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f7291090bfe14fc6df33c64d5eeb8c89fdfcc10b | 338,371 | py | Python | tests/example_responses.py | vishalbelsare/gradient-cli | c0e06252925cad3ad73d47ded1100f6b0cb0989a | [
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] | 125 | 2019-06-05T16:34:19.000Z | 2022-03-30T18:46:06.000Z | tests/example_responses.py | vishalbelsare/gradient-cli | c0e06252925cad3ad73d47ded1100f6b0cb0989a | [
"0BSD"
] | 11 | 2019-07-16T06:48:55.000Z | 2021-12-15T12:41:51.000Z | LIST_OF_EXPERIMENTS_RESPONSE_JSON = {
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"params": {
"clusterId": "clmtkpnm2",
"command": "python3 hello.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "K80",
"name": "job 2",
"projectId": "pr4yxj956",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr4yxj956/jst4l3hoi0hcyn/hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-06T13:52:48.106674+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-06T13:56:29.890444+00:00",
"dtModified": "2019-03-06T13:52:48.106674+00:00",
"dtProvisioningFinished": "2019-03-06T13:56:24.678388+00:00",
"dtProvisioningStarted": "2019-03-06T13:55:32.325678+00:00",
"dtStarted": "2019-03-06T13:56:24.678388+00:00",
"dtTeardownFinished": "2019-03-06T13:56:30.178571+00:00",
"dtTeardownStarted": "2019-03-06T13:56:30.024471+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 31989,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "esbipxwngbmji4",
"id": 31987,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 31989,
"params": {
"clusterId": "clmtkpnm2",
"command": "python3 hello.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "K80",
"name": "job 1",
"projectId": "pr4yxj956",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr4yxj956/j5cidd1koia24/hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr4yxj956"
},
"totalItems": 12
}
},
{
"data": [
{
"dtCreated": "2019-03-25T14:35:05.738112+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-25T14:35:21.314193+00:00",
"dtModified": "2019-03-25T14:35:05.738112+00:00",
"dtProvisioningFinished": "2019-03-25T14:35:12.777000+00:00",
"dtProvisioningStarted": "2019-03-25T14:35:12.777000+00:00",
"dtStarted": "2019-03-25T14:35:12.777000+00:00",
"dtTeardownFinished": "2019-03-25T14:35:21.314193+00:00",
"dtTeardownStarted": "2019-03-25T14:35:21.314193+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 33353,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "e8lufvr2bh8ld",
"id": 33351,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 7,
"templateHistory": {
"dtCreated": "2019-03-25T14:35:05.738112+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 33353,
"params": {
"command": "nvidia-smi",
"container": "Test-Container",
"isPreemptible": False,
"machineType": "C2",
"projectId": "pr1opq9uz",
"workspaceFileName": "none"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-25T14:20:07.758313+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-25T14:20:07.758313+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": "Expecting value: line 1 column 1 (char 0)",
"experimentTemplateHistoryId": 33352,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "esasysj42i2j2s",
"id": 33350,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 7,
"templateHistory": {
"dtCreated": "2019-03-25T14:20:06.517435+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 33352,
"params": {
"experimentTypeId": 1,
"name": "dsfads",
"ports": 5000,
"project_handle": "pr1opq9uz",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "C2"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-15T11:46:32.978544+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-15T11:46:50.541797+00:00",
"dtModified": "2019-03-15T11:46:32.978544+00:00",
"dtProvisioningFinished": "2019-03-15T11:46:45.501290+00:00",
"dtProvisioningStarted": "2019-03-15T11:46:41.431717+00:00",
"dtStarted": "2019-03-15T11:46:45.501290+00:00",
"dtTeardownFinished": "2019-03-15T11:46:50.598748+00:00",
"dtTeardownStarted": "2019-03-15T11:46:50.561267+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 32879,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "estgm33hfbqf5k",
"id": 32877,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 32879,
"params": {
"clusterId": "cls28l0qm",
"command": "python3 myscript.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "P5000",
"name": "job 4",
"projectId": "pr1opq9uz",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/j8bxv3ni2zc17/myscript.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-15T11:46:32.513024+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-15T11:46:50.363855+00:00",
"dtModified": "2019-03-15T11:46:32.513024+00:00",
"dtProvisioningFinished": "2019-03-15T11:46:45.324783+00:00",
"dtProvisioningStarted": "2019-03-15T11:46:40.947880+00:00",
"dtStarted": "2019-03-15T11:46:45.324783+00:00",
"dtTeardownFinished": "2019-03-15T11:46:50.419813+00:00",
"dtTeardownStarted": "2019-03-15T11:46:50.383192+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 32878,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "esbo3pisqq3t68",
"id": 32876,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 32878,
"params": {
"clusterId": "cls28l0qm",
"command": "python3 myscript.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "P5000",
"name": "job 3",
"projectId": "pr1opq9uz",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/jpk82qnq71w9c/myscript.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-15T11:46:16.422317+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-15T11:46:30.820071+00:00",
"dtModified": "2019-03-15T11:46:16.422317+00:00",
"dtProvisioningFinished": "2019-03-15T11:46:25.776927+00:00",
"dtProvisioningStarted": "2019-03-15T11:46:21.616491+00:00",
"dtStarted": "2019-03-15T11:46:25.776927+00:00",
"dtTeardownFinished": "2019-03-15T11:46:30.873777+00:00",
"dtTeardownStarted": "2019-03-15T11:46:30.839238+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 32877,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "eso8s7e014681",
"id": 32875,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 32877,
"params": {
"clusterId": "cls28l0qm",
"command": "python3 test_remote.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "P5000",
"name": "job 2",
"projectId": "pr1opq9uz",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/jsuqgzne50tlg1/test_remote.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-15T11:26:14.607276+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-15T11:26:32.872835+00:00",
"dtModified": "2019-03-15T11:26:14.607276+00:00",
"dtProvisioningFinished": "2019-03-15T11:26:27.823314+00:00",
"dtProvisioningStarted": "2019-03-15T11:26:23.367983+00:00",
"dtStarted": "2019-03-15T11:26:27.823314+00:00",
"dtTeardownFinished": "2019-03-15T11:26:32.930538+00:00",
"dtTeardownStarted": "2019-03-15T11:26:32.897522+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 32876,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "esml2ioetny5o6",
"id": 32874,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 32876,
"params": {
"clusterId": "cls28l0qm",
"command": "python3 myscript.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "P5000",
"name": "job 1",
"projectId": "pr1opq9uz",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/jsnze3m7s2xlpr/myscript.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr1opq9uz"
},
"totalItems": 6
}
}
],
"message": "success",
"meta": {
"filter": [],
"limit": 1000000,
"offset": 0,
"totalItems": 18
}
}
LIST_OF_EXPERIMENTS_FILTERED_WITH_TWO_PROJECTS_STDOUT = """+--------+----------------+---------+
| Name | ID | Status |
+--------+----------------+---------+
| dsfads | esj8mcpaayh5kx | failed |
| dsfads | estun7jhqta8sm | failed |
| dsfads | es08mwh7ye6zpm | failed |
| None | eshdufkt9ti8mx | stopped |
| None | e07h0ym8fewm | stopped |
| None | e0fq31vyla039 | stopped |
| None | esxz3ihfl9qxs2 | stopped |
| job 5 | ecqq0rz0gok9i | stopped |
| job 4 | es310784c1o1x6 | stopped |
| job 3 | esabwtehoekn6w | stopped |
| job 2 | ea1d6t082wq7i | stopped |
| job 1 | esbipxwngbmji4 | stopped |
| None | e8lufvr2bh8ld | failed |
| dsfads | esasysj42i2j2s | failed |
| job 4 | estgm33hfbqf5k | stopped |
| job 3 | esbo3pisqq3t68 | stopped |
| job 2 | eso8s7e014681 | stopped |
| job 1 | esml2ioetny5o6 | stopped |
+--------+----------------+---------+
"""
LIST_OF_EXPERIMENTS_FILTERED_BUT_NONE_FOUND = {
"data": [
{
"data": [],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr4yxj95"
},
"totalItems": 0
}
},
{
"data": [],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr1opq9u"
},
"totalItems": 0
}
}
],
"message": "success",
"meta": {
"filter": [],
"limit": 1000000,
"offset": 0,
"totalItems": 0
}
}
UPDATE_TAGS_RESPONSE = [
{
"tagId": 1,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.506Z",
"dtDeleted": None,
"userId": 1,
"id": 1,
"entity_id": 3,
"tag": {
"name": "test0",
"dtCreated": "2020-02-01T12:46:58.480Z",
"id": 1
}
},
{
"tagId": 2,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.507Z",
"dtDeleted": None,
"userId": 1,
"id": 2,
"entity_id": 3,
"tag": {
"name": "test2",
"dtCreated": "2020-02-01T12:46:58.481Z",
"id": 2
}
},
{
"tagId": 3,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.509Z",
"dtDeleted": None,
"userId": 1,
"id": 3,
"entity_id": 3,
"tag": {
"name": "test1",
"dtCreated": "2020-02-01T12:46:58.482Z",
"id": 3
}
},
{
"tagId": 4,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.510Z",
"dtDeleted": None,
"userId": 1,
"id": 4,
"entity_id": 3,
"tag": {
"name": "test3",
"dtCreated": "2020-02-01T12:46:58.483Z",
"id": 4
}
}
]
GET_TAGS_RESPONSE = {
"some_id": [
{
"tagId": 1,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.506Z",
"dtDeleted": None,
"userId": 1,
"id": 1,
"entity_id": 3,
"tag": {
"name": "test0",
"dtCreated": "2020-02-01T12:46:58.480Z",
"id": 1
}
},
{
"tagId": 2,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.507Z",
"dtDeleted": None,
"userId": 1,
"id": 2,
"entity_id": 3,
"tag": {
"name": "test2",
"dtCreated": "2020-02-01T12:46:58.481Z",
"id": 2
}
},
{
"tagId": 3,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.509Z",
"dtDeleted": None,
"userId": 1,
"id": 3,
"entity_id": 3,
"tag": {
"name": "test1",
"dtCreated": "2020-02-01T12:46:58.482Z",
"id": 3
}
},
{
"tagId": 4,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.510Z",
"dtDeleted": None,
"userId": 1,
"id": 4,
"entity_id": 3,
"tag": {
"name": "test3",
"dtCreated": "2020-02-01T12:46:58.483Z",
"id": 4
}
}
]
}
CREATE_DEPLOYMENT_WITH_BASIC_OPTIONS_RESPONSE = {
"deployment": {
"id": "sadkfhlskdjh",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "batman.bin.supraman@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://www.latlmes.com/breaking/paperspace-now-has-a-100-bilion-valuation",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "some_oauth_secret",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desanw1jptk7woh:predict",
"ports": None,
"dtCreated": "2019-04-04T10:27:22.659Z",
"dtModified": "2019-04-04T10:27:22.659Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": None,
"dtDeleted": None,
"isDeleted": False
}
}
LIST_DEPLOYMENTS = {
"deploymentList": [
{
"id": "dev61ity7lx232",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": "some_tag",
"params": None,
"code": "some_code",
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": "some_username",
"imagePassword": "some_password",
"workspaceUrl": "https://www.example.com",
"workspaceUsername": "some_username",
"workspacePassword": "some_password",
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "some_auth_token",
"annotations": {},
"oauthKey": "some_oauth_key",
"oauthSecret": "some_oauth_secret",
"serviceType": "some_service_type",
"apiType": "some_api_type",
"endpoint": "https://development-services.paperspace.io/model-serving/dev61ity7lx232:predict",
"ports": "5000",
"dtCreated": "2019-04-04T10:53:56.665Z",
"dtModified": "2019-04-04T10:53:56.729Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:53:56.711Z",
"dtDeleted": None,
"isDeleted": False,
"metricsURL": "aws-testing.paperspace.io",
},
{
"id": "desanw1jptk7woh",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desanw1jptk7woh:predict",
"ports": None,
"dtCreated": "2019-04-04T10:27:22.659Z",
"dtModified": "2019-04-04T10:27:22.726Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:27:22.710Z",
"dtDeleted": None,
"isDeleted": False
},
{
"id": "desfnnrqt1v633v",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desfnnrqt1v633v:predict",
"ports": None,
"dtCreated": "2019-04-04T10:27:10.180Z",
"dtModified": "2019-04-04T10:27:10.248Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:27:10.230Z",
"dtDeleted": None,
"isDeleted": False
},
{
"id": "desdyn55d2e02su",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desdyn55d2e02su:predict",
"ports": None,
"dtCreated": "2019-04-04T10:11:10.204Z",
"dtModified": "2019-04-04T10:11:10.281Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:11:10.260Z",
"dtDeleted": None,
"isDeleted": False
},
{
"id": "des3tmqa3s627o9",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/des3tmqa3s627o9:predict",
"ports": None,
"dtCreated": "2019-04-04T09:29:22.277Z",
"dtModified": "2019-04-04T09:29:22.345Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T09:29:22.327Z",
"dtDeleted": None,
"isDeleted": False
}
],
"total": 5,
"displayTotal": 5,
"runningTotal": 0
}
CREATE_MACHINE_RESPONSE = {
"id": "psclbvqpc",
"name": "some_machine",
"os": None,
"ram": None,
"cpus": 1,
"gpu": None,
"storageTotal": None,
"storageUsed": None,
"usageRate": "Pro hourly",
"shutdownTimeoutInHours": 168,
"shutdownTimeoutForces": False,
"performAutoSnapshot": False,
"autoSnapshotFrequency": None,
"autoSnapshotSaveCount": None,
"dynamicPublicIp": False,
"agentType": "WindowsDesktop",
"dtCreated": "2019-04-10T14:19:49.852Z",
"state": "provisioning",
"updatesPending": False,
"networkId": None,
"privateIpAddress": None,
"publicIpAddress": None,
"region": None,
"userId": "u3z4be26",
"teamId": "te6hh34n6",
"scriptId": None,
"dtLastRun": None
}
LIST_MACHINES_RESPONSE = [
{
"ram": None,
"userId": "u3z4be26",
"cpus": 1,
"teamId": "te6hh34n6",
"updatesPending": False,
"dynamicPublicIp": False,
"storageTotal": None,
"shutdownTimeoutForces": False,
"id": "psclbvqpc",
"shutdownTimeoutInHours": 168,
"state": "provisioning",
"usageRate": "Pro hourly",
"publicIpAddress": None,
"gp": None,
"privateIpAddress": None,
"dtCreated": "2019-04-10T14:19:49.852Z",
"dtLastRun": None,
"storageUsed": None,
"scriptId": None,
"autoSnapshotSaveCount": None,
"name": "keton2",
"agentType": "WindowsDesktop",
"performAutoSnapshot": False,
"networkId": None,
"autoSnapshotFrequency": None,
"os": None, "region": None
},
{
"ram": "536870912",
"userId": "u3z4be26",
"cpus": 1,
"teamId": "te6hh34n6",
"updatesPending": False,
"dynamicPublicIp": False,
"storageTotal": "53687091200",
"shutdownTimeoutForces": False,
"id": "psbtuwfvt",
"shutdownTimeoutInHours": 1,
"state": "off",
"usageRate": "C1 Hourly",
"publicIpAddress": None,
"gp": "None",
"privateIpAddress": "10.64.14.135",
"dtCreated": "2019-04-11T18:10:29.665Z",
"dtLastRun": None,
"storageUsed": "110080",
"scriptId": None,
"autoSnapshotSaveCount": 1,
"name": "New Machine 1",
"agentType": "LinuxHeadless",
"performAutoSnapshot": True,
"networkId": "nng82wb",
"autoSnapshotFrequency": "month",
"os": "Ubuntu 18.04.1 LTS; uname: 4.15.0-38-generic; distro: ubuntu; major: 18; minor: 04",
"region": "East Coast (NY2)"
}
]
SHOW_MACHINE_RESPONSE = {
"id": "some_id",
"name": "New Machine 1",
"os": "Ubuntu 18.04.1 LTS; uname: 4.15.0-38-generic; distro: ubuntu; major: 18; minor: 04",
"ram": "536870912",
"cpus": 1,
"gpu": "None",
"storageTotal": "53687091200",
"storageUsed": "110080",
"usageRate": "C1 Hourly",
"shutdownTimeoutInHours": 1,
"shutdownTimeoutForces": False,
"performAutoSnapshot": True,
"autoSnapshotFrequency": "month",
"autoSnapshotSaveCount": 1,
"dynamicPublicIp": False,
"agentType": "LinuxHeadless",
"dtCreated": "2019-04-11T18:10:29.665Z",
"state": "off",
"updatesPending": False,
"networkId": "nng82wb",
"privateIpAddress": "10.64.14.135",
"publicIpAddress": None,
"region": "East Coast (NY2)",
"userId": "u3z4be26",
"teamId": "te6hh34n6",
"scriptId": None,
"dtLastRun": None,
"events": [
{
"name": "restart",
"state": "done",
"errorMsg": "",
"handle": "042454c4-af4c-489a-8bff-b4d3e9f39f9e",
"dtModified": "2019-04-12T12:19:05.801Z",
"dtFinished": "2019-04-12T12:19:05.801Z",
"dtCreated": "2019-04-12T12:19:03.814Z"
},
{
"name": "stop",
"state": "done",
"errorMsg": "",
"handle": "07fe20ce-35c4-4949-afbd-103d1afc6797",
"dtModified": "2019-04-11T19:11:19.942Z",
"dtFinished": "2019-04-11T19:11:19.942Z",
"dtCreated": "2019-04-11T19:11:14.205Z"
},
{
"name": "snapshot-create",
"state": "done",
"errorMsg": "",
"handle": "7fa0ab4f-896d-42ac-a2c5-54f0a94c2310",
"dtModified": "2019-04-11T18:14:36.702Z",
"dtFinished": "2019-04-11T18:14:36.702Z",
"dtCreated": "2019-04-11T18:14:33.216Z"
},
{
"name": "start",
"state": "done",
"errorMsg": "",
"handle": "c04cccdf-3823-4ef1-b1ab-83b3ccdcfcb7",
"dtModified": "2019-04-11T18:11:00.371Z",
"dtFinished": "2019-04-11T18:11:00.371Z",
"dtCreated": "2019-04-11T18:10:45.468Z"
},
{
"name": "create",
"state": "done",
"errorMsg": "",
"handle": "34f64d24-1161-42f0-871d-1ed03b10dfc0",
"dtModified": "2019-04-11T18:10:45.473Z",
"dtFinished": "2019-04-11T18:10:45.473Z",
"dtCreated": "2019-04-11T18:10:29.665Z"
}
]
}
SHOW_MACHINE_UTILIZATION_RESPONSE = {
"machineId": "some_key",
"utilization": {
"machineId": "some_key",
"secondsUsed": 0,
"billingMonth": "2017-09",
"hourlyRate": 0,
},
"storageUtilization": {
"machineId": "some_key",
"secondsUsed": 256798.902394,
"monthlyRate": "5.00",
"billingMonth": "2017-09",
},
}
LIST_PROJECTS_RESPONSE = {
"data": [
{
"name": "test_project",
"handle": "prq70zy79",
"dtCreated": "2019-03-18T13:24:46.666Z",
"dtDeleted": None,
"lastJobSeqNum": 2,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"experiments": {
"data": [
{
"dtCreated": "2019-04-05T15:10:55.692629+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-04-05T15:10:55.692629+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 22159,
"experimentTemplateId": 60,
"experimentTypeId": 1,
"handle": "estgcoux8igx32",
"id": 22123,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1655,
"state": 1,
"templateHistory": {
"dtCreated": "2019-04-05T15:10:54.923725+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 22159,
"params": {
"is_preemptible": False,
"name": "dsfads",
"ports": 5000,
"project_handle": "prq70zy79",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "C2",
"worker_use_dockerfile": False,
"workspaceUrl": "example.com"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "prq70zy79"
},
"totalItems": 1
}
}
},
{
"name": "keton",
"handle": "prmr22ve0",
"dtCreated": "2019-03-25T14:50:43.202Z",
"dtDeleted": None,
"lastJobSeqNum": 8,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"experiments": {
"data": [
{
"dtCreated": "2019-04-02T15:17:03.393886+00:00",
"dtDeleted": None,
"dtFinished": "2019-04-02T17:02:54.654569+00:00",
"dtModified": "2019-04-02T15:17:03.393886+00:00",
"dtProvisioningFinished": "2019-04-02T15:17:10.978198+00:00",
"dtProvisioningStarted": "2019-04-02T15:17:10.978198+00:00",
"dtStarted": "2019-04-02T15:17:10.978198+00:00",
"dtTeardownFinished": "2019-04-02T17:02:54.654569+00:00",
"dtTeardownStarted": "2019-04-02T17:02:54.654569+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 22130,
"experimentTemplateId": 174,
"experimentTypeId": 1,
"handle": "ehla1kvbwzaco",
"id": 22094,
"projectHandle": "prmr22ve0",
"projectId": 626,
"started_by_user_id": 1655,
"state": 5,
"templateHistory": {
"dtCreated": "2019-04-02T15:17:02.663449+00:00",
"dtDeleted": None,
"experimentTemplateId": 174,
"id": 22130,
"params": {
"is_preemptible": False,
"model_path": "/artifacts",
"model_type": "Tensorflow",
"name": "Test1",
"ports": 5000,
"project_handle": "prmr22ve0",
"worker_command": "python mnist.py --data_format=channels_last",
"worker_container": "tensorflow/tensorflow:1.13.1-py3",
"worker_machine_type": "K80",
"workspaceUrl": "https://github.com/Paperspace/mnist-sample"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "prmr22ve0"
},
"totalItems": 1
}
}
},
{
"name": "paperspace-python",
"handle": "przhbct98",
"dtCreated": "2019-04-04T15:12:34.229Z",
"dtDeleted": None,
"lastJobSeqNum": 3,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"experiments": {
"data": [
{
"dtCreated": "2019-04-24T10:18:30.523193+00:00",
"dtDeleted": None,
"dtFinished": "2019-04-24T10:18:43.613748+00:00",
"dtModified": "2019-04-24T10:18:30.523193+00:00",
"dtProvisioningFinished": "2019-04-24T10:18:35.010792+00:00",
"dtProvisioningStarted": "2019-04-24T10:18:35.010792+00:00",
"dtStarted": "2019-04-24T10:18:35.010792+00:00",
"dtTeardownFinished": "2019-04-24T10:18:43.613748+00:00",
"dtTeardownStarted": "2019-04-24T10:18:43.613748+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 22311,
"experimentTemplateId": 186,
"experimentTypeId": 1,
"handle": "es47og38wzhnuo",
"id": 22270,
"projectHandle": "przhbct98",
"projectId": 649,
"started_by_user_id": 1655,
"state": 7,
"templateHistory": {
"dtCreated": "2019-04-24T10:18:30.523193+00:00",
"dtDeleted": None,
"experimentTemplateId": 186,
"id": 22311,
"params": {
"command": ". test.sh\npython2 hello.py",
"container": "paperspace/tensorflow-python",
"machineType": "G1",
"project": "paperspace-python",
"workspaceFileName": "temp.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "przhbct98"
},
"totalItems": 1
}
}
}
],
"meta": {
"totalItems": 3
}
}
LIST_JOBS_RESPONSE_JSON = {
"jobList": [
{
"id": "jsxeeba5qq99yn",
"name": "job 1",
"state": "Error",
"workspaceUrl": "none",
"workingDirectory": "/paperspace",
"artifactsDirectory": "/artifacts",
"entrypoint": "nvidia-smi",
"projectId": "prmr22ve0",
"project": "keton",
"container": "Test-Container",
"containerUrl": "paperspace/tensorflow:1.5.0-gpu",
"baseContainer": "Test-Container",
"baseContainerUrl": "paperspace/tensorflow:1.5.0-gpu",
"machineType": "K80",
"cluster": "PS Jobs on GCP",
"clusterId": "clkyczmyz",
"usageRate": "K80 hourly",
"startedByUserId": "ukgvw4i8",
"parentJobId": None,
"jobError": "Error quering for experiment for job: sql: Scan error on column index 4, name \"model_path\": unsupported Scan, storing driver.Value type <nil> into type *string",
"dtCreated": "2019-03-25T14:51:16.118Z",
"dtModified": "2019-03-25T14:51:16.118Z",
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-03-27T13:53:34.188Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"exitCode": None,
"queuePosition": None,
"seqNum": 1,
"storageRegion": "GCP West",
"clusterMachine": "gradient-host-1553525480",
"fqdn": "jsxeeba5qq99yn.dgradient.paperspace.com",
"ports": None,
"isPublic": None,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": None,
"cpuCount": None,
"cpuModel": None,
"cpuFlags": None,
"cpuMem": None,
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"targetNodeAttrs": None,
"jobEnv": None,
"sharedMemMBytes": None,
"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "aws-testing.paperspace.io",
"customMetrics": None,
"experimentId": "esk8lny3pxyqd6",
"tags": [
"tag1",
"tag2",
],
},
{
"id": "jfl063dsv634h",
"name": "job 2",
"state": "Stopped",
"workspaceUrl": "none",
"workingDirectory": "/paperspace",
"artifactsDirectory": "/artifacts",
"entrypoint": "nvidia-smi",
"projectId": "prmr22ve0",
"project": "keton",
"container": "Test-Container",
"containerUrl": "paperspace/tensorflow:1.5.0-gpu",
"baseContainer": "Test-Container",
"baseContainerUrl": "paperspace/tensorflow:1.5.0-gpu",
"machineType": "P100",
"cluster": "PS Jobs on GCP",
"clusterId": "clkyczmyz",
"usageRate": "P100 hourly",
"startedByUserId": "ukgvw4i8",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-03-25T14:54:30.866Z",
"dtModified": "2019-03-25T14:54:30.866Z",
"dtProvisioningStarted": "2019-03-25T14:59:15.818Z",
"dtProvisioningFinished": "2019-03-25T14:59:20.542Z",
"dtStarted": "2019-03-25T14:59:20.542Z",
"dtFinished": "2019-03-25T14:59:25.631Z",
"dtTeardownStarted": "2019-03-25T14:59:25.669Z",
"dtTeardownFinished": "2019-03-25T14:59:25.758Z",
"dtDeleted": None,
"exitCode": 0,
"queuePosition": None,
"seqNum": 2,
"storageRegion": "GCP West",
"clusterMachine": "gradient-host-1553525723",
"fqdn": "jfl063dsv634h.dgradient.paperspace.com",
"ports": None,
"isPublic": None,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": "gradient-host-1553525723",
"cpuCount": 4,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat arch_capabilities",
"cpuMem": "24683148 kB",
"gpuName": "Tesla P100-PCIE-16GB",
"gpuSerial": "0324317067114",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "410.48",
"gpuCount": 1,
"gpuMem": "16280 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"targetNodeAttrs": None,
"jobEnv": None,
"sharedMemMBytes": None,
"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "metrics-gcp-dev.paperspace.io",
"customMetrics": None,
"experimentId": "ejd2v80p7cw6m"
},
{
"id": "jsvau8w47k78zm",
"name": "Clone - jfl063dsv634h",
"state": "Stopped",
"workspaceUrl": "none",
"workingDirectory": "/paperspace",
"artifactsDirectory": "/artifacts",
"entrypoint": "nvidia-smi",
"projectId": "prmr22ve0",
"project": "keton",
"container": "Test-Container",
"containerUrl": "paperspace/tensorflow:1.5.0-gpu",
"baseContainer": "Test-Container",
"baseContainerUrl": "paperspace/tensorflow:1.5.0-gpu",
"machineType": "P100",
"cluster": "PS Jobs on GCP",
"clusterId": "clkyczmyz",
"usageRate": "P100 hourly",
"startedByUserId": "ukgvw4i8",
"parentJobId": "jfl063dsv634h",
"jobError": None,
"dtCreated": "2019-03-25T15:04:43.844Z",
"dtModified": "2019-03-25T15:04:43.844Z",
"dtProvisioningStarted": "2019-03-25T15:07:43.854Z",
"dtProvisioningFinished": "2019-03-25T15:07:48.435Z",
"dtStarted": "2019-03-25T15:07:48.435Z",
"dtFinished": "2019-03-25T15:07:53.523Z",
"dtTeardownStarted": "2019-03-25T15:07:53.561Z",
"dtTeardownFinished": "2019-03-25T15:07:53.649Z",
"dtDeleted": None,
"exitCode": 0,
"queuePosition": None,
"seqNum": 3,
"storageRegion": "GCP West",
"clusterMachine": "gradient-host-1553526309",
"fqdn": "jsvau8w47k78zm.dgradient.paperspace.com",
"ports": None,
"isPublic": None,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": "gradient-host-1553526309",
"cpuCount": 4,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
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"gpuCount": 1,
"gpuMem": "16280 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"targetNodeAttrs": None,
"jobEnv": None,
"sharedMemMBytes": None,
"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "metrics-gcp-dev.paperspace.io",
"customMetrics": None,
"experimentId": None
},
{
"id": "j2eq99xhvgtum",
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"entrypoint": "echo keton",
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"dtTeardownStarted": None,
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"dtDeleted": None,
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"hasCode": None,
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"codeCommit": None,
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"gpuDriver": None,
"gpuCount": None,
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"tpuType": None,
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"tpuDatasetDir": None,
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"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "metrics-gcp-dev.paperspace.io",
"customMetrics": None,
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},
{
"id": "jzzinybinuxf9",
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"usageRate": "P100 hourly",
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"dtCreated": "2019-03-25T15:18:51.461Z",
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"seqNum": 5,
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"isPublic": False,
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"hasCode": None,
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"runTillCancelled": None,
"pushOnCompletion": None,
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"cpuHostname": "gradient-host-1553526309",
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"jobEnv": None,
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"shutdownTimeout": None,
"isPreemptible": False,
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"customMetrics": None,
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},
{
"id": "jsb37duc1zlbz0",
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"dtCreated": "2019-03-25T15:29:04.601Z",
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"cpuHostname": "gradient-host-1553526309",
"cpuCount": 4,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
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{
"id": "jq41vipwy18f7",
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"dtCreated": "2019-03-25T15:29:06.765Z",
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"containerUser": None,
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"cpuHostname": "gradient-host-1553526384",
"cpuCount": 4,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
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"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "metrics-gcp-dev.paperspace.io",
"customMetrics": None,
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},
{
"id": "jsigkjnyb6m3qm",
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"dtCreated": "2019-04-02T15:17:05.618Z",
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"ports": "5000:5000",
"isPublic": False,
"containerUser": None,
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"metricsURL": "metrics-gcp-dev.paperspace.io",
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},
{
"id": "j4g76vuppxqao",
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"dtCreated": "2019-04-04T15:12:34.414Z",
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"dtDeleted": None,
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"seqNum": 1,
"storageRegion": "GCP West",
"clusterMachine": "gradient-host-1553793082-82415ed3",
"fqdn": "j4g76vuppxqao.dgradient.paperspace.com",
"ports": None,
"isPublic": None,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": "gradient-host-1553793082-82415ed3",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat arch_capabilities",
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"metricsURL": "metrics-gcp-dev.paperspace.io",
"customMetrics": None,
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},
{
"id": "jsbnvdhwb46vr9",
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"dtCreated": "2019-04-24T09:09:53.645Z",
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"dtDeleted": None,
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"storageRegion": "GCP West",
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"fqdn": "jsbnvdhwb46vr9.dgradient.paperspace.com",
"ports": None,
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"hasCode": None,
"codeUploaded": None,
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"runTillCancelled": None,
"pushOnCompletion": None,
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"cpuHostname": "gradient-host-1556074006",
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"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
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"cpuMem": "1783384 kB",
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},
{
"id": "jt8alwzv28kha",
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"triggerEventId": None
}
},
"message": "success"
}
LIST_OF_LOGS_FOR_EXPERIMENT = [
{
"jobId": "jsy2ibsz1l026y",
"line": 1,
"timestamp": "2019-07-08T12:40:59.139Z",
"message": "2019-07-08 12:40:59.139494: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 2,
"timestamp": "2019-07-08T12:40:59.271Z",
"message": "2019-07-08 12:40:59.270783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 3,
"timestamp": "2019-07-08T12:40:59.271Z",
"message": "2019-07-08 12:40:59.271379: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5642a00 executing computations on platform CUDA. Devices:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 4,
"timestamp": "2019-07-08T12:40:59.271Z",
"message": "2019-07-08 12:40:59.271427: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): Tesla K80, Compute Capability 3.7"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 5,
"timestamp": "2019-07-08T12:40:59.274Z",
"message": "2019-07-08 12:40:59.274589: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 6,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.274952: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56aba20 executing computations on platform Host. Devices:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 7,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.274983: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): \u003cundefined\u003e, \u003cundefined\u003e"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 8,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.275358: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 9,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 10,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "pciBusID: 0000:00:04.0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 11,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "totalMemory: 11.17GiB freeMemory: 11.09GiB"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 12,
"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.275392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 13,
"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276439: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 14,
"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 15,
"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276483: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 16,
"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 17,
"timestamp": "2019-07-08T12:40:59.28Z",
"message": "WARNING: Logging before flag parsing goes to stderr."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 18,
"timestamp": "2019-07-08T12:40:59.28Z",
"message": "I0708 12:40:59.280403 140451862599424 mnist.py:265] ==================== Environment Variables ===================="
},
{
"jobId": "jsy2ibsz1l026y",
"line": 19,
"timestamp": "2019-07-08T12:40:59.28Z",
"message": "I0708 12:40:59.280700 140451862599424 mnist.py:267] NVIDIA_VISIBLE_DEVICES: all"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 20,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.280864 140451862599424 mnist.py:267] PS_DOCKER_AGENT: 6136343261613365"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 21,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.280993 140451862599424 mnist.py:267] EVAL_SECS: 10"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 22,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281114 140451862599424 mnist.py:267] PATH: /usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 23,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281230 140451862599424 mnist.py:267] PS_HOST_PRIVATE_IP_ADDRESS: 10.138.0.15"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 24,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281354 140451862599424 mnist.py:267] PS_HOSTNAME: gradient-host-1562589161"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 25,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281510 140451862599424 mnist.py:267] HOSTNAME: 9b41cad9ccda"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 26,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281623 140451862599424 mnist.py:267] EPOCHS_EVAL: 5"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 27,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281736 140451862599424 mnist.py:267] PS_JOBSPACE: /storage"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 28,
