class KrakenExchangeTests(unittest.TestCase): """ Tests that functions within KrakenExchange class perform as intended. """ def test_initialisation_with_valid_market(self): try: k = KrakenExchange('BTC-EUR') pass except KeyError: self.fail( 'Initialising KrakenExchange with BTC-EUR raised KeyError.' ) def test_initialisation_with_invalid_market(self): with self.assertRaises(KeyError): k = KrakenExchange('REDDDDDDDDDD-BLUEEEEEEEEEE') def test_fetch_l1_quote_on_supported_market(self): try: k = KrakenExchange('BTC-EUR') k.fetch_l1_quote() pass except Exception as e: self.fail( 'Fetch l1 quote on supported market failed: {}'.format( str(e) ) ) def test_fetch_l1_quote_on_unsupported_market(self): with self.assertRaises(ExchangeAPICallFailedException): k = KrakenExchange('LTC-GBP') k.fetch_l1_quote() def test_latest_l1_quote_to_csv(self): k = KrakenExchange('BTC-EUR') k.latest_l1_quote = { "best ask size": 0.65333759, "best bid price": 5780.1, "best ask price": 5781.24, "timestamp": datetime.utcnow(), "best bid size": 0.001006 } k.latest_l1_quote_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/kraken_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) def test_fetch_l1_quote_and_write_to_csv(self): k = KrakenExchange('BTC-EUR') with HiddenPrints(): k.fetch_l1_quote_and_write_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/kraken_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) class BitstampExchangeTests(unittest.TestCase): """ Tests that functions within BitstampExchange class perform as intended. """ def test_initialisation_with_valid_market(self): try: k = BitstampExchange('BTC-EUR') pass except KeyError: self.fail( 'Initialising BitstampExchange with BTC-EUR raised KeyError.' ) def test_initialisation_with_invalid_market(self): with self.assertRaises(KeyError): k = BitstampExchange('REDDDDDDDDDD-BLUEEEEEEEEEE') def test_fetch_l1_quote_on_supported_market(self): try: k = BitstampExchange('BTC-EUR') k.fetch_l1_quote() pass except Exception as e: self.fail( 'Fetch l1 quote on supported market failed: {}'.format( str(e) ) ) def test_fetch_l1_quote_on_unsupported_market(self): with self.assertRaises(ExchangeAPICallFailedException): k = BitstampExchange('LTC-GBP') k.fetch_l1_quote() def test_latest_l1_quote_to_csv(self): k = BitstampExchange('BTC-EUR') k.latest_l1_quote = { "best ask size": 0.65333759, "best bid price": 5780.1, "best ask price": 5781.24, "timestamp": datetime.utcnow(), "best bid size": 0.001006 } k.latest_l1_quote_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/bitstamp_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) def test_fetch_l1_quote_and_write_to_csv(self): k = BitstampExchange('BTC-EUR') with HiddenPrints(): k.fetch_l1_quote_and_write_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/bitstamp_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) class BitfinexExchangeTests(unittest.TestCase): """ Tests that functions within BitfinexExchange class perform as intended. """ def test_initialisation_with_valid_market(self): try: k = BitfinexExchange('BTC-EUR') pass except KeyError: self.fail( 'Initialising BitfinexExchange with BTC-EUR raised KeyError.' ) def test_initialisation_with_invalid_market(self): with self.assertRaises(KeyError): k = BitfinexExchange('REDDDDDDDDDD-BLUEEEEEEEEEE') def test_fetch_l1_quote_on_supported_market(self): try: k = BitfinexExchange('BTC-EUR') k.fetch_l1_quote() pass except Exception as e: self.fail( 'Fetch l1 quote on supported market failed: {}'.format( str(e) ) ) def test_fetch_l1_quote_on_unsupported_market(self): with self.assertRaises(ExchangeAPICallFailedException): k = BitfinexExchange('LTC-GBP') k.fetch_l1_quote() def test_latest_l1_quote_to_csv(self): k = BitfinexExchange('BTC-EUR') k.latest_l1_quote = { "best ask size": 0.65333759, "best bid price": 5780.1, "best ask price": 5781.24, "timestamp": datetime.utcnow(), "best bid size": 0.001006 } k.latest_l1_quote_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/bitfinex_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) def test_fetch_l1_quote_and_write_to_csv(self): k = BitfinexExchange('BTC-EUR') with HiddenPrints(): k.fetch_l1_quote_and_write_to_csv( path_to_folder=os.path.dirname(os.path.realpath(__file__)) + '/' ) # Test that csv file exists path = ( os.path.dirname(os.path.realpath(__file__)) + '/bitfinex_BTC-EUR.csv' ) self.assertTrue(os.path.exists(path)) os.remove(path) if __name__ == '__main__': unittest.main(exit=False)

30.094118

79

0.583268

1,126

10,232

5.027531

0.119005

0.049461

0.059353

0.042395

0.804098

0.803745

0.775481

0.730613

0.699523

0

0.021301

0.320954

10,232

339

80

30.182891

0.793466

0.082389

0

0.724

0

0.116272

0.020259

0

0.064

1

0.104

false

0.032

0.036

0

0.16

0

null

0

1

0

1

0

1

0

null

0

6

6f08e7a44962b3d4ce1d67b7f28da022e46eb7fe

4,097

py

Python

src/bindings/python/tests/test_ngraph/test_eye.py

si-eun-kim/openvino

1db4446e2a6ead55d066e0b4e718fa37f509353a

[ "Apache-2.0" ]

2

2021-12-14T15:27:46.000Z

2021-12-14T15:34:16.000Z

src/bindings/python/tests/test_ngraph/test_eye.py

si-eun-kim/openvino

1db4446e2a6ead55d066e0b4e718fa37f509353a

[ "Apache-2.0" ]

33

2021-09-23T04:14:30.000Z

2022-01-24T13:21:32.000Z

src/bindings/python/tests/test_ngraph/test_eye.py

si-eun-kim/openvino

1db4446e2a6ead55d066e0b4e718fa37f509353a

[ "Apache-2.0" ]

11

2021-11-09T00:51:40.000Z

2021-11-10T12:04:16.000Z

# Copyright (C) 2018-2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import openvino.runtime.opset9 as ov import numpy as np import pytest from tests.runtime import get_runtime from openvino.runtime.utils.types import get_element_type_str from openvino.runtime.utils.types import get_element_type @pytest.mark.parametrize( "num_rows, num_columns, diagonal_index, out_type", [ pytest.param(2, 5, 0, np.float32), pytest.param(5, 3, 2, np.int64), pytest.param(3, 3, -1, np.float16), pytest.param(5, 5, -10, np.float32), ], ) def test_eye_rectangle(num_rows, num_columns, diagonal_index, out_type): num_rows_array = np.array([num_rows], np.int32) num_columns_array = np.array([num_columns], np.int32) diagonal_index_array = np.array([diagonal_index], np.int32) num_rows_tensor = ov.constant(num_rows_array) num_columns_tensor = ov.constant(num_columns_array) diagonal_index_tensor = ov.constant(diagonal_index_array) # Create with param names eye_node = ov.eye(num_rows=num_rows_tensor, num_columns=num_columns_tensor, diagonal_index=diagonal_index_tensor, output_type=get_element_type_str(out_type)) # Create with default orded eye_node = ov.eye(num_rows_tensor, num_columns_tensor, diagonal_index_tensor, get_element_type_str(out_type)) expected_results = np.eye(num_rows, M=num_columns, k=diagonal_index, dtype=np.float32) assert eye_node.get_type_name() == "Eye" assert eye_node.get_output_size() == 1 assert eye_node.get_output_element_type(0) == get_element_type(out_type) assert tuple(eye_node.get_output_shape(0)) == expected_results.shape # TODO: Enable with Eye reference implementation # runtime = get_runtime() # computation = runtime.computation(eye_node) # eye_results = computation() # assert np.allclose(eye_results, expected_results) @pytest.mark.parametrize( "num_rows, num_columns, diagonal_index, batch_shape, out_type", [ pytest.param(2, 5, 0, [1], np.float32), pytest.param(5, 3, 2, [2, 2], np.int64), pytest.param(3, 3, -1, [1, 3, 2], np.float16), pytest.param(5, 5, -10, [1, 1], np.float32), ], ) def test_eye_batch_shape(num_rows, num_columns, diagonal_index, batch_shape, out_type): num_rows_array = np.array([num_rows], np.int32) num_columns_array = np.array([num_columns], np.int32) diagonal_index_array = np.array([diagonal_index], np.int32) batch_shape_array = np.array(batch_shape, np.int32) num_rows_tensor = ov.constant(num_rows_array) num_columns_tensor = ov.constant(num_columns_array) diagonal_index_tensor = ov.constant(diagonal_index_array) batch_shape_tensor = ov.constant(batch_shape_array) # Create with param names eye_node = ov.eye(num_rows=num_rows_tensor, num_columns=num_columns_tensor, diagonal_index=diagonal_index_tensor, batch_shape=batch_shape_tensor, output_type=get_element_type_str(out_type)) # Create with default orded eye_node = ov.eye(num_rows_tensor, num_columns_tensor, diagonal_index_tensor, get_element_type_str(out_type), batch_shape_tensor) output_shape = [*batch_shape, 1, 1] one_matrix = np.eye(num_rows, M=num_columns, k=diagonal_index, dtype=np.float32) expected_results = np.tile(one_matrix, output_shape) assert eye_node.get_type_name() == "Eye" assert eye_node.get_output_size() == 1 assert eye_node.get_output_element_type(0) == get_element_type(out_type) assert tuple(eye_node.get_output_shape(0)) == expected_results.shape # TODO: Enable with Eye reference implementation # runtime = get_runtime() # computation = runtime.computation(eye_node) # eye_results = computation() # assert np.allclose(eye_results, expected_results)

39.776699

90

0.686112

567

4,097

4.619048

0.141093

0.053456

0.042764

0.036655

0.857197

0.842688

0.796487

0.765559

0.700649

0

0.027052

0.215035

4,097

102

91

40.166667

0.787313

0.137418

0

0.542857

0

0.03213

0

0.009804

0.114286

1

0.028571

false

0

0.085714

0

0.114286

0

null

0

1

0

null

0

6

6f3bc48d07d6db347089edf80b48b6fd74fd6c76

2,108

py

Python

download_cifar100_teacher.py

valeoai/QuEST

02a23d2d8e0d059b4a30433f92eec5db146467f4

[ "Apache-2.0" ]

3

2021-06-03T22:45:47.000Z

2022-03-27T18:50:06.000Z

download_cifar100_teacher.py

valeoai/QuEST

02a23d2d8e0d059b4a30433f92eec5db146467f4

[ "Apache-2.0" ]

null

download_cifar100_teacher.py

valeoai/QuEST

02a23d2d8e0d059b4a30433f92eec5db146467f4

[ "Apache-2.0" ]

1

2021-08-20T15:39:40.000Z

import os import urllib.request os.makedirs('saved_models', exist_ok=True) model_path = 'http://shape2prog.csail.mit.edu/repo/wrn_40_2_vanilla/ckpt_epoch_240.pth' model_dir = 'saved_models/wrn_40_2_vanilla' os.makedirs(model_dir, exist_ok=True) urllib.request.urlretrieve(model_path, os.path.join(model_dir, model_path.split('/')[-1])) print(f"Downloaded {model_path.split('repo/')[-1]} to saved_models/") model_path = 'http://shape2prog.csail.mit.edu/repo/resnet56_vanilla/ckpt_epoch_240.pth' model_dir = 'saved_models/resnet56_vanilla' os.makedirs(model_dir, exist_ok=True) urllib.request.urlretrieve(model_path, os.path.join(model_dir, model_path.split('/')[-1])) print(f"Downloaded {model_path.split('repo/')[-1]} to saved_models/") model_path = 'http://shape2prog.csail.mit.edu/repo/resnet110_vanilla/ckpt_epoch_240.pth' model_dir = 'saved_models/resnet110_vanilla' os.makedirs(model_dir, exist_ok=True) urllib.request.urlretrieve(model_path, os.path.join(model_dir, model_path.split('/')[-1])) print(f"Downloaded {model_path.split('repo/')[-1]} to saved_models/") model_path = 'http://shape2prog.csail.mit.edu/repo/resnet32x4_vanilla/ckpt_epoch_240.pth' model_dir = 'saved_models/resnet32x4_vanilla' os.makedirs(model_dir, exist_ok=True) urllib.request.urlretrieve(model_path, os.path.join(model_dir, model_path.split('/')[-1])) print(f"Downloaded {model_path.split('repo/')[-1]} to saved_models/") model_path = 'http://shape2prog.csail.mit.edu/repo/vgg13_vanilla/ckpt_epoch_240.pth' model_dir = 'saved_models/vgg13_vanilla' os.makedirs(model_dir, exist_ok=True) urllib.request.urlretrieve(model_path, os.path.join(model_dir, model_path.split('/')[-1])) print(f"Downloaded {model_path.split('repo/')[-1]} to saved_models/") model_path = 'http://shape2prog.csail.mit.edu/repo/ResNet50_vanilla/ckpt_epoch_240.pth' model_dir = 'saved_models/ResNet50_vanilla' os.makedirs(model_dir, exist_ok=True) urllib.request.urlretrieve(model_path, os.path.join(model_dir, model_path.split('/')[-1])) print(f"Downloaded {model_path.split('repo/')[-1]} to saved_models/")

51.414634

91

0.766129

328

2,108

4.64939

0.121951

0.141639

0.110164

0.090492

0.900984

0.876066

0.714754

0

0.033588

0.067837

2,108

40

92

52.7

0.742494

0

0.545455

0

0.472921

0.174081

0

1

0

false

0

0.060606

0

0.060606

0.181818

0

null

0

1

0

null

0

6

6f3d81cff53a00e04f111ddf20aa94a2c2b57bda

3,885

py

Python

test/lazy/test_cat_lazy_tensor.py

Mehdishishehbor/gpytorch

432e537b3f6679ea4ab3acf33b14626b7e161c92

[ "MIT" ]

null

test/lazy/test_cat_lazy_tensor.py

Mehdishishehbor/gpytorch

432e537b3f6679ea4ab3acf33b14626b7e161c92

[ "MIT" ]

null

test/lazy/test_cat_lazy_tensor.py

Mehdishishehbor/gpytorch

432e537b3f6679ea4ab3acf33b14626b7e161c92

[ "MIT" ]

null

#!/usr/bin/env python3 import unittest import torch from Lgpytorch.lazy import CatLazyTensor, NonLazyTensor from Lgpytorch.test.lazy_tensor_test_case import LazyTensorTestCase class TestCatLazyTensor(LazyTensorTestCase, unittest.TestCase): seed = 1 def create_lazy_tensor(self): root = torch.randn(6, 7) self.psd_mat = root.matmul(root.t()) slice1_mat = self.psd_mat[:2, :].requires_grad_() slice2_mat = self.psd_mat[2:4, :].requires_grad_() slice3_mat = self.psd_mat[4:6, :].requires_grad_() slice1 = NonLazyTensor(slice1_mat) slice2 = NonLazyTensor(slice2_mat) slice3 = NonLazyTensor(slice3_mat) return CatLazyTensor(slice1, slice2, slice3, dim=-2) def evaluate_lazy_tensor(self, lazy_tensor): return self.psd_mat.detach().clone().requires_grad_() class TestCatLazyTensorColumn(LazyTensorTestCase, unittest.TestCase): seed = 1 def create_lazy_tensor(self): root = torch.randn(6, 7) self.psd_mat = root.matmul(root.t()) slice1_mat = self.psd_mat[:, :2].requires_grad_() slice2_mat = self.psd_mat[:, 2:4].requires_grad_() slice3_mat = self.psd_mat[:, 4:6].requires_grad_() slice1 = NonLazyTensor(slice1_mat) slice2 = NonLazyTensor(slice2_mat) slice3 = NonLazyTensor(slice3_mat) return CatLazyTensor(slice1, slice2, slice3, dim=-1) def evaluate_lazy_tensor(self, lazy_tensor): return self.psd_mat.detach().clone().requires_grad_() class TestCatLazyTensorBatch(LazyTensorTestCase, unittest.TestCase): seed = 0 def create_lazy_tensor(self): root = torch.randn(3, 6, 7) self.psd_mat = root.matmul(root.transpose(-2, -1)) slice1_mat = self.psd_mat[..., :2, :].requires_grad_() slice2_mat = self.psd_mat[..., 2:4, :].requires_grad_() slice3_mat = self.psd_mat[..., 4:6, :].requires_grad_() slice1 = NonLazyTensor(slice1_mat) slice2 = NonLazyTensor(slice2_mat) slice3 = NonLazyTensor(slice3_mat) return CatLazyTensor(slice1, slice2, slice3, dim=-2) def evaluate_lazy_tensor(self, lazy_tensor): return self.psd_mat.detach().clone().requires_grad_() class TestCatLazyTensorMultiBatch(LazyTensorTestCase, unittest.TestCase): seed = 0 # Because these LTs are large, we'll skil the big tests skip_slq_tests = True def create_lazy_tensor(self): root = torch.randn(4, 3, 6, 7) self.psd_mat = root.matmul(root.transpose(-2, -1)) slice1_mat = self.psd_mat[..., :2, :].requires_grad_() slice2_mat = self.psd_mat[..., 2:4, :].requires_grad_() slice3_mat = self.psd_mat[..., 4:6, :].requires_grad_() slice1 = NonLazyTensor(slice1_mat) slice2 = NonLazyTensor(slice2_mat) slice3 = NonLazyTensor(slice3_mat) return CatLazyTensor(slice1, slice2, slice3, dim=-2) def evaluate_lazy_tensor(self, lazy_tensor): return self.psd_mat.detach().clone().requires_grad_() class TestCatLazyTensorBatchCat(LazyTensorTestCase, unittest.TestCase): seed = 0 # Because these LTs are large, we'll skil the big tests skip_slq_tests = True def create_lazy_tensor(self): root = torch.randn(5, 3, 6, 7) self.psd_mat = root.matmul(root.transpose(-2, -1)) slice1_mat = self.psd_mat[:2, ...].requires_grad_() slice2_mat = self.psd_mat[2:3, ...].requires_grad_() slice3_mat = self.psd_mat[3:, ...].requires_grad_() slice1 = NonLazyTensor(slice1_mat) slice2 = NonLazyTensor(slice2_mat) slice3 = NonLazyTensor(slice3_mat) return CatLazyTensor(slice1, slice2, slice3, dim=0) def evaluate_lazy_tensor(self, lazy_tensor): return self.psd_mat.detach().clone().requires_grad_() if __name__ == "__main__": unittest.main()

31.844262

73

0.667954

488

3,885

5.040984

0.141393

0.071138

0.101626

0.079268

0.879675

0.863821

0.85122

0.836179

0

0.037748

0.209009

3,885

121

74

32.107438

0.762773

0.033205

0

0.679487

0

0.002132

0

1

0.128205

false

0

0.051282

0.064103

0.461538

0

null

0

1

0

null

0

6

4894cec7ad1d16f91926da91173205b79ee1b463

1,620

py

Python

tests/test_compound_where.py

WinVector/data_algebra

3d6002ddf8231d310e03537a0435df0554b62234

[ "BSD-3-Clause" ]

37

2019-08-28T08:16:48.000Z

2022-03-14T21:18:39.000Z

tests/test_compound_where.py

WinVector/data_algebra

3d6002ddf8231d310e03537a0435df0554b62234

[ "BSD-3-Clause" ]

1

2019-09-02T23:13:29.000Z

2019-09-08T01:43:10.000Z

tests/test_compound_where.py

WinVector/data_algebra

3d6002ddf8231d310e03537a0435df0554b62234

[ "BSD-3-Clause" ]

3

2019-08-28T12:23:11.000Z

2020-02-08T19:22:31.000Z

import data_algebra import data_algebra.test_util from data_algebra.data_ops import * # https://github.com/WinVector/data_algebra import data_algebra.util import data_algebra.SQLite def test_compount_where_and(): d = data_algebra.default_data_model.pd.DataFrame( { "a": ["a", "b", None, None], "b": ["c", None, "d", None], "x": [1, 2, None, None], "y": [3, None, 4, None], } ) ops = describe_table(d, table_name="d").select_rows( 'a == "a" and b == "c" and x > 0 and y < 4' ) db_handle = data_algebra.SQLite.SQLiteModel().db_handle(conn=None) sql = db_handle.to_sql(ops) assert isinstance(sql, str) expect = data_algebra.default_data_model.pd.DataFrame( {"a": ["a"], "b": ["c"], "x": [1.0], "y": [3.0],} ) data_algebra.test_util.check_transform(ops=ops, data=d, expect=expect) def test_compount_where_amp(): d = data_algebra.default_data_model.pd.DataFrame( { "a": ["a", "b", None, None], "b": ["c", None, "d", None], "x": [1, 2, None, None], "y": [3, None, 4, None], } ) ops = describe_table(d, table_name="d").select_rows( 'a == "a" & b == "c" & x > 0 & y < 4' ) db_handle = data_algebra.SQLite.SQLiteModel().db_handle(conn=None) sql = db_handle.to_sql(ops) assert isinstance(sql, str) expect = data_algebra.default_data_model.pd.DataFrame( {"a": ["a"], "b": ["c"], "x": [1.0], "y": [3.0],} ) data_algebra.test_util.check_transform(ops=ops, data=d, expect=expect)

28.421053

80

0.56358

231

1,620

3.744589

0.21645

0.178035

0.017341

0.101734

0.824277

0.756069

0

0.016556

0.254321

1,620

56

81

28.928571

0.699503

0.025309

0

0.55814

0

0.067216

0

0.046512

1

0.046512

false

0

0.116279

0

0.162791

0

null

0

1

0

null

0

6

4896bd7de479f88113218577909931ad2456610b

18,819

py

Python

lshmm/viterbi/vit_diploid_variants_samples.py

jeromekelleher/lshmm

58e0c3395f222e756bb10a0063f5118b20176a01

[ "MIT" ]

null

lshmm/viterbi/vit_diploid_variants_samples.py

jeromekelleher/lshmm

58e0c3395f222e756bb10a0063f5118b20176a01

[ "MIT" ]

9

2022-02-24T14:20:09.000Z

2022-03-01T17:54:47.000Z

lshmm/vit_diploid_variants_samples.py

astheeggeggs/ls_hmm

11af1eb886ef3db2869cdd50954fba5565fcef51

[ "MIT" ]

1

2022-02-28T17:07:36.000Z

"""Collection of functions to run Viterbi algorithms on dipoid genotype data, where the data is structured as variants x samples.""" import numba as nb import numpy as np # https://github.com/numba/numba/issues/1269 @nb.njit def np_apply_along_axis(func1d, axis, arr): """Create numpy-like functions for max, sum etc.""" assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: result = np.empty(arr.shape[1]) for i in range(len(result)): result[i] = func1d(arr[:, i]) else: result = np.empty(arr.shape[0]) for i in range(len(result)): result[i] = func1d(arr[i, :]) return result @nb.njit def np_amax(array, axis): """Numba implementation of numpy vectorised maximum.""" return np_apply_along_axis(np.amax, axis, array) @nb.njit def np_sum(array, axis): """Numba implementation of numpy vectorised sum.""" return np_apply_along_axis(np.sum, axis, array) @nb.njit def np_argmax(array, axis): """Numba implementation of numpy vectorised argmax.""" return np_apply_along_axis(np.argmax, axis, array) # def forwards_viterbi_dip_naive(n, m, G, s, e, r): # # Initialise # V = np.zeros((m, n, n)) # P = np.zeros((m, n, n)).astype(np.int64) # c = np.ones(m) # index = ( # 4*np.equal(G[0,:,:], s[0,0]).astype(np.int64) + # 2*(G[0,:,:] == 1).astype(np.int64) + # np.int64(s[0,0] == 1) # ) # V[0,:,:] = 1/(n**2) * e[0,index] # r_n = r/n # for l in range(1,m): # index = ( # 4*np.equal(G[l,:,:], s[0,l]).astype(np.int64) + # 2*(G[l,:,:] == 1).astype(np.int64) + # np.int64(s[0,l] == 1) # ) # for j1 in range(n): # for j2 in range(n): # # Get the vector to maximise over # v = np.zeros((n,n)) # for k1 in range(n): # for k2 in range(n): # v[k1, k2] = V[l-1,k1, k2] # if ((k1 == j1) and (k2 == j2)): # v[k1, k2] *= ((1 - r[l])**2 + 2*(1-r[l]) * r_n[l] + r_n[l]**2) # elif ((k1 == j1) or (k2 == j2)): # v[k1, k2] *= (r_n[l] * (1 - r[l]) + r_n[l]**2) # else: # v[k1, k2] *= r_n[l]**2 # V[l,j1,j2] = np.amax(v) * e[l, index[j1, j2]] # P[l,j1,j2] = np.argmax(v) # c[l] = np.amax(V[l,:,:]) # V[l,:,:] *= 1/c[l] # ll = np.sum(np.log10(c)) # return V, P, ll @nb.njit def forwards_viterbi_dip_naive(n, m, G, s, e, r): """Naive implementation of LS diploid Viterbi algorithm.""" # Initialise V = np.zeros((m, n, n)) P = np.zeros((m, n, n)).astype(np.int64) c = np.ones(m) r_n = r / n for j1 in range(n): for j2 in range(n): index_tmp = ( 4 * np.int64(np.equal(G[0, j1, j2], s[0, 0])) + 2 * np.int64((G[0, j1, j2] == 1)) + np.int64(s[0, 0] == 1) ) V[0, j1, j2] = 1 / (n ** 2) * e[0, index_tmp] for l in range(1, m): index = ( 4 * np.equal(G[l, :, :], s[0, l]).astype(np.int64) + 2 * (G[l, :, :] == 1).astype(np.int64) + np.int64(s[0, l] == 1) ) for j1 in range(n): for j2 in range(n): # Get the vector to maximise over v = np.zeros((n, n)) for k1 in range(n): for k2 in range(n): v[k1, k2] = V[l - 1, k1, k2] if (k1 == j1) and (k2 == j2): v[k1, k2] *= ( (1 - r[l]) ** 2 + 2 * (1 - r[l]) * r_n[l] + r_n[l] ** 2 ) elif (k1 == j1) or (k2 == j2): v[k1, k2] *= r_n[l] * (1 - r[l]) + r_n[l] ** 2 else: v[k1, k2] *= r_n[l] ** 2 V[l, j1, j2] = np.amax(v) * e[l, index[j1, j2]] P[l, j1, j2] = np.argmax(v) c[l] = np.amax(V[l, :, :]) V[l, :, :] *= 1 / c[l] ll = np.sum(np.log10(c)) return V, P, ll # def forwards_viterbi_dip_naive_low_mem(n, m, G, s, e, r): # # Initialise # V = np.zeros((n,n)) # P = np.zeros((m,n,n)).astype(np.int64) # c = np.ones(m) # index = ( # 4*np.equal(G[0,:,:], s[0,0]).astype(np.int64) + # 2*(G[0,:,:] == 1).astype(np.int64) + # np.int64(s[0,0] == 1) # ) # V_previous = 1/(n**2) * e[0,index] # r_n = r/n # # Take a look at Haploid Viterbi implementation in Jeromes code and see if we can pinch some ideas. # # Diploid Viterbi, with smaller memory footprint. # for l in range(1,m): # index = ( # 4*np.equal(G[l,:,:], s[0,l]).astype(np.int64) + # 2*(G[l,:,:] == 1).astype(np.int64) + # np.int64(s[0,l] == 1) # ) # for j1 in range(n): # for j2 in range(n): # # Get the vector to maximise over # v = np.zeros((n,n)) # for k1 in range(n): # for k2 in range(n): # v[k1, k2] = V_previous[k1, k2] # if ((k1 == j1) and (k2 == j2)): # v[k1, k2] *= ((1 - r[l])**2 + 2*(1-r[l]) * r_n[l] + r_n[l]**2) # elif ((k1 == j1) or (k2 == j2)): # v[k1, k2] *= (r_n[l] * (1 - r[l]) + r_n[l]**2) # else: # v[k1, k2] *= r_n[l]**2 # V[j1,j2] = np.amax(v) * e[l,index[j1, j2]] # P[l,j1,j2] = np.argmax(v) # c[l] = np.amax(V) # V_previous = np.copy(V) / c[l] # ll = np.sum(np.log10(c)) # return V, P, ll @nb.njit def forwards_viterbi_dip_naive_low_mem(n, m, G, s, e, r): """Naive implementation of LS diploid Viterbi algorithm, with reduced memory.""" # Initialise V = np.zeros((n, n)) V_previous = np.zeros((n, n)) P = np.zeros((m, n, n)).astype(np.int64) c = np.ones(m) r_n = r / n for j1 in range(n): for j2 in range(n): index_tmp = ( 4 * np.int64(np.equal(G[0, j1, j2], s[0, 0])) + 2 * np.int64((G[0, j1, j2] == 1)) + np.int64(s[0, 0] == 1) ) V_previous[j1, j2] = 1 / (n ** 2) * e[0, index_tmp] # Take a look at Haploid Viterbi implementation in Jeromes code and see if we can pinch some ideas. # Diploid Viterbi, with smaller memory footprint. for l in range(1, m): index = ( 4 * np.equal(G[l, :, :], s[0, l]).astype(np.int64) + 2 * (G[l, :, :] == 1).astype(np.int64) + np.int64(s[0, l] == 1) ) for j1 in range(n): for j2 in range(n): # Get the vector to maximise over v = np.zeros((n, n)) for k1 in range(n): for k2 in range(n): v[k1, k2] = V_previous[k1, k2] if (k1 == j1) and (k2 == j2): v[k1, k2] *= ( (1 - r[l]) ** 2 + 2 * (1 - r[l]) * r_n[l] + r_n[l] ** 2 ) elif (k1 == j1) or (k2 == j2): v[k1, k2] *= r_n[l] * (1 - r[l]) + r_n[l] ** 2 else: v[k1, k2] *= r_n[l] ** 2 V[j1, j2] = np.amax(v) * e[l, index[j1, j2]] P[l, j1, j2] = np.argmax(v) c[l] = np.amax(V) V_previous = np.copy(V) / c[l] ll = np.sum(np.log10(c)) return V, P, ll # def forwards_viterbi_dip_low_mem(n, m, G, s, e, r): # # Initialise # V = np.zeros((n, n)) # P = np.zeros((m,n,n)).astype(np.int64) # index = ( # 4*np.equal(G[0,:,:], s[0,0]).astype(np.int64) + # 2*(G[0,:,:] == 1).astype(np.int64) + # np.int64(s[0,0] == 1) # ) # V_previous = 1/(n**2) * e[0,index] # c = np.ones(m) # r_n = r/n # # Diploid Viterbi, with smaller memory footprint, rescaling, and using the structure of the HMM. # for l in range(1,m): # index = ( # 4*np.equal(G[l,:,:], s[0,l]).astype(np.int64) + # 2*(G[l,:,:] == 1).astype(np.int64) + # np.int64(s[0,l] == 1) # ) # c[l] = np.amax(V_previous) # argmax = np.argmax(V_previous) # V_previous *= 1/c[l] # V_rowcol_max = np_amax(V_previous, 0) # arg_rowcol_max = np_argmax(V_previous, 0) # no_switch = (1 - r[l])**2 + 2*(r_n[l]*(1 - r[l])) + r_n[l]**2 # single_switch = r_n[l]*(1 - r[l]) + r_n[l]**2 # double_switch = r_n[l]**2 # j1_j2 = 0 # for j1 in range(n): # for j2 in range(n): # V_single_switch = max(V_rowcol_max[j1], V_rowcol_max[j2]) # P_single_switch = np.argmax(np.array([V_rowcol_max[j1], V_rowcol_max[j2]])) # if P_single_switch == 0: # template_single_switch = j1*n + arg_rowcol_max[j1] # else: # template_single_switch = arg_rowcol_max[j2]*n + j2 # V[j1,j2] = V_previous[j1,j2] * no_switch # No switch in either # P[l, j1, j2] = j1_j2 # # Single or double switch? # single_switch_tmp = single_switch * V_single_switch # if (single_switch_tmp > double_switch): # # Then single switch is the alternative # if (V[j1,j2] < single_switch * V_single_switch): # V[j1,j2] = single_switch * V_single_switch # P[l, j1, j2] = template_single_switch # else: # # Double switch is the alternative # if V[j1, j2] < double_switch: # V[j1, j2] = double_switch # P[l, j1, j2] = argmax # V[j1,j2] *= e[l, index[j1, j2]] # j1_j2 += 1 # V_previous = np.copy(V) # ll = np.sum(np.log10(c)) + np.log10(np.amax(V)) # return V, P, ll @nb.njit def forwards_viterbi_dip_low_mem(n, m, G, s, e, r): """LS diploid Viterbi algorithm, with reduced memory.""" # Initialise V = np.zeros((n, n)) V_previous = np.zeros((n, n)) P = np.zeros((m, n, n)).astype(np.int64) c = np.ones(m) r_n = r / n for j1 in range(n): for j2 in range(n): index_tmp = ( 4 * np.int64(np.equal(G[0, j1, j2], s[0, 0])) + 2 * np.int64((G[0, j1, j2] == 1)) + np.int64(s[0, 0] == 1) ) V_previous[j1, j2] = 1 / (n ** 2) * e[0, index_tmp] # Diploid Viterbi, with smaller memory footprint, rescaling, and using the structure of the HMM. for l in range(1, m): index = ( 4 * np.equal(G[l, :, :], s[0, l]).astype(np.int64) + 2 * (G[l, :, :] == 1).astype(np.int64) + np.int64(s[0, l] == 1) ) c[l] = np.amax(V_previous) argmax = np.argmax(V_previous) V_previous *= 1 / c[l] V_rowcol_max = np_amax(V_previous, 0) arg_rowcol_max = np_argmax(V_previous, 0) no_switch = (1 - r[l]) ** 2 + 2 * (r_n[l] * (1 - r[l])) + r_n[l] ** 2 single_switch = r_n[l] * (1 - r[l]) + r_n[l] ** 2 double_switch = r_n[l] ** 2 j1_j2 = 0 for j1 in range(n): for j2 in range(n): V_single_switch = max(V_rowcol_max[j1], V_rowcol_max[j2]) P_single_switch = np.argmax( np.array([V_rowcol_max[j1], V_rowcol_max[j2]]) ) if P_single_switch == 0: template_single_switch = j1 * n + arg_rowcol_max[j1] else: template_single_switch = arg_rowcol_max[j2] * n + j2 V[j1, j2] = V_previous[j1, j2] * no_switch # No switch in either P[l, j1, j2] = j1_j2 # Single or double switch? single_switch_tmp = single_switch * V_single_switch if single_switch_tmp > double_switch: # Then single switch is the alternative if V[j1, j2] < single_switch * V_single_switch: V[j1, j2] = single_switch * V_single_switch P[l, j1, j2] = template_single_switch else: # Double switch is the alternative if V[j1, j2] < double_switch: V[j1, j2] = double_switch P[l, j1, j2] = argmax V[j1, j2] *= e[l, index[j1, j2]] j1_j2 += 1 V_previous = np.copy(V) ll = np.sum(np.log10(c)) + np.log10(np.amax(V)) return V, P, ll # def forwards_viterbi_dip_naive_vec(n, m, G, s, e, r): # # Initialise # V = np.zeros((m,n,n)) # P = np.zeros((m,n,n)).astype(np.int64) # c = np.ones(m) # index = ( # 4*np.equal(G[0,:,:], s[0,0]).astype(np.int64) + # 2*(G[0,:,:] == 1).astype(np.int64) + # np.int64(s[0,0] == 1) # ) # V[0,:,:] = 1/(n**2) * e[0,index] # r_n = r/n # # Jumped the gun - vectorising. # for l in range(1,m): # index = ( # 4*np.equal(G[l,:,:], s[0,l]).astype(np.int64) + # 2*(G[l,:,:] == 1).astype(np.int64) + # np.int64(s[0,l] == 1) # ) # for j1 in range(n): # for j2 in range(n): # v = (r_n[l]**2) * np.ones((n,n)) # v[j1,j2] += (1-r[l])**2 # v[j1, :] += (r_n[l] * (1 - r[l])) # v[:, j2] += (r_n[l] * (1 - r[l])) # v *= V[l-1,:,:] # V[l,j1,j2] = np.amax(v) * e[l,index[j1, j2]] # P[l,j1,j2] = np.argmax(v) # c[l] = np.amax(V[l,:,:]) # V[l,:,:] *= 1/c[l] # ll = np.sum(np.log10(c)) # return V, P, ll @nb.jit def forwards_viterbi_dip_naive_vec(n, m, G, s, e, r): """Vectorised LS diploid Viterbi algorithm using numpy.""" # Initialise V = np.zeros((m, n, n)) P = np.zeros((m, n, n)).astype(np.int64) c = np.ones(m) r_n = r / n for j1 in range(n): for j2 in range(n): index_tmp = ( 4 * np.int64(np.equal(G[0, j1, j2], s[0, 0])) + 2 * np.int64((G[0, j1, j2] == 1)) + np.int64(s[0, 0] == 1) ) V[0, j1, j2] = 1 / (n ** 2) * e[0, index_tmp] # Jumped the gun - vectorising. for l in range(1, m): index = ( 4 * np.equal(G[l, :, :], s[0, l]).astype(np.int64) + 2 * (G[l, :, :] == 1).astype(np.int64) + np.int64(s[0, l] == 1) ) for j1 in range(n): for j2 in range(n): v = (r_n[l] ** 2) * np.ones((n, n)) v[j1, j2] += (1 - r[l]) ** 2 v[j1, :] += r_n[l] * (1 - r[l]) v[:, j2] += r_n[l] * (1 - r[l]) v *= V[l - 1, :, :] V[l, j1, j2] = np.amax(v) * e[l, index[j1, j2]] P[l, j1, j2] = np.argmax(v) c[l] = np.amax(V[l, :, :]) V[l, :, :] *= 1 / c[l] ll = np.sum(np.log10(c)) return V, P, ll def forwards_viterbi_dip_naive_full_vec(n, m, G, s, e, r): """Fully vectorised naive LS diploid Viterbi algorithm using numpy.""" char_both = np.eye(n * n).ravel().reshape((n, n, n, n)) char_col = np.tile(np.sum(np.eye(n * n).reshape((n, n, n, n)), 3), (n, 1, 1, 1)) char_row = np.copy(char_col).T rows, cols = np.ogrid[:n, :n] # Initialise V = np.zeros((m, n, n)) P = np.zeros((m, n, n)).astype(np.int64) c = np.ones(m) index = ( 4 * np.equal(G[0, :, :], s[0, 0]).astype(np.int64) + 2 * (G[0, :, :] == 1).astype(np.int64) + np.int64(s[0, 0] == 1) ) V[0, :, :] = 1 / (n ** 2) * e[0, index] r_n = r / n for l in range(1, m): index = ( 4 * np.equal(G[l, :, :], s[0, l]).astype(np.int64) + 2 * (G[l, :, :] == 1).astype(np.int64) + np.int64(s[0, l] == 1) ) v = ( (r_n[l] ** 2) + (1 - r[l]) ** 2 * char_both + (r_n[l] * (1 - r[l])) * (char_col + char_row) ) v *= V[l - 1, :, :] P[l, :, :] = np.argmax(v.reshape(n, n, -1), 2) # Have to flatten to use argmax V[l, :, :] = v.reshape(n, n, -1)[rows, cols, P[l, :, :]] * e[l, index] c[l] = np.amax(V[l, :, :]) V[l, :, :] *= 1 / c[l] ll = np.sum(np.log10(c)) return V, P, ll @nb.jit def backwards_viterbi_dip(m, V_last, P): """Run a backwards pass to determine the most likely path.""" assert V_last.ndim == 2 assert V_last.shape[0] == V_last.shape[1] # Initialisation path = np.zeros(m).astype(np.int64) path[m - 1] = np.argmax(V_last) # Backtrace for j in range(m - 2, -1, -1): path[j] = P[j + 1, :, :].ravel()[path[j + 1]] return path def get_phased_path(n, path): """Obtain the phased path.""" return np.unravel_index(path, (n, n)) @nb.jit def path_ll_dip(n, m, G, phased_path, s, e, r): """Evaluate log-likelihood path through a reference panel which results in sequence s.""" index = ( 4 * np.int64(np.equal(G[0, phased_path[0][0], phased_path[1][0]], s[0, 0])) + 2 * np.int64(G[0, phased_path[0][0], phased_path[1][0]] == 1) + np.int64(s[0, 0] == 1) ) log_prob_path = np.log10(1 / (n ** 2) * e[0, index]) old_phase = np.array([phased_path[0][0], phased_path[1][0]]) r_n = r / n for l in range(1, m): index = ( 4 * np.int64(np.equal(G[l, phased_path[0][l], phased_path[1][l]], s[0, l])) + 2 * np.int64(G[l, phased_path[0][l], phased_path[1][l]] == 1) + np.int64(s[0, l] == 1) ) current_phase = np.array([phased_path[0][l], phased_path[1][l]]) phase_diff = np.sum(~np.equal(current_phase, old_phase)) if phase_diff == 0: log_prob_path += np.log10( (1 - r[l]) ** 2 + 2 * (r_n[l] * (1 - r[l])) + r_n[l] ** 2 ) elif phase_diff == 1: log_prob_path += np.log10(r_n[l] * (1 - r[l]) + r_n[l] ** 2) else: log_prob_path += np.log10(r_n[l] ** 2) log_prob_path += np.log10(e[l, index]) old_phase = current_phase return log_prob_path

33.307965

132

0.435145

2,901

18,819

2.720786

0.064461

0.06208

0.016344

0.012163

0.862283

0.848347

0.813632

0.789814

0.775877

0

0.065352

0.380413

18,819

564

133

33.367021

0.611578

0.418035

0

0.558052

0

0.014981

1

0.044944

false

0

0.007491

0

0.097378

0

null

0

1

0

null

0

6

48eca2b30f95acacb8513624eb0235e73603734b

183

py

Python

src/c3nav/site/templatetags/route_render.py

johnjohndoe/c3nav

a17f863a3512e305595c16b0300796b6bae81241

[ "Apache-2.0" ]

132

2016-11-12T01:45:23.000Z

2022-03-08T15:17:10.000Z

src/c3nav/site/templatetags/route_render.py

johnjohndoe/c3nav

a17f863a3512e305595c16b0300796b6bae81241

[ "Apache-2.0" ]

66

2016-09-29T09:46:19.000Z

2022-03-11T23:26:18.000Z

src/c3nav/site/templatetags/route_render.py

johnjohndoe/c3nav

a17f863a3512e305595c16b0300796b6bae81241

[ "Apache-2.0" ]

42

2016-09-29T08:34:57.000Z

2022-03-08T15:17:15.000Z

from django import template register = template.Library() @register.filter def negate(value): return -value @register.filter def subtract(value, arg): return value - arg

13.071429

29

0.726776

23

183

5.782609

0.565217

0.210526

0.255639

0

0.180328

183

13

30

14.076923

0.886667

0

0.25

0

1

0.25

false

0

0.125

0.25

0.625

0

1

0

null

1

0

null

0

1

0

1

0

6

5b1a7c8341406690f20aa12accdb9fc9001deadc

238

py

Python

speechpro/cloud/speech/synthesis/rest/cloud_client/api/__init__.py

speechpro/cloud-python

dfcfc19a1f008b55c5290599c594fe8de777018b

[ "MIT" ]

15

2020-05-27T09:35:32.000Z

2022-03-29T18:35:36.000Z

speechpro/cloud/speech/synthesis/rest/cloud_client/api/__init__.py

speechpro/cloud-python

dfcfc19a1f008b55c5290599c594fe8de777018b

[ "MIT" ]

null

speechpro/cloud/speech/synthesis/rest/cloud_client/api/__init__.py

speechpro/cloud-python

dfcfc19a1f008b55c5290599c594fe8de777018b

[ "MIT" ]

1

2021-04-06T21:39:29.000Z

from __future__ import absolute_import # flake8: noqa # import apis into api package import speechpro.cloud.speech.synthesis.rest.cloud_client.api.session_api import speechpro.cloud.speech.synthesis.rest.cloud_client.api.synthesize_api

29.75

76

0.848739

34

238

5.676471

0.529412

0.15544

0.207254

0.26943

0.549223

0

0.004566

0.079832

238

7

77

34

0.876712

0.172269

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

d294cefa293f8d84c96bacb7467d9cfe88246372

147

py

Python

armageddon/__init__.py

acse-ns1321/asteroid-impact-simulator

986c12ff1276e5d0547a4f760e1d2cb90fe4ba11

[ "MIT" ]

null

armageddon/__init__.py

acse-ns1321/asteroid-impact-simulator

986c12ff1276e5d0547a4f760e1d2cb90fe4ba11

[ "MIT" ]

null

armageddon/__init__.py

acse-ns1321/asteroid-impact-simulator

986c12ff1276e5d0547a4f760e1d2cb90fe4ba11

[ "MIT" ]

null

# flake8:NOQA """Python asteroid airburst calculator""" from .solver import * from .damage import * from .locator import * from .mapping import *

18.375

41

0.734694

18

147

6

0.666667

0.277778

0

0.008065

0.156463

147

7

42

21

0.862903

0.326531

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

d2a2c147c06d327188733c71e9a83b70f75131b1

27

py

Python

micro-benchmark-key-errs/snippets/dicts/type_coercion/main.py

WenJinfeng/PyCG

b45e8e04fe697d8301cf27222a8f37646d69f168

[ "Apache-2.0" ]

121

2020-12-16T20:31:37.000Z

2022-03-21T20:32:43.000Z

micro-benchmark-key-errs/snippets/dicts/type_coercion/main.py

WenJinfeng/PyCG

b45e8e04fe697d8301cf27222a8f37646d69f168

[ "Apache-2.0" ]

24

2021-03-13T00:04:00.000Z

2022-03-21T17:28:11.000Z

micro-benchmark-key-errs/snippets/dicts/type_coercion/main.py

WenJinfeng/PyCG

b45e8e04fe697d8301cf27222a8f37646d69f168

[ "Apache-2.0" ]

19

2021-03-23T10:58:47.000Z

2022-03-24T19:46:50.000Z

d = {"1": "a"} d[1] d["1"]

6.75

14

0.259259

7

27

1

0.428571

0.857143

0

0.142857

0.222222

27

3

15

9

0.190476

0

0.111111

0

1

0

false

0

1

null

1

0

1

0

1

0

null

0

6

d2bc823500d7e835a13076bd5554f0f404893ff4

243

py

Python

jmeter_api/timers/__init__.py

dashawn888/jmeter_api

1ab5b02f3a7c8ad1b84fc50db4fe1fc2fa7c91bd

[ "Apache-2.0" ]

11

2020-03-22T13:30:21.000Z

2021-12-25T06:23:44.000Z

jmeter_api/timers/__init__.py

dashawn888/jmeter_api

1ab5b02f3a7c8ad1b84fc50db4fe1fc2fa7c91bd

[ "Apache-2.0" ]

2

2020-03-23T00:06:42.000Z

2021-02-24T21:41:40.000Z

jmeter_api/timers/__init__.py

dashawn888/jmeter_api

1ab5b02f3a7c8ad1b84fc50db4fe1fc2fa7c91bd

[ "Apache-2.0" ]

3

2020-11-09T14:14:25.000Z

2021-05-27T02:54:38.000Z

from jmeter_api.timers.constant_throughput_timer.elements import ConstantThroughputTimer, BasedOn from jmeter_api.timers.constant_timer.elements import ConstantTimer from jmeter_api.timers.uniform_random_timer.elements import UniformRandTimer

60.75

97

0.90535

30

243

7.066667

0.5

0.141509

0.183962

0.268868

0.254717

0

0.053498

243

3

98

81

0.921739

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

d2d3eacc8c8caee95603f50b68c177c406992381

83

py

Python

backend/grant/task/__init__.py

DSBUGAY2/zcash-grant-system

729b9edda13bd1eeb3f445d889264230c6470d7e

[ "MIT" ]

8

2019-06-03T16:29:49.000Z

2021-05-11T20:38:36.000Z

backend/grant/task/__init__.py

DSBUGAY2/zcash-grant-system

729b9edda13bd1eeb3f445d889264230c6470d7e

[ "MIT" ]

342

2019-01-15T19:13:58.000Z

2020-03-24T16:38:13.000Z

backend/grant/task/__init__.py

DSBUGAY2/zcash-grant-system

729b9edda13bd1eeb3f445d889264230c6470d7e

[ "MIT" ]

5

2019-02-15T09:06:47.000Z

2022-01-24T21:38:41.000Z

from . import models from . import views from . import commands from . import jobs

16.6

22

0.759036

12

83

5.25

0.5

0.634921

0

0.192771

83

5

23

16.6

0.940299

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

d2e9f3e2143b7da446094a72db5befcb7fc0a728

54,559

py

Python

autogalaxy/profiles/mass_profiles/stellar_mass_profiles.py

Jammy2211/PyAutoModel

02f54e71900de9ec12c9070dc00a4bd001b25afa

[ "MIT" ]

4

2019-10-29T13:27:23.000Z

2020-03-24T11:13:35.000Z

autogalaxy/profiles/mass_profiles/stellar_mass_profiles.py

Jammy2211/PyAutoModel

02f54e71900de9ec12c9070dc00a4bd001b25afa

[ "MIT" ]

null

autogalaxy/profiles/mass_profiles/stellar_mass_profiles.py

Jammy2211/PyAutoModel

02f54e71900de9ec12c9070dc00a4bd001b25afa

[ "MIT" ]

3

2020-02-12T10:29:59.000Z

2020-03-24T11:13:53.000Z

import copy import numpy as np from scipy.special import wofz from scipy.integrate import quad from typing import List, Tuple import autoarray as aa from autogalaxy.profiles.mass_profiles import MassProfile from autogalaxy.profiles.mass_profiles.mass_profiles import ( MassProfileMGE, MassProfileCSE, ) from autogalaxy.profiles.mass_profiles.mass_profiles import psi_from class StellarProfile: pass class EllGaussian(MassProfile, StellarProfile): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, sigma: float = 0.01, mass_to_light_ratio: float = 1.0, ): """ The elliptical Gaussian light profile. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall intensity normalisation of the light profile (units are dimensionless and derived from the data the light profile's image is compared too, which is expected to be electrons per second). sigma The sigma value of the Gaussian. """ super(EllGaussian, self).__init__( centre=centre, elliptical_comps=elliptical_comps ) super(MassProfile, self).__init__( centre=centre, elliptical_comps=elliptical_comps ) self.mass_to_light_ratio = mass_to_light_ratio self.intensity = intensity self.sigma = sigma def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike): """ Calculate the deflection angles at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the deflection angles are computed on. """ return self.deflections_2d_via_analytic_from(grid=grid) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_analytic_from(self, grid: aa.type.Grid2DLike): """ Calculate the deflection angles at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the deflection angles are computed on. """ deflections = ( self.mass_to_light_ratio * self.intensity * self.sigma * np.sqrt((2 * np.pi) / (1.0 - self.axis_ratio ** 2.0)) * self.zeta_from(grid=grid) ) return self.rotate_grid_from_reference_frame( np.multiply( 1.0, np.vstack((-1.0 * np.imag(deflections), np.real(deflections))).T ) ) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_integral_from(self, grid: aa.type.Grid2DLike): """ Calculate the deflection angles at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the deflection angles are computed on. Note: sigma is divided by sqrt(q) here. """ def calculate_deflection_component(npow, index): deflection_grid = self.axis_ratio * grid[:, index] for i in range(grid.shape[0]): deflection_grid[i] *= ( self.intensity * self.mass_to_light_ratio * quad( self.deflection_func, a=0.0, b=1.0, args=( grid[i, 0], grid[i, 1], npow, self.axis_ratio, self.sigma / np.sqrt(self.axis_ratio), ), )[0] ) return deflection_grid deflection_y = calculate_deflection_component(1.0, 0) deflection_x = calculate_deflection_component(0.0, 1) return self.rotate_grid_from_reference_frame( np.multiply(1.0, np.vstack((deflection_y, deflection_x)).T) ) @staticmethod def deflection_func(u, y, x, npow, axis_ratio, sigma): eta_u = np.sqrt(axis_ratio) * np.sqrt( (u * ((x ** 2) + (y ** 2 / (1 - (1 - axis_ratio ** 2) * u)))) ) return np.exp(-0.5 * np.square(np.divide(eta_u, sigma))) / ( (1 - (1 - axis_ratio ** 2) * u) ** (npow + 0.5) ) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def convergence_2d_from(self, grid: aa.type.Grid2DLike): """Calculate the projected convergence at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ return self.convergence_func(self.grid_to_eccentric_radii(grid)) def convergence_func(self, grid_radius: float) -> float: return self.mass_to_light_ratio * self.image_2d_via_radii_from(grid_radius) @aa.grid_dec.grid_2d_to_structure def potential_2d_from(self, grid: aa.type.Grid2DLike): return np.zeros(shape=grid.shape[0]) def image_2d_via_radii_from(self, grid_radii: np.ndarray): """Calculate the intensity of the Gaussian light profile on a grid of radial coordinates. Parameters ---------- grid_radii The radial distance from the centre of the profile. for each coordinate on the grid. Note: sigma is divided by sqrt(q) here. """ return np.multiply( self.intensity, np.exp( -0.5 * np.square( np.divide(grid_radii, self.sigma / np.sqrt(self.axis_ratio)) ) ), ) @property def axis_ratio(self): axis_ratio = super().axis_ratio return axis_ratio if axis_ratio < 0.9999 else 0.9999 def zeta_from(self, grid: aa.type.Grid2DLike): q2 = self.axis_ratio ** 2.0 ind_pos_y = grid[:, 0] >= 0 shape_grid = np.shape(grid) output_grid = np.zeros((shape_grid[0]), dtype=np.complex128) scale_factor = self.axis_ratio / (self.sigma * np.sqrt(2.0 * (1.0 - q2))) xs_0 = grid[:, 1][ind_pos_y] * scale_factor ys_0 = grid[:, 0][ind_pos_y] * scale_factor xs_1 = grid[:, 1][~ind_pos_y] * scale_factor ys_1 = -grid[:, 0][~ind_pos_y] * scale_factor output_grid[ind_pos_y] = -1j * ( wofz(xs_0 + 1j * ys_0) - np.exp(-(xs_0 ** 2.0) * (1.0 - q2) - ys_0 * ys_0 * (1.0 / q2 - 1.0)) * wofz(self.axis_ratio * xs_0 + 1j * ys_0 / self.axis_ratio) ) output_grid[~ind_pos_y] = np.conj( -1j * ( wofz(xs_1 + 1j * ys_1) - np.exp(-(xs_1 ** 2.0) * (1.0 - q2) - ys_1 * ys_1 * (1.0 / q2 - 1.0)) * wofz(self.axis_ratio * xs_1 + 1j * ys_1 / self.axis_ratio) ) ) return output_grid def with_new_normalization(self, normalization): mass_profile = copy.copy(self) mass_profile.mass_to_light_ratio = normalization return mass_profile # noinspection PyAbstractClass class AbstractEllSersic(MassProfile, MassProfileMGE, MassProfileCSE, StellarProfile): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, sersic_index: float = 0.6, mass_to_light_ratio: float = 1.0, ): """ The Sersic mass profile, the mass profiles of the light profiles that are used to fit and subtract the lens \ model_galaxy's light. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The radius containing half the light of this profile. sersic_index Controls the concentration of the profile (lower -> less concentrated, higher -> more concentrated). mass_to_light_ratio The mass-to-light ratio of the light profiles """ super(AbstractEllSersic, self).__init__( centre=centre, elliptical_comps=elliptical_comps ) super(MassProfile, self).__init__( centre=centre, elliptical_comps=elliptical_comps ) super(MassProfileMGE, self).__init__() super(MassProfileCSE, self).__init__() self.mass_to_light_ratio = mass_to_light_ratio self.intensity = intensity self.effective_radius = effective_radius self.sersic_index = sersic_index def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike): return self.deflections_2d_via_cse_from(grid=grid) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_mge_from(self, grid: aa.type.Grid2DLike): """ Calculate the projected 2D deflection angles from a grid of (y,x) arc second coordinates, by computing and summing the convergence of each individual cse used to decompose the mass profile. The cored steep elliptical (cse) decomposition of a the elliptical NFW mass profile (e.g. `decompose_convergence_via_cse`) is using equation (12) of Oguri 2021 (https://arxiv.org/abs/2106.11464). Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ return self._deflections_2d_via_mge_from( grid=grid, sigmas_factor=np.sqrt(self.axis_ratio) ) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_cse_from(self, grid: aa.type.Grid2DLike): """ Calculate the projected 2D deflection angles from a grid of (y,x) arc second coordinates, by computing and summing the convergence of each individual cse used to decompose the mass profile. The cored steep elliptical (cse) decomposition of a the elliptical NFW mass profile (e.g. `decompose_convergence_via_cse`) is using equation (12) of Oguri 2021 (https://arxiv.org/abs/2106.11464). Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ return self._deflections_2d_via_cse_from(grid=grid) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def convergence_2d_from(self, grid: aa.type.Grid2DLike): """Calculate the projected convergence at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ return self.convergence_func(self.grid_to_eccentric_radii(grid)) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def convergence_2d_via_mge_from(self, grid: aa.type.Grid2DLike): """ Calculate the projected convergence at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ eccentric_radii = self.grid_to_eccentric_radii(grid=grid) return self._convergence_2d_via_mge_from(grid_radii=eccentric_radii) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def convergence_2d_via_cse_from(self, grid: aa.type.Grid2DLike): """ Calculate the projected 2D convergence from a grid of (y,x) arc second coordinates, by computing and summing the convergence of each individual cse used to decompose the mass profile. The cored steep elliptical (cse) decomposition of a the elliptical NFW mass profile (e.g. `decompose_convergence_via_cse`) is using equation (12) of Oguri 2021 (https://arxiv.org/abs/2106.11464). Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ elliptical_radii = self.grid_to_elliptical_radii(grid=grid) return self._convergence_2d_via_cse_from(grid_radii=elliptical_radii) def convergence_func(self, grid_radius: float) -> float: return self.mass_to_light_ratio * self.image_2d_via_radii_from(grid_radius) @aa.grid_dec.grid_2d_to_structure def potential_2d_from(self, grid: aa.type.Grid2DLike): return np.zeros(shape=grid.shape[0]) def image_2d_via_radii_from(self, radius: np.ndarray): """ Returns the intensity of the profile at a given radius. Parameters ---------- radius The distance from the centre of the profile. """ return self.intensity * np.exp( -self.sersic_constant * (((radius / self.effective_radius) ** (1.0 / self.sersic_index)) - 1) ) def decompose_convergence_via_mge(self) -> Tuple[List, List]: radii_min = self.effective_radius / 100.0 radii_max = self.effective_radius * 20.0 def sersic_2d(r): return ( self.mass_to_light_ratio * self.intensity * np.exp( -self.sersic_constant * (((r / self.effective_radius) ** (1.0 / self.sersic_index)) - 1.0) ) ) return self._decompose_convergence_via_mge( func=sersic_2d, radii_min=radii_min, radii_max=radii_max ) def decompose_convergence_via_cse(self,) -> Tuple[List, List]: """ Decompose the convergence of the Sersic profile into cored steep elliptical (cse) profiles. This decomposition uses the standard 2d profile of a Sersic mass profile. Parameters ---------- func The function representing the profile that is decomposed into CSEs. radii_min: The minimum radius to fit radii_max: The maximum radius to fit total_cses The number of CSEs used to approximate the input func. sample_points: int (should be larger than 'total_cses') The number of data points to fit Returns ------- Tuple[List, List] A list of amplitudes and core radii of every cored steep elliptical (cse) the mass profile is decomposed into. """ upper_dex, lower_dex, total_cses, sample_points = cse_settings_from( effective_radius=self.effective_radius, sersic_index=self.sersic_index, sersic_constant=self.sersic_constant, mass_to_light_gradient=0.0, ) scaled_effective_radius = self.effective_radius / np.sqrt(self.axis_ratio) radii_min = scaled_effective_radius / 10.0 ** lower_dex radii_max = scaled_effective_radius * 10.0 ** upper_dex def sersic_2d(r): return ( self.mass_to_light_ratio * self.intensity * np.exp( -self.sersic_constant * ( ((r / scaled_effective_radius) ** (1.0 / self.sersic_index)) - 1.0 ) ) ) return self._decompose_convergence_via_cse_from( func=sersic_2d, radii_min=radii_min, radii_max=radii_max, total_cses=total_cses, sample_points=sample_points, ) @property def sersic_constant(self): """A parameter derived from Sersic index which ensures that effective radius contains 50% of the profile's total integrated light. """ return ( (2 * self.sersic_index) - (1.0 / 3.0) + (4.0 / (405.0 * self.sersic_index)) + (46.0 / (25515.0 * self.sersic_index ** 2)) + (131.0 / (1148175.0 * self.sersic_index ** 3)) - (2194697.0 / (30690717750.0 * self.sersic_index ** 4)) ) @property def ellipticity_rescale(self): return 1.0 - ((1.0 - self.axis_ratio) / 2.0) @property def elliptical_effective_radius(self): """ The effective_radius of a Sersic light profile is defined as the circular effective radius. This is the \ radius within which a circular aperture contains half the profiles's total integrated light. For elliptical \ systems, this won't robustly capture the light profile's elliptical shape. The elliptical effective radius instead describes the major-axis radius of the ellipse containing \ half the light, and may be more appropriate for highly flattened systems like disk galaxies. """ return self.effective_radius / np.sqrt(self.axis_ratio) def with_new_normalization(self, normalization): mass_profile = copy.copy(self) mass_profile.mass_to_light_ratio = normalization return mass_profile class EllSersic(AbstractEllSersic, MassProfileMGE, MassProfileCSE): @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_integral_from(self, grid: aa.type.Grid2DLike): """ Calculate the deflection angles at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the deflection angles are computed on. """ def calculate_deflection_component(npow, index): sersic_constant = self.sersic_constant deflection_grid = self.axis_ratio * grid[:, index] for i in range(grid.shape[0]): deflection_grid[i] *= ( self.intensity * self.mass_to_light_ratio * quad( self.deflection_func, a=0.0, b=1.0, args=( grid[i, 0], grid[i, 1], npow, self.axis_ratio, self.sersic_index, self.effective_radius, sersic_constant, ), )[0] ) return deflection_grid deflection_y = calculate_deflection_component(1.0, 0) deflection_x = calculate_deflection_component(0.0, 1) return self.rotate_grid_from_reference_frame( np.multiply(1.0, np.vstack((deflection_y, deflection_x)).T) ) @staticmethod def deflection_func( u, y, x, npow, axis_ratio, sersic_index, effective_radius, sersic_constant ): eta_u = np.sqrt(axis_ratio) * np.sqrt( (u * ((x ** 2) + (y ** 2 / (1 - (1 - axis_ratio ** 2) * u)))) ) return np.exp( -sersic_constant * (((eta_u / effective_radius) ** (1.0 / sersic_index)) - 1) ) / ((1 - (1 - axis_ratio ** 2) * u) ** (npow + 0.5)) class SphSersic(EllSersic): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, sersic_index: float = 0.6, mass_to_light_ratio: float = 1.0, ): """ The Sersic mass profile, the mass profiles of the light profiles that are used to fit and subtract the lens model_galaxy's light. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre intensity Overall flux intensity normalisation in the light profiles (electrons per second) effective_radius The circular radius containing half the light of this profile. sersic_index Controls the concentration of the profile (lower -> less concentrated, higher -> more concentrated). mass_to_light_ratio The mass-to-light ratio of the light profile. """ super().__init__( centre=centre, elliptical_comps=(0.0, 0.0), intensity=intensity, effective_radius=effective_radius, sersic_index=sersic_index, mass_to_light_ratio=mass_to_light_ratio, ) class EllExponential(EllSersic): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, mass_to_light_ratio: float = 1.0, ): """ The EllExponential mass profile, the mass profiles of the light profiles that are used to fit and subtract the lens model_galaxy's light. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The circular radius containing half the light of this profile. mass_to_light_ratio The mass-to-light ratio of the light profiles """ super().__init__( centre=centre, elliptical_comps=elliptical_comps, intensity=intensity, effective_radius=effective_radius, sersic_index=1.0, mass_to_light_ratio=mass_to_light_ratio, ) class SphExponential(EllExponential): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, mass_to_light_ratio: float = 1.0, ): """ The Exponential mass profile, the mass profiles of the light profiles that are used to fit and subtract the lens model_galaxy's light. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The circular radius containing half the light of this profile. mass_to_light_ratio The mass-to-light ratio of the light profiles. """ super().__init__( centre=centre, elliptical_comps=(0.0, 0.0), intensity=intensity, effective_radius=effective_radius, mass_to_light_ratio=mass_to_light_ratio, ) class EllDevVaucouleurs(EllSersic): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, mass_to_light_ratio: float = 1.0, ): """ The EllDevVaucouleurs mass profile, the mass profiles of the light profiles that are used to fit and subtract the lens model_galaxy's light. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The radius containing half the light of this profile. mass_to_light_ratio The mass-to-light ratio of the light profile. """ super().__init__( centre=centre, elliptical_comps=elliptical_comps, intensity=intensity, effective_radius=effective_radius, sersic_index=4.0, mass_to_light_ratio=mass_to_light_ratio, ) class SphDevVaucouleurs(EllDevVaucouleurs): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, mass_to_light_ratio: float = 1.0, ): """ The DevVaucouleurs mass profile, the mass profiles of the light profiles that are used to fit and subtract the lens model_galaxy's light. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The circular radius containing half the light of this profile. mass_to_light_ratio The mass-to-light ratio of the light profiles. """ super().__init__( centre=centre, elliptical_comps=(0.0, 0.0), intensity=intensity, effective_radius=effective_radius, mass_to_light_ratio=mass_to_light_ratio, ) class EllSersicRadialGradient(AbstractEllSersic): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, sersic_index: float = 0.6, mass_to_light_ratio: float = 1.0, mass_to_light_gradient: float = 0.0, ): """ Setup a Sersic mass and light profiles. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The circular radius containing half the light of this profile. sersic_index Controls the concentration of the profile (lower -> less concentrated, higher -> more concentrated). mass_to_light_ratio The mass-to-light ratio of the light profile. mass_to_light_gradient The mass-to-light radial gradient. """ super().__init__( centre=centre, elliptical_comps=elliptical_comps, intensity=intensity, effective_radius=effective_radius, sersic_index=sersic_index, mass_to_light_ratio=mass_to_light_ratio, ) self.mass_to_light_gradient = mass_to_light_gradient @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_integral_from(self, grid: aa.type.Grid2DLike): """ Calculate the deflection angles at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the deflection angles are computed on. """ def calculate_deflection_component(npow, index): sersic_constant = self.sersic_constant deflection_grid = self.axis_ratio * grid[:, index] for i in range(grid.shape[0]): deflection_grid[i] *= ( self.intensity * self.mass_to_light_ratio * quad( self.deflection_func, a=0.0, b=1.0, args=( grid[i, 0], grid[i, 1], npow, self.axis_ratio, self.sersic_index, self.effective_radius, self.mass_to_light_gradient, sersic_constant, ), )[0] ) return deflection_grid deflection_y = calculate_deflection_component(1.0, 0) deflection_x = calculate_deflection_component(0.0, 1) return self.rotate_grid_from_reference_frame( np.multiply(1.0, np.vstack((deflection_y, deflection_x)).T) ) @staticmethod def deflection_func( u, y, x, npow, axis_ratio, sersic_index, effective_radius, mass_to_light_gradient, sersic_constant, ): eta_u = np.sqrt(axis_ratio) * np.sqrt( (u * ((x ** 2) + (y ** 2 / (1 - (1 - axis_ratio ** 2) * u)))) ) return ( (((axis_ratio * eta_u) / effective_radius) ** -mass_to_light_gradient) * np.exp( -sersic_constant * (((eta_u / effective_radius) ** (1.0 / sersic_index)) - 1) ) / ((1 - (1 - axis_ratio ** 2) * u) ** (npow + 0.5)) ) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def convergence_2d_from(self, grid: aa.type.Grid2DLike): """Calculate the projected convergence at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ return self.convergence_func(self.grid_to_eccentric_radii(grid)) def convergence_func(self, grid_radius: float) -> float: return ( self.mass_to_light_ratio * ( ((self.axis_ratio * grid_radius) / self.effective_radius) ** -self.mass_to_light_gradient ) * self.image_2d_via_radii_from(grid_radius) ) def decompose_convergence_via_mge(self): radii_min = self.effective_radius / 100.0 radii_max = self.effective_radius * 20.0 def sersic_radial_gradient_2D(r): return ( self.mass_to_light_ratio * self.intensity * ( ((self.axis_ratio * r) / self.effective_radius) ** -self.mass_to_light_gradient ) * np.exp( -self.sersic_constant * (((r / self.effective_radius) ** (1.0 / self.sersic_index)) - 1.0) ) ) return self._decompose_convergence_via_mge( func=sersic_radial_gradient_2D, radii_min=radii_min, radii_max=radii_max ) def decompose_convergence_via_cse(self) -> Tuple[List, List]: """ Decompose the convergence of the Sersic profile into singular isothermal elliptical (sie) profiles. This decomposition uses the standard 2d profile of a Sersic mass profile. Parameters ---------- func The function representing the profile that is decomposed into CSEs. radii_min: The minimum radius to fit radii_max: The maximum radius to fit total_sies The number of SIEs used to approximate the input func. sample_points: int (should be larger than 'total_sies') The number of data points to fit Returns ------- Tuple[List, List] A list of amplitudes and core radii of every singular isothernal ellipsoids (sie) the mass profile is decomposed into. """ upper_dex, lower_dex, total_cses, sample_points = cse_settings_from( effective_radius=self.effective_radius, sersic_index=self.sersic_index, sersic_constant=self.sersic_constant, mass_to_light_gradient=self.mass_to_light_gradient, ) scaled_effective_radius = self.effective_radius / np.sqrt(self.axis_ratio) radii_min = scaled_effective_radius / 10.0 ** lower_dex radii_max = scaled_effective_radius * 10.0 ** upper_dex def sersic_radial_gradient_2D(r): return ( self.mass_to_light_ratio * self.intensity * ( ((self.axis_ratio * r) / scaled_effective_radius) ** -self.mass_to_light_gradient ) * np.exp( -self.sersic_constant * ( ((r / scaled_effective_radius) ** (1.0 / self.sersic_index)) - 1.0 ) ) ) return self._decompose_convergence_via_cse_from( func=sersic_radial_gradient_2D, radii_min=radii_min, radii_max=radii_max, total_cses=total_cses, sample_points=sample_points, ) class SphSersicRadialGradient(EllSersicRadialGradient): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, effective_radius: float = 0.6, sersic_index: float = 0.6, mass_to_light_ratio: float = 1.0, mass_to_light_gradient: float = 0.0, ): """ Setup a Sersic mass and light profiles. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. intensity Overall flux intensity normalisation in the light profiles (electrons per second). effective_radius The circular radius containing half the light of this profile. sersic_index Controls the concentration of the profile (lower -> less concentrated, higher -> more concentrated). mass_to_light_ratio The mass-to-light ratio of the light profile. mass_to_light_gradient The mass-to-light radial gradient. """ super().__init__( centre=centre, elliptical_comps=(0.0, 0.0), intensity=intensity, effective_radius=effective_radius, sersic_index=sersic_index, mass_to_light_ratio=mass_to_light_ratio, mass_to_light_gradient=mass_to_light_gradient, ) class EllSersicCore(EllSersic): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), effective_radius: float = 0.6, sersic_index: float = 4.0, radius_break: float = 0.01, intensity_break: float = 0.05, gamma: float = 0.25, alpha: float = 3.0, mass_to_light_ratio: float = 1.0, ): """ The elliptical cored-Sersic light profile. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall intensity normalisation of the light profile (units are dimensionless and derived from the data the light profile's image is compared too, which is expected to be electrons per second). effective_radius The circular radius containing half the light of this profile. sersic_index Controls the concentration of the profile (lower -> less concentrated, higher -> more concentrated). radius_break The break radius separating the inner power-law (with logarithmic slope gamma) and outer Sersic function. intensity_break The intensity at the break radius. gamma The logarithmic power-law slope of the inner core profiles alpha : Controls the sharpness of the transition between the inner core / outer Sersic profiles. """ super().__init__( centre=centre, elliptical_comps=elliptical_comps, intensity=intensity_break, effective_radius=effective_radius, sersic_index=sersic_index, mass_to_light_ratio=mass_to_light_ratio, ) self.radius_break = radius_break self.intensity_break = intensity_break self.alpha = alpha self.gamma = gamma def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike): return self.deflections_2d_via_mge_from(grid=grid) def image_2d_via_radii_from(self, grid_radii: np.ndarray): """ Calculate the intensity of the cored-Sersic light profile on a grid of radial coordinates. Parameters ---------- grid_radii The radial distance from the centre of the profile. for each coordinate on the grid. """ return np.multiply( np.multiply( self.intensity_prime, np.power( np.add( 1, np.power(np.divide(self.radius_break, grid_radii), self.alpha), ), (self.gamma / self.alpha), ), ), np.exp( np.multiply( -self.sersic_constant, ( np.power( np.divide( np.add( np.power(grid_radii, self.alpha), (self.radius_break ** self.alpha), ), (self.effective_radius ** self.alpha), ), (1.0 / (self.alpha * self.sersic_index)), ) ), ) ), ) def decompose_convergence_via_mge(self): radii_min = self.effective_radius / 50.0 radii_max = self.effective_radius * 20.0 def core_sersic_2D(r): return ( self.mass_to_light_ratio * self.intensity_prime * (1.0 + (self.radius_break / r) ** self.alpha) ** (self.gamma / self.alpha) * np.exp( -self.sersic_constant * ( (r ** self.alpha + self.radius_break ** self.alpha) / self.effective_radius ** self.alpha ) ** (1.0 / (self.sersic_index * self.alpha)) ) ) return self._decompose_convergence_via_mge( func=core_sersic_2D, radii_min=radii_min, radii_max=radii_max ) @property def intensity_prime(self): """Overall intensity normalisation in the rescaled Core-Sersic light profiles (electrons per second)""" return ( self.intensity_break * (2.0 ** (-self.gamma / self.alpha)) * np.exp( self.sersic_constant * ( ((2.0 ** (1.0 / self.alpha)) * self.radius_break) / self.effective_radius ) ** (1.0 / self.sersic_index) ) ) class SphSersicCore(EllSersicCore): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), effective_radius: float = 0.6, sersic_index: float = 4.0, radius_break: float = 0.01, intensity_break: float = 0.05, gamma: float = 0.25, alpha: float = 3.0, ): """ The elliptical cored-Sersic light profile. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. intensity Overall intensity normalisation of the light profile (units are dimensionless and derived from the data the light profile's image is compared too, which is expected to be electrons per second). effective_radius The circular radius containing half the light of this profile. sersic_index Controls the concentration of the profile (lower -> less concentrated, higher -> more concentrated). radius_break The break radius separating the inner power-law (with logarithmic slope gamma) and outer Sersic function. intensity_break The intensity at the break radius. gamma The logarithmic power-law slope of the inner core profiles alpha : Controls the sharpness of the transition between the inner core / outer Sersic profiles. """ super().__init__( centre=centre, elliptical_comps=(0.0, 0.0), effective_radius=effective_radius, sersic_index=sersic_index, radius_break=radius_break, intensity_break=intensity_break, gamma=gamma, alpha=alpha, ) self.radius_break = radius_break self.intensity_break = intensity_break self.alpha = alpha self.gamma = gamma class EllChameleon(MassProfile, StellarProfile): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), elliptical_comps: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, core_radius_0: float = 0.01, core_radius_1: float = 0.02, mass_to_light_ratio: float = 1.0, ): """ The elliptical Chamelon mass profile. Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall intensity normalisation of the light profile (units are dimensionless and derived from the data the light profile's image is compared too, which is expected to be electrons per second). core_radius_0 : the core size of the first elliptical cored Isothermal profile. core_radius_1 : core_radius_0 + core_radius_1 is the core size of the second elliptical cored Isothermal profile. We use core_radius_1 here is to avoid negative values. Profile form: mass_to_light_ratio * intensity *\ (1.0 / Sqrt(x^2 + (y/q)^2 + core_radius_0^2) - 1.0 / Sqrt(x^2 + (y/q)^2 + (core_radius_0 + core_radius_1)**2.0)) """ super(EllChameleon, self).__init__( centre=centre, elliptical_comps=elliptical_comps ) super(MassProfile, self).__init__( centre=centre, elliptical_comps=elliptical_comps ) self.mass_to_light_ratio = mass_to_light_ratio self.intensity = intensity self.core_radius_0 = core_radius_0 self.core_radius_1 = core_radius_1 def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike): return self.deflections_2d_via_analytic_from(grid=grid) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def deflections_2d_via_analytic_from(self, grid: aa.type.Grid2DLike): """ Calculate the deflection angles at a given set of arc-second gridded coordinates. Following Eq. (15) and (16), but the parameters are slightly different. Parameters ---------- grid The grid of (y,x) arc-second coordinates the deflection angles are computed on. """ factor = ( 2.0 * self.mass_to_light_ratio * self.intensity / (1 + self.axis_ratio) * self.axis_ratio / np.sqrt(1.0 - self.axis_ratio ** 2.0) ) core_radius_0 = np.sqrt( (4.0 * self.core_radius_0 ** 2.0) / (1.0 + self.axis_ratio) ** 2 ) core_radius_1 = np.sqrt( (4.0 * self.core_radius_1 ** 2.0) / (1.0 + self.axis_ratio) ** 2 ) psi0 = psi_from( grid=grid, axis_ratio=self.axis_ratio, core_radius=core_radius_0 ) psi1 = psi_from( grid=grid, axis_ratio=self.axis_ratio, core_radius=core_radius_1 ) deflection_y0 = np.arctanh( np.divide( np.multiply(np.sqrt(1.0 - self.axis_ratio ** 2.0), grid[:, 0]), np.add(psi0, self.axis_ratio ** 2.0 * core_radius_0), ) ) deflection_x0 = np.arctan( np.divide( np.multiply(np.sqrt(1.0 - self.axis_ratio ** 2.0), grid[:, 1]), np.add(psi0, core_radius_0), ) ) deflection_y1 = np.arctanh( np.divide( np.multiply(np.sqrt(1.0 - self.axis_ratio ** 2.0), grid[:, 0]), np.add(psi1, self.axis_ratio ** 2.0 * core_radius_1), ) ) deflection_x1 = np.arctan( np.divide( np.multiply(np.sqrt(1.0 - self.axis_ratio ** 2.0), grid[:, 1]), np.add(psi1, core_radius_1), ) ) deflection_y = np.subtract(deflection_y0, deflection_y1) deflection_x = np.subtract(deflection_x0, deflection_x1) return self.rotate_grid_from_reference_frame( np.multiply(factor, np.vstack((deflection_y, deflection_x)).T) ) @aa.grid_dec.grid_2d_to_structure @aa.grid_dec.transform @aa.grid_dec.relocate_to_radial_minimum def convergence_2d_from(self, grid: aa.type.Grid2DLike): """Calculate the projected convergence at a given set of arc-second gridded coordinates. Parameters ---------- grid The grid of (y,x) arc-second coordinates the convergence is computed on. """ return self.convergence_func(self.grid_to_elliptical_radii(grid)) def convergence_func(self, grid_radius: float) -> float: return self.mass_to_light_ratio * self.image_2d_via_radii_from(grid_radius) @aa.grid_dec.grid_2d_to_structure def potential_2d_from(self, grid: aa.type.Grid2DLike): return np.zeros(shape=grid.shape[0]) def image_2d_via_radii_from(self, grid_radii: np.ndarray): """Calculate the intensity of the Chamelon light profile on a grid of radial coordinates. Parameters ---------- grid_radii The radial distance from the centre of the profile. for each coordinate on the grid. """ axis_ratio_factor = (1.0 + self.axis_ratio) ** 2.0 return np.multiply( self.intensity / (1 + self.axis_ratio), np.add( np.divide( 1.0, np.sqrt( np.add( np.square(grid_radii), (4.0 * self.core_radius_0 ** 2.0) / axis_ratio_factor, ) ), ), -np.divide( 1.0, np.sqrt( np.add( np.square(grid_radii), (4.0 * self.core_radius_1 ** 2.0) / axis_ratio_factor, ) ), ), ), ) @property def axis_ratio(self): axis_ratio = super().axis_ratio return axis_ratio if axis_ratio < 0.99999 else 0.99999 def with_new_normalization(self, normalization): mass_profile = copy.copy(self) mass_profile.mass_to_light_ratio = normalization return mass_profile class SphChameleon(EllChameleon): def __init__( self, centre: Tuple[float, float] = (0.0, 0.0), intensity: float = 0.1, core_radius_0: float = 0.01, core_radius_1: float = 0.02, mass_to_light_ratio: float = 1.0, ): """ The spherica; Chameleon mass profile. Profile form: mass_to_light_ratio * intensity *\ (1.0 / Sqrt(x^2 + (y/q)^2 + core_radius_0^2) - 1.0 / Sqrt(x^2 + (y/q)^2 + (core_radius_0 + core_radius_1)**2.0)) Parameters ---------- centre The (y,x) arc-second coordinates of the profile centre. elliptical_comps The first and second ellipticity components of the elliptical coordinate system, (see the module `autogalaxy -> convert.py` for the convention). intensity Overall intensity normalisation of the light profile (units are dimensionless and derived from the data the light profile's image is compared too, which is expected to be electrons per second). core_radius_0 : the core size of the first elliptical cored Isothermal profile. core_radius_1 : core_radius_0 + core_radius_1 is the core size of the second elliptical cored Isothermal profile. We use core_radius_1 here is to avoid negative values. """ super().__init__( centre=centre, elliptical_comps=(0.0, 0.0), intensity=intensity, core_radius_0=core_radius_0, core_radius_1=core_radius_1, mass_to_light_ratio=mass_to_light_ratio, ) def cse_settings_from( effective_radius, sersic_index, sersic_constant, mass_to_light_gradient ): if mass_to_light_gradient > 0.5: if effective_radius > 0.2: lower_dex = 6.0 upper_dex = np.min( [np.log10((18.0 / sersic_constant) ** sersic_index), 1.1] ) if sersic_index <= 1.2: total_cses = 50 sample_points = 80 elif sersic_index > 3.8: total_cses = 40 sample_points = 50 lower_dex = 6.5 else: total_cses = 30 sample_points = 50 else: if sersic_index <= 1.2: upper_dex = 1.0 total_cses = 50 sample_points = 80 lower_dex = 4.5 elif sersic_index > 3.8: total_cses = 40 sample_points = 50 lower_dex = 6.0 upper_dex = 1.5 else: upper_dex = 1.1 lower_dex = 6.0 total_cses = 30 sample_points = 50 else: upper_dex = np.min( [ np.log10((23.0 / sersic_constant) ** sersic_index), 0.85 - np.log10(effective_radius), ] ) if (sersic_index <= 0.9) and (sersic_index > 0.8): total_cses = 50 sample_points = 80 upper_dex = np.log10((18.0 / sersic_constant) ** sersic_index) lower_dex = 4.3 + np.log10(effective_radius) elif sersic_index <= 0.8: total_cses = 50 sample_points = 80 upper_dex = np.log10((16.0 / sersic_constant) ** sersic_index) lower_dex = 4.0 + np.log10(effective_radius) elif sersic_index > 3.8: total_cses = 40 sample_points = 50 lower_dex = 4.5 + np.log10(effective_radius) else: lower_dex = 3.5 + np.log10(effective_radius) total_cses = 30 sample_points = 50 return upper_dex, lower_dex, total_cses, sample_points

36.372667

129

0.560604

6,276

54,559

4.647068

0.058158

0.018927

0.034699

0.038951

0.890588

0.870701

0.85246

0.835522

0.801166

0.789782

0

0.026702

0.357503

54,559

1,499

130

36.396931

0.8053

0.291813

0

0.647399

0

1

0.078613

false

0.001156

0.010405

0.018497

0.169942

0

null

0

1

0

null

0

6

d2f89e6b57c9a1b93947576a30ec79f4c0bc634e

88

py

Python

Workflow/packages/__init__.py

MATS64664-2021-Group-2/Hydride-Connect-Group-2

fa95d38174ffd85461bf66f923c38a3908a469a7

[ "MIT" ]

null

Workflow/packages/__init__.py

MATS64664-2021-Group-2/Hydride-Connect-Group-2

fa95d38174ffd85461bf66f923c38a3908a469a7

[ "MIT" ]

2

2021-04-12T20:30:48.000Z

2021-05-24T14:07:24.000Z

Workflow/packages/__init__.py

MATS64664-2021-Group-2/Hydride_Connection

fa95d38174ffd85461bf66f923c38a3908a469a7

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Apr 15 11:31:06 2021 @author: a77510jm """

11

35

0.579545

14

88

3.642857

1

0

0.257143

0.204545

88

7

36

12.571429

0.471429

0.875

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

d2ff009598eedc70cbe497c5d19827bdffd07954

144,055

py

Python

test/test_parameters.py

HubukiNinten/imgaug

2570c5651ed1c90addbaffc0f8be226646c55334

[ "MIT" ]

1

2019-10-25T17:43:20.000Z

test/test_parameters.py

HubukiNinten/imgaug

2570c5651ed1c90addbaffc0f8be226646c55334

[ "MIT" ]

null

test/test_parameters.py

HubukiNinten/imgaug

2570c5651ed1c90addbaffc0f8be226646c55334

[ "MIT" ]