"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281847 140451862599424 mnist.py:267] PS_JOB_RUNNER: 1"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 29,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.281965 140451862599424 mnist.py:267] NVIDIA_REQUIRE_CUDA: cuda\u003e=10.0 brand=tesla,driver\u003e=384,driver\u003c385 brand=tesla,driver\u003e=410,driver\u003c411"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 30,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282079 140451862599424 mnist.py:267] CUDA_PKG_VERSION: 10-0=10.0.130-1"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 31,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282189 140451862599424 mnist.py:267] LD_LIBRARY_PATH: /usr/local/cuda/extras/CUPTI/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 32,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282299 140451862599424 mnist.py:267] TYPE: master"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 33,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282416 140451862599424 mnist.py:267] PS_DATASETS: /datasets"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 34,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282531 140451862599424 mnist.py:267] TRAIN_EPOCHS: 10"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 35,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282641 140451862599424 mnist.py:267] MAX_STEPS: 1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 36,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282753 140451862599424 mnist.py:267] PS_HOME: /paperspace"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 37,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.282861 140451862599424 mnist.py:267] INDEX: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 38,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.282970 140451862599424 mnist.py:267] CUDA_VERSION: 10.0.130"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 39,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283078 140451862599424 mnist.py:267] PORTS: 5000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 40,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283186 140451862599424 mnist.py:267] PS_ARTIFACTS: /artifacts"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 41,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283293 140451862599424 mnist.py:267] NVIDIA_DRIVER_CAPABILITIES: compute,utility"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 42,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283408 140451862599424 mnist.py:267] PS_HOST_PUBLIC_IP_ADDRESS: 35.233.198.134"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 43,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283516 140451862599424 mnist.py:267] LANG: C.UTF-8"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 44,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283623 140451862599424 mnist.py:267] PS_JOB_ID: jsy2ibsz1l026y"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 45,
"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283730 140451862599424 mnist.py:267] HOME: /root"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 46,
"timestamp": "2019-07-08T12:40:59.862Z",
"message": "I0708 12:40:59.862003 140451862599424 run_config.py:532] Initializing RunConfig with distribution strategies."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 47,
"timestamp": "2019-07-08T12:40:59.862Z",
"message": "I0708 12:40:59.862317 140451862599424 estimator_training.py:166] Not using Distribute Coordinator."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 48,
"timestamp": "2019-07-08T12:40:59.863Z",
"message": "I0708 12:40:59.862842 140451862599424 estimator.py:201] Using config: {'_master': '', '_num_worker_replicas': 1, '_num_ps_replicas': 0, '_save_checkpoints_secs': None, '_log_step_count_steps': 100, '_train_distribute': \u003ctensorflow.contrib.distribute.python.one_device_strategy.OneDeviceStrategy object at 0x7fbcb5adecc0\u003e, '_protocol': None, '_session_config': allow_soft_placement: True"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 49,
"timestamp": "2019-07-08T12:40:59.863Z",
"message": ", '_tf_random_seed': None, '_global_id_in_cluster': 0, '_is_chief': True, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/paperspace/models/mnist', '_save_summary_steps': 100, '_service': None, '_experimental_distribute': None, '_cluster_spec': \u003ctensorflow.python.training.server_lib.ClusterSpec object at 0x7fbcb34f7828\u003e, '_save_checkpoints_steps': 600, '_evaluation_master': '', '_eval_distribute': None, '_task_id': 0, '_distribute_coordinator_mode': None, '_task_type': 'worker', '_device_fn': None}"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 50,
"timestamp": "2019-07-08T12:40:59.863Z",
"message": "I0708 12:40:59.863682 140451862599424 estimator_training.py:185] Not using Distribute Coordinator."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 51,
"timestamp": "2019-07-08T12:40:59.864Z",
"message": "I0708 12:40:59.864074 140451862599424 training.py:610] Running training and evaluation locally (non-distributed)."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 52,
"timestamp": "2019-07-08T12:40:59.864Z",
"message": "I0708 12:40:59.864597 140451862599424 training.py:698] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 600 or save_checkpoints_secs None."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 53,
"timestamp": "2019-07-08T12:41:01.276Z",
"message": "W0708 12:41:01.276278 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/data/ops/dataset_ops.py:1419: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 54,
"timestamp": "2019-07-08T12:41:01.276Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 55,
"timestamp": "2019-07-08T12:41:01.276Z",
"message": "Colocations handled automatically by placer."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 56,
"timestamp": "2019-07-08T12:41:01.292Z",
"message": "I0708 12:41:01.291953 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 57,
"timestamp": "2019-07-08T12:41:01.389Z",
"message": "W0708 12:41:01.389566 140451862599424 deprecation.py:506] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 58,
"timestamp": "2019-07-08T12:41:01.389Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 59,
"timestamp": "2019-07-08T12:41:01.389Z",
"message": "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 60,
"timestamp": "2019-07-08T12:41:01.537Z",
"message": "W0708 12:41:01.536658 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/losses/losses_impl.py:209: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 61,
"timestamp": "2019-07-08T12:41:01.537Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 62,
"timestamp": "2019-07-08T12:41:01.537Z",
"message": "Use tf.cast instead."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 63,
"timestamp": "2019-07-08T12:41:01.86Z",
"message": "I0708 12:41:01.860109 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 64,
"timestamp": "2019-07-08T12:41:01.909Z",
"message": "I0708 12:41:01.909625 140451862599424 basic_session_run_hooks.py:527] Create CheckpointSaverHook."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 65,
"timestamp": "2019-07-08T12:41:02.104Z",
"message": "I0708 12:41:02.104314 140451862599424 monitored_session.py:222] Graph was finalized."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 66,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105148: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 67,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 68,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 69,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 70,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 71,
"timestamp": "2019-07-08T12:41:03.052Z",
"message": "I0708 12:41:03.051607 140451862599424 session_manager.py:491] Running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 72,
"timestamp": "2019-07-08T12:41:03.064Z",
"message": "I0708 12:41:03.064292 140451862599424 session_manager.py:493] Done running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 73,
"timestamp": "2019-07-08T12:41:03.405Z",
"message": "I0708 12:41:03.404671 140451862599424 basic_session_run_hooks.py:594] Saving checkpoints for 0 into /paperspace/models/mnist/model.ckpt."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 74,
"timestamp": "2019-07-08T12:41:03.616Z",
"message": "I0708 12:41:03.616098 140451862599424 util.py:164] Initialize strategy"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 75,
"timestamp": "2019-07-08T12:41:03.715Z",
"message": "2019-07-08 12:41:03.715695: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 76,
"timestamp": "2019-07-08T12:41:09.915Z",
"message": "I0708 12:41:09.915456 140451862599424 basic_session_run_hooks.py:249] cross_entropy = 2.3025289, learning_rate = 1e-04, train_accuracy = 0.08"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 77,
"timestamp": "2019-07-08T12:41:09.916Z",
"message": "I0708 12:41:09.915941 140451862599424 basic_session_run_hooks.py:249] loss = 2.3025289, step = 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 78,
"timestamp": "2019-07-08T12:41:11.313Z",
"message": "I0708 12:41:11.313229 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 71.5095"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 79,
"timestamp": "2019-07-08T12:41:11.314Z",
"message": "I0708 12:41:11.314374 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.3830725, learning_rate = 1e-04, train_accuracy = 0.49 (1.399 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 80,
"timestamp": "2019-07-08T12:41:11.314Z",
"message": "I0708 12:41:11.314673 140451862599424 basic_session_run_hooks.py:247] loss = 0.3830725, step = 100 (1.399 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 81,
"timestamp": "2019-07-08T12:41:12.369Z",
"message": "I0708 12:41:12.368999 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 94.7225"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 82,
"timestamp": "2019-07-08T12:41:12.37Z",
"message": "I0708 12:41:12.370402 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.19424663, learning_rate = 1e-04, train_accuracy = 0.6433333 (1.056 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 83,
"timestamp": "2019-07-08T12:41:12.371Z",
"message": "I0708 12:41:12.370768 140451862599424 basic_session_run_hooks.py:247] loss = 0.19424663, step = 200 (1.056 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 84,
"timestamp": "2019-07-08T12:41:13.424Z",
"message": "I0708 12:41:13.423866 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 94.7932"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 85,
"timestamp": "2019-07-08T12:41:13.425Z",
"message": "I0708 12:41:13.425122 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.20186995, learning_rate = 1e-04, train_accuracy = 0.7125 (1.055 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 86,
"timestamp": "2019-07-08T12:41:13.425Z",
"message": "I0708 12:41:13.425472 140451862599424 basic_session_run_hooks.py:247] loss = 0.20186995, step = 300 (1.055 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 87,
"timestamp": "2019-07-08T12:41:14.457Z",
"message": "I0708 12:41:14.457170 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 96.7791"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 88,
"timestamp": "2019-07-08T12:41:14.458Z",
"message": "I0708 12:41:14.458280 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.14686151, learning_rate = 1e-04, train_accuracy = 0.762 (1.033 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 89,
"timestamp": "2019-07-08T12:41:14.458Z",
"message": "I0708 12:41:14.458536 140451862599424 basic_session_run_hooks.py:247] loss = 0.14686151, step = 400 (1.033 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 90,
"timestamp": "2019-07-08T12:41:15.531Z",
"message": "I0708 12:41:15.530984 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 93.1259"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 91,
"timestamp": "2019-07-08T12:41:15.532Z",
"message": "I0708 12:41:15.532083 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.115687564, learning_rate = 1e-04, train_accuracy = 0.795 (1.074 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 92,
"timestamp": "2019-07-08T12:41:15.532Z",
"message": "I0708 12:41:15.532342 140451862599424 basic_session_run_hooks.py:247] loss = 0.115687564, step = 500 (1.074 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 93,
"timestamp": "2019-07-08T12:41:16.563Z",
"message": "I0708 12:41:16.563513 140451862599424 basic_session_run_hooks.py:594] Saving checkpoints for 600 into /paperspace/models/mnist/model.ckpt."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 94,
"timestamp": "2019-07-08T12:41:17.176Z",
"message": "I0708 12:41:17.176506 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 95,
"timestamp": "2019-07-08T12:41:17.385Z",
"message": "I0708 12:41:17.385438 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 96,
"timestamp": "2019-07-08T12:41:17.41Z",
"message": "I0708 12:41:17.410446 140451862599424 evaluation.py:257] Starting evaluation at 2019-07-08T12:41:17Z"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 97,
"timestamp": "2019-07-08T12:41:17.517Z",
"message": "I0708 12:41:17.517657 140451862599424 monitored_session.py:222] Graph was finalized."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 98,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 99,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 100,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 101,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 102,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 103,
"timestamp": "2019-07-08T12:41:17.519Z",
"message": "W0708 12:41:17.519085 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 104,
"timestamp": "2019-07-08T12:41:17.519Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 105,
"timestamp": "2019-07-08T12:41:17.519Z",
"message": "Use standard file APIs to check for files with this prefix."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 106,
"timestamp": "2019-07-08T12:41:17.52Z",
"message": "I0708 12:41:17.520449 140451862599424 saver.py:1270] Restoring parameters from /paperspace/models/mnist/model.ckpt-600"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 107,
"timestamp": "2019-07-08T12:41:17.572Z",
"message": "I0708 12:41:17.572486 140451862599424 session_manager.py:491] Running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 108,
"timestamp": "2019-07-08T12:41:17.584Z",
"message": "I0708 12:41:17.584268 140451862599424 session_manager.py:493] Done running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 109,
"timestamp": "2019-07-08T12:41:19.062Z",
"message": "I0708 12:41:19.062499 140451862599424 evaluation.py:277] Finished evaluation at 2019-07-08-12:41:19"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 110,
"timestamp": "2019-07-08T12:41:19.063Z",
"message": "I0708 12:41:19.062822 140451862599424 estimator.py:1979] Saving dict for global step 600: accuracy = 0.9691, global_step = 600, loss = 0.10409811"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 111,
"timestamp": "2019-07-08T12:41:19.113Z",
"message": "I0708 12:41:19.113427 140451862599424 estimator.py:2039] Saving 'checkpoint_path' summary for global step 600: /paperspace/models/mnist/model.ckpt-600"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 112,
"timestamp": "2019-07-08T12:41:19.126Z",
"message": "I0708 12:41:19.125805 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 27.817"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 113,
"timestamp": "2019-07-08T12:41:19.126Z",
"message": "I0708 12:41:19.126667 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.108451165, learning_rate = 1e-04, train_accuracy = 0.82285714 (3.595 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 114,
"timestamp": "2019-07-08T12:41:19.127Z",
"message": "I0708 12:41:19.126947 140451862599424 basic_session_run_hooks.py:247] loss = 0.108451165, step = 600 (3.595 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 115,
"timestamp": "2019-07-08T12:41:20.185Z",
"message": "I0708 12:41:20.185327 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 94.39"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 116,
"timestamp": "2019-07-08T12:41:20.186Z",
"message": "I0708 12:41:20.186514 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.122035645, learning_rate = 1e-04, train_accuracy = 0.8425 (1.060 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 117,
"timestamp": "2019-07-08T12:41:20.186Z",
"message": "I0708 12:41:20.186774 140451862599424 basic_session_run_hooks.py:247] loss = 0.122035645, step = 700 (1.060 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 118,
"timestamp": "2019-07-08T12:41:21.256Z",
"message": "I0708 12:41:21.255784 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 93.4188"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 119,
"timestamp": "2019-07-08T12:41:21.257Z",
"message": "I0708 12:41:21.256838 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.07952806, learning_rate = 1e-04, train_accuracy = 0.8577778 (1.070 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 120,
"timestamp": "2019-07-08T12:41:21.257Z",
"message": "I0708 12:41:21.257101 140451862599424 basic_session_run_hooks.py:247] loss = 0.07952806, step = 800 (1.070 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 121,
"timestamp": "2019-07-08T12:41:22.288Z",
"message": "I0708 12:41:22.287836 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 96.8942"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 122,
"timestamp": "2019-07-08T12:41:22.289Z",
"message": "I0708 12:41:22.288971 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.12382953, learning_rate = 1e-04, train_accuracy = 0.868 (1.032 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 123,
"timestamp": "2019-07-08T12:41:22.289Z",
"message": "I0708 12:41:22.289424 140451862599424 basic_session_run_hooks.py:247] loss = 0.12382953, step = 900 (1.032 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 124,
"timestamp": "2019-07-08T12:41:23.297Z",
"message": "I0708 12:41:23.297554 140451862599424 basic_session_run_hooks.py:594] Saving checkpoints for 1000 into /paperspace/models/mnist/model.ckpt."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 125,
"timestamp": "2019-07-08T12:41:23.399Z",
"message": "I0708 12:41:23.398564 140451862599424 training.py:525] Skip the current checkpoint eval due to throttle secs (10 secs)."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 126,
"timestamp": "2019-07-08T12:41:23.451Z",
"message": "I0708 12:41:23.450761 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 127,
"timestamp": "2019-07-08T12:41:23.655Z",
"message": "I0708 12:41:23.655447 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 128,
"timestamp": "2019-07-08T12:41:23.681Z",
"message": "I0708 12:41:23.681046 140451862599424 evaluation.py:257] Starting evaluation at 2019-07-08T12:41:23Z"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 129,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "I0708 12:41:23.871646 140451862599424 monitored_session.py:222] Graph was finalized."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 130,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 131,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 132,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872338: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 133,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872353: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 134,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 135,
"timestamp": "2019-07-08T12:41:23.874Z",
"message": "I0708 12:41:23.874099 140451862599424 saver.py:1270] Restoring parameters from /paperspace/models/mnist/model.ckpt-1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 136,
"timestamp": "2019-07-08T12:41:23.932Z",
"message": "I0708 12:41:23.932351 140451862599424 session_manager.py:491] Running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 137,
"timestamp": "2019-07-08T12:41:23.946Z",
"message": "I0708 12:41:23.946145 140451862599424 session_manager.py:493] Done running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 138,
"timestamp": "2019-07-08T12:41:25.49Z",
"message": "I0708 12:41:25.490408 140451862599424 evaluation.py:277] Finished evaluation at 2019-07-08-12:41:25"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 139,
"timestamp": "2019-07-08T12:41:25.491Z",
"message": "I0708 12:41:25.490850 140451862599424 estimator.py:1979] Saving dict for global step 1000: accuracy = 0.9787, global_step = 1000, loss = 0.0693267"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 140,
"timestamp": "2019-07-08T12:41:25.491Z",
"message": "I0708 12:41:25.491545 140451862599424 estimator.py:2039] Saving 'checkpoint_path' summary for global step 1000: /paperspace/models/mnist/model.ckpt-1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 141,
"timestamp": "2019-07-08T12:41:25.492Z",
"message": "I0708 12:41:25.492296 140451862599424 training.py:928] Calling exporter with the `is_the_final_export=True`."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 142,
"timestamp": "2019-07-08T12:41:25.492Z",
"message": "I0708 12:41:25.492531 140451862599424 util.py:168] Finalize strategy."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 143,
"timestamp": "2019-07-08T12:41:25.572Z",
"message": "I0708 12:41:25.571871 140451862599424 estimator.py:359] Loss for final step: 0.03955281."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 144,
"timestamp": "2019-07-08T12:41:25.572Z",
"message": "I0708 12:41:25.572580 140451862599424 mnist.py:240] Starting to Export model to /paperspace/models"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 145,
"timestamp": "2019-07-08T12:41:25.583Z",
"message": "I0708 12:41:25.583298 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 146,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "I0708 12:41:25.744162 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 147,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "W0708 12:41:25.744491 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:205: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 148,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 149,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 150,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745018 140451862599424 export.py:587] Signatures INCLUDED in export for Regress: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 151,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745139 140451862599424 export.py:587] Signatures INCLUDED in export for Train: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 152,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745274 140451862599424 export.py:587] Signatures INCLUDED in export for Predict: ['serving_default', 'classify']"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 153,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745424 140451862599424 export.py:587] Signatures INCLUDED in export for Classify: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 154,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745549 140451862599424 export.py:587] Signatures INCLUDED in export for Eval: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 155,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 156,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746099: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 157,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 158,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 159,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 160,
"timestamp": "2019-07-08T12:41:25.779Z",
"message": "I0708 12:41:25.778900 140451862599424 saver.py:1270] Restoring parameters from /paperspace/models/mnist/model.ckpt-1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 161,
"timestamp": "2019-07-08T12:41:25.814Z",
"message": "I0708 12:41:25.814109 140451862599424 builder_impl.py:654] Assets added to graph."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 162,
"timestamp": "2019-07-08T12:41:25.814Z",
"message": "I0708 12:41:25.814344 140451862599424 builder_impl.py:449] No assets to write."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 163,
"timestamp": "2019-07-08T12:41:25.878Z",
"message": "I0708 12:41:25.877661 140451862599424 builder_impl.py:414] SavedModel written to: /paperspace/models/temp-b'1562589685'/saved_model.pb"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 164,
"timestamp": "2019-07-08T12:41:25.878Z",
"message": "I0708 12:41:25.878680 140451862599424 mnist.py:247] Model Exported"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 165,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Gradient SDK not installed. Distributed training is not possible"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 166,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": ""
},
{
"jobId": "jsy2ibsz1l026y",
"line": 167,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 168,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "For more information, please see:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 169,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 170,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": " * https://github.com/tensorflow/addons"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 171,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "If you depend on functionality not listed there, please file an issue."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 172,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": ""
},
{
"jobId": "jsy2ibsz1l026y",
"line": 173,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz to /tmp/tmp5a21acuc.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 174,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz to /tmp/tmpl6cdsahk.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 175,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz to /tmp/tmplh4ob02m.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 176,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz to /tmp/tmpbrss4txl.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 177,
"timestamp": "2019-07-08T12:41:36.525Z",
"message": "PSEOF"
}
]
DETAILS_OF_SINGLE_NODE_EXPERIMENT_RESPONSE_JSON = {
"data": {
"dtCreated": "2020-04-02T21:37:00.000000+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-22T13:22:27.200591+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 21814,
"experimentTemplateId": 60,
"experimentTypeId": 1,
"handle": "esro6mbmiulvbl",
"id": 21784,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1655,
"state": 1,
"tags": [
{
"name": "tag1",
},
{
"name": "tag2",
},
],
"templateHistory": {
"dtCreated": "2019-03-22T13:22:26.375543+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 21814,
"params": {
"experimentTypeId": 1,
"name": "dsfads",
"ports": 5000,
"project_handle": "prq70zy79",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "C2"
},
"triggerEvent": None,
"triggerEventId": None
},
"datasets": [
{
"uri": "s3://some/dataset",
"volume_options": {
"kind": "dynamic",
"size": "10Gi",
},
},
],
},
"message": "success"
}
DETAILS_OF_MULTI_NODE_EXPERIMENT_RESPONSE_JSON = {
"data": {
"dtCreated": "2019-12-16T16:18:47.881765+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-12-16T16:18:47.881765+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 34335,
"experimentTemplateId": 439,
"experimentTypeId": 3,
"handle": "emarbao6t6tsn",
"id": 34272,
"projectHandle": "pr85u3sfa",
"projectId": 1824,
"started_by_user_id": 1655,
"state": 10,
"tags": [
{
"name": "tag1",
},
{
"name": "tag2",
},
],
"templateHistory": {
"cliCommand": "gradient experiments create multinode --name some_name --projectId pr85u3sfa --experimentType MPI --workerContainer python --workerMachineType c5.xlarge --workerCommand \"echo worker\" --workerCount 2 --masterContainer python --masterMachineType c5.xlarge --masterCommand \"echo master\" --masterCount 1 --workspaceUrl some.url --workingDirectory /some/working/directory --clusterId clqr4b0ox --experimentEnv '{\"key\": \"value\"}' --ports 5000 --modelType some_type --modelPath /some/model/path",
"dtCreated": "2019-12-16T16:18:46.577973+00:00",
"dtDeleted": None,
"experimentTemplateId": 439,
"id": 34335,
"params": {
"artifact_directory": "/some/artifact/directory",
"cluster_id": "clqr4b0ox",
"experiment_env": {
"key": "value"
},
"is_preemptible": False,
"model_path": "/some/model/path",
"model_type": "some_type",
"name": "some_name",
"ports": "5000",
"project_handle": "pr85u3sfa",
"version": "v2",
"working_directory": "/some/working/directory",
"workspace_url": "some.url"
},
"triggerEvent": None,
"triggerEventId": None
}
},
"message": "success"
}
NOTEBOOK_GET_RESPONSE = {
"name": "some_name",
"handle": "ngw7piq9",
"jobHandle": "jzhmk7fpluqje",
"project": "Notebook-some_name",
"projectHandle": "prg284tu2",
"state": "Running",
"token": "dc2c331ce694ebe5a615f6e5885f6a933fcb1be49cb0d17f",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ngw7piq9.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "some_namespace",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-03T11:06:18.154Z",
"dtModified": "2019-09-03T11:06:18.154Z",
"dtProvisioningStarted": "2019-09-03T11:08:36.286Z",
"dtProvisioningFinished": "2019-09-03T11:10:36.471Z",
"dtStarted": "2019-09-03T11:10:36.471Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20206,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567508793",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617088427",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1823,
"metricsURL": "aws-testing.paperspace.io",
}
NOTEBOOK_GET_RESPONSE_WITH_TAGS = {
"name": "some_name",
"handle": "ngw7piq9",
"jobHandle": "jzhmk7fpluqje",
"project": "Notebook-some_name",
"projectHandle": "prg284tu2",
"state": "Running",
"token": "dc2c331ce694ebe5a615f6e5885f6a933fcb1be49cb0d17f",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ngw7piq9.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "some_namespace",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-03T11:06:18.154Z",
"dtModified": "2019-09-03T11:06:18.154Z",
"dtProvisioningStarted": "2019-09-03T11:08:36.286Z",
"dtProvisioningFinished": "2019-09-03T11:10:36.471Z",
"dtStarted": "2019-09-03T11:10:36.471Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20206,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567508793",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617088427",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1823,
"tags": [
"tag1",
"tag2"
],
}
NOTEBOOKS_LIST_RESPONSE_JSON = {
"notebookList": [
{
"name": "job 1",
"handle": "n1vmfj6x",
"jobHandle": "jsh0692p80dphg",
"project": "Notebook-undefined",
"projectHandle": "pr231zktg",
"state": "Running",
"token": "80426c989ef8d42b4dff6806c9592b1111a47c63c0f4a36f",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "n1vmfj6x.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T11:06:12.667Z",
"dtModified": "2019-09-04T11:06:12.667Z",
"dtProvisioningStarted": "2019-09-04T11:08:28.305Z",
"dtProvisioningFinished": "2019-09-04T11:10:30.628Z",
"dtStarted": "2019-09-04T11:10:30.628Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": None,
"jobId": 20221,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567595186",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617028675",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1832
},
{
"name": "job 1",
"handle": "nhdf8zf3",
"jobHandle": "jsyvcxoxch3jgu",
"project": "Notebook-undefined",
"projectHandle": "pr2u2sfja",
"state": "Running",
"token": "1d763ce770a195c98ea3d30588f3ad007c2b8403608ab091",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nhdf8zf3.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T10:23:04.762Z",
"dtModified": "2019-09-04T10:23:04.762Z",
"dtProvisioningStarted": "2019-09-04T10:26:05.190Z",
"dtProvisioningFinished": "2019-09-04T10:28:13.609Z",
"dtStarted": "2019-09-04T10:28:13.609Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": None,
"jobId": 20219,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567592650-d5337953",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297204 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617086962",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1831
},
{
"name": "My Notebook 123",
"handle": "nslk5r03",
"jobHandle": "jskm7amsly7mmj",
"project": "Notebook-My Notebook 123",
"projectHandle": "pr3qq8qlg",
"state": "Stopped",
"token": "7751a516535bf3d52c164315d6187c1c9a04f15c15cf1c15",
"container": "nslk5r03",
"containerUrl": "us.gcr.io/ps-development-229517/paperspace/pr3qq8qlg:nslk5r03",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nslk5r03.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T10:22:43.248Z",
"dtModified": "2019-09-04T10:22:43.248Z",
"dtProvisioningStarted": "2019-09-04T10:25:26.545Z",
"dtProvisioningFinished": "2019-09-04T10:27:24.319Z",
"dtStarted": "2019-09-04T10:27:24.319Z",
"dtFinished": "2019-09-04T16:27:52.044Z",
"dtTeardownStarted": "2019-09-04T16:27:56.915Z",
"dtTeardownFinished": "2019-09-04T16:28:57.796Z",
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20218,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567592577",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617029024",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1830
},
{
"name": "My Notebook 123",
"handle": "ng9a3tp4",
"jobHandle": "jg5vkj6d799z8",
"project": "Notebook-My Notebook 123",
"projectHandle": "pr5ngrxr9",
"state": "Stopped",
"token": "d9ba60e2bf7abd8ebd0c9988507ce203dd9baa7bc2b77284",
"container": "ng9a3tp4",
"containerUrl": "us.gcr.io/ps-development-229517/paperspace/pr5ngrxr9:ng9a3tp4",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ng9a3tp4.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T10:16:22.362Z",
"dtModified": "2019-09-04T10:16:22.362Z",
"dtProvisioningStarted": "2019-09-04T10:18:46.309Z",
"dtProvisioningFinished": "2019-09-04T10:20:45.879Z",
"dtStarted": "2019-09-04T10:20:45.879Z",
"dtFinished": "2019-09-04T16:20:51.718Z",
"dtTeardownStarted": "2019-09-04T16:20:56.745Z",
"dtTeardownFinished": "2019-09-04T16:22:03.922Z",
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20217,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567592197",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617086541",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1829
},
{
"name": "some_name",
"handle": "ngw7piq9",
"jobHandle": "jzhmk7fpluqje",
"project": "Notebook-some_name",
"projectHandle": "prg284tu2",
"state": "Stopped",
"token": "dc2c331ce694ebe5a615f6e5885f6a933fcb1be49cb0d17f",
"container": "ngw7piq9",
"containerUrl": "us.gcr.io/ps-development-229517/paperspace/prg284tu2:ngw7piq9",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ngw7piq9.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-03T11:06:18.154Z",