null

from __future__ import print_function, division, absolute_import import itertools import sys # unittest only added in 3.4 self.subTest() if sys.version_info[0] < 3 or sys.version_info[1] < 4: import unittest2 as unittest else: import unittest # unittest.mock is not available in 2.7 (though unittest2 might contain it?) try: import unittest.mock as mock except ImportError: import mock import matplotlib matplotlib.use('Agg') # fix execution of tests involving matplotlib on travis import numpy as np import six.moves as sm import skimage import skimage.data import skimage.morphology import scipy import scipy.special import imgaug as ia import imgaug.random as iarandom from imgaug import parameters as iap from imgaug.testutils import reseed def _eps(arr): if ia.is_np_array(arr) and arr.dtype.kind == "f": return np.finfo(arr.dtype).eps return 1e-4 class Test_handle_continuous_param(unittest.TestCase): def test_value_range_is_none(self): result = iap.handle_continuous_param( 1, "[test1]", value_range=None, tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_range_is_tuple_of_nones(self): result = iap.handle_continuous_param( 1, "[test1b]", value_range=(None, None), tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_param_is_stochastic_parameter(self): result = iap.handle_continuous_param( iap.Deterministic(1), "[test2]", value_range=None, tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_range_is_tuple_of_integers(self): result = iap.handle_continuous_param( 1, "[test3]", value_range=(0, 10), tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_param_is_outside_of_value_range(self): with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( 1, "[test4]", value_range=(2, 12), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test4]" in str(context.exception)) def test_param_is_inside_value_range_and_no_lower_bound(self): # value within value range (without lower bound) result = iap.handle_continuous_param( 1, "[test5]", value_range=(None, 12), tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_param_is_outside_of_value_range_and_no_lower_bound(self): # value outside of value range (without lower bound) with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( 1, "[test6]", value_range=(None, 0), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test6]" in str(context.exception)) def test_param_is_inside_value_range_and_no_upper_bound(self): # value within value range (without upper bound) result = iap.handle_continuous_param( 1, "[test7]", value_range=(-1, None), tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_param_is_outside_of_value_range_and_no_upper_bound(self): # value outside of value range (without upper bound) with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( 1, "[test8]", value_range=(2, None), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test8]" in str(context.exception)) def test_tuple_as_value_but_no_tuples_allowed(self): # tuple as value, but no tuples allowed with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( (1, 2), "[test9]", value_range=None, tuple_to_uniform=False, list_to_choice=True) self.assertTrue("[test9]" in str(context.exception)) def test_tuple_as_value_and_tuples_allowed(self): # tuple as value and tuple allowed result = iap.handle_continuous_param( (1, 2), "[test10]", value_range=None, tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Uniform)) def test_tuple_as_value_and_tuples_allowed_and_inside_value_range(self): # tuple as value and tuple allowed and tuple within value range result = iap.handle_continuous_param( (1, 2), "[test11]", value_range=(0, 10), tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Uniform)) def test_tuple_value_and_allowed_and_partially_outside_value_range(self): # tuple as value and tuple allowed and tuple partially outside of # value range with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( (1, 2), "[test12]", value_range=(1.5, 13), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test12]" in str(context.exception)) def test_tuple_value_and_allowed_and_fully_outside_value_range(self): # tuple as value and tuple allowed and tuple fully outside of value # range with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( (1, 2), "[test13]", value_range=(3, 13), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test13]" in str(context.exception)) def test_list_as_value_but_no_lists_allowed(self): # list as value, but no list allowed with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( [1, 2, 3], "[test14]", value_range=None, tuple_to_uniform=True, list_to_choice=False) self.assertTrue("[test14]" in str(context.exception)) def test_list_as_value_and_lists_allowed(self): # list as value and list allowed result = iap.handle_continuous_param( [1, 2, 3], "[test15]", value_range=None, tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Choice)) def test_list_value_and_allowed_and_partially_outside_value_range(self): # list as value and list allowed and list partially outside of value # range with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( [1, 2], "[test16]", value_range=(1.5, 13), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test16]" in str(context.exception)) def test_list_value_and_allowed_and_fully_outside_of_value_range(self): # list as value and list allowed and list fully outside of value range with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( [1, 2], "[test17]", value_range=(3, 13), tuple_to_uniform=True, list_to_choice=True) self.assertTrue("[test17]" in str(context.exception)) def test_value_inside_value_range_and_value_range_given_as_callable(self): # single value within value range given as callable def _value_range(x): return -1 < x < 1 result = iap.handle_continuous_param( 1, "[test18]", value_range=_value_range, tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_bad_datatype_as_value_range(self): # bad datatype for value range with self.assertRaises(Exception) as context: _ = iap.handle_continuous_param( 1, "[test19]", value_range=False, tuple_to_uniform=True, list_to_choice=True) self.assertTrue( "Unexpected input for value_range" in str(context.exception)) class Test_handle_discrete_param(unittest.TestCase): def test_float_value_inside_value_range_but_no_floats_allowed(self): # float value without value range when no float value is allowed with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( 1.5, "[test0]", value_range=None, tuple_to_uniform=True, list_to_choice=True, allow_floats=False) self.assertTrue("[test0]" in str(context.exception)) def test_value_range_is_none(self): # value without value range result = iap.handle_discrete_param( 1, "[test1]", value_range=None, tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_range_is_tuple_of_nones(self): # value without value range as (None, None) result = iap.handle_discrete_param( 1, "[test1b]", value_range=(None, None), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_is_stochastic_parameter(self): # stochastic parameter result = iap.handle_discrete_param( iap.Deterministic(1), "[test2]", value_range=None, tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_inside_value_range(self): # value within value range result = iap.handle_discrete_param( 1, "[test3]", value_range=(0, 10), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_outside_value_range(self): # value outside of value range with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( 1, "[test4]", value_range=(2, 12), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test4]" in str(context.exception)) def test_value_inside_value_range_no_lower_bound(self): # value within value range (without lower bound) result = iap.handle_discrete_param( 1, "[test5]", value_range=(None, 12), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_outside_value_range_no_lower_bound(self): # value outside of value range (without lower bound) with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( 1, "[test6]", value_range=(None, 0), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test6]" in str(context.exception)) def test_value_inside_value_range_no_upper_bound(self): # value within value range (without upper bound) result = iap.handle_discrete_param( 1, "[test7]", value_range=(-1, None), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_value_outside_value_range_no_upper_bound(self): # value outside of value range (without upper bound) with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( 1, "[test8]", value_range=(2, None), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test8]" in str(context.exception)) def test_value_is_tuple_but_no_tuples_allowed(self): # tuple as value, but no tuples allowed with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( (1, 2), "[test9]", value_range=None, tuple_to_uniform=False, list_to_choice=True, allow_floats=True) self.assertTrue("[test9]" in str(context.exception)) def test_value_is_tuple_and_tuples_allowed(self): # tuple as value and tuple allowed result = iap.handle_discrete_param( (1, 2), "[test10]", value_range=None, tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.DiscreteUniform)) def test_value_tuple_and_allowed_and_inside_value_range(self): # tuple as value and tuple allowed and tuple within value range result = iap.handle_discrete_param( (1, 2), "[test11]", value_range=(0, 10), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.DiscreteUniform)) def test_value_tuple_and_allowed_and_inside_vr_allow_floats_false(self): # tuple as value and tuple allowed and tuple within value range with # allow_floats=False result = iap.handle_discrete_param( (1, 2), "[test11b]", value_range=(0, 10), tuple_to_uniform=True, list_to_choice=True, allow_floats=False) self.assertTrue(isinstance(result, iap.DiscreteUniform)) def test_value_tuple_and_allowed_and_partially_outside_value_range(self): # tuple as value and tuple allowed and tuple partially outside of # value range with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( (1, 3), "[test12]", value_range=(2, 13), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test12]" in str(context.exception)) def test_value_tuple_and_allowed_and_fully_outside_value_range(self): # tuple as value and tuple allowed and tuple fully outside of value # range with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( (1, 2), "[test13]", value_range=(3, 13), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test13]" in str(context.exception)) def test_value_list_but_not_allowed(self): # list as value, but no list allowed with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( [1, 2, 3], "[test14]", value_range=None, tuple_to_uniform=True, list_to_choice=False, allow_floats=True) self.assertTrue("[test14]" in str(context.exception)) def test_value_list_and_allowed(self): # list as value and list allowed result = iap.handle_discrete_param( [1, 2, 3], "[test15]", value_range=None, tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue(isinstance(result, iap.Choice)) def test_value_list_and_allowed_and_partially_outside_value_range(self): # list as value and list allowed and list partially outside of value range with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( [1, 3], "[test16]", value_range=(2, 13), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test16]" in str(context.exception)) def test_value_list_and_allowed_and_fully_outside_value_range(self): # list as value and list allowed and list fully outside of value range with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( [1, 2], "[test17]", value_range=(3, 13), tuple_to_uniform=True, list_to_choice=True, allow_floats=True) self.assertTrue("[test17]" in str(context.exception)) def test_value_inside_value_range_given_as_callable(self): # single value within value range given as callable def _value_range(x): return -1 < x < 1 result = iap.handle_discrete_param( 1, "[test18]", value_range=_value_range, tuple_to_uniform=True, list_to_choice=True) self.assertTrue(isinstance(result, iap.Deterministic)) def test_bad_datatype_as_value_range(self): # bad datatype for value range with self.assertRaises(Exception) as context: _ = iap.handle_discrete_param( 1, "[test19]", value_range=False, tuple_to_uniform=True, list_to_choice=True) self.assertTrue( "Unexpected input for value_range" in str(context.exception)) class Test_handle_categorical_string_param(unittest.TestCase): def test_arg_is_all(self): valid_values = ["class1", "class2"] param = iap.handle_categorical_string_param( ia.ALL, "foo", valid_values) assert isinstance(param, iap.Choice) assert param.a == valid_values def test_arg_is_valid_str(self): valid_values = ["class1", "class2"] param = iap.handle_categorical_string_param( "class1", "foo", valid_values) assert isinstance(param, iap.Deterministic) assert param.value == "class1" def test_arg_is_invalid_str(self): valid_values = ["class1", "class2"] with self.assertRaises(AssertionError) as ctx: _param = iap.handle_categorical_string_param( "class3", "foo", valid_values) expected = ( "Expected parameter 'foo' to be one of: class1, class2. " "Got: class3.") assert expected == str(ctx.exception) def test_arg_is_valid_list(self): valid_values = ["class1", "class2", "class3"] param = iap.handle_categorical_string_param( ["class1", "class3"], "foo", valid_values) assert isinstance(param, iap.Choice) assert param.a == ["class1", "class3"] def test_arg_is_list_with_invalid_types(self): valid_values = ["class1", "class2", "class3"] with self.assertRaises(AssertionError) as ctx: _param = iap.handle_categorical_string_param( ["class1", False], "foo", valid_values) expected = ( "Expected list provided for parameter 'foo' to only contain " "strings, got types: str, bool." ) assert expected in str(ctx.exception) def test_arg_is_invalid_list(self): valid_values = ["class1", "class2", "class3"] with self.assertRaises(AssertionError) as ctx: _param = iap.handle_categorical_string_param( ["class1", "class4"], "foo", valid_values) expected = ( "Expected list provided for parameter 'foo' to only contain " "the following allowed strings: class1, class2, class3. " "Got strings: class1, class4." ) assert expected in str(ctx.exception) def test_arg_is_stochastic_param(self): param = iap.Deterministic("class1") param_out = iap.handle_categorical_string_param( param, "foo", ["class1"]) assert param_out is param def test_arg_is_invalid_datatype(self): with self.assertRaises(Exception) as ctx: _ = iap.handle_categorical_string_param( False, "foo", ["class1"]) expected = "Expected parameter 'foo' to be imgaug.ALL" assert expected in str(ctx.exception) class Test_handle_probability_param(unittest.TestCase): def test_bool_like_values(self): for val in [True, False, 0, 1, 0.0, 1.0]: with self.subTest(param=val): p = iap.handle_probability_param(val, "[test1]") assert isinstance(p, iap.Deterministic) assert p.value == int(val) def test_float_probabilities(self): for val in [0.0001, 0.001, 0.01, 0.1, 0.9, 0.99, 0.999, 0.9999]: with self.subTest(param=val): p = iap.handle_probability_param(val, "[test2]") assert isinstance(p, iap.Binomial) assert isinstance(p.p, iap.Deterministic) assert val-1e-8 < p.p.value < val+1e-8 def test_probability_is_stochastic_parameter(self): det = iap.Deterministic(1) p = iap.handle_probability_param(det, "[test3]") assert p == det def test_probability_has_bad_datatype(self): with self.assertRaises(Exception) as context: _p = iap.handle_probability_param("test", "[test4]") self.assertTrue("Expected " in str(context.exception)) def test_probability_is_negative(self): with self.assertRaises(AssertionError): _p = iap.handle_probability_param(-0.01, "[test5]") def test_probability_is_above_100_percent(self): with self.assertRaises(AssertionError): _p = iap.handle_probability_param(1.01, "[test6]") class Test_force_np_float_dtype(unittest.TestCase): def test_common_dtypes(self): dtypes = [ ("float16", "float16"), ("float32", "float32"), ("float64", "float64"), ("uint8", "float64"), ("int32", "float64") ] for dtype_in, expected in dtypes: with self.subTest(dtype_in=dtype_in): arr = np.zeros((1,), dtype=dtype_in) observed = iap.force_np_float_dtype(arr).dtype assert observed.name == expected class Test_both_np_float_if_one_is_float(unittest.TestCase): def test_float16_float32(self): a1 = np.zeros((1,), dtype=np.float16) b1 = np.zeros((1,), dtype=np.float32) a2, b2 = iap.both_np_float_if_one_is_float(a1, b1) assert a2.dtype.name == "float16" assert b2.dtype.name == "float32" def test_float16_int32(self): a1 = np.zeros((1,), dtype=np.float16) b1 = np.zeros((1,), dtype=np.int32) a2, b2 = iap.both_np_float_if_one_is_float(a1, b1) assert a2.dtype.name == "float16" assert b2.dtype.name == "float64" def test_int32_float16(self): a1 = np.zeros((1,), dtype=np.int32) b1 = np.zeros((1,), dtype=np.float16) a2, b2 = iap.both_np_float_if_one_is_float(a1, b1) assert a2.dtype.name == "float64" assert b2.dtype.name == "float16" def test_int32_uint8(self): a1 = np.zeros((1,), dtype=np.int32) b1 = np.zeros((1,), dtype=np.uint8) a2, b2 = iap.both_np_float_if_one_is_float(a1, b1) assert a2.dtype.name == "float64" assert b2.dtype.name == "float64" class Test_draw_distributions_grid(unittest.TestCase): def setUp(self): reseed() def test_basic_functionality(self): params = [mock.Mock(), mock.Mock()] params[0].draw_distribution_graph.return_value = \ np.zeros((1, 1, 3), dtype=np.uint8) params[1].draw_distribution_graph.return_value = \ np.zeros((1, 1, 3), dtype=np.uint8) draw_grid_mock = mock.Mock() draw_grid_mock.return_value = np.zeros((4, 3, 2), dtype=np.uint8) with mock.patch('imgaug.imgaug.draw_grid', draw_grid_mock): grid_observed = iap.draw_distributions_grid( params, rows=2, cols=3, graph_sizes=(20, 21), sample_sizes=[(1, 2), (3, 4)], titles=["A", "B"]) assert grid_observed.shape == (4, 3, 2) assert params[0].draw_distribution_graph.call_count == 1 assert params[1].draw_distribution_graph.call_count == 1 assert params[0].draw_distribution_graph.call_args[1]["size"] == (1, 2) assert params[0].draw_distribution_graph.call_args[1]["title"] == "A" assert params[1].draw_distribution_graph.call_args[1]["size"] == (3, 4) assert params[1].draw_distribution_graph.call_args[1]["title"] == "B" assert draw_grid_mock.call_count == 1 assert draw_grid_mock.call_args[0][0][0].shape == (20, 21, 3) assert draw_grid_mock.call_args[0][0][1].shape == (20, 21, 3) assert draw_grid_mock.call_args[1]["rows"] == 2 assert draw_grid_mock.call_args[1]["cols"] == 3 class Test_draw_distributions_graph(unittest.TestCase): def test_basic_functionality(self): # this test is very rough as we get a not-very-well-defined image out # of the function param = iap.Uniform(0.0, 1.0) graph_img = param.draw_distribution_graph(title=None, size=(10000,), bins=100) # at least 10% of the image should be white-ish (background) nb_white = np.sum(graph_img[..., :] > [200, 200, 200]) nb_all = np.prod(graph_img.shape) graph_img_title = param.draw_distribution_graph(title="test", size=(10000,), bins=100) assert graph_img.ndim == 3 assert graph_img.shape[2] == 3 assert nb_white > 0.1 * nb_all assert graph_img_title.ndim == 3 assert graph_img_title.shape[2] == 3 assert not np.array_equal(graph_img_title, graph_img) class TestStochasticParameter(unittest.TestCase): def setUp(self): reseed() def test_copy(self): other_param = iap.Uniform(1.0, 10.0) param = iap.Discretize(other_param) other_param.a = [1.0] param_copy = param.copy() param.other_param.a[0] += 1 assert isinstance(param_copy, iap.Discretize) assert isinstance(param_copy.other_param, iap.Uniform) assert param_copy.other_param.a[0] == param.other_param.a[0] def test_deepcopy(self): other_param = iap.Uniform(1.0, 10.0) param = iap.Discretize(other_param) other_param.a = [1.0] param_copy = param.deepcopy() param.other_param.a[0] += 1 assert isinstance(param_copy, iap.Discretize) assert isinstance(param_copy.other_param, iap.Uniform) assert param_copy.other_param.a[0] != param.other_param.a[0] class TestStochasticParameterOperators(unittest.TestCase): def setUp(self): reseed() def test_multiply_stochasic_params(self): param1 = iap.Normal(0, 1) param2 = iap.Uniform(-1.0, 1.0) param3 = param1 * param2 assert isinstance(param3, iap.Multiply) assert param3.other_param == param1 assert param3.val == param2 def test_multiply_stochastic_param_with_integer(self): param1 = iap.Normal(0, 1) param3 = param1 * 2 assert isinstance(param3, iap.Multiply) assert param3.other_param == param1 assert isinstance(param3.val, iap.Deterministic) assert param3.val.value == 2 def test_multiply_integer_with_stochastic_param(self): param1 = iap.Normal(0, 1) param3 = 2 * param1 assert isinstance(param3, iap.Multiply) assert isinstance(param3.other_param, iap.Deterministic) assert param3.other_param.value == 2 assert param3.val == param1 def test_multiply_string_with_stochastic_param_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = "test" * param1 self.assertTrue("Invalid datatypes" in str(context.exception)) def test_multiply_stochastic_param_with_string_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1 * "test" self.assertTrue("Invalid datatypes" in str(context.exception)) def test_divide_stochastic_params(self): # Divide (__truediv__) param1 = iap.Normal(0, 1) param2 = iap.Uniform(-1.0, 1.0) param3 = param1 / param2 assert isinstance(param3, iap.Divide) assert param3.other_param == param1 assert param3.val == param2 def test_divide_stochastic_param_by_integer(self): param1 = iap.Normal(0, 1) param3 = param1 / 2 assert isinstance(param3, iap.Divide) assert param3.other_param == param1 assert isinstance(param3.val, iap.Deterministic) assert param3.val.value == 2 def test_divide_integer_by_stochastic_param(self): param1 = iap.Normal(0, 1) param3 = 2 / param1 assert isinstance(param3, iap.Divide) assert isinstance(param3.other_param, iap.Deterministic) assert param3.other_param.value == 2 assert param3.val == param1 def test_divide_string_by_stochastic_param_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = "test" / param1 self.assertTrue("Invalid datatypes" in str(context.exception)) def test_divide_stochastic_param_by_string_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1 / "test" self.assertTrue("Invalid datatypes" in str(context.exception)) def test_div_stochastic_params(self): # Divide (__div__) param1 = iap.Normal(0, 1) param2 = iap.Uniform(-1.0, 1.0) param3 = param1.__div__(param2) assert isinstance(param3, iap.Divide) assert param3.other_param == param1 assert param3.val == param2 def test_div_stochastic_param_by_integer(self): param1 = iap.Normal(0, 1) param3 = param1.__div__(2) assert isinstance(param3, iap.Divide) assert param3.other_param == param1 assert isinstance(param3.val, iap.Deterministic) assert param3.val.value == 2 def test_div_stochastic_param_by_string_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1.__div__("test") self.assertTrue("Invalid datatypes" in str(context.exception)) def test_rdiv_stochastic_param_by_integer(self): # Divide (__rdiv__) param1 = iap.Normal(0, 1) param3 = param1.__rdiv__(2) assert isinstance(param3, iap.Divide) assert isinstance(param3.other_param, iap.Deterministic) assert param3.other_param.value == 2 assert param3.val == param1 def test_rdiv_stochastic_param_by_string_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1.__rdiv__("test") self.assertTrue("Invalid datatypes" in str(context.exception)) def test_floordiv_stochastic_params(self): # Divide (__floordiv__) param1_int = iap.DiscreteUniform(0, 10) param2_int = iap.Choice([1, 2]) param3 = param1_int // param2_int assert isinstance(param3, iap.Discretize) assert isinstance(param3.other_param, iap.Divide) assert param3.other_param.other_param == param1_int assert param3.other_param.val == param2_int def test_floordiv_symbol_stochastic_param_by_integer(self): param1_int = iap.DiscreteUniform(0, 10) param3 = param1_int // 2 assert isinstance(param3, iap.Discretize) assert isinstance(param3.other_param, iap.Divide) assert param3.other_param.other_param == param1_int assert isinstance(param3.other_param.val, iap.Deterministic) assert param3.other_param.val.value == 2 def test_floordiv_symbol_integer_by_stochastic_param(self): param1_int = iap.DiscreteUniform(0, 10) param3 = 2 // param1_int assert isinstance(param3, iap.Discretize) assert isinstance(param3.other_param, iap.Divide) assert isinstance(param3.other_param.other_param, iap.Deterministic) assert param3.other_param.other_param.value == 2 assert param3.other_param.val == param1_int def test_floordiv_symbol_string_by_stochastic_should_fail(self): param1_int = iap.DiscreteUniform(0, 10) with self.assertRaises(Exception) as context: _ = "test" // param1_int self.assertTrue("Invalid datatypes" in str(context.exception)) def test_floordiv_symbol_stochastic_param_by_string_should_fail(self): param1_int = iap.DiscreteUniform(0, 10) with self.assertRaises(Exception) as context: _ = param1_int // "test" self.assertTrue("Invalid datatypes" in str(context.exception)) def test_add_stochastic_params(self): param1 = iap.Normal(0, 1) param2 = iap.Uniform(-1.0, 1.0) param3 = param1 + param2 assert isinstance(param3, iap.Add) assert param3.other_param == param1 assert param3.val == param2 def test_add_integer_to_stochastic_param(self): param1 = iap.Normal(0, 1) param3 = param1 + 2 assert isinstance(param3, iap.Add) assert param3.other_param == param1 assert isinstance(param3.val, iap.Deterministic) assert param3.val.value == 2 def test_add_stochastic_param_to_integer(self): param1 = iap.Normal(0, 1) param3 = 2 + param1 assert isinstance(param3, iap.Add) assert isinstance(param3.other_param, iap.Deterministic) assert param3.other_param.value == 2 assert param3.val == param1 def test_add_stochastic_param_to_string(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = "test" + param1 self.assertTrue("Invalid datatypes" in str(context.exception)) def test_add_string_to_stochastic_param(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1 + "test" self.assertTrue("Invalid datatypes" in str(context.exception)) def test_subtract_stochastic_params(self): param1 = iap.Normal(0, 1) param2 = iap.Uniform(-1.0, 1.0) param3 = param1 - param2 assert isinstance(param3, iap.Subtract) assert param3.other_param == param1 assert param3.val == param2 def test_subtract_integer_from_stochastic_param(self): param1 = iap.Normal(0, 1) param3 = param1 - 2 assert isinstance(param3, iap.Subtract) assert param3.other_param == param1 assert isinstance(param3.val, iap.Deterministic) assert param3.val.value == 2 def test_subtract_stochastic_param_from_integer(self): param1 = iap.Normal(0, 1) param3 = 2 - param1 assert isinstance(param3, iap.Subtract) assert isinstance(param3.other_param, iap.Deterministic) assert param3.other_param.value == 2 assert param3.val == param1 def test_subtract_stochastic_param_from_string_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = "test" - param1 self.assertTrue("Invalid datatypes" in str(context.exception)) def test_subtract_string_from_stochastic_param_should_fail(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1 - "test" self.assertTrue("Invalid datatypes" in str(context.exception)) def test_exponentiate_stochastic_params(self): param1 = iap.Normal(0, 1) param2 = iap.Uniform(-1.0, 1.0) param3 = param1 ** param2 assert isinstance(param3, iap.Power) assert param3.other_param == param1 assert param3.val == param2 def test_exponentiate_stochastic_param_by_integer(self): param1 = iap.Normal(0, 1) param3 = param1 ** 2 assert isinstance(param3, iap.Power) assert param3.other_param == param1 assert isinstance(param3.val, iap.Deterministic) assert param3.val.value == 2 def test_exponentiate_integer_by_stochastic_param(self): param1 = iap.Normal(0, 1) param3 = 2 ** param1 assert isinstance(param3, iap.Power) assert isinstance(param3.other_param, iap.Deterministic) assert param3.other_param.value == 2 assert param3.val == param1 def test_exponentiate_string_by_stochastic_param(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = "test" ** param1 self.assertTrue("Invalid datatypes" in str(context.exception)) def test_exponentiate_stochastic_param_by_string(self): param1 = iap.Normal(0, 1) with self.assertRaises(Exception) as context: _ = param1 ** "test" self.assertTrue("Invalid datatypes" in str(context.exception)) class TestBinomial(unittest.TestCase): def setUp(self): reseed() def test___init___p_is_zero(self): param = iap.Binomial(0) assert ( param.__str__() == param.__repr__() == "Binomial(Deterministic(int 0))" ) def test___init___p_is_one(self): param = iap.Binomial(1.0) assert ( param.__str__() == param.__repr__() == "Binomial(Deterministic(float 1.00000000))" ) def test_p_is_zero(self): param = iap.Binomial(0) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == 0 assert np.all(samples == 0) def test_p_is_one(self): param = iap.Binomial(1.0) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == 1 assert np.all(samples == 1) def test_p_is_50_percent(self): param = iap.Binomial(0.5) sample = param.draw_sample() samples = param.draw_samples((10000,)) unique, counts = np.unique(samples, return_counts=True) assert sample.shape == tuple() assert samples.shape == (10000,) assert sample in [0, 1] assert len(unique) == 2 for val, count in zip(unique, counts): if val == 0: assert 5000 - 500 < count < 5000 + 500 elif val == 1: assert 5000 - 500 < count < 5000 + 500 else: assert False def test_p_is_list(self): param = iap.Binomial(iap.Choice([0.25, 0.75])) for _ in sm.xrange(10): samples = param.draw_samples((1000,)) p = np.sum(samples) / samples.size assert ( (0.25 - 0.05 < p < 0.25 + 0.05) or (0.75 - 0.05 < p < 0.75 + 0.05) ) def test_p_is_tuple(self): param = iap.Binomial((0.0, 1.0)) last_p = 0.5 diffs = [] for _ in sm.xrange(30): samples = param.draw_samples((1000,)) p = np.sum(samples).astype(np.float32) / samples.size diffs.append(abs(p - last_p)) last_p = p nb_p_changed = sum([diff > 0.05 for diff in diffs]) assert nb_p_changed > 15 def test_samples_same_values_for_same_seeds(self): param = iap.Binomial(0.5) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.array_equal(samples1, samples2) class TestChoice(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Choice([0, 1, 2]) assert ( param.__str__() == param.__repr__() == "Choice(a=[0, 1, 2], replace=True, p=None)" ) def test_value_is_list(self): param = iap.Choice([0, 1, 2]) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [0, 1, 2] assert np.all( np.logical_or( np.logical_or(samples == 0, samples == 1), samples == 2 ) ) def test_sampled_values_match_expected_counts(self): param = iap.Choice([0, 1, 2]) samples = param.draw_samples((10000,)) expected = 10000/3 expected_tolerance = expected * 0.05 for v in [0, 1, 2]: count = np.sum(samples == v) assert ( expected - expected_tolerance < count < expected + expected_tolerance ) def test_value_is_list_containing_negative_number(self): param = iap.Choice([-1, 1]) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [-1, 1] assert np.all(np.logical_or(samples == -1, samples == 1)) def test_value_is_list_of_floats(self): param = iap.Choice([-1.2, 1.7]) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert ( ( -1.2 - _eps(sample) < sample < -1.2 + _eps(sample) ) or ( 1.7 - _eps(sample) < sample < 1.7 + _eps(sample) ) ) assert np.all( np.logical_or( np.logical_and( -1.2 - _eps(sample) < samples, samples < -1.2 + _eps(sample) ), np.logical_and( 1.7 - _eps(sample) < samples, samples < 1.7 + _eps(sample) ) ) ) def test_value_is_list_of_strings(self): param = iap.Choice(["first", "second", "third"]) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in ["first", "second", "third"] assert np.all( np.logical_or( np.logical_or( samples == "first", samples == "second" ), samples == "third" ) ) def test_sample_without_replacing(self): param = iap.Choice([1+i for i in sm.xrange(100)], replace=False) samples = param.draw_samples((50,)) seen = [0 for _ in sm.xrange(100)] for sample in samples: seen[sample-1] += 1 assert all([count in [0, 1] for count in seen]) def test_non_uniform_probabilities_over_elements(self): param = iap.Choice([0, 1], p=[0.25, 0.75]) samples = param.draw_samples((10000,)) unique, counts = np.unique(samples, return_counts=True) assert len(unique) == 2 for val, count in zip(unique, counts): if val == 0: assert 2500 - 500 < count < 2500 + 500 elif val == 1: assert 7500 - 500 < count < 7500 + 500 else: assert False def test_list_contains_stochastic_parameter(self): param = iap.Choice([iap.Choice([0, 1]), 2]) samples = param.draw_samples((10000,)) unique, counts = np.unique(samples, return_counts=True) assert len(unique) == 3 for val, count in zip(unique, counts): if val in [0, 1]: assert 2500 - 500 < count < 2500 + 500 elif val == 2: assert 5000 - 500 < count < 5000 + 500 else: assert False def test_samples_same_values_for_same_seeds(self): param = iap.Choice([-1, 0, 1, 2, 3]) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.array_equal(samples1, samples2) def test_value_is_bad_datatype(self): with self.assertRaises(Exception) as context: _ = iap.Choice(123) self.assertTrue( "Expected a to be an iterable" in str(context.exception)) def test_p_is_bad_datatype(self): with self.assertRaises(Exception) as context: _ = iap.Choice([1, 2], p=123) self.assertTrue("Expected p to be" in str(context.exception)) def test_value_and_p_have_unequal_lengths(self): with self.assertRaises(Exception) as context: _ = iap.Choice([1, 2], p=[1]) self.assertTrue("Expected lengths of" in str(context.exception)) class TestDiscreteUniform(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.DiscreteUniform(0, 2) assert ( param.__str__() == param.__repr__() == "DiscreteUniform(Deterministic(int 0), Deterministic(int 2))" ) def test_bounds_are_ints(self): param = iap.DiscreteUniform(0, 2) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [0, 1, 2] assert np.all( np.logical_or( np.logical_or(samples == 0, samples == 1), samples == 2 ) ) def test_samples_match_expected_counts(self): param = iap.DiscreteUniform(0, 2) samples = param.draw_samples((10000,)) expected = 10000/3 expected_tolerance = expected * 0.05 for v in [0, 1, 2]: count = np.sum(samples == v) assert ( expected - expected_tolerance < count < expected + expected_tolerance ) def test_lower_bound_is_negative(self): param = iap.DiscreteUniform(-1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [-1, 0, 1] assert np.all( np.logical_or( np.logical_or(samples == -1, samples == 0), samples == 1 ) ) def test_bounds_are_floats(self): param = iap.DiscreteUniform(-1.2, 1.2) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [-1, 0, 1] assert np.all( np.logical_or( np.logical_or( samples == -1, samples == 0 ), samples == 1 ) ) def test_lower_and_upper_bound_have_wrong_order(self): param = iap.DiscreteUniform(1, -1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [-1, 0, 1] assert np.all( np.logical_or( np.logical_or( samples == -1, samples == 0 ), samples == 1 ) ) def test_lower_and_upper_bound_are_the_same(self): param = iap.DiscreteUniform(1, 1) sample = param.draw_sample() samples = param.draw_samples((100,)) assert sample == 1 assert np.all(samples == 1) def test_samples_same_values_for_same_seeds(self): param = iap.Uniform(-1, 1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.array_equal(samples1, samples2) class TestPoisson(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Poisson(1) assert ( param.__str__() == param.__repr__() == "Poisson(Deterministic(int 1))" ) def test_draw_sample(self): param = iap.Poisson(1) sample = param.draw_sample() assert sample.shape == tuple() assert 0 <= sample def test_via_comparison_to_np_poisson(self): param = iap.Poisson(1) samples = param.draw_samples((100, 1000)) samples_direct = iarandom.RNG(1234).poisson( lam=1, size=(100, 1000)) assert samples.shape == (100, 1000) for i in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]: count_direct = int(np.sum(samples_direct == i)) count = np.sum(samples == i) tolerance = max(count_direct * 0.1, 250) assert count_direct - tolerance < count < count_direct + tolerance def test_samples_same_values_for_same_seeds(self): param = iap.Poisson(1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.array_equal(samples1, samples2) class TestNormal(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Normal(0, 1) assert ( param.__str__() == param.__repr__() == "Normal(loc=Deterministic(int 0), scale=Deterministic(int 1))" ) def test_draw_sample(self): param = iap.Normal(0, 1) sample = param.draw_sample() assert sample.shape == tuple() def test_via_comparison_to_np_normal(self): param = iap.Normal(0, 1) samples = param.draw_samples((100, 1000)) samples_direct = iarandom.RNG(1234).normal(loc=0, scale=1, size=(100, 1000)) samples = np.clip(samples, -1, 1) samples_direct = np.clip(samples_direct, -1, 1) nb_bins = 10 hist, _ = np.histogram(samples, bins=nb_bins, range=(-1.0, 1.0), density=False) hist_direct, _ = np.histogram(samples_direct, bins=nb_bins, range=(-1.0, 1.0), density=False) tolerance = 0.05 for nb_samples, nb_samples_direct in zip(hist, hist_direct): density = nb_samples / samples.size density_direct = nb_samples_direct / samples_direct.size assert ( density_direct - tolerance < density < density_direct + tolerance ) def test_loc_is_stochastic_parameter(self): param = iap.Normal(iap.Choice([-100, 100]), 1) seen = [0, 0] for _ in sm.xrange(1000): samples = param.draw_samples((100,)) exp = np.mean(samples) if -100 - 10 < exp < -100 + 10: seen[0] += 1 elif 100 - 10 < exp < 100 + 10: seen[1] += 1 else: assert False assert 500 - 100 < seen[0] < 500 + 100 assert 500 - 100 < seen[1] < 500 + 100 def test_scale(self): param1 = iap.Normal(0, 1) param2 = iap.Normal(0, 100) samples1 = param1.draw_samples((1000,)) samples2 = param2.draw_samples((1000,)) assert np.std(samples1) < np.std(samples2) assert 100 - 10 < np.std(samples2) < 100 + 10 def test_samples_same_values_for_same_seeds(self): param = iap.Normal(0, 1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestTruncatedNormal(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.TruncatedNormal(0, 1) expected = ( "TruncatedNormal(" "loc=Deterministic(int 0), " "scale=Deterministic(int 1), " "low=Deterministic(float -inf), " "high=Deterministic(float inf)" ")" ) assert ( param.__str__() == param.__repr__() == expected ) def test___init___custom_range(self): param = iap.TruncatedNormal(0, 1, low=-100, high=50.0) expected = ( "TruncatedNormal(" "loc=Deterministic(int 0), " "scale=Deterministic(int 1), " "low=Deterministic(int -100), " "high=Deterministic(float 50.00000000)" ")" ) assert ( param.__str__() == param.__repr__() == expected ) def test_scale_is_zero(self): param = iap.TruncatedNormal(0.5, 0, low=-10, high=10) samples = param.draw_samples((100,)) assert np.allclose(samples, 0.5) def test_scale(self): param1 = iap.TruncatedNormal(0.0, 0.1, low=-100, high=100) param2 = iap.TruncatedNormal(0.0, 5.0, low=-100, high=100) samples1 = param1.draw_samples((1000,)) samples2 = param2.draw_samples((1000,)) assert np.std(samples1) < np.std(samples2) assert np.isclose(np.std(samples1), 0.1, rtol=0, atol=0.20) assert np.isclose(np.std(samples2), 5.0, rtol=0, atol=0.40) def test_loc_is_stochastic_parameter(self): param = iap.TruncatedNormal(iap.Choice([-100, 100]), 0.01, low=-1000, high=1000) seen = [0, 0] for _ in sm.xrange(200): samples = param.draw_samples((5,)) observed = np.mean(samples) dist1 = np.abs(-100 - observed) dist2 = np.abs(100 - observed) if dist1 < 1: seen[0] += 1 elif dist2 < 1: seen[1] += 1 else: assert False assert np.isclose(seen[0], 100, rtol=0, atol=20) assert np.isclose(seen[1], 100, rtol=0, atol=20) def test_samples_are_within_bounds(self): param = iap.TruncatedNormal(0, 10.0, low=-5, high=7.5) samples = param.draw_samples((1000,)) # are all within bounds assert np.all(samples >= -5.0 - 1e-4) assert np.all(samples <= 7.5 + 1e-4) # at least some samples close to bounds assert np.any(samples <= -4.5) assert np.any(samples >= 7.0) # at least some samples close to loc assert np.any(np.abs(samples) < 0.5) def test_samples_same_values_for_same_seeds(self): param = iap.TruncatedNormal(0, 1) samples1 = param.draw_samples((10, 5), random_state=1234) samples2 = param.draw_samples((10, 5), random_state=1234) assert np.allclose(samples1, samples2) def test_samples_different_values_for_different_seeds(self): param = iap.TruncatedNormal(0, 1) samples1 = param.draw_samples((10, 5), random_state=1234) samples2 = param.draw_samples((10, 5), random_state=2345) assert not np.allclose(samples1, samples2) class TestLaplace(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Laplace(0, 1) assert ( param.__str__() == param.__repr__() == "Laplace(loc=Deterministic(int 0), scale=Deterministic(int 1))" ) def test_draw_sample(self): param = iap.Laplace(0, 1) sample = param.draw_sample() assert sample.shape == tuple() def test_via_comparison_to_np_laplace(self): param = iap.Laplace(0, 1) samples = param.draw_samples((100, 1000)) samples_direct = iarandom.RNG(1234).laplace(loc=0, scale=1, size=(100, 1000)) assert samples.shape == (100, 1000) samples = np.clip(samples, -1, 1) samples_direct = np.clip(samples_direct, -1, 1) nb_bins = 10 hist, _ = np.histogram(samples, bins=nb_bins, range=(-1.0, 1.0), density=False) hist_direct, _ = np.histogram(samples_direct, bins=nb_bins, range=(-1.0, 1.0), density=False) tolerance = 0.05 for nb_samples, nb_samples_direct in zip(hist, hist_direct): density = nb_samples / samples.size density_direct = nb_samples_direct / samples_direct.size assert ( density_direct - tolerance < density < density_direct + tolerance ) def test_loc_is_stochastic_parameter(self): param = iap.Laplace(iap.Choice([-100, 100]), 1) seen = [0, 0] for _ in sm.xrange(1000): samples = param.draw_samples((100,)) exp = np.mean(samples) if -100 - 10 < exp < -100 + 10: seen[0] += 1 elif 100 - 10 < exp < 100 + 10: seen[1] += 1 else: assert False assert 500 - 100 < seen[0] < 500 + 100 assert 500 - 100 < seen[1] < 500 + 100 def test_scale(self): param1 = iap.Laplace(0, 1) param2 = iap.Laplace(0, 100) samples1 = param1.draw_samples((1000,)) samples2 = param2.draw_samples((1000,)) assert np.var(samples1) < np.var(samples2) def test_scale_is_zero(self): param1 = iap.Laplace(1, 0) samples = param1.draw_samples((100,)) assert np.all(np.logical_and( samples > 1 - _eps(samples), samples < 1 + _eps(samples) )) def test_samples_same_values_for_same_seeds(self): param = iap.Laplace(0, 1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestChiSquare(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.ChiSquare(1) assert ( param.__str__() == param.__repr__() == "ChiSquare(df=Deterministic(int 1))" ) def test_draw_sample(self): param = iap.ChiSquare(1) sample = param.draw_sample() assert sample.shape == tuple() assert 0 <= sample def test_via_comparison_to_np_chisquare(self): param = iap.ChiSquare(1) samples = param.draw_samples((100, 1000)) samples_direct = iarandom.RNG(1234).chisquare(df=1, size=(100, 1000)) assert samples.shape == (100, 1000) assert np.all(0 <= samples) samples = np.clip(samples, 0, 3) samples_direct = np.clip(samples_direct, 0, 3) nb_bins = 10 hist, _ = np.histogram(samples, bins=nb_bins, range=(0, 3.0), density=False) hist_direct, _ = np.histogram(samples_direct, bins=nb_bins, range=(0, 3.0), density=False) tolerance = 0.05 for nb_samples, nb_samples_direct in zip(hist, hist_direct): density = nb_samples / samples.size density_direct = nb_samples_direct / samples_direct.size assert ( density_direct - tolerance < density < density_direct + tolerance ) def test_df_is_stochastic_parameter(self): param = iap.ChiSquare(iap.Choice([1, 10])) seen = [0, 0] for _ in sm.xrange(1000): samples = param.draw_samples((100,)) exp = np.mean(samples) if 1 - 1.0 < exp < 1 + 1.0: seen[0] += 1 elif 10 - 4.0 < exp < 10 + 4.0: seen[1] += 1 else: assert False assert 500 - 100 < seen[0] < 500 + 100 assert 500 - 100 < seen[1] < 500 + 100 def test_larger_df_leads_to_more_variance(self): param1 = iap.ChiSquare(1) param2 = iap.ChiSquare(10) samples1 = param1.draw_samples((1000,)) samples2 = param2.draw_samples((1000,)) assert np.var(samples1) < np.var(samples2) assert 2*1 - 1.0 < np.var(samples1) < 2*1 + 1.0 assert 2*10 - 5.0 < np.var(samples2) < 2*10 + 5.0 def test_samples_same_values_for_same_seeds(self): param = iap.ChiSquare(1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestWeibull(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Weibull(1) assert ( param.__str__() == param.__repr__() == "Weibull(a=Deterministic(int 1))" ) def test_draw_sample(self): param = iap.Weibull(1) sample = param.draw_sample() assert sample.shape == tuple() assert 0 <= sample def test_via_comparison_to_np_weibull(self): param = iap.Weibull(1) samples = param.draw_samples((100, 1000)) samples_direct = iarandom.RNG(1234).weibull(a=1, size=(100, 1000)) assert samples.shape == (100, 1000) assert np.all(0 <= samples) samples = np.clip(samples, 0, 2) samples_direct = np.clip(samples_direct, 0, 2) nb_bins = 10 hist, _ = np.histogram(samples, bins=nb_bins, range=(0, 2.0), density=False) hist_direct, _ = np.histogram(samples_direct, bins=nb_bins, range=(0, 2.0), density=False) tolerance = 0.05 for nb_samples, nb_samples_direct in zip(hist, hist_direct): density = nb_samples / samples.size density_direct = nb_samples_direct / samples_direct.size assert ( density_direct - tolerance < density < density_direct + tolerance ) def test_argument_is_stochastic_parameter(self): param = iap.Weibull(iap.Choice([1, 0.5])) expected_first = scipy.special.gamma(1 + 1/1) expected_second = scipy.special.gamma(1 + 1/0.5) seen = [0, 0] for _ in sm.xrange(100): samples = param.draw_samples((50000,)) observed = np.mean(samples) matches_first = ( expected_first - 0.2 * expected_first < observed < expected_first + 0.2 * expected_first ) matches_second = ( expected_second - 0.2 * expected_second < observed < expected_second + 0.2 * expected_second ) if matches_first: seen[0] += 1 elif matches_second: seen[1] += 1 else: assert False assert 50 - 25 < seen[0] < 50 + 25 assert 50 - 25 < seen[1] < 50 + 25 def test_different_strengths(self): param1 = iap.Weibull(1) param2 = iap.Weibull(0.5) samples1 = param1.draw_samples((10000,)) samples2 = param2.draw_samples((10000,)) expected_first = ( scipy.special.gamma(1 + 2/1) - (scipy.special.gamma(1 + 1/1))**2 ) expected_second = ( scipy.special.gamma(1 + 2/0.5) - (scipy.special.gamma(1 + 1/0.5))**2 ) assert np.var(samples1) < np.var(samples2) assert ( expected_first - 0.2 * expected_first < np.var(samples1) < expected_first + 0.2 * expected_first ) assert ( expected_second - 0.2 * expected_second < np.var(samples2) < expected_second + 0.2 * expected_second ) def test_samples_same_values_for_same_seeds(self): param = iap.Weibull(1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestUniform(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Uniform(0, 1.0) assert ( param.__str__() == param.__repr__() == "Uniform(Deterministic(int 0), Deterministic(float 1.00000000))" ) def test_draw_sample(self): param = iap.Uniform(0, 1.0) sample = param.draw_sample() assert sample.shape == tuple() assert 0 - _eps(sample) < sample < 1.0 + _eps(sample) def test_draw_samples(self): param = iap.Uniform(0, 1.0) samples = param.draw_samples((10, 5)) assert samples.shape == (10, 5) assert np.all( np.logical_and( 0 - _eps(samples) < samples, samples < 1.0 + _eps(samples) ) ) def test_via_density_histogram(self): param = iap.Uniform(0, 1.0) samples = param.draw_samples((10000,)) nb_bins = 10 hist, _ = np.histogram(samples, bins=nb_bins, range=(0.0, 1.0), density=False) density_expected = 1.0/nb_bins density_tolerance = 0.05 for nb_samples in hist: density = nb_samples / samples.size assert ( density_expected - density_tolerance < density < density_expected + density_tolerance ) def test_negative_value(self): param = iap.Uniform(-1.0, 1.0) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert -1.0 - _eps(sample) < sample < 1.0 + _eps(sample) assert np.all( np.logical_and( -1.0 - _eps(samples) < samples, samples < 1.0 + _eps(samples) ) ) def test_wrong_argument_order(self): param = iap.Uniform(1.0, -1.0) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert -1.0 - _eps(sample) < sample < 1.0 + _eps(sample) assert np.all( np.logical_and( -1.0 - _eps(samples) < samples, samples < 1.0 + _eps(samples) ) ) def test_arguments_are_integers(self): param = iap.Uniform(-1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert -1.0 - _eps(sample) < sample < 1.0 + _eps(sample) assert np.all( np.logical_and( -1.0 - _eps(samples) < samples, samples < 1.0 + _eps(samples) ) ) def test_arguments_are_identical(self): param = iap.Uniform(1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert 1.0 - _eps(sample) < sample < 1.0 + _eps(sample) assert np.all( np.logical_and( 1.0 - _eps(samples) < samples, samples < 1.0 + _eps(samples) ) ) def test_samples_same_values_for_same_seeds(self): param = iap.Uniform(-1.0, 1.0) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestBeta(unittest.TestCase): @classmethod def _mean(cls, alpha, beta): return alpha / (alpha + beta) @classmethod def _var(cls, alpha, beta): return (alpha * beta) / ((alpha + beta)**2 * (alpha + beta + 1)) def setUp(self): reseed() def test___init__(self): param = iap.Beta(0.5, 0.5) assert ( param.__str__() == param.__repr__() == "Beta(" "Deterministic(float 0.50000000), " "Deterministic(float 0.50000000)" ")" ) def test_draw_sample(self): param = iap.Beta(0.5, 0.5) sample = param.draw_sample() assert sample.shape == tuple() assert 0 - _eps(sample) < sample < 1.0 + _eps(sample) def test_draw_samples(self): param = iap.Beta(0.5, 0.5) samples = param.draw_samples((100, 1000)) assert samples.shape == (100, 1000) assert np.all( np.logical_and( 0 - _eps(samples) <= samples, samples <= 1.0 + _eps(samples) ) ) def test_via_comparison_to_np_beta(self): param = iap.Beta(0.5, 0.5) samples = param.draw_samples((100, 1000)) samples_direct = iarandom.RNG(1234).beta( a=0.5, b=0.5, size=(100, 1000)) nb_bins = 10 hist, _ = np.histogram(samples, bins=nb_bins, range=(0, 1.0), density=False) hist_direct, _ = np.histogram(samples_direct, bins=nb_bins, range=(0, 1.0), density=False) tolerance = 0.05 for nb_samples, nb_samples_direct in zip(hist, hist_direct): density = nb_samples / samples.size density_direct = nb_samples_direct / samples_direct.size assert ( density_direct - tolerance < density < density_direct + tolerance ) def test_argument_is_stochastic_parameter(self): param = iap.Beta(iap.Choice([0.5, 2]), 0.5) expected_first = self._mean(0.5, 0.5) expected_second = self._mean(2, 0.5) seen = [0, 0] for _ in sm.xrange(100): samples = param.draw_samples((10000,)) observed = np.mean(samples) if expected_first - 0.05 < observed < expected_first + 0.05: seen[0] += 1 elif expected_second - 0.05 < observed < expected_second + 0.05: seen[1] += 1 else: assert False assert 50 - 25 < seen[0] < 50 + 25 assert 50 - 25 < seen[1] < 50 + 25 def test_compare_curves_of_different_arguments(self): param1 = iap.Beta(2, 2) param2 = iap.Beta(0.5, 0.5) samples1 = param1.draw_samples((10000,)) samples2 = param2.draw_samples((10000,)) expected_first = self._var(2, 2) expected_second = self._var(0.5, 0.5) assert np.var(samples1) < np.var(samples2) assert ( expected_first - 0.1 * expected_first < np.var(samples1) < expected_first + 0.1 * expected_first ) assert ( expected_second - 0.1 * expected_second < np.var(samples2) < expected_second + 0.1 * expected_second ) def test_samples_same_values_for_same_seeds(self): param = iap.Beta(0.5, 0.5) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestDeterministic(unittest.TestCase): def setUp(self): reseed() def test___init__(self): pairs = [ (0, "Deterministic(int 0)"), (1.0, "Deterministic(float 1.00000000)"), ("test", "Deterministic(test)") ] for value, expected in pairs: with self.subTest(value=value): param = iap.Deterministic(value) assert ( param.__str__() == param.__repr__() == expected ) def test_samples_same_values_for_same_seeds(self): values = [ -100, -54, -1, 0, 1, 54, 100, -100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0 ] for value in values: with self.subTest(value=value): param = iap.Deterministic(value) rs1 = iarandom.RNG(123456) rs2 = iarandom.RNG(123456) samples1 = param.draw_samples(20, random_state=rs1) samples2 = param.draw_samples(20, random_state=rs2) assert np.array_equal(samples1, samples2) def test_draw_sample_int(self): values = [-100, -54, -1, 0, 1, 54, 100] for value in values: with self.subTest(value=value): param = iap.Deterministic(value) sample1 = param.draw_sample() sample2 = param.draw_sample() assert sample1.shape == tuple() assert sample1 == sample2 def test_draw_sample_float(self): values = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for value in values: with self.subTest(value=value): param = iap.Deterministic(value) sample1 = param.draw_sample() sample2 = param.draw_sample() assert sample1.shape == tuple() assert np.isclose( sample1, sample2, rtol=0, atol=_eps(sample1)) def test_draw_samples_int(self): values = [-100, -54, -1, 0, 1, 54, 100] shapes = [10, 10, (5, 3), (5, 3), (4, 5, 3), (4, 5, 3)] for value, shape in itertools.product(values, shapes): with self.subTest(value=value, shape=shape): param = iap.Deterministic(value) samples = param.draw_samples(shape) shape_expected = ( shape if isinstance(shape, tuple) else tuple([shape])) assert samples.shape == shape_expected assert np.all(samples == value) def test_draw_samples_float(self): values = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] shapes = [10, 10, (5, 3), (5, 3), (4, 5, 3), (4, 5, 3)] for value, shape in itertools.product(values, shapes): with self.subTest(value=value, shape=shape): param = iap.Deterministic(value) samples = param.draw_samples(shape) shape_expected = ( shape if isinstance(shape, tuple) else tuple([shape])) assert samples.shape == shape_expected assert np.allclose(samples, value, rtol=0, atol=_eps(samples)) def test_argument_is_stochastic_parameter(self): seen = [0, 0] for _ in sm.xrange(200): param = iap.Deterministic(iap.Choice([0, 1])) seen[param.value] += 1 assert 100 - 50 < seen[0] < 100 + 50 assert 100 - 50 < seen[1] < 100 + 50 def test_argument_has_invalid_type(self): with self.assertRaises(Exception) as context: _ = iap.Deterministic([1, 2, 3]) self.assertTrue( "Expected StochasticParameter object or number or string" in str(context.exception)) class TestFromLowerResolution(unittest.TestCase): def setUp(self): reseed() def test___init___size_percent(self): param = iap.FromLowerResolution(other_param=iap.Deterministic(0), size_percent=1, method="nearest") assert ( param.__str__() == param.__repr__() == "FromLowerResolution(" "size_percent=Deterministic(int 1), " "method=Deterministic(nearest), " "other_param=Deterministic(int 0)" ")" ) def test___init___size_px(self): param = iap.FromLowerResolution(other_param=iap.Deterministic(0), size_px=1, method="nearest") assert ( param.__str__() == param.__repr__() == "FromLowerResolution(" "size_px=Deterministic(int 1), " "method=Deterministic(nearest), " "other_param=Deterministic(int 0)" ")" ) def test_binomial_hwc(self): param = iap.FromLowerResolution(iap.Binomial(0.5), size_px=8) samples = param.draw_samples((8, 8, 1)) uq = np.unique(samples) assert samples.shape == (8, 8, 1) assert len(uq) == 2 assert 0 in uq assert 1 in uq def test_binomial_nhwc(self): param = iap.FromLowerResolution(iap.Binomial(0.5), size_px=8) samples_nhwc = param.draw_samples((1, 8, 8, 1)) uq = np.unique(samples_nhwc) assert samples_nhwc.shape == (1, 8, 8, 1) assert len(uq) == 2 assert 0 in uq assert 1 in uq def test_draw_samples_with_too_many_dimensions(self): # (N, H, W, C, something) causing error param = iap.FromLowerResolution(iap.Binomial(0.5), size_px=8) with self.assertRaises(Exception) as context: _ = param.draw_samples((1, 8, 8, 1, 1)) self.assertTrue( "FromLowerResolution can only generate samples of shape" in str(context.exception) ) def test_binomial_hw3(self): # C=3 param = iap.FromLowerResolution(iap.Binomial(0.5), size_px=8) samples = param.draw_samples((8, 8, 3)) uq = np.unique(samples) assert samples.shape == (8, 8, 3) assert len(uq) == 2 assert 0 in uq assert 1 in uq def test_different_size_px_arguments(self): # different sizes in px param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2) param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=16) seen_components = [0, 0] seen_pixels = [0, 0] for _ in sm.xrange(100): samples1 = param1.draw_samples((16, 16, 1)) samples2 = param2.draw_samples((16, 16, 1)) _, num1 = skimage.morphology.label(samples1, connectivity=1, background=0, return_num=True) _, num2 = skimage.morphology.label(samples2, connectivity=1, background=0, return_num=True) seen_components[0] += num1 seen_components[1] += num2 seen_pixels[0] += np.sum(samples1 == 1) seen_pixels[1] += np.sum(samples2 == 1) assert seen_components[0] < seen_components[1] assert ( seen_pixels[0] / seen_components[0] > seen_pixels[1] / seen_components[1] ) def test_different_size_px_arguments_with_tuple(self): # different sizes in px, one given as tuple (a, b) param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2) param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=(2, 16)) seen_components = [0, 0] seen_pixels = [0, 0] for _ in sm.xrange(400): samples1 = param1.draw_samples((16, 16, 1)) samples2 = param2.draw_samples((16, 16, 1)) _, num1 = skimage.morphology.label(samples1, connectivity=1, background=0, return_num=True) _, num2 = skimage.morphology.label(samples2, connectivity=1, background=0, return_num=True) seen_components[0] += num1 seen_components[1] += num2 seen_pixels[0] += np.sum(samples1 == 1) seen_pixels[1] += np.sum(samples2 == 1) assert seen_components[0] < seen_components[1] assert ( seen_pixels[0] / seen_components[0] > seen_pixels[1] / seen_components[1] ) def test_different_size_px_argument_with_stochastic_parameters(self): # different sizes in px, given as StochasticParameter param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=iap.Deterministic(1)) param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=iap.Choice([8, 16])) seen_components = [0, 0] seen_pixels = [0, 0] for _ in sm.xrange(100): samples1 = param1.draw_samples((16, 16, 1)) samples2 = param2.draw_samples((16, 16, 1)) _, num1 = skimage.morphology.label(samples1, connectivity=1, background=0, return_num=True) _, num2 = skimage.morphology.label(samples2, connectivity=1, background=0, return_num=True) seen_components[0] += num1 seen_components[1] += num2 seen_pixels[0] += np.sum(samples1 == 1) seen_pixels[1] += np.sum(samples2 == 1) assert seen_components[0] < seen_components[1] assert ( seen_pixels[0] / seen_components[0] > seen_pixels[1] / seen_components[1] ) def test_size_px_has_invalid_datatype(self): # bad datatype for size_px with self.assertRaises(Exception) as context: _ = iap.FromLowerResolution(iap.Binomial(0.5), size_px=False) self.assertTrue("Expected " in str(context.exception)) def test_min_size(self): # min_size param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2) param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=1, min_size=16) seen_components = [0, 0] seen_pixels = [0, 0] for _ in sm.xrange(100): samples1 = param1.draw_samples((16, 16, 1)) samples2 = param2.draw_samples((16, 16, 1)) _, num1 = skimage.morphology.label(samples1, connectivity=1, background=0, return_num=True) _, num2 = skimage.morphology.label(samples2, connectivity=1, background=0, return_num=True) seen_components[0] += num1 seen_components[1] += num2 seen_pixels[0] += np.sum(samples1 == 1) seen_pixels[1] += np.sum(samples2 == 1) assert seen_components[0] < seen_components[1] assert ( seen_pixels[0] / seen_components[0] > seen_pixels[1] / seen_components[1] ) def test_size_percent(self): # different sizes in percent param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=0.01) param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=0.8) seen_components = [0, 0] seen_pixels = [0, 0] for _ in sm.xrange(100): samples1 = param1.draw_samples((16, 16, 1)) samples2 = param2.draw_samples((16, 16, 1)) _, num1 = skimage.morphology.label(samples1, connectivity=1, background=0, return_num=True) _, num2 = skimage.morphology.label(samples2, connectivity=1, background=0, return_num=True) seen_components[0] += num1 seen_components[1] += num2 seen_pixels[0] += np.sum(samples1 == 1) seen_pixels[1] += np.sum(samples2 == 1) assert seen_components[0] < seen_components[1] assert ( seen_pixels[0] / seen_components[0] > seen_pixels[1] / seen_components[1] ) def test_size_percent_as_stochastic_parameters(self): # different sizes in percent, given as StochasticParameter param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=iap.Deterministic(0.01)) param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=iap.Choice([0.4, 0.8])) seen_components = [0, 0] seen_pixels = [0, 0] for _ in sm.xrange(100): samples1 = param1.draw_samples((16, 16, 1)) samples2 = param2.draw_samples((16, 16, 1)) _, num1 = skimage.morphology.label(samples1, connectivity=1, background=0, return_num=True) _, num2 = skimage.morphology.label(samples2, connectivity=1, background=0, return_num=True) seen_components[0] += num1 seen_components[1] += num2 seen_pixels[0] += np.sum(samples1 == 1) seen_pixels[1] += np.sum(samples2 == 1) assert seen_components[0] < seen_components[1] assert ( seen_pixels[0] / seen_components[0] > seen_pixels[1] / seen_components[1] ) def test_size_percent_has_invalid_datatype(self): # bad datatype for size_percent with self.assertRaises(Exception) as context: _ = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=False) self.assertTrue("Expected " in str(context.exception)) def test_method(self): # method given as StochasticParameter param = iap.FromLowerResolution( iap.Binomial(0.5), size_px=4, method=iap.Choice(["nearest", "linear"])) seen = [0, 0] for _ in sm.xrange(200): samples = param.draw_samples((16, 16, 1)) nb_in_between = np.sum( np.logical_and(0.05 < samples, samples < 0.95)) if nb_in_between == 0: seen[0] += 1 else: seen[1] += 1 assert 100 - 50 < seen[0] < 100 + 50 assert 100 - 50 < seen[1] < 100 + 50 def test_method_has_invalid_datatype(self): # bad datatype for method with self.assertRaises(Exception) as context: _ = iap.FromLowerResolution(iap.Binomial(0.5), size_px=4, method=False) self.assertTrue("Expected " in str(context.exception)) def test_samples_same_values_for_same_seeds(self): # multiple calls with same random_state param = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2) samples1 = param.draw_samples((10, 5, 1), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5, 1), random_state=iarandom.RNG(1234)) assert np.allclose(samples1, samples2) class TestClip(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Clip(iap.Deterministic(0), -1, 1) assert ( param.__str__() == param.__repr__() == "Clip(Deterministic(int 0), -1.000000, 1.000000)" ) def test_value_within_bounds(self): param = iap.Clip(iap.Deterministic(0), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == 0 assert np.all(samples == 0) def test_value_exactly_at_upper_bound(self): param = iap.Clip(iap.Deterministic(1), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == 1 assert np.all(samples == 1) def test_value_exactly_at_lower_bound(self): param = iap.Clip(iap.Deterministic(-1), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == -1 assert np.all(samples == -1) def test_value_is_within_bounds_and_float(self): param = iap.Clip(iap.Deterministic(0.5), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert 0.5 - _eps(sample) < sample < 0.5 + _eps(sample) assert np.all( np.logical_and( 0.5 - _eps(sample) <= samples, samples <= 0.5 + _eps(sample) ) ) def test_value_is_above_upper_bound(self): param = iap.Clip(iap.Deterministic(2), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == 1 assert np.all(samples == 1) def test_value_is_below_lower_bound(self): param = iap.Clip(iap.Deterministic(-2), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == -1 assert np.all(samples == -1) def test_value_is_sometimes_without_bounds_sometimes_beyond(self): param = iap.Clip(iap.Choice([0, 2]), -1, 1) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [0, 1] assert np.all(np.logical_or(samples == 0, samples == 1)) def test_samples_same_values_for_same_seeds(self): param = iap.Clip(iap.Choice([0, 2]), -1, 1) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.array_equal(samples1, samples2) def test_lower_bound_is_none(self): param = iap.Clip(iap.Deterministic(0), None, 1) sample = param.draw_sample() assert sample == 0 assert ( param.__str__() == param.__repr__() == "Clip(Deterministic(int 0), None, 1.000000)" ) def test_upper_bound_is_none(self): param = iap.Clip(iap.Deterministic(0), 0, None) sample = param.draw_sample() assert sample == 0 assert ( param.__str__() == param.__repr__() == "Clip(Deterministic(int 0), 0.000000, None)" ) def test_both_bounds_are_none(self): param = iap.Clip(iap.Deterministic(0), None, None) sample = param.draw_sample() assert sample == 0 assert ( param.__str__() == param.__repr__() == "Clip(Deterministic(int 0), None, None)" ) class TestDiscretize(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Discretize(iap.Deterministic(0)) assert ( param.__str__() == param.__repr__() == "Discretize(Deterministic(int 0))" ) def test_applied_to_deterministic(self): values = [-100.2, -54.3, -1.0, -1, -0.7, -0.00043, 0, 0.00043, 0.7, 1.0, 1, 54.3, 100.2] for value in values: with self.subTest(value=value): param = iap.Discretize(iap.Deterministic(value)) value_expected = np.round( np.float64([value]) ).astype(np.int32)[0] sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample == value_expected assert np.all(samples == value_expected) # TODO why are these tests applied to DiscreteUniform instead of Uniform? def test_applied_to_discrete_uniform(self): param_orig = iap.DiscreteUniform(0, 1) param = iap.Discretize(param_orig) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) assert sample in [0, 1] assert np.all(np.logical_or(samples == 0, samples == 1)) def test_applied_to_discrete_uniform_with_wider_range(self): param_orig = iap.DiscreteUniform(0, 2) param = iap.Discretize(param_orig) samples1 = param_orig.draw_samples((10000,)) samples2 = param.draw_samples((10000,)) assert np.all(np.abs(samples1 - samples2) < 0.2*(10000/3)) def test_samples_same_values_for_same_seeds(self): param_orig = iap.DiscreteUniform(0, 2) param = iap.Discretize(param_orig) samples1 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((10, 5), random_state=iarandom.RNG(1234)) assert np.array_equal(samples1, samples2) class TestMultiply(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Multiply(iap.Deterministic(0), 1, elementwise=False) assert ( param.__str__() == param.__repr__() == "Multiply(Deterministic(int 0), Deterministic(int 1), False)" ) def test_multiply_example_integer_values(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): with self.subTest(left=v1, right=v2): p = iap.Multiply(iap.Deterministic(v1), v2) samples = p.draw_samples((2, 3)) assert p.draw_sample() == v1 * v2 assert samples.dtype.kind == "i" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.int64) + v1 * v2 ) def test_multiply_example_integer_values_both_deterministic(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): with self.subTest(left=v1, right=v2): p = iap.Multiply(iap.Deterministic(v1), iap.Deterministic(v2)) samples = p.draw_samples((2, 3)) assert p.draw_sample() == v1 * v2 assert samples.dtype.name == "int32" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.int32) + v1 * v2 ) def test_multiply_example_float_values(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): with self.subTest(left=v1, right=v2): p = iap.Multiply(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert np.isclose(sample, v1 * v2, atol=1e-3, rtol=0) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float32) + v1 * v2 ) def test_multiply_example_float_values_both_deterministic(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): with self.subTest(left=v1, right=v2): p = iap.Multiply(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert np.isclose(sample, v1 * v2, atol=1e-3, rtol=0) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float32) + v1 * v2 ) def test_multiply_by_stochastic_parameter(self): param = iap.Multiply(iap.Deterministic(1.0), (1.0, 2.0), elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 * 1.0 - _eps(samples)) assert np.all(samples < 1.0 * 2.0 + _eps(samples)) assert ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_multiply_by_stochastic_parameter_elementwise(self): param = iap.Multiply(iap.Deterministic(1.0), (1.0, 2.0), elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 * 1.0 - _eps(samples)) assert np.all(samples < 1.0 * 2.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_multiply_stochastic_parameter_by_fixed_value(self): param = iap.Multiply(iap.Uniform(1.0, 2.0), 1.0, elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 * 1.0 - _eps(samples)) assert np.all(samples < 2.0 * 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_multiply_stochastic_parameter_by_fixed_value_elementwise(self): param = iap.Multiply(iap.Uniform(1.0, 2.0), 1.0, elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 * 1.0 - _eps(samples)) assert np.all(samples < 2.0 * 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) class TestDivide(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Divide(iap.Deterministic(0), 1, elementwise=False) assert ( param.__str__() == param.__repr__() == "Divide(Deterministic(int 0), Deterministic(int 1), False)" ) def test_divide_integers(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): if v2 == 0: v2 = 1 with self.subTest(left=v1, right=v2): p = iap.Divide(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert sample == (v1 / v2) assert samples.dtype.kind == "f" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.float64) + (v1 / v2) ) def test_divide_integers_both_deterministic(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): if v2 == 0: v2 = 1 with self.subTest(left=v1, right=v2): p = iap.Divide(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert sample == (v1 / v2) assert samples.dtype.kind == "f" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.float64) + (v1 / v2) ) def test_divide_floats(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): if v2 == 0: v2 = 1 with self.subTest(left=v1, right=v2): p = iap.Divide(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert ( (v1 / v2) - _eps(sample) <= sample <= (v1 / v2) + _eps(sample) ) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float64) + (v1 / v2) ) def test_divide_floats_both_deterministic(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): if v2 == 0: v2 = 1 with self.subTest(left=v1, right=v2): p = iap.Divide(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert ( (v1 / v2) - _eps(sample) <= sample <= (v1 / v2) + _eps(sample) ) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float64) + (v1 / v2) ) def test_divide_by_stochastic_parameter(self): param = iap.Divide(iap.Deterministic(1.0), (1.0, 2.0), elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > (1.0 / 2.0) - _eps(samples)) assert np.all(samples < (1.0 / 1.0) + _eps(samples)) assert ( samples_sorted[0] - _eps(samples) < samples_sorted[-1] < samples_sorted[0] + _eps(samples) ) def test_divide_by_stochastic_parameter_elementwise(self): param = iap.Divide(iap.Deterministic(1.0), (1.0, 2.0), elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > (1.0 / 2.0) - _eps(samples)) assert np.all(samples < (1.0 / 1.0) + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples) < samples_sorted[-1] < samples_sorted[0] + _eps(samples) ) def test_divide_stochastic_parameter_by_float(self): param = iap.Divide(iap.Uniform(1.0, 2.0), 1.0, elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > (1.0 / 1.0) - _eps(samples)) assert np.all(samples < (2.0 / 1.0) + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples) < samples_sorted[-1] < samples_sorted[0] + _eps(samples) ) def test_divide_stochastic_parameter_by_float_elementwise(self): param = iap.Divide(iap.Uniform(1.0, 2.0), 1.0, elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > (1.0 / 1.0) - _eps(samples)) assert np.all(samples < (2.0 / 1.0) + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted) < samples_sorted[-1] < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted) ) def test_divide_by_stochastic_parameter_that_can_by_zero(self): # test division by zero automatically being converted to division by 1 param = iap.Divide(2, iap.Choice([0, 2]), elementwise=True) samples = param.draw_samples((10, 20)) samples_unique = np.sort(np.unique(samples.flatten())) assert samples_unique[0] == 1 and samples_unique[1] == 2 def test_divide_by_zero(self): param = iap.Divide(iap.Deterministic(1), 0, elementwise=False) sample = param.draw_sample() assert sample == 1 class TestAdd(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Add(iap.Deterministic(0), 1, elementwise=False) assert ( param.__str__() == param.__repr__() == "Add(Deterministic(int 0), Deterministic(int 1), False)" ) def test_add_integers(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): with self.subTest(left=v1, right=v2): p = iap.Add(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert sample == v1 + v2 assert samples.dtype.kind == "i" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.int32) + v1 + v2 ) def test_add_integers_both_deterministic(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): with self.subTest(left=v1, right=v2): p = iap.Add(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert sample == v1 + v2 assert samples.dtype.kind == "i" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.int32) + v1 + v2 ) def test_add_floats(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): with self.subTest(left=v1, right=v2): p = iap.Add(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert np.isclose(sample, v1 + v2, atol=1e-3, rtol=0) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float32) + v1 + v2 ) def test_add_floats_both_deterministic(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): with self.subTest(left=v1, right=v2): p = iap.Add(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert np.isclose(sample, v1 + v2, atol=1e-3, rtol=0) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float32) + v1 + v2 ) def test_add_stochastic_parameter(self): param = iap.Add(iap.Deterministic(1.0), (1.0, 2.0), elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples >= 1.0 + 1.0 - _eps(samples)) assert np.all(samples <= 1.0 + 2.0 + _eps(samples)) assert ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_add_stochastic_parameter_elementwise(self): param = iap.Add(iap.Deterministic(1.0), (1.0, 2.0), elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples >= 1.0 + 1.0 - _eps(samples)) assert np.all(samples <= 1.0 + 2.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_add_to_stochastic_parameter(self): param = iap.Add(iap.Uniform(1.0, 2.0), 1.0, elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples >= 1.0 + 1.0 - _eps(samples)) assert np.all(samples <= 2.0 + 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_add_to_stochastic_parameter_elementwise(self): param = iap.Add(iap.Uniform(1.0, 2.0), 1.0, elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples >= 1.0 + 1.0 - _eps(samples)) assert np.all(samples <= 2.0 + 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) class TestSubtract(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Subtract(iap.Deterministic(0), 1, elementwise=False) assert ( param.__str__() == param.__repr__() == "Subtract(Deterministic(int 0), Deterministic(int 1), False)" ) def test_subtract_integers(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): with self.subTest(left=v1, right=v2): p = iap.Subtract(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert sample == v1 - v2 assert samples.dtype.kind == "i" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.int64) + v1 - v2 ) def test_subtract_integers_both_deterministic(self): values_int = [-100, -54, -1, 0, 1, 54, 100] for v1, v2 in itertools.product(values_int, values_int): with self.subTest(left=v1, right=v2): p = iap.Subtract(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert sample == v1 - v2 assert samples.dtype.kind == "i" assert np.array_equal( samples, np.zeros((2, 3), dtype=np.int64) + v1 - v2 ) def test_subtract_floats(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): with self.subTest(left=v1, right=v2): p = iap.Subtract(iap.Deterministic(v1), v2) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert v1 - v2 - _eps(sample) < sample < v1 - v2 + _eps(sample) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float64) + v1 - v2 ) def test_subtract_floats_both_deterministic(self): values_float = [-100.0, -54.3, -1.0, 0.1, 0.0, 0.1, 1.0, 54.4, 100.0] for v1, v2 in itertools.product(values_float, values_float): with self.subTest(left=v1, right=v2): p = iap.Subtract(iap.Deterministic(v1), iap.Deterministic(v2)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert v1 - v2 - _eps(sample) < sample < v1 - v2 + _eps(sample) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float64) + v1 - v2 ) def test_subtract_stochastic_parameter(self): param = iap.Subtract(iap.Deterministic(1.0), (1.0, 2.0), elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 - 2.0 - _eps(samples)) assert np.all(samples < 1.0 - 1.0 + _eps(samples)) assert ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_subtract_stochastic_parameter_elementwise(self): param = iap.Subtract(iap.Deterministic(1.0), (1.0, 2.0), elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 - 2.0 - _eps(samples)) assert np.all(samples < 1.0 - 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_subtract_from_stochastic_parameter(self): param = iap.Subtract(iap.Uniform(1.0, 2.0), 1.0, elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 - 1.0 - _eps(samples)) assert np.all(samples < 2.0 - 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_subtract_from_stochastic_parameter_elementwise(self): param = iap.Subtract(iap.Uniform(1.0, 2.0), 1.0, elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 - 1.0 - _eps(samples)) assert np.all(samples < 2.0 - 1.0 + _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) class TestPower(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Power(iap.Deterministic(0), 1, elementwise=False) assert ( param.__str__() == param.__repr__() == "Power(Deterministic(int 0), Deterministic(int 1), False)" ) def test_pairs(self): values = [ -100, -54, -1, 0, 1, 54, 100, -100.0, -54.0, -1.0, 0.0, 1.0, 54.0, 100.0 ] exponents = [-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2] for base, exponent in itertools.product(values, exponents): if base < 0 and ia.is_single_float(exponent): continue if base == 0 and exponent < 0: continue with self.subTest(base=base, exponent=exponent): p = iap.Power(iap.Deterministic(base), exponent) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert ( base ** exponent - _eps(sample) < sample < base ** exponent + _eps(sample) ) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float64) + base ** exponent ) def test_pairs_both_deterministic(self): values = [ -100, -54, -1, 0, 1, 54, 100, -100.0, -54.0, -1.0, 0.0, 1.0, 54.0, 100.0 ] exponents = [-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2] for base, exponent in itertools.product(values, exponents): if base < 0 and ia.is_single_float(exponent): continue if base == 0 and exponent < 0: continue with self.subTest(base=base, exponent=exponent): p = iap.Power(iap.Deterministic(base), iap.Deterministic(exponent)) sample = p.draw_sample() samples = p.draw_samples((2, 3)) assert ( base ** exponent - _eps(sample) < sample < base ** exponent + _eps(sample) ) assert samples.dtype.kind == "f" assert np.allclose( samples, np.zeros((2, 3), dtype=np.float64) + base ** exponent ) def test_exponent_is_stochastic_parameter(self): param = iap.Power(iap.Deterministic(1.5), (1.0, 2.0), elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.5 ** 1.0 - 2 * _eps(samples)) assert np.all(samples < 1.5 ** 2.0 + 2 * _eps(samples)) assert ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_exponent_is_stochastic_parameter_elementwise(self): param = iap.Power(iap.Deterministic(1.5), (1.0, 2.0), elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.5 ** 1.0 - 2 * _eps(samples)) assert np.all(samples < 1.5 ** 2.0 + 2 * _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_value_is_uniform(self): param = iap.Power(iap.Uniform(1.0, 2.0), 1.0, elementwise=False) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 ** 1.0 - 2 * _eps(samples)) assert np.all(samples < 2.0 ** 1.0 + 2 * _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) def test_value_is_uniform_elementwise(self): param = iap.Power(iap.Uniform(1.0, 2.0), 1.0, elementwise=True) samples = param.draw_samples((10, 20)) samples_sorted = np.sort(samples.flatten()) assert samples.shape == (10, 20) assert np.all(samples > 1.0 ** 1.0 - 2 * _eps(samples)) assert np.all(samples < 2.0 ** 1.0 + 2 * _eps(samples)) assert not ( samples_sorted[0] - _eps(samples_sorted[0]) < samples_sorted[-1] < samples_sorted[0] + _eps(samples_sorted[0]) ) class TestAbsolute(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Absolute(iap.Deterministic(0)) assert ( param.__str__() == param.__repr__() == "Absolute(Deterministic(int 0))" ) def test_fixed_values(self): simple_values = [-1.5, -1, -1.0, -0.1, 0, 0.0, 0.1, 1, 1.0, 1.5] for value in simple_values: with self.subTest(value=value): param = iap.Absolute(iap.Deterministic(value)) sample = param.draw_sample() samples = param.draw_samples((10, 5)) assert sample.shape == tuple() assert samples.shape == (10, 5) if ia.is_single_float(value): assert ( abs(value) - _eps(sample) < sample < abs(value) + _eps(sample) ) assert np.all(abs(value) - _eps(samples) < samples) assert np.all(samples < abs(value) + _eps(samples)) else: assert sample == abs(value) assert np.all(samples == abs(value)) def test_value_is_stochastic_parameter(self): param = iap.Absolute(iap.Choice([-3, -1, 1, 3])) sample = param.draw_sample() samples = param.draw_samples((10, 10)) samples_uq = np.sort(np.unique(samples)) assert sample.shape == tuple() assert sample in [3, 1] assert samples.shape == (10, 10) assert len(samples_uq) == 2 assert samples_uq[0] == 1 and samples_uq[1] == 3 class TestRandomSign(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.RandomSign(iap.Deterministic(0), 0.5) assert ( param.__str__() == param.__repr__() == "RandomSign(Deterministic(int 0), 0.50)" ) def test_value_is_deterministic(self): param = iap.RandomSign(iap.Deterministic(1)) samples = param.draw_samples((1000,)) n_positive = np.sum(samples == 1) n_negative = np.sum(samples == -1) assert samples.shape == (1000,) assert n_positive + n_negative == 1000 assert 350 < n_positive < 750 def test_value_is_deterministic_many_samples(self): param = iap.RandomSign(iap.Deterministic(1)) seen = [0, 0] for _ in sm.xrange(1000): sample = param.draw_sample() assert sample.shape == tuple() if sample == 1: seen[1] += 1 else: seen[0] += 1 n_negative, n_positive = seen assert n_positive + n_negative == 1000 assert 350 < n_positive < 750 def test_value_is_stochastic_parameter(self): param = iap.RandomSign(iap.Choice([1, 2])) samples = param.draw_samples((4000,)) seen = [0, 0, 0, 0] seen[0] = np.sum(samples == -2) seen[1] = np.sum(samples == -1) seen[2] = np.sum(samples == 1) seen[3] = np.sum(samples == 2) assert np.sum(seen) == 4000 assert all([700 < v < 1300 for v in seen]) def test_samples_same_values_for_same_seeds(self): param = iap.RandomSign(iap.Choice([1, 2])) samples1 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) assert samples1.shape == (100, 10) assert samples2.shape == (100, 10) assert np.array_equal(samples1, samples2) assert np.sum(samples1 == -2) > 50 assert np.sum(samples1 == -1) > 50 assert np.sum(samples1 == 1) > 50 assert np.sum(samples1 == 2) > 50 class TestForceSign(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.ForceSign(iap.Deterministic(0), True, "invert", 1) assert ( param.__str__() == param.__repr__() == "ForceSign(Deterministic(int 0), True, invert, 1)" ) def test_single_sample_positive(self): param = iap.ForceSign(iap.Deterministic(1), positive=True, mode="invert") sample = param.draw_sample() assert sample.shape == tuple() assert sample == 1 def test_single_sample_negative(self): param = iap.ForceSign(iap.Deterministic(1), positive=False, mode="invert") sample = param.draw_sample() assert sample.shape == tuple() assert sample == -1 def test_many_samples_positive(self): param = iap.ForceSign(iap.Deterministic(1), positive=True, mode="invert") samples = param.draw_samples(100) assert samples.shape == (100,) assert np.all(samples == 1) def test_many_samples_negative(self): param = iap.ForceSign(iap.Deterministic(1), positive=False, mode="invert") samples = param.draw_samples(100) assert samples.shape == (100,) assert np.all(samples == -1) def test_many_samples_negative_value_to_positive(self): param = iap.ForceSign(iap.Deterministic(-1), positive=True, mode="invert") samples = param.draw_samples(100) assert samples.shape == (100,) assert np.all(samples == 1) def test_many_samples_negative_value_to_negative(self): param = iap.ForceSign(iap.Deterministic(-1), positive=False, mode="invert") samples = param.draw_samples(100) assert samples.shape == (100,) assert np.all(samples == -1) def test_many_samples_stochastic_value_to_positive(self): param = iap.ForceSign(iap.Choice([-2, 1]), positive=True, mode="invert") samples = param.draw_samples(1000) n_twos = np.sum(samples == 2) n_ones = np.sum(samples == 1) assert samples.shape == (1000,) assert n_twos + n_ones == 1000 assert 200 < n_twos < 700 assert 200 < n_ones < 700 def test_many_samples_stochastic_value_to_positive_reroll(self): param = iap.ForceSign(iap.Choice([-2, 1]), positive=True, mode="reroll") samples = param.draw_samples(1000) n_twos = np.sum(samples == 2) n_ones = np.sum(samples == 1) assert samples.shape == (1000,) assert n_twos + n_ones == 1000 assert n_twos > 0 assert n_ones > 0 def test_many_samples_stochastic_value_to_positive_reroll_max_count(self): param = iap.ForceSign(iap.Choice([-2, 1]), positive=True, mode="reroll", reroll_count_max=100) samples = param.draw_samples(100) n_twos = np.sum(samples == 2) n_ones = np.sum(samples == 1) assert samples.shape == (100,) assert n_twos + n_ones == 100 assert n_twos < 5 def test_samples_same_values_for_same_seeds(self): param = iap.ForceSign(iap.Choice([-2, 1]), positive=True, mode="invert") samples1 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) assert samples1.shape == (100, 10) assert samples2.shape == (100, 10) assert np.array_equal(samples1, samples2) class TestPositive(unittest.TestCase): def setUp(self): reseed() def test_many_samples_reroll(self): param = iap.Positive(iap.Deterministic(-1), mode="reroll", reroll_count_max=1) samples = param.draw_samples((100,)) assert samples.shape == (100,) assert np.all(samples == 1) class TestNegative(unittest.TestCase): def setUp(self): reseed() def test_many_samples_reroll(self): param = iap.Negative(iap.Deterministic(1), mode="reroll", reroll_count_max=1) samples = param.draw_samples((100,)) assert samples.shape == (100,) assert np.all(samples == -1) class TestIterativeNoiseAggregator(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.IterativeNoiseAggregator(iap.Deterministic(0), iterations=(1, 3), aggregation_method="max") assert ( param.__str__() == param.__repr__() == ( "IterativeNoiseAggregator(" "Deterministic(int 0), " "DiscreteUniform(Deterministic(int 1), " "Deterministic(int 3)" "), " "Deterministic(max)" ")" ) ) def test_value_is_deterministic_max_1_iter(self): param = iap.IterativeNoiseAggregator(iap.Deterministic(1), iterations=1, aggregation_method="max") sample = param.draw_sample() samples = param.draw_samples((2, 4)) assert sample.shape == tuple() assert samples.shape == (2, 4) assert sample == 1 assert np.all(samples == 1) def test_value_is_stochastic_avg_200_iter(self): param = iap.IterativeNoiseAggregator(iap.Choice([0, 50]), iterations=200, aggregation_method="avg") sample = param.draw_sample() samples = param.draw_samples((2, 4)) assert sample.shape == tuple() assert samples.shape == (2, 4) assert 25 - 10 < sample < 25 + 10 assert np.all(np.logical_and(25 - 10 < samples, samples < 25 + 10)) def test_value_is_stochastic_max_100_iter(self): param = iap.IterativeNoiseAggregator(iap.Choice([0, 50]), iterations=100, aggregation_method="max") sample = param.draw_sample() samples = param.draw_samples((2, 4)) assert sample.shape == tuple() assert samples.shape == (2, 4) assert sample == 50 assert np.all(samples == 50) def test_value_is_stochastic_min_100_iter(self): param = iap.IterativeNoiseAggregator(iap.Choice([0, 50]), iterations=100, aggregation_method="min") sample = param.draw_sample() samples = param.draw_samples((2, 4)) assert sample.shape == tuple() assert samples.shape == (2, 4) assert sample == 0 assert np.all(samples == 0) def test_value_is_stochastic_avg_or_max_100_iter_evaluate_counts(self): seen = [0, 0, 0, 0] for _ in sm.xrange(100): param = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=100, aggregation_method=["avg", "max"]) samples = param.draw_samples((1, 1)) diff_0 = abs(0 - samples[0, 0]) diff_25 = abs(25 - samples[0, 0]) diff_50 = abs(50 - samples[0, 0]) if diff_25 < 10.0: seen[0] += 1 elif diff_50 < _eps(samples): seen[1] += 1 elif diff_0 < _eps(samples): seen[2] += 1 else: seen[3] += 1 assert seen[2] <= 2 # around 0.0 assert seen[3] <= 2 # 0.0+eps <= x < 15.0 or 35.0 < x < 50.0 or >50.0 assert 50 - 20 < seen[0] < 50 + 20 assert 50 - 20 < seen[1] < 50 + 20 def test_value_is_stochastic_avg_tuple_as_iter_evaluate_histograms(self): # iterations as tuple param = iap.IterativeNoiseAggregator( iap.Uniform(-1.0, 1.0), iterations=(1, 100), aggregation_method="avg") diffs = [] for _ in sm.xrange(100): samples = param.draw_samples((1, 1)) diff = abs(samples[0, 0] - 0.0) diffs.append(diff) nb_bins = 3 hist, _ = np.histogram(diffs, bins=nb_bins, range=(-1.0, 1.0), density=False) assert hist[1] > hist[0] assert hist[1] > hist[2] def test_value_is_stochastic_max_list_as_iter_evaluate_counts(self): # iterations as list seen = [0, 0] for _ in sm.xrange(400): param = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=[1, 100], aggregation_method=["max"]) samples = param.draw_samples((1, 1)) diff_0 = abs(0 - samples[0, 0]) diff_50 = abs(50 - samples[0, 0]) if diff_50 < _eps(samples): seen[0] += 1 elif diff_0 < _eps(samples): seen[1] += 1 else: assert False assert 300 - 50 < seen[0] < 300 + 50 assert 100 - 50 < seen[1] < 100 + 50 def test_value_is_stochastic_all_100_iter(self): # test ia.ALL as aggregation_method # note that each method individually and list of methods are already # tested, so no in depth test is needed here param = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=100, aggregation_method=ia.ALL) assert isinstance(param.aggregation_method, iap.Choice) assert len(param.aggregation_method.a) == 3 assert [v in param.aggregation_method.a for v in ["min", "avg", "max"]] def test_value_is_stochastic_max_2_iter(self): param = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=2, aggregation_method="max") samples = param.draw_samples((2, 1000)) nb_0 = np.sum(samples == 0) nb_50 = np.sum(samples == 50) assert nb_0 + nb_50 == 2 * 1000 assert 0.25 - 0.05 < nb_0 / (2 * 1000) < 0.25 + 0.05 def test_samples_same_values_for_same_seeds(self): param = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=5, aggregation_method="avg") samples1 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) assert samples1.shape == (100, 10) assert samples2.shape == (100, 10) assert np.allclose(samples1, samples2) def test_stochastic_param_as_aggregation_method(self): param = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=5, aggregation_method=iap.Deterministic("max")) assert isinstance(param.aggregation_method, iap.Deterministic) assert param.aggregation_method.value == "max" def test_bad_datatype_for_aggregation_method(self): with self.assertRaises(Exception) as context: _ = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=5, aggregation_method=False) self.assertTrue( "Expected aggregation_method to be" in str(context.exception)) def test_bad_datatype_for_iterations(self): with self.assertRaises(Exception) as context: _ = iap.IterativeNoiseAggregator( iap.Choice([0, 50]), iterations=False, aggregation_method="max") self.assertTrue("Expected iterations to be" in str(context.exception)) class TestSigmoid(unittest.TestCase): def setUp(self): reseed() def test___init__(self): param = iap.Sigmoid( iap.Deterministic(0), threshold=(-10, 10), activated=True, mul=1, add=0) assert ( param.__str__() == param.__repr__() == ( "Sigmoid(" "Deterministic(int 0), " "Uniform(" "Deterministic(int -10), " "Deterministic(int 10)" "), " "Deterministic(int 1), " "1, " "0)" ) ) def test_activated_is_true(self): param = iap.Sigmoid( iap.Deterministic(5), add=0, mul=1, threshold=0.5, activated=True) expected = 1 / (1 + np.exp(-(5 * 1 + 0 - 0.5))) sample = param.draw_sample() samples = param.draw_samples((5, 10)) assert sample.shape == tuple() assert samples.shape == (5, 10) assert expected - _eps(sample) < sample < expected + _eps(sample) assert np.all( np.logical_and( expected - _eps(samples) < samples, samples < expected + _eps(samples) ) ) def test_activated_is_false(self): param = iap.Sigmoid( iap.Deterministic(5), add=0, mul=1, threshold=0.5, activated=False) expected = 5 sample = param.draw_sample() samples = param.draw_samples((5, 10)) assert sample.shape == tuple() assert samples.shape == (5, 10) assert expected - _eps(sample) < sample < expected + _eps(sample) assert np.all( np.logical_and( expected - _eps(sample) < samples, samples < expected + _eps(sample) ) ) def test_activated_is_probabilistic(self): param = iap.Sigmoid( iap.Deterministic(5), add=0, mul=1, threshold=0.5, activated=0.5) expected_first = 5 expected_second = 1 / (1 + np.exp(-(5 * 1 + 0 - 0.5))) seen = [0, 0] for _ in sm.xrange(1000): sample = param.draw_sample() diff_first = abs(sample - expected_first) diff_second = abs(sample - expected_second) if diff_first < _eps(sample): seen[0] += 1 elif diff_second < _eps(sample): seen[1] += 1 else: assert False assert 500 - 150 < seen[0] < 500 + 150 assert 500 - 150 < seen[1] < 500 + 150 def test_value_is_stochastic_param(self): param = iap.Sigmoid( iap.Choice([1, 10]), add=0, mul=1, threshold=0.5, activated=True) expected_first = 1 / (1 + np.exp(-(1 * 1 + 0 - 0.5))) expected_second = 1 / (1 + np.exp(-(10 * 1 + 0 - 0.5))) seen = [0, 0] for _ in sm.xrange(1000): sample = param.draw_sample() diff_first = abs(sample - expected_first) diff_second = abs(sample - expected_second) if diff_first < _eps(sample): seen[0] += 1 elif diff_second < _eps(sample): seen[1] += 1 else: assert False assert 500 - 150 < seen[0] < 500 + 150 assert 500 - 150 < seen[1] < 500 + 150 def test_mul_add_threshold_with_various_fixed_values(self): muls = [0.1, 1, 10.3] adds = [-5.7, -0.0734, 0, 0.0734, 5.7] vals = [-1, -0.7, 0, 0.7, 1] threshs = [-5.7, -0.0734, 0, 0.0734, 5.7] for mul, add, val, thresh in itertools.product(muls, adds, vals, threshs): with self.subTest(mul=mul, add=add, val=val, threshold=thresh): param = iap.Sigmoid( iap.Deterministic(val), add=add, mul=mul, threshold=thresh) sample = param.draw_sample() samples = param.draw_samples((2, 3)) dt = sample.dtype val_ = np.array([val], dtype=dt) mul_ = np.array([mul], dtype=dt) add_ = np.array([add], dtype=dt) thresh_ = np.array([thresh], dtype=dt) expected = ( 1 / ( 1 + np.exp( -(val_ * mul_ + add_ - thresh_) ) ) ) assert sample.shape == tuple() assert samples.shape == (2, 3) assert ( expected - 5*_eps(sample) < sample < expected + 5*_eps(sample) ) assert np.all( np.logical_and( expected - 5*_eps(sample) < samples, samples < expected + 5*_eps(sample) ) ) def test_samples_same_values_for_same_seeds(self): param = iap.Sigmoid( iap.Choice([1, 10]), add=0, mul=1, threshold=0.5, activated=True) samples1 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) samples2 = param.draw_samples((100, 10), random_state=iarandom.RNG(1234)) assert samples1.shape == (100, 10) assert samples2.shape == (100, 10) assert np.array_equal(samples1, samples2)