"dtModified": "2019-09-03T11:06:18.154Z",
"dtProvisioningStarted": "2019-09-03T11:08:36.286Z",
"dtProvisioningFinished": "2019-09-03T11:10:36.471Z",
"dtStarted": "2019-09-03T11:10:36.471Z",
"dtFinished": "2019-09-03T17:10:53.440Z",
"dtTeardownStarted": "2019-09-03T17:10:54.455Z",
"dtTeardownFinished": "2019-09-03T17:12:01.889Z",
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20206,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567508793",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617088427",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1823
},
{
"name": "some_notebook_name",
"handle": "n8h0d5lf",
"jobHandle": "js63sf787xc3mx",
"project": "Notebook-some_notebook_name",
"projectHandle": "prupasg3e",
"state": "Error",
"token": None,
"container": "some_name",
"containerUrl": "some_name",
"baseContainer": "some_name",
"baseContainerUrl": "some_name",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "n8h0d5lf.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job js63sf787xc3mx: Error pulling image 'some_name': Error response from daemon: Get https://registry-1.docker.io/v2/library/some_name/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:31:43.392Z",
"dtModified": "2019-08-30T12:31:43.392Z",
"dtProvisioningStarted": "2019-08-30T12:33:34.650Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:33:35.479Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20163,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567168315",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1811
},
{
"name": "some_notebook_name",
"handle": "nl0b6cn0",
"jobHandle": "jss28gdrarcbrw",
"project": "Notebook-some_notebook_name",
"projectHandle": "pr43jj028",
"state": "Error",
"token": None,
"container": "some_name",
"containerUrl": "some_name",
"baseContainer": "some_name",
"baseContainerUrl": "some_name",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nl0b6cn0.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job jss28gdrarcbrw: Error pulling image 'some_name': Error response from daemon: Get https://registry-1.docker.io/v2/library/some_name/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:16:11.944Z",
"dtModified": "2019-08-30T12:16:11.944Z",
"dtProvisioningStarted": "2019-08-30T12:16:19.646Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:16:20.382Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20162,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567167274",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1810
},
{
"name": "some_notebook_name",
"handle": "njmq1zju",
"jobHandle": "jd35vd65dkqch",
"project": "Notebook-some_notebook_name",
"projectHandle": "prflq2sy0",
"state": "Error",
"token": None,
"container": "None",
"containerUrl": "None",
"baseContainer": "None",
"baseContainerUrl": "None",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "njmq1zju.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job jd35vd65dkqch: Error pulling image 'None': Error response from daemon: Get https://registry-1.docker.io/v2/library/None/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:14:32.296Z",
"dtModified": "2019-08-30T12:14:32.296Z",
"dtProvisioningStarted": "2019-08-30T12:16:08.347Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:16:09.132Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20161,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567167274",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1809
},
{
"name": "some_notebook_name",
"handle": "nfcuwqu5",
"jobHandle": "je2cmiigxwhy0",
"project": "Notebook-some_notebook_name",
"projectHandle": "prl9nu5p7",
"state": "Error",
"token": None,
"container": "some_name",
"containerUrl": "some_name",
"baseContainer": "some_name",
"baseContainerUrl": "some_name",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nfcuwqu5.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job je2cmiigxwhy0: Error pulling image 'some_name': Error response from daemon: Get https://registry-1.docker.io/v2/library/some_name/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:13:30.657Z",
"dtModified": "2019-08-30T12:13:30.657Z",
"dtProvisioningStarted": "2019-08-30T12:15:11.388Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:15:12.207Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20160,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567167224",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1808
}
],
"availableMachines": [
{
"vmTypeId": 5,
"clusterId": 1,
"isAvailable": False,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "0",
"numAvailableNodes": "0",
"id": 11,
"cluster": {
"name": "PS Notebooks",
"type": 1,
"regionId": 1,
"showDisabled": False,
"dtCreated": "2017-11-03T19:09:41.077Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "cls28l0qm",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 1
}
},
{
"vmTypeId": 7,
"clusterId": 1,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "1",
"id": 15,
"cluster": {
"name": "PS Notebooks",
"type": 1,
"regionId": 1,
"showDisabled": False,
"dtCreated": "2017-11-03T19:09:41.077Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "cls28l0qm",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 1
}
},
{
"vmTypeId": 20,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "2",
"numAvailableNodes": "0",
"id": 7,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 20,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": True,
"showDisabled": False,
"numActiveNodes": "2",
"numAvailableNodes": "0",
"id": 6,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 21,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": True,
"showDisabled": False,
"numActiveNodes": "0",
"numAvailableNodes": "0",
"id": 22,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 21,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "0",
"numAvailableNodes": "0",
"id": 23,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 25,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "0",
"id": 27,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 25,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": True,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "0",
"id": 26,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 31,
"clusterId": 90,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "1",
"id": 1,
"cluster": {
"name": "Free Public Notebooks",
"type": 1,
"regionId": 2,
"showDisabled": False,
"dtCreated": "2019-06-18T23:33:58.997Z",
"dtModified": "2019-08-12T17:06:19.492Z",
"dtDeleted": None,
"handle": "cltwhzxx6",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 90
}
},
{
"vmTypeId": 32,
"clusterId": 90,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "1",
"id": 12,
"cluster": {
"name": "Free Public Notebooks",
"type": 1,
"regionId": 2,
"showDisabled": False,
"dtCreated": "2019-06-18T23:33:58.997Z",
"dtModified": "2019-08-12T17:06:19.492Z",
"dtDeleted": None,
"handle": "cltwhzxx6",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 90
}
}
],
"total": 9,
"runningTotal": 2,
"displayTotal": 9
}
TENSORBOARD_CREATE_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": None,
"id": "some_id",
"instance": None,
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id"
}, {
"project_id": "some_project_id",
"id": "some_other_experiment_id"
}
],
"dt_deleted": None
}
}
TENSORBOARD_DETAIL_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": "https://aws-testing.paperspace.io/tensorboard/some_id/",
"id": "some_id",
"instance": {
"type": "cpu",
"size": "large",
"count": 2
},
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id",
"state": "Some State"
}, {
"project_id": "some_project_id",
"id": "some_other_experiment_id",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
}
TENSORBOARD_UPDATE_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": "https://aws-testing.paperspace.io/tensorboard/some_id/",
"id": "some_id",
"instance": {
"type": "cpu",
"size": "large",
"count": 2
},
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id",
"state": "Some State"
},
{
"project_id": "some_project_id",
"id": "some_other_experiment_id",
"state": "Some State"
},
{
"project_id": "some_project_id",
"id": "some_third_experiment_id",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
}
TENSORBOARD_UPDATE_REMOVE_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": "https://aws-testing.paperspace.io/tensorboard/some_id/",
"id": "some_id",
"instance": {
"type": "cpu",
"size": "large",
"count": 2
},
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
}
TENSORBOARD_LIST_RESPONSE_JSON = {
"message": "success",
"data": [
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbrs2kcjman4ly/",
"id": "tbrs2kcjman4ly",
"instance": None,
"dt_created": "2019-09-16T09:28:10.415478+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
},
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbskzep6d9po04d/",
"id": "tbskzep6d9po04d",
"instance": None,
"dt_created": "2019-09-16T09:28:56.908450+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
},
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbsaq6hggzxcnet/",
"id": "tbsaq6hggzxcnet",
"instance": None,
"dt_created": "2019-09-16T12:59:20.788663+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
},
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbwuzalec7ik58/",
"id": "tbwuzalec7ik58",
"instance": {
"size": "large",
"type": "cpu",
"count": 2
},
"dt_created": "2019-09-17T07:43:39.679876+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
]
}
TENSORBOARD_DELETE_RESPONSE_JSON = {
"message": "success"
}
LIST_JOB_FILES_RESPONSE_JSON = {
'data': [
{
"file": "hello.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/artifacts/hello.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=7CT5k6buEmZe5k5E7g6BXMs2xV4%3D&response-content-disposition=attachment%3Bfilename%3D%22hello.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "hello2.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/artifacts/hello2.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=L1lI47cNyiROzdYkf%2FF3Cm3165E%3D&response-content-disposition=attachment%3Bfilename%3D%22hello2.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "keton/elo.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/artifacts/keton/elo.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=tHriojGx03S%2FKkVGQGVI5CQRFTo%3D&response-content-disposition=attachment%3Bfilename%3D%22elo.txt%22&x-amz-security-token=some_amz_security_token"
}
],
}
GET_PRESIGNED_URL_FOR_S3_BUCKET_RESPONSE_JSON = {
"data": {
"bucket_name": "ps-projects",
"fields": {
"AWSAccessKeyId": "SOME_AWS_ACCESS_KEY_ID",
"key": "some/path/to/file/demo.zip",
"policy": "base64policy=",
"signature": "base64signature="
},
"url": "https://ps-projects.s3.amazonaws.com/"
},
"message": "success"
}
DELETE_MODEL_404_RESPONSE_JSON = {
"error": {
"name": "Error",
"status": 404,
"message": "Unable to find model",
},
}
MODEL_CREATE_RESPONSE_JSON_V2 = {
"id": "some_model_id",
"projectId": None,
"experimentId": None,
"updatedByUserId": "ukgvw4i8",
"updatedByUserEmail": "asd@paperspace.com",
"modelType": "Custom",
"name": "some_name",
"tag": None,
"summary": None,
"detail": None,
"params": None,
"url": None,
"notes": None,
"isDeleted": False,
"isPublic": False,
"dtCreated": "2020-04-01T14:15:39.371Z",
"dtModified": "2020-04-01T14:15:39.371Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": None,
"tags": None
}
MODEL_UPLOAD_GET_PRESIGNED_URL_RESPONSE = "https://ps-customstorage-development.s3.amazonaws.com/teo6raui0/models/" \
"moslrdsp1q4z4a2/keton.txt?AWSAccessKeyId=AKIAVWO7J5OJXEKIB7EJ&Content-Typ" \
"e=text%2Fplain&Expires=1585779340&Signature=82UPgvg0yBayLJCLMiNq6jc4mys%3D"
MODEL_UPLOAD_RESPONSE_JSON = {
"id": "some_model_id",
"projectId": None,
"experimentId": None,
"updatedByUserId": "ukgvw4i8",
"updatedByUserEmail": "some_email@paperspace.com",
"modelType": "Tensorflow",
"name": "some_name",
"tag": None,
"summary": {"key": "value"},
"detail": None,
"params": None,
"url": "s3://ps-projects-development/teo6raui0/models/some_model_id/saved_model.pb",
"notes": "some notes",
"isDeleted": False,
"isPublic": False,
"dtCreated": "2019-11-26T13:33:11.001Z", "dtModified": "2019-11-26T13:33:11.001Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": None,
}
MODEL_DETAILS_RESPONSE_JSON = {
"modelList": [
{
"id": "some_id",
"projectId": "some_project_id",
"experimentId": "some_experiment_id",
"updatedByUserId": "some_user_id",
"updatedByUserEmail": "paperspace@paperspace.com",
"modelType": "Tensorflow",
"name": "some_name",
"tag": None,
"summary": None,
"detail": None,
"params": None,
"url": "s3://ps-projects-development/asdf/some_project_id/some_experiment_id/model",
"notes": None,
"isDeleted": False,
"isPublic": False,
"dtCreated": "2019-12-13T23:00:26.077Z",
"dtModified": "2019-12-13T23:00:26.077Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": "Stopped"
}
],
"total": 96,
"displayTotal": 1
}
MODEL_DETAILS_RESPONSE_JSON_WITH_TAGS = {
"modelList": [
{
"id": "some_id",
"projectId": "some_project_id",
"experimentId": "some_experiment_id",
"updatedByUserId": "some_user_id",
"updatedByUserEmail": "paperspace@paperspace.com",
"modelType": "Tensorflow",
"name": "some_name",
"tag": None,
"tags": [
"tag1",
"tag2"
],
"summary": None,
"detail": None,
"params": None,
"url": "s3://ps-projects-development/asdf/some_project_id/some_experiment_id/model",
"notes": None,
"isDeleted": False,
"isPublic": False,
"dtCreated": "2019-12-13T23:00:26.077Z",
"dtModified": "2019-12-13T23:00:26.077Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": "Stopped"
}
],
"total": 96,
"displayTotal": 1
}
LIST_MODEL_FILES_RESPONSE_JSON = [
{
"file": "hello.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/model/hello.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=7CT5k6buEmZe5k5E7g6BXMs2xV4%3D&response-content-disposition=attachment%3Bfilename%3D%22hello.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "hello2.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/model/hello2.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=L1lI47cNyiROzdYkf%2FF3Cm3165E%3D&response-content-disposition=attachment%3Bfilename%3D%22hello2.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "keton/elo.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/model/keton/elo.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=tHriojGx03S%2FKkVGQGVI5CQRFTo%3D&response-content-disposition=attachment%3Bfilename%3D%22elo.txt%22&x-amz-security-token=some_amz_security_token"
}
]
GET_DEPLOYMENT_DETAILS_JSON_RESPONSE = {
"deploymentList": [
{
"id": "some_id",
"ownerId": "some_owner_id",
"projectId": "some_project_id",
"experimentId": "some_experiment_id",
"state": "Stopped",
"startedByUserId": "some_user_id",
"startedByUserEmail": "some@address.com",
"deploymentType": "TFServing",
"deploymentTypeDescription": "Tensorflow Serving on K8s",
"modelId": "some_model_id",
"modelUrl": "s3://ps-projects/asdf/some/model/url",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "KPS Jobs",
"clusterId": "some_cluster_id",
"machineType": "p3.2xlarge",
"imageUrl": "tensorflow/serving",
"imageUsername": None,
"imagePassword": None,
"imageServer": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": None,
"annotations": {},
"oauthKey": None,
"oauthSecret": None,
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://paperspace.io/model-serving/some_id:predict",
"ports": None,
"dtCreated": "2020-01-13T22:21:28.210Z",
"dtModified": "2020-01-14T00:32:27.577Z",
"dtBuildStarted": "2020-01-13T22:21:28.482Z",
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2020-01-13T22:48:40.107Z",
"dtDeleted": None,
"isDeleted": False,
"modelType": "Tensorflow",
"modelPath": None,
"command": "some deployment command",
"args": None,
"env": None,
"containerModelPath": None,
"containerUrlPath": None,
"endpointUrlPath": None,
"method": None,
"tags": ["tag1", "tag2"],
"metricsURL": "aws-testing.paperspace.io",
"autoscaling": {
"minInstanceCount": 4,
"maxInstanceCount": 64,
"scaleCooldownPeriod": 123,
"metrics": [
{
"type": "Resource",
"name": "cpu",
"valueType": "targetAverage",
"value": 10,
},
{
"type": "Metric",
"name": "loss",
"valueType": "target",
"value": 2,
},
{
"type": "Metric",
"name": "keton",
"valueType": "target",
"value": 21.37,
},
],
},
},
],
"total": 129,
"displayTotal": 129,
"runningTotal": 0
}
GET_CLUSTER_DETAILS_RESPONSE = {
"id": "some_cluster_id",
"name": "EKS testing",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "some_team_id",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 1,
"isPrivate": True,
"modelName": "team",
"modelId": 1,
"nodes": [
{
"id": "cmsq4u0gf0m971f",
"name": "default",
"clusterId": "cluwffvkb",
"activeJobId": None,
"machineId": None,
"dtCreated": "2019-11-21T10:06:27.602Z",
"dtModified": "2019-11-21T10:06:27.602Z",
"dtDeleted": None,
"dtHeartbeat": None,
"nodeAttrs": None
}
]
}
GET_V1_CLUSTER_DETAILS_RESPONSE = {
"id": "some_cluster_id",
"name": "EKS testing",
"type": "Job Cluster",
"region": "Private",
"cloud": "aws",
"teamId": "some_team_id",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 1,
"isPrivate": True,
"modelName": "team",
"modelId": 1,
"nodes": [
{
"id": "some_node_id",
"name": "default",
"clusterId": "some_cluster_id",
"activeJobId": None,
"machineId": None,
"dtCreated": "2019-11-21T10:06:27.602Z",
"dtModified": "2019-11-21T10:06:27.602Z",
"dtDeleted": None,
"dtHeartbeat": None,
"nodeAttrs": None
}
]
}
DETAILS_OF_PROJECT = {
"data": [
{
"name": "some_name",
"handle": "some_id",
"dtCreated": "2020-02-07T11:43:34.335Z",
"dtDeleted": None,
"lastJobSeqNum": 44,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"tags": None,
"buildPullRequests": True,
"buildForks": False,
"buildBranches": "default",
"experiments": {
"data": [
{
"dtCreated": "2020-02-13T16:02:22.978027+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2020-02-13T16:02:22.978027+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 70590,
"experimentTemplateId": 7156,
"experimentTypeId": 2,
"handle": "some_handle",
"id": 70557,
"projectHandle": "some_id",
"projectId": 49107,
"started_by_user_id": 192354,
"state": 1,
"templateHistory": {
"cliCommand": "gradient experiments create multinode --name some_name --projectId some_id --experimentType GRPC --workerContainer python --workerMachineType c5.xlarge --workerCommand \"python script.py\" --workerCount 2 --parameterServerContainer python --parameterServerMachineType c5.xlarge --parameterServerCommand \"python script.py\" --parameterServerCount 1 --workspaceUrl s3://some_workspace.url --clusterId some_cluster_id --ports 5000:5000",
"dtCreated": "2020-02-13T16:02:21.263923+00:00",
"dtDeleted": None,
"experimentTemplateId": 7156,
"id": 70590,
"params": {
"cluster_id": "some_cluster_id",
"is_preemptible": False,
"name": "some_name",
"parameter_server_command": "python script.py",
"parameter_server_container": "python",
"parameter_server_count": 1,
"parameter_server_machine_type": "c5.xlarge",
"parameter_server_use_dockerfile": False,
"ports": "5000:5000",
"project_handle": "some_id",
"version": "v2",
"worker_command": "python script.py",
"worker_container": "python",
"worker_count": 2,
"worker_machine_type": "c5.xlarge",
"worker_use_dockerfile": False,
"workspace_url": "s3://some_workspace.url"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "some_id"
},
"totalItems": 40
}
}
}
],
"meta": {
"where": {
"handle": "some_id"
},
"totalItems": 1
},
"tagFilter": []
}
DETAILS_OF_PROJECT_WITH_TAGS = {
"data": [
{
"name": "some_name",
"handle": "some_id",
"dtCreated": "2020-02-07T11:43:34.335Z",
"dtDeleted": None,
"lastJobSeqNum": 44,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"tags": [
"tag1",
"tag2"
],
"buildPullRequests": True,
"buildForks": False,
"buildBranches": "default",
"experiments": {
"data": [
{
"dtCreated": "2020-02-13T16:02:22.978027+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2020-02-13T16:02:22.978027+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 70590,
"experimentTemplateId": 7156,
"experimentTypeId": 2,
"handle": "some_handle",
"id": 70557,
"projectHandle": "some_id",
"projectId": 49107,
"started_by_user_id": 192354,
"state": 1,
"templateHistory": {
"cliCommand": "gradient experiments create multinode --name some_name --projectId some_id --experimentType GRPC --workerContainer python --workerMachineType c5.xlarge --workerCommand \"python script.py\" --workerCount 2 --parameterServerContainer python --parameterServerMachineType c5.xlarge --parameterServerCommand \"python script.py\" --parameterServerCount 1 --workspaceUrl s3://some_workspace.url --clusterId some_cluster_id --ports 5000:5000",
"dtCreated": "2020-02-13T16:02:21.263923+00:00",
"dtDeleted": None,
"experimentTemplateId": 7156,
"id": 70590,
"params": {
"cluster_id": "some_cluster_id",
"is_preemptible": False,
"name": "some_name",
"parameter_server_command": "python script.py",
"parameter_server_container": "python",
"parameter_server_count": 1,
"parameter_server_machine_type": "c5.xlarge",
"parameter_server_use_dockerfile": False,
"ports": "5000:5000",
"project_handle": "some_id",
"version": "v2",
"worker_command": "python script.py",
"worker_container": "python",
"worker_count": 2,
"worker_machine_type": "c5.xlarge",
"worker_use_dockerfile": False,
"workspace_url": "s3://some_workspace.url"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "some_id"
},
"totalItems": 40
}
}
}
],
"meta": {
"where": {
"handle": "some_id"
},
"totalItems": 1
},
"tagFilter": []
}
EXAMPLE_CLUSTERS_LIST_RESPONSE = [
{
"id": "cluster_id_1",
"name": "cluster name 1",
"type": "Job Cluster",
"region": "Private",
"cloud": "private",
"teamId": "team_id",
"isDefault": True,
"dtCreated": "2019-07-05T23:28:17.416Z",
"dtModified": "2019-07-05T23:28:17.416Z",
"clusterId": 91,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
},
{
"id": "cluster_id_2",
"name": "cluster name 2",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "team_id",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
},
{
"id": "cluster_id_3",
"name": "cluster name 3",
"type": "Job Cluster",
"region": "Private",
"cloud": "gcp",
"teamId": "team_id",
"isDefault": False,
"dtCreated": "2019-10-29T18:42:50.985Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 100,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
LIMITED_EXAMPLE_CLUSTERS_LIST_RESPONSE = [
{
"id": "cluster_id_1",
"name": "cluster name 1",
"type": "Job Cluster",
"region": "Private",
"cloud": "private",
"teamId": "team_id",
"isDefault": True,
"dtCreated": "2019-07-05T23:28:17.416Z",
"dtModified": "2019-07-05T23:28:17.416Z",
"clusterId": 91,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
},
{
"id": "cluster_id_2",
"name": "cluster name 2",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "team_id",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
LIST_OF_VM_MACHINE_TYPES = {
"92": [
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 35,
"capacityId": 21,
"vmType": {
"label": "c5.xlarge",
"kind": "aws-cpu",
"cpus": 4,
"ram": "8589934592",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:12:41.506Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "small",
"id": 35,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 35,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 131,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clqr4b0ox",
"name": "KPS Jobs",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
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"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 66,
"type": "hourly",
"usageRateId": 18,
"dtCreated": "2019-11-27T11:59:58.059Z",
"isPreemptible": False,
"id": 151,
"usageRate": {
"description": "VIP",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "VIP",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": False,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 18
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "cluwffvkb",
"name": "EKS testing",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 103,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
}
],
"175": [
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 35,
"capacityId": 134,
"vmType": {
"label": "c5.xlarge",
"kind": "aws-cpu",
"cpus": 4,
"ram": "8589934592",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:12:41.506Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "small",
"id": 35,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 35,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 131,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clrvkwq6l",
"name": "test",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2020-03-09T17:15:08.080Z",
"dtModified": "2020-03-09T17:15:08.080Z",
"clusterId": 175,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 36,
"capacityId": 135,
"vmType": {
"label": "c5.4xlarge",
"kind": "aws-cpu",
"cpus": 16,
"ram": "34359738368",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:14:06.425Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "medium",
"id": 36,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 36,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 132,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clrvkwq6l",
"name": "test",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2020-03-09T17:15:08.080Z",
"dtModified": "2020-03-09T17:15:08.080Z",
"clusterId": 175,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 38,
"capacityId": 136,
"vmType": {
"label": "p2.xlarge",
"kind": "aws-gpu",
"cpus": 4,
"ram": "65498251264",
"gpuModelId": 11,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "small",
"id": 38,
"gpuModel": {
"model": "passthrough",
"label": "Tesla K80",
"gpuGroupId": 8,
"memInBytes": "12884901888",
"memInMb": 12288,
"memInGb": "12",
"id": 11
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 38,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:05.626Z",
"isPreemptible": False,
"id": 134,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clrvkwq6l",
"name": "test",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2020-03-09T17:15:08.080Z",
"dtModified": "2020-03-09T17:15:08.080Z",
"clusterId": 175,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 39,
"capacityId": 137,
"vmType": {
"label": "p3.2xlarge",
"kind": "aws-gpu",
"cpus": 8,
"ram": "65498251264",
"gpuModelId": 10,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "medium",
"id": 39,
"gpuModel": {
"model": "passthrough",
"label": "Tesla V100",
"gpuGroupId": 7,
"memInBytes": "17179869184",
"memInMb": 16384,
"memInGb": "16",
"id": 10
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 39,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:05.626Z",
"isPreemptible": False,
"id": 135,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clrvkwq6l",
"name": "test",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2020-03-09T17:15:08.080Z",
"dtModified": "2020-03-09T17:15:08.080Z",
"clusterId": 175,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 40,
"capacityId": 138,
"vmType": {
"label": "p3.16xlarge",
"kind": "aws-gpu",
"cpus": 64,
"ram": "523986010112",
"gpuModelId": 10,
"gpuCount": 8,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "large",
"id": 40,
"gpuModel": {
"model": "passthrough",
"label": "Tesla V100",
"gpuGroupId": 7,
"memInBytes": "17179869184",
"memInMb": 16384,
"memInGb": "16",
"id": 10
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 40,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:14.282Z",
"isPreemptible": False,
"id": 136,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clrvkwq6l",
"name": "test",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2020-03-09T17:15:08.080Z",
"dtModified": "2020-03-09T17:15:08.080Z",
"clusterId": 175,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 66,
"capacityId": 139,
"vmType": {
"label": "c5.xlarge",
"kind": "eks-cpu",
"cpus": 4,
"ram": "8589934592",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-11-27T11:08:38.641Z",
"dtModified": "2019-11-27T11:08:38.641Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "small",
"id": 66,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 66,
"type": "hourly",
"usageRateId": 18,
"dtCreated": "2019-11-27T11:59:58.059Z",
"isPreemptible": False,
"id": 151,
"usageRate": {
"description": "VIP",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "VIP",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": False,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 18
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clrvkwq6l",
"name": "test",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2020-03-09T17:15:08.080Z",
"dtModified": "2020-03-09T17:15:08.080Z",
"clusterId": 175,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
}
]
}
EXPERIMENTS_METRICS_GET_RESPONSE = {
"handle": "ess57ozwb5ijpt",
"object_type": "experiment",
"charts": {
"cpuPercentage": {
"mljob-esro6mbmiulvbl-0-worker": [
{"time_stamp": 1587375065, "value": "0"},
{"time_stamp": 1587375095, "value": "0"},
{"time_stamp": 1587375125, "value": "0"},
{"time_stamp": 1587375155, "value": "0"},
],
"mljob-esro6mbmiulvbl-1-worker": [
{"time_stamp": 1587375065, "value": "0"},
{"time_stamp": 1587375095, "value": "0"},
{"time_stamp": 1587375125, "value": "0"},
{"time_stamp": 1587375155, "value": "0"},
],
},
"memoryUsage": {
"mljob-esro6mbmiulvbl-0-worker": [
{"time_stamp": 1587375005, "value": "0"},
{"time_stamp": 1587375035, "value": "0"},
{"time_stamp": 1587375065, "value": "761856"},
{"time_stamp": 1587375095, "value": "761856"},
{"time_stamp": 1587375125, "value": "761856"},
],
"mljob-esro6mbmiulvbl-1-worker": [
{"time_stamp": 1587375005, "value": "0"},
{"time_stamp": 1587375035, "value": "0"},
{"time_stamp": 1587375065, "value": "761856"},
{"time_stamp": 1587375095, "value": "761856"},
{"time_stamp": 1587375125, "value": "761856"},
],
},
},
}
EXPERIMENTS_METRICS_GET_RESPONSE_WHEN_NO_DATA_WAS_FOUND = {
"handle": "ess57ozwb5ijpt",
"object_type": "experiment",
"charts": {},
}
DEPLOYMENTS_METRICS_GET_RESPONSE = {
"handle": "desgffa3mtgepvm",
"object_type": "modelDeployment",
"charts": {
"cpuPercentage": {
"desgffa3mtgepvm-0": [
{"time_stamp": 1587340800, "value": "0.0388702066666724"},
{"time_stamp": 1587370800, "value": "0.04452898888887249"},
{"time_stamp": 1587400800, "value": "0.044658617777757724"},
{"time_stamp": 1587430800, "value": "0.04922275555555997"},
{"time_stamp": 1587460800, "value": "0.0589409911111084"},
{"time_stamp": 1587490800, "value": "0.02873176888891117"},
{"time_stamp": 1587520800, "value": "0.042048226666666876"},
{"time_stamp": 1587550800, "value": "0.04952780222222625"},
],
"desgffa3mtgepvm-1": [
{"time_stamp": 1587340800, "value": "0.05044751111111307"},
{"time_stamp": 1587370800, "value": "0.04381767555555724"},
{"time_stamp": 1587400800, "value": "0.03436263111110646"},
{"time_stamp": 1587430800, "value": "0.048889264444432624"},
{"time_stamp": 1587460800, "value": "0.041525960000020255"},
{"time_stamp": 1587490800, "value": "0.04574227333332853"},
{"time_stamp": 1587520800, "value": "0.03383691777780011"},
{"time_stamp": 1587550800, "value": "0.045942304444426756"},
],
},
"memoryUsage": {
"desgffa3mtgepvm-0": [
{"time_stamp": 1587340800, "value": "34910208"},
{"time_stamp": 1587370800, "value": "34910208"},
{"time_stamp": 1587400800, "value": "34914304"},
{"time_stamp": 1587430800, "value": "34914304"},
{"time_stamp": 1587460800, "value": "34914304"},
{"time_stamp": 1587490800, "value": "34914304"},
{"time_stamp": 1587520800, "value": "34914304"},
{"time_stamp": 1587550800, "value": "34914304"},
],
"desgffa3mtgepvm-1": [
{"time_stamp": 1587340800, "value": "35942400"},
{"time_stamp": 1587370800, "value": "35942400"},
{"time_stamp": 1587400800, "value": "35942400"},
{"time_stamp": 1587430800, "value": "35942400"},
{"time_stamp": 1587460800, "value": "35942400"},
{"time_stamp": 1587490800, "value": "35942400"},
{"time_stamp": 1587520800, "value": "35942400"},
{"time_stamp": 1587550800, "value": "35942400"},
],
},
},
}
GET_JOB_RESPONSE = {
"job": {
"id": "jstkd2lapucirs",
"name": "ufoym/deepo:all-py36-worker-1",
"state": "Running",
"workspaceUrl": "git+https://github.com/dte/stylegan.git",
"workingDirectory": "/paperspace",
"artifactsDirectory": "/artifacts",
"entrypoint": "sleep 3600 ; python generate_figures.py",
"projectId": "pr85u3sfa",
"project": "keton",
"container": "ufoym/deepo:all-py36",
"containerUrl": "ufoym/deepo:all-py36",
"baseContainer": "ufoym/deepo:all-py36",
"baseContainerUrl": "ufoym/deepo:all-py36",
"machineType": "c5.xlarge",
"cluster": "KPS Jobs",
"clusterId": "clqr4b0ox",
"usageRate": "Employee",
"startedByUserId": "ukgvw4i8",
"parentJobId": None,
"jobError": None,
"dtCreated": "2020-04-29T10:11:07.924Z",
"dtModified": "2020-04-29T10:11:07.924Z",
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"exitCode": None,
"queuePosition": None,
"seqNum": 155,
"storageRegion": "Private",
"clusterMachine": None,
"fqdn": "undefined.dgradient.paperspace.com",
"ports": "5000:5000", "isPublic": False,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": None,
"cpuCount": None,
"cpuModel": None,
"cpuFlags": None,
"cpuMem": None,
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"targetNodeAttrs": None,
"jobEnv": None,
"sharedMemMBytes": None,
"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "aws-testing.paperspace.io",
"customMetrics": None,
"experimentId": "ecgrgm7ok8chv",
"tags": None,
},
"user": {
"firstName": "first",
"lastName": "last",
"email": "bartosz@paperspace.com",
"handle": "ukgvw4i8",
},
}
GET_JOB_METRICS_RESPONSE = {
"handle": "jstkd2lapucirs",
"object_type": "mljob",
"charts": {
"cpuPercentage": {
"mljob-ecgrgm7ok8chv-0-worker": [
{"time_stamp": 1588155157, "value": "0"},
{"time_stamp": 1588155187, "value": "0"},
{"time_stamp": 1588155217, "value": "0"},
],
},
"memoryUsage": {
"mljob-ecgrgm7ok8chv-0-worker": [
{"time_stamp": 1588155097, "value": "0"},
{"time_stamp": 1588155127, "value": "5881856"},
{"time_stamp": 1588155157, "value": "5881856"},
{"time_stamp": 1588155187, "value": "5881856"},
],
},
},
}
JOBS_METRICS_GET_RESPONSE_WHEN_NO_DATA_WAS_FOUND = {
"handle": "jsy5wnxbtqw1e9",
"object_type": "mljob",
"charts": {
"cpuPercentage": None,
"memoryUsage": None,
},
}
NOTEBOOKS_METRICS_GET_RESPONSE = {
"handle": "npmnnm6e",
"object_type": "notebook",
"charts": {
"cpuPercentage": {
"npmnnm6e": [
{"time_stamp": 1587993000, "value": "0"},
{"time_stamp": 1587993030, "value": "0"},
{"time_stamp": 1587993060, "value": "0"},
{"time_stamp": 1587993090, "value": "0"},
{"time_stamp": 1587993120, "value": "0"},
{"time_stamp": 1587993150, "value": "0"},
{"time_stamp": 1587993180, "value": "0"},
{"time_stamp": 1587993210, "value": "0"},
{"time_stamp": 1587993240, "value": "0"},
{"time_stamp": 1587993270, "value": "0"},
{"time_stamp": 1587993300, "value": "0"},
{"time_stamp": 1587993330, "value": "0"},
{"time_stamp": 1587993360, "value": "0"},
],
},
"memoryUsage": {
"npmnnm6e": [
{"time_stamp": 1587992970, "value": "0"},
{"time_stamp": 1587993000, "value": "782336"},
{"time_stamp": 1587993030, "value": "782336"},
{"time_stamp": 1587993060, "value": "782336"},
{"time_stamp": 1587993090, "value": "782336"},
{"time_stamp": 1587993120, "value": "782336"},
{"time_stamp": 1587993150, "value": "782336"},
{"time_stamp": 1587993180, "value": "782336"},
{"time_stamp": 1587993210, "value": "782336"},
{"time_stamp": 1587993240, "value": "782336"},
{"time_stamp": 1587993270, "value": "782336"},
{"time_stamp": 1587993300, "value": "782336"},
{"time_stamp": 1587993330, "value": "782336"},