34.712048

83

0.557204

17,098

144,055

4.486022

0.033747

0.028383

0.023467

0.029686

0.861881

0.827514

0.785156

0.762001

0.737882

0.709929

0

0.060091

0.330672

144,055

4,149

84

34.720415

0.735408

0.02152

0

0.656814

0

0.028436

0.007439

0

0.000241

0.238955

1

0.107654

false

0

0.006223

0.001245

0.127256

0.000311

0

null

0

1

0

null

0

6

827ac159b8342adeb18a832d9a86cfcb0600fb29

62

py

Python

modules/vqvc/__init__.py

reppy4620/VCon

cac3441443cb9b28ffbaa0646ed1826d71cb16e0

[ "MIT" ]

4

2021-05-22T03:14:44.000Z

2022-01-03T04:32:54.000Z

modules/vqvc/__init__.py

reppy4620/VCon

cac3441443cb9b28ffbaa0646ed1826d71cb16e0

[ "MIT" ]

null

modules/vqvc/__init__.py

reppy4620/VCon

cac3441443cb9b28ffbaa0646ed1826d71cb16e0

[ "MIT" ]

null

from .model import VQVCModel from .pl_model import VQVCModule

20.666667

32

0.83871

9

62

5.666667

0.666667

0.431373

0

0.129032

62

2

33

31

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

827fdac046ac07902d8fa5e1aeb478e27e40e24c

11,538

py

Python

integration_tests/test_router.py

madfish-solutions/quipuswap-token2token-core

41fd4293029e2094a564141fb389fd9a1ef19185

[ "MIT" ]

null

integration_tests/test_router.py

madfish-solutions/quipuswap-token2token-core

41fd4293029e2094a564141fb389fd9a1ef19185

[ "MIT" ]

null

integration_tests/test_router.py

madfish-solutions/quipuswap-token2token-core

41fd4293029e2094a564141fb389fd9a1ef19185

[ "MIT" ]

null

from unittest import TestCase import json from helpers import * from pytezos import ContractInterface, pytezos, MichelsonRuntimeError from pytezos.context.mixin import ExecutionContext token_a = "KT1AxaBxkFLCUi3f8rdDAAxBKHfzY8LfKDRA" token_b = "KT1PgHxzUXruWG5XAahQzJAjkk4c2sPcM3Ca" token_c = "KT1RJ6PbjHpwc3M5rw5s2Nbmefwbuwbdxton" token_d = "KT1Wz32jY2WEwWq8ZaA2C6cYFHGchFYVVczC" pair_ab = { "token_a_type" : { "fa2": { "token_address": token_a, "token_id": 0 } }, "token_b_type": { "fa2": { "token_address": token_b, "token_id": 1 } }, } pair_bc = { "token_a_type": { "fa2": { "token_address": token_b, "token_id": 1 } }, "token_b_type" : { "fa2": { "token_address": token_c, "token_id": 2 } } } pair_ac = { "token_a_type" : { "fa2": { "token_address": token_a, "token_id": 0 } }, "token_b_type" : { "fa2": { "token_address": token_c, "token_id": 2 } } } pair_cd = { "token_a_type" : { "fa2": { "token_address": token_c, "token_id": 2 } }, "token_b_type" : { "fa2": { "token_address": token_d, "token_id": 3 } } } class TokenToTokenRouterTest(TestCase): @classmethod def setUpClass(cls): cls.maxDiff = None dex_code = open("./integration_tests/compiled/Dex.tz", 'r').read() cls.dex = ContractInterface.from_michelson(dex_code) initial_storage_michelson = json.load(open("./integration_tests/compiled/storage.json", 'r')) cls.init_storage = cls.dex.storage.decode(initial_storage_michelson) def test_tt_token_to_token_router(self): amount_in=10_000 chain = LocalChain(storage=self.init_storage) res = chain.execute(self.dex.addPair(pair_ab, 100_000, 300_000)) res = chain.execute(self.dex.addPair(pair_bc, 500_000, 700_000)) # interpret the call without applying it res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 1, "operation": "a_to_b", } ], "amount_in" : amount_in, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) contract_in = next(v for v in transfers if v["destination"] == contract_self_address) self.assertEqual(contract_in["token_address"], token_a) self.assertEqual(contract_in["amount"], 10_000) routed_out = next(v for v in transfers if v["destination"] == julian) self.assertEqual(routed_out["token_address"], token_c) # same swap but one by one res = chain.interpret(self.dex.swap( swaps=[{ "pair_id": 0, "operation": "a_to_b", }], amount_in=amount_in, min_amount_out=1, receiver=julian, deadline=100_000 )) transfers = parse_token_transfers(res) token_b_out = next(v for v in transfers if v["destination"] == julian) res = chain.interpret(self.dex.swap( swaps=[{ "pair_id": 1, "operation": "a_to_b", }], amount_in=token_b_out["amount"], min_amount_out=1, receiver=julian, deadline=100_000, )) transfers = parse_token_transfers(res) token_c_out = next(v for v in transfers if v["destination"] == julian) self.assertEqual(routed_out["amount"], token_c_out["amount"]) def test_tt_router_triangle(self): chain = LocalChain(storage=self.init_storage) res = chain.execute(self.dex.addPair(pair_ab, 100_000_000_000, 100_000_000_000)) res = chain.execute(self.dex.addPair(pair_bc, 100_000_000_000, 100_000_000_000)) res = chain.execute(self.dex.addPair(pair_ac, 100_000_000_000, 100_000_000_000)) # interpret the call without applying it res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 1, "operation": "a_to_b", }, { "pair_id": 2, "operation": "b_to_a", } ], "amount_in" : 10_000, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) token_c_out = next(v for v in transfers if v["destination"] == julian) self.assertEqual(token_c_out["amount"], 9909) # ~ 9910 by compound interest formula def test_tt_router_ab_ba(self): chain = LocalChain(storage=self.init_storage) res = chain.execute(self.dex.addPair(pair_ab, 100_000_000_000, 100_000_000_000)) res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 0, "operation": "b_to_a", } ], "amount_in" : 10_000, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) token_out = next(v for v in transfers if v["destination"] == julian) self.assertEqual(token_out["amount"], 9939) def test_tt_router_impossible_path(self): chain = LocalChain(storage=self.init_storage) res = chain.execute(self.dex.addPair(pair_ab, 1111, 3333)) res = chain.execute(self.dex.addPair(pair_cd, 5555, 7777)) # can't find path with self.assertRaises(MichelsonRuntimeError): res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 1, "operation": "a_to_b", } ], "amount_in" : 334, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) with self.assertRaises(MichelsonRuntimeError): res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 0, "operation": "a_to_b", } ], "amount_in" : 334, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) def test_tt_router_cant_overbuy(self): chain = LocalChain(storage=self.init_storage) res = chain.execute(self.dex.addPair(pair_ab, 100_000, 100_000)) res = chain.execute(self.dex.addPair(pair_bc, 10_000, 10_000)) res = chain.execute(self.dex.addPair(pair_ac, 1_000_000, 1_000_000)) # overbuy at the very beginning res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", } ], "amount_in" : 100_000_000_000, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) token_out = next(v for v in transfers if v["destination"] == julian) self.assertEqual(token_out["amount"], 99_999) # overbuy at the end res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 1, "operation": "a_to_b", } ], "amount_in" : 100_000_000, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) token_out = next(v for v in transfers if v["destination"] == julian) self.assertLess(token_out["amount"], 9_999) # overbuy in the middle res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "a_to_b", }, { "pair_id": 1, "operation": "a_to_b", }, { "pair_id": 2, "operation": "b_to_a", } ], "amount_in" : 10_000_000_000, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) token_out = next(v for v in transfers if v["destination"] == julian) self.assertLess(token_out["amount"], 9_999) def test_tt_router_mixed_fa2_fa12(self): pair_ab = { "token_a_type" : { "fa12": token_b, }, "token_b_type": { "fa2": { "token_address": token_a, "token_id": 1 } }, } pair_bc = { "token_a_type" : { "fa12": token_b, }, "token_b_type" : { "fa2": { "token_address": token_c, "token_id": 2 } } } amount_in=10_000 chain = LocalChain(storage=self.init_storage) res = chain.execute(self.dex.addPair(pair_ab, 100_000, 300_000)) res = chain.execute(self.dex.addPair(pair_bc, 500_000, 700_000)) # interpret the call without applying it res = chain.interpret(self.dex.swap({ "swaps" : [ { "pair_id": 0, "operation": "b_to_a", }, { "pair_id": 1, "operation": "a_to_b", } ], "amount_in" : amount_in, "min_amount_out" : 1, "receiver" : julian, "deadline": 100_000 })) transfers = parse_token_transfers(res) contract_in = next(v for v in transfers if v["destination"] == contract_self_address) self.assertEqual(contract_in["token_address"], token_a) self.assertEqual(contract_in["amount"], 10_000) routed_out = next(v for v in transfers if v["destination"] == julian) self.assertEqual(routed_out["token_address"], token_c)