{"time_stamp": 1587993360, "value": "782336"},
],
},
},
}
NOTEBOOKS_METRICS_GET_RESPONSE_WHEN_NO_METRICS_WERE_FOUND = {
"handle": "nrwed38p",
"object_type": "notebook",
"charts": {"cpuPercentage": None, "memoryUsage": None},
}
DEPLOYMENTS_LOGS_RESPONSE = [
{
"jobId": "deshj1l4nuw6sd1",
"line": 1,
"timestamp": "2020-05-11T11:27:56.552Z",
"message": "line1\n",
"instanceName": {
"String": "deshj1l4nuw6sd1-1-artifact-1-5nd94",
"Valid": True
},
"uuid": {
"String": "1ef5d86b-d05e-4610-8f81-b42b556cede7",
"Valid": True
},
"instanceCount": {
"Int64": 1,
"Valid": True
}
},
{
"jobId": "deshj1l4nuw6sd1",
"line": 2,
"timestamp": "2020-05-11T11:27:56.552Z",
"message": "line2\n",
"instanceName": {
"String": "deshj1l4nuw6sd1-1-artifact-1-5nd94",
"Valid": True
},
"uuid": {
"String": "1ef5d86b-d05e-4610-8f81-b42b556cede7",
"Valid": True
},
"instanceCount": {
"Int64": 1,
"Valid": True
}
},
{
"jobId": "deshj1l4nuw6sd1",
"line": 3,
"timestamp": "2020-05-11T11:27:56.557Z",
"message": "line3",
"instanceName": {
"String": "deshj1l4nuw6sd1-1-artifact-1-5nd94",
"Valid": True
},
"uuid": {
"String": "1ef5d86b-d05e-4610-8f81-b42b556cede7",
"Valid": True
},
"instanceCount": {
"Int64": 1,
"Valid": True
}
},
{
"jobId": "deshj1l4nuw6sd1",
"line": 4,
"timestamp": "2020-05-11T11:27:56.558Z",
"message": "PSEOF"
}
]
LIST_SECRETS_RESPONSE = [
{
"name": "aws_access_key_id"
},
{
"name": "aws_secret_access_key"
}
]
LIST_DATASETS_RESPONSE = [
{
"description": None,
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
]
SHOW_DATASET_DETAILS_RESPONSE = {
"description": None,
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
CREATE_DATASET_RESPONSE = {
"description": None,
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
UPDATE_DATASET_RESPONSE = {
"description": "Test dataset",
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
LIST_DATASET_VERSIONS_RESPONSE = [
{
"dtCreated": "2020-10-29T22:56:15.514Z",
"dtModified": "2020-10-29T22:56:16.213Z",
"isCommitted": True,
"message": None,
"tags": [
{
"name": "hello"
}
],
"version": "1rn19s2"
}
]
SHOW_DATASET_VERSION_DETAILS_RESPONSE = {
"dtCreated": "2020-10-29T22:56:15.514Z",
"dtModified": "2020-10-29T22:56:16.213Z",
"isCommitted": True,
"message": None,
"tags": [
{
"name": "hello"
}
],
"version": "1rn19s2"
}
UPDATE_DATASET_VERSION_RESPONSE = {
"dtCreated": "2020-10-29T22:56:15.514Z",
"dtModified": "2020-10-29T23:27:14.955Z",
"isCommitted": True,
"message": "Test message",
"tags": [
{
"name": "hello"
}
],
"version": "1rn19s2"
}
SET_DATASET_VERSION_TAG_RESPONSE = {
"dtCreated": "2020-10-29T22:57:40.723Z",
"dtModified": "2020-10-29T23:48:42.948Z",
"name": "hello",
"version": {
"message": "Test message",
"version": "1rn19s2"
}
}
LIST_STORAGE_PROVIDERS_RESPONSE = [
{
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
]
SHOW_STORAGE_PROVIDER_DETAILS_RESPONSE = {
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
CREATE_STORAGE_PROVIDER_RESPONSE = {
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
UPDATE_STORAGE_PROVIDER_RESPONSE = {
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test2",
"type": "s3"
}
| 40.738141 | 570 | 0.435959 | LIST_OF_EXPERIMENTS_RESPONSE_JSON = {
"data": [
{
"dtCreated": "2019-03-21T07:47:05.616096+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-21T07:47:05.616096+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 6315,
"experimentTemplateId": 60,
"experimentTypeId": 1,
"handle": "ea2lfbbpdyzsq",
"id": 6292,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1,
"state": 1,
"templateHistory": {
"dtCreated": "2019-03-21T07:47:04.925852+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 6315,
"params": {
"experimentTypeId": 1,
"name": "dsfads",
"ports": 5000,
"project_handle": "prq70zy79",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "sadas"
},
"triggerEvent": None,
"triggerEventId": None
},
"datasets": [
{
"uri": "s3://some/dataset",
"volume_options": {
"kind": "dynamic",
"size": "10Gi",
},
},
],
},
{
"dtCreated": "2019-03-21T07:46:57.706055+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-21T07:46:57.706055+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 6314,
"experimentTemplateId": 60,
"experimentTypeId": 1,
"handle": "em6btk2vtb7it",
"id": 6291,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1,
"state": 1,
"templateHistory": {
"dtCreated": "2019-03-21T07:46:56.949590+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 6314,
"params": {
"experimentTypeId": 1,
"name": "dsfads",
"ports": 5000,
"project_handle": "prq70zy79",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "sadas"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-20T19:56:50.154853+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-20T19:56:50.154853+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 6297,
"experimentTemplateId": 60,
"experimentTypeId": 3,
"handle": "ew69ls0vy3eto",
"id": 6286,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1,
"state": 1,
"templateHistory": {
"dtCreated": "2019-03-20T19:56:49.427354+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 6297,
"params": {
"artifactDirectory": "/artdir",
"clusterId": 2,
"experimentEnv": {
"key": "val"
},
"experimentTypeId": 3,
"name": "multinode_mpi",
"parameter_server_command": "ls",
"parameter_server_container": "pscon",
"parameter_server_container_user": "pscuser",
"parameter_server_count": 2,
"parameter_server_machine_type": "psmtype",
"parameter_server_registry_password": "psrpass",
"parameter_server_registry_username": "psrcus",
"ports": 3456,
"project_handle": "prq70zy79",
"project_id": 34,
"trigger_event_id": 12,
"worker_command": "wcom",
"worker_container": "wcon",
"worker_container_user": "usr",
"worker_count": 2,
"worker_machine_type": "mty",
"worker_registry_password": "rpass",
"worker_registry_username": "rusr",
"workingDirectory": "/dir",
"workspaceUrl": "wurl"
},
"triggerEvent": {
"dtCreated": "2019-03-11T14:47:57+00:00",
"eventData": {
"author": {
"email": "bluckey@paperspace.com",
"login": "ultrabluewolf",
"name": "Britney Luckey"
},
"branch": "feature/test-1",
"message": "Update readme #2",
"repo_node_id": "MDEwOlJlcG9zaXRvcnkxNzQ3MjI3NDc=",
"sender": {
"id": 4633049,
"login": "ultrabluewolf"
},
"sha": "daa117a00cd1e0e9b1b55695031e698a560cca29",
"timestamp": "2019-03-11T10:47:57-04:00"
},
"id": 12,
"type": "github"
},
"triggerEventId": 12
}
}
],
"message": "success",
"meta": {
"filter": [],
"limit": 11,
"offset": 0,
"totalItems": 27
}
}
LIST_OF_EXPERIMENTS_FILTERED_WITH_TWO_PROJECTS = {
"data": [
{
"data": [
{
"dtCreated": "2019-03-21T08:48:49.449397+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-21T08:48:49.449397+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": "Expecting value: line 1 column 1 (char 0)",
"experimentTemplateHistoryId": 297,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "esj8mcpaayh5kx",
"id": 295,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 1,
"state": 7,
"templateHistory": {
"dtCreated": "2019-03-21T08:48:48.436994+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 297,
"params": {
"artifactDirectory": "sagdf",
"clusterId": 342,
"experimentEnv": {
"k": "v"
},
"experimentTypeId": 1,
"name": "dsfads",
"ports": 3245,
"project_handle": "pr4yxj956",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "C2",
"workingDirectory": "sadfas",
"workspaceUrl": "sadfds"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-21T08:48:29.553574+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-21T08:48:29.553574+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": "{'name': 'Error', 'status': 400, 'message': 'Invalid machine type'}",
"experimentTemplateHistoryId": 296,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "estun7jhqta8sm",
"id": 294,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 1,
"state": 7,
"templateHistory": {
"dtCreated": "2019-03-21T08:48:28.467167+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 296,
"params": {
"artifactDirectory": "sagdf",
"clusterId": 342,
"experimentEnv": {
"k": "v"
},
"experimentTypeId": 1,
"name": "dsfads",
"ports": 3245,
"project_handle": "pr4yxj956",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "c2",
"workingDirectory": "sadfas",
"workspaceUrl": "sadfds"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-21T08:44:42.581919+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-21T08:44:42.581919+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": "{'name': 'Error', 'status': 400, 'message': 'Invalid machine type'}",
"experimentTemplateHistoryId": 295,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "es08mwh7ye6zpm",
"id": 293,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 1,
"state": 7,
"templateHistory": {
"dtCreated": "2019-03-21T08:44:41.366528+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 295,
"params": {
"artifactDirectory": "sagdf",
"clusterId": 342,
"experimentEnv": {
"k": "v"
},
"experimentTypeId": 1,
"name": "dsfads",
"ports": 3245,
"project_handle": "pr4yxj956",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "sadas",
"workingDirectory": "sadfas",
"workspaceUrl": "sadfds"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-19T16:35:28.425184+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-19T16:38:40.664560+00:00",
"dtModified": "2019-03-19T16:35:28.425184+00:00",
"dtProvisioningFinished": "2019-03-19T16:37:37.516138+00:00",
"dtProvisioningStarted": "2019-03-19T16:37:37.516138+00:00",
"dtStarted": "2019-03-19T16:37:37.516138+00:00",
"dtTeardownFinished": "2019-03-19T16:38:40.664560+00:00",
"dtTeardownStarted": "2019-03-19T16:38:40.664560+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 38,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "eshdufkt9ti8mx",
"id": 38,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-19T16:35:28.425184+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 38,
"params": {
"command": "python2 hello.py",
"container": "paperspace/tensorflow-python",
"project": "paperspace-python",
"workspaceFileName": "hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-19T14:52:42.476592+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-19T14:53:00.850701+00:00",
"dtModified": "2019-03-19T14:52:42.476592+00:00",
"dtProvisioningFinished": "2019-03-19T14:52:46.844619+00:00",
"dtProvisioningStarted": "2019-03-19T14:52:46.844619+00:00",
"dtStarted": "2019-03-19T14:52:46.844619+00:00",
"dtTeardownFinished": "2019-03-19T14:53:00.850701+00:00",
"dtTeardownStarted": "2019-03-19T14:53:00.850701+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 34,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "e07h0ym8fewm",
"id": 34,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-19T14:52:42.476592+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 34,
"params": {
"command": "python2 hello.py",
"container": "paperspace/tensorflow-python",
"project": "paperspace-python",
"workspaceFileName": "hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-19T14:46:21.605202+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-19T14:46:40.360111+00:00",
"dtModified": "2019-03-19T14:46:21.605202+00:00",
"dtProvisioningFinished": "2019-03-19T14:46:26.094384+00:00",
"dtProvisioningStarted": "2019-03-19T14:46:26.094384+00:00",
"dtStarted": "2019-03-19T14:46:26.094384+00:00",
"dtTeardownFinished": "2019-03-19T14:46:40.360111+00:00",
"dtTeardownStarted": "2019-03-19T14:46:40.360111+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 33,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "e0fq31vyla039",
"id": 33,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-19T14:46:21.605202+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 33,
"params": {
"command": "python3 hello.py",
"container": "paperspace/tensorflow-python",
"project": "paperspace-python",
"workspaceFileName": "hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-19T14:38:38.227536+00:00",
"dtDeleted": None,
"dtFinished": "2019-03-19T14:42:41.723400+00:00",
"dtModified": "2019-03-19T14:38:38.227536+00:00",
"dtProvisioningFinished": "2019-03-19T14:41:34.630690+00:00",
"dtProvisioningStarted": "2019-03-19T14:41:34.630690+00:00",
"dtStarted": "2019-03-19T14:41:34.630690+00:00",
"dtTeardownFinished": "2019-03-19T14:42:41.723400+00:00",
"dtTeardownStarted": "2019-03-19T14:42:41.723400+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 32,
"experimentTemplateId": 8,
"experimentTypeId": 1,
"handle": "esxz3ihfl9qxs2",
"id": 32,
"projectHandle": "pr4yxj956",
"projectId": 14898,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-19T14:38:38.227536+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 32,
"params": {
"command": "python3 hello.py",
"container": "paperspace/tensorflow-python",
"project": "paperspace-python",
"workspaceFileName": "hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
"dtCreated": "2019-03-14T14:06:27.383232+00:00",
"dtDeleted": None,
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},
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},
{
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"templateHistory": {
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"dtDeleted": None,
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"workspaceUrl": "s3://ps-projects/pr4yxj956/jafb4f2w30afo/hello.py.zip"
},
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}
},
{
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"experimentError": None,
"experimentTemplateHistoryId": 31991,
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"handle": "ea1d6t082wq7i",
"id": 31989,
"projectHandle": "pr4yxj956",
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"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 8,
"id": 31991,
"params": {
"clusterId": "clmtkpnm2",
"command": "python3 hello.py",
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"machineType": "K80",
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"projectId": "pr4yxj956",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr4yxj956/jst4l3hoi0hcyn/hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
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"handle": "esbipxwngbmji4",
"id": 31987,
"projectHandle": "pr4yxj956",
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"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
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"params": {
"clusterId": "clmtkpnm2",
"command": "python3 hello.py",
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"workspaceUrl": "s3://ps-projects/pr4yxj956/j5cidd1koia24/hello.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
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},
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},
{
"data": [
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"workspaceFileName": "none"
},
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}
},
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"name": "dsfads",
"ports": 5000,
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"worker_command": "sadas",
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"worker_machine_type": "C2"
},
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}
},
{
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"name": "job 4",
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"workspaceUrl": "s3://ps-projects/pr1opq9uz/j8bxv3ni2zc17/myscript.py.zip"
},
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}
},
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"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/jpk82qnq71w9c/myscript.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
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"handle": "eso8s7e014681",
"id": 32875,
"projectHandle": "pr1opq9uz",
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"state": 5,
"templateHistory": {
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"id": 32877,
"params": {
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"command": "python3 test_remote.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "P5000",
"name": "job 2",
"projectId": "pr1opq9uz",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/jsuqgzne50tlg1/test_remote.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
},
{
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"dtDeleted": None,
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"experimentError": None,
"experimentTemplateHistoryId": 32876,
"experimentTemplateId": 2257,
"experimentTypeId": 1,
"handle": "esml2ioetny5o6",
"id": 32874,
"projectHandle": "pr1opq9uz",
"projectId": 15548,
"started_by_user_id": 199654,
"state": 5,
"templateHistory": {
"dtCreated": "2019-03-22T23:31:15.372159+00:00",
"dtDeleted": None,
"experimentTemplateId": 2257,
"id": 32876,
"params": {
"clusterId": "cls28l0qm",
"command": "python3 myscript.py",
"containerUrl": "paperspace/tensorflow-python",
"isPreemptible": False,
"machineType": "P5000",
"name": "job 1",
"projectId": "pr1opq9uz",
"startedByUserId": "u3z4be26",
"workspaceUrl": "s3://ps-projects/pr1opq9uz/jsnze3m7s2xlpr/myscript.py.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr1opq9uz"
},
"totalItems": 6
}
}
],
"message": "success",
"meta": {
"filter": [],
"limit": 1000000,
"offset": 0,
"totalItems": 18
}
}
LIST_OF_EXPERIMENTS_FILTERED_WITH_TWO_PROJECTS_STDOUT = """+--------+----------------+---------+
| Name | ID | Status |
+--------+----------------+---------+
| dsfads | esj8mcpaayh5kx | failed |
| dsfads | estun7jhqta8sm | failed |
| dsfads | es08mwh7ye6zpm | failed |
| None | eshdufkt9ti8mx | stopped |
| None | e07h0ym8fewm | stopped |
| None | e0fq31vyla039 | stopped |
| None | esxz3ihfl9qxs2 | stopped |
| job 5 | ecqq0rz0gok9i | stopped |
| job 4 | es310784c1o1x6 | stopped |
| job 3 | esabwtehoekn6w | stopped |
| job 2 | ea1d6t082wq7i | stopped |
| job 1 | esbipxwngbmji4 | stopped |
| None | e8lufvr2bh8ld | failed |
| dsfads | esasysj42i2j2s | failed |
| job 4 | estgm33hfbqf5k | stopped |
| job 3 | esbo3pisqq3t68 | stopped |
| job 2 | eso8s7e014681 | stopped |
| job 1 | esml2ioetny5o6 | stopped |
+--------+----------------+---------+
"""
LIST_OF_EXPERIMENTS_FILTERED_BUT_NONE_FOUND = {
"data": [
{
"data": [],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr4yxj95"
},
"totalItems": 0
}
},
{
"data": [],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "pr1opq9u"
},
"totalItems": 0
}
}
],
"message": "success",
"meta": {
"filter": [],
"limit": 1000000,
"offset": 0,
"totalItems": 0
}
}
UPDATE_TAGS_RESPONSE = [
{
"tagId": 1,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.506Z",
"dtDeleted": None,
"userId": 1,
"id": 1,
"entity_id": 3,
"tag": {
"name": "test0",
"dtCreated": "2020-02-01T12:46:58.480Z",
"id": 1
}
},
{
"tagId": 2,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.507Z",
"dtDeleted": None,
"userId": 1,
"id": 2,
"entity_id": 3,
"tag": {
"name": "test2",
"dtCreated": "2020-02-01T12:46:58.481Z",
"id": 2
}
},
{
"tagId": 3,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.509Z",
"dtDeleted": None,
"userId": 1,
"id": 3,
"entity_id": 3,
"tag": {
"name": "test1",
"dtCreated": "2020-02-01T12:46:58.482Z",
"id": 3
}
},
{
"tagId": 4,
"entity": "experiment",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.510Z",
"dtDeleted": None,
"userId": 1,
"id": 4,
"entity_id": 3,
"tag": {
"name": "test3",
"dtCreated": "2020-02-01T12:46:58.483Z",
"id": 4
}
}
]
GET_TAGS_RESPONSE = {
"some_id": [
{
"tagId": 1,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.506Z",
"dtDeleted": None,
"userId": 1,
"id": 1,
"entity_id": 3,
"tag": {
"name": "test0",
"dtCreated": "2020-02-01T12:46:58.480Z",
"id": 1
}
},
{
"tagId": 2,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.507Z",
"dtDeleted": None,
"userId": 1,
"id": 2,
"entity_id": 3,
"tag": {
"name": "test2",
"dtCreated": "2020-02-01T12:46:58.481Z",
"id": 2
}
},
{
"tagId": 3,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.509Z",
"dtDeleted": None,
"userId": 1,
"id": 3,
"entity_id": 3,
"tag": {
"name": "test1",
"dtCreated": "2020-02-01T12:46:58.482Z",
"id": 3
}
},
{
"tagId": 4,
"entity": "entity",
"entityId": 3,
"dtCreated": "2020-02-01T12:46:58.510Z",
"dtDeleted": None,
"userId": 1,
"id": 4,
"entity_id": 3,
"tag": {
"name": "test3",
"dtCreated": "2020-02-01T12:46:58.483Z",
"id": 4
}
}
]
}
CREATE_DEPLOYMENT_WITH_BASIC_OPTIONS_RESPONSE = {
"deployment": {
"id": "sadkfhlskdjh",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "batman.bin.supraman@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://www.latlmes.com/breaking/paperspace-now-has-a-100-bilion-valuation",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "some_oauth_secret",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desanw1jptk7woh:predict",
"ports": None,
"dtCreated": "2019-04-04T10:27:22.659Z",
"dtModified": "2019-04-04T10:27:22.659Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": None,
"dtDeleted": None,
"isDeleted": False
}
}
LIST_DEPLOYMENTS = {
"deploymentList": [
{
"id": "dev61ity7lx232",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": "some_tag",
"params": None,
"code": "some_code",
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": "some_username",
"imagePassword": "some_password",
"workspaceUrl": "https://www.example.com",
"workspaceUsername": "some_username",
"workspacePassword": "some_password",
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "some_auth_token",
"annotations": {},
"oauthKey": "some_oauth_key",
"oauthSecret": "some_oauth_secret",
"serviceType": "some_service_type",
"apiType": "some_api_type",
"endpoint": "https://development-services.paperspace.io/model-serving/dev61ity7lx232:predict",
"ports": "5000",
"dtCreated": "2019-04-04T10:53:56.665Z",
"dtModified": "2019-04-04T10:53:56.729Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:53:56.711Z",
"dtDeleted": None,
"isDeleted": False,
"metricsURL": "aws-testing.paperspace.io",
},
{
"id": "desanw1jptk7woh",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desanw1jptk7woh:predict",
"ports": None,
"dtCreated": "2019-04-04T10:27:22.659Z",
"dtModified": "2019-04-04T10:27:22.726Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:27:22.710Z",
"dtDeleted": None,
"isDeleted": False
},
{
"id": "desfnnrqt1v633v",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desfnnrqt1v633v:predict",
"ports": None,
"dtCreated": "2019-04-04T10:27:10.180Z",
"dtModified": "2019-04-04T10:27:10.248Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:27:10.230Z",
"dtDeleted": None,
"isDeleted": False
},
{
"id": "desdyn55d2e02su",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/desdyn55d2e02su:predict",
"ports": None,
"dtCreated": "2019-04-04T10:11:10.204Z",
"dtModified": "2019-04-04T10:11:10.281Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T10:11:10.260Z",
"dtDeleted": None,
"isDeleted": False
},
{
"id": "des3tmqa3s627o9",
"ownerId": "tejxgn5bt",
"projectId": "prmr22ve0",
"experimentId": "ehla1kvbwzaco",
"state": "Stopped",
"startedByUserId": "ukgvw4i8",
"startedByUserEmail": "bartosz@paperspace.com",
"deploymentType": "TFServing",
"modelId": "mosu30xm7q8vb0p",
"modelUrl": "s3://ps-projects-development/prmr22ve0/ehla1kvbwzaco/model/",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "PS Jobs on GCP",
"machineType": "K80",
"imageUrl": "https://github.com/Paperspace/mnist-sample",
"imageUsername": None,
"imagePassword": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": "-----",
"annotations": {},
"oauthKey": "-----",
"oauthSecret": "-----",
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://development-services.paperspace.io/model-serving/des3tmqa3s627o9:predict",
"ports": None,
"dtCreated": "2019-04-04T09:29:22.277Z",
"dtModified": "2019-04-04T09:29:22.345Z",
"dtBuildStarted": None,
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2019-04-04T09:29:22.327Z",
"dtDeleted": None,
"isDeleted": False
}
],
"total": 5,
"displayTotal": 5,
"runningTotal": 0
}
CREATE_MACHINE_RESPONSE = {
"id": "psclbvqpc",
"name": "some_machine",
"os": None,
"ram": None,
"cpus": 1,
"gpu": None,
"storageTotal": None,
"storageUsed": None,
"usageRate": "Pro hourly",
"shutdownTimeoutInHours": 168,
"shutdownTimeoutForces": False,
"performAutoSnapshot": False,
"autoSnapshotFrequency": None,
"autoSnapshotSaveCount": None,
"dynamicPublicIp": False,
"agentType": "WindowsDesktop",
"dtCreated": "2019-04-10T14:19:49.852Z",
"state": "provisioning",
"updatesPending": False,
"networkId": None,
"privateIpAddress": None,
"publicIpAddress": None,
"region": None,
"userId": "u3z4be26",
"teamId": "te6hh34n6",
"scriptId": None,
"dtLastRun": None
}
LIST_MACHINES_RESPONSE = [
{
"ram": None,
"userId": "u3z4be26",
"cpus": 1,
"teamId": "te6hh34n6",
"updatesPending": False,
"dynamicPublicIp": False,
"storageTotal": None,
"shutdownTimeoutForces": False,
"id": "psclbvqpc",
"shutdownTimeoutInHours": 168,
"state": "provisioning",
"usageRate": "Pro hourly",
"publicIpAddress": None,
"gp": None,
"privateIpAddress": None,
"dtCreated": "2019-04-10T14:19:49.852Z",
"dtLastRun": None,
"storageUsed": None,
"scriptId": None,
"autoSnapshotSaveCount": None,
"name": "keton2",
"agentType": "WindowsDesktop",
"performAutoSnapshot": False,
"networkId": None,
"autoSnapshotFrequency": None,
"os": None, "region": None
},
{
"ram": "536870912",
"userId": "u3z4be26",
"cpus": 1,
"teamId": "te6hh34n6",
"updatesPending": False,
"dynamicPublicIp": False,
"storageTotal": "53687091200",
"shutdownTimeoutForces": False,
"id": "psbtuwfvt",
"shutdownTimeoutInHours": 1,
"state": "off",
"usageRate": "C1 Hourly",
"publicIpAddress": None,
"gp": "None",
"privateIpAddress": "10.64.14.135",
"dtCreated": "2019-04-11T18:10:29.665Z",
"dtLastRun": None,
"storageUsed": "110080",
"scriptId": None,
"autoSnapshotSaveCount": 1,
"name": "New Machine 1",
"agentType": "LinuxHeadless",
"performAutoSnapshot": True,
"networkId": "nng82wb",
"autoSnapshotFrequency": "month",
"os": "Ubuntu 18.04.1 LTS; uname: 4.15.0-38-generic; distro: ubuntu; major: 18; minor: 04",
"region": "East Coast (NY2)"
}
]
SHOW_MACHINE_RESPONSE = {
"id": "some_id",
"name": "New Machine 1",
"os": "Ubuntu 18.04.1 LTS; uname: 4.15.0-38-generic; distro: ubuntu; major: 18; minor: 04",
"ram": "536870912",
"cpus": 1,
"gpu": "None",
"storageTotal": "53687091200",
"storageUsed": "110080",
"usageRate": "C1 Hourly",
"shutdownTimeoutInHours": 1,
"shutdownTimeoutForces": False,
"performAutoSnapshot": True,
"autoSnapshotFrequency": "month",
"autoSnapshotSaveCount": 1,
"dynamicPublicIp": False,
"agentType": "LinuxHeadless",
"dtCreated": "2019-04-11T18:10:29.665Z",
"state": "off",
"updatesPending": False,
"networkId": "nng82wb",
"privateIpAddress": "10.64.14.135",
"publicIpAddress": None,
"region": "East Coast (NY2)",
"userId": "u3z4be26",
"teamId": "te6hh34n6",
"scriptId": None,
"dtLastRun": None,
"events": [
{
"name": "restart",
"state": "done",
"errorMsg": "",
"handle": "042454c4-af4c-489a-8bff-b4d3e9f39f9e",
"dtModified": "2019-04-12T12:19:05.801Z",
"dtFinished": "2019-04-12T12:19:05.801Z",
"dtCreated": "2019-04-12T12:19:03.814Z"
},
{
"name": "stop",
"state": "done",
"errorMsg": "",
"handle": "07fe20ce-35c4-4949-afbd-103d1afc6797",
"dtModified": "2019-04-11T19:11:19.942Z",
"dtFinished": "2019-04-11T19:11:19.942Z",
"dtCreated": "2019-04-11T19:11:14.205Z"
},
{
"name": "snapshot-create",
"state": "done",
"errorMsg": "",
"handle": "7fa0ab4f-896d-42ac-a2c5-54f0a94c2310",
"dtModified": "2019-04-11T18:14:36.702Z",
"dtFinished": "2019-04-11T18:14:36.702Z",
"dtCreated": "2019-04-11T18:14:33.216Z"
},
{
"name": "start",
"state": "done",
"errorMsg": "",
"handle": "c04cccdf-3823-4ef1-b1ab-83b3ccdcfcb7",
"dtModified": "2019-04-11T18:11:00.371Z",
"dtFinished": "2019-04-11T18:11:00.371Z",
"dtCreated": "2019-04-11T18:10:45.468Z"
},
{
"name": "create",
"state": "done",
"errorMsg": "",
"handle": "34f64d24-1161-42f0-871d-1ed03b10dfc0",
"dtModified": "2019-04-11T18:10:45.473Z",
"dtFinished": "2019-04-11T18:10:45.473Z",
"dtCreated": "2019-04-11T18:10:29.665Z"
}
]
}
SHOW_MACHINE_UTILIZATION_RESPONSE = {
"machineId": "some_key",
"utilization": {
"machineId": "some_key",
"secondsUsed": 0,
"billingMonth": "2017-09",
"hourlyRate": 0,
},
"storageUtilization": {
"machineId": "some_key",
"secondsUsed": 256798.902394,
"monthlyRate": "5.00",
"billingMonth": "2017-09",
},
}
LIST_PROJECTS_RESPONSE = {
"data": [
{
"name": "test_project",
"handle": "prq70zy79",
"dtCreated": "2019-03-18T13:24:46.666Z",
"dtDeleted": None,
"lastJobSeqNum": 2,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"experiments": {
"data": [
{
"dtCreated": "2019-04-05T15:10:55.692629+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-04-05T15:10:55.692629+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 22159,
"experimentTemplateId": 60,
"experimentTypeId": 1,
"handle": "estgcoux8igx32",
"id": 22123,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1655,
"state": 1,
"templateHistory": {
"dtCreated": "2019-04-05T15:10:54.923725+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 22159,
"params": {
"is_preemptible": False,
"name": "dsfads",
"ports": 5000,
"project_handle": "prq70zy79",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "C2",
"worker_use_dockerfile": False,
"workspaceUrl": "example.com"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "prq70zy79"
},
"totalItems": 1
}
}
},
{
"name": "keton",
"handle": "prmr22ve0",
"dtCreated": "2019-03-25T14:50:43.202Z",
"dtDeleted": None,
"lastJobSeqNum": 8,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"experiments": {
"data": [
{
"dtCreated": "2019-04-02T15:17:03.393886+00:00",
"dtDeleted": None,
"dtFinished": "2019-04-02T17:02:54.654569+00:00",
"dtModified": "2019-04-02T15:17:03.393886+00:00",
"dtProvisioningFinished": "2019-04-02T15:17:10.978198+00:00",
"dtProvisioningStarted": "2019-04-02T15:17:10.978198+00:00",
"dtStarted": "2019-04-02T15:17:10.978198+00:00",
"dtTeardownFinished": "2019-04-02T17:02:54.654569+00:00",
"dtTeardownStarted": "2019-04-02T17:02:54.654569+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 22130,
"experimentTemplateId": 174,
"experimentTypeId": 1,
"handle": "ehla1kvbwzaco",
"id": 22094,
"projectHandle": "prmr22ve0",
"projectId": 626,
"started_by_user_id": 1655,
"state": 5,
"templateHistory": {
"dtCreated": "2019-04-02T15:17:02.663449+00:00",
"dtDeleted": None,
"experimentTemplateId": 174,
"id": 22130,
"params": {
"is_preemptible": False,
"model_path": "/artifacts",
"model_type": "Tensorflow",
"name": "Test1",
"ports": 5000,
"project_handle": "prmr22ve0",
"worker_command": "python mnist.py --data_format=channels_last",
"worker_container": "tensorflow/tensorflow:1.13.1-py3",
"worker_machine_type": "K80",
"workspaceUrl": "https://github.com/Paperspace/mnist-sample"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "prmr22ve0"
},
"totalItems": 1
}
}
},
{
"name": "paperspace-python",
"handle": "przhbct98",
"dtCreated": "2019-04-04T15:12:34.229Z",
"dtDeleted": None,
"lastJobSeqNum": 3,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"experiments": {
"data": [
{
"dtCreated": "2019-04-24T10:18:30.523193+00:00",
"dtDeleted": None,
"dtFinished": "2019-04-24T10:18:43.613748+00:00",
"dtModified": "2019-04-24T10:18:30.523193+00:00",
"dtProvisioningFinished": "2019-04-24T10:18:35.010792+00:00",
"dtProvisioningStarted": "2019-04-24T10:18:35.010792+00:00",
"dtStarted": "2019-04-24T10:18:35.010792+00:00",
"dtTeardownFinished": "2019-04-24T10:18:43.613748+00:00",
"dtTeardownStarted": "2019-04-24T10:18:43.613748+00:00",
"experimentError": None,
"experimentTemplateHistoryId": 22311,
"experimentTemplateId": 186,
"experimentTypeId": 1,
"handle": "es47og38wzhnuo",
"id": 22270,
"projectHandle": "przhbct98",
"projectId": 649,
"started_by_user_id": 1655,
"state": 7,
"templateHistory": {
"dtCreated": "2019-04-24T10:18:30.523193+00:00",
"dtDeleted": None,
"experimentTemplateId": 186,
"id": 22311,
"params": {
"command": ". test.sh\npython2 hello.py",
"container": "paperspace/tensorflow-python",
"machineType": "G1",
"project": "paperspace-python",
"workspaceFileName": "temp.zip"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "przhbct98"
},
"totalItems": 1
}
}
}
],
"meta": {
"totalItems": 3
}
}
LIST_JOBS_RESPONSE_JSON = {
"jobList": [
{
"id": "jsxeeba5qq99yn",
"name": "job 1",
"state": "Error",
"workspaceUrl": "none",
"workingDirectory": "/paperspace",
"artifactsDirectory": "/artifacts",
"entrypoint": "nvidia-smi",
"projectId": "prmr22ve0",
"project": "keton",
"container": "Test-Container",
"containerUrl": "paperspace/tensorflow:1.5.0-gpu",
"baseContainer": "Test-Container",
"baseContainerUrl": "paperspace/tensorflow:1.5.0-gpu",
"machineType": "K80",
"cluster": "PS Jobs on GCP",
"clusterId": "clkyczmyz",
"usageRate": "K80 hourly",
"startedByUserId": "ukgvw4i8",
"parentJobId": None,
"jobError": "Error quering for experiment for job: sql: Scan error on column index 4, name \"model_path\": unsupported Scan, storing driver.Value type <nil> into type *string",
"dtCreated": "2019-03-25T14:51:16.118Z",
"dtModified": "2019-03-25T14:51:16.118Z",
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-03-27T13:53:34.188Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"exitCode": None,
"queuePosition": None,
"seqNum": 1,
"storageRegion": "GCP West",
"clusterMachine": "gradient-host-1553525480",
"fqdn": "jsxeeba5qq99yn.dgradient.paperspace.com",
"ports": None,
"isPublic": None,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": None,
"cpuCount": None,
"cpuModel": None,
"cpuFlags": None,
"cpuMem": None,
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
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{
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{
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{
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{
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},
{
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"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.274983: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): \u003cundefined\u003e, \u003cundefined\u003e"
},
{
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"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.275358: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: "
},
{
"jobId": "jsy2ibsz1l026y",
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"timestamp": "2019-07-08T12:40:59.275Z",
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{
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{
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},
{
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"timestamp": "2019-07-08T12:40:59.275Z",
"message": "2019-07-08 12:40:59.275392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
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{
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"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276439: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
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"line": 14,
"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
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{
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"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276483: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
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"timestamp": "2019-07-08T12:40:59.276Z",
"message": "2019-07-08 12:40:59.276778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 17,
"timestamp": "2019-07-08T12:40:59.28Z",
"message": "WARNING: Logging before flag parsing goes to stderr."