30.68617

101

0.477639

1,172

11,538

4.403584

0.116041

0.029064

0.039527

0.042821

0.804108

0.801783

0.787638

0.778338

0.735323

0

0.06408

0.410296

11,538

375

102

30.768

0.694444

0.022881

0

0.654321

0

0.151367

0.019531

0

0.04321

1

0.021605

false

0

0.015432

0

0.040123

0

null

0

1

0

null

0

6

7d57cb53958a854e64b6d878a9826f34dbca7a63

96

py

Python

venv/lib/python3.8/site-packages/pip/_internal/operations/install/editable_legacy.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/pip/_internal/operations/install/editable_legacy.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/pip/_internal/operations/install/editable_legacy.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/6e/30/4e/6df13ab33dd498623bcb8f860a029ad969938275a514553b6fe8b4b10b

96

0.895833

9

96

9.555556

1

0

0.4375

0

96

1

96

0.458333

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

7d58f75c60cd92e49b8842d06b9c5d9c9a1f2ca8

91

py

Python

skfda/exploratory/__init__.py

jiduque/scikit-fda

5ea71e78854801b259aa3a01eb6b154aa63bf54b

[ "BSD-3-Clause" ]

147

2019-05-10T20:46:42.000Z

2022-03-25T17:23:19.000Z

skfda/exploratory/__init__.py

jiduque/scikit-fda

5ea71e78854801b259aa3a01eb6b154aa63bf54b

[ "BSD-3-Clause" ]

306

2019-04-26T08:56:05.000Z

2022-03-30T11:12:48.000Z

skfda/exploratory/__init__.py

jiduque/scikit-fda

5ea71e78854801b259aa3a01eb6b154aa63bf54b

[ "BSD-3-Clause" ]

38

2019-09-03T17:24:04.000Z

2022-01-06T05:09:18.000Z

from . import depth from . import outliers from . import stats from . import visualization

18.2

27

0.78022

12

91

5.916667

0.5

0.56338

0

0.175824

91

4

28

22.75

0.946667

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

7d6a9fc0ae2c18fcc1e9420cc0d5c546fe26cbe4

1,267

py

Python

Home_Work_2_B_Naychuk_Anastasiya/Task1.py

NaychukAnastasiya/goiteens-python3-naychuk

a79d0af238a15f58a822bb5d8e4d48227d4a7bc1

[ "MIT" ]

null

Home_Work_2_B_Naychuk_Anastasiya/Task1.py

NaychukAnastasiya/goiteens-python3-naychuk

a79d0af238a15f58a822bb5d8e4d48227d4a7bc1

[ "MIT" ]

null

Home_Work_2_B_Naychuk_Anastasiya/Task1.py

NaychukAnastasiya/goiteens-python3-naychuk

a79d0af238a15f58a822bb5d8e4d48227d4a7bc1

[ "MIT" ]

null

# Яке з 3 чисел найбільш наближене до середнього print("Введіть перше число") var1 = float(input()) print("Введіть друге число") var2 = float(input()) print("Введіть третє число") var3 = float(input()) # Avg = (var1+var2+var3)/3 # Варіант розв'язку з порівнянням чисел із середнім арифметичним: if ((var1 > var2) and (var1 < var3)) or (var1 < var2) and (var1 > var3): print ("Найбільш наближеним числом до середнього є ",var1) elif ((var2 > var1) and (var2 < var3)) or ((var2 < var1) and (var12 > var3)): print ("Найбільш наближеним числом до середнього є ",var2) else: print ("Найбільш наближеним числом до середнього є ",var3) # # Варіант розв'язку з порівнянням чисел із середнім арифметичним: # if (abs(var1-Avg))>(abs(var2-Avg)): # if (abs(var2-Avg))>(abs(var3-Avg)): # print ("Найбільш наближеним числом до середнього є ",var3) # else: #(abs(var2-Avg))<(abs(var3-Avg)) # print ("Найбільш наближеним числом до середнього є ",var2) # else: #(abs(var1-Avg))<(abs(var2-Avg)) # if (abs(var1-Avg))>(abs(var3-Avg)): # print ("Найбільш наближеним числом до середнього є ",var3) # else: #(abs(var1-Avg))<(abs(var3-Avg)) # print ("Найбільш наближеним числом до середнього є ",var1)

45.25

93

0.648777

175

1,267

4.697143

0.211429

0.116788

0.195864

0.246959

0.749392

0.708029

0.527981

0.457421

0

0.043265

0.197317

1,267

28

94

45.25

0.764995

0.558011

0

0.361165

0

1

0

false

0

0.5

0

null

0

1

0

1

0

null

0

1

0

6

7d72c0bcd96eb18d89e4b84f9f4aa4228039c607

102

py

Python

urlmiddleware/base.py

dbramwell/django-urlmiddleware

8f7f4a571730805cdd04f321548c8d1dc7751ec7

[ "MIT" ]

4

2015-04-10T10:41:18.000Z

2016-06-16T01:19:15.000Z

urlmiddleware/base.py

dbramwell/django-urlmiddleware

8f7f4a571730805cdd04f321548c8d1dc7751ec7

[ "MIT" ]

2

2015-12-18T12:24:05.000Z

2015-12-18T17:00:27.000Z

urlmiddleware/base.py

dbramwell/django-urlmiddleware

8f7f4a571730805cdd04f321548c8d1dc7751ec7

[ "MIT" ]

7

2015-11-17T17:53:37.000Z

2016-03-29T06:21:17.000Z

from django.core.urlresolvers import Resolver404 class MiddlewareResolver404(Resolver404): pass

17

48

0.823529

10

102

8.4

0.9

0

0.101124

0.127451

102

5

49

20.4

0.842697

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

7db8db74363fb05b1c46621fca683280e13e4190

67

py

Python

Solutions/Python/Posix command(7 kyu).py

collenirwin/Codewars-Solutions

14bad3878d3fc37c7e73cbaaaa24cd28f759ce3b

[ "MIT" ]

null

Solutions/Python/Posix command(7 kyu).py

collenirwin/Codewars-Solutions

14bad3878d3fc37c7e73cbaaaa24cd28f759ce3b

[ "MIT" ]

null

Solutions/Python/Posix command(7 kyu).py

collenirwin/Codewars-Solutions

14bad3878d3fc37c7e73cbaaaa24cd28f759ce3b

[ "MIT" ]

null

from os import popen def get_output(s): return popen(s).read()

16.75

26

0.701493

12

67

3.833333

0.833333

0

0.179104

67

4

26

16.75

0.836364

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

8196db5a9a3e9b1ef0fc71ca07363d90aa3c3237

4,386

py

Python

aindex/demo.py

ad3002/Lyrebird

8c0a186e32d61189f073401152c52a89bfed46ed

[ "MIT" ]

null

aindex/demo.py

ad3002/Lyrebird

8c0a186e32d61189f073401152c52a89bfed46ed

[ "MIT" ]

null

aindex/demo.py

ad3002/Lyrebird

8c0a186e32d61189f073401152c52a89bfed46ed

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # #@created: 07.01.2018 #@author: Aleksey Komissarov #@contact: [email protected] from aindex import * settings = { "index_prefix": "tests/kmers.23", "aindex_prefix": "tests/kmers.23", "reads_file": "tests/reads.reads", } index = load_aindex(settings) k = 23 sequence = "TAAGTTATTATTTAGTTAATACTTTTAACAATATTATTAAGGTATTTAAAAAATACTATTATAGTATTTAACATAGTTAAATACCTTCCTTAATACTGTTAAATTATATTCAATCAATACATATATAATATTATTAAAATACTTGATAAGTATTATTTAGATATTAGACAAATACTAATTTTATATTGCTTTAATACTTAATAAATACTACTTATGTATTAAGTAAATATTACTGTAATACTAATAACAATATTATTACAATATGCTAGAATAATATTGCTAGTATCAATAATTACTAATATAGTATTAGGAAAATACCATAATAATATTTCTACATAATACTAAGTTAATACTATGTGTAGAATAATAAATAATCAGATTAAAAAAATTTTATTTATCTGAAACATATTTAATCAATTGAACTGATTATTTTCAGCAGTAATAATTACATATGTACATAGTACATATGTAAAATATCATTAATTTCTGTTATATATAATAGTATCTATTTTAGAGAGTATTAATTATTACTATAATTAAGCATTTATGCTTAATTATAAGCTTTTTATGAACAAAATTATAGACATTTTAGTTCTTATAATAAATAATAGATATTAAAGAAAATAAAAAAATAGAAATAAATATCATAACCCTTGATAACCCAGAAATTAATACTTAATCAAAAATGAAAATATTAATTAATAAAAGTGAATTGAATAAAATTTTGAAAAAAATGAATAACGTTATTATTTCCAATAACAAAATAAAACCACATCATTCATATTTTTTAATAGAGGCAAAAGAAAAAGAAATAAACTTTTATGCTAACAATGAATACTTTTCTGTCAAATGTAATTTAAATAAAAATATTGATATTCTTGAACAAGGCTCCTTAATTGTTAAAGGAAAAATTTTTAACGATCTTATTAATGGCATAAAAGAAGAGATTATTACTATTCAAGAAAAAGATCAAACACTTTTGGTTAAAACAAAAAAAACAAGTATTAATTTAAACACAATTAATGTGAATGAATTTCCAAGAATAAGGTTTAATGAAAAAAACGATTTAAGTGAATTTAATCAATTCAAAATAAATTATTCACTTTTAGTAAAAGGCATTAAAAAAATTTTTCACTCAGTTTCAAATAATCGTGAAATATCTTCTAAATTTAATGGAGTAAATTTCAATGGATCCAATGGAAAAGAAATATTTTTAGAAGCTTCTGACACTTATAAACTATCTGTTTTTGAGATAAAGCAAGAAACAGAACCATTTGATTTCATTTTGGAGAGTAATTTACTTAGTTTCATTAATTCTTTTAATCCTGAAGAAGATAAATCTATTGTTTTTTATTACAGAAAAGATAATAAAGATAGCTTTAGTACAGAAATGTTGATTTCAATGGATAACTTTATGATTAGTTACACATCGGTTAATGAAAAATTTCCAGAGGTAAACTACTTTTTTGAATTTGAACCTGAAACTAAAATAGTTGTTCAAAAAAATGAATTAAAAGATGCACTTCAAAGAATTCAAACTTTGGCTCAAAATGAAAGAACTTTTTTATGCGATATGCAAATTAACAGTTCTGAATTAAAAATAAGAGCTATTGTTAATAATATCGGAAATTCTCTTGAGGAAATTTCTTGTCTTAAATTTGAAGGTTATAAACTTAATATTTCTTTTAACCCAAGTTCTCTATTAGATCACATAGAGTCTTTTGAATCAAATGAAATAAATTTTGATTTCCAAGGAAATAGTAAGTATTTTTTGATAACCTCTAAAAGTGAACCTGAACTTAAGCAAATATTGGTTCCTTCAAGATAATGAATCTTTACGATCTTTTAGAACTACCAACTACAGCATCAATAAAAGAAATAAAAATTGCTTATAAAAGATTAGCAAAGCGTTATCACCCTGATGTAAATAAATTAGGTTCGCAAACTTTTGTTGAAATTAATAATGCTTATTCAATATTAAGTGATCCTAACCAAAAGGAAAAATATGATTCAATGCTGAAAGTTAATGATTTTCAAAATCGCATCAAAAATTTAGATATTAGTGTTAGATGACATGAAAATTTCATGGAAGAACTCGAACTTCGTAAGAACTGAGAATTTGATTTTTTTTCATCTGATGAAGATTTCTTTTATTCTCCATTTACAAAAA" test_kmer = "TAAGTTATTATTTAGTTAATACT" right_kmer = "AGTTAATACTTTTAACAATATTA" print("Task 1. Get kmer frequency") # raw_input("\nReady?") for i in range(len(sequence)-k+1): kmer = sequence[i:i+k] print("Position %s kmer %s freq = %s" % (i, kmer, index[kmer])) print("Task 2. Iter read by read, print the first 20 reads") # raw_input("\nReady?") for i, read in enumerate(index.iter_reads()): if i == 20: break print(i, read) print("Task 3. Iter reads by kmer, returs (start, next_read_start, read, pos_if_uniq|None, all_poses)") # raw_input("\nReady?") for read in iter_reads_by_kmer(test_kmer, index): print(read) print("Task 4. Get distances in reads for two kmers, returns a list of (rid, left_kmer_pos, right_kmer_pos) tuples.") # raw_input("\nReady?") print(get_left_right_distances(test_kmer, right_kmer, index)) print("Task 5. Get layout for kmer, returns (max_pos, reads, lefts, rights, rids, starts), for details see source code") # raw_input("\nReady?") max_pos, reads, lefts, rights, rids, starts = get_layout_for_kmer(right_kmer, index) print("Central layout:") for read in reads: print(read) print("Left flanks:") print(lefts) print("Right flanks:") print(rights) print("Task 6. Iter reads by sequence, returтs (start, next_read_start, read, pos_if_uniq|None, all_poses)") # raw_input("\nReady?") sequence = "AATATTATTAAGGTATTTAAAAAATACTATTATAGTATTTAACATA" for read in iter_reads_by_sequence(sequence, index): print(read) print("Task 7. Iter reads by kmer with reads as SE, returns (start, next_read_start, subread, kmere_pos, -1|0|1 for spring_pos, was_reversed, poses_in_read)") # raw_input("\nReady?") user_reads = set() sequence = "AATATTATTAAGGTATTTAAAAAATACTATTATAGTATTTAACATA" for rid, nextrid, read, pos, spring_pos, was_reversed, poses in get_reads_se_by_kmer(kmer, index, user_reads, k=23): print(rid, read, pos)

58.48

2,183

0.858413

338

4,386

10.95858

0.331361

0.017009

0.026458

0.013769

0.107991

0.073434

0.048056

0.030778

0

0.00883

0.070451

4,386

74

2,184

59.27027

0.899681

0.061332

0

0.116279

0

0.116279

0.754694

0.56279

0

1

0

1

0

false

0

0.023256

0

0.023256

0.44186

0

1

null

0

1

0

1

null

1

0

1

0

6

819a475b581f4721e5c8b8ee781500a5749d808c

8,054

py

Python

transformation_fnc.py

usrmaia/transformation-fnc

37ef77708892417ac985bb6f1cf62285834560d8

[ "MIT" ]

null

transformation_fnc.py

usrmaia/transformation-fnc

37ef77708892417ac985bb6f1cf62285834560d8

[ "MIT" ]

null

transformation_fnc.py

usrmaia/transformation-fnc

37ef77708892417ac985bb6f1cf62285834560d8

[ "MIT" ]

null

from useful import * from os import system def remove_implication(formula): while ">" in formula: operator = formula.find(">") print(formula, operator) subform_left = get_subform_left(formula, operator) subform_right = get_subform_right(formula, operator) formula = get_remove_implication(formula, subform_left, subform_right, operator) return formula def get_remove_implication(formula, subform_left, subform_right, operator): # ...(A>B)... |-> ...(-A#B)... no_modification_right = formula[operator + len(subform_right) + 1:] no_modification_left = formula[:operator - len(subform_left)] return f"{no_modification_left}-{subform_left}#{subform_right}{no_modification_right}" def morgan_law(formula): while "-(" in formula: index = formula.find("-(") print(formula, index) operator = get_operator(formula, index + 1) subform_left = get_subform_left(formula, operator) subform_right = get_subform_right(formula, operator) formula = get_morgan_law(formula, subform_left, subform_right, operator) return formula def get_morgan_law(formula, subform_left, subform_right, operator): # ...-(A&B)... |-> ...(-A#-B)... # ...-(A#B)... |-> ...(-A&-B)... match formula[operator]: case "#": new_operator = "&" case "&": new_operator = "#" no_modification_right = formula[operator + len(subform_right) + 1:] no_modification_left = formula[:operator - len(subform_left) - 1 - 1] return f"{no_modification_left}(-{subform_left}{new_operator}-{subform_right}{no_modification_right}" def remove_double_negation(formula): # --A |-> A formula = formula.replace("--", "") return formula def distributivity(formula): index = 0 while index < len(formula): # Existir "#(" ou ")#" é apenas a primeira condição para se aplicar a distributividade # A segunda condição é existir "#(A&B)" ou "(A&B)#" if "#(" in formula[index:index + 2]: # "#(" operator_and = get_operator(formula, index + 1) if formula[operator_and] == "&": # "#(A&B)" print(formula, index, operator_and) formula, index = get_distributivity_lr(formula, index, operator_and) if ")#" in formula[index:index + 2]: # "(#" len_subform_left = len(get_subform_left(formula, index + 1)) operator_and = get_operator(formula, index + 1 - len_subform_left) if formula[operator_and] == "&": # "(A&B)#" print(formula, index + 1, operator_and) formula, index = get_distributivity_rl(formula, index + 1, operator_and) index += 1 return formula def get_distributivity_lr(formula, operator_or, operator_and): # ...(A#(B&C))... |-> ...((A#B)&(A#C))... # Parenteses externo da fórmula subform_left = get_subform_left(formula, operator_or) no_modification_left = formula[:operator_or - len(subform_left)] subform_right = get_subform_right(formula, operator_or) no_modification_right = formula[operator_or + len(subform_right) + 1:] # Parenteses interno da fórmula subform_middle = get_subform_left(formula, operator_and) subform_right = get_subform_right(formula, operator_and) return f"{no_modification_left}({subform_left}#{subform_middle})&({subform_left}#{subform_right}){no_modification_right}", 0 def get_distributivity_rl(formula, operator_or, operator_and): # ...((A&B)#C)... |-> ...((A#C)&(B#C))... # Parenteses externo da fórmula subform_left = get_subform_left(formula, operator_or) no_modification_left = formula[:operator_or - len(subform_left)] subform_right = get_subform_right(formula, operator_or) no_modification_right = formula[operator_or + len(subform_right) + 1:] # Parenteses interno da fórmula subform_left = get_subform_left(formula, operator_and) subform_middle = get_subform_right(formula, operator_and) return f"{no_modification_left}({subform_left}#{subform_right})&({subform_middle}#{subform_right}){no_modification_right}", 0 def distributivity_new_aton(formula): index = 0 while index < len(formula): # Existir "#(" ou ")#" é apenas a primeira condição para se aplicar a distributividade # A segunda condição é existir "#(A&B)" ou "(A&B)#" if "#(" in formula[index:index + 2]: # "#(" operator_and = get_operator(formula, index + 1) if formula[operator_and] == "&": # "#(A&B)" print(formula, index, operator_and) formula, index = get_distributivity_new_atom_lr(formula, index, operator_and) if ")#" in formula[index:index + 2]: # "(#" len_subform_left = len(get_subform_left(formula, index + 1)) operator_and = get_operator(formula, index + 1 - len_subform_left) if formula[operator_and] == "&": # "(A&B)#" print(formula, index + 1, operator_and) formula, index = get_distributivity_new_atom_rl(formula, index + 1, operator_and) index += 1 return formula def get_distributivity_new_atom_lr(formula, operator_or, operator_and): # ...(A#(B&C))... |-> ...(((A#p)&((¬p#B)&(¬p#C)))&((¬B#¬C)#p))... # Parenteses externo da fórmula subform_left = get_subform_left(formula, operator_or) no_modification_left = formula[:operator_or - len(subform_left)] subform_right = get_subform_right(formula, operator_or) no_modification_right = formula[operator_or + len(subform_right) + 1:] # Parenteses interno da fórmula subform_middle = get_subform_left(formula, operator_and) subform_right = get_subform_right(formula, operator_and) new_operator = get_unprecedented(formula) return f"{no_modification_left}(({subform_left}#{new_operator})&((¬{new_operator}#{subform_middle})&(¬{new_operator}#{subform_right})))&((¬{subform_middle}#¬{subform_right})#{new_operator}){no_modification_right}", 0 #return f"{no_modification_left}({subform_left}#{new_operator})&(¬{new_operator}#{subform_middle})&(¬{new_operator}#{subform_right})&(¬{subform_middle}#¬{subform_right}#{new_operator}){no_modification_right}", 0 def get_distributivity_new_atom_rl(formula, operator_or, operator_and): # ...((A&B)#C)... |-> ...(((C#p)&((¬p#A)&(¬p#B)))&((¬A#¬B)#p))... # Parenteses externo da fórmula subform_left = get_subform_left(formula, operator_or) no_modification_left = formula[:operator_or - len(subform_left)] subform_right = get_subform_right(formula, operator_or) no_modification_right = formula[operator_or + len(subform_right) + 1:] # Parenteses interno da fórmula subform_left = get_subform_left(formula, operator_and) subform_middle = get_subform_right(formula, operator_and) new_operator = get_unprecedented(formula) return f"{no_modification_left}(({subform_right}#{new_operator})&((¬{new_operator}#{subform_left})&(¬{new_operator}#{subform_middle})))&((¬{subform_left}#¬{subform_middle})#{new_operator}){no_modification_right}", 0 #return f"{no_modification_left}({subform_right}#{new_operator})&(¬{new_operator}#{subform_left})&(¬{new_operator}#{subform_middle})&(¬{subform_left}#¬{subform_middle}#{new_operator}){no_modification_right}", 0 if __name__ == "__main__": system("cls") #system("clear") while(True): formula = input("Fórmula: ") if formula == 'q': break print(formula) print("Removendo implicações: ") A1 = remove_implication(formula) print(A1) print("Aplicando Lei de Morgan: ") A2 = morgan_law(A1) print(A2) print("Removendo dupla negação: ") A3 = remove_double_negation(A2) print(A3) print("Aplicando distributividade: ") A4 = distributivity(A3) print(A4) print("Aplicando distributividade com novo átomo: ") A5 = distributivity_new_aton(A3) print(A5) system("pause")

47.099415

220

0.661286

1,006

8,054

5.011928

0.089463

0.098175

0.067434

0.04998

0.849663

0.842721

0.831813

0.804641

0.801864

0.758429

0

0.007252

0.195307

8,054

171

221

47.099415

0.767011

0.157561

0

0.495935

0

0.01626

0.147458

0.118533

0

1

0.089431

false

0

0.01626

0

0.195122

0.138211

0

null

0

1

0

null

0

6

c4b5547f1e3ecbc952e52b926351b009c451edf6

22

py

Python

celestial/client/system/__init__.py

ams-tech/celestial

0c4c264563fe79d6838a1c40a1d114c1d6fcf23f

[ "MIT" ]

null

celestial/client/system/__init__.py

ams-tech/celestial

0c4c264563fe79d6838a1c40a1d114c1d6fcf23f

[ "MIT" ]

null

celestial/client/system/__init__.py

ams-tech/celestial

0c4c264563fe79d6838a1c40a1d114c1d6fcf23f

[ "MIT" ]

null

from . import cmdline

11

21

0.772727

3

22

5.666667

1

0

0.181818

22

1

22

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c4fbbf35cb97942fd780038b58bdfd3ad398e637

248

py

Python

w1data/metadata.py

swork/w1-datalogger

26191d57ff1c05e5c6e9de90870c5c63916f9a8c

[ "MIT" ]

null

w1data/metadata.py

swork/w1-datalogger

26191d57ff1c05e5c6e9de90870c5c63916f9a8c

[ "MIT" ]

null

w1data/metadata.py

swork/w1-datalogger

26191d57ff1c05e5c6e9de90870c5c63916f9a8c

[ "MIT" ]

null

import logging, sys logger = logging.getLogger(__name__) def measurement_for_skey(sensor_key, metadata): # logger.debug("sensor_key:{} metadata:{}".format(sensor_key, metadata)) return metadata['collector']['sensors'][sensor_key]['name']

31

76

0.741935

30

248

5.8

0.6

0.206897

0.293103

0

0.100806

248

7

77

35.428571

0.780269

0.282258

0

0.114286

0

1

0.25

false

0

0.25

0.75

0

null

1

0

1

0

null

0

1

0

1

0

6

482318efaad6f890a578bab42ca3ad7a7b532213

27

py

Python

src/euler_python_package/euler_python/medium/p207.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p207.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p207.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

def problem207(): pass

9

17

0.62963

3

27

5.666667

1

0

0.15

0.259259

27

2

18

13.5

0.7

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

48246329c18e90c00165cc92ef48bb7d9a328558

5,200

py

Python

tests/unit_tests/prepare_email/test_mail_segmenting.py

farthur/melusine

121fbb17da221b12186a275d5843b466ce65d954

[ "Apache-2.0" ]

null

tests/unit_tests/prepare_email/test_mail_segmenting.py

farthur/melusine

121fbb17da221b12186a275d5843b466ce65d954

[ "Apache-2.0" ]

null

tests/unit_tests/prepare_email/test_mail_segmenting.py

farthur/melusine

121fbb17da221b12186a275d5843b466ce65d954

[ "Apache-2.0" ]

null

import pandas as pd from melusine.prepare_email.mail_segmenting import structure_email, tag_signature structured_historic = [ { "text": " \n \n \n Bonjours, \n \n Suite a notre conversation \ téléphonique de Mardi , pourriez vous me dire la \n somme que je vous \ dois afin d'd'être en régularisation . \n \n Merci bonne journée", "meta": "", }, { "text": " \n Bonjour. \n \n Merci de bien vouloir prendre connaissance \ du document ci-joint : \n 1 - Relevé d'identité postal MUTUELLE \ (contrats) \n \n Sentiments mutualistes. \n \n La Mutuelle \n \n \ La visualisation des fichiers PDF nécessite Adobe Reader. \n ", "meta": " \n \n Le mar. 22 mai 2018 à 10:20, \ <[email protected]> a écrit\xa0:", }, ] output = [ { "meta": {"date": None, "from": None, "to": None}, "structured_text": { "header": None, "text": [ {"part": " Bonjours, ", "tags": "HELLO"}, { "part": " Suite a notre conversation \ téléphonique de Mardi , pourriez vous me dire la somme que je vous dois \ afin d'd'être en régularisation . \n \n ", "tags": "BODY", }, {"part": "Merci bonne journée", "tags": "GREETINGS"}, ], }, }, { "meta": { "date": " mar. 22 mai 2018 à 10:20", "from": " <[email protected]> ", "to": None, }, "structured_text": { "header": None, "text": [ {"part": " Bonjour. \n \n ", "tags": "HELLO"}, { "part": "Merci de bien vouloir prendre \ connaissance du document ci-joint : 1 - Relevé d'identité postal MUTUELLE \ (contrats) ", "tags": "BODY", }, {"part": " Sentiments mutualistes. ", "tags": "GREETINGS"}, {"part": " La Mutuelle ", "tags": "BODY"}, { "part": " La visualisation des fichiers \ PDF nécessite Adobe Reader. \n", "tags": "FOOTER", }, ], }, }, ] def test_structure_email(): input_df = pd.DataFrame({"structured_historic": [structured_historic]}) output_df = pd.Series([output]) result = input_df.apply(structure_email, axis=1) pd.testing.assert_series_equal(result, output_df) structured_historic_signature = [ { "text": " \n \n \n Bonjours, \n \n Suite a notre conversation \ téléphonique de Mardi , pourriez vous me dire la \n somme que je vous \ dois afin d'd'être en régularisation . \n \n Merci bonne journée\nJean Dupont", "meta": "", }, { "text": " \n Bonjour. \n \n Merci de bien vouloir prendre connaissance \ du document ci-joint : \n 1 - Relevé d'identité postal MUTUELLE \ (contrats) \n \n Sentiments mutualistes. \n \n La Mutuelle \n \n \ La visualisation des fichiers PDF nécessite Adobe Reader. \n ", "meta": " \n \n Le mar. 22 mai 2018 à 10:20, \ <[email protected]> a écrit\xa0:", }, ] output_signature = [ { "meta": {"date": None, "from": None, "to": None}, "structured_text": { "header": None, "text": [ {"part": " Bonjours, ", "tags": "HELLO"}, { "part": " Suite a notre conversation \ téléphonique de Mardi , pourriez vous me dire la somme que je vous dois \ afin d'd'être en régularisation . \n \n ", "tags": "BODY", }, {"part": "Merci bonne journée", "tags": "GREETINGS"}, {"part": "Jean Dupont", "tags": "SIGNATURE"}, ], }, }, { "meta": { "date": " mar. 22 mai 2018 à 10:20", "from": " <[email protected]> ", "to": None, }, "structured_text": { "header": None, "text": [ {"part": " Bonjour. \n \n ", "tags": "HELLO"}, { "part": "Merci de bien vouloir prendre \ connaissance du document ci-joint : 1 - Relevé d'identité postal MUTUELLE \ (contrats) ", "tags": "BODY", }, {"part": " Sentiments mutualistes. ", "tags": "GREETINGS"}, {"part": " La Mutuelle ", "tags": "BODY"}, { "part": " La visualisation des fichiers PDF nécessite Adobe Reader. \n", "tags": "FOOTER", }, ], }, }, ] def test_tag_signature(): input_df = pd.DataFrame({"structured_historic": [structured_historic_signature]}) output_df = pd.Series([output_signature]) input_df["structured_body"] = input_df.apply(structure_email, axis=1) result = input_df.apply(tag_signature, axis=1) pd.testing.assert_series_equal(result, output_df)

35.616438

93

0.497308

537

5,200

4.741155

0.195531

0.017282

0.02828

0.036135

0.890416

0.873134

0.850746

0.808327

0

0.014889

0.367115

5,200

145

94

35.862069

0.758736

0

0.556391

0

0.015038

0.170524

0.012265

0

0.015038

1

0.015038

false

0

0.015038

0

0.030075

0

null

0

1

0

null

0

6

48285ffa4d4045b7cf655571731a70ba6854e4b3

19,519

py

Python

cogv3/admin/managecommands.py

XFazze/discordbot

6b4201a6d6ff1bed5f65de4b4d30738b4d51e223

[ "MIT" ]

2

2021-07-29T02:39:36.000Z

2021-07-29T02:39:38.000Z

cogv3/admin/managecommands.py

XFazze/discordbot

6b4201a6d6ff1bed5f65de4b4d30738b4d51e223

[ "MIT" ]

2

2021-08-16T08:31:24.000Z

2021-09-20T16:34:58.000Z

cogv3/admin/managecommands.py

XFazze/discordbot

6b4201a6d6ff1bed5f65de4b4d30738b4d51e223

[ "MIT" ]

null

import discord from discord import embeds from discord.ext import commands from discord.ext.commands.core import command from pymongo import MongoClient, collation from discord_components import Button, Select, SelectOption, ComponentsBot from discord.utils import get class managecommands(commands.Cog): def __init__(self, bot): self.bot = bot # Enable/disable command @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def disable(self, ctx, command: str = None, role: discord.Role = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return if role == None: role = ctx.guild.default_role collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] if command not in settings.keys(): settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {} } if role.id not in settings[command]['disabled_guild']: settings[command]['disabled_guild'].append(role.id) else: await ctx.reply(embed=discord.Embed(title="Command is already disabled", color=0xFD3333)) return if role.id in settings[command]['guild']: settings[command]['guild'].remove(role.id) newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Disabled "+command+" on server for "+role.name, color=0x00FF42)) @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def disablecategory(self, ctx, category: discord.CategoryChannel = None, command: str = None, role: discord.Role = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return if role == None: role = ctx.guild.default_role collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] if command not in settings.keys(): settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {} } if str(category.id) not in settings[command]['disabled_category'].keys(): settings[command]['disabled_category'][str(category.id)] = [ role.id] else: if role.id in settings[command]['disabled_category'][str(category.id)]: await ctx.reply(embed=discord.Embed(title="Command is already disabled", color=0xFD3333)) return else: settings[command]['disabled_category'][str( category.id)].append(role.id) if str(category.id) in settings[command]['category'].keys(): if role.id in settings[command]['category'][str(category.id)]: settings[command]['category'][str(category.id)].remove(role.id) newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Disabled "+command+" in category " + category.name+" for "+role.name + category.name, color=0x00FF42)) @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def disablechannel(self, ctx, channel: discord.TextChannel = None, command: str = None, role: discord.Role = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return if role == None: role = ctx.guild.default_role collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] if command not in settings.keys(): settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {} } if str(channel.id) not in settings[command]['disabled_channel'].keys(): settings[command]['disabled_channel'][str(channel.id)] = [role.id] else: if role.id in settings[command]['disabled_channel'][str(channel.id)]: await ctx.reply(embed=discord.Embed(title="Command is already disabled", color=0xFD3333)) return else: settings[command]['disabled_channel'][str( channel.id)].append(role.id) if str(channel.id) in settings[command]['channel'].keys(): if role.id in settings[command]['channel'][str(channel.id)]: settings[command]['channel'][str(channel.id)].remove(role.id) newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Disabled "+command+" in channel " + channel.name+" for "+role.name, color=0x00FF42)) @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def enable(self, ctx, command: str = None, role: discord.Role = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return if role == None: role = ctx.guild.default_role collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] if command not in settings.keys(): settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {} } if role.id not in settings[command]['guild']: settings[command]['guild'].append(role.id) else: await ctx.reply(embed=discord.Embed(title="Command is already enabled", color=0xFD3333)) return if role.id in settings[command]['disabled_guild']: settings[command]['disabled_guild'].remove(role.id) newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Enabled "+command+" on server for "+role.name, color=0x00FF42)) @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def enablecategory(self, ctx, category: discord.CategoryChannel = None, command: str = None, role: discord.Role = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return if role == None: role = ctx.guild.default_role collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] if command not in settings.keys(): settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {} } if str(category.id) not in settings[command]['category'].keys(): settings[command]['category'][str(category.id)] = [role.id] else: if role.id in settings[command]['category'][str(category.id)]: await ctx.reply(embed=discord.Embed(title="Command is already disabled", color=0xFD3333)) return else: settings[command]['category'][str(category.id)].append(role.id) if str(category.id) in settings[command]['disabled_category'].keys(): if role.id in settings[command]['disabled_category'][str(category.id)]: settings[command]['disabled_category'][str( category.id)].remove(role.id) newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Enabled "+command+" in category " + category.name + " for "+role.name, color=0x00FF42)) @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def enablechannel(self, ctx, channel: discord.TextChannel = None, command: str = None, role: discord.Role = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return if role == None: role = ctx.guild.default_role collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] if command not in settings.keys(): settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {} } if str(channel.id) not in settings[command]['channel'].keys(): settings[command]['channel'][str(channel.id)] = [role.id] else: if role.id in settings[command]['channel'][str(channel.id)]: await ctx.reply(embed=discord.Embed(title="Command is already disabled", color=0xFD3333)) return else: settings[command]['channel'][str(channel.id)].append(role.id) if str(channel.id) in settings[command]['disabled_channel'].keys(): if role.id in settings[command]['disabled_channel'][str(channel.id)]: settings[command]['disabled_channel'][str( channel.id)].remove(role.id) newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Enabled "+command+" in channel " + channel.name + " for "+role.name, color=0x00FF42)) @commands.command(pass_context=True) @commands.has_permissions(manage_guild=True) async def resetperms(self, ctx, command: str = None): validcommand = False for cmd in self.bot.commands: if command == cmd.name: validcommand = True break if not validcommand: await ctx.reply(embed=discord.Embed(title="Provide a valid command", color=0xFD3333)) return collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] settings[command] = { "guild": [], "disabled_guild": [], "category": {}, "disabled_category": {}, "channel": {}, "disabled_channel": {}} newvalue = {"$set": {"settings": settings}} collection.update_one(myquery, newvalue) await ctx.reply(embed=discord.Embed(title="Reset command permissions", color=0x00FF42)) @commands.command(pass_context=True) async def showperms(self, ctx): collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": ctx.guild.id} settings = collection.find_one(myquery)["settings"] options=[] for setting in settings.keys(): options.append(SelectOption(label=setting, value=setting)) message = await ctx.reply("The lower in the hiearchy will go over the other. So channel enable will go over guild disable.", components=[Select(placeholder="Select something!", options=options, custom_id="commandperms",)]) while True: interaction = await self.bot.wait_for("select_option") embed = discord.Embed(name="Command permissions for ", value=interaction.values[0], color=0xFFFFFF) if len(settings[interaction.values[0]]["guild"]) > 0: msg = "" for roleid in settings[interaction.values[0]]["guild"]: role_obj = get(ctx.guild.roles, id=roleid) msg += role_obj.name+'\n' else: msg="None" embed.add_field(name="Guild wide allowed", value=msg) if len(settings[interaction.values[0]]["guild"]) > 0: msg = "" for roleid in settings[interaction.values[0]]["disabled_guild"]: role_obj = get(ctx.guild.roles, id=roleid) msg += role_obj.name+'\n' else: msg="None" embed.add_field(name="Guild wide denied", value=msg) # this is no longer a list # its a dictionary embed.add_field(name="Category wide allowed", value="\u200b", inline=False) if len(settings[interaction.values[0]]["category"].keys()) > 0: for key in settings[interaction.values[0]]["category"].keys(): if len(settings[interaction.values[0]]["category"][key]) == 0: continue msg = "" for roleid in settings[interaction.values[0]]["category"][key]: role_obj = get(ctx.guild.roles, id=roleid) msg += role_obj.name+'\n' name = get(ctx.guild.categories, id=int(key)) embed.add_field(name=name, value=msg) else: msg = "None" embed.add_field(name="Category wide denied", value="\u200b", inline=False) if len(settings[interaction.values[0]]["disabled_category"].keys()) > 0: for key in settings[interaction.values[0]]["disabled_category"].keys(): if len(settings[interaction.values[0]]["disabled_category"][key]) == 0: continue msg = "" for roleid in settings[interaction.values[0]]["disabled_category"][key]: role_obj = get(ctx.guild.roles, id=roleid) msg += role_obj.name+'\n' name = get(ctx.guild.categories, id=int(key)) embed.add_field(name=name, value=msg) else: msg = "None" embed.add_field(name="Channel wide allowed", value="\u200b", inline=False) if len(settings[interaction.values[0]]["channel"].keys()) > 0: for key in settings[interaction.values[0]]["channel"].keys(): if len(settings[interaction.values[0]]["channel"][key]) == 0: continue msg = "" for roleid in settings[interaction.values[0]]["channel"][key]: role_obj = get(ctx.guild.roles, id=roleid) msg += role_obj.name+'\n' name = get(ctx.guild.text_channels, id=int(key)) embed.add_field(name=name, value=msg) else: msg = "None" embed.add_field(name="Channel wide denied", value="\u200b", inline=False) if len(settings[interaction.values[0]]["disabled_channel"].keys()) > 0: for key in settings[interaction.values[0]]["disabled_channel"].keys(): if len(settings[interaction.values[0]]["disabled_channel"][key]) == 0: continue msg = "" for roleid in settings[interaction.values[0]]["disabled_channel"][key]: role_obj = get(ctx.guild.roles, id=roleid) msg += role_obj.name+'\n' name = get(ctx.guild.text_channels, id=int(key)) embed.add_field(name=name, value=msg) else: msg = "There " await message.edit(embed=embed,components=[Select(placeholder="Select something!", options=options, custom_id="commandperms",)]) def setup(bot): bot.add_cog(managecommands(bot)) def perms(context): command = context.command.name #str guild_id = context.guild.id channel_id = str(context.message.channel.id) category_id = str(context.message.channel.category_id) roles = [] for role in context.author.roles: roles.append(role.id) collection = MongoClient('localhost', 27017).maindb.guilds myquery = {"id": guild_id} settings = collection.find_one(myquery)["settings"] if command in settings.keys(): if channel_id in settings[command]["channel"].keys(): print("channels exist") if bool(set(roles) & set(settings[command]["channel"][channel_id])): return True elif channel_id in settings[command]["disabled_channel"].keys(): if bool(set(roles) & set(settings[command]["disabled_channel"][channel_id])): return False elif category_id in settings[command]["category"].keys(): if bool(set(roles) & set(settings[command]["category"][category_id])): return True elif category_id in settings[command]["disabled_category"].keys(): if bool(set(roles) & set(settings[command]["disabled_category"][category_id])): return False elif bool(set(roles) & set(settings[command]["disabled_guild"])): return False elif bool(set(roles) & set(settings[command]["guild"])): return True return True

42.06681

230

0.56217

2,046

19,519

5.292766

0.074291

0.073414

0.037677

0.033244

0.890479

0.881984

0.869055

0.845784

0.81448

0.768861

0

0.014043

0.310467

19,519

464

231

42.06681

0.790549

0.003433

0

0.687831

0

0.120886

0

0.008638

0

1

0.007937

false

0.021164

0.018519

0

0.082011

0.002646

0

null

0

1

0

null

0

6

6f9177f95c9276da027118820c1944dc489b0063

137

py

Python

backend/elasticsurgery/views/__init__.py

EDITD/ElasticSurgery

458571d48541d1ddbbfeb20e04703592e5f869e0

[ "MIT" ]

null

backend/elasticsurgery/views/__init__.py

EDITD/ElasticSurgery

458571d48541d1ddbbfeb20e04703592e5f869e0

[ "MIT" ]

27

2019-09-25T14:19:44.000Z

2022-02-12T21:39:17.000Z

backend/elasticsurgery/views/__init__.py

EDITD/ElasticSurgery

458571d48541d1ddbbfeb20e04703592e5f869e0

[ "MIT" ]

null

from flask import jsonify from ..app import app @app.route('/ping', methods=('GET',)) def get_ping(): return jsonify(ping='pong')

15.222222

37

0.671533

20

137

4.55

0.6

0

0.153285

137

8

38

17.125

0.784483

0

0.087591

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

6fa6de489d3ecbdc05135c1a882460c438344d63

149

py

Python

tests/node_test.py

allenai/beaker-py

99c8d7f6e9938807ca5405964ef35633a19e8d68

[ "Apache-2.0" ]

null

tests/node_test.py

allenai/beaker-py

99c8d7f6e9938807ca5405964ef35633a19e8d68

[ "Apache-2.0" ]

20

2021-12-16T13:23:07.000Z

2022-03-31T16:40:02.000Z

tests/node_test.py

allenai/beaker-py

99c8d7f6e9938807ca5405964ef35633a19e8d68

[ "Apache-2.0" ]

null

from beaker import Beaker def test_node_get(client: Beaker, beaker_node_id: str): assert client.node.get(beaker_node_id).limits.gpu_count == 8

24.833333

64

0.778523

25

149

4.36

0.6

0.12844

0.220183

0

0.007692

0.127517

149

5

65

29.8

0.830769

0

0.333333

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

6fe3cc82a26ac5744b2544116ad6a32d14b35afa

30

py

Python

sigal/plugins/encrypt/__init__.py

fidergo-stephane-gourichon/sigal

b1f2e947700e618425e170e8758b1fbb82c91acb

[ "MIT" ]

null

sigal/plugins/encrypt/__init__.py

fidergo-stephane-gourichon/sigal

b1f2e947700e618425e170e8758b1fbb82c91acb

[ "MIT" ]

null

sigal/plugins/encrypt/__init__.py

fidergo-stephane-gourichon/sigal

b1f2e947700e618425e170e8758b1fbb82c91acb

[ "MIT" ]

null

from .encrypt import register

15

29

0.833333

4

30

6.25

1

0

0.133333

30

1

30

0.961538

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6ffae9b25573e5f7348c89e03b62b498cbca2ea9