},
{
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"line": 18,
"timestamp": "2019-07-08T12:40:59.28Z",
"message": "I0708 12:40:59.280403 140451862599424 mnist.py:265] ==================== Environment Variables ===================="
},
{
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"line": 19,
"timestamp": "2019-07-08T12:40:59.28Z",
"message": "I0708 12:40:59.280700 140451862599424 mnist.py:267] NVIDIA_VISIBLE_DEVICES: all"
},
{
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"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.280864 140451862599424 mnist.py:267] PS_DOCKER_AGENT: 6136343261613365"
},
{
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"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.280993 140451862599424 mnist.py:267] EVAL_SECS: 10"
},
{
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"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281114 140451862599424 mnist.py:267] PATH: /usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
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{
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"message": "I0708 12:40:59.281230 140451862599424 mnist.py:267] PS_HOST_PRIVATE_IP_ADDRESS: 10.138.0.15"
},
{
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"message": "I0708 12:40:59.281354 140451862599424 mnist.py:267] PS_HOSTNAME: gradient-host-1562589161"
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{
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"message": "I0708 12:40:59.281510 140451862599424 mnist.py:267] HOSTNAME: 9b41cad9ccda"
},
{
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"message": "I0708 12:40:59.281623 140451862599424 mnist.py:267] EPOCHS_EVAL: 5"
},
{
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"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281736 140451862599424 mnist.py:267] PS_JOBSPACE: /storage"
},
{
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"timestamp": "2019-07-08T12:40:59.281Z",
"message": "I0708 12:40:59.281847 140451862599424 mnist.py:267] PS_JOB_RUNNER: 1"
},
{
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"line": 29,
"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.281965 140451862599424 mnist.py:267] NVIDIA_REQUIRE_CUDA: cuda\u003e=10.0 brand=tesla,driver\u003e=384,driver\u003c385 brand=tesla,driver\u003e=410,driver\u003c411"
},
{
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"message": "I0708 12:40:59.282079 140451862599424 mnist.py:267] CUDA_PKG_VERSION: 10-0=10.0.130-1"
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{
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"message": "I0708 12:40:59.282189 140451862599424 mnist.py:267] LD_LIBRARY_PATH: /usr/local/cuda/extras/CUPTI/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64"
},
{
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"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282299 140451862599424 mnist.py:267] TYPE: master"
},
{
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"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282416 140451862599424 mnist.py:267] PS_DATASETS: /datasets"
},
{
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"message": "I0708 12:40:59.282531 140451862599424 mnist.py:267] TRAIN_EPOCHS: 10"
},
{
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"message": "I0708 12:40:59.282641 140451862599424 mnist.py:267] MAX_STEPS: 1000"
},
{
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"timestamp": "2019-07-08T12:40:59.282Z",
"message": "I0708 12:40:59.282753 140451862599424 mnist.py:267] PS_HOME: /paperspace"
},
{
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"message": "I0708 12:40:59.282861 140451862599424 mnist.py:267] INDEX: 0"
},
{
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"message": "I0708 12:40:59.282970 140451862599424 mnist.py:267] CUDA_VERSION: 10.0.130"
},
{
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"message": "I0708 12:40:59.283078 140451862599424 mnist.py:267] PORTS: 5000"
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{
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"message": "I0708 12:40:59.283186 140451862599424 mnist.py:267] PS_ARTIFACTS: /artifacts"
},
{
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"message": "I0708 12:40:59.283293 140451862599424 mnist.py:267] NVIDIA_DRIVER_CAPABILITIES: compute,utility"
},
{
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"message": "I0708 12:40:59.283408 140451862599424 mnist.py:267] PS_HOST_PUBLIC_IP_ADDRESS: 35.233.198.134"
},
{
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"message": "I0708 12:40:59.283516 140451862599424 mnist.py:267] LANG: C.UTF-8"
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{
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"message": "I0708 12:40:59.283623 140451862599424 mnist.py:267] PS_JOB_ID: jsy2ibsz1l026y"
},
{
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"timestamp": "2019-07-08T12:40:59.283Z",
"message": "I0708 12:40:59.283730 140451862599424 mnist.py:267] HOME: /root"
},
{
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"line": 46,
"timestamp": "2019-07-08T12:40:59.862Z",
"message": "I0708 12:40:59.862003 140451862599424 run_config.py:532] Initializing RunConfig with distribution strategies."
},
{
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"line": 47,
"timestamp": "2019-07-08T12:40:59.862Z",
"message": "I0708 12:40:59.862317 140451862599424 estimator_training.py:166] Not using Distribute Coordinator."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 48,
"timestamp": "2019-07-08T12:40:59.863Z",
"message": "I0708 12:40:59.862842 140451862599424 estimator.py:201] Using config: {'_master': '', '_num_worker_replicas': 1, '_num_ps_replicas': 0, '_save_checkpoints_secs': None, '_log_step_count_steps': 100, '_train_distribute': \u003ctensorflow.contrib.distribute.python.one_device_strategy.OneDeviceStrategy object at 0x7fbcb5adecc0\u003e, '_protocol': None, '_session_config': allow_soft_placement: True"
},
{
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"timestamp": "2019-07-08T12:40:59.863Z",
"message": ", '_tf_random_seed': None, '_global_id_in_cluster': 0, '_is_chief': True, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/paperspace/models/mnist', '_save_summary_steps': 100, '_service': None, '_experimental_distribute': None, '_cluster_spec': \u003ctensorflow.python.training.server_lib.ClusterSpec object at 0x7fbcb34f7828\u003e, '_save_checkpoints_steps': 600, '_evaluation_master': '', '_eval_distribute': None, '_task_id': 0, '_distribute_coordinator_mode': None, '_task_type': 'worker', '_device_fn': None}"
},
{
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"line": 50,
"timestamp": "2019-07-08T12:40:59.863Z",
"message": "I0708 12:40:59.863682 140451862599424 estimator_training.py:185] Not using Distribute Coordinator."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 51,
"timestamp": "2019-07-08T12:40:59.864Z",
"message": "I0708 12:40:59.864074 140451862599424 training.py:610] Running training and evaluation locally (non-distributed)."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 52,
"timestamp": "2019-07-08T12:40:59.864Z",
"message": "I0708 12:40:59.864597 140451862599424 training.py:698] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 600 or save_checkpoints_secs None."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 53,
"timestamp": "2019-07-08T12:41:01.276Z",
"message": "W0708 12:41:01.276278 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/data/ops/dataset_ops.py:1419: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 54,
"timestamp": "2019-07-08T12:41:01.276Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 55,
"timestamp": "2019-07-08T12:41:01.276Z",
"message": "Colocations handled automatically by placer."
},
{
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"line": 56,
"timestamp": "2019-07-08T12:41:01.292Z",
"message": "I0708 12:41:01.291953 140451862599424 estimator.py:1111] Calling model_fn."
},
{
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"line": 57,
"timestamp": "2019-07-08T12:41:01.389Z",
"message": "W0708 12:41:01.389566 140451862599424 deprecation.py:506] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version."
},
{
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"line": 58,
"timestamp": "2019-07-08T12:41:01.389Z",
"message": "Instructions for updating:"
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{
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"line": 59,
"timestamp": "2019-07-08T12:41:01.389Z",
"message": "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`."
},
{
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"line": 60,
"timestamp": "2019-07-08T12:41:01.537Z",
"message": "W0708 12:41:01.536658 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/losses/losses_impl.py:209: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version."
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{
"jobId": "jsy2ibsz1l026y",
"line": 61,
"timestamp": "2019-07-08T12:41:01.537Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 62,
"timestamp": "2019-07-08T12:41:01.537Z",
"message": "Use tf.cast instead."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 63,
"timestamp": "2019-07-08T12:41:01.86Z",
"message": "I0708 12:41:01.860109 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 64,
"timestamp": "2019-07-08T12:41:01.909Z",
"message": "I0708 12:41:01.909625 140451862599424 basic_session_run_hooks.py:527] Create CheckpointSaverHook."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 65,
"timestamp": "2019-07-08T12:41:02.104Z",
"message": "I0708 12:41:02.104314 140451862599424 monitored_session.py:222] Graph was finalized."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 66,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105148: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 67,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 68,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 69,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 70,
"timestamp": "2019-07-08T12:41:02.105Z",
"message": "2019-07-08 12:41:02.105612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 71,
"timestamp": "2019-07-08T12:41:03.052Z",
"message": "I0708 12:41:03.051607 140451862599424 session_manager.py:491] Running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 72,
"timestamp": "2019-07-08T12:41:03.064Z",
"message": "I0708 12:41:03.064292 140451862599424 session_manager.py:493] Done running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 73,
"timestamp": "2019-07-08T12:41:03.405Z",
"message": "I0708 12:41:03.404671 140451862599424 basic_session_run_hooks.py:594] Saving checkpoints for 0 into /paperspace/models/mnist/model.ckpt."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 74,
"timestamp": "2019-07-08T12:41:03.616Z",
"message": "I0708 12:41:03.616098 140451862599424 util.py:164] Initialize strategy"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 75,
"timestamp": "2019-07-08T12:41:03.715Z",
"message": "2019-07-08 12:41:03.715695: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 76,
"timestamp": "2019-07-08T12:41:09.915Z",
"message": "I0708 12:41:09.915456 140451862599424 basic_session_run_hooks.py:249] cross_entropy = 2.3025289, learning_rate = 1e-04, train_accuracy = 0.08"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 77,
"timestamp": "2019-07-08T12:41:09.916Z",
"message": "I0708 12:41:09.915941 140451862599424 basic_session_run_hooks.py:249] loss = 2.3025289, step = 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 78,
"timestamp": "2019-07-08T12:41:11.313Z",
"message": "I0708 12:41:11.313229 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 71.5095"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 79,
"timestamp": "2019-07-08T12:41:11.314Z",
"message": "I0708 12:41:11.314374 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.3830725, learning_rate = 1e-04, train_accuracy = 0.49 (1.399 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 80,
"timestamp": "2019-07-08T12:41:11.314Z",
"message": "I0708 12:41:11.314673 140451862599424 basic_session_run_hooks.py:247] loss = 0.3830725, step = 100 (1.399 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 81,
"timestamp": "2019-07-08T12:41:12.369Z",
"message": "I0708 12:41:12.368999 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 94.7225"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 82,
"timestamp": "2019-07-08T12:41:12.37Z",
"message": "I0708 12:41:12.370402 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.19424663, learning_rate = 1e-04, train_accuracy = 0.6433333 (1.056 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 83,
"timestamp": "2019-07-08T12:41:12.371Z",
"message": "I0708 12:41:12.370768 140451862599424 basic_session_run_hooks.py:247] loss = 0.19424663, step = 200 (1.056 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 84,
"timestamp": "2019-07-08T12:41:13.424Z",
"message": "I0708 12:41:13.423866 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 94.7932"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 85,
"timestamp": "2019-07-08T12:41:13.425Z",
"message": "I0708 12:41:13.425122 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.20186995, learning_rate = 1e-04, train_accuracy = 0.7125 (1.055 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 86,
"timestamp": "2019-07-08T12:41:13.425Z",
"message": "I0708 12:41:13.425472 140451862599424 basic_session_run_hooks.py:247] loss = 0.20186995, step = 300 (1.055 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 87,
"timestamp": "2019-07-08T12:41:14.457Z",
"message": "I0708 12:41:14.457170 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 96.7791"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 88,
"timestamp": "2019-07-08T12:41:14.458Z",
"message": "I0708 12:41:14.458280 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.14686151, learning_rate = 1e-04, train_accuracy = 0.762 (1.033 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 89,
"timestamp": "2019-07-08T12:41:14.458Z",
"message": "I0708 12:41:14.458536 140451862599424 basic_session_run_hooks.py:247] loss = 0.14686151, step = 400 (1.033 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 90,
"timestamp": "2019-07-08T12:41:15.531Z",
"message": "I0708 12:41:15.530984 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 93.1259"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 91,
"timestamp": "2019-07-08T12:41:15.532Z",
"message": "I0708 12:41:15.532083 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.115687564, learning_rate = 1e-04, train_accuracy = 0.795 (1.074 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 92,
"timestamp": "2019-07-08T12:41:15.532Z",
"message": "I0708 12:41:15.532342 140451862599424 basic_session_run_hooks.py:247] loss = 0.115687564, step = 500 (1.074 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 93,
"timestamp": "2019-07-08T12:41:16.563Z",
"message": "I0708 12:41:16.563513 140451862599424 basic_session_run_hooks.py:594] Saving checkpoints for 600 into /paperspace/models/mnist/model.ckpt."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 94,
"timestamp": "2019-07-08T12:41:17.176Z",
"message": "I0708 12:41:17.176506 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 95,
"timestamp": "2019-07-08T12:41:17.385Z",
"message": "I0708 12:41:17.385438 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 96,
"timestamp": "2019-07-08T12:41:17.41Z",
"message": "I0708 12:41:17.410446 140451862599424 evaluation.py:257] Starting evaluation at 2019-07-08T12:41:17Z"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 97,
"timestamp": "2019-07-08T12:41:17.517Z",
"message": "I0708 12:41:17.517657 140451862599424 monitored_session.py:222] Graph was finalized."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 98,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 99,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 100,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 101,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 102,
"timestamp": "2019-07-08T12:41:17.518Z",
"message": "2019-07-08 12:41:17.518626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 103,
"timestamp": "2019-07-08T12:41:17.519Z",
"message": "W0708 12:41:17.519085 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 104,
"timestamp": "2019-07-08T12:41:17.519Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 105,
"timestamp": "2019-07-08T12:41:17.519Z",
"message": "Use standard file APIs to check for files with this prefix."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 106,
"timestamp": "2019-07-08T12:41:17.52Z",
"message": "I0708 12:41:17.520449 140451862599424 saver.py:1270] Restoring parameters from /paperspace/models/mnist/model.ckpt-600"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 107,
"timestamp": "2019-07-08T12:41:17.572Z",
"message": "I0708 12:41:17.572486 140451862599424 session_manager.py:491] Running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 108,
"timestamp": "2019-07-08T12:41:17.584Z",
"message": "I0708 12:41:17.584268 140451862599424 session_manager.py:493] Done running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 109,
"timestamp": "2019-07-08T12:41:19.062Z",
"message": "I0708 12:41:19.062499 140451862599424 evaluation.py:277] Finished evaluation at 2019-07-08-12:41:19"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 110,
"timestamp": "2019-07-08T12:41:19.063Z",
"message": "I0708 12:41:19.062822 140451862599424 estimator.py:1979] Saving dict for global step 600: accuracy = 0.9691, global_step = 600, loss = 0.10409811"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 111,
"timestamp": "2019-07-08T12:41:19.113Z",
"message": "I0708 12:41:19.113427 140451862599424 estimator.py:2039] Saving 'checkpoint_path' summary for global step 600: /paperspace/models/mnist/model.ckpt-600"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 112,
"timestamp": "2019-07-08T12:41:19.126Z",
"message": "I0708 12:41:19.125805 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 27.817"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 113,
"timestamp": "2019-07-08T12:41:19.126Z",
"message": "I0708 12:41:19.126667 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.108451165, learning_rate = 1e-04, train_accuracy = 0.82285714 (3.595 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 114,
"timestamp": "2019-07-08T12:41:19.127Z",
"message": "I0708 12:41:19.126947 140451862599424 basic_session_run_hooks.py:247] loss = 0.108451165, step = 600 (3.595 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 115,
"timestamp": "2019-07-08T12:41:20.185Z",
"message": "I0708 12:41:20.185327 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 94.39"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 116,
"timestamp": "2019-07-08T12:41:20.186Z",
"message": "I0708 12:41:20.186514 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.122035645, learning_rate = 1e-04, train_accuracy = 0.8425 (1.060 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 117,
"timestamp": "2019-07-08T12:41:20.186Z",
"message": "I0708 12:41:20.186774 140451862599424 basic_session_run_hooks.py:247] loss = 0.122035645, step = 700 (1.060 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 118,
"timestamp": "2019-07-08T12:41:21.256Z",
"message": "I0708 12:41:21.255784 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 93.4188"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 119,
"timestamp": "2019-07-08T12:41:21.257Z",
"message": "I0708 12:41:21.256838 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.07952806, learning_rate = 1e-04, train_accuracy = 0.8577778 (1.070 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 120,
"timestamp": "2019-07-08T12:41:21.257Z",
"message": "I0708 12:41:21.257101 140451862599424 basic_session_run_hooks.py:247] loss = 0.07952806, step = 800 (1.070 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 121,
"timestamp": "2019-07-08T12:41:22.288Z",
"message": "I0708 12:41:22.287836 140451862599424 basic_session_run_hooks.py:680] global_step/sec: 96.8942"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 122,
"timestamp": "2019-07-08T12:41:22.289Z",
"message": "I0708 12:41:22.288971 140451862599424 basic_session_run_hooks.py:247] cross_entropy = 0.12382953, learning_rate = 1e-04, train_accuracy = 0.868 (1.032 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 123,
"timestamp": "2019-07-08T12:41:22.289Z",
"message": "I0708 12:41:22.289424 140451862599424 basic_session_run_hooks.py:247] loss = 0.12382953, step = 900 (1.032 sec)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 124,
"timestamp": "2019-07-08T12:41:23.297Z",
"message": "I0708 12:41:23.297554 140451862599424 basic_session_run_hooks.py:594] Saving checkpoints for 1000 into /paperspace/models/mnist/model.ckpt."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 125,
"timestamp": "2019-07-08T12:41:23.399Z",
"message": "I0708 12:41:23.398564 140451862599424 training.py:525] Skip the current checkpoint eval due to throttle secs (10 secs)."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 126,
"timestamp": "2019-07-08T12:41:23.451Z",
"message": "I0708 12:41:23.450761 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 127,
"timestamp": "2019-07-08T12:41:23.655Z",
"message": "I0708 12:41:23.655447 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 128,
"timestamp": "2019-07-08T12:41:23.681Z",
"message": "I0708 12:41:23.681046 140451862599424 evaluation.py:257] Starting evaluation at 2019-07-08T12:41:23Z"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 129,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "I0708 12:41:23.871646 140451862599424 monitored_session.py:222] Graph was finalized."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 130,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 131,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 132,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872338: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 133,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872353: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 134,
"timestamp": "2019-07-08T12:41:23.872Z",
"message": "2019-07-08 12:41:23.872548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 135,
"timestamp": "2019-07-08T12:41:23.874Z",
"message": "I0708 12:41:23.874099 140451862599424 saver.py:1270] Restoring parameters from /paperspace/models/mnist/model.ckpt-1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 136,
"timestamp": "2019-07-08T12:41:23.932Z",
"message": "I0708 12:41:23.932351 140451862599424 session_manager.py:491] Running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 137,
"timestamp": "2019-07-08T12:41:23.946Z",
"message": "I0708 12:41:23.946145 140451862599424 session_manager.py:493] Done running local_init_op."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 138,
"timestamp": "2019-07-08T12:41:25.49Z",
"message": "I0708 12:41:25.490408 140451862599424 evaluation.py:277] Finished evaluation at 2019-07-08-12:41:25"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 139,
"timestamp": "2019-07-08T12:41:25.491Z",
"message": "I0708 12:41:25.490850 140451862599424 estimator.py:1979] Saving dict for global step 1000: accuracy = 0.9787, global_step = 1000, loss = 0.0693267"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 140,
"timestamp": "2019-07-08T12:41:25.491Z",
"message": "I0708 12:41:25.491545 140451862599424 estimator.py:2039] Saving 'checkpoint_path' summary for global step 1000: /paperspace/models/mnist/model.ckpt-1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 141,
"timestamp": "2019-07-08T12:41:25.492Z",
"message": "I0708 12:41:25.492296 140451862599424 training.py:928] Calling exporter with the `is_the_final_export=True`."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 142,
"timestamp": "2019-07-08T12:41:25.492Z",
"message": "I0708 12:41:25.492531 140451862599424 util.py:168] Finalize strategy."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 143,
"timestamp": "2019-07-08T12:41:25.572Z",
"message": "I0708 12:41:25.571871 140451862599424 estimator.py:359] Loss for final step: 0.03955281."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 144,
"timestamp": "2019-07-08T12:41:25.572Z",
"message": "I0708 12:41:25.572580 140451862599424 mnist.py:240] Starting to Export model to /paperspace/models"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 145,
"timestamp": "2019-07-08T12:41:25.583Z",
"message": "I0708 12:41:25.583298 140451862599424 estimator.py:1111] Calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 146,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "I0708 12:41:25.744162 140451862599424 estimator.py:1113] Done calling model_fn."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 147,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "W0708 12:41:25.744491 140451862599424 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:205: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 148,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "Instructions for updating:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 149,
"timestamp": "2019-07-08T12:41:25.744Z",
"message": "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 150,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745018 140451862599424 export.py:587] Signatures INCLUDED in export for Regress: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 151,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745139 140451862599424 export.py:587] Signatures INCLUDED in export for Train: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 152,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745274 140451862599424 export.py:587] Signatures INCLUDED in export for Predict: ['serving_default', 'classify']"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 153,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745424 140451862599424 export.py:587] Signatures INCLUDED in export for Classify: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 154,
"timestamp": "2019-07-08T12:41:25.745Z",
"message": "I0708 12:41:25.745549 140451862599424 export.py:587] Signatures INCLUDED in export for Eval: None"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 155,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 156,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746099: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 157,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 158,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N "
},
{
"jobId": "jsy2ibsz1l026y",
"line": 159,
"timestamp": "2019-07-08T12:41:25.746Z",
"message": "2019-07-08 12:41:25.746278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10787 MB memory) -\u003e physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 160,
"timestamp": "2019-07-08T12:41:25.779Z",
"message": "I0708 12:41:25.778900 140451862599424 saver.py:1270] Restoring parameters from /paperspace/models/mnist/model.ckpt-1000"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 161,
"timestamp": "2019-07-08T12:41:25.814Z",
"message": "I0708 12:41:25.814109 140451862599424 builder_impl.py:654] Assets added to graph."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 162,
"timestamp": "2019-07-08T12:41:25.814Z",
"message": "I0708 12:41:25.814344 140451862599424 builder_impl.py:449] No assets to write."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 163,
"timestamp": "2019-07-08T12:41:25.878Z",
"message": "I0708 12:41:25.877661 140451862599424 builder_impl.py:414] SavedModel written to: /paperspace/models/temp-b'1562589685'/saved_model.pb"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 164,
"timestamp": "2019-07-08T12:41:25.878Z",
"message": "I0708 12:41:25.878680 140451862599424 mnist.py:247] Model Exported"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 165,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Gradient SDK not installed. Distributed training is not possible"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 166,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": ""
},
{
"jobId": "jsy2ibsz1l026y",
"line": 167,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 168,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "For more information, please see:"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 169,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 170,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": " * https://github.com/tensorflow/addons"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 171,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "If you depend on functionality not listed there, please file an issue."
},
{
"jobId": "jsy2ibsz1l026y",
"line": 172,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": ""
},
{
"jobId": "jsy2ibsz1l026y",
"line": 173,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz to /tmp/tmp5a21acuc.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 174,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz to /tmp/tmpl6cdsahk.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 175,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz to /tmp/tmplh4ob02m.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 176,
"timestamp": "2019-07-08T12:41:25.879Z",
"message": "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz to /tmp/tmpbrss4txl.gz"
},
{
"jobId": "jsy2ibsz1l026y",
"line": 177,
"timestamp": "2019-07-08T12:41:36.525Z",
"message": "PSEOF"
}
]
DETAILS_OF_SINGLE_NODE_EXPERIMENT_RESPONSE_JSON = {
"data": {
"dtCreated": "2020-04-02T21:37:00.000000+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-03-22T13:22:27.200591+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 21814,
"experimentTemplateId": 60,
"experimentTypeId": 1,
"handle": "esro6mbmiulvbl",
"id": 21784,
"projectHandle": "prq70zy79",
"projectId": 612,
"started_by_user_id": 1655,
"state": 1,
"tags": [
{
"name": "tag1",
},
{
"name": "tag2",
},
],
"templateHistory": {
"dtCreated": "2019-03-22T13:22:26.375543+00:00",
"dtDeleted": None,
"experimentTemplateId": 60,
"id": 21814,
"params": {
"experimentTypeId": 1,
"name": "dsfads",
"ports": 5000,
"project_handle": "prq70zy79",
"worker_command": "sadas",
"worker_container": "asd",
"worker_machine_type": "C2"
},
"triggerEvent": None,
"triggerEventId": None
},
"datasets": [
{
"uri": "s3://some/dataset",
"volume_options": {
"kind": "dynamic",
"size": "10Gi",
},
},
],
},
"message": "success"
}
DETAILS_OF_MULTI_NODE_EXPERIMENT_RESPONSE_JSON = {
"data": {
"dtCreated": "2019-12-16T16:18:47.881765+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2019-12-16T16:18:47.881765+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 34335,
"experimentTemplateId": 439,
"experimentTypeId": 3,
"handle": "emarbao6t6tsn",
"id": 34272,
"projectHandle": "pr85u3sfa",
"projectId": 1824,
"started_by_user_id": 1655,
"state": 10,
"tags": [
{
"name": "tag1",
},
{
"name": "tag2",
},
],
"templateHistory": {
"cliCommand": "gradient experiments create multinode --name some_name --projectId pr85u3sfa --experimentType MPI --workerContainer python --workerMachineType c5.xlarge --workerCommand \"echo worker\" --workerCount 2 --masterContainer python --masterMachineType c5.xlarge --masterCommand \"echo master\" --masterCount 1 --workspaceUrl some.url --workingDirectory /some/working/directory --clusterId clqr4b0ox --experimentEnv '{\"key\": \"value\"}' --ports 5000 --modelType some_type --modelPath /some/model/path",
"dtCreated": "2019-12-16T16:18:46.577973+00:00",
"dtDeleted": None,
"experimentTemplateId": 439,
"id": 34335,
"params": {
"artifact_directory": "/some/artifact/directory",
"cluster_id": "clqr4b0ox",
"experiment_env": {
"key": "value"
},
"is_preemptible": False,
"model_path": "/some/model/path",
"model_type": "some_type",
"name": "some_name",
"ports": "5000",
"project_handle": "pr85u3sfa",
"version": "v2",
"working_directory": "/some/working/directory",
"workspace_url": "some.url"
},
"triggerEvent": None,
"triggerEventId": None
}
},
"message": "success"
}
NOTEBOOK_GET_RESPONSE = {
"name": "some_name",
"handle": "ngw7piq9",
"jobHandle": "jzhmk7fpluqje",
"project": "Notebook-some_name",
"projectHandle": "prg284tu2",
"state": "Running",
"token": "dc2c331ce694ebe5a615f6e5885f6a933fcb1be49cb0d17f",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ngw7piq9.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "some_namespace",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-03T11:06:18.154Z",
"dtModified": "2019-09-03T11:06:18.154Z",
"dtProvisioningStarted": "2019-09-03T11:08:36.286Z",
"dtProvisioningFinished": "2019-09-03T11:10:36.471Z",
"dtStarted": "2019-09-03T11:10:36.471Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20206,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567508793",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617088427",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1823,
"metricsURL": "aws-testing.paperspace.io",
}
NOTEBOOK_GET_RESPONSE_WITH_TAGS = {
"name": "some_name",
"handle": "ngw7piq9",
"jobHandle": "jzhmk7fpluqje",
"project": "Notebook-some_name",
"projectHandle": "prg284tu2",
"state": "Running",
"token": "dc2c331ce694ebe5a615f6e5885f6a933fcb1be49cb0d17f",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ngw7piq9.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "some_namespace",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-03T11:06:18.154Z",
"dtModified": "2019-09-03T11:06:18.154Z",
"dtProvisioningStarted": "2019-09-03T11:08:36.286Z",
"dtProvisioningFinished": "2019-09-03T11:10:36.471Z",
"dtStarted": "2019-09-03T11:10:36.471Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20206,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567508793",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617088427",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1823,
"tags": [
"tag1",
"tag2"
],
}
NOTEBOOKS_LIST_RESPONSE_JSON = {
"notebookList": [
{
"name": "job 1",
"handle": "n1vmfj6x",
"jobHandle": "jsh0692p80dphg",
"project": "Notebook-undefined",
"projectHandle": "pr231zktg",
"state": "Running",
"token": "80426c989ef8d42b4dff6806c9592b1111a47c63c0f4a36f",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "n1vmfj6x.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T11:06:12.667Z",
"dtModified": "2019-09-04T11:06:12.667Z",
"dtProvisioningStarted": "2019-09-04T11:08:28.305Z",
"dtProvisioningFinished": "2019-09-04T11:10:30.628Z",
"dtStarted": "2019-09-04T11:10:30.628Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": None,
"jobId": 20221,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567595186",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617028675",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1832
},
{
"name": "job 1",
"handle": "nhdf8zf3",
"jobHandle": "jsyvcxoxch3jgu",
"project": "Notebook-undefined",
"projectHandle": "pr2u2sfja",
"state": "Running",
"token": "1d763ce770a195c98ea3d30588f3ad007c2b8403608ab091",
"container": "Paperspace + Fast.AI 1.0 (V3)",
"containerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nhdf8zf3.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T10:23:04.762Z",
"dtModified": "2019-09-04T10:23:04.762Z",
"dtProvisioningStarted": "2019-09-04T10:26:05.190Z",
"dtProvisioningFinished": "2019-09-04T10:28:13.609Z",
"dtStarted": "2019-09-04T10:28:13.609Z",
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": None,
"jobId": 20219,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567592650-d5337953",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297204 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617086962",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1831
},
{
"name": "My Notebook 123",
"handle": "nslk5r03",
"jobHandle": "jskm7amsly7mmj",
"project": "Notebook-My Notebook 123",
"projectHandle": "pr3qq8qlg",
"state": "Stopped",
"token": "7751a516535bf3d52c164315d6187c1c9a04f15c15cf1c15",
"container": "nslk5r03",
"containerUrl": "us.gcr.io/ps-development-229517/paperspace/pr3qq8qlg:nslk5r03",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nslk5r03.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T10:22:43.248Z",
"dtModified": "2019-09-04T10:22:43.248Z",
"dtProvisioningStarted": "2019-09-04T10:25:26.545Z",
"dtProvisioningFinished": "2019-09-04T10:27:24.319Z",
"dtStarted": "2019-09-04T10:27:24.319Z",
"dtFinished": "2019-09-04T16:27:52.044Z",
"dtTeardownStarted": "2019-09-04T16:27:56.915Z",
"dtTeardownFinished": "2019-09-04T16:28:57.796Z",
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20218,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567592577",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617029024",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1830
},
{
"name": "My Notebook 123",
"handle": "ng9a3tp4",
"jobHandle": "jg5vkj6d799z8",
"project": "Notebook-My Notebook 123",
"projectHandle": "pr5ngrxr9",
"state": "Stopped",
"token": "d9ba60e2bf7abd8ebd0c9988507ce203dd9baa7bc2b77284",
"container": "ng9a3tp4",
"containerUrl": "us.gcr.io/ps-development-229517/paperspace/pr5ngrxr9:ng9a3tp4",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ng9a3tp4.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-04T10:16:22.362Z",
"dtModified": "2019-09-04T10:16:22.362Z",
"dtProvisioningStarted": "2019-09-04T10:18:46.309Z",
"dtProvisioningFinished": "2019-09-04T10:20:45.879Z",
"dtStarted": "2019-09-04T10:20:45.879Z",
"dtFinished": "2019-09-04T16:20:51.718Z",
"dtTeardownStarted": "2019-09-04T16:20:56.745Z",
"dtTeardownFinished": "2019-09-04T16:22:03.922Z",
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20217,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567592197",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617086541",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1829
},
{
"name": "some_name",
"handle": "ngw7piq9",
"jobHandle": "jzhmk7fpluqje",
"project": "Notebook-some_name",
"projectHandle": "prg284tu2",
"state": "Stopped",
"token": "dc2c331ce694ebe5a615f6e5885f6a933fcb1be49cb0d17f",
"container": "ngw7piq9",
"containerUrl": "us.gcr.io/ps-development-229517/paperspace/prg284tu2:ngw7piq9",
"baseContainer": "Paperspace + Fast.AI 1.0 (V3)",
"baseContainerUrl": "paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6",
"vmType": "K80",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "ngw7piq9.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": None,
"dtCreated": "2019-09-03T11:06:18.154Z",
"dtModified": "2019-09-03T11:06:18.154Z",
"dtProvisioningStarted": "2019-09-03T11:08:36.286Z",
"dtProvisioningFinished": "2019-09-03T11:10:36.471Z",
"dtStarted": "2019-09-03T11:10:36.471Z",
"dtFinished": "2019-09-03T17:10:53.440Z",
"dtTeardownStarted": "2019-09-03T17:10:54.455Z",
"dtTeardownFinished": "2019-09-03T17:12:01.889Z",
"dtDeleted": None,
"shutdownTimeout": 6,
"jobId": 20206,
"isPublic": False,
"isPreemptible": False,
"cpuHostname": "gradient-host-1567508793",
"cpuCount": 2,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "12297212 kB",
"gpuName": "Tesla K80",
"gpuSerial": "0320617088427",
"gpuDevice": "/dev/nvidia0",
"gpuDriver": "418.67",
"gpuCount": 1,
"gpuMem": "11441 MiB",
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1823
},
{
"name": "some_notebook_name",
"handle": "n8h0d5lf",
"jobHandle": "js63sf787xc3mx",
"project": "Notebook-some_notebook_name",
"projectHandle": "prupasg3e",
"state": "Error",
"token": None,
"container": "some_name",
"containerUrl": "some_name",
"baseContainer": "some_name",
"baseContainerUrl": "some_name",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "n8h0d5lf.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job js63sf787xc3mx: Error pulling image 'some_name': Error response from daemon: Get https://registry-1.docker.io/v2/library/some_name/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:31:43.392Z",
"dtModified": "2019-08-30T12:31:43.392Z",
"dtProvisioningStarted": "2019-08-30T12:33:34.650Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:33:35.479Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20163,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567168315",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1811
},
{
"name": "some_notebook_name",
"handle": "nl0b6cn0",
"jobHandle": "jss28gdrarcbrw",
"project": "Notebook-some_notebook_name",
"projectHandle": "pr43jj028",
"state": "Error",
"token": None,
"container": "some_name",
"containerUrl": "some_name",
"baseContainer": "some_name",
"baseContainerUrl": "some_name",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nl0b6cn0.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job jss28gdrarcbrw: Error pulling image 'some_name': Error response from daemon: Get https://registry-1.docker.io/v2/library/some_name/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:16:11.944Z",
"dtModified": "2019-08-30T12:16:11.944Z",
"dtProvisioningStarted": "2019-08-30T12:16:19.646Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:16:20.382Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20162,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567167274",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1810
},
{
"name": "some_notebook_name",
"handle": "njmq1zju",
"jobHandle": "jd35vd65dkqch",
"project": "Notebook-some_notebook_name",
"projectHandle": "prflq2sy0",
"state": "Error",
"token": None,
"container": "None",
"containerUrl": "None",
"baseContainer": "None",
"baseContainerUrl": "None",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "njmq1zju.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job jd35vd65dkqch: Error pulling image 'None': Error response from daemon: Get https://registry-1.docker.io/v2/library/None/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:14:32.296Z",
"dtModified": "2019-08-30T12:14:32.296Z",
"dtProvisioningStarted": "2019-08-30T12:16:08.347Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:16:09.132Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20161,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567167274",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1809
},
{
"name": "some_notebook_name",
"handle": "nfcuwqu5",
"jobHandle": "je2cmiigxwhy0",
"project": "Notebook-some_notebook_name",
"projectHandle": "prl9nu5p7",
"state": "Error",
"token": None,
"container": "some_name",
"containerUrl": "some_name",
"baseContainer": "some_name",
"baseContainerUrl": "some_name",
"vmType": "G1",
"cluster": "PS Notebooks on GCP",
"clusterId": "clmtkpnm2",
"fqdn": "nfcuwqu5.dgradient.paperspace.com",
"startedByUser": "first last",
"startedByUserId": "ukgvw4i8",
"namespace": "username",
"parentJobId": None,
"jobError": "Error pulling container during provisioning for job je2cmiigxwhy0: Error pulling image 'some_name': Error response from daemon: Get https://registry-1.docker.io/v2/library/some_name/manifests/latest: unauthorized: incorrect username or password",
"dtCreated": "2019-08-30T12:13:30.657Z",
"dtModified": "2019-08-30T12:13:30.657Z",
"dtProvisioningStarted": "2019-08-30T12:15:11.388Z",
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": "2019-08-30T12:15:12.207Z",
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"shutdownTimeout": 8,
"jobId": 20160,
"isPublic": False,
"isPreemptible": True,
"cpuHostname": "gradient-host-1567167224",
"cpuCount": 1,
"cpuModel": "Intel(R) Xeon(R) CPU @ 2.30GHz",
"cpuFlags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities",
"cpuMem": "1783380 kB",
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"id": 1808
}
],
"availableMachines": [
{
"vmTypeId": 5,
"clusterId": 1,
"isAvailable": False,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "0",
"numAvailableNodes": "0",
"id": 11,
"cluster": {
"name": "PS Notebooks",
"type": 1,
"regionId": 1,
"showDisabled": False,
"dtCreated": "2017-11-03T19:09:41.077Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "cls28l0qm",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 1
}
},
{
"vmTypeId": 7,
"clusterId": 1,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "1",
"id": 15,
"cluster": {
"name": "PS Notebooks",
"type": 1,
"regionId": 1,
"showDisabled": False,
"dtCreated": "2017-11-03T19:09:41.077Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "cls28l0qm",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 1
}
},
{
"vmTypeId": 20,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "2",
"numAvailableNodes": "0",
"id": 7,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 20,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": True,
"showDisabled": False,
"numActiveNodes": "2",
"numAvailableNodes": "0",
"id": 6,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 21,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": True,
"showDisabled": False,
"numActiveNodes": "0",
"numAvailableNodes": "0",
"id": 22,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 21,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "0",
"numAvailableNodes": "0",
"id": 23,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 25,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "0",
"id": 27,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 25,
"clusterId": 3,
"isAvailable": True,
"isPreemptible": True,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "0",
"id": 26,
"cluster": {
"name": "PS Notebooks on GCP",
"type": 1,
"regionId": 4,
"showDisabled": False,
"dtCreated": "2018-03-02T18:27:16.323Z",
"dtModified": "2019-05-29T15:56:16.039Z",
"dtDeleted": None,
"handle": "clmtkpnm2",
"cloud": "gcp",
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 3
}
},
{
"vmTypeId": 31,
"clusterId": 90,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "1",
"id": 1,
"cluster": {
"name": "Free Public Notebooks",
"type": 1,
"regionId": 2,
"showDisabled": False,
"dtCreated": "2019-06-18T23:33:58.997Z",
"dtModified": "2019-08-12T17:06:19.492Z",
"dtDeleted": None,
"handle": "cltwhzxx6",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 90
}
},
{
"vmTypeId": 32,
"clusterId": 90,
"isAvailable": True,
"isPreemptible": False,
"showDisabled": False,
"numActiveNodes": "1",
"numAvailableNodes": "1",
"id": 12,
"cluster": {
"name": "Free Public Notebooks",
"type": 1,
"regionId": 2,
"showDisabled": False,
"dtCreated": "2019-06-18T23:33:58.997Z",
"dtModified": "2019-08-12T17:06:19.492Z",
"dtDeleted": None,
"handle": "cltwhzxx6",
"cloud": None,
"isDeleted": False,
"isPrivate": False,
"isDefault": False,
"fqdn": None,
"id": 90
}
}
],
"total": 9,
"runningTotal": 2,
"displayTotal": 9
}
TENSORBOARD_CREATE_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": None,
"id": "some_id",
"instance": None,
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id"
}, {
"project_id": "some_project_id",
"id": "some_other_experiment_id"
}
],
"dt_deleted": None
}
}
TENSORBOARD_DETAIL_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": "https://aws-testing.paperspace.io/tensorboard/some_id/",
"id": "some_id",
"instance": {
"type": "cpu",
"size": "large",
"count": 2
},
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id",
"state": "Some State"
}, {
"project_id": "some_project_id",
"id": "some_other_experiment_id",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
}
TENSORBOARD_UPDATE_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": "https://aws-testing.paperspace.io/tensorboard/some_id/",
"id": "some_id",
"instance": {
"type": "cpu",
"size": "large",
"count": 2
},
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id",
"state": "Some State"
},
{
"project_id": "some_project_id",
"id": "some_other_experiment_id",
"state": "Some State"
},
{
"project_id": "some_project_id",
"id": "some_third_experiment_id",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
}
TENSORBOARD_UPDATE_REMOVE_RESPONSE_JSON = {
"message": "success",
"data": {
"cluster_id": "some_cluster_id",
"url": "https://aws-testing.paperspace.io/tensorboard/some_id/",
"id": "some_id",
"instance": {
"type": "cpu",
"size": "large",
"count": 2
},
"dt_created": "2019-09-18T12:10:09.817102+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "some_project_id",
"id": "some_experiment_id",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
}
TENSORBOARD_LIST_RESPONSE_JSON = {
"message": "success",
"data": [
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbrs2kcjman4ly/",
"id": "tbrs2kcjman4ly",
"instance": None,
"dt_created": "2019-09-16T09:28:10.415478+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
},
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbskzep6d9po04d/",
"id": "tbskzep6d9po04d",
"instance": None,
"dt_created": "2019-09-16T09:28:56.908450+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
},
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbsaq6hggzxcnet/",
"id": "tbsaq6hggzxcnet",
"instance": None,
"dt_created": "2019-09-16T12:59:20.788663+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
},
{
"cluster_id": "clmtkpnm2",
"url": "https://aws-testing.paperspace.io/tensorboard/tbwuzalec7ik58/",
"id": "tbwuzalec7ik58",
"instance": {
"size": "large",
"type": "cpu",
"count": 2
},
"dt_created": "2019-09-17T07:43:39.679876+00:00",
"image": "tensorflow/tensorflow:latest-py3",
"experiments": [
{
"project_id": "prrdpg67x",
"id": "ers2kcjman4ly",
"state": "Some State"
}
],
"dt_deleted": None,
"state": "Some State"
}
]
}
TENSORBOARD_DELETE_RESPONSE_JSON = {
"message": "success"
}
LIST_JOB_FILES_RESPONSE_JSON = {
'data': [
{
"file": "hello.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/artifacts/hello.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=7CT5k6buEmZe5k5E7g6BXMs2xV4%3D&response-content-disposition=attachment%3Bfilename%3D%22hello.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "hello2.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/artifacts/hello2.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=L1lI47cNyiROzdYkf%2FF3Cm3165E%3D&response-content-disposition=attachment%3Bfilename%3D%22hello2.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "keton/elo.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/artifacts/keton/elo.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=tHriojGx03S%2FKkVGQGVI5CQRFTo%3D&response-content-disposition=attachment%3Bfilename%3D%22elo.txt%22&x-amz-security-token=some_amz_security_token"
}
],
}
GET_PRESIGNED_URL_FOR_S3_BUCKET_RESPONSE_JSON = {
"data": {
"bucket_name": "ps-projects",
"fields": {
"AWSAccessKeyId": "SOME_AWS_ACCESS_KEY_ID",
"key": "some/path/to/file/demo.zip",
"policy": "base64policy=",
"signature": "base64signature="
},
"url": "https://ps-projects.s3.amazonaws.com/"
},
"message": "success"
}
DELETE_MODEL_404_RESPONSE_JSON = {
"error": {
"name": "Error",
"status": 404,
"message": "Unable to find model",
},
}
MODEL_CREATE_RESPONSE_JSON_V2 = {
"id": "some_model_id",
"projectId": None,
"experimentId": None,
"updatedByUserId": "ukgvw4i8",
"updatedByUserEmail": "asd@paperspace.com",
"modelType": "Custom",
"name": "some_name",
"tag": None,
"summary": None,
"detail": None,
"params": None,
"url": None,
"notes": None,
"isDeleted": False,
"isPublic": False,
"dtCreated": "2020-04-01T14:15:39.371Z",
"dtModified": "2020-04-01T14:15:39.371Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": None,
"tags": None
}
MODEL_UPLOAD_GET_PRESIGNED_URL_RESPONSE = "https://ps-customstorage-development.s3.amazonaws.com/teo6raui0/models/" \
"moslrdsp1q4z4a2/keton.txt?AWSAccessKeyId=AKIAVWO7J5OJXEKIB7EJ&Content-Typ" \
"e=text%2Fplain&Expires=1585779340&Signature=82UPgvg0yBayLJCLMiNq6jc4mys%3D"
MODEL_UPLOAD_RESPONSE_JSON = {
"id": "some_model_id",
"projectId": None,
"experimentId": None,
"updatedByUserId": "ukgvw4i8",
"updatedByUserEmail": "some_email@paperspace.com",
"modelType": "Tensorflow",
"name": "some_name",
"tag": None,
"summary": {"key": "value"},
"detail": None,
"params": None,
"url": "s3://ps-projects-development/teo6raui0/models/some_model_id/saved_model.pb",
"notes": "some notes",
"isDeleted": False,
"isPublic": False,
"dtCreated": "2019-11-26T13:33:11.001Z", "dtModified": "2019-11-26T13:33:11.001Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": None,
}
MODEL_DETAILS_RESPONSE_JSON = {
"modelList": [
{
"id": "some_id",
"projectId": "some_project_id",
"experimentId": "some_experiment_id",
"updatedByUserId": "some_user_id",
"updatedByUserEmail": "paperspace@paperspace.com",
"modelType": "Tensorflow",
"name": "some_name",
"tag": None,
"summary": None,
"detail": None,
"params": None,
"url": "s3://ps-projects-development/asdf/some_project_id/some_experiment_id/model",
"notes": None,
"isDeleted": False,
"isPublic": False,
"dtCreated": "2019-12-13T23:00:26.077Z",
"dtModified": "2019-12-13T23:00:26.077Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": "Stopped"
}
],
"total": 96,
"displayTotal": 1
}
MODEL_DETAILS_RESPONSE_JSON_WITH_TAGS = {
"modelList": [
{
"id": "some_id",
"projectId": "some_project_id",
"experimentId": "some_experiment_id",
"updatedByUserId": "some_user_id",
"updatedByUserEmail": "paperspace@paperspace.com",
"modelType": "Tensorflow",
"name": "some_name",
"tag": None,
"tags": [
"tag1",
"tag2"
],
"summary": None,
"detail": None,
"params": None,
"url": "s3://ps-projects-development/asdf/some_project_id/some_experiment_id/model",
"notes": None,
"isDeleted": False,
"isPublic": False,
"dtCreated": "2019-12-13T23:00:26.077Z",
"dtModified": "2019-12-13T23:00:26.077Z",
"dtUploaded": None,
"dtDeleted": None,
"modelPath": None,
"deploymentState": "Stopped"
}
],
"total": 96,
"displayTotal": 1
}
LIST_MODEL_FILES_RESPONSE_JSON = [
{
"file": "hello.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/model/hello.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=7CT5k6buEmZe5k5E7g6BXMs2xV4%3D&response-content-disposition=attachment%3Bfilename%3D%22hello.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "hello2.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/model/hello2.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=L1lI47cNyiROzdYkf%2FF3Cm3165E%3D&response-content-disposition=attachment%3Bfilename%3D%22hello2.txt%22&x-amz-security-token=some_amz_security_token"
},
{
"file": "keton/elo.txt",
"url": "https://ps-projects.s3.amazonaws.com/some/path/model/keton/elo.txt?AWSAccessKeyId=some_aws_access_key_id&Expires=713274132&Signature=tHriojGx03S%2FKkVGQGVI5CQRFTo%3D&response-content-disposition=attachment%3Bfilename%3D%22elo.txt%22&x-amz-security-token=some_amz_security_token"
}
]
GET_DEPLOYMENT_DETAILS_JSON_RESPONSE = {
"deploymentList": [
{
"id": "some_id",
"ownerId": "some_owner_id",
"projectId": "some_project_id",
"experimentId": "some_experiment_id",
"state": "Stopped",
"startedByUserId": "some_user_id",
"startedByUserEmail": "some@address.com",
"deploymentType": "TFServing",
"deploymentTypeDescription": "Tensorflow Serving on K8s",
"modelId": "some_model_id",
"modelUrl": "s3://ps-projects/asdf/some/model/url",
"name": "some_name",
"tag": None,
"params": None,
"code": None,
"cluster": "KPS Jobs",
"clusterId": "some_cluster_id",
"machineType": "p3.2xlarge",
"imageUrl": "tensorflow/serving",
"imageUsername": None,
"imagePassword": None,
"imageServer": None,
"workspaceUrl": None,
"workspaceUsername": None,
"workspacePassword": None,
"instanceCount": 1,
"runningCount": 0,
"error": None,
"authToken": None,
"annotations": {},
"oauthKey": None,
"oauthSecret": None,
"serviceType": "model",
"apiType": "REST",
"endpoint": "https://paperspace.io/model-serving/some_id:predict",
"ports": None,
"dtCreated": "2020-01-13T22:21:28.210Z",
"dtModified": "2020-01-14T00:32:27.577Z",
"dtBuildStarted": "2020-01-13T22:21:28.482Z",
"dtBuildFinished": None,
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtStopped": "2020-01-13T22:48:40.107Z",
"dtDeleted": None,
"isDeleted": False,
"modelType": "Tensorflow",
"modelPath": None,
"command": "some deployment command",
"args": None,
"env": None,
"containerModelPath": None,
"containerUrlPath": None,
"endpointUrlPath": None,
"method": None,
"tags": ["tag1", "tag2"],
"metricsURL": "aws-testing.paperspace.io",
"autoscaling": {
"minInstanceCount": 4,
"maxInstanceCount": 64,
"scaleCooldownPeriod": 123,
"metrics": [
{
"type": "Resource",
"name": "cpu",
"valueType": "targetAverage",
"value": 10,
},
{
"type": "Metric",
"name": "loss",
"valueType": "target",
"value": 2,
},
{
"type": "Metric",
"name": "keton",
"valueType": "target",
"value": 21.37,
},
],
},
},
],
"total": 129,
"displayTotal": 129,
"runningTotal": 0
}
GET_CLUSTER_DETAILS_RESPONSE = {
"id": "some_cluster_id",
"name": "EKS testing",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "some_team_id",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 1,
"isPrivate": True,
"modelName": "team",
"modelId": 1,
"nodes": [
{
"id": "cmsq4u0gf0m971f",
"name": "default",
"clusterId": "cluwffvkb",
"activeJobId": None,
"machineId": None,
"dtCreated": "2019-11-21T10:06:27.602Z",
"dtModified": "2019-11-21T10:06:27.602Z",
"dtDeleted": None,
"dtHeartbeat": None,
"nodeAttrs": None
}
]
}
GET_V1_CLUSTER_DETAILS_RESPONSE = {
"id": "some_cluster_id",
"name": "EKS testing",
"type": "Job Cluster",
"region": "Private",
"cloud": "aws",
"teamId": "some_team_id",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 1,
"isPrivate": True,
"modelName": "team",
"modelId": 1,
"nodes": [
{
"id": "some_node_id",
"name": "default",
"clusterId": "some_cluster_id",
"activeJobId": None,
"machineId": None,
"dtCreated": "2019-11-21T10:06:27.602Z",
"dtModified": "2019-11-21T10:06:27.602Z",
"dtDeleted": None,
"dtHeartbeat": None,
"nodeAttrs": None
}
]
}
DETAILS_OF_PROJECT = {
"data": [
{
"name": "some_name",
"handle": "some_id",
"dtCreated": "2020-02-07T11:43:34.335Z",
"dtDeleted": None,
"lastJobSeqNum": 44,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"tags": None,
"buildPullRequests": True,
"buildForks": False,
"buildBranches": "default",
"experiments": {
"data": [
{
"dtCreated": "2020-02-13T16:02:22.978027+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2020-02-13T16:02:22.978027+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 70590,
"experimentTemplateId": 7156,
"experimentTypeId": 2,
"handle": "some_handle",
"id": 70557,
"projectHandle": "some_id",
"projectId": 49107,
"started_by_user_id": 192354,
"state": 1,
"templateHistory": {
"cliCommand": "gradient experiments create multinode --name some_name --projectId some_id --experimentType GRPC --workerContainer python --workerMachineType c5.xlarge --workerCommand \"python script.py\" --workerCount 2 --parameterServerContainer python --parameterServerMachineType c5.xlarge --parameterServerCommand \"python script.py\" --parameterServerCount 1 --workspaceUrl s3://some_workspace.url --clusterId some_cluster_id --ports 5000:5000",
"dtCreated": "2020-02-13T16:02:21.263923+00:00",
"dtDeleted": None,
"experimentTemplateId": 7156,
"id": 70590,
"params": {
"cluster_id": "some_cluster_id",
"is_preemptible": False,
"name": "some_name",
"parameter_server_command": "python script.py",
"parameter_server_container": "python",
"parameter_server_count": 1,
"parameter_server_machine_type": "c5.xlarge",
"parameter_server_use_dockerfile": False,
"ports": "5000:5000",
"project_handle": "some_id",
"version": "v2",
"worker_command": "python script.py",
"worker_container": "python",
"worker_count": 2,
"worker_machine_type": "c5.xlarge",
"worker_use_dockerfile": False,
"workspace_url": "s3://some_workspace.url"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "some_id"
},
"totalItems": 40
}
}
}
],
"meta": {
"where": {
"handle": "some_id"
},
"totalItems": 1
},
"tagFilter": []
}
DETAILS_OF_PROJECT_WITH_TAGS = {
"data": [
{
"name": "some_name",
"handle": "some_id",
"dtCreated": "2020-02-07T11:43:34.335Z",
"dtDeleted": None,
"lastJobSeqNum": 44,
"repoNodeId": None,
"repoName": None,
"repoUrl": None,
"tags": [
"tag1",
"tag2"
],
"buildPullRequests": True,
"buildForks": False,
"buildBranches": "default",
"experiments": {
"data": [
{
"dtCreated": "2020-02-13T16:02:22.978027+00:00",
"dtDeleted": None,
"dtFinished": None,
"dtModified": "2020-02-13T16:02:22.978027+00:00",
"dtProvisioningFinished": None,
"dtProvisioningStarted": None,
"dtStarted": None,
"dtTeardownFinished": None,
"dtTeardownStarted": None,
"experimentError": None,
"experimentTemplateHistoryId": 70590,
"experimentTemplateId": 7156,
"experimentTypeId": 2,
"handle": "some_handle",
"id": 70557,
"projectHandle": "some_id",
"projectId": 49107,
"started_by_user_id": 192354,
"state": 1,
"templateHistory": {
"cliCommand": "gradient experiments create multinode --name some_name --projectId some_id --experimentType GRPC --workerContainer python --workerMachineType c5.xlarge --workerCommand \"python script.py\" --workerCount 2 --parameterServerContainer python --parameterServerMachineType c5.xlarge --parameterServerCommand \"python script.py\" --parameterServerCount 1 --workspaceUrl s3://some_workspace.url --clusterId some_cluster_id --ports 5000:5000",
"dtCreated": "2020-02-13T16:02:21.263923+00:00",
"dtDeleted": None,
"experimentTemplateId": 7156,
"id": 70590,
"params": {
"cluster_id": "some_cluster_id",
"is_preemptible": False,
"name": "some_name",
"parameter_server_command": "python script.py",
"parameter_server_container": "python",
"parameter_server_count": 1,
"parameter_server_machine_type": "c5.xlarge",
"parameter_server_use_dockerfile": False,
"ports": "5000:5000",
"project_handle": "some_id",
"version": "v2",
"worker_command": "python script.py",
"worker_container": "python",
"worker_count": 2,
"worker_machine_type": "c5.xlarge",
"worker_use_dockerfile": False,
"workspace_url": "s3://some_workspace.url"
},
"triggerEvent": None,
"triggerEventId": None
}
}
],
"meta": {
"itemGroup": {
"key": "projectHandle",
"value": "some_id"
},
"totalItems": 40
}
}
}
],
"meta": {
"where": {
"handle": "some_id"
},
"totalItems": 1
},
"tagFilter": []
}
EXAMPLE_CLUSTERS_LIST_RESPONSE = [
{
"id": "cluster_id_1",
"name": "cluster name 1",
"type": "Job Cluster",
"region": "Private",
"cloud": "private",
"teamId": "team_id",
"isDefault": True,
"dtCreated": "2019-07-05T23:28:17.416Z",
"dtModified": "2019-07-05T23:28:17.416Z",
"clusterId": 91,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
},
{
"id": "cluster_id_2",
"name": "cluster name 2",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "team_id",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
},
{
"id": "cluster_id_3",
"name": "cluster name 3",
"type": "Job Cluster",
"region": "Private",
"cloud": "gcp",
"teamId": "team_id",
"isDefault": False,
"dtCreated": "2019-10-29T18:42:50.985Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 100,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
LIMITED_EXAMPLE_CLUSTERS_LIST_RESPONSE = [
{
"id": "cluster_id_1",
"name": "cluster name 1",
"type": "Job Cluster",
"region": "Private",
"cloud": "private",
"teamId": "team_id",
"isDefault": True,
"dtCreated": "2019-07-05T23:28:17.416Z",
"dtModified": "2019-07-05T23:28:17.416Z",
"clusterId": 91,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
},
{
"id": "cluster_id_2",
"name": "cluster name 2",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "team_id",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2019-11-21T18:12:27.723Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
LIST_OF_VM_MACHINE_TYPES = {
"92": [
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 35,
"capacityId": 21,
"vmType": {
"label": "c5.xlarge",
"kind": "aws-cpu",
"cpus": 4,
"ram": "8589934592",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:12:41.506Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "small",
"id": 35,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 35,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 131,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clqr4b0ox",
"name": "KPS Jobs",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 36,
"capacityId": 22,
"vmType": {
"label": "c5.4xlarge",
"kind": "aws-cpu",
"cpus": 16,
"ram": "34359738368",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:14:06.425Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "medium",
"id": 36,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 36,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 132,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clqr4b0ox",
"name": "KPS Jobs",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 38,
"capacityId": 23,
"vmType": {
"label": "p2.xlarge",
"kind": "aws-gpu",
"cpus": 4,
"ram": "65498251264",
"gpuModelId": 11,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "small",
"id": 38,
"gpuModel": {
"model": "passthrough",
"label": "Tesla K80",
"gpuGroupId": 8,
"memInBytes": "12884901888",
"memInMb": 12288,
"memInGb": "12",
"id": 11
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 38,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:05.626Z",
"isPreemptible": False,
"id": 134,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clqr4b0ox",
"name": "KPS Jobs",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 39,
"capacityId": 24,
"vmType": {
"label": "p3.2xlarge",
"kind": "aws-gpu",
"cpus": 8,
"ram": "65498251264",
"gpuModelId": 10,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "medium",
"id": 39,
"gpuModel": {
"model": "passthrough",
"label": "Tesla V100",
"gpuGroupId": 7,
"memInBytes": "17179869184",
"memInMb": 16384,
"memInGb": "16",
"id": 10
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 39,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:05.626Z",
"isPreemptible": False,
"id": 135,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clqr4b0ox",
"name": "KPS Jobs",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 40,
"capacityId": 25,
"vmType": {
"label": "p3.16xlarge",
"kind": "aws-gpu",
"cpus": 64,
"ram": "523986010112",
"gpuModelId": 10,
"gpuCount": 8,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "large",
"id": 40,
"gpuModel": {
"model": "passthrough",
"label": "Tesla V100",
"gpuGroupId": 7,
"memInBytes": "17179869184",
"memInMb": 16384,
"memInGb": "16",
"id": 10
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 40,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:14.282Z",
"isPreemptible": False,
"id": 136,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clqr4b0ox",
"name": "KPS Jobs",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-07-22T14:50:10.170Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 92,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
}
],
"102": [
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 65,
"capacityId": 32,
"vmType": {
"label": "Wolfpass-CPU",
"kind": "cpu",
"cpus": 24,
"ram": "34359738368",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-11-11T19:53:21.298Z",
"dtModified": "2019-11-19T19:09:03.288Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "small",
"id": 65,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 65,
"type": "hourly",
"usageRateId": 18,
"dtCreated": "2019-11-11T19:57:04.870Z",
"isPreemptible": False,
"id": 150,
"usageRate": {
"description": "VIP",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "VIP",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": False,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 18
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "clfe0kr2p",
"name": "Intel Wolfpass KPS",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "intelwolfpass",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-11-11T16:34:29.495Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 102,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
}
],
"103": [
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 35,
"capacityId": 33,
"vmType": {
"label": "c5.xlarge",
"kind": "aws-cpu",
"cpus": 4,
"ram": "8589934592",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:12:41.506Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "small",
"id": 35,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 35,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 131,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "cluwffvkb",
"name": "EKS testing",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 103,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 36,
"capacityId": 34,
"vmType": {
"label": "c5.4xlarge",
"kind": "aws-cpu",
"cpus": 16,
"ram": "34359738368",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:14:06.425Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "medium",
"id": 36,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 36,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:32:41.602Z",
"isPreemptible": False,
"id": 132,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "cluwffvkb",
"name": "EKS testing",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 103,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 37,
"capacityId": 35,
"vmType": {
"label": "c5.24xlarge",