184

py

Python

reikna/core/__init__.py

ringw/reikna

0f27f86e35a9f06405de2d99580f766a1b504562

[ "MIT" ]

122

2015-05-01T12:42:34.000Z

2021-09-30T22:47:59.000Z

lib/python/reikna-0.7.5/reikna/core/__init__.py

voxie-viewer/voxie

d2b5e6760519782e9ef2e51f5322a3baa0cb1198

[ "MIT" ]

42

2015-05-04T16:55:47.000Z

2021-09-18T04:53:34.000Z

lib/python/reikna-0.7.5/reikna/core/__init__.py

voxie-viewer/voxie

d2b5e6760519782e9ef2e51f5322a3baa0cb1198

[ "MIT" ]

14

2015-05-01T19:22:52.000Z

2021-09-30T22:48:03.000Z

from reikna.core.signature import Type, Annotation, Parameter, Signature from reikna.core.computation import Computation from reikna.core.transformation import Transformation, Indices

46

72

0.858696

22

184

7.181818

0.5

0.189873

0.265823

0

0.086957

184

3

73

61.333333

0.940476

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

d230b8b07301d92ab203c4ea79e6dcb73031cdf8

36

py

Python

deepleaps/workspace/src/ipc/CustomCommand.py

Longseabear/deep-leaps-pytorch

abcb87f3079c0612bde4a4f94c75d7c05d5aee3a

[ "MIT" ]

1

2021-02-27T18:00:39.000Z

deepleaps/workspace/src/ipc/CustomCommand.py

Longseabear/deep-leaps-pytorch

abcb87f3079c0612bde4a4f94c75d7c05d5aee3a

[ "MIT" ]

null

deepleaps/workspace/src/ipc/CustomCommand.py

Longseabear/deep-leaps-pytorch

abcb87f3079c0612bde4a4f94c75d7c05d5aee3a

[ "MIT" ]

null

import deepleaps.ipc.RunningCommand

18

35

0.888889

4

36

8

1

0

0.055556

36

1

36

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

d24ca4e55e2ea29a960fa8ecd6a05a6ef87a0584

8,346

py

Python

network.py

tonyhu20116543/Playing-20-Question-Game-with-Policy-Based-Reinforcement-Learning

fb9b20181dd3e3273fcbc28144d60f01185ceffd

[ "MIT" ]

12

2020-07-24T13:21:35.000Z

2021-11-08T10:13:24.000Z

network.py

tonyhu20116543/Playing-20-Question-Game-with-Policy-Based-Reinforcement-Learning

fb9b20181dd3e3273fcbc28144d60f01185ceffd

[ "MIT" ]

null

network.py

tonyhu20116543/Playing-20-Question-Game-with-Policy-Based-Reinforcement-Learning

fb9b20181dd3e3273fcbc28144d60f01185ceffd

[ "MIT" ]

7

2020-07-24T13:28:44.000Z

2021-11-08T10:13:25.000Z

import os import tensorflow as tf from util import masked_softmax class PolicyNetwork(object): """ Policy Function approximator. """ def __init__(self, input_size, output_size, learning_rate=0.001, summaries_dir=None, scope="policy_estimator"): with tf.variable_scope(scope): # Writes Tensorboard summaries to disk self.summary_writer = None if summaries_dir: summary_dir = os.path.join(summaries_dir, "summaries_{}".format(scope)) if not os.path.exists(summary_dir): os.makedirs(summary_dir) self.summary_writer = tf.summary.FileWriter(summary_dir) self.state = tf.placeholder(dtype=tf.float64, shape=[1, input_size], name="state") self.action = tf.placeholder(dtype=tf.int32, name="action") self.target = tf.placeholder(dtype=tf.float64, name="target") self.mask = tf.placeholder(dtype=tf.float64, shape=[1, output_size], name="mask") # This is just table lookup estimator # self.fc_layer1 = tf.contrib.layers.fully_connected( # inputs=self.state, # num_outputs=len(env.state), # activation_fn=tf.nn.relu) self.output_layer = tf.contrib.layers.fully_connected( inputs=self.state, num_outputs=output_size, activation_fn=None) # self.action_probs = tf.squeeze(tf.nn.softmax(self.output_layer)) self.action_probs = tf.squeeze(masked_softmax(self.output_layer, self.mask)) self.picked_action_prob = tf.gather(self.action_probs, self.action) # Loss and train op self.loss = -tf.log(self.picked_action_prob) * self.target self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) self.train_op = self.optimizer.minimize( self.loss, global_step=tf.train.get_global_step()) def predict(self, state, mask, sess=None): sess = sess or tf.get_default_session() return sess.run(self.action_probs, {self.state: state.reshape(1, -1), self.mask: mask.reshape(1, -1)}) def update(self, state, target, action, mask, sess=None): sess = sess or tf.get_default_session() feed_dict = {self.state: state.reshape(1, -1), self.target: target, self.action: action, self.mask: mask.reshape(1, -1)} _, loss = sess.run([self.train_op, self.loss], feed_dict) return loss def restore(self, sess, checkpoint_file): sess = sess or tf.get_default_session() self.saver = tf.train.Saver(tf.global_variables()) self.saver.restore(sess=sess, save_path=checkpoint_file) class ValueNetwork(object): """ Value Function approximator. """ def __init__(self, input_size, output_size=1, learning_rate=0.01, scope="value_estimator"): with tf.variable_scope(scope): self.state = tf.placeholder(dtype=tf.float64, shape=[1, input_size], name="state") self.target = tf.placeholder(dtype=tf.float64, name="target") # This is just table lookup estimator # self.fc_layer1 = tf.contrib.layers.fully_connected( # inputs=self.state, # num_outputs=input_size, # activation_fn=tf.nn.relu) self.output_layer = tf.contrib.layers.fully_connected( inputs=self.state, num_outputs=output_size, activation_fn=None) self.value_estimate = tf.squeeze(self.output_layer) self.loss = tf.squared_difference(self.value_estimate, self.target) self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) self.train_op = self.optimizer.minimize( self.loss, global_step=tf.train.get_global_step()) def predict(self, state, sess=None): sess = sess or tf.get_default_session() return sess.run(self.value_estimate, {self.state: state.reshape(1, -1)}) def update(self, state, target, sess=None): sess = sess or tf.get_default_session() feed_dict = {self.state: state.reshape(1, -1), self.target: target} _, loss = sess.run([self.train_op, self.loss], feed_dict) return loss class ObjectAwareRewardNetwork(object): """ Object-aware Reward Function approximator. """ def __init__(self, input_size, output_size, action_num, learning_rate=0.01, scope="reward_estimator"): with tf.variable_scope(scope): self.state = tf.placeholder(shape=[1, input_size], dtype=tf.float64, name="state") self.action = tf.placeholder(shape=[], dtype=tf.int32, name="question_idx") self.object = tf.placeholder(shape=[], dtype=tf.int32, name="person_idx") self.target = tf.placeholder(dtype=tf.float64, name="target") object_vec = tf.one_hot(self.object, input_size, dtype=tf.float64) action_vec = tf.one_hot(self.action, action_num, dtype=tf.float64) concat_vec = tf.concat([object_vec, action_vec], 0) self.output_layer = tf.contrib.layers.fully_connected( inputs=tf.concat([self.state, tf.expand_dims(concat_vec, 0)], 1), num_outputs=output_size, activation_fn=tf.nn.sigmoid) self.value_estimate = tf.squeeze(self.output_layer) self.loss = tf.squared_difference(self.value_estimate, self.target) self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) self.train_op = self.optimizer.minimize( self.loss, global_step=tf.train.get_global_step()) def predict(self, state, action, object, sess=None): sess = sess or tf.get_default_session() return sess.run(self.value_estimate, {self.state: state.reshape(1, -1), self.action: action, self.object: object}) def update(self, state, action, object, target, sess=None): sess = sess or tf.get_default_session() feed_dict = {self.state: state.reshape(1, -1), self.action: action, self.object: object, self.target: target} _, loss = sess.run([self.train_op, self.loss], feed_dict) def restore(self, sess, checkpoint_file): sess = sess or tf.get_default_session() self.saver = tf.train.Saver(tf.global_variables()) self.saver.restore(sess=sess, save_path=checkpoint_file) class RewardNetwork(object): """ Reward Function approximator. """ def __init__(self, input_size, output_size, action_num, learning_rate=0.01, scope="reward_estimator"): with tf.variable_scope(scope): self.state = tf.placeholder(shape=[1, input_size], dtype=tf.float64, name="state") self.action = tf.placeholder(shape=[], dtype=tf.int32, name="question_idx") self.target = tf.placeholder(dtype=tf.float64, name="target") action_vec = tf.one_hot(self.action, action_num, dtype=tf.float64) self.output_layer = tf.contrib.layers.fully_connected( inputs=tf.concat([self.state, tf.expand_dims(action_vec, 0)], 1), num_outputs=output_size, activation_fn=tf.nn.sigmoid) self.value_estimate = tf.squeeze(self.output_layer) self.loss = tf.squared_difference(self.value_estimate, self.target) self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) self.train_op = self.optimizer.minimize( self.loss, global_step=tf.train.get_global_step()) def predict(self, state, action, sess=None): sess = sess or tf.get_default_session() return sess.run(self.value_estimate, {self.state: state.reshape(1, -1), self.action: action}) def update(self, state, action, target, sess=None): sess = sess or tf.get_default_session() feed_dict = {self.state: state.reshape(1, -1), self.action: action, self.target: target} _, loss = sess.run([self.train_op, self.loss], feed_dict) def restore(self, sess, checkpoint_file): sess = sess or tf.get_default_session() self.saver = tf.train.Saver(tf.global_variables()) self.saver.restore(sess=sess, save_path=checkpoint_file)

46.88764

122

0.642104

1,071

8,346

4.815126

0.122316

0.045375

0.032577

0.025596

0.858639

0.819469

0.803762

0.785534

0.775645

0.738802

0

0.012276

0.238677

8,346

177

123

47.152542

0.799339

0.072011

0

0.638655

0

0.02115

0

1

0.12605

false

0

0.02521

0

0.235294

0

null

0

1

0

null

0

6

96359eac01afe317df5fd3c215b39bdd662a534c

14,568

py

Python

test/pdu.py

praekelt/python-smpp

8a0753fc498ab6bcd6243aed5953cddd69cef2c0

[ "BSD-3-Clause" ]

36

2015-01-15T09:38:06.000Z

2021-06-14T15:27:34.000Z

test/pdu.py

komuW/smpp_server

10ef5c2ebc09e2ef88bdd62c55a4280a187d1eb2

[ "BSD-3-Clause" ]

8

2015-02-12T15:52:53.000Z

2017-05-22T12:28:45.000Z

test/pdu.py

komuW/smpp_server

10ef5c2ebc09e2ef88bdd62c55a4280a187d1eb2

[ "BSD-3-Clause" ]

22

2015-04-29T15:06:17.000Z

2021-05-25T11:19:41.000Z

pdu_objects = [ { 'header': { 'command_length': 0, 'command_id': 'bind_transmitter', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', 'password': 'abc123', 'system_type': '', 'interface_version': '34', 'addr_ton': 1, 'addr_npi': 1, 'address_range': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'bind_transmitter_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'bind_receiver', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', 'password': 'abc123', 'system_type': '', 'interface_version': '34', 'addr_ton': 1, 'addr_npi': 1, 'address_range': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'bind_receiver_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'bind_transceiver', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', 'password': 'abc123', 'system_type': '', 'interface_version': '34', 'addr_ton': 1, 'addr_npi': 1, 'address_range': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'bind_transceiver_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'outbind', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'system_id': 'test_system', 'password': 'abc123', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'unbind', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'unbind_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'generic_nack', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'submit_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'service_type': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': '', 'esm_class': 0, 'protocol_id': 0, 'priority_flag': 0, 'schedule_delivery_time': '', 'validity_period': '', 'registered_delivery': 0, 'replace_if_present_flag': 0, 'data_coding': 0, 'sm_default_msg_id': 0, 'sm_length': 1, 'short_message': 'testing 123', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'submit_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'service_type': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': '', 'esm_class': 0, 'protocol_id': 0, 'priority_flag': 0, 'schedule_delivery_time': '', 'validity_period': '', 'registered_delivery': 0, 'replace_if_present_flag': 0, 'data_coding': 0, 'sm_default_msg_id': 0, 'sm_length': 0, 'short_message': None, # 'short_message' can be of zero length }, 'optional_parameters': [ { 'tag': 'message_payload', 'length': 0, 'value': '5666', }, ], }, }, # ] # breaker = [ { 'header': { 'command_length': 0, 'command_id': 'submit_sm_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'submit_sm_resp', 'command_status': 'ESME_RSYSERR', 'sequence_number': 0, }, # submit_sm_resp can have no body for failures }, { 'header': { 'command_length': 0, 'command_id': 'submit_multi', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'service_type': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'number_of_dests': 0, 'dest_address': [ { 'dest_flag': 1, 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': 'the address' }, { 'dest_flag': 2, 'dl_name': 'the list', }, { 'dest_flag': 2, 'dl_name': 'the other list', }, # {} ], 'esm_class': 0, 'protocol_id': 0, 'priority_flag': 0, 'schedule_delivery_time': '', 'validity_period': '', 'registered_delivery': 0, 'replace_if_present_flag': 0, 'data_coding': 0, 'sm_default_msg_id': 0, 'sm_length': 1, 'short_message': 'testing 123', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'submit_multi_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', 'no_unsuccess': 5, 'unsuccess_sme': [ { 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': '', 'error_status_code': 0, }, { 'dest_addr_ton': 3, 'dest_addr_npi': 1, 'destination_addr': '555', 'error_status_code': 0, }, ], }, }, }, # ] # breaker = [ { 'header': { 'command_length': 0, 'command_id': 'deliver_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'service_type': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': '', 'esm_class': 0, 'protocol_id': 0, 'priority_flag': 0, 'schedule_delivery_time': '', 'validity_period': '', 'registered_delivery': 0, 'replace_if_present_flag': 0, 'data_coding': 0, 'sm_default_msg_id': 0, 'sm_length': 1, 'short_message': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'deliver_sm_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'data_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'service_type': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': '', 'esm_class': 0, 'registered_delivery': 0, 'data_coding': 0, }, 'optional_parameters': [ { 'tag': 'message_payload', 'length': 0, 'value': '', }, ], }, }, { 'header': { 'command_length': 0, 'command_id': 'data_sm_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'query_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'query_sm_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', 'final_date': '', 'message_state': 0, 'error_code': 0, }, }, }, { 'header': { 'command_length': 0, 'command_id': 'cancel_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'service_type': '', 'message_id': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'dest_addr_ton': 1, 'dest_addr_npi': 1, 'destination_addr': '', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'cancel_sm_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'replace_sm', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'message_id': '', 'source_addr_ton': 1, 'source_addr_npi': 1, 'source_addr': '', 'schedule_delivery_time': '', 'validity_period': '', 'registered_delivery': 0, 'replace_if_present_flag': 0, 'data_coding': 0, 'sm_default_msg_id': 0, 'sm_length': 1, 'short_message': 'is this an = sign?', }, }, }, { 'header': { 'command_length': 0, 'command_id': 'replace_sm_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'enquire_link', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'enquire_link_resp', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, }, { 'header': { 'command_length': 0, 'command_id': 'alert_notification', 'command_status': 'ESME_ROK', 'sequence_number': 0, }, 'body': { 'mandatory_parameters': { 'source_addr_ton': 'international', 'source_addr_npi': 1, 'source_addr': '', 'esme_addr_ton': 9, 'esme_addr_npi': '', 'esme_addr': '', }, }, }, ]

28.17795

57

0.376922

1,059

14,568

4.766761

0.106704

0.044374

0.109152

0.114897

0.918582

0.901347

0.890254

0.792591

0

0.024103

0.48737

14,568

516

58

28.232558

0.651848

0.007757

0

0.617357

0

0.363932

0.018484

0

1

0

false

0.00789

0

null

0

1

0

null

0

6

96af8b4a48adf5297e31757c90f73a77f6edf704

101

py

Python

vault_password.py

RMuskovets/empireofcode

a2a9cfe2c43c7f28999b426601063dd0af352db5

[ "Apache-2.0" ]

1

2018-02-20T12:11:45.000Z

vault_password.py

RMuskovets/empireofcode

a2a9cfe2c43c7f28999b426601063dd0af352db5

[ "Apache-2.0" ]

null

vault_password.py

RMuskovets/empireofcode

a2a9cfe2c43c7f28999b426601063dd0af352db5

[ "Apache-2.0" ]

null

def golf(p): return len(p)>9 and p!=p.lower() and p!=p.upper() and any('0'<=l and l<='9' for l in p)

50.5

100

0.584158

26

101

2.269231

0.538462

0.135593

0.169492

0

0.035294

0.158416

101

1

101

0.658824

0

0.019802

0

1

false

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

73d14617d94420a3d56d21a483a4a8f9476f65c1

170

py

Python

notebooks/container/__init__.py

DanieleBaranzini/sktime-tutorial-pydata-amsterdam-2020

eb9d76a8dc7fff29e4123b940200d58eed87147c

[ "BSD-3-Clause" ]

114

2020-06-16T09:29:30.000Z

2022-03-12T09:06:52.000Z

notebooks/container/__init__.py

DanieleBaranzini/sktime-tutorial-pydata-amsterdam-2020

eb9d76a8dc7fff29e4123b940200d58eed87147c

[ "BSD-3-Clause" ]

5

2020-11-06T13:02:26.000Z

2021-06-10T18:34:37.000Z

notebooks/container/__init__.py

DanieleBaranzini/sktime-tutorial-pydata-amsterdam-2020

eb9d76a8dc7fff29e4123b940200d58eed87147c

[ "BSD-3-Clause" ]

62

2020-06-16T09:25:05.000Z

2022-03-01T21:02:10.000Z

from container.base import TimeBase from container.array import TimeArray, TimeDtype from container.timeseries import TimeSeries from container.timeframe import TimeFrame

42.5

48

0.876471

21

170

7.095238

0.47619

0.348993

0

0.094118

170

4

49

42.5

0.967532

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

fb52ea45a86609e7040cf2f5adb9df43b0bf1496

265

py

Python

todo/main.py

shuayb/simple-todo

7a6c840d38ada098b5cc3458d652c7db02ffd791

[ "MIT" ]

null

todo/main.py

shuayb/simple-todo

7a6c840d38ada098b5cc3458d652c7db02ffd791

[ "MIT" ]

null

todo/main.py

shuayb/simple-todo

7a6c840d38ada098b5cc3458d652c7db02ffd791

[ "MIT" ]

null

from app import app, db import models import views if __name__ == '__main__': app.run() # No need to do (debug=True), as in config.py, debug = true is already set. # app.run(debug=True) # app.run(debug=True, use_debugger=False, use_reloader=False)

26.5

79

0.683019

43

265

3.976744

0.627907

0.210526

0.128655

0.175439

0

0.196226

265

9

80

29.444444

0.802817

0.577358

0

0.074074

0

1

0

true

0

0.6

0

0.6

0

null

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

fb6262762a9edf203b455a0bed2e167c184ce590

1,947

py

Python

Twitter Data Extraction.py

scottblender/twitter-covid-19-vaccine-analysis

a4d273b8b885fc33db075dfc910fa39645fa3789

[ "MIT" ]

null

Twitter Data Extraction.py

scottblender/twitter-covid-19-vaccine-analysis

a4d273b8b885fc33db075dfc910fa39645fa3789

[ "MIT" ]

null

Twitter Data Extraction.py

scottblender/twitter-covid-19-vaccine-analysis

a4d273b8b885fc33db075dfc910fa39645fa3789

[ "MIT" ]

null

import snscrape.modules.twitter as sntwitter import pandas as pd # Creating list to append tweet data to tweets_list2 = [] # Using TwitterSearchScraper to scrape data and append tweets to list for i,tweet in enumerate(sntwitter.TwitterSearchScraper('covid vaccine until:2021-05-24').get_items()): if i>100000: break tweets_list2.append([tweet.date, tweet.id, tweet.content, tweet.user.username, tweet.user.verified, tweet.user.followersCount, tweet.user.friendsCount, tweet.likeCount, tweet.retweetCount, tweet.quoteCount, tweet.user.created, tweet.user.location, tweet.user.displayname, tweet.lang, tweet.coordinates, tweet.place]) # Creating a dataframe from the tweets list above tweets_df2 = pd.DataFrame(tweets_list2, columns=['Datetime', 'Tweet Id', 'Text', 'Username', 'Verified', 'Followers Count', 'Friends Count', 'Like Count', 'Retweet Count', 'Quote Count', 'Created','Location','Display Name', 'Language', 'Coordinates', 'Place']) tweets_df2.to_csv('First Extract.csv') # Creating list to append tweet data to tweets_list2 = [] # Using TwitterSearchScraper to scrape data and append tweets to list for i,tweet in enumerate(sntwitter.TwitterSearchScraper('covid vaccine until:2021-05-13').get_items()): if i>100000: break tweets_list2.append([tweet.date, tweet.id, tweet.content, tweet.user.username, tweet.user.verified, tweet.user.followersCount, tweet.user.friendsCount, tweet.likeCount, tweet.retweetCount, tweet.quoteCount, tweet.user.created, tweet.user.location, tweet.user.displayname, tweet.lang, tweet.coordinates, tweet.place]) # Creating a dataframe from the tweets list above tweets_df3 = pd.DataFrame(tweets_list2, columns=['Datetime', 'Tweet Id', 'Text', 'Username', 'Verified', 'Followers Count', 'Friends Count', 'Like Count', 'Retweet Count', 'Quote Count', 'Created','Location','Display Name', 'Language', 'Coordinates', 'Place']) tweets_df3.to_csv('Second Extract.csv')

69.535714

320

0.757062

260

1,947

5.615385

0.292308

0.086301

0.019178

0.027397

0.923288

0

0.021965

0.111454

1,947

27

321

72.111111

0.821965

0.157678

0

0.5

0

0.240661

0

1

0

false

0

0.125

0

0.125

0

null

0

1

0

null

0

6

fb7dc85f21a97ece3e0b036a3c4e6d6962f9001a

49

py

Python

netvisor_api_client/schemas/sales_payments/__init__.py

kiuru/netvisor-api-client

2af3e4ca400497ace5a86d0a1807ec3b9c530cf4

[ "MIT" ]

5

2019-04-17T08:10:47.000Z

2021-11-27T12:26:15.000Z

netvisor_api_client/schemas/sales_payments/__init__.py

kiuru/netvisor-api-client

2af3e4ca400497ace5a86d0a1807ec3b9c530cf4

[ "MIT" ]

7

2019-06-25T17:02:50.000Z

2021-07-21T10:14:38.000Z

netvisor_api_client/schemas/sales_payments/__init__.py

kiuru/netvisor-api-client

2af3e4ca400497ace5a86d0a1807ec3b9c530cf4

[ "MIT" ]

10

2019-06-25T15:37:33.000Z

2021-10-16T19:40:37.000Z

from .list import SalesPaymentListSchema # noqa

24.5

48

0.816327

5

49

8

1

0

0.142857

49

1

49

0.952381

0.081633

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fb8b63ad2ffbee810610ac48848eca279fdeb691

47

py

Python

primeiro programa/primeiro_programa.py

Cesario115/Ola-mundo

2949ff2c9dc1b5f8bc70825072751b19920019af

[ "MIT" ]

null

primeiro programa/primeiro_programa.py

Cesario115/Ola-mundo

2949ff2c9dc1b5f8bc70825072751b19920019af

[ "MIT" ]

null

primeiro programa/primeiro_programa.py

Cesario115/Ola-mundo

2949ff2c9dc1b5f8bc70825072751b19920019af

[ "MIT" ]

null

print('='*50) print("Olá mundo!") print('='*50)

15.666667

19

0.574468

7

47

3.857143

0.571429

0.518519

0

0.090909

0.06383

47

3

20

15.666667

0.522727

0

0.666667

0

0.25

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

838511c8e3372a6ae2d5fbb109dbbc9156779d54

171

py

Python

stdlib/getpass_qs.py

bpuderer/python-snippets27

8d51ff34c48bee1247575536d8ed506eafde8631

[ "MIT" ]

3

2015-11-20T14:30:53.000Z

2015-12-19T05:55:19.000Z

stdlib/getpass_qs.py

bpuderer/python-snippets27

8d51ff34c48bee1247575536d8ed506eafde8631

[ "MIT" ]

null

stdlib/getpass_qs.py

bpuderer/python-snippets27

8d51ff34c48bee1247575536d8ed506eafde8631

[ "MIT" ]

1

2016-01-05T20:54:49.000Z

import getpass # prompt user without echoing output print getpass.getpass() print getpass.getpass(prompt="Custom Prompt:") print "user login name:", getpass.getuser()

17.1

46

0.766082

22

171

5.954545

0.545455

0.198473

0.290076

0

0.128655

171

9

47

19

0.879195

0.19883

0

0.222222

0

null

1

0.25

null

0.75

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

f7d56596394f7bfd79f8b0a1466fae7aaa135fac

2,104

py

Python

test/torch/mpc/test_fss.py

NicoSerranoP/PySyft

87fcd566c46fce4c16d363c94396dd26bd82a016

[ "Apache-2.0" ]

3

2020-11-24T05:15:57.000Z

2020-12-07T09:52:45.000Z

test/torch/mpc/test_fss.py

NicoSerranoP/PySyft

87fcd566c46fce4c16d363c94396dd26bd82a016

[ "Apache-2.0" ]

1

2020-09-29T00:24:31.000Z

test/torch/mpc/test_fss.py

NicoSerranoP/PySyft

87fcd566c46fce4c16d363c94396dd26bd82a016

[ "Apache-2.0" ]

1

2021-09-04T16:27:41.000Z

import pytest import torch as th from syft.frameworks.torch.mpc.fss import DPF, DIF, n @pytest.mark.parametrize("op", ["eq", "le"]) def test_fss_class(op): class_ = {"eq": DPF, "le": DIF}[op] th_op = {"eq": th.eq, "le": th.le}[op] gather_op = {"eq": "__add__", "le": "__xor__"}[op] # single value primitive = class_.keygen(n_values=1) alpha, s_00, s_01, *CW = primitive mask = th.randint(0, 2 ** n, alpha.shape) k0, k1 = [((alpha - mask) % 2 ** n, s_00, *CW), (mask, s_01, *CW)] x = th.tensor([0]) x_masked = x + k0[0] + k1[0] y0 = class_.eval(0, x_masked, *k0[1:]) y1 = class_.eval(1, x_masked, *k1[1:]) assert (getattr(y0, gather_op)(y1) == th_op(x, 0)).all() # 1D tensor primitive = class_.keygen(n_values=3) alpha, s_00, s_01, *CW = primitive mask = th.randint(0, 2 ** n, alpha.shape) k0, k1 = [((alpha - mask) % 2 ** n, s_00, *CW), (mask, s_01, *CW)] x = th.tensor([0, 2, -2]) x_masked = x + k0[0] + k1[0] y0 = class_.eval(0, x_masked, *k0[1:]) y1 = class_.eval(1, x_masked, *k1[1:]) assert (getattr(y0, gather_op)(y1) == th_op(x, 0)).all() # 2D tensor primitive = class_.keygen(n_values=4) alpha, s_00, s_01, *CW = primitive mask = th.randint(0, 2 ** n, alpha.shape) k0, k1 = [((alpha - mask) % 2 ** n, s_00, *CW), (mask, s_01, *CW)] x = th.tensor([[0, 2], [-2, 0]]) x_masked = x + k0[0].reshape(x.shape) + k1[0].reshape(x.shape) y0 = class_.eval(0, x_masked, *k0[1:]) y1 = class_.eval(1, x_masked, *k1[1:]) assert (getattr(y0, gather_op)(y1) == th_op(x, 0)).all() # 3D tensor primitive = class_.keygen(n_values=8) alpha, s_00, s_01, *CW = primitive mask = th.randint(0, 2 ** n, alpha.shape) k0, k1 = [((alpha - mask) % 2 ** n, s_00, *CW), (mask, s_01, *CW)] x = th.tensor([[[0, 2], [-2, 0]], [[0, 2], [-2, 0]]]) x_masked = x + k0[0].reshape(x.shape) + k1[0].reshape(x.shape) y0 = class_.eval(0, x_masked, *k0[1:]) y1 = class_.eval(1, x_masked, *k1[1:]) assert (getattr(y0, gather_op)(y1) == th_op(x, 0)).all()

32.875

70

0.551331

369

2,104

2.96748

0.157182

0.076712

0.03653

0.076712

0.827397

0.80274

0.712329

0

0.082822

0.225285

2,104

63

71

33.396825

0.588957

0.019962

0

0.636364

0

0.015557

0

0.090909

1

0.022727

false

0

0.068182

0

0.090909

0

null

0

1

0

null

0

6

790a31602a2e6231958a1ed23fbe61a5ef5fd6fa

23

py

Python

examples/ndfd/ndfd.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

123

2015-01-12T06:43:22.000Z

2022-03-20T18:06:46.000Z

examples/ndfd/ndfd.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

103

2015-01-08T18:35:57.000Z

2022-01-18T01:44:14.000Z

examples/ndfd/ndfd.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

54

2015-02-15T17:12:00.000Z

2022-03-07T23:02:32.000Z

from raw.ndfd import *

11.5

22

0.73913

4

23

4.25

1

0

0.173913

23

1

23

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

790a4f9b1ca5315576470030e7218150601d0818

56

py

Python

pandoc_mustache/__init__.py

copart/pandoc-mustache

f6ace29cd0c8d6b4d8f182eedcf36ad38a2412fa

[ "CC0-1.0" ]

43

2017-12-27T05:57:00.000Z

2022-03-18T10:07:28.000Z

pandoc_mustache/__init__.py

copart/pandoc-mustache

f6ace29cd0c8d6b4d8f182eedcf36ad38a2412fa

[ "CC0-1.0" ]

10

2018-02-07T11:20:37.000Z

2021-04-22T21:44:19.000Z

pandoc_mustache/__init__.py

copart/pandoc-mustache

f6ace29cd0c8d6b4d8f182eedcf36ad38a2412fa

[ "CC0-1.0" ]

8

2018-11-05T13:10:35.000Z

2021-08-30T18:14:02.000Z

from .version import __version__ import pandoc_mustache

18.666667

32

0.875

7

56

6.285714

0.714286

0.590909

0

0.107143

56

2

33

28

0.88

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

7911efa6a596e02ff81a8a1e7aa08e6a17b34751

721

py

Python

tests/validation/test_is_subnational1.py

StuartMacKay/ebird-api

14b5c777548416a58abec05e25cd4b9a8e22f210

[ "MIT" ]

9

2020-05-16T20:26:33.000Z

2021-11-02T06:24:46.000Z

tests/validation/test_is_subnational1.py

StuartMacKay/ebird-api

14b5c777548416a58abec05e25cd4b9a8e22f210

[ "MIT" ]

17

2019-06-22T09:41:22.000Z

2020-09-11T06:25:21.000Z

tests/validation/test_is_subnational1.py

ProjectBabbler/ebird-api

14b5c777548416a58abec05e25cd4b9a8e22f210

[ "MIT" ]

null

import unittest from ebird.api.validation import is_subnational1 class IsSubnational1Tests(unittest.TestCase): """Tests for the is_subnational1 validation function.""" def test_is_subnational1(self): self.assertTrue(is_subnational1("US-NV")) def test_invalid_code_is_not_subnational1(self): self.assertFalse(is_subnational1("U")) self.assertFalse(is_subnational1("US-")) def test_country_is_not_subnational1(self): self.assertFalse(is_subnational1("US")) def test_subnational2_is_not_subnational1(self): self.assertFalse(is_subnational1("US-NV-VMT")) def test_location_is_not_subnational1(self): self.assertFalse(is_subnational1("L123456"))

30.041667

60

0.744799

87

721

5.862069

0.356322

0.247059

0.196078

0.284314

0.488235

0.4

0.203922

0

0.03437

0.152566

721

23

61

31.347826

0.800327

0.069348

0

0.040602

0

0.428571

1

0.357143

false

0

0.142857

0

0.571429

0

null

1

0

null

0

1

0

1

0

1

0

6

f70e1eec634ed0c89cd786687c6b726187e816d5

11,426

py

Python

src/train.py

Gordonbuck/ml-oov-we

ce28cd8b556a16125ba36cd41781a3e60bb26422

[ "MIT" ]

null

src/train.py

Gordonbuck/ml-oov-we

ce28cd8b556a16125ba36cd41781a3e60bb26422

[ "MIT" ]

null

src/train.py

Gordonbuck/ml-oov-we

ce28cd8b556a16125ba36cd41781a3e60bb26422

[ "MIT" ]

null

import higher from leap import Leap import numpy as np import os import torch import torch.nn as nn import gc def train(model, source_corpus, char2idx, args, device): model = model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr_init) lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=args.lr_decay, patience=args.patience, threshold=args.threshold) best_valid_cosine = 1 for epoch in np.arange(args.n_epochs): valid_cosine = [] valid_ce = [] model.train() for batch in np.arange(args.n_batch): train_contexts, train_targets, train_vocabs, train_inds = source_corpus.get_batch(args.batch_size, args.n_shot, char2idx, device, fixed=args.fixed_shot, return_inds=True) optimizer.zero_grad() if args.lang_model: pred_emb, pred_ind = model.forward(train_contexts, train_vocabs, lang_model=args.lang_model) loss = nn.functional.cross_entropy(pred_ind, train_inds) loss += -nn.functional.cosine_similarity(pred_emb, train_targets).mean() else: pred_emb = model.forward(train_contexts, train_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, train_targets).mean() loss.backward() optimizer.step() model.eval() with torch.no_grad(): for batch in np.arange(args.n_batch): valid_contexts, valid_targets, valid_vocabs, valid_inds = source_corpus.get_batch(args.batch_size, args.n_shot, char2idx, device, use_valid=True, fixed=args.fixed_shot, return_inds=True) if args.lang_model: pred_emb, pred_ind = model.forward(valid_contexts, valid_vocabs, lang_model=args.lang_model) loss = nn.functional.cross_entropy(pred_ind, valid_inds).mean() valid_ce += [loss.cpu().numpy()] else: pred_emb = model.forward(valid_contexts, valid_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, valid_targets).mean() valid_cosine += [loss.cpu().numpy()] avg_valid = np.average(valid_cosine) lr_scheduler.step(avg_valid) if args.lang_model: avg_ce = np.average(valid_ce) print(f"Average cosine loss: {avg_valid}; Average cross entropy loss: {avg_ce}") else: print(f"Average cosine loss: {avg_valid}") if avg_valid < best_valid_cosine: best_valid_cosine = avg_valid torch.save(model.state_dict(), os.path.join(args.save_dir, 'model.pt')) if optimizer.param_groups[0]['lr'] < args.lr_early_stop: print('LR early stop') break def maml_adapt(model, source_corpus, target_corpus, char2idx, args, device, lang_model_n_words=0): model = model.to(device) meta_optimizer = torch.optim.Adam(model.parameters(), lr=args.maml_meta_lr_init) lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(meta_optimizer, factor=args.lr_decay, patience=args.patience, threshold=args.threshold) best_score = 3 for meta_epoch in np.arange(args.n_meta_epochs): gc.collect() source_valid_cosine = [] target_valid_cosine = [] model.train() with torch.backends.cudnn.flags(benchmark=True): for meta_batch in np.arange(args.n_meta_batch): inner_optimizer = torch.optim.Adam(model.parameters(), lr=args.maml_inner_lr_init) meta_optimizer.zero_grad() with higher.innerloop_ctx(model, inner_optimizer, copy_initial_weights=False) as (fmodel, diffopt): for inner_batch in np.arange(args.n_inner_batch): source_train_contexts, source_train_targets, source_train_vocabs = source_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, fixed=args.fixed_shot) pred_emb = fmodel.forward(source_train_contexts, source_train_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, source_train_targets).mean() diffopt.step(loss) target_train_contexts, target_train_targets, target_train_vocabs = target_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, fixed=args.fixed_shot, repeat_ctxs=args.meta_repeat_ctxs) pred_emb = fmodel.forward(target_train_contexts, target_train_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, target_train_targets).mean() loss.backward() meta_optimizer.step() model.eval() with torch.no_grad(): for batch in np.arange(args.n_batch): source_valid_contexts, source_valid_targets, source_valid_vocabs = source_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, use_valid=True, fixed=args.fixed_shot) pred_emb = model.forward(source_valid_contexts, source_valid_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, source_valid_targets).mean() source_valid_cosine += [loss.cpu().numpy()] target_valid_contexts, target_valid_targets, target_valid_vocabs = target_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, use_valid=True, fixed=args.fixed_shot, repeat_ctxs=args.meta_repeat_ctxs) pred_emb = model.forward(target_valid_contexts, target_valid_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, target_valid_targets).mean() target_valid_cosine += [loss.cpu().numpy()] avg_source_valid, avg_target_valid = np.average(source_valid_cosine), np.average(target_valid_cosine) score = avg_target_valid lr_scheduler.step(score) print(f"Average source cosine loss: {avg_source_valid}; Average target cosine loss: {avg_target_valid}") if score < best_score: best_score = score torch.save(model.state_dict(), os.path.join(args.save_dir, 'maml_model.pt')) if meta_optimizer.param_groups[0]['lr'] < args.maml_lr_early_stop: print('LR early stop') break def leap_adapt(model, source_corpus, target_corpus, char2idx, args, device, lang_model_n_words=0): model = model.to(device) leap = Leap(model) meta_optimizer = torch.optim.Adam(leap.parameters(), lr=args.leap_meta_lr_init) lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(meta_optimizer, factor=args.lr_decay, patience=args.patience, threshold=args.threshold) best_score = 3 for meta_epoch in np.arange(args.n_meta_epochs): source_valid_cosine = [] target_valid_cosine = [] model.train() for meta_batch in np.arange(args.n_meta_batch): meta_optimizer.zero_grad() leap.init_task() leap.to(model) inner_optimizer = torch.optim.Adam(model.parameters(), lr=args.leap_inner_lr_init) for inner_batch in np.arange(args.n_task_steps): inner_optimizer.zero_grad() source_train_contexts, source_train_targets, source_train_vocabs = source_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, fixed=args.fixed_shot) pred_emb = model.forward(source_train_contexts, source_train_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, source_train_targets).mean() loss.backward() leap.update(loss, model) inner_optimizer.step() leap.init_task() leap.to(model) inner_optimizer = torch.optim.Adam(model.parameters(), lr=args.leap_inner_lr_init) for inner_batch in np.arange(args.n_task_steps): inner_optimizer.zero_grad() target_train_contexts, target_train_targets, target_train_vocabs = target_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, fixed=args.fixed_shot, repeat_ctxs=args.meta_repeat_ctxs) pred_emb = model.forward(target_train_contexts, target_train_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, target_train_targets).mean() loss.backward() leap.update(loss, model) inner_optimizer.step() leap.normalize() meta_optimizer.step() leap.to(model) model.eval() with torch.no_grad(): for batch in np.arange(args.n_batch): source_valid_contexts, source_valid_targets, source_valid_vocabs = source_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, use_valid=True, fixed=args.fixed_shot) pred_emb = model.forward(source_valid_contexts, source_valid_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, source_valid_targets).mean() source_valid_cosine += [loss.cpu().numpy()] target_valid_contexts, target_valid_targets, target_valid_vocabs = target_corpus.get_batch( args.meta_batch_size, args.n_shot, char2idx, device, use_valid=True, fixed=args.fixed_shot, repeat_ctxs=args.meta_repeat_ctxs) pred_emb = model.forward(target_valid_contexts, target_valid_vocabs) loss = -nn.functional.cosine_similarity(pred_emb, target_valid_targets).mean() target_valid_cosine += [loss.cpu().numpy()] avg_source_valid, avg_target_valid = np.average(source_valid_cosine), np.average(target_valid_cosine) score = avg_target_valid lr_scheduler.step(score) print(f"Average source cosine loss: {avg_source_valid}; Average target cosine loss: {avg_target_valid}") if score < best_score: best_score = score torch.save(model.state_dict(), os.path.join(args.save_dir, 'leap_model.pt')) if meta_optimizer.param_groups[0]['lr'] < args.leap_lr_early_stop: print('LR early stop') break

52.412844

120

0.591896

1,304

11,426

4.858129

0.092025

0.041673

0.032833

0.026519

0.86614

0.85588

0.830781

0.816575

0.781847

0.735754

0

0.002718

0.323823

11,426

217

121

52.654378

0.81724

0

0.668508

0

0.032295

0

1

0.016575

false

0

0.038674

0

0.055249

0.038674

0

null

0

1

0

null

0

6

f72ddd7241194452b55a3968e1f8f4807cdc48eb

1,166

py

Python

pact/test/test_constants.py

dwang7/pact-python

da03551e812508652e062fc4ba6071f1119e5bf2

[ "MIT" ]

null

pact/test/test_constants.py

dwang7/pact-python

da03551e812508652e062fc4ba6071f1119e5bf2

[ "MIT" ]

null

pact/test/test_constants.py

dwang7/pact-python

da03551e812508652e062fc4ba6071f1119e5bf2

[ "MIT" ]

null

from unittest import TestCase from mock import patch from .. import constants class mock_service_exeTestCase(TestCase): def setUp(self): super(mock_service_exeTestCase, self).setUp() self.addCleanup(patch.stopall) self.mock_os = patch.object(constants, 'os', autospec=True).start() def test_other(self): self.mock_os.name = 'posix' self.assertEqual(constants.mock_service_exe(), 'pact-mock-service') def test_windows(self): self.mock_os.name = 'nt' self.assertEqual(constants.mock_service_exe(), 'pact-mock-service.bat') class provider_verifier_exeTestCase(TestCase): def setUp(self): super(provider_verifier_exeTestCase, self).setUp() self.addCleanup(patch.stopall) self.mock_os = patch.object(constants, 'os', autospec=True).start() def test_other(self): self.mock_os.name = 'posix' self.assertEqual( constants.provider_verifier_exe(), 'pact-provider-verifier') def test_windows(self): self.mock_os.name = 'nt' self.assertEqual( constants.provider_verifier_exe(), 'pact-provider-verifier.bat')

30.684211

79

0.679245

141

1,166

5.432624

0.234043

0.086162

0.078329

0.073107

0.825065

0.731071

0.553525

0

0.202401

1,166

37

80

31.513514

0.823656

0

0.592593

0

0.089194

0.059177

0

0.148148

1

0.222222

false

0

0.111111

0

0.407407

0

null

0

1

0

null

0

1

0

6

f740e9188e23989d7d8cb429eceb0134b86a65bd

194

py

Python

hallucinate/api.py

SySS-Research/hallucinate

f6dbeea0599e232707e6cf27c3fe592edba92f6f

[ "MIT" ]

199

2021-07-27T13:47:14.000Z

2022-03-05T09:18:56.000Z

hallucinate/api.py

avineshwar/hallucinate

f6dbeea0599e232707e6cf27c3fe592edba92f6f

[ "MIT" ]

1

2021-12-08T19:32:29.000Z

hallucinate/api.py

avineshwar/hallucinate

f6dbeea0599e232707e6cf27c3fe592edba92f6f

[ "MIT" ]

13

2021-07-27T18:55:03.000Z

2021-08-09T06:15:35.000Z

class BaseHandler: def send(self, data, p): pass def recv(self, data, p): pass def shutdown(self, p, direction=2): pass def close(self): pass

13.857143

39

0.525773

25

194

4.08

0.52

0.205882

0.176471

0.254902

0.313725

0

0.008197

0.371134

194

13

40

14.923077

0.827869

0

0.444444

0

1

0.444444

false

0.444444

0

0.555556

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f783069506127a9b55df9ae0fb7a072477dcbc3b

32

py

Python

tests/unit/cli/test_repo.py

tehlingchu/anchore-cli

b0df36337f443749991a49263227c1d40989debb

[ "Apache-2.0" ]

110

2017-09-14T02:15:15.000Z

2022-03-30T20:14:21.000Z

tests/unit/cli/test_repo.py

tehlingchu/anchore-cli

b0df36337f443749991a49263227c1d40989debb

[ "Apache-2.0" ]

115

2017-09-22T12:15:30.000Z

2022-01-17T12:31:21.000Z

tests/unit/cli/test_repo.py

tehlingchu/anchore-cli

b0df36337f443749991a49263227c1d40989debb

[ "Apache-2.0" ]

56

2017-09-22T11:26:25.000Z

2022-03-03T14:14:58.000Z

from anchorecli.cli import repo

16

31

0.84375

5

32

5.4

1

0

0.125

32

1

32

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5421bfc32b86a8ee54dfb925ef8eac6e4d16b3b0

212

py

Python

pycache/__init__.py

HuiiBuh/pycache

300bd51f9e575fd77014d6c86497dd58f313f752

[ "MIT" ]

1

2021-09-04T05:34:26.000Z

pycache/__init__.py

HuiiBuh/pycache

300bd51f9e575fd77014d6c86497dd58f313f752

[ "MIT" ]

1

2021-03-14T19:26:01.000Z

2021-03-16T18:46:38.000Z

pycache/__init__.py

HuiiBuh/pycache

300bd51f9e575fd77014d6c86497dd58f313f752

[ "MIT" ]

null

__version__ = '0.3.2' # noinspection PyUnresolvedReferences from ._cache._cache import cache # noinspection PyUnresolvedReferences from ._scheduler._scheduler import add_schedule, schedule, ScheduleSubscription

30.285714

79

0.84434

21

212

8.095238

0.619048

0.4

0.447059

0

0.015625

0.09434

212

6

80

35.333333

0.869792

0.334906

0

0.036232

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

583a1302a3f7562a97c1476d70bc500c24d60c4f

174

py

Python

glanceclient/common/exceptions.py

citrix-openstack-build/python-glanceclient

32d9c42816b608220ae5692e573142dab6534604

[ "Apache-2.0" ]

1

2019-09-11T11:56:19.000Z

tools/dockerize/webportal/usr/lib/python2.7/site-packages/glanceclient/common/exceptions.py

foruy/openflow-multiopenstack

74140b041ac25ed83898ff3998e8dcbed35572bb

[ "Apache-2.0" ]

null

tools/dockerize/webportal/usr/lib/python2.7/site-packages/glanceclient/common/exceptions.py

foruy/openflow-multiopenstack

74140b041ac25ed83898ff3998e8dcbed35572bb

[ "Apache-2.0" ]

null

# This is here for compatability purposes. Once all known OpenStack clients # are updated to use glanceclient.exc, this file should be removed from glanceclient.exc import *