"kind": "aws-cpu",
"cpus": 94,
"ram": "206158430208",
"gpuModelId": 6,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:14:06.425Z",
"dtModified": "2019-08-28T17:40:50.773Z",
"isPreemptible": False,
"deploymentType": "cpu",
"deploymentSize": "large",
"id": 37,
"gpuModel": {
"model": "None",
"label": "None",
"gpuGroupId": 3,
"memInBytes": "0",
"memInMb": 0,
"memInGb": "0",
"id": 6
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 37,
"type": "hourly",
"usageRateId": 10,
"dtCreated": "2019-07-22T15:33:05.626Z",
"isPreemptible": False,
"id": 133,
"usageRate": {
"description": "Employee",
"rate": "0.00",
"type": "monthly",
"gpuModelId": 1,
"rateHourly": "0.00",
"rateMonthly": "0.00",
"label": "Employee",
"period": "monthly",
"kind": "air",
"isEarlyAccess": False,
"isEmployeeOnly": True,
"numCpus": 2,
"ramInBytes": "4294967296",
"id": 10
}
}
],
"defaultUsageRateOverrides": []
},
"clusters": [
{
"id": "cluwffvkb",
"name": "EKS testing",
"type": "Kubernetes Processing Site",
"region": "Private",
"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 103,
"isPrivate": True,
"modelName": "team",
"modelId": 1170
}
]
},
{
"showDisabled": False,
"isAvailable": True,
"isPreemptible": False,
"vmTypeId": 39,
"capacityId": 36,
"vmType": {
"label": "p3.2xlarge",
"kind": "aws-gpu",
"cpus": 8,
"ram": "65498251264",
"gpuModelId": 10,
"gpuCount": 1,
"internalDescription": None,
"dtCreated": "2019-07-22T15:20:22.108Z",
"dtModified": "2019-08-28T17:40:50.779Z",
"isPreemptible": False,
"deploymentType": "gpu",
"deploymentSize": "medium",
"id": 39,
"gpuModel": {
"model": "passthrough",
"label": "Tesla V100",
"gpuGroupId": 7,
"memInBytes": "17179869184",
"memInMb": 16384,
"memInGb": "16",
"id": 10
},
"availableTemplatesWithOperatingSystems": [],
"availableRegions": [],
"permissions": [],
"defaultUsageRates": [
{
"vmTypeId": 39,
"type": "hourly",
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],
"defaultUsageRateOverrides": []
},
"clusters": [
{
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"cloud": "aws",
"teamId": "teo6raui0",
"isDefault": False,
"dtCreated": "2019-11-21T07:27:37.010Z",
"dtModified": "2020-02-28T20:58:26.134Z",
"clusterId": 103,
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},
{
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"availableTemplatesWithOperatingSystems": [],
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"permissions": [],
"defaultUsageRates": [
{
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"defaultUsageRateOverrides": []
},
"clusters": [
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{
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},
"availableTemplatesWithOperatingSystems": [],
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"defaultUsageRates": [
{
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"dtCreated": "2019-11-27T11:59:58.059Z",
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],
"defaultUsageRateOverrides": []
},
"clusters": [
{
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},
{
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"defaultUsageRateOverrides": []
},
"clusters": [
{
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},
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}
}
],
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},
"clusters": [
{
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"cloud": "aws",
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"dtModified": "2020-03-09T17:15:08.080Z",
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}
]
},
{
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},
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{
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}
}
],
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},
"clusters": [
{
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"cloud": "aws",
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}
]
},
{
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},
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{
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}
]
},
{
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},
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],
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},
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}
]
},
{
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},
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"kind": "air",
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}
}
],
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},
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}
]
},
{
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},
"availableTemplatesWithOperatingSystems": [],
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{
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],
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}
]
}
]
}
EXPERIMENTS_METRICS_GET_RESPONSE = {
"handle": "ess57ozwb5ijpt",
"object_type": "experiment",
"charts": {
"cpuPercentage": {
"mljob-esro6mbmiulvbl-0-worker": [
{"time_stamp": 1587375065, "value": "0"},
{"time_stamp": 1587375095, "value": "0"},
{"time_stamp": 1587375125, "value": "0"},
{"time_stamp": 1587375155, "value": "0"},
],
"mljob-esro6mbmiulvbl-1-worker": [
{"time_stamp": 1587375065, "value": "0"},
{"time_stamp": 1587375095, "value": "0"},
{"time_stamp": 1587375125, "value": "0"},
{"time_stamp": 1587375155, "value": "0"},
],
},
"memoryUsage": {
"mljob-esro6mbmiulvbl-0-worker": [
{"time_stamp": 1587375005, "value": "0"},
{"time_stamp": 1587375035, "value": "0"},
{"time_stamp": 1587375065, "value": "761856"},
{"time_stamp": 1587375095, "value": "761856"},
{"time_stamp": 1587375125, "value": "761856"},
],
"mljob-esro6mbmiulvbl-1-worker": [
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{"time_stamp": 1587375035, "value": "0"},
{"time_stamp": 1587375065, "value": "761856"},
{"time_stamp": 1587375095, "value": "761856"},
{"time_stamp": 1587375125, "value": "761856"},
],
},
},
}
EXPERIMENTS_METRICS_GET_RESPONSE_WHEN_NO_DATA_WAS_FOUND = {
"handle": "ess57ozwb5ijpt",
"object_type": "experiment",
"charts": {},
}
DEPLOYMENTS_METRICS_GET_RESPONSE = {
"handle": "desgffa3mtgepvm",
"object_type": "modelDeployment",
"charts": {
"cpuPercentage": {
"desgffa3mtgepvm-0": [
{"time_stamp": 1587340800, "value": "0.0388702066666724"},
{"time_stamp": 1587370800, "value": "0.04452898888887249"},
{"time_stamp": 1587400800, "value": "0.044658617777757724"},
{"time_stamp": 1587430800, "value": "0.04922275555555997"},
{"time_stamp": 1587460800, "value": "0.0589409911111084"},
{"time_stamp": 1587490800, "value": "0.02873176888891117"},
{"time_stamp": 1587520800, "value": "0.042048226666666876"},
{"time_stamp": 1587550800, "value": "0.04952780222222625"},
],
"desgffa3mtgepvm-1": [
{"time_stamp": 1587340800, "value": "0.05044751111111307"},
{"time_stamp": 1587370800, "value": "0.04381767555555724"},
{"time_stamp": 1587400800, "value": "0.03436263111110646"},
{"time_stamp": 1587430800, "value": "0.048889264444432624"},
{"time_stamp": 1587460800, "value": "0.041525960000020255"},
{"time_stamp": 1587490800, "value": "0.04574227333332853"},
{"time_stamp": 1587520800, "value": "0.03383691777780011"},
{"time_stamp": 1587550800, "value": "0.045942304444426756"},
],
},
"memoryUsage": {
"desgffa3mtgepvm-0": [
{"time_stamp": 1587340800, "value": "34910208"},
{"time_stamp": 1587370800, "value": "34910208"},
{"time_stamp": 1587400800, "value": "34914304"},
{"time_stamp": 1587430800, "value": "34914304"},
{"time_stamp": 1587460800, "value": "34914304"},
{"time_stamp": 1587490800, "value": "34914304"},
{"time_stamp": 1587520800, "value": "34914304"},
{"time_stamp": 1587550800, "value": "34914304"},
],
"desgffa3mtgepvm-1": [
{"time_stamp": 1587340800, "value": "35942400"},
{"time_stamp": 1587370800, "value": "35942400"},
{"time_stamp": 1587400800, "value": "35942400"},
{"time_stamp": 1587430800, "value": "35942400"},
{"time_stamp": 1587460800, "value": "35942400"},
{"time_stamp": 1587490800, "value": "35942400"},
{"time_stamp": 1587520800, "value": "35942400"},
{"time_stamp": 1587550800, "value": "35942400"},
],
},
},
}
GET_JOB_RESPONSE = {
"job": {
"id": "jstkd2lapucirs",
"name": "ufoym/deepo:all-py36-worker-1",
"state": "Running",
"workspaceUrl": "git+https://github.com/dte/stylegan.git",
"workingDirectory": "/paperspace",
"artifactsDirectory": "/artifacts",
"entrypoint": "sleep 3600 ; python generate_figures.py",
"projectId": "pr85u3sfa",
"project": "keton",
"container": "ufoym/deepo:all-py36",
"containerUrl": "ufoym/deepo:all-py36",
"baseContainer": "ufoym/deepo:all-py36",
"baseContainerUrl": "ufoym/deepo:all-py36",
"machineType": "c5.xlarge",
"cluster": "KPS Jobs",
"clusterId": "clqr4b0ox",
"usageRate": "Employee",
"startedByUserId": "ukgvw4i8",
"parentJobId": None,
"jobError": None,
"dtCreated": "2020-04-29T10:11:07.924Z",
"dtModified": "2020-04-29T10:11:07.924Z",
"dtProvisioningStarted": None,
"dtProvisioningFinished": None,
"dtStarted": None,
"dtFinished": None,
"dtTeardownStarted": None,
"dtTeardownFinished": None,
"dtDeleted": None,
"exitCode": None,
"queuePosition": None,
"seqNum": 155,
"storageRegion": "Private",
"clusterMachine": None,
"fqdn": "undefined.dgradient.paperspace.com",
"ports": "5000:5000", "isPublic": False,
"containerUser": None,
"hasCode": None,
"codeUploaded": None,
"codeCommit": None,
"runTillCancelled": None,
"pushOnCompletion": None,
"newImageName": None,
"cpuHostname": None,
"cpuCount": None,
"cpuModel": None,
"cpuFlags": None,
"cpuMem": None,
"gpuName": None,
"gpuSerial": None,
"gpuDevice": None,
"gpuDriver": None,
"gpuCount": None,
"gpuMem": None,
"tpuType": None,
"tpuName": None,
"tpuGrpcUrl": None,
"tpuTFVersion": None,
"tpuDatasetDir": None,
"tpuModelDir": None,
"targetNodeAttrs": None,
"jobEnv": None,
"sharedMemMBytes": None,
"shutdownTimeout": None,
"isPreemptible": False,
"metricsURL": "aws-testing.paperspace.io",
"customMetrics": None,
"experimentId": "ecgrgm7ok8chv",
"tags": None,
},
"user": {
"firstName": "first",
"lastName": "last",
"email": "bartosz@paperspace.com",
"handle": "ukgvw4i8",
},
}
GET_JOB_METRICS_RESPONSE = {
"handle": "jstkd2lapucirs",
"object_type": "mljob",
"charts": {
"cpuPercentage": {
"mljob-ecgrgm7ok8chv-0-worker": [
{"time_stamp": 1588155157, "value": "0"},
{"time_stamp": 1588155187, "value": "0"},
{"time_stamp": 1588155217, "value": "0"},
],
},
"memoryUsage": {
"mljob-ecgrgm7ok8chv-0-worker": [
{"time_stamp": 1588155097, "value": "0"},
{"time_stamp": 1588155127, "value": "5881856"},
{"time_stamp": 1588155157, "value": "5881856"},
{"time_stamp": 1588155187, "value": "5881856"},
],
},
},
}
JOBS_METRICS_GET_RESPONSE_WHEN_NO_DATA_WAS_FOUND = {
"handle": "jsy5wnxbtqw1e9",
"object_type": "mljob",
"charts": {
"cpuPercentage": None,
"memoryUsage": None,
},
}
NOTEBOOKS_METRICS_GET_RESPONSE = {
"handle": "npmnnm6e",
"object_type": "notebook",
"charts": {
"cpuPercentage": {
"npmnnm6e": [
{"time_stamp": 1587993000, "value": "0"},
{"time_stamp": 1587993030, "value": "0"},
{"time_stamp": 1587993060, "value": "0"},
{"time_stamp": 1587993090, "value": "0"},
{"time_stamp": 1587993120, "value": "0"},
{"time_stamp": 1587993150, "value": "0"},
{"time_stamp": 1587993180, "value": "0"},
{"time_stamp": 1587993210, "value": "0"},
{"time_stamp": 1587993240, "value": "0"},
{"time_stamp": 1587993270, "value": "0"},
{"time_stamp": 1587993300, "value": "0"},
{"time_stamp": 1587993330, "value": "0"},
{"time_stamp": 1587993360, "value": "0"},
],
},
"memoryUsage": {
"npmnnm6e": [
{"time_stamp": 1587992970, "value": "0"},
{"time_stamp": 1587993000, "value": "782336"},
{"time_stamp": 1587993030, "value": "782336"},
{"time_stamp": 1587993060, "value": "782336"},
{"time_stamp": 1587993090, "value": "782336"},
{"time_stamp": 1587993120, "value": "782336"},
{"time_stamp": 1587993150, "value": "782336"},
{"time_stamp": 1587993180, "value": "782336"},
{"time_stamp": 1587993210, "value": "782336"},
{"time_stamp": 1587993240, "value": "782336"},
{"time_stamp": 1587993270, "value": "782336"},
{"time_stamp": 1587993300, "value": "782336"},
{"time_stamp": 1587993330, "value": "782336"},
{"time_stamp": 1587993360, "value": "782336"},
],
},
},
}
NOTEBOOKS_METRICS_GET_RESPONSE_WHEN_NO_METRICS_WERE_FOUND = {
"handle": "nrwed38p",
"object_type": "notebook",
"charts": {"cpuPercentage": None, "memoryUsage": None},
}
DEPLOYMENTS_LOGS_RESPONSE = [
{
"jobId": "deshj1l4nuw6sd1",
"line": 1,
"timestamp": "2020-05-11T11:27:56.552Z",
"message": "line1\n",
"instanceName": {
"String": "deshj1l4nuw6sd1-1-artifact-1-5nd94",
"Valid": True
},
"uuid": {
"String": "1ef5d86b-d05e-4610-8f81-b42b556cede7",
"Valid": True
},
"instanceCount": {
"Int64": 1,
"Valid": True
}
},
{
"jobId": "deshj1l4nuw6sd1",
"line": 2,
"timestamp": "2020-05-11T11:27:56.552Z",
"message": "line2\n",
"instanceName": {
"String": "deshj1l4nuw6sd1-1-artifact-1-5nd94",
"Valid": True
},
"uuid": {
"String": "1ef5d86b-d05e-4610-8f81-b42b556cede7",
"Valid": True
},
"instanceCount": {
"Int64": 1,
"Valid": True
}
},
{
"jobId": "deshj1l4nuw6sd1",
"line": 3,
"timestamp": "2020-05-11T11:27:56.557Z",
"message": "line3",
"instanceName": {
"String": "deshj1l4nuw6sd1-1-artifact-1-5nd94",
"Valid": True
},
"uuid": {
"String": "1ef5d86b-d05e-4610-8f81-b42b556cede7",
"Valid": True
},
"instanceCount": {
"Int64": 1,
"Valid": True
}
},
{
"jobId": "deshj1l4nuw6sd1",
"line": 4,
"timestamp": "2020-05-11T11:27:56.558Z",
"message": "PSEOF"
}
]
LIST_SECRETS_RESPONSE = [
{
"name": "aws_access_key_id"
},
{
"name": "aws_secret_access_key"
}
]
LIST_DATASETS_RESPONSE = [
{
"description": None,
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
]
SHOW_DATASET_DETAILS_RESPONSE = {
"description": None,
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
CREATE_DATASET_RESPONSE = {
"description": None,
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
UPDATE_DATASET_RESPONSE = {
"description": "Test dataset",
"dtCreated": "2020-10-09T18:34:07.097Z",
"dtModified": "2020-10-09T18:34:07.097Z",
"id": "dsttn2y7j1ux882",
"name": "test1",
"storageProvider": {
"config": {
"accessKey": "AKIAVWO7J5OJV4XSVOPA",
"bucket": "chris-dev-cluster",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
}
LIST_DATASET_VERSIONS_RESPONSE = [
{
"dtCreated": "2020-10-29T22:56:15.514Z",
"dtModified": "2020-10-29T22:56:16.213Z",
"isCommitted": True,
"message": None,
"tags": [
{
"name": "hello"
}
],
"version": "1rn19s2"
}
]
SHOW_DATASET_VERSION_DETAILS_RESPONSE = {
"dtCreated": "2020-10-29T22:56:15.514Z",
"dtModified": "2020-10-29T22:56:16.213Z",
"isCommitted": True,
"message": None,
"tags": [
{
"name": "hello"
}
],
"version": "1rn19s2"
}
UPDATE_DATASET_VERSION_RESPONSE = {
"dtCreated": "2020-10-29T22:56:15.514Z",
"dtModified": "2020-10-29T23:27:14.955Z",
"isCommitted": True,
"message": "Test message",
"tags": [
{
"name": "hello"
}
],
"version": "1rn19s2"
}
SET_DATASET_VERSION_TAG_RESPONSE = {
"dtCreated": "2020-10-29T22:57:40.723Z",
"dtModified": "2020-10-29T23:48:42.948Z",
"name": "hello",
"version": {
"message": "Test message",
"version": "1rn19s2"
}
}
LIST_STORAGE_PROVIDERS_RESPONSE = [
{
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
]
SHOW_STORAGE_PROVIDER_DETAILS_RESPONSE = {
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
CREATE_STORAGE_PROVIDER_RESPONSE = {
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test1",
"type": "s3"
}
UPDATE_STORAGE_PROVIDER_RESPONSE = {
"config": {
"accessKey": "AKIBAEG7J3OJ24XAV33B",
"bucket": "bucket",
"secretAccessKey": "********"
},
"dtCreated": "2020-10-09T18:17:34.192Z",
"dtModified": "2020-10-09T18:29:22.368Z",
"id": "spltautet072md4",
"name": "test2",
"type": "s3"
}
| true | true |
f729118340ff69c98db4837c348747f22c6dba5c | 2,323 | py | Python | sdk/python/pulumi_azure_nextgen/analysisservices/latest/list_server_gateway_status.py | test-wiz-sec/pulumi-azure-nextgen | 20a695af0d020b34b0f1c336e1b69702755174cc | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_nextgen/analysisservices/latest/list_server_gateway_status.py | test-wiz-sec/pulumi-azure-nextgen | 20a695af0d020b34b0f1c336e1b69702755174cc | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_nextgen/analysisservices/latest/list_server_gateway_status.py | test-wiz-sec/pulumi-azure-nextgen | 20a695af0d020b34b0f1c336e1b69702755174cc | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
__all__ = [
'ListServerGatewayStatusResult',
'AwaitableListServerGatewayStatusResult',
'list_server_gateway_status',
]
@pulumi.output_type
class ListServerGatewayStatusResult:
"""
Status of gateway is live.
"""
def __init__(__self__, status=None):
if status and not isinstance(status, str):
raise TypeError("Expected argument 'status' to be a str")
pulumi.set(__self__, "status", status)
@property
@pulumi.getter
def status(self) -> Optional[str]:
"""
Live message of list gateway.
"""
return pulumi.get(self, "status")
class AwaitableListServerGatewayStatusResult(ListServerGatewayStatusResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return ListServerGatewayStatusResult(
status=self.status)
def list_server_gateway_status(resource_group_name: Optional[str] = None,
server_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListServerGatewayStatusResult:
"""
Use this data source to access information about an existing resource.
:param str resource_group_name: The name of the Azure Resource group of which a given Analysis Services server is part. This name must be at least 1 character in length, and no more than 90.
:param str server_name: The name of the Analysis Services server.
"""
__args__ = dict()
__args__['resourceGroupName'] = resource_group_name
__args__['serverName'] = server_name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-nextgen:analysisservices/latest:listServerGatewayStatus', __args__, opts=opts, typ=ListServerGatewayStatusResult).value
return AwaitableListServerGatewayStatusResult(
status=__ret__.status)
| 35.738462 | 194 | 0.695222 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
__all__ = [
'ListServerGatewayStatusResult',
'AwaitableListServerGatewayStatusResult',
'list_server_gateway_status',
]
@pulumi.output_type
class ListServerGatewayStatusResult:
def __init__(__self__, status=None):
if status and not isinstance(status, str):
raise TypeError("Expected argument 'status' to be a str")
pulumi.set(__self__, "status", status)
@property
@pulumi.getter
def status(self) -> Optional[str]:
return pulumi.get(self, "status")
class AwaitableListServerGatewayStatusResult(ListServerGatewayStatusResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return ListServerGatewayStatusResult(
status=self.status)
def list_server_gateway_status(resource_group_name: Optional[str] = None,
server_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListServerGatewayStatusResult:
__args__ = dict()
__args__['resourceGroupName'] = resource_group_name
__args__['serverName'] = server_name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-nextgen:analysisservices/latest:listServerGatewayStatus', __args__, opts=opts, typ=ListServerGatewayStatusResult).value
return AwaitableListServerGatewayStatusResult(
status=__ret__.status)
| true | true |
f729125438703c7fe62b6594e277054ba09d4f2e | 90 | py | Python | tests/test_git_speed.py | git-scientist/git-speed | a3c8c6b91568faf6bbd3a8cf5293f0cde1b1565b | [
"MIT"
] | 2 | 2020-04-29T10:06:51.000Z | 2020-08-13T09:31:09.000Z | tests/test_git_speed.py | git-scientist/git-speed | a3c8c6b91568faf6bbd3a8cf5293f0cde1b1565b | [
"MIT"
] | null | null | null | tests/test_git_speed.py | git-scientist/git-speed | a3c8c6b91568faf6bbd3a8cf5293f0cde1b1565b | [
"MIT"
] | 1 | 2020-07-30T16:28:57.000Z | 2020-07-30T16:28:57.000Z | from git_speed import __version__
def test_version():
assert __version__ == "1.1.1"
| 15 | 33 | 0.722222 | from git_speed import __version__
def test_version():
assert __version__ == "1.1.1"
| true | true |
f72912f0f3a8e96ead98134dbd6365fdf2ba8754 | 24,020 | py | Python | validator/tests/test_state_delta_processor/tests.py | suparnadhar/SuparnaGit | bec2704d8b6bc1802523ec26dcb902f59a747a4d | [
"Apache-2.0"
] | 1 | 2017-08-04T10:31:00.000Z | 2017-08-04T10:31:00.000Z | validator/tests/test_state_delta_processor/tests.py | suparnadhar/SuparnaGit | bec2704d8b6bc1802523ec26dcb902f59a747a4d | [
"Apache-2.0"
] | null | null | null | validator/tests/test_state_delta_processor/tests.py | suparnadhar/SuparnaGit | bec2704d8b6bc1802523ec26dcb902f59a747a4d | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------
import unittest
from unittest.mock import Mock
from sawtooth_validator.database.dict_database import DictDatabase
from sawtooth_validator.networking.dispatch import Handler
from sawtooth_validator.networking.dispatch import HandlerResult
from sawtooth_validator.networking.dispatch import HandlerStatus
from sawtooth_validator.state.state_delta_store import StateDeltaStore
from sawtooth_validator.state.state_delta_processor import \
StateDeltaProcessor
from sawtooth_validator.state.state_delta_processor import \
NoKnownBlockError
from sawtooth_validator.state.state_delta_processor import \
StateDeltaSubscriberValidationHandler
from sawtooth_validator.state.state_delta_processor import \
StateDeltaAddSubscriberHandler
from sawtooth_validator.state.state_delta_processor import \
StateDeltaUnsubscriberHandler
from sawtooth_validator.state.state_delta_processor import \
GetStateDeltaEventsHandler
from sawtooth_validator.protobuf.state_delta_pb2 import StateChange
from sawtooth_validator.protobuf.state_delta_pb2 import StateDeltaEvent
from sawtooth_validator.protobuf.state_delta_pb2 import \
RegisterStateDeltaSubscriberRequest
from sawtooth_validator.protobuf.state_delta_pb2 import \
RegisterStateDeltaSubscriberResponse
from sawtooth_validator.protobuf.state_delta_pb2 import \
UnregisterStateDeltaSubscriberRequest
from sawtooth_validator.protobuf.state_delta_pb2 import \
UnregisterStateDeltaSubscriberResponse
from sawtooth_validator.protobuf.state_delta_pb2 import \
GetStateDeltaEventsRequest
from sawtooth_validator.protobuf.state_delta_pb2 import \
GetStateDeltaEventsResponse
from sawtooth_validator.protobuf import validator_pb2
from test_journal.block_tree_manager import BlockTreeManager
class StateDeltaSubscriberValidationHandlerTest(unittest.TestCase):
def test_register_subscriber(self):
"""Tests that the handler will validate a correct subscriber and return an OK
response.
"""
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaSubscriberValidationHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=[block_tree_manager.chain_head.identifier],
address_prefixes=['000000']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN_AND_PASS, response.status)
self.assertEqual(RegisterStateDeltaSubscriberResponse.OK,
response.message_out.status)
def test_register_with_uknown_block_ids(self):
"""Tests that the handler will respond with an UNKNOWN_BLOCK
when a subscriber does not supply a known block id in
last_known_block_ids
"""
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaSubscriberValidationHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=['a'],
address_prefixes=['000000']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(
RegisterStateDeltaSubscriberResponse.UNKNOWN_BLOCK,
response.message_out.status)
class StateDeltaAddSubscriberHandlerTest(unittest.TestCase):
def test_add_subscriber(self):
"""Tests that the handler for adding the subscriptions will properly
add a subscriber.
"""
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaAddSubscriberHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=[block_tree_manager.chain_head.identifier],
address_prefixes=['0123456']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.PASS, response.status)
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
def test_register_with_unknown_block_ids(self):
"""Tests that the handler will respond with a DROP
when a subscriber does not supply a known block id in
last_known_block_ids, but the subscriber is added. It passed
validation, but a fork may have occured between validation and now.
"""
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaAddSubscriberHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=['a'],
address_prefixes=['000000']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.DROP, response.status)
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
class StateDeltaUnregisterSubscriberHandlerTest(unittest.TestCase):
def test_unregister_subscriber(self):
"""Test that a handler will unregister a subscriber via a requestor's
connection id.
"""
mock_delta_processor = Mock()
handler = StateDeltaUnsubscriberHandler(mock_delta_processor)
request = UnregisterStateDeltaSubscriberRequest().SerializeToString()
response = handler.handle('test_conn_id', request)
mock_delta_processor.remove_subscriber.assert_called_with(
'test_conn_id')
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(UnregisterStateDeltaSubscriberResponse.OK,
response.message_out.status)
class GetStateDeltaEventsHandlerTest(unittest.TestCase):
def test_get_events(self):
"""Tests that the GetStateDeltaEventsHandler will return a response
with the state event for the block requested.
"""
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_store.save_state_deltas(
block_tree_manager.chain_head.state_root_hash,
[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
handler = GetStateDeltaEventsHandler(block_tree_manager.block_store,
delta_store)
request = GetStateDeltaEventsRequest(
block_ids=[block_tree_manager.chain_head.identifier],
address_prefixes=['deadbeef']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(GetStateDeltaEventsResponse.OK,
response.message_out.status)
chain_head = block_tree_manager.chain_head
self.assertEqual(
[StateDeltaEvent(
block_id=chain_head.identifier,
block_num=chain_head.block_num,
state_root_hash=chain_head.state_root_hash,
previous_block_id=chain_head.previous_block_id,
state_changes=[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET)])],
[event for event in response.message_out.events])
def test_get_events_ignore_bad_blocks(self):
"""Tests that the GetStateDeltaEventsHandler will return a response
containing only the events for blocks that exists.
"""
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_store.save_state_deltas(
block_tree_manager.chain_head.state_root_hash,
[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
handler = GetStateDeltaEventsHandler(block_tree_manager.block_store,
delta_store)
request = GetStateDeltaEventsRequest(
block_ids=[block_tree_manager.chain_head.identifier,
'somebadblockid'],
address_prefixes=['deadbeef']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(GetStateDeltaEventsResponse.OK,
response.message_out.status)
chain_head = block_tree_manager.chain_head
self.assertEqual(
[StateDeltaEvent(
block_id=chain_head.identifier,
block_num=chain_head.block_num,
state_root_hash=chain_head.state_root_hash,
previous_block_id=chain_head.previous_block_id,
state_changes=[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET)])],
[event for event in response.message_out.events])
def test_get_events_no_valid_block_ids(self):
"""Tests that the GetStateDeltaEventsHandler will return a response
with NO_VALID_BLOCKS_SPECIFIED error when no valid blocks are
specified in the request.
"""
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
handler = GetStateDeltaEventsHandler(block_tree_manager.block_store,
delta_store)
request = GetStateDeltaEventsRequest(
block_ids=['somebadblockid'],
address_prefixes=['deadbeef']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(GetStateDeltaEventsResponse.NO_VALID_BLOCKS_SPECIFIED,
response.message_out.status)
class StateDeltaProcessorTest(unittest.TestCase):
def test_add_subscriber(self):
"""Test adding a subscriber, who has no known blocks.
This scenerio is valid for subscribers who have never connected and
would need to receive all deltas since the genesis block.
On registration, the subscriber should receive one event, comprised
of the state changes for the genesis block.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_store.save_state_deltas(
block_tree_manager.chain_head.state_root_hash,
[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
delta_processor.add_subscriber(
'test_conn_id',
[],
['deadbeef'])
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
# test that it catches up, and receives the events from the chain head.
# In this case, it should just be once, as the chain head is the
# genesis block
chain_head = block_tree_manager.chain_head
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=chain_head.identifier,
block_num=chain_head.block_num,
state_root_hash=chain_head.state_root_hash,
previous_block_id=chain_head.previous_block_id,
state_changes=[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET)]
).SerializeToString(),
connection_id='test_conn_id')
def test_add_subscriber_known_block_id(self):
"""Test adding a subscriber, whose known block id is the current
chainhead.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'test_conn_id',
[block_tree_manager.chain_head.identifier],
['deadbeef'])
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
self.assertTrue(not mock_service.send.called)
def test_add_subscriber_unknown_block_id(self):
"""Test adding a subscriber, whose known block id is not in the
current chain.
"""
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
with self.assertRaises(NoKnownBlockError):
delta_processor.add_subscriber(
'test_conn_id',
['deadbeefb10c4'],
['deadbeef'])
def test_remove_subscriber(self):
"""Test adding a subscriber, whose known block id is the current
chainhead, followed by removing the subscriber.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'test_conn_id',
[block_tree_manager.chain_head.identifier],
['deadbeef' 'beefdead'])
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
delta_processor.remove_subscriber('test_conn_id')
self.assertEqual([], delta_processor.subscriber_ids)
def test_publish_deltas(self):
"""Tests that a subscriber filtering on an address prefix receives only
the changes in an event that match.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
database = DictDatabase()
delta_store = StateDeltaStore(database)
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'test_conn_id',
[block_tree_manager.chain_head.identifier],
['deadbeef'])
next_block = block_tree_manager.generate_block()
# State added during context squash for our block
delta_store.save_state_deltas(
next_block.header.state_root_hash,
[StateChange(address='deadbeef01',
value='my_state_Value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
# call to publish deltas for that block to the subscribers
delta_processor.publish_deltas(next_block)
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=next_block.identifier,
block_num=next_block.header.block_num,
state_root_hash=next_block.header.state_root_hash,
previous_block_id=next_block.header.previous_block_id,
state_changes=[StateChange(address='deadbeef01',
value='my_state_Value'.encode(),
type=StateChange.SET)]
).SerializeToString(),
connection_id='test_conn_id')
def test_publish_deltas_subscriber_matches_no_addresses(self):
"""Given a subscriber whose prefix filters don't match any addresses
in the current state delta, it should still receive an event with the
block change information.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
database = DictDatabase()
delta_store = StateDeltaStore(database)
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'settings_conn_id',
[block_tree_manager.chain_head.identifier],
['000000'])
next_block = block_tree_manager.generate_block()
# State added during context squash for our block
delta_store.save_state_deltas(
next_block.header.state_root_hash,
[StateChange(address='deadbeef01',
value='my_state_Value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
# call to publish deltas for that block to the subscribers
delta_processor.publish_deltas(next_block)
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=next_block.identifier,
block_num=next_block.header.block_num,
state_root_hash=next_block.header.state_root_hash,
previous_block_id=next_block.header.previous_block_id,
state_changes=[]
).SerializeToString(),
connection_id='settings_conn_id')
def test_publish_deltas_no_state_changes(self):
"""Given a block transition, where no state changes happened (e.g. it
only had transactions which did not change state), the
StateDeltaProcessor should still publish an event with the block change
information.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
database = DictDatabase()
delta_store = StateDeltaStore(database)
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'subscriber_conn_id',
[block_tree_manager.chain_head.identifier],
['000000'])
next_block = block_tree_manager.generate_block()
delta_processor.publish_deltas(next_block)
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=next_block.identifier,
block_num=next_block.header.block_num,
state_root_hash=next_block.header.state_root_hash,
previous_block_id=next_block.header.previous_block_id,
state_changes=[]
).SerializeToString(),
connection_id='subscriber_conn_id')
def test_is_valid_subscription_no_known_blocks(self):
"""Test that a check for a valid subscription with no known block ids
returns True.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription([]))
def test_is_valid_subscription_known_chain_head(self):
"""Test that a check for a valid subscription with the known block id
is the chain head returns True.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription([
block_tree_manager.chain_head.identifier]))
def test_is_valid_subscription_known_old_chain(self):
"""Test that a check for a valid subscription where the known block id
is a block in the middle of the chain should return True.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
chain = block_tree_manager.generate_chain(
block_tree_manager.chain_head, 5)
# Grab an id from the middle of the chain
known_id = chain[3].identifier
block_tree_manager.block_store.update_chain(chain)
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription([known_id]))
def test_is_valid_subscription_known_shared_predecessor(self):
"""Test that a check for a valid subscription, where the known block ids
contain a shared ancestor, should return True.