43.5

75

0.804598

26

174

5.384615

0.884615

0.214286

0

0.155172

174

3

76

58

0.952381

0.793103

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

587f22d6d391706fced03d26fcfcf342a5722cf3

1,394

py

Python

deepmedic_config.py

farrokhkarimi/deepmedic_project

b0c916171673ce3259d2458146f2db941f0bf270

[ "MIT" ]

2

2021-07-15T18:40:18.000Z

2021-08-03T17:10:12.000Z

deepmedic_config.py

farrokhkarimi/deepmedic_project

b0c916171673ce3259d2458146f2db941f0bf270

[ "MIT" ]

null

deepmedic_config.py

farrokhkarimi/deepmedic_project

b0c916171673ce3259d2458146f2db941f0bf270

[ "MIT" ]

1

2022-01-17T12:11:51.000Z

import os def deepmedic_config(config_files_path, niftis_path, test_flair_file_name, test_t1c_file_name, mask, prediction_file_name, output_path): with open(os.path.join(config_files_path, 'model', 'modelConfig.cfg'), 'r') as f: lines = f.readlines() lines[8] = 'folderForOutput = "%s"\n' % output_path with open(os.path.join(config_files_path, 'model', 'modelConfig.cfg'), 'w') as f: f.writelines(lines) with open(os.path.join(config_files_path, 'test', 'testConfig.cfg'), 'r') as f: lines = f.readlines() lines[8] = 'folderForOutput = "%s"\n' % output_path with open(os.path.join(config_files_path, 'test', 'testConfig.cfg'), 'w') as f: f.writelines(lines) with open(os.path.join(config_files_path, 'test', 'testChannels_flair.cfg'), 'w') as f: f.write(os.path.join(niftis_path, test_flair_file_name)) with open (os.path.join(config_files_path, 'test', 'testChannels_t1c.cfg'), 'w') as f: f.write(os.path.join(niftis_path, test_t1c_file_name)) with open(os.path.join(config_files_path, 'test', 'testRoiMasks.cfg'), 'w') as f: f.write(os.path.join(niftis_path, mask)) with open(os.path.join(config_files_path, 'test' 'testNamesOfPredictions.cfg'), 'w') as f: f.write(prediction_file_name)

48.068966

137

0.636298

199

1,394

4.236181

0.190955

0.078292

0.130486

0.132859

0.827995

0.774614

0.730724

0

0.00457

0.215208

1,394

29

138

48.068966

0.765996

0

0.3

0

0.169715

0.035113

0

1

0.05

false

0

0.05

0

0.1

0

null

0

1

0

null

0

6

5439f19ce894429f825edd092b433b960bae49d4

9,411

py

Python

src/peering/azext_peering/custom.py

michimune/azure-cli-extensions

697e2c674e5c0825d44c72d714542fe01331e107

[ "MIT" ]

1

2022-03-22T15:02:32.000Z

src/peering/azext_peering/custom.py

michimune/azure-cli-extensions

697e2c674e5c0825d44c72d714542fe01331e107

[ "MIT" ]

1

2021-02-10T22:04:59.000Z

src/peering/azext_peering/custom.py

michimune/azure-cli-extensions

697e2c674e5c0825d44c72d714542fe01331e107

[ "MIT" ]

1

2021-06-03T19:31:10.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # pylint: disable=line-too-long # pylint: disable=too-many-statements # pylint: disable=too-many-lines # pylint: disable=too-many-locals # pylint: disable=unused-argument import json def list_peering_legacy(cmd, client, peering_location=None, kind=None): return client.list(peering_location=peering_location, kind=kind) def create_peering_asn(cmd, client, name, peer_asn=None, emails=None, phone=None, peer_name=None, validation_state=None): body = {} body['peer_asn'] = peer_asn # number body.setdefault('peer_contact_info', {})['emails'] = None if emails is None else emails.split(',') body.setdefault('peer_contact_info', {})['phone'] = None if phone is None else phone.split(',') body['peer_name'] = peer_name # str body['validation_state'] = validation_state # str return client.create_or_update(peer_asn_name=name, peer_asn=body) def update_peering_asn(cmd, client, name, peer_asn=None, emails=None, phone=None, peer_name=None, validation_state=None): body = client.get(peer_asn_name=name).as_dict() body.peer_asn = peer_asn # number body.peer_contact_info.emails = None if emails is None else emails.split(',') body.peer_contact_info.phone = None if phone is None else phone.split(',') body.peer_name = peer_name # str body.validation_state = validation_state # str return client.create_or_update(peer_asn_name=name, peer_asn=body) def delete_peering_asn(cmd, client, name): return client.delete(peer_asn_name=name) def list_peering_asn(cmd, client): return client.list_by_subscription() def list_peering_location(cmd, client, kind=None, direct_peering_type=None): return client.list(kind=kind, direct_peering_type=direct_peering_type) def create_peering(cmd, client, resource_group, name, kind, location, sku_name=None, sku_tier=None, sku_family=None, sku_size=None, direct_connections=None, direct_peer_asn=None, direct_direct_peering_type=None, exchange_connections=None, exchange_peer_asn=None, peering_location=None, tags=None): body = {} body.setdefault('sku', {})['name'] = sku_name # str body.setdefault('sku', {})['tier'] = sku_tier # str body.setdefault('sku', {})['family'] = sku_family # str body.setdefault('sku', {})['size'] = sku_size # str body['kind'] = kind # str body.setdefault('direct', {})['connections'] = json.loads(direct_connections) if isinstance(direct_connections, str) else direct_connections body.setdefault('direct', {}).setdefault('peer_asn', {})['id'] = direct_peer_asn body.setdefault('direct', {})['direct_peering_type'] = direct_direct_peering_type # str # body.setdefault('exchange', {})['connections'] = json.loads(exchange_connections) if isinstance(exchange_connections, str) else exchange_connections # body.setdefault('exchange', {}).setdefault('peer_asn', {})['id'] = exchange_peer_asn body['peering_location'] = peering_location # str body['location'] = location # str body['tags'] = tags # dictionary return client.create_or_update(resource_group_name=resource_group, peering_name=name, peering=body) def update_peering(cmd, client, resource_group, name, sku_name=None, sku_tier=None, sku_family=None, sku_size=None, kind=None, direct_connections=None, direct_peer_asn=None, direct_direct_peering_type=None, exchange_connections=None, exchange_peer_asn=None, peering_location=None, location=None, tags=None): body = client.get(resource_group_name=resource_group, peering_name=name).as_dict() body.sku.name = sku_name # str body.sku.tier = sku_tier # str body.sku.family = sku_family # str body.sku.size = sku_size # str body.kind = kind # str body.direct.connections = json.loads(direct_connections) if isinstance(direct_connections, str) else direct_connections body.direct.peer_asn = direct_peer_asn body.direct.direct_peering_type = direct_direct_peering_type # str body.exchange.connections = json.loads(exchange_connections) if isinstance(exchange_connections, str) else exchange_connections body.exchange.peer_asn = exchange_peer_asn body.peering_location = peering_location # str body.location = location # str body.tags = tags # dictionary return client.create_or_update(resource_group_name=resource_group, peering_name=name, peering=body) def delete_peering(cmd, client, resource_group, name): return client.delete(resource_group_name=resource_group, peering_name=name) def list_peering(cmd, client, resource_group): if resource_group is not None: return client.list_by_resource_group(resource_group_name=resource_group) return client.list_by_subscription() def list_peering_service_location(cmd, client): return client.list() def create_peering_service_prefix(cmd, client, resource_group, peering_service_name, name, prefix=None): return client.create_or_update(resource_group_name=resource_group, peering_service_name=peering_service_name, prefix_name=name, prefix=prefix) def update_peering_service_prefix(cmd, client, resource_group, peering_service_name, name, prefix=None): return client.create_or_update(resource_group_name=resource_group, peering_service_name=peering_service_name, prefix_name=name, prefix=prefix) def delete_peering_service_prefix(cmd, client, resource_group, peering_service_name, name): return client.delete(resource_group_name=resource_group, peering_service_name=peering_service_name, prefix_name=name) def list_peering_service_prefix(cmd, client, resource_group, peering_service_name): return client.list_by_peering_service(resource_group_name=resource_group, peering_service_name=peering_service_name) def list_peering_service_provider(cmd, client): return client.list() def create_peering_service(cmd, client, resource_group, name, location, peering_service_location=None, peering_service_provider=None, tags=None): body = {} body['peering_service_location'] = peering_service_location # str body['peering_service_provider'] = peering_service_provider # str body['location'] = location # str body['tags'] = tags # dictionary return client.create_or_update(resource_group_name=resource_group, peering_service_name=name, peering_service=body) def update_peering_service(cmd, client, resource_group, name, peering_service_location=None, peering_service_provider=None, location=None, tags=None): body = client.get(resource_group_name=resource_group, peering_service_name=name).as_dict() body.peering_service_location = peering_service_location # str body.peering_service_provider = peering_service_provider # str body.location = location # str body.tags = tags # dictionary return client.create_or_update(resource_group_name=resource_group, peering_service_name=name, peering_service=body) def delete_peering_service(cmd, client, resource_group, name): return client.delete(resource_group_name=resource_group, peering_service_name=name) def list_peering_service(cmd, client, resource_group): if resource_group is not None: return client.list_by_resource_group(resource_group_name=resource_group) return client.list_by_subscription()

42.013393

154

0.604824

1,011

9,411

5.317507

0.086053

0.106399

0.063244

0.065104

0.837798

0.806734

0.763021

0.737723

0.705357

0.68657

0

0.294974

9,411

223

155

42.201794

0.810249

0.094889

0

0.596491

0

0.030918

0.005664

0

1

0.116959

false

0

0.005848

0.070175

0.251462

0

null

0

1

0

null

0

6

543c4f51f177e890cbcf4f4101beb26f2ee15486

81

py

Python

tests/integration/testdata/buildcmd/PyLayerMake/layer.py

renanmontebelo/aws-sam-cli

b5cfc46aa9726b5cd006df8ecc08d1b4eedeb9ea

[ "BSD-2-Clause", "Apache-2.0" ]

2,959

2018-05-08T21:48:56.000Z

2020-08-24T14:35:39.000Z

tests/integration/testdata/buildcmd/PyLayerMake/layer.py

renanmontebelo/aws-sam-cli

b5cfc46aa9726b5cd006df8ecc08d1b4eedeb9ea

[ "BSD-2-Clause", "Apache-2.0" ]

1,469

2018-05-08T22:44:28.000Z

2020-08-24T20:19:24.000Z

tests/integration/testdata/buildcmd/PyLayerMake/layer.py

renanmontebelo/aws-sam-cli

b5cfc46aa9726b5cd006df8ecc08d1b4eedeb9ea

[ "BSD-2-Clause", "Apache-2.0" ]

642

2018-05-08T22:09:19.000Z

2020-08-17T09:04:37.000Z

import numpy def layer_method(): return {"pi": "{0:.2f}".format(numpy.pi)}

13.5

45

0.617284

12

81

4.083333

0.833333

0

0.029412

0.160494

81

5

46

16.2

0.691176

0

0.111111

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

54562608a59ce9476a71d70e032f5d5bf8f6d75b

138

py

Python

datx/base_station.py

ipipdotnet/datx-python

68d6e99363abc6ae48714be38aa90a5ae6e20fd4

[ "Apache-2.0" ]

39

2018-03-13T02:48:36.000Z

2021-03-18T07:51:54.000Z

datx/base_station.py

ipipdotnet/datx-python

68d6e99363abc6ae48714be38aa90a5ae6e20fd4

[ "Apache-2.0" ]

1

2018-11-06T08:30:31.000Z

datx/base_station.py

ipipdotnet/datx-python

68d6e99363abc6ae48714be38aa90a5ae6e20fd4

[ "Apache-2.0" ]

10

2018-04-28T02:07:08.000Z

2020-11-09T04:26:47.000Z

15.333333

33

0.623188

17

138

5.117647

0.882353

0

0.045872

0.210145

138

9

34

15.333333

0.743119

0.376812

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

5468c394ce1fe6e2cc2dd6fce2fd7d4c6e567c44

3,494

py

Python

bem/teq_planet.py

DanielAndreasen/bem

c4cca79322f08b5e9a3f3d39749c11d9f6296aae

[ "MIT" ]

null

bem/teq_planet.py

DanielAndreasen/bem

c4cca79322f08b5e9a3f3d39749c11d9f6296aae

[ "MIT" ]

null

bem/teq_planet.py

DanielAndreasen/bem

c4cca79322f08b5e9a3f3d39749c11d9f6296aae

[ "MIT" ]

null

import numpy as np from uncertainties import umath as um def getTeqpl(Teffst, aR, ecc, A=0, f=1/4.): """Return the planet equilibrium temperature. Relation adapted from equation 4 page 4 in http://www.mpia.de/homes/ppvi/chapter/madhusudhan.pdf and https://en.wikipedia.org/wiki/Stefan%E2%80%93Boltzmann_law and later updated to include the effect of excentricity on the average stellar planet distance according to equation 5 p 25 of Laughlin & Lissauer 2015arXiv150105685L (1501.05685) Plus Exoplanet atmospheres, physical processes, Sara Seager, p30 eq 3.9 for f contribution. :param float/np.ndarray Teffst: Effective temperature of the star :param float/np.ndarray aR: Ration of the planetary orbital semi-major axis over the stellar radius (without unit) :param float/np.ndarray A: Bond albedo (should be between 0 and 1) :param float/np.ndarray f: Redistribution factor. If 1/4 the energy is uniformly redistributed over the planetary surface. If f = 2/3, no redistribution at all, the atmosphere immediately reradiate whithout advection. :return float/np.ndarray Teqpl: Equilibrium temperature of the planet """ return Teffst * (f * (1 - A))**(1 / 4.) * np.sqrt(1 / aR) / (1 - ecc**2)**(1/8.) def getTeqpl_error(Teffst, aR, ecc, A=0, f=1/4.): """Return the planet equilibrium temperature. Relation adapted from equation 4 page 4 in http://www.mpia.de/homes/ppvi/chapter/madhusudhan.pdf and https://en.wikipedia.org/wiki/Stefan%E2%80%93Boltzmann_law and later updated to include the effect of excentricity on the average stellar planet distance according to equation 5 p 25 of Laughlin & Lissauer 2015arXiv150105685L (1501.05685) Plus Exoplanet atmospheres, physical processes, Sara Seager, p30 eq 3.9 for f contribution. :param float/np.ndarray Teffst: Effective temperature of the star :param float/np.ndarray aR: Ration of the planetary orbital semi-major axis over the stellar radius (without unit) :param float/np.ndarray A: Bond albedo (should be between 0 and 1) :param float/np.ndarray f: Redistribution factor. If 1/4 the energy is uniformly redistributed over the planetary surface. If f = 2/3, no redistribution at all, the atmosphere immediately reradiate whithout advection. :return float/np.ndarray Teqpl: Equilibrium temperature of the planet """ return Teffst * (f * (1 - A))**(1 / 4.) * um.sqrt(1 / aR) / (1 - ecc**2)**(1/8.) def getHtidal(Ms, Rp, a, e): # a -- in AU, semi major axis # Teq -- in Kelvins, planetary equilibrium temperature # M -- in Jupiter masses, planetary mass # Z -- [Fe/H], stellar metallicity # Rp -- radius planet # Ms -- stellar mass # e -- eccentricity # G -- gravitational constant # # G = 6.67408 * 10**(-11) # m3 kg-1 s-2 # Equation from Enoch et al. 2012 # Q = 10**5 # Tidal dissipation factor for high mass planets ...? # k = 0.51 # Love number # H_tidal = (63/4) * ((G * Ms)**(3/2) * Ms * Rp**5 * a**(-15/2)*e**2) / ((3*Q) / (2*k)) # Equation from Jackson 2008 # Qp' = (3*Qp) / (2*k) Qp = 500 # with Love number 0.3 for terrestrial planets H_tidal = (63 / 16*np.pi) * (((G*Ms)**(3/2) * Ms * Rp**3) / (Qp)) * a**(-15/2) * e**2 return H_tidal def safronov_nb(Mp, Ms, Rp, a): # Ozturk 2018, Safronov 1972 return (Mp/Ms) * (a/Rp)

48.527778

100

0.660561

529

3,494

4.349716

0.334594

0.030422

0.060843

0.066058

0.767492

0.762277

0.754455

0.739678

0

0.06338

0.227819

3,494

71

101

49.211268

0.789474

0.763022

0

1

0.307692

false

0

0.153846

0.076923

0.769231

0

null

0

1

0

null

0

1

0

6

5480e17b073b3d2de7a418823c0645c307bf4d95

183

py

Python

reward/utils/device.py

lgvaz/torchrl

cfff8acaf70d1fec72169162b95ab5ad3547d17a

[ "MIT" ]

5

2018-06-21T14:33:40.000Z

2018-08-18T02:26:03.000Z

reward/utils/device.py

lgvaz/reward

cfff8acaf70d1fec72169162b95ab5ad3547d17a

[ "MIT" ]

null

reward/utils/device.py

lgvaz/reward

cfff8acaf70d1fec72169162b95ab5ad3547d17a

[ "MIT" ]

2

2018-05-08T03:34:49.000Z

2018-06-22T15:04:17.000Z

import torch CONFIG = {"device": torch.device("cuda" if torch.cuda.is_available() else "cpu")} def get(): return CONFIG["device"] def set_device(device): CONFIG["device"] = device

22.875

81

0.704918

26

183

4.884615

0.538462

0.283465

0

0.120219

183

7

82

26.142857

0.78882

0

0.136612

0

1

0.5

false

0

0.25

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

54b976c7100ab785c654b0c7ca7597f8b6235530

2,979

py

Python

tests/integration/test_labels.py

spmistry/crux-python

15a6b705d1eec7e789f6f62819429f93e02349c1

[ "MIT" ]

null

tests/integration/test_labels.py

spmistry/crux-python

15a6b705d1eec7e789f6f62819429f93e02349c1

[ "MIT" ]

null

tests/integration/test_labels.py

spmistry/crux-python

15a6b705d1eec7e789f6f62819429f93e02349c1

[ "MIT" ]

null

import pytest @pytest.mark.usefixtures("dataset", "helpers") def test_add_get_label(dataset, helpers): file_1 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) label_result = file_1.add_label("label1", "value1") assert label_result is True assert file_1.labels.get("label1") == "value1" @pytest.mark.usefixtures("dataset", "helpers") def test_add_labels_set_labels(dataset, helpers): file_1 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) labels = {"label1": "value1", "label2": "value2"} labels_result = file_1.add_labels(labels) assert labels_result is True assert file_1.labels == labels # Negative Test case which verifies label search by searching unset labels without pagination. @pytest.mark.usefixtures("dataset", "helpers") def test_search_label(dataset, helpers): file_1 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) file_2 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) label_result_1 = file_1.add_label("label1", "value1") label_result_2 = file_2.add_label("label1", "value1") assert label_result_1 is True assert label_result_2 is True predicates = [{"op": "eq", "key": "label4", "val": "value4"}] resources = dataset.find_resources_by_label(predicates=predicates) resource_ids = [resource.id for resource in resources] assert len(resource_ids) == 0 # Negative Test case which verifies label search by searching unset labels with pagination. @pytest.mark.usefixtures("dataset", "helpers") def test_search_label_page(dataset, helpers): file_1 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) file_2 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) label_result_1 = file_1.add_label("label2", "value2") label_result_2 = file_2.add_label("label2", "value2") assert label_result_1 is True assert label_result_2 is True predicates = [{"op": "eq", "key": "label3", "val": "value3"}] resources = dataset.find_resources_by_label(predicates=predicates, max_per_page=1) resource_ids = [resource.id for resource in resources] assert len(resource_ids) == 0 @pytest.mark.usefixtures("dataset", "helpers") def test_delete_label(dataset, helpers): file_1 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) file_2 = dataset.create_file( path="/test_file_" + helpers.generate_random_string(16) + ".csv" ) file_1.add_label("label1", "value1") file_2.add_label("label1", "value1") d1_result = file_1.delete_label(label_key="label1") assert d1_result is True d2_result = file_2.delete_label(label_key="label1") assert d2_result is True

35.891566

94

0.700906

401

2,979

4.902743

0.164589

0.033062

0.069176

0.085453

0.865209

0.801628

0.703459

0.600712

0

0.031199

0.171534

2,979

82

95

36.329268

0.765397

0.061094

0

0.453125

1

0

0.130995

0

0.1875

1

0.078125

false

0

0.015625

0

0.09375

0

null

0

1

0

1

0

1

0

null

0

6

49b5f0ea075bbb7b79a2d40b2e4b0bdffec0743f

12,388

py

Python

weasyl/test/web/test_site_updates.py

sl1-1/weasyl

d4f6bf3e33b85a2289a451d95d5b90ff24f5d539

[ "Apache-2.0" ]

1

2019-02-15T04:21:48.000Z

weasyl/test/web/test_site_updates.py

sl1-1/weasyl

d4f6bf3e33b85a2289a451d95d5b90ff24f5d539

[ "Apache-2.0" ]

254

2017-12-23T19:36:43.000Z

2020-04-14T21:46:13.000Z

weasyl/test/web/test_site_updates.py

sl1-1/weasyl

d4f6bf3e33b85a2289a451d95d5b90ff24f5d539

[ "Apache-2.0" ]

1

2017-12-23T18:42:16.000Z

from __future__ import absolute_import, unicode_literals import pytest from libweasyl import staff from libweasyl.legacy import UNIXTIME_OFFSET from weasyl import errorcode from weasyl import siteupdate from weasyl.define import sessionmaker from weasyl.test import db_utils _FORM = { u'title': u'Title', u'content': u'Content', } @pytest.fixture(name='site_updates') @pytest.mark.usefixtures('db') def _site_updates(): user = db_utils.create_user(username='test_username') updates = [ siteupdate.create(user, u'foo', u'content one'), siteupdate.create(user, u'bar', u'content two'), siteupdate.create(user, u'baz', u'content three'), ] for update in updates: sessionmaker().expunge(update) return (user, updates) @pytest.mark.usefixtures('db') def test_select_last_empty(app): assert siteupdate.select_last() is None @pytest.mark.usefixtures('db') def test_select_last(app, site_updates): user, updates = site_updates most_recent = updates[-1] selected = siteupdate.select_last() assert 'display_url' in selected.pop('user_media')['avatar'][0] assert selected == { 'updateid': most_recent.updateid, 'userid': user, 'username': 'test_username', 'title': most_recent.title, 'content': most_recent.content, 'unixtime': most_recent.unixtime.timestamp + UNIXTIME_OFFSET, 'comment_count': 0, } @pytest.mark.usefixtures('db', 'cache') def test_index_empty(app): resp = app.get('/') assert resp.html.find(id='home-content') is not None assert resp.html.find(id='hc-update') is None @pytest.mark.usefixtures('db', 'cache') def test_index(app, site_updates): _, updates = site_updates resp = app.get('/') update = resp.html.find(id='hc-update') assert update is not None assert update.h3.string == updates[-1].title assert update.figure.img['alt'] == u'avatar of test_username' @pytest.mark.usefixtures('db') def test_list_empty(app): resp = app.get('/site-updates/') assert resp.html.find(None, 'content').p.string == u'No site updates to show.' @pytest.mark.usefixtures('db') def test_list(app, monkeypatch, site_updates): _, updates = site_updates resp = app.get('/site-updates/') assert len(resp.html.findAll(None, 'text-post-item')) == 3 assert resp.html.find(None, 'text-post-actions') is None assert len(resp.html.findAll(None, 'text-post-group-header')) == 1 user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.get('/site-updates/', headers={'Cookie': cookie}) assert len(resp.html.findAll(None, 'text-post-item')) == 3 assert resp.html.find(None, 'text-post-actions').a['href'] == '/site-updates/%d/edit' % (updates[-1].updateid,) @pytest.mark.usefixtures('db', 'no_csrf') def test_create(app, monkeypatch): user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post('/admincontrol/siteupdate', _FORM, headers={'Cookie': cookie}).follow() assert resp.html.find(None, 'content').h3.string == _FORM['title'] @pytest.mark.usefixtures('db', 'no_csrf') def test_create_strip(app, monkeypatch): user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post( '/admincontrol/siteupdate', dict(_FORM, title=' test title \t '), headers={'Cookie': cookie}, ).follow() assert resp.html.find(None, 'content').h3.string == u'test title' @pytest.mark.usefixtures('db') def test_create_csrf(app, monkeypatch): user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post('/admincontrol/siteupdate', _FORM, headers={'Cookie': cookie}, status=403) assert resp.html.find(id='error_content').p.string == errorcode.token @pytest.mark.usefixtures('db') def test_create_restricted(app, monkeypatch): resp = app.get('/admincontrol/siteupdate') assert resp.html.find(id='error_content').contents[0].strip() == errorcode.unsigned resp = app.post('/admincontrol/siteupdate', _FORM) assert resp.html.find(id='error_content').contents[0].strip() == errorcode.unsigned user = db_utils.create_user() cookie = db_utils.create_session(user) resp = app.get('/admincontrol/siteupdate', headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission resp = app.post('/admincontrol/siteupdate', _FORM, headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission monkeypatch.setattr(staff, 'TECHNICAL', frozenset([user])) monkeypatch.setattr(staff, 'MODS', frozenset([user])) resp = app.get('/admincontrol/siteupdate', headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission resp = app.post('/admincontrol/siteupdate', _FORM, headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.get('/admincontrol/siteupdate', headers={'Cookie': cookie}) assert resp.html.find(id='error_content') is None @pytest.mark.usefixtures('db', 'no_csrf') def test_create_validation(app, monkeypatch): user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post('/admincontrol/siteupdate', {'title': u'', 'content': u'Content'}, headers={'Cookie': cookie}, status=422) assert resp.html.find(id='error_content').p.string == errorcode.error_messages['titleInvalid'] resp = app.post('/admincontrol/siteupdate', {'title': u'Title', 'content': u''}, headers={'Cookie': cookie}, status=422) assert resp.html.find(id='error_content').p.string == errorcode.error_messages['contentInvalid'] @pytest.mark.usefixtures('db', 'no_csrf') def test_create_notifications(app, monkeypatch): admin_user = db_utils.create_user() normal_user = db_utils.create_user() admin_cookie = db_utils.create_session(admin_user) monkeypatch.setattr(staff, 'ADMINS', frozenset([admin_user])) resp = app.post('/admincontrol/siteupdate', _FORM, headers={'Cookie': admin_cookie}).follow() assert resp.html.find(None, 'content').h3.string == _FORM['title'] normal_cookie = db_utils.create_session(normal_user) resp = app.get('/messages/notifications', headers={'Cookie': normal_cookie}) assert list(resp.html.find(id='header-messages').find(title='Notifications').stripped_strings)[1] == '1' assert resp.html.find(id='site_updates').find(None, 'item').a.string == _FORM['title'] @pytest.mark.usefixtures('db', 'no_csrf') def test_edit(app, monkeypatch, site_updates): _, updates = site_updates user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post('/site-updates/%d' % (updates[-1].updateid,), _FORM, headers={'Cookie': cookie}).follow() assert resp.html.find(None, 'content').h3.string == _FORM['title'] @pytest.mark.usefixtures('db', 'no_csrf') def test_edit_strip(app, monkeypatch, site_updates): _, updates = site_updates user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post( '/site-updates/%d' % (updates[-1].updateid,), dict(_FORM, title=' test title \t '), headers={'Cookie': cookie}, ).follow() assert resp.html.find(None, 'content').h3.string == u'test title' @pytest.mark.usefixtures('db', 'no_csrf') def test_edit_nonexistent(app, monkeypatch, site_updates): _, updates = site_updates user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) app.post('/site-updates/%d' % (updates[-1].updateid + 1,), _FORM, headers={'Cookie': cookie}, status=404) @pytest.mark.usefixtures('db') def test_edit_csrf(app, monkeypatch, site_updates): _, updates = site_updates user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post('/site-updates/%d' % (updates[-1].updateid,), _FORM, headers={'Cookie': cookie}, status=403) assert resp.html.find(id='error_content').p.string == errorcode.token @pytest.mark.usefixtures('db') def test_edit_restricted(app, monkeypatch, site_updates): _, updates = site_updates resp = app.get('/site-updates/%d/edit' % (updates[-1].updateid,)) assert resp.html.find(id='error_content').contents[0].strip() == errorcode.unsigned resp = app.post('/site-updates/%d' % (updates[-1].updateid,), _FORM) assert resp.html.find(id='error_content').contents[0].strip() == errorcode.unsigned user = db_utils.create_user() cookie = db_utils.create_session(user) resp = app.get('/site-updates/%d/edit' % (updates[-1].updateid,), headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission resp = app.post('/site-updates/%d' % (updates[-1].updateid,), _FORM, headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission monkeypatch.setattr(staff, 'TECHNICAL', frozenset([user])) monkeypatch.setattr(staff, 'MODS', frozenset([user])) resp = app.get('/site-updates/%d/edit' % (updates[-1].updateid,), headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission resp = app.post('/site-updates/%d' % (updates[-1].updateid,), _FORM, headers={'Cookie': cookie}) assert resp.html.find(id='error_content').p.string == errorcode.permission monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.get('/site-updates/%d/edit' % (updates[-1].updateid,), headers={'Cookie': cookie}) assert resp.html.find(id='error_content') is None @pytest.mark.usefixtures('db', 'no_csrf') def test_edit_validation(app, monkeypatch, site_updates): _, updates = site_updates user = db_utils.create_user() cookie = db_utils.create_session(user) monkeypatch.setattr(staff, 'ADMINS', frozenset([user])) resp = app.post('/site-updates/%d' % (updates[-1].updateid,), {'title': u'', 'content': u'Content'}, headers={'Cookie': cookie}, status=422) assert resp.html.find(id='error_content').p.string == errorcode.error_messages['titleInvalid'] resp = app.post('/site-updates/%d' % (updates[-1].updateid,), {'title': u'Title', 'content': u''}, headers={'Cookie': cookie}, status=422) assert resp.html.find(id='error_content').p.string == errorcode.error_messages['contentInvalid'] @pytest.mark.usefixtures('db', 'no_csrf') def test_edit_notifications(app, monkeypatch): admin_user = db_utils.create_user() normal_user = db_utils.create_user() admin_cookie = db_utils.create_session(admin_user) monkeypatch.setattr(staff, 'ADMINS', frozenset([admin_user])) resp = app.post('/admincontrol/siteupdate', _FORM, headers={'Cookie': admin_cookie}).follow() assert resp.html.find(None, 'content').h3.string == _FORM['title'] normal_cookie = db_utils.create_session(normal_user) resp = app.get('/messages/notifications', headers={'Cookie': normal_cookie}) assert list(resp.html.find(id='header-messages').find(title='Notifications').stripped_strings)[1] == '1' assert resp.html.find(id='site_updates').find(None, 'item').a.string == _FORM['title'] resp = app.post( '/site-updates/%d' % (siteupdate.select_last()['updateid'],), dict(_FORM, title=u'New title'), headers={'Cookie': admin_cookie}, ).follow() assert resp.html.find(None, 'content').h3.string == u'New title' resp = app.get('/messages/notifications', headers={'Cookie': normal_cookie}) assert list(resp.html.find(id='header-messages').find(title='Notifications').stripped_strings)[1] == '1' assert resp.html.find(id='site_updates').find(None, 'item').a.string == u'New title'

39.705128

144

0.690265

1,609

12,388

5.168428

0.085146

0.055556

0.056277

0.075758

0.863035

0.846922

0.823954

0.798581

0.766114

0.757937

0

0.005712

0.137875

12,388

311

145

39.832797

0.77294

0

0.61674

0

0.173636

0.042945

0

0.207048

1

0.088106

false

0

0.035242

0

0.127753

0

null

0

1

0

null

0

6

49c8b96abc3f198aa66587406ab8b7e9c78fd259

31

py

Python

lemkelcp/__init__.py

pritam-dey3/lemkelcp

4d963a6d0e6ba531496f5b0e99a52c0d288e4a6e

[ "MIT" ]

10

2019-03-17T19:37:25.000Z

2022-01-02T04:29:05.000Z

lemkelcp/__init__.py

pritam-dey3/lemkelcp

4d963a6d0e6ba531496f5b0e99a52c0d288e4a6e

[ "MIT" ]

1

2019-09-25T09:32:49.000Z

2021-12-28T05:05:55.000Z

lemkelcp/__init__.py

pritam-dey3/lemkelcp

4d963a6d0e6ba531496f5b0e99a52c0d288e4a6e

[ "MIT" ]

4

2019-02-24T11:49:10.000Z

2020-06-06T14:07:11.000Z

from .lemkelcp import lemkelcp

15.5

30

0.83871

4

31

6.5

0.75

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b71f0ad71dba6e7fc8ad5d041a1cde7948bbc25f

117

py

Python

contacts/views/contact_views.py

Onlynfk/Freshdesk-CRM-Platform

67137af09f7daf6fa2d19a9e70d573548137c9db

[ "MIT" ]

null

contacts/views/contact_views.py

Onlynfk/Freshdesk-CRM-Platform

67137af09f7daf6fa2d19a9e70d573548137c9db

[ "MIT" ]

null

contacts/views/contact_views.py

Onlynfk/Freshdesk-CRM-Platform

67137af09f7daf6fa2d19a9e70d573548137c9db

[ "MIT" ]

null

from django.shortcuts import render def contact(request): return render(request, 'contacts/contact.html')

19.5

52

0.735043

14

117

6.142857

0.785714

0

0.17094

117

5

53

23.4

0.886598

0

0.1875

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b771d6a65389f019399e4105e7ca9559208f9b9c

271

py

Python

pycon_project/apps/proposals/admin.py

mitsuhiko/pycon

73688a82080539a1c0d575cf3248f55fefb6b9ba

[ "BSD-3-Clause" ]

1

2017-09-04T08:19:08.000Z

pycon_project/apps/proposals/admin.py

mitsuhiko/pycon

73688a82080539a1c0d575cf3248f55fefb6b9ba

[ "BSD-3-Clause" ]

null

pycon_project/apps/proposals/admin.py

mitsuhiko/pycon

73688a82080539a1c0d575cf3248f55fefb6b9ba

[ "BSD-3-Clause" ]

null

from django.contrib import admin from proposals.models import Proposal, ProposalSessionType admin.site.register(ProposalSessionType) admin.site.register(Proposal, list_display = ["title", "session_type", "audience_level", "cancelled", "extreme_pycon", "invited"] )

30.111111

103

0.785978

30

271

6.966667

0.733333

0.229665

0.267943

0.344498

0

0.095941

271

9

104

30.111111

0.853061

0

0.220588

0

1

0

true

0

0.333333

0

0.333333

0

null

1

0

1

0

null

0

1

0

1

0

6

b7a7213417448a10f646593e2af28f99d94c2f47

3,144

py

Python

paper_plots/small_vs_large_box.py

finn-dodgson/DeepHalos

86e0ac6c24ac97a0a2a0a60a7ea3721a04bd050c

[ "MIT" ]

null

paper_plots/small_vs_large_box.py

finn-dodgson/DeepHalos

86e0ac6c24ac97a0a2a0a60a7ea3721a04bd050c

[ "MIT" ]

null

paper_plots/small_vs_large_box.py

finn-dodgson/DeepHalos

86e0ac6c24ac97a0a2a0a60a7ea3721a04bd050c

[ "MIT" ]

null

import numpy as np from plots import plots_for_predictions as pp from utilss import distinct_colours as dc import matplotlib.pyplot as plt c = dc.get_distinct(4) path = '/Users/luisals/Documents/deep_halos_files/mass_range_13.4/random_20sims_200k/lr5e-5/' p1 = np.load(path + "seed_20/predicted_sim_6_epoch_09.npy") t1 = np.load(path + "seed_20/true_sim_6_epoch_09.npy") p_big = np.load("/Users/luisals/Projects/DLhalos/bigbox/raw/predicted_sim_L200_N1024_genetIC3_epoch_10.npy") t_big = np.load("/Users/luisals/Projects/DLhalos/bigbox/raw/true_sim_L200_N1024_genetIC3_epoch_10.npy") path_av = "/Users/luisals/Documents/deep_halos_files/mass_range_13.4/random_20sims_200k/averaged_boxes/log_alpha_-4.3/" p_av = np.load(path_av + "predicted_sim_6_epoch_32.npy") t_av = np.load(path_av + "true_sim_6_epoch_32.npy") p_av_big = np.load("/Users/luisals/Projects/DLhalos/bigbox/avg/predicted_sim_L200_N1024_genetIC3_epoch_18.npy") t_av_big = np.load("/Users/luisals/Projects/DLhalos/bigbox/avg/true_sim_L200_N1024_genetIC3_epoch_18.npy") # Raw-density case f1, a, m = pp.plot_histogram_predictions(p1, t1, radius_bins=False, particle_ids=None, errorbars=False, label=r"$L_\mathrm{box}=50 \, \mathrm{Mpc} \,/ \,h$", color="C0") f11, a1, m1 = pp.plot_histogram_predictions(p_big, t_big, radius_bins=False, particle_ids=None, errorbars=False, fig=f1, axes=a, color="C1", label=r"$L_\mathrm{box}=200 \, \mathrm{Mpc} \,/ \,h$") a1[0].set_ylabel(r"$n_{\mathrm{particles}}$", fontsize=16) [a.set_xlabel(r"$\log(M_{\mathrm{predicted}}/M_{\mathrm{true}})$", fontsize=16) for a in a1] plt.savefig("/Users/lls/Documents/Papers/dlhalos_paper/small_vs_large_box.pdf") # Averaged-density case f1, a, m = pp.plot_histogram_predictions(p_av, t_av, radius_bins=False, particle_ids=None, errorbars=False, label=r"$L_\mathrm{box}=50 \, \mathrm{Mpc} \,/ \,h$", color="C0") f11, a1, m1 = pp.plot_histogram_predictions(p_av_big, t_av_big, radius_bins=False, particle_ids=None, errorbars=False, fig=f1, axes=a, color="C1", label=r"$L_\mathrm{box}=200 \, \mathrm{Mpc} \,/ \,h$") a1[0].set_ylabel(r"$n_{\mathrm{particles}}$", fontsize=16) [a.set_xlabel(r"$\log(M_{\mathrm{predicted}}/M_{\mathrm{true}})$", fontsize=16) for a in a1] plt.savefig("/Users/luisals/Documents/Papers/dlhalos_paper/averaged_small_vs_large_box.pdf") # Averaged-density case f1, a, m = pp.plot_histogram_predictions(p_big, t_big, radius_bins=False, particle_ids=None, errorbars=False, label="Raw density", color="C0") f11, a1, m1 = pp.plot_histogram_predictions(p_av_big, t_av_big, radius_bins=False, particle_ids=None, errorbars=False, fig=f1, axes=a, color="C1", label="Averaged density") a1[0].set_ylabel(r"$n_{\mathrm{particles}}$", fontsize=16) [a.set_xlabel(r"$\log(M_{\mathrm{predicted}}/M_{\mathrm{true}})$", fontsize=16) for a in a1] plt.savefig("/Users/luisals/Documents/Papers/dlhalos_paper/raw_vs_averaged_large_box.pdf")

60.461538

126

0.699746

501

3,144

4.105788

0.227545

0.04667

0.043753

0.075839

0.85367

0.797278

0.783666

0.725328

0.659212

0

0.050558

0.144402

3,144

51

127

61.647059

0.714126

0.019084

0

0.333333

0

0.027778

0.422078

0.352922

0

1

0

false

0

0.111111

0

0.111111

0

null

0

1

0

null

0

6

b7ebf597cf4af041d284ceb92dfc3840fcf8cea7

146

py

Python

annuaire/commands/__init__.py

djacomy/layer-annuaire

b0312534e31dd98d98568a83918cf7dd583aa4c7

[ "MIT" ]

null

annuaire/commands/__init__.py

djacomy/layer-annuaire

b0312534e31dd98d98568a83918cf7dd583aa4c7

[ "MIT" ]

null

annuaire/commands/__init__.py

djacomy/layer-annuaire

b0312534e31dd98d98568a83918cf7dd583aa4c7

[ "MIT" ]

null

"""Package groups the different commands modules.""" from annuaire.commands import download, import_lawyers __all__ = [download, import_lawyers]

29.2

54

0.80137

17

146

6.529412

0.705882

0.252252

0.378378

0

0.109589

146

4

55

36.5

0.853846

0.315068

0

1

0

false

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

4d0941aea75adaa006d884337e5c4d550547f131

6,030

py

Python

updates.py

knowledgetechnologyuhh/hipss

518bf3e6a4d02e234cbe29506b9afda0a6ccb187

[ "MIT" ]

null

updates.py

knowledgetechnologyuhh/hipss

518bf3e6a4d02e234cbe29506b9afda0a6ccb187

[ "MIT" ]

null

updates.py

knowledgetechnologyuhh/hipss

518bf3e6a4d02e234cbe29506b9afda0a6ccb187

[ "MIT" ]

null

import torch import numpy as np import torch.nn.functional as F from torch.nn.utils.clip_grad import clip_grad_norm_ from mpi_utils.mpi_utils import sync_grads def update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg): if cfg.automatic_entropy_tuning: alpha_loss = -(log_alpha * (log_pi + target_entropy).detach()).mean() alpha_optim.zero_grad() alpha_loss.backward() alpha_optim.step() alpha = log_alpha.exp() alpha_tlogs = alpha.clone() else: alpha_loss = torch.tensor(0.) alpha_tlogs = torch.tensor(alpha) return alpha_loss, alpha_tlogs def update_flat(actor_network, critic_network, critic_target_network, policy_optim, critic_optim, alpha, log_alpha, target_entropy, alpha_optim, obs_norm, ag_norm, g_norm, obs_next_norm, actions, rewards, cfg): inputs_norm = np.concatenate([obs_norm, ag_norm, g_norm], axis=1) inputs_next_norm = np.concatenate([obs_next_norm, ag_norm, g_norm], axis=1) inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32) inputs_next_norm_tensor = torch.tensor(inputs_next_norm, dtype=torch.float32) actions_tensor = torch.tensor(actions, dtype=torch.float32) r_tensor = torch.tensor(rewards, dtype=torch.float32).reshape(rewards.shape[0], 1) if cfg.cuda: inputs_norm_tensor = inputs_norm_tensor.cuda() inputs_next_norm_tensor = inputs_next_norm_tensor.cuda() actions_tensor = actions_tensor.cuda() r_tensor = r_tensor.cuda() with torch.no_grad(): actions_next, log_pi_next, _ = actor_network.sample(inputs_next_norm_tensor) qf_next_target = critic_target_network(inputs_next_norm_tensor, actions_next) min_qf_next_target = torch.min(qf_next_target, dim=0).values - alpha * log_pi_next next_q_value = r_tensor + cfg.gamma * min_qf_next_target # the q loss qf = critic_network(inputs_norm_tensor, actions_tensor) qf_loss = torch.stack([F.mse_loss(_qf, next_q_value) for _qf in qf]).mean() # the actor loss pi, log_pi, _ = actor_network.sample(inputs_norm_tensor) qf_pi = critic_network(inputs_norm_tensor, pi) min_qf_pi = torch.min(qf_pi, dim=0).values policy_loss = ((alpha * log_pi) - min_qf_pi).mean() # update actor network policy_optim.zero_grad() policy_loss.backward() sync_grads(actor_network) policy_optim.step() # update the critic_network critic_optim.zero_grad() qf_loss.backward() if cfg.clip_grad_norm: clip_grad_norm_(critic_network.parameters(), cfg.max_norm) sync_grads(critic_network) critic_optim.step() alpha_loss, alpha_tlogs = update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg) train_metrics = dict(q_loss=qf_loss.item(), next_q=next_q_value.mean().item(), policy_loss=policy_loss.item(), alpha_loss=alpha_loss.item(), alpha_tlogs=alpha_tlogs.item()) for idx, (_qf, _qtarget) in enumerate(zip(qf, qf_next_target)): train_metrics[f'q_{idx}'] = _qf.mean().item() train_metrics[f'q_target_{idx}'] = _qtarget.mean().item() return train_metrics def update_language(actor_network, critic_network, critic_target_network, policy_optim, critic_optim, alpha, log_alpha, target_entropy, alpha_optim, obs_norm, instruction, obs_next_norm, actions, rewards, cfg): inputs_norm = obs_norm inputs_next_norm = obs_next_norm inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32) inputs_next_norm_tensor = torch.tensor(inputs_next_norm, dtype=torch.float32) actions_tensor = torch.tensor(actions, dtype=torch.float32) r_tensor = torch.tensor(rewards, dtype=torch.float32).reshape(rewards.shape[0], 1) instruction_tensor = torch.tensor(instruction, dtype=torch.long) if cfg.cuda: inputs_norm_tensor = inputs_norm_tensor.cuda() inputs_next_norm_tensor = inputs_next_norm_tensor.cuda() actions_tensor = actions_tensor.cuda() r_tensor = r_tensor.cuda() instruction_tensor = instruction_tensor.cuda() with torch.no_grad(): actions_next, log_pi_next, _ = actor_network.sample(inputs_next_norm_tensor, instruction_tensor) qf_next_target = critic_target_network(inputs_next_norm_tensor, actions_next, instruction_tensor) min_qf_next_target = torch.min(qf_next_target, dim=0).values - alpha * log_pi_next next_q_value = r_tensor + cfg.gamma * min_qf_next_target # the q loss qf = critic_network(inputs_norm_tensor, actions_tensor, instruction_tensor) qf_loss = torch.stack([F.mse_loss(_qf, next_q_value) for _qf in qf]).mean() # the actor loss pi, log_pi, _ = actor_network.sample(inputs_norm_tensor, instruction_tensor) qf_pi = critic_network(inputs_norm_tensor, pi, instruction_tensor) min_qf_pi = torch.min(qf_pi, dim=0).values policy_loss = ((alpha * log_pi) - min_qf_pi).mean() # update actor network policy_optim.zero_grad() policy_loss.backward() sync_grads(actor_network) policy_optim.step() # update the critic_network critic_optim.zero_grad() qf_loss.backward() if cfg.clip_grad_norm: clip_grad_norm_(critic_network.parameters(), cfg.max_norm) sync_grads(critic_network) critic_optim.step() alpha_loss, alpha_tlogs = update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg) train_metrics = dict(q_loss=qf_loss.item(), next_q=next_q_value.mean().item(), policy_loss=policy_loss.item(), alpha_loss=alpha_loss.item(), alpha_tlogs=alpha_tlogs.item()) for idx, (_qf, _qtarget) in enumerate(zip(qf, qf_next_target)): train_metrics[f'q_{idx}'] = _qf.mean().item() train_metrics[f'q_target_{idx}'] = _qtarget.mean().item() return train_metrics

42.167832

119

0.703814

852

6,030

4.579812

0.105634

0.056381

0.050231

0.051256

0.844951

0.839313

0.835725

0.804459

0.784982

0

0.005575

0.196849

6,030

142

120

42.464789

0.800124

0.024046

0

0.654206

0

0.007148

0

1

0.028037

false

0

0.046729

0

0.102804

0

null

0

1

0

null

0

6

4d240f3eb85f0adcecd00489cbe4d3ad31ec57c5

27

py

Python

test.py

justin-th/linux-pasword-protect

feba8712d5bc25c417cb7297aac9c0d23566378e

[ "MIT" ]

null

test.py

justin-th/linux-pasword-protect

feba8712d5bc25c417cb7297aac9c0d23566378e

[ "MIT" ]

null

test.py

justin-th/linux-pasword-protect

feba8712d5bc25c417cb7297aac9c0d23566378e

[ "MIT" ]

null

import os print(os.curdir)

9

16

0.777778

5

27

4.2

0.8

0

0.111111

27

3

16

9

0.875

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

4d50bed8c76e8e60cc01b8081cea63dca711f207

805

py

Python

test/test_vlan_group.py

nrfta/python-netbox-client

68ba6dd4d7306513dc1ad38f3ac59122ba4f70a8

[ "MIT" ]

null

test/test_vlan_group.py

nrfta/python-netbox-client

68ba6dd4d7306513dc1ad38f3ac59122ba4f70a8

[ "MIT" ]

null

test/test_vlan_group.py

nrfta/python-netbox-client

68ba6dd4d7306513dc1ad38f3ac59122ba4f70a8

[ "MIT" ]

null

# coding: utf-8 """ NetBox API API to access NetBox # noqa: E501 OpenAPI spec version: 2.8 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import netbox_client from netbox_client.models.vlan_group import VLANGroup # noqa: E501 from netbox_client.rest import ApiException class TestVLANGroup(unittest.TestCase): """VLANGroup unit test stubs""" def setUp(self): pass def tearDown(self): pass def testVLANGroup(self): """Test VLANGroup""" # FIXME: construct object with mandatory attributes with example values # model = netbox_client.models.vlan_group.VLANGroup() # noqa: E501 pass if __name__ == '__main__': unittest.main()

19.634146

79

0.680745

97

805

5.453608

0.57732

0.090737

0.060491

0.083176

0.102079

0

0.019355

0.229814

805

40

80

20.125

0.833871

0.423602

0

0.214286

1

0

0.018957

0

0.025

0

1

0.214286

false

0.214286

0.357143

0

0.642857

0

null

0

1

0

null

0

1

0

1

0

1

0

1

0

6

4d656673d216ce0be4fe64d21204d4348b38598e

60

py

Python

pyroombaadapter/__init__.py

ymollard/PyRoombaAdapter

a4b63e9b97ac2e27a8b472f596a1111eb3c254b9

[ "MIT" ]

null

pyroombaadapter/__init__.py

ymollard/PyRoombaAdapter

a4b63e9b97ac2e27a8b472f596a1111eb3c254b9

[ "MIT" ]

null

pyroombaadapter/__init__.py

ymollard/PyRoombaAdapter

a4b63e9b97ac2e27a8b472f596a1111eb3c254b9

[ "MIT" ]

null

from pyroombaadapter.pyroombaadapter import PyRoombaAdapter

30

59

0.916667

5

60

11

0.6

0

0.066667

60

1

60

0.982143

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

4ddf8f7618bc1ce4a506f069f1a4aa3da6ef6a1b

22

py

Python

pefile/__init__.py

0x1F9F1/binja-msvc

be2577c22c8d37fd1e2e211f80b1c9a920705bd2

[ "MIT" ]

9

2019-02-08T10:01:39.000Z

2021-04-29T12:27:34.000Z

pefile/__init__.py

DatBrick/binja-msvc

751ffc1450c569bad23ac67a761d0f1fbd4ca4c4

[ "MIT" ]

1

2019-07-04T20:09:57.000Z

2019-07-12T11:10:15.000Z

pefile/__init__.py

DatBrick/binja-msvc

751ffc1450c569bad23ac67a761d0f1fbd4ca4c4

[ "MIT" ]

2

2019-03-03T13:00:14.000Z

2020-05-01T05:35:04.000Z

from .pefile import *

11

21

0.727273

3

22

5.333333

1

0

0.181818

22

1

22

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4dfab55975cccc588661b8464faec98ada96eafa

11,800

py

Python

posthog/test/test_update_person_props.py

csmatar/posthog

4587cfe18625f302726c531f06a32c18e9749e9d

[ "MIT" ]

58

2020-08-26T16:26:18.000Z

2022-03-30T05:32:23.000Z

posthog/test/test_update_person_props.py

csmatar/posthog

4587cfe18625f302726c531f06a32c18e9749e9d

[ "MIT" ]

15

2021-11-09T10:49:34.000Z

2021-11-09T16:11:01.000Z

posthog/test/test_update_person_props.py

csmatar/posthog

4587cfe18625f302726c531f06a32c18e9749e9d

[ "MIT" ]

13

2020-09-08T13:27:07.000Z

2022-03-19T17:27:10.000Z

from datetime import datetime from django.db import connection from posthog.models import Person from posthog.test.base import BaseTest # How we expect this function to behave: # | call | value exists | call TS is ___ existing TS | previous fn | write/override # 1| set | no | N/A | N/A | yes # 2| set_once | no | N/A | N/A | yes # 3| set | yes | before | set | no # 4| set | yes | before | set_once | yes # 5| set | yes | after | set | yes # 6| set | yes | after | set_once | yes # 7| set_once | yes | before | set | no # 8| set_once | yes | before | set_once | yes # 9| set_once | yes | after | set | no # 10| set_once | yes | after | set_once | no # 11| set | yes | equal | set | no # 12| set_once | yes | equal | set | no # 13| set | yes | equal | set_once | yes # 14| set_once | yes | equal | set_once | no FUTURE_TIMESTAMP = datetime(2050, 1, 1, 1, 1, 1).isoformat() PAST_TIMESTAMP = datetime(2000, 1, 1, 1, 1, 1).isoformat() # Refers to migration 0176_update_person_props_function # This is a Postgres function we use in the plugin server class TestShouldUpdatePersonProp(BaseTest): def test_update_without_properties_last_updated_at(self): person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0}, properties_last_updated_at={}, properties_last_operation={"a": "set", "b": "set_once"}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set', 'a', '1'::jsonb)::person_property_update, row('set_once', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # dont update set_once call self.assertEqual(updated_person.properties, {"a": 1, "b": 0}) self.assertEqual(updated_person.properties_last_operation, {"a": "set", "b": "set_once"}) self.assertIsNotNone(updated_person.properties_last_updated_at["a"]) def test_update_without_properties_last_operation(self): person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0}, properties_last_updated_at={"a": FUTURE_TIMESTAMP, "b": FUTURE_TIMESTAMP,}, properties_last_operation={}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set', 'a', '1'::jsonb)::person_property_update, row('set_once', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # dont update set_once call self.assertEqual(updated_person.properties, {"a": 1, "b": 0}) self.assertEqual(updated_person.properties_last_operation, {"a": "set"}) self.assertNotEqual(updated_person.properties_last_updated_at["a"], FUTURE_TIMESTAMP) # tests cases 1 and 2 from the table def test_update_non_existent_prop(self): person = Person.objects.create( team=self.team, properties={}, properties_last_updated_at={}, properties_last_operation={} ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set', 'a', '1'::jsonb)::person_property_update, row('set_once', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # both updated self.assertEqual(updated_person.properties, {"a": 1, "b": 1}) self.assertEqual(updated_person.properties_last_operation, {"a": "set", "b": "set_once"}) self.assertIsNotNone(updated_person.properties_last_updated_at["a"]) self.assertIsNotNone(updated_person.properties_last_updated_at["b"]) # # tests cases 3 and 4 from the table def test_set_operation_with_earlier_timestamp(self): person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0}, properties_last_updated_at={"a": FUTURE_TIMESTAMP, "b": FUTURE_TIMESTAMP,}, properties_last_operation={"a": "set", "b": "set_once"}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set', 'a', '1'::jsonb)::person_property_update, row('set', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # b updated self.assertEqual(updated_person.properties, {"a": 0, "b": 1}) self.assertEqual(updated_person.properties_last_operation, {"a": "set", "b": "set"}) self.assertEqual(updated_person.properties_last_updated_at["a"], FUTURE_TIMESTAMP) self.assertNotEqual(updated_person.properties_last_updated_at["b"], FUTURE_TIMESTAMP) # # tests cases 5 and 6 from the table def test_set_operation_with_older_timestamp(self): person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0}, properties_last_updated_at={"a": PAST_TIMESTAMP, "b": PAST_TIMESTAMP,}, properties_last_operation={"a": "set", "b": "set_once"}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set', 'a', '1'::jsonb)::person_property_update, row('set', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # both updated self.assertEqual(updated_person.properties, {"a": 1, "b": 1}) self.assertEqual(updated_person.properties_last_operation, {"a": "set", "b": "set"}) self.assertNotEqual(updated_person.properties_last_updated_at["a"], PAST_TIMESTAMP) self.assertNotEqual(updated_person.properties_last_updated_at["b"], PAST_TIMESTAMP) # tests cases 7 and 8 from the table def test_set_once_operation_with_earlier_timestamp(self): person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0}, properties_last_updated_at={"a": FUTURE_TIMESTAMP, "b": FUTURE_TIMESTAMP,}, properties_last_operation={"a": "set", "b": "set_once"}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set_once', 'a', '1'::jsonb)::person_property_update, row('set_once', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # b updated self.assertEqual(updated_person.properties, {"a": 0, "b": 1}) self.assertEqual(updated_person.properties_last_operation, {"a": "set", "b": "set_once"}) self.assertEqual(updated_person.properties_last_updated_at["a"], FUTURE_TIMESTAMP) self.assertNotEqual(updated_person.properties_last_updated_at["b"], FUTURE_TIMESTAMP) # tests cases 9 and 10 from the table def test_set_once_operation_with_older_timestamp(self): person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0}, properties_last_updated_at={"a": PAST_TIMESTAMP, "b": PAST_TIMESTAMP,}, properties_last_operation={"a": "set", "b": "set_once"}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, now()::text, array[ row('set_once', 'a', '1'::jsonb)::person_property_update, row('set_once', 'b', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # neither updated self.assertEqual(updated_person.properties, {"a": 0, "b": 0}) self.assertEqual(updated_person.properties_last_operation, {"a": "set", "b": "set_once"}) self.assertEqual(updated_person.properties_last_updated_at["a"], PAST_TIMESTAMP) self.assertEqual(updated_person.properties_last_updated_at["b"], PAST_TIMESTAMP) # # tests cases 11-14 from the table def test_equal_timestamps(self): timestamp = PAST_TIMESTAMP person = Person.objects.create( team=self.team, properties={"a": 0, "b": 0, "c": 0, "d": 0}, properties_last_updated_at={"a": timestamp, "b": timestamp, "c": timestamp, "d": timestamp}, properties_last_operation={"a": "set", "b": "set", "c": "set_once", "d": "set_once"}, ) with connection.cursor() as cursor: cursor.execute( f""" SELECT update_person_props( {person.id}, '{timestamp}', array[ row('set', 'a', '1'::jsonb)::person_property_update, row('set_once', 'b', '1'::jsonb)::person_property_update, row('set', 'c', '1'::jsonb)::person_property_update, row('set_once', 'd', '1'::jsonb)::person_property_update ] ) """ ) updated_person = Person.objects.get(id=person.id) # update if current op is set and last op is set_once i.e. "c" self.assertEqual(updated_person.properties, {"a": 0, "b": 0, "c": 1, "d": 0}) self.assertEqual( updated_person.properties_last_operation, {"a": "set", "b": "set", "c": "set", "d": "set_once"} ) # c changed self.assertEqual(updated_person.properties_last_updated_at["a"], PAST_TIMESTAMP) self.assertEqual(updated_person.properties_last_updated_at["b"], PAST_TIMESTAMP) self.assertEqual(updated_person.properties_last_updated_at["c"], PAST_TIMESTAMP) self.assertEqual(updated_person.properties_last_updated_at["c"], PAST_TIMESTAMP)

42.446043

107

0.527203

1,250

11,800

4.7328

0.0976

0.099391

0.124408

0.097194

0.869337

0.839926

0.816092

0.811021

0.78668

0.745098

0

0.014057

0.348898

11,800

277

108

42.599278

0.755955

0.145678

0

0.674641

0

0.314317

0.06372

0

0.15311

1

0.038278

false

0

0.019139

0

0.062201

0

null

0

1

0

null

0

6

1290da62e7e73de3c4c75ef861a9d5a9bcbe1f4b

2,924

py

Python

tests/test_utils.py

jamesmcclain/pystac

993b54f5a10b0d55db18dbda81c5ad7acc06d921

[ "Apache-2.0" ]

1

2018-08-04T05:24:58.000Z

tests/test_utils.py

jamesmcclain/pystac

993b54f5a10b0d55db18dbda81c5ad7acc06d921

[ "Apache-2.0" ]

4

2017-12-11T22:15:44.000Z

2018-06-15T15:20:34.000Z

tests/test_utils.py

jamesmcclain/pystac

993b54f5a10b0d55db18dbda81c5ad7acc06d921

[ "Apache-2.0" ]

5

2018-06-15T14:51:50.000Z

2019-08-22T05:33:55.000Z

import unittest from pystac.utils import (make_relative_href, make_absolute_href, is_absolute_href) class UtilsTest(unittest.TestCase): def test_make_relative_href(self): # Test cases of (source_href, start_href, expected) test_cases = [ ('/a/b/c/d/catalog.json', '/a/b/c/catalog.json', './d/catalog.json'), ('/a/b/catalog.json', '/a/b/c/catalog.json', '../catalog.json'), ('/a/catalog.json', '/a/b/c/catalog.json', '../../catalog.json'), ('http://stacspec.org/a/b/c/d/catalog.json', 'http://stacspec.org/a/b/c/catalog.json', './d/catalog.json'), ('http://stacspec.org/a/b/catalog.json', 'http://stacspec.org/a/b/c/catalog.json', '../catalog.json'), ('http://stacspec.org/a/catalog.json', 'http://stacspec.org/a/b/c/catalog.json', '../../catalog.json'), ('http://stacspec.org/a/catalog.json', 'http://cogeo.org/a/b/c/catalog.json', 'http://stacspec.org/a/catalog.json'), ('http://stacspec.org/a/catalog.json', 'https://stacspec.org/a/b/c/catalog.json', 'http://stacspec.org/a/catalog.json') ] for source_href, start_href, expected in test_cases: actual = make_relative_href(source_href, start_href) self.assertEqual(actual, expected) def test_make_absolute_href(self): # Test cases of (source_href, start_href, expected) test_cases = [ ('item.json', '/a/b/c/catalog.json', '/a/b/c/item.json'), ('./item.json', '/a/b/c/catalog.json', '/a/b/c/item.json'), ('./z/item.json', '/a/b/c/catalog.json', '/a/b/c/z/item.json'), ('../item.json', '/a/b/c/catalog.json', '/a/b/item.json'), ('item.json', 'https://stacgeo.org/a/b/c/catalog.json', 'https://stacgeo.org/a/b/c/item.json'), ('./item.json', 'https://stacgeo.org/a/b/c/catalog.json', 'https://stacgeo.org/a/b/c/item.json'), ('./z/item.json', 'https://stacgeo.org/a/b/c/catalog.json', 'https://stacgeo.org/a/b/c/z/item.json'), ('../item.json', 'https://stacgeo.org/a/b/c/catalog.json', 'https://stacgeo.org/a/b/item.json') ] for source_href, start_href, expected in test_cases: actual = make_absolute_href(source_href, start_href) self.assertEqual(actual, expected) def test_is_absolute_href(self): # Test cases of (href, expected) test_cases = [('item.json', False), ('./item.json', False), ('../item.json', False), ('/item.json', True), ('http://stacgeo.org/item.json', True)] for href, expected in test_cases: actual = is_absolute_href(href) self.assertEqual(actual, expected)

46.412698

77

0.548906

389

2,924

4.025707

0.105398

0.224777

0.045977

0.102171

0.863346

0.840996

0.784163

0.772031

0.695402

0.66539

0

0.25171

2,924

62

78

47.16129

0.715722

0.04446

0

0.235294

0

0.442294

0.007527

0

0.058824

1

0.058824

false

0

0.039216

0

0.117647

0

null

1

0

1

0

1

0

null

0

6

12df0714eb5fa8ab8f6068ed158fd58746d6bc32

37

py

Python

npd_well_decoder/__init__.py

fmell/npd-well-name-decoder

a44ec28a6ef3b32ba38751eeffff479008b53e2d

[ "MIT" ]

null

npd_well_decoder/__init__.py

fmell/npd-well-name-decoder

a44ec28a6ef3b32ba38751eeffff479008b53e2d

[ "MIT" ]

null

npd_well_decoder/__init__.py

fmell/npd-well-name-decoder

a44ec28a6ef3b32ba38751eeffff479008b53e2d

[ "MIT" ]

null

from .npd import parse_wellbore_name

18.5

36

0.864865

6

37

5

1

0

0.108108

37

1

37

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

12fda5a81fde9ab3c46b39a497e89d5ab29b6639

17,673

py

Python

symbols/block.py

zerofo/sdu-face-alignment

f4b57fde0576d2327369884fd5d5e9a7765a0790

[ "MIT" ]

192

2019-03-27T02:40:41.000Z

2022-03-18T15:35:17.000Z

symbols/block.py

zerofo/sdu-face-alignment

f4b57fde0576d2327369884fd5d5e9a7765a0790

[ "MIT" ]

4

2019-04-01T14:51:22.000Z

2020-11-25T08:22:04.000Z

symbols/block.py

zerofo/sdu-face-alignment

f4b57fde0576d2327369884fd5d5e9a7765a0790

[ "MIT" ]

38

2019-03-30T05:33:48.000Z

2021-10-01T06:08:17.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import mxnet as mx import numpy as np from config import config def Conv(**kwargs): body = mx.sym.Convolution(**kwargs) return body def Act(data, act_type, name): if act_type=='prelu': body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) else: body = mx.symbol.Activation(data=data, act_type=act_type, name=name) return body def ConvFactory(data, num_filter, kernel, stride=(1, 1), pad=(0, 0), act_type="relu", mirror_attr={}, with_act=True, dcn=False, name=''): bn_mom = config.bn_mom workspace = config.workspace if not dcn: conv = mx.symbol.Convolution( data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, workspace=workspace, name=name+'_conv') else: conv_offset = mx.symbol.Convolution(name=name+'_conv_offset', data = data, num_filter=18, pad=(1, 1), kernel=(3, 3), stride=(1, 1)) conv = mx.contrib.symbol.DeformableConvolution(name=name+"_conv", data=data, offset=conv_offset, num_filter=num_filter, pad=(1,1), kernel=(3,3), num_deformable_group=1, stride=stride, dilate=(1, 1), no_bias=False) bn = mx.symbol.BatchNorm(data=conv, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name+'_bn') if with_act: act = Act(bn, act_type, name=name+'_relu') #act = mx.symbol.Activation( # data=bn, act_type=act_type, attr=mirror_attr, name=name+'_relu') return act else: return bn def conv_resnet(data, num_filter, stride, dim_match, name, binarize, dcn, dilate, **kwargs): bit = 1 ACT_BIT = config.ACT_BIT bn_mom = config.bn_mom workspace = config.workspace memonger = config.memonger #print('in unit2') # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') if not binarize: act1 = Act(data=bn1, act_type='relu', name=name + '_relu1') conv1 = Conv(data=act1, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') else: act1 = mx.sym.QActivation(data=bn1, act_bit=ACT_BIT, name=name + '_relu1', backward_only=True) conv1 = mx.sym.QConvolution(data=act1, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1', act_bit=ACT_BIT, weight_bit=bit) bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') if not binarize: act2 = Act(data=bn2, act_type='relu', name=name + '_relu2') conv2 = Conv(data=act2, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') else: act2 = mx.sym.QActivation(data=bn2, act_bit=ACT_BIT, name=name + '_relu2', backward_only=True) conv2 = mx.sym.QConvolution(data=act2, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2', act_bit=ACT_BIT, weight_bit=bit) bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if not binarize: act3 = Act(data=bn3, act_type='relu', name=name + '_relu3') conv3 = Conv(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') else: act3 = mx.sym.QActivation(data=bn3, act_bit=ACT_BIT, name=name + '_relu3', backward_only=True) conv3 = mx.sym.QConvolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3', act_bit=ACT_BIT, weight_bit=bit) #if binarize: # conv3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') if dim_match: shortcut = data else: if not binarize: shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') else: shortcut = mx.sym.QConvolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_sc', act_bit=ACT_BIT, weight_bit=bit) if memonger: shortcut._set_attr(mirror_stage='True') return conv3 + shortcut def conv_hpm(data, num_filter, stride, dim_match, name, binarize, dcn, dilation, **kwargs): bit = 1 ACT_BIT = config.ACT_BIT bn_mom = config.bn_mom workspace = config.workspace memonger = config.memonger #print('in unit2') # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') if not binarize: act1 = Act(data=bn1, act_type='relu', name=name + '_relu1') if not dcn: conv1 = Conv(data=act1, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(dilation,dilation), dilate=(dilation,dilation), no_bias=True, workspace=workspace, name=name + '_conv1') else: conv1_offset = mx.symbol.Convolution(name=name+'_conv1_offset', data = act1, num_filter=18, pad=(1, 1), kernel=(3, 3), stride=(1, 1)) conv1 = mx.contrib.symbol.DeformableConvolution(name=name+'_conv1', data=act1, offset=conv1_offset, num_filter=int(num_filter*0.5), pad=(1,1), kernel=(3, 3), num_deformable_group=1, stride=(1, 1), dilate=(1, 1), no_bias=True) else: act1 = mx.sym.QActivation(data=bn1, act_bit=ACT_BIT, name=name + '_relu1', backward_only=True) conv1 = mx.sym.QConvolution_v1(data=act1, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1', act_bit=ACT_BIT, weight_bit=bit) bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') if not binarize: act2 = Act(data=bn2, act_type='relu', name=name + '_relu2') if not dcn: conv2 = Conv(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(dilation,dilation), dilate=(dilation,dilation), no_bias=True, workspace=workspace, name=name + '_conv2') else: conv2_offset = mx.symbol.Convolution(name=name+'_conv2_offset', data = act2, num_filter=18, pad=(1, 1), kernel=(3, 3), stride=(1, 1)) conv2 = mx.contrib.symbol.DeformableConvolution(name=name+'_conv2', data=act2, offset=conv2_offset, num_filter=int(num_filter*0.25), pad=(1,1), kernel=(3, 3), num_deformable_group=1, stride=(1, 1), dilate=(1, 1), no_bias=True) else: act2 = mx.sym.QActivation(data=bn2, act_bit=ACT_BIT, name=name + '_relu2', backward_only=True) conv2 = mx.sym.QConvolution_v1(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2', act_bit=ACT_BIT, weight_bit=bit) bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if not binarize: act3 = Act(data=bn3, act_type='relu', name=name + '_relu3') if not dcn: conv3 = Conv(data=act3, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(dilation,dilation), dilate=(dilation,dilation), no_bias=True, workspace=workspace, name=name + '_conv3') else: conv3_offset = mx.symbol.Convolution(name=name+'_conv3_offset', data = act3, num_filter=18, pad=(1, 1), kernel=(3, 3), stride=(1, 1)) conv3 = mx.contrib.symbol.DeformableConvolution(name=name+'_conv3', data=act3, offset=conv3_offset, num_filter=int(num_filter*0.25), pad=(1,1), kernel=(3, 3), num_deformable_group=1, stride=(1, 1), dilate=(1, 1), no_bias=True) else: act3 = mx.sym.QActivation(data=bn3, act_bit=ACT_BIT, name=name + '_relu3', backward_only=True) conv3 = mx.sym.QConvolution_v1(data=act3, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv3', act_bit=ACT_BIT, weight_bit=bit) conv4 = mx.symbol.Concat(*[conv1, conv2, conv3]) if binarize: conv4 = mx.sym.BatchNorm(data=conv4, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') if dim_match: shortcut = data else: if not binarize: shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') else: #assert(False) shortcut = mx.sym.QConvolution_v1(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_sc', act_bit=ACT_BIT, weight_bit=bit) shortcut = mx.sym.BatchNorm(data=shortcut, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn') if memonger: shortcut._set_attr(mirror_stage='True') return conv4 + shortcut #return bn4 + shortcut #return act4 + shortcut def block17(net, input_num_channels, scale=1.0, with_act=True, act_type='relu', mirror_attr={}, name=''): tower_conv = ConvFactory(net, 192, (1, 1), name=name+'_conv') tower_conv1_0 = ConvFactory(net, 129, (1, 1), name=name+'_conv1_0') tower_conv1_1 = ConvFactory(tower_conv1_0, 160, (1, 7), pad=(1, 2), name=name+'_conv1_1') tower_conv1_2 = ConvFactory(tower_conv1_1, 192, (7, 1), pad=(2, 1), name=name+'_conv1_2') tower_mixed = mx.symbol.Concat(*[tower_conv, tower_conv1_2]) tower_out = ConvFactory( tower_mixed, input_num_channels, (1, 1), with_act=False, name=name+'_conv_out') net = net+scale * tower_out if with_act: act = mx.symbol.Activation( data=net, act_type=act_type, attr=mirror_attr) return act else: return net def block35(net, input_num_channels, scale=1.0, with_act=True, act_type='relu', mirror_attr={}, name=''): M = 1.0 tower_conv = ConvFactory(net, int(input_num_channels*0.25*M), (1, 1), name=name+'_conv') tower_conv1_0 = ConvFactory(net, int(input_num_channels*0.25*M), (1, 1), name=name+'_conv1_0') tower_conv1_1 = ConvFactory(tower_conv1_0, int(input_num_channels*0.25*M), (3, 3), pad=(1, 1), name=name+'_conv1_1') tower_conv2_0 = ConvFactory(net, int(input_num_channels*0.25*M), (1, 1), name=name+'_conv2_0') tower_conv2_1 = ConvFactory(tower_conv2_0, int(input_num_channels*0.375*M), (3, 3), pad=(1, 1), name=name+'_conv2_1') tower_conv2_2 = ConvFactory(tower_conv2_1, int(input_num_channels*0.5*M), (3, 3), pad=(1, 1), name=name+'_conv2_2') tower_mixed = mx.symbol.Concat(*[tower_conv, tower_conv1_1, tower_conv2_2]) tower_out = ConvFactory( tower_mixed, input_num_channels, (1, 1), with_act=False, name=name+'_conv_out') net = net+scale * tower_out if with_act: act = mx.symbol.Activation( data=net, act_type=act_type, attr=mirror_attr) return act else: return net def conv_inception(data, num_filter, stride, dim_match, name, binarize, dcn, dilate, **kwargs): assert not binarize if stride[0]>1 or not dim_match: return conv_resnet(data, num_filter, stride, dim_match, name, binarize, dcn, dilate, **kwargs) conv4 = block35(data, num_filter, name=name+'_block35') return conv4 def conv_cab(data, num_filter, stride, dim_match, name, binarize, dcn, dilate, **kwargs): workspace = config.workspace if stride[0]>1 or not dim_match: return conv_hpm(data, num_filter, stride, dim_match, name, binarize, dcn, dilate, **kwargs) cab = CAB(data, num_filter, 1, 4, workspace, name, dilate, 1) return cab.get() def conv_block(data, num_filter, stride, dim_match, name, binarize, dcn, dilate): if config.net_block=='resnet': return conv_resnet(data, num_filter, stride, dim_match, name, binarize, dcn, dilate) elif config.net_block=='inception': return conv_inception(data, num_filter, stride, dim_match, name, binarize, dcn, dilate) elif config.net_block=='hpm': return conv_hpm(data, num_filter, stride, dim_match, name, binarize, dcn, dilate) elif config.net_block=='cab': return conv_cab(data, num_filter, stride, dim_match, name, binarize, dcn, dilate) #def lin(data, num_filter, workspace, name, binarize, dcn): # bit = 1 # ACT_BIT = config.ACT_BIT # bn_mom = config.bn_mom # workspace = config.workspace # if not binarize: # if not dcn: # conv1 = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), # no_bias=True, workspace=workspace, name=name + '_conv') # bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn') # act1 = Act(data=bn1, act_type='relu', name=name + '_relu') # return act1 # else: # bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn') # act1 = Act(data=bn1, act_type='relu', name=name + '_relu') # conv1_offset = mx.symbol.Convolution(name=name+'_conv_offset', data = act1, # num_filter=18, pad=(1, 1), kernel=(3, 3), stride=(1, 1)) # conv1 = mx.contrib.symbol.DeformableConvolution(name=name+"_conv", data=act1, offset=conv1_offset, # num_filter=num_filter, pad=(1,1), kernel=(3, 3), num_deformable_group=1, stride=(1, 1), dilate=(1, 1), no_bias=False) # #conv1 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), # # no_bias=False, workspace=workspace, name=name + '_conv') # return conv1 # else: # bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn') # act1 = Act(data=bn1, act_type='relu', name=name + '_relu') # conv1 = mx.sym.QConvolution_v1(data=act1, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), # no_bias=True, workspace=workspace, name=name + '_conv', act_bit=ACT_BIT, weight_bit=bit) # conv1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') # return conv1 def lin3(data, num_filter, workspace, name, k, g=1, d=1): bn_mom = config.bn_mom workspace = config.workspace if k!=3: conv1 = Conv(data=data, num_filter=num_filter, kernel=(k,k), stride=(1,1), pad=((k-1)//2,(k-1)//2), num_group=g, no_bias=True, workspace=workspace, name=name + '_conv') else: conv1 = Conv(data=data, num_filter=num_filter, kernel=(k,k), stride=(1,1), pad=(d,d), num_group=g, dilate=(d, d), no_bias=True, workspace=workspace, name=name + '_conv') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn') act1 = Act(data=bn1, act_type='relu', name=name + '_relu') ret = act1 return ret class CAB: def __init__(self, data, nFilters, nModules, n, workspace, name, dilate, group): self.data = data self.nFilters = nFilters self.nModules = nModules self.n = n self.workspace = workspace self.name = name self.dilate = dilate self.group = group self.sym_map = {} def get_output(self, w, h): key = (w, h) if key in self.sym_map: return self.sym_map[key] ret = None if h==self.n: if w==self.n: ret = (self.data, self.nFilters) else: x = self.get_output(w+1, h) f = int(x[1]*0.5) if w!=self.n-1: body = lin3(x[0], f, self.workspace, "%s_w%d_h%d_1"%(self.name, w, h), 3, self.group, 1) else: body = lin3(x[0], f, self.workspace, "%s_w%d_h%d_1"%(self.name, w, h), 3, self.group, self.dilate) ret = (body,f) else: x = self.get_output(w+1, h+1) y = self.get_output(w, h+1) if h%2==1 and h!=w: xbody = lin3(x[0], x[1], self.workspace, "%s_w%d_h%d_2"%(self.name, w, h), 3, x[1]) #xbody = xbody+x[0] else: xbody = x[0] #xbody = x[0] #xbody = lin3(x[0], x[1], self.workspace, "%s_w%d_h%d_2"%(self.name, w, h), 3, x[1]) if w==0: ybody = lin3(y[0], y[1], self.workspace, "%s_w%d_h%d_3"%(self.name, w, h), 3, self.group) else: ybody = y[0] ybody = mx.sym.concat(y[0], ybody, dim=1) body = mx.sym.add_n(xbody,ybody, name="%s_w%d_h%d_add"%(self.name, w, h)) body = body/2 ret = (body, x[1]) self.sym_map[key] = ret return ret def get(self): return self.get_output(1, 1)[0]

54.378462

148

0.62106

2,661

17,673

3.937242

0.062758

0.06185

0.020617

0.054596

0.806433

0.784576

0.760619

0.737329

0.721199

0.683593

0

0.044666

0.227239

17,673

325

149

54.378462

0.722487

0.139026

0

0.486381

0

0.035333

0

0.003891

1

0.054475

false

0

0.023346

0.003891

0.167315

0.003891

0

null

0

1

0

null

0

6

42500bb71a15c0815810b37eafb946db4fb96b64

3,713

py

Python

Ch2_Linked_Lists/test/test_CTCI_Ch2_Ex6.py

mtrdazzo/CTCI

30a82aed96b05fe21b7d337a138e4ec19950eb9d

[ "MIT" ]

null

Ch2_Linked_Lists/test/test_CTCI_Ch2_Ex6.py

mtrdazzo/CTCI

30a82aed96b05fe21b7d337a138e4ec19950eb9d

[ "MIT" ]

null

Ch2_Linked_Lists/test/test_CTCI_Ch2_Ex6.py

mtrdazzo/CTCI

30a82aed96b05fe21b7d337a138e4ec19950eb9d

[ "MIT" ]

null

from unittest import TestCase from CTCI.Ch2_Linked_Lists.common.SinglyLinkedList import Empty, Node from CTCI.Ch2_Linked_Lists.exercises.CTCI_Ch2_Ex6 import PalindromeSinglyLinkedList, is_palindrome_brute_force from CTCI.Ch2_Linked_Lists.exercises.CTCI_Ch2_Ex6 import is_palindrome_reverse class TestPalindromeSinglyLinkedList(TestCase): def setUp(self): self.pll = PalindromeSinglyLinkedList() def tearDown(self): self.pll = None def test_empty_list(self): with self.assertRaises(Empty): self.pll.is_palindrome() def test_single_element(self): self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) def test_two_elements(self): self.pll.add(1) self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) self.pll.remove(1) self.pll.add(2) self.assertFalse(self.pll.is_palindrome()) def test_more_than_two_elements_even(self): self.pll.add(1) self.pll.add(2) self.pll.add(2) self.pll.add(2) self.assertFalse(self.pll.is_palindrome()) self.pll.remove(2) self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) def test_more_than_two_elements_odd(self): self.pll.add(1) self.pll.add(2) self.pll.add(2) self.assertFalse(self.pll.is_palindrome()) self.pll.remove(2) self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) class TestPalindromeBruteForce(TestCase): def setUp(self): pass def tearDown(self): pass def test_empty_linked_list(self): self.assertIsNone(is_palindrome_brute_force(None)) def test_single_element(self): list = Node(1) self.assertTrue(is_palindrome_brute_force(list)) def test_two_elements(self): list = Node(1) list.next = Node(2) self.assertFalse(is_palindrome_brute_force(list)) list.next = Node(1) self.assertTrue(is_palindrome_brute_force(list)) def test_odd_elements(self): list = Node(1) list.next = Node(2) list.next.next = Node(2) self.assertFalse(is_palindrome_brute_force(list)) list.next.next = Node(1) self.assertTrue(is_palindrome_brute_force(list)) def test_even_elements(self): list = Node(1) list.next = Node(2) list.next.next = Node(2) list.next.next.next = Node(3) self.assertFalse(is_palindrome_brute_force(list)) list.next.next.next = Node(1) self.assertTrue(is_palindrome_brute_force(list)) class TestPalindromeReverse(TestCase): def setUp(self): pass def tearDown(self): pass def test_empty_node(self): self.assertIsNone(is_palindrome_reverse(None)) def test_single_node(self): self.assertTrue(is_palindrome_reverse(Node(1))) def test_two_nodes(self): l_list = Node(1) l_list.next = Node(2) self.assertFalse(is_palindrome_reverse(l_list)) l_list.next = Node(1) self.assertTrue(is_palindrome_reverse(l_list)) def test_odd_nodes(self): l_list = Node(1) l_list.next = Node(2) l_list.next.next = Node(3) self.assertFalse(is_palindrome_reverse(l_list)) l_list.next.next = Node(1) self.assertTrue(is_palindrome_reverse(l_list)) def test_even_nodes(self): l_list = Node(1) l_list.next = Node(2) l_list.next = Node(2) l_list.next = Node(3) self.assertFalse(is_palindrome_reverse(l_list)) l_list.next.next = Node(1) self.assertTrue(is_palindrome_reverse(l_list))

24.919463

110

0.649879

501

3,713

4.580838

0.107784

0.135948

0.056645

0.086275

0.806536

0.732462

0.715468

0.709804

0.697603

0.682353

0

0.01745

0.243738

3,713

148

111

25.087838

0.799858

0

0.67

0

0.24

1

0.21

false

0.04

0

0.28

0

null

0

1

0

1

0

null

0

1

0

6

4259a696e067dbb5b562342c586a116816461462

29

py

Python

src/svr/tests/__init__.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

3

2019-05-04T12:30:00.000Z

2020-05-14T06:28:51.000Z

src/svr/tests/__init__.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

1

2019-05-05T01:30:37.000Z

2019-05-16T02:50:04.000Z

src/svr/tests/__init__.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

1

2020-03-27T07:12:40.000Z

from .test_db import TestDal

14.5

28

0.827586

5

29

4.6

1

0

0.137931

29

1

29

0.92

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

428e0c3390f490eb7e09d675c22baad9bedb5ba6

171

py

Python

nndet/evaluator/detection/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

242

2021-05-17T12:31:39.000Z

2022-03-31T11:51:29.000Z

nndet/evaluator/detection/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

59

2021-06-02T07:32:10.000Z

2022-03-31T18:45:52.000Z

nndet/evaluator/detection/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

38

2021-05-31T14:01:37.000Z

2022-03-21T08:24:40.000Z

from nndet.evaluator.detection.froc import FROCMetric from nndet.evaluator.detection.coco import COCOMetric from nndet.evaluator.detection.hist import PredictionHistogram

42.75

62

0.877193

21

171

7.142857

0.52381

0.18

0.36

0.54

0

0.070175

171

3

63

57

0.943396

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

42a78f723d388f6c17abd15949a96f2a870ca42a

1,933

py

Python

mindhome_alpha/erpnext/stock/doctype/stock_settings/test_stock_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:55:29.000Z

mindhome_alpha/erpnext/stock/doctype/stock_settings/test_stock_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

null

mindhome_alpha/erpnext/stock/doctype/stock_settings/test_stock_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:39:01.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2017, Frappe Technologies Pvt. Ltd. and Contributors # See license.txt from __future__ import unicode_literals import frappe import unittest class TestStockSettings(unittest.TestCase): def setUp(self): frappe.db.set_value("Stock Settings", None, "clean_description_html", 0) def test_settings(self): item = frappe.get_doc(dict( doctype = 'Item', item_code = 'Item for description test', item_group = 'Products', description = 'Drawing No. 07-xxx-PO132 1800 x 1685 x 750 All parts made of Marine Ply Top w/ Corian dd CO, CS, VIP Day Cabin' )).insert() settings = frappe.get_single('Stock Settings') settings.clean_description_html = 1 settings.save() item.reload() self.assertEqual(item.description, 'Drawing No. 07-xxx-PO132 1800 x 1685 x 750 All parts made of Marine Ply Top w/ Corian dd CO, CS, VIP Day Cabin') item.delete() def test_clean_html(self): settings = frappe.get_single('Stock Settings') settings.clean_description_html = 1 settings.save() item = frappe.get_doc(dict( doctype = 'Item', item_code = 'Item for description test', item_group = 'Products', description = 'Drawing No. 07-xxx-PO132 1800 x 1685 x 750 All parts made of Marine Ply Top w/ Corian dd CO, CS, VIP Day Cabin' )).insert() self.assertEqual(item.description, 'Drawing No. 07-xxx-PO132 1800 x 1685 x 750 All parts made of Marine Ply Top w/ Corian dd CO, CS, VIP Day Cabin') item.delete()

42.021739

338

0.698914

311

1,933

4.270096

0.273312

0.067771

0.097892

0.128012

0.787651

0

0.055057

0.135541

1,933

45

339

42.955556

0.739677

0.053802

0

0.709677

0

0.129032

0.566575

0.04274

0

0.064516

1

0.096774

false

0

0.096774

0

0.225806

0

null

0

1

0

1

0

null

0

6

35f85f5cb5fab6226fab7a5a01b0882ca5ca7ca9

54

py

Python

tests/src/import_func.py

bayashi-cl/expander

b3623b656a71801233797e05781295a6101fefd8

[ "CC0-1.0" ]

null

tests/src/import_func.py

bayashi-cl/expander

b3623b656a71801233797e05781295a6101fefd8

[ "CC0-1.0" ]

1

2022-03-12T20:41:21.000Z

2022-03-13T06:34:30.000Z

tests/src/import_func.py

bayashi-cl/expander

b3623b656a71801233797e05781295a6101fefd8

[ "CC0-1.0" ]

null

from testlib_a.main_a import print_name print_name()

13.5

39

0.833333

10

54

4.1

0.7

0.439024

0

0.111111

54

3

40

18

0.854167

0

1

0

true

0

0.5

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

c41f3f30efc1128fe0e35981a452b93b464ce15f

304

py

Python

configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_09_10PottedMeatCan.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

33

2021-12-15T07:11:47.000Z

2022-03-29T08:58:32.000Z

configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_09_10PottedMeatCan.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

3

2021-12-15T11:39:54.000Z

2022-03-29T07:24:23.000Z

configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_09_10PottedMeatCan.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

null

_base_ = "./resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_01_02MasterChefCan.py" OUTPUT_DIR = ( "output/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/09_10PottedMeatCan" ) DATASETS = dict(TRAIN=("ycbv_010_potted_meat_can_train_pbr",))

50.666667

117

0.871711

37

304

6.459459

0.72973

0.200837

0.23431

0.292887

0.527197

0

0.100346

0.049342

304

5

118

60.8

0.726644

0

0.786184

0

1

0

false

0

null

1

0

1

0

1

null

0

6

c441a8d53ebaea6e35e7d68f0992cf2efeee375b

2,429

py

Python

tests/sequence_utils_test.py

rmcolq/genofunk

ffa031fb361fc736e839d0e36d36f8ed7ade30dc

[ "MIT" ]

1

2021-01-09T23:25:02.000Z

tests/sequence_utils_test.py

rmcolq/genofunk

ffa031fb361fc736e839d0e36d36f8ed7ade30dc

[ "MIT" ]

null

tests/sequence_utils_test.py

rmcolq/genofunk

ffa031fb361fc736e839d0e36d36f8ed7ade30dc

[ "MIT" ]

null

import os import unittest import json import filecmp from genofunk.sequence_utils import * this_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) class TestSequenceUtils(unittest.TestCase): def test_get_coordinates_from_json_simple_pairs(self): json_value = { "start": 30, "end": 40, "strand": 1 } coordinates = get_coordinates_from_json(json_value, pairs=True) expected = [[30, 40]] self.assertEqual(expected, coordinates) def test_get_coordinates_from_json_simple_no_pairs(self): json_value = { "start": 30, "end": 40, "strand": 1 } coordinates = get_coordinates_from_json(json_value, pairs=False) expected = [30, 40] self.assertEqual(expected, coordinates) def test_get_coordinates_from_json_join_pairs(self): json_value = { "join": [ { "start": 0, "end": 11, "strand": 1 }, { "start": 10, "end": 20, "strand": 1 } ] } coordinates = get_coordinates_from_json(json_value, pairs=True) expected = [[0,11],[10,20]] self.assertEqual(expected, coordinates) def test_get_coordinates_from_json_join_no_pairs(self): json_value = { "join": [ { "start": 0, "end": 11, "strand": 1 }, { "start": 10, "end": 20, "strand": 1 } ] } coordinates = get_coordinates_from_json(json_value, pairs=False) expected = [0,11,10,20] self.assertEqual(expected, coordinates) def test_is_open_reading_frame_wrong_start(self): amino_acid_sequence = "NATIL*" result = is_open_reading_frame(amino_acid_sequence) self.assertFalse(result) def test_is_open_reading_frame_wrong_end(self): amino_acid_sequence = "MNATIL*S" result = is_open_reading_frame(amino_acid_sequence) self.assertFalse(result) def test_is_open_reading_frame_stop_in_middle(self): amino_acid_sequence = "MNATIL*S*" result = is_open_reading_frame(amino_acid_sequence, allow_stop_codons_in_middle=False) self.assertFalse(result) def test_is_open_reading_frame_stop_in_middle_allowed(self): amino_acid_sequence = "MNATIL*S*" result = is_open_reading_frame(amino_acid_sequence, allow_stop_codons_in_middle=True) self.assertTrue(result)

34.211268

94

0.645533

295

2,429

4.925424

0.20678

0.038541

0.099105

0.121129

0.860977

0.836201

0.788713

0

0.027518

0.251956

2,429

71

95

34.211268

0.772152

0

0.508197

0

0.051029

0

0.131148

1

0.131148

false

0

0.081967

0

0.229508

0

null

0

1

0

null

0

6

674628d16822f8d4efcc764dcb583fc1ae5fb351

86

py

Python

tests/syntax/scripts/annotated_comments.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

tests/syntax/scripts/annotated_comments.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

tests/syntax/scripts/annotated_comments.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

#$ header variable x :: int #$ acc parallel private(idx) #$ omp parallel private(idx)

21.5

28

0.697674

12

86

5

0.75

0.5

0.6

0

0.162791

86

3

29

28.666667

0.833333

0.930233

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

6

674979db2e403ec19a4fc12df3f2a373c9172b77

86

py

Python

OIL/__init__.py

vjdad4m/OIL

a664fe213723fe354796245632f58f31583bcba0

[ "MIT" ]

1

2021-06-22T22:14:16.000Z

OIL/__init__.py

vjdad4m/OIL

a664fe213723fe354796245632f58f31583bcba0

[ "MIT" ]

null

OIL/__init__.py

vjdad4m/OIL

a664fe213723fe354796245632f58f31583bcba0

[ "MIT" ]

null

import OIL.color import OIL.label import OIL.parser import OIL.tools import OIL.errors

17.2

17

0.837209

15

86

4.8

0.466667

0.625

0

0.104651

86

5

18

17.2

0.935065

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