"""
mock_service = Mock()
block_tree_manager = BlockTreeManager()
chain = block_tree_manager.generate_chain(
block_tree_manager.chain_head, 5)
# Grab an id from the middle of the chain
known_id = chain[3].identifier
block_tree_manager.block_store.update_chain(chain)
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription(
[known_id, 'someforkedblock01', 'someforkedblock02']))
| 39.571664 | 85 | 0.663863 |
import unittest
from unittest.mock import Mock
from sawtooth_validator.database.dict_database import DictDatabase
from sawtooth_validator.networking.dispatch import Handler
from sawtooth_validator.networking.dispatch import HandlerResult
from sawtooth_validator.networking.dispatch import HandlerStatus
from sawtooth_validator.state.state_delta_store import StateDeltaStore
from sawtooth_validator.state.state_delta_processor import \
StateDeltaProcessor
from sawtooth_validator.state.state_delta_processor import \
NoKnownBlockError
from sawtooth_validator.state.state_delta_processor import \
StateDeltaSubscriberValidationHandler
from sawtooth_validator.state.state_delta_processor import \
StateDeltaAddSubscriberHandler
from sawtooth_validator.state.state_delta_processor import \
StateDeltaUnsubscriberHandler
from sawtooth_validator.state.state_delta_processor import \
GetStateDeltaEventsHandler
from sawtooth_validator.protobuf.state_delta_pb2 import StateChange
from sawtooth_validator.protobuf.state_delta_pb2 import StateDeltaEvent
from sawtooth_validator.protobuf.state_delta_pb2 import \
RegisterStateDeltaSubscriberRequest
from sawtooth_validator.protobuf.state_delta_pb2 import \
RegisterStateDeltaSubscriberResponse
from sawtooth_validator.protobuf.state_delta_pb2 import \
UnregisterStateDeltaSubscriberRequest
from sawtooth_validator.protobuf.state_delta_pb2 import \
UnregisterStateDeltaSubscriberResponse
from sawtooth_validator.protobuf.state_delta_pb2 import \
GetStateDeltaEventsRequest
from sawtooth_validator.protobuf.state_delta_pb2 import \
GetStateDeltaEventsResponse
from sawtooth_validator.protobuf import validator_pb2
from test_journal.block_tree_manager import BlockTreeManager
class StateDeltaSubscriberValidationHandlerTest(unittest.TestCase):
def test_register_subscriber(self):
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaSubscriberValidationHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=[block_tree_manager.chain_head.identifier],
address_prefixes=['000000']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN_AND_PASS, response.status)
self.assertEqual(RegisterStateDeltaSubscriberResponse.OK,
response.message_out.status)
def test_register_with_uknown_block_ids(self):
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaSubscriberValidationHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=['a'],
address_prefixes=['000000']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(
RegisterStateDeltaSubscriberResponse.UNKNOWN_BLOCK,
response.message_out.status)
class StateDeltaAddSubscriberHandlerTest(unittest.TestCase):
def test_add_subscriber(self):
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaAddSubscriberHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=[block_tree_manager.chain_head.identifier],
address_prefixes=['0123456']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.PASS, response.status)
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
def test_register_with_unknown_block_ids(self):
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
handler = StateDeltaAddSubscriberHandler(delta_processor)
request = RegisterStateDeltaSubscriberRequest(
last_known_block_ids=['a'],
address_prefixes=['000000']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.DROP, response.status)
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
class StateDeltaUnregisterSubscriberHandlerTest(unittest.TestCase):
def test_unregister_subscriber(self):
mock_delta_processor = Mock()
handler = StateDeltaUnsubscriberHandler(mock_delta_processor)
request = UnregisterStateDeltaSubscriberRequest().SerializeToString()
response = handler.handle('test_conn_id', request)
mock_delta_processor.remove_subscriber.assert_called_with(
'test_conn_id')
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(UnregisterStateDeltaSubscriberResponse.OK,
response.message_out.status)
class GetStateDeltaEventsHandlerTest(unittest.TestCase):
def test_get_events(self):
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_store.save_state_deltas(
block_tree_manager.chain_head.state_root_hash,
[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
handler = GetStateDeltaEventsHandler(block_tree_manager.block_store,
delta_store)
request = GetStateDeltaEventsRequest(
block_ids=[block_tree_manager.chain_head.identifier],
address_prefixes=['deadbeef']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(GetStateDeltaEventsResponse.OK,
response.message_out.status)
chain_head = block_tree_manager.chain_head
self.assertEqual(
[StateDeltaEvent(
block_id=chain_head.identifier,
block_num=chain_head.block_num,
state_root_hash=chain_head.state_root_hash,
previous_block_id=chain_head.previous_block_id,
state_changes=[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET)])],
[event for event in response.message_out.events])
def test_get_events_ignore_bad_blocks(self):
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_store.save_state_deltas(
block_tree_manager.chain_head.state_root_hash,
[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
handler = GetStateDeltaEventsHandler(block_tree_manager.block_store,
delta_store)
request = GetStateDeltaEventsRequest(
block_ids=[block_tree_manager.chain_head.identifier,
'somebadblockid'],
address_prefixes=['deadbeef']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(GetStateDeltaEventsResponse.OK,
response.message_out.status)
chain_head = block_tree_manager.chain_head
self.assertEqual(
[StateDeltaEvent(
block_id=chain_head.identifier,
block_num=chain_head.block_num,
state_root_hash=chain_head.state_root_hash,
previous_block_id=chain_head.previous_block_id,
state_changes=[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET)])],
[event for event in response.message_out.events])
def test_get_events_no_valid_block_ids(self):
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
handler = GetStateDeltaEventsHandler(block_tree_manager.block_store,
delta_store)
request = GetStateDeltaEventsRequest(
block_ids=['somebadblockid'],
address_prefixes=['deadbeef']).SerializeToString()
response = handler.handle('test_conn_id', request)
self.assertEqual(HandlerStatus.RETURN, response.status)
self.assertEqual(GetStateDeltaEventsResponse.NO_VALID_BLOCKS_SPECIFIED,
response.message_out.status)
class StateDeltaProcessorTest(unittest.TestCase):
def test_add_subscriber(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_store.save_state_deltas(
block_tree_manager.chain_head.state_root_hash,
[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
delta_processor.add_subscriber(
'test_conn_id',
[],
['deadbeef'])
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
chain_head = block_tree_manager.chain_head
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=chain_head.identifier,
block_num=chain_head.block_num,
state_root_hash=chain_head.state_root_hash,
previous_block_id=chain_head.previous_block_id,
state_changes=[StateChange(address='deadbeef0000000',
value='my_genesis_value'.encode(),
type=StateChange.SET)]
).SerializeToString(),
connection_id='test_conn_id')
def test_add_subscriber_known_block_id(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'test_conn_id',
[block_tree_manager.chain_head.identifier],
['deadbeef'])
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
self.assertTrue(not mock_service.send.called)
def test_add_subscriber_unknown_block_id(self):
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=Mock(),
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
with self.assertRaises(NoKnownBlockError):
delta_processor.add_subscriber(
'test_conn_id',
['deadbeefb10c4'],
['deadbeef'])
def test_remove_subscriber(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=Mock(),
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'test_conn_id',
[block_tree_manager.chain_head.identifier],
['deadbeef' 'beefdead'])
self.assertEqual(['test_conn_id'], delta_processor.subscriber_ids)
delta_processor.remove_subscriber('test_conn_id')
self.assertEqual([], delta_processor.subscriber_ids)
def test_publish_deltas(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
database = DictDatabase()
delta_store = StateDeltaStore(database)
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'test_conn_id',
[block_tree_manager.chain_head.identifier],
['deadbeef'])
next_block = block_tree_manager.generate_block()
delta_store.save_state_deltas(
next_block.header.state_root_hash,
[StateChange(address='deadbeef01',
value='my_state_Value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
delta_processor.publish_deltas(next_block)
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=next_block.identifier,
block_num=next_block.header.block_num,
state_root_hash=next_block.header.state_root_hash,
previous_block_id=next_block.header.previous_block_id,
state_changes=[StateChange(address='deadbeef01',
value='my_state_Value'.encode(),
type=StateChange.SET)]
).SerializeToString(),
connection_id='test_conn_id')
def test_publish_deltas_subscriber_matches_no_addresses(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
database = DictDatabase()
delta_store = StateDeltaStore(database)
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'settings_conn_id',
[block_tree_manager.chain_head.identifier],
['000000'])
next_block = block_tree_manager.generate_block()
delta_store.save_state_deltas(
next_block.header.state_root_hash,
[StateChange(address='deadbeef01',
value='my_state_Value'.encode(),
type=StateChange.SET),
StateChange(address='a14ea01',
value='some other state value'.encode(),
type=StateChange.SET)])
delta_processor.publish_deltas(next_block)
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=next_block.identifier,
block_num=next_block.header.block_num,
state_root_hash=next_block.header.state_root_hash,
previous_block_id=next_block.header.previous_block_id,
state_changes=[]
).SerializeToString(),
connection_id='settings_conn_id')
def test_publish_deltas_no_state_changes(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
database = DictDatabase()
delta_store = StateDeltaStore(database)
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
delta_processor.add_subscriber(
'subscriber_conn_id',
[block_tree_manager.chain_head.identifier],
['000000'])
next_block = block_tree_manager.generate_block()
delta_processor.publish_deltas(next_block)
mock_service.send.assert_called_with(
validator_pb2.Message.STATE_DELTA_EVENT,
StateDeltaEvent(
block_id=next_block.identifier,
block_num=next_block.header.block_num,
state_root_hash=next_block.header.state_root_hash,
previous_block_id=next_block.header.previous_block_id,
state_changes=[]
).SerializeToString(),
connection_id='subscriber_conn_id')
def test_is_valid_subscription_no_known_blocks(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription([]))
def test_is_valid_subscription_known_chain_head(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription([
block_tree_manager.chain_head.identifier]))
def test_is_valid_subscription_known_old_chain(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
chain = block_tree_manager.generate_chain(
block_tree_manager.chain_head, 5)
known_id = chain[3].identifier
block_tree_manager.block_store.update_chain(chain)
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription([known_id]))
def test_is_valid_subscription_known_shared_predecessor(self):
mock_service = Mock()
block_tree_manager = BlockTreeManager()
chain = block_tree_manager.generate_chain(
block_tree_manager.chain_head, 5)
known_id = chain[3].identifier
block_tree_manager.block_store.update_chain(chain)
delta_store = StateDeltaStore(DictDatabase())
delta_processor = StateDeltaProcessor(
service=mock_service,
state_delta_store=delta_store,
block_store=block_tree_manager.block_store)
self.assertTrue(delta_processor.is_valid_subscription(
[known_id, 'someforkedblock01', 'someforkedblock02']))
| true | true |
f729136de3f0a611238aeb65acbeabe598efccb8 | 1,388 | py | Python | Python/String/The Minion Game/Python3.py | sanskritilakhmani/Hackerrank | 500694c1e6888fc657becc2c503fb5860bf39ec7 | [
"Apache-2.0"
] | 1 | 2022-02-16T18:37:49.000Z | 2022-02-16T18:37:49.000Z | Python/String/The Minion Game/Python3.py | TAFFAHACHRAF/Hackerrank | 500694c1e6888fc657becc2c503fb5860bf39ec7 | [
"Apache-2.0"
] | null | null | null | Python/String/The Minion Game/Python3.py | TAFFAHACHRAF/Hackerrank | 500694c1e6888fc657becc2c503fb5860bf39ec7 | [
"Apache-2.0"
] | 4 | 2021-07-18T16:24:02.000Z | 2022-02-15T21:42:03.000Z | """
Kevin and Stuart want to play the 'The Minion Game'.
Game Rules:
Both players are given the same string, S.
Both players have to make substrings using the letters of the string S.
Stuart has to make words starting with consonants.
Kevin has to make words starting with vowels.
The game ends when both players have made all possible substrings.
Scoring
A player gets +1 point for each occurrence of the substring in the string S.
For Example:
String S = BANANA
Kevin's vowel beginning word = ANA
Here, ANA occurs twice in BANANA. Hence, Kevin will get 2 Points.
Your task is to determine the winner of the game and their score.
Input Format
A single line of input containing the string S.
Note: The string S will contain only uppercase letters: [A-Z].
Output Format
Print one line: the name of the winner and their score separated by a space.
If the game is a draw, print Draw.
Question Link: https://www.hackerrank.com/challenges/the-minion-game/problem
Sample Input
BANANA
Sample Output
Stuart 12
"""
1.
def minion_game(string):
vowels = 'AEIOU'
k,s = 0,0
l =len(string)
for i in range(l):
if string[i] in vowels:
k += (l-i)
else:
s += (l-i)
if k > s:
print("Kevin", k)
elif k < s:
print("Stuart", s)
else:
print("Draw")
if __name__ == '__main__':
s = input()
minion_game(s)
2.
| 28.326531 | 76 | 0.68732 | 1.
def minion_game(string):
vowels = 'AEIOU'
k,s = 0,0
l =len(string)
for i in range(l):
if string[i] in vowels:
k += (l-i)
else:
s += (l-i)
if k > s:
print("Kevin", k)
elif k < s:
print("Stuart", s)
else:
print("Draw")
if __name__ == '__main__':
s = input()
minion_game(s)
2.
| true | true |
f72913cb4fa2ae202b84b187f1e0eb8b2fdba626 | 3,376 | py | Python | survae/transforms/bijections/conv1x1.py | Nintorac/survae_experiments | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | [
"MIT"
] | null | null | null | survae/transforms/bijections/conv1x1.py | Nintorac/survae_experiments | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | [
"MIT"
] | null | null | null | survae/transforms/bijections/conv1x1.py | Nintorac/survae_experiments | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | [
"MIT"
] | null | null | null | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from survae.transforms.bijections import Bijection
class Conv1x1(Bijection):
"""
Invertible 1x1 Convolution [1].
The weight matrix is initialized as a random rotation matrix
as described in Section 3.2 of [1].
Args:
num_channels (int): Number of channels in the input and output.
orthogonal_init: bool, if True initialize weights to be a random orthogonal matrix (default=True).
References:
[1] Glow: Generative Flow with Invertible 1×1 Convolutions,
Kingma & Dhariwal, 2018, https://arxiv.org/abs/1807.03039
"""
def __init__(self, num_channels, orthogonal_init=True):
super(Conv1x1, self).__init__()
self.num_channels = num_channels
self.weight = nn.Parameter(torch.Tensor(num_channels, num_channels))
self.reset_parameters(orthogonal_init)
def reset_parameters(self, orthogonal_init):
self.orthogonal_init = orthogonal_init
if self.orthogonal_init:
nn.init.orthogonal_(self.weight)
else:
bound = 1.0 / np.sqrt(self.num_channels)
nn.init.uniform_(self.weight, -bound, bound)
def _conv(self, weight, v):
return F.conv2d(v, weight.unsqueeze(-1).unsqueeze(-1))
def _logdet(self, x_shape):
b, c, h, w = x_shape
_, ldj_per_pixel = torch.slogdet(self.weight)
ldj = ldj_per_pixel * h * w
return ldj.expand([b])
def forward(self, x):
z = self._conv(self.weight, x)
ldj = self._logdet(x.shape)
return z, ldj
def inverse(self, z):
weight_inv = torch.inverse(self.weight)
x = self._conv(weight_inv, z)
return x
class Conv1x11d(Bijection):
"""
Invertible 1x1 Convolution [1].
The weight matrix is initialized as a random rotation matrix
as described in Section 3.2 of [1].
Args:
num_channels (int): Number of channels in the input and output.
orthogonal_init: bool, if True initialize weights to be a random orthogonal matrix (default=True).
References:
[1] Glow: Generative Flow with Invertible 1×1 Convolutions,
Kingma & Dhariwal, 2018, https://arxiv.org/abs/1807.03039
"""
def __init__(self, num_channels, orthogonal_init=True):
super(Conv1x11d, self).__init__()
self.num_channels = num_channels
self.weight = nn.Parameter(torch.Tensor(num_channels, num_channels))
self.reset_parameters(orthogonal_init)
def reset_parameters(self, orthogonal_init):
self.orthogonal_init = orthogonal_init
if self.orthogonal_init:
nn.init.orthogonal_(self.weight)
else:
bound = 1.0 / np.sqrt(self.num_channels)
nn.init.uniform_(self.weight, -bound, bound)
def _conv(self, weight, v):
return F.conv1d(v, weight.unsqueeze(-1))
def _logdet(self, x_shape):
b, c, l = x_shape
_, ldj_per_pixel = torch.slogdet(self.weight)
ldj = ldj_per_pixel * l
return ldj.expand([b])
def forward(self, x):
z = self._conv(self.weight, x)
ldj = self._logdet(x.shape)
return z, ldj
def inverse(self, z):
weight_inv = torch.inverse(self.weight)
x = self._conv(weight_inv, z)
return x
| 32.461538 | 106 | 0.643957 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from survae.transforms.bijections import Bijection
class Conv1x1(Bijection):
def __init__(self, num_channels, orthogonal_init=True):
super(Conv1x1, self).__init__()
self.num_channels = num_channels
self.weight = nn.Parameter(torch.Tensor(num_channels, num_channels))
self.reset_parameters(orthogonal_init)
def reset_parameters(self, orthogonal_init):
self.orthogonal_init = orthogonal_init
if self.orthogonal_init:
nn.init.orthogonal_(self.weight)
else:
bound = 1.0 / np.sqrt(self.num_channels)
nn.init.uniform_(self.weight, -bound, bound)
def _conv(self, weight, v):
return F.conv2d(v, weight.unsqueeze(-1).unsqueeze(-1))
def _logdet(self, x_shape):
b, c, h, w = x_shape
_, ldj_per_pixel = torch.slogdet(self.weight)
ldj = ldj_per_pixel * h * w
return ldj.expand([b])
def forward(self, x):
z = self._conv(self.weight, x)
ldj = self._logdet(x.shape)
return z, ldj
def inverse(self, z):
weight_inv = torch.inverse(self.weight)
x = self._conv(weight_inv, z)
return x
class Conv1x11d(Bijection):
def __init__(self, num_channels, orthogonal_init=True):
super(Conv1x11d, self).__init__()
self.num_channels = num_channels
self.weight = nn.Parameter(torch.Tensor(num_channels, num_channels))
self.reset_parameters(orthogonal_init)
def reset_parameters(self, orthogonal_init):
self.orthogonal_init = orthogonal_init
if self.orthogonal_init:
nn.init.orthogonal_(self.weight)
else:
bound = 1.0 / np.sqrt(self.num_channels)
nn.init.uniform_(self.weight, -bound, bound)
def _conv(self, weight, v):
return F.conv1d(v, weight.unsqueeze(-1))
def _logdet(self, x_shape):
b, c, l = x_shape
_, ldj_per_pixel = torch.slogdet(self.weight)
ldj = ldj_per_pixel * l
return ldj.expand([b])
def forward(self, x):
z = self._conv(self.weight, x)
ldj = self._logdet(x.shape)
return z, ldj
def inverse(self, z):
weight_inv = torch.inverse(self.weight)
x = self._conv(weight_inv, z)
return x
| true | true |
f729144d89ee4319375af6b0e2f57088061d7e52 | 46,630 | py | Python | .venv/lib/python3.8/site-packages/moto/batch/models.py | taharh/label-studio | fab68de11bdc6699472d12a78390375928258e1e | [
"Apache-2.0"
] | null | null | null | .venv/lib/python3.8/site-packages/moto/batch/models.py | taharh/label-studio | fab68de11bdc6699472d12a78390375928258e1e | [
"Apache-2.0"
] | 1 | 2020-11-19T23:20:04.000Z | 2020-11-19T23:20:04.000Z | .venv/lib/python3.8/site-packages/moto/batch/models.py | taharh/label-studio | fab68de11bdc6699472d12a78390375928258e1e | [
"Apache-2.0"
] | 1 | 2020-11-22T11:04:01.000Z | 2020-11-22T11:04:01.000Z | from __future__ import unicode_literals
import re
from itertools import cycle
import six
import datetime
import time
import uuid
import logging
import docker
import threading
import dateutil.parser
from boto3 import Session
from moto.core import BaseBackend, BaseModel, CloudFormationModel
from moto.iam import iam_backends
from moto.ec2 import ec2_backends
from moto.ecs import ecs_backends
from moto.logs import logs_backends
from .exceptions import InvalidParameterValueException, InternalFailure, ClientException
from .utils import (
make_arn_for_compute_env,
make_arn_for_job_queue,
make_arn_for_task_def,
lowercase_first_key,
)
from moto.ec2.exceptions import InvalidSubnetIdError
from moto.ec2.models import INSTANCE_TYPES as EC2_INSTANCE_TYPES
from moto.iam.exceptions import IAMNotFoundException
from moto.core import ACCOUNT_ID as DEFAULT_ACCOUNT_ID
from moto.utilities.docker_utilities import DockerModel
logger = logging.getLogger(__name__)
COMPUTE_ENVIRONMENT_NAME_REGEX = re.compile(
r"^[A-Za-z0-9][A-Za-z0-9_-]{1,126}[A-Za-z0-9]$"
)
def datetime2int(date):
return int(time.mktime(date.timetuple()))
class ComputeEnvironment(CloudFormationModel):
def __init__(
self,
compute_environment_name,
_type,
state,
compute_resources,
service_role,
region_name,
):
self.name = compute_environment_name
self.env_type = _type
self.state = state
self.compute_resources = compute_resources
self.service_role = service_role
self.arn = make_arn_for_compute_env(
DEFAULT_ACCOUNT_ID, compute_environment_name, region_name
)
self.instances = []
self.ecs_arn = None
self.ecs_name = None
def add_instance(self, instance):
self.instances.append(instance)
def set_ecs(self, arn, name):
self.ecs_arn = arn
self.ecs_name = name
@property
def physical_resource_id(self):
return self.arn
@staticmethod
def cloudformation_name_type():
return "ComputeEnvironmentName"
@staticmethod
def cloudformation_type():
# https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html
return "AWS::Batch::ComputeEnvironment"
@classmethod
def create_from_cloudformation_json(
cls, resource_name, cloudformation_json, region_name
):
backend = batch_backends[region_name]
properties = cloudformation_json["Properties"]
env = backend.create_compute_environment(
resource_name,
properties["Type"],
properties.get("State", "ENABLED"),
lowercase_first_key(properties["ComputeResources"]),
properties["ServiceRole"],
)
arn = env[1]
return backend.get_compute_environment_by_arn(arn)
class JobQueue(CloudFormationModel):
def __init__(
self, name, priority, state, environments, env_order_json, region_name
):
"""
:param name: Job queue name
:type name: str
:param priority: Job queue priority
:type priority: int
:param state: Either ENABLED or DISABLED
:type state: str
:param environments: Compute Environments
:type environments: list of ComputeEnvironment
:param env_order_json: Compute Environments JSON for use when describing
:type env_order_json: list of dict
:param region_name: Region name
:type region_name: str
"""
self.name = name
self.priority = priority
self.state = state
self.environments = environments
self.env_order_json = env_order_json
self.arn = make_arn_for_job_queue(DEFAULT_ACCOUNT_ID, name, region_name)
self.status = "VALID"
self.jobs = []
def describe(self):
result = {
"computeEnvironmentOrder": self.env_order_json,
"jobQueueArn": self.arn,
"jobQueueName": self.name,
"priority": self.priority,
"state": self.state,
"status": self.status,
}
return result
@property
def physical_resource_id(self):
return self.arn
@staticmethod
def cloudformation_name_type():
return "JobQueueName"
@staticmethod
def cloudformation_type():
# https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html
return "AWS::Batch::JobQueue"
@classmethod
def create_from_cloudformation_json(
cls, resource_name, cloudformation_json, region_name
):
backend = batch_backends[region_name]
properties = cloudformation_json["Properties"]
# Need to deal with difference case from cloudformation compute_resources, e.g. instanceRole vs InstanceRole
# Hacky fix to normalise keys, is making me think I want to start spamming cAsEiNsEnSiTiVe dictionaries
compute_envs = [
lowercase_first_key(dict_item)
for dict_item in properties["ComputeEnvironmentOrder"]
]
queue = backend.create_job_queue(
queue_name=resource_name,
priority=properties["Priority"],
state=properties.get("State", "ENABLED"),
compute_env_order=compute_envs,
)
arn = queue[1]
return backend.get_job_queue_by_arn(arn)
class JobDefinition(CloudFormationModel):
def __init__(
self,
name,
parameters,
_type,
container_properties,
region_name,
revision=0,
retry_strategy=0,
):
self.name = name
self.retries = retry_strategy
self.type = _type
self.revision = revision
self._region = region_name
self.container_properties = container_properties
self.arn = None
self.status = "ACTIVE"
if parameters is None:
parameters = {}
self.parameters = parameters
self._validate()
self._update_arn()
def _update_arn(self):
self.revision += 1
self.arn = make_arn_for_task_def(
DEFAULT_ACCOUNT_ID, self.name, self.revision, self._region
)
def _validate(self):
if self.type not in ("container",):
raise ClientException('type must be one of "container"')
# For future use when containers arnt the only thing in batch
if self.type != "container":
raise NotImplementedError()
if not isinstance(self.parameters, dict):
raise ClientException("parameters must be a string to string map")
if "image" not in self.container_properties:
raise ClientException("containerProperties must contain image")
if "memory" not in self.container_properties:
raise ClientException("containerProperties must contain memory")
if self.container_properties["memory"] < 4:
raise ClientException("container memory limit must be greater than 4")
if "vcpus" not in self.container_properties:
raise ClientException("containerProperties must contain vcpus")
if self.container_properties["vcpus"] < 1:
raise ClientException("container vcpus limit must be greater than 0")
def update(self, parameters, _type, container_properties, retry_strategy):
if parameters is None:
parameters = self.parameters
if _type is None:
_type = self.type
if container_properties is None:
container_properties = self.container_properties
if retry_strategy is None:
retry_strategy = self.retries
return JobDefinition(
self.name,
parameters,
_type,
container_properties,
region_name=self._region,
revision=self.revision,
retry_strategy=retry_strategy,
)
def describe(self):
result = {
"jobDefinitionArn": self.arn,
"jobDefinitionName": self.name,
"parameters": self.parameters,
"revision": self.revision,
"status": self.status,
"type": self.type,
}
if self.container_properties is not None:
result["containerProperties"] = self.container_properties
if self.retries is not None and self.retries > 0:
result["retryStrategy"] = {"attempts": self.retries}
return result
@property
def physical_resource_id(self):
return self.arn
@staticmethod
def cloudformation_name_type():
return "JobDefinitionName"
@staticmethod
def cloudformation_type():
# https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html
return "AWS::Batch::JobDefinition"
@classmethod
def create_from_cloudformation_json(
cls, resource_name, cloudformation_json, region_name
):
backend = batch_backends[region_name]
properties = cloudformation_json["Properties"]
res = backend.register_job_definition(
def_name=resource_name,
parameters=lowercase_first_key(properties.get("Parameters", {})),
_type="container",
retry_strategy=lowercase_first_key(properties["RetryStrategy"]),
container_properties=lowercase_first_key(properties["ContainerProperties"]),
)
arn = res[1]
return backend.get_job_definition_by_arn(arn)
class Job(threading.Thread, BaseModel, DockerModel):
def __init__(self, name, job_def, job_queue, log_backend, container_overrides):
"""
Docker Job
:param name: Job Name
:param job_def: Job definition
:type: job_def: JobDefinition
:param job_queue: Job Queue
:param log_backend: Log backend
:type log_backend: moto.logs.models.LogsBackend
"""
threading.Thread.__init__(self)
DockerModel.__init__(self)
self.job_name = name
self.job_id = str(uuid.uuid4())
self.job_definition = job_def
self.container_overrides = container_overrides or {}
self.job_queue = job_queue
self.job_state = "SUBMITTED" # One of SUBMITTED | PENDING | RUNNABLE | STARTING | RUNNING | SUCCEEDED | FAILED
self.job_queue.jobs.append(self)
self.job_started_at = datetime.datetime(1970, 1, 1)
self.job_stopped_at = datetime.datetime(1970, 1, 1)
self.job_stopped = False
self.job_stopped_reason = None
self.stop = False
self.daemon = True
self.name = "MOTO-BATCH-" + self.job_id
self._log_backend = log_backend
self.log_stream_name = None
def describe(self):
result = {
"jobDefinition": self.job_definition.arn,
"jobId": self.job_id,
"jobName": self.job_name,
"jobQueue": self.job_queue.arn,
"status": self.job_state,
"dependsOn": [],
}
if result["status"] not in ["SUBMITTED", "PENDING", "RUNNABLE", "STARTING"]:
result["startedAt"] = datetime2int(self.job_started_at)
if self.job_stopped:
result["stoppedAt"] = datetime2int(self.job_stopped_at)
result["container"] = {}
result["container"]["command"] = [
'/bin/sh -c "for a in `seq 1 10`; do echo Hello World; sleep 1; done"'
]
result["container"]["privileged"] = False
result["container"]["readonlyRootFilesystem"] = False
result["container"]["ulimits"] = {}
result["container"]["vcpus"] = 1
result["container"]["volumes"] = ""
result["container"]["logStreamName"] = self.log_stream_name
if self.job_stopped_reason is not None:
result["statusReason"] = self.job_stopped_reason
return result
def _get_container_property(self, p, default):
return self.container_overrides.get(
p, self.job_definition.container_properties.get(p, default)
)
def run(self):
"""
Run the container.
Logic is as follows:
Generate container info (eventually from task definition)
Start container
Loop whilst not asked to stop and the container is running.
Get all logs from container between the last time I checked and now.
Convert logs into cloudwatch format
Put logs into cloudwatch
:return:
"""
try:
self.job_state = "PENDING"
time.sleep(1)
image = self.job_definition.container_properties.get(
"image", "alpine:latest"
)
privileged = self.job_definition.container_properties.get(
"privileged", False
)
cmd = self._get_container_property(
"command",
'/bin/sh -c "for a in `seq 1 10`; do echo Hello World; sleep 1; done"',
)
environment = {
e["name"]: e["value"]
for e in self._get_container_property("environment", [])
}
volumes = {
v["name"]: v["host"]
for v in self._get_container_property("volumes", [])
}
mounts = [
docker.types.Mount(
m["containerPath"],
volumes[m["sourceVolume"]]["sourcePath"],
type="bind",
read_only=m["readOnly"],
)
for m in self._get_container_property("mountPoints", [])
]
name = "{0}-{1}".format(self.job_name, self.job_id)
self.job_state = "RUNNABLE"
# TODO setup ecs container instance
time.sleep(1)
self.job_state = "STARTING"
log_config = docker.types.LogConfig(type=docker.types.LogConfig.types.JSON)
container = self.docker_client.containers.run(
image,
cmd,
detach=True,
name=name,
log_config=log_config,
environment=environment,
mounts=mounts,
privileged=privileged,
)
self.job_state = "RUNNING"
self.job_started_at = datetime.datetime.now()
try:
# Log collection
logs_stdout = []
logs_stderr = []
container.reload()
# Dodgy hack, we can only check docker logs once a second, but we want to loop more
# so we can stop if asked to in a quick manner, should all go away if we go async
# There also be some dodgyness when sending an integer to docker logs and some
# events seem to be duplicated.
now = datetime.datetime.now()
i = 1
while container.status == "running" and not self.stop:
time.sleep(0.2)
if i % 5 == 0:
logs_stderr.extend(
container.logs(
stdout=False,
stderr=True,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
logs_stdout.extend(
container.logs(
stdout=True,
stderr=False,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
now = datetime.datetime.now()
container.reload()
i += 1
# Container should be stopped by this point... unless asked to stop
if container.status == "running":
container.kill()
self.job_stopped_at = datetime.datetime.now()
# Get final logs
logs_stderr.extend(
container.logs(
stdout=False,
stderr=True,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
logs_stdout.extend(
container.logs(
stdout=True,
stderr=False,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
self.job_state = "SUCCEEDED" if not self.stop else "FAILED"
# Process logs
logs_stdout = [x for x in logs_stdout if len(x) > 0]
logs_stderr = [x for x in logs_stderr if len(x) > 0]
logs = []
for line in logs_stdout + logs_stderr:
date, line = line.split(" ", 1)
date = dateutil.parser.parse(date)
# TODO: Replace with int(date.timestamp()) once we yeet Python2 out of the window
date = int(
(time.mktime(date.timetuple()) + date.microsecond / 1000000.0)
)
logs.append({"timestamp": date, "message": line.strip()})
# Send to cloudwatch
log_group = "/aws/batch/job"
stream_name = "{0}/default/{1}".format(
self.job_definition.name, self.job_id
)
self.log_stream_name = stream_name
self._log_backend.ensure_log_group(log_group, None)
self._log_backend.create_log_stream(log_group, stream_name)
self._log_backend.put_log_events(log_group, stream_name, logs, None)
except Exception as err:
logger.error(
"Failed to run AWS Batch container {0}. Error {1}".format(
self.name, err
)
)
self.job_state = "FAILED"
container.kill()
finally:
container.remove()
except Exception as err:
logger.error(
"Failed to run AWS Batch container {0}. Error {1}".format(
self.name, err
)
)
self.job_state = "FAILED"
self.job_stopped = True
self.job_stopped_at = datetime.datetime.now()
def terminate(self, reason):
if not self.stop:
self.stop = True
self.job_stopped_reason = reason
class BatchBackend(BaseBackend):
def __init__(self, region_name=None):
super(BatchBackend, self).__init__()
self.region_name = region_name
self._compute_environments = {}
self._job_queues = {}
self._job_definitions = {}
self._jobs = {}
@property
def iam_backend(self):
"""
:return: IAM Backend
:rtype: moto.iam.models.IAMBackend
"""
return iam_backends["global"]
@property
def ec2_backend(self):
"""
:return: EC2 Backend
:rtype: moto.ec2.models.EC2Backend
"""
return ec2_backends[self.region_name]
@property
def ecs_backend(self):
"""
:return: ECS Backend
:rtype: moto.ecs.models.EC2ContainerServiceBackend
"""
return ecs_backends[self.region_name]
@property
def logs_backend(self):
"""
:return: ECS Backend
:rtype: moto.logs.models.LogsBackend
"""
return logs_backends[self.region_name]
def reset(self):
region_name = self.region_name
for job in self._jobs.values():
if job.job_state not in ("FAILED", "SUCCEEDED"):
job.stop = True
# Try to join
job.join(0.2)
self.__dict__ = {}
self.__init__(region_name)
def get_compute_environment_by_arn(self, arn):
return self._compute_environments.get(arn)
def get_compute_environment_by_name(self, name):
for comp_env in self._compute_environments.values():
if comp_env.name == name:
return comp_env
return None
def get_compute_environment(self, identifier):
"""
Get compute environment by name or ARN
:param identifier: Name or ARN
:type identifier: str
:return: Compute Environment or None
:rtype: ComputeEnvironment or None
"""
env = self.get_compute_environment_by_arn(identifier)
if env is None:
env = self.get_compute_environment_by_name(identifier)
return env
def get_job_queue_by_arn(self, arn):
return self._job_queues.get(arn)
def get_job_queue_by_name(self, name):
for comp_env in self._job_queues.values():
if comp_env.name == name:
return comp_env
return None
def get_job_queue(self, identifier):
"""
Get job queue by name or ARN
:param identifier: Name or ARN
:type identifier: str
:return: Job Queue or None
:rtype: JobQueue or None
"""
env = self.get_job_queue_by_arn(identifier)
if env is None:
env = self.get_job_queue_by_name(identifier)
return env
def get_job_definition_by_arn(self, arn):
return self._job_definitions.get(arn)
def get_job_definition_by_name(self, name):
latest_revision = -1
latest_job = None
for job_def in self._job_definitions.values():
if job_def.name == name and job_def.revision > latest_revision:
latest_job = job_def
latest_revision = job_def.revision
return latest_job
def get_job_definition_by_name_revision(self, name, revision):
for job_def in self._job_definitions.values():
if job_def.name == name and job_def.revision == revision:
return job_def
return None
def get_job_definition(self, identifier):
"""
Get job definitions by name or ARN
:param identifier: Name or ARN
:type identifier: str
:return: Job definition or None
:rtype: JobDefinition or None
"""
job_def = self.get_job_definition_by_arn(identifier)
if job_def is None:
if ":" in identifier:
job_def = self.get_job_definition_by_name_revision(
*identifier.split(":", 1)
)
else:
job_def = self.get_job_definition_by_name(identifier)
return job_def
def get_job_definitions(self, identifier):
"""
Get job definitions by name or ARN
:param identifier: Name or ARN
:type identifier: str
:return: Job definition or None
:rtype: list of JobDefinition
"""
result = []
env = self.get_job_definition_by_arn(identifier)
if env is not None:
result.append(env)
else:
for value in self._job_definitions.values():
if value.name == identifier:
result.append(value)
return result
def get_job_by_id(self, identifier):
"""
Get job by id
:param identifier: Job ID
:type identifier: str
:return: Job
:rtype: Job
"""
try:
return self._jobs[identifier]
except KeyError:
return None
def describe_compute_environments(
self, environments=None, max_results=None, next_token=None
):
envs = set()
if environments is not None:
envs = set(environments)
result = []
for arn, environment in self._compute_environments.items():
# Filter shortcut
if len(envs) > 0 and arn not in envs and environment.name not in envs:
continue
json_part = {
"computeEnvironmentArn": arn,
"computeEnvironmentName": environment.name,
"ecsClusterArn": environment.ecs_arn,
"serviceRole": environment.service_role,
"state": environment.state,
"type": environment.env_type,
"status": "VALID",
}
if environment.env_type == "MANAGED":
json_part["computeResources"] = environment.compute_resources
result.append(json_part)
return result
def create_compute_environment(
self, compute_environment_name, _type, state, compute_resources, service_role
):
# Validate
if COMPUTE_ENVIRONMENT_NAME_REGEX.match(compute_environment_name) is None:
raise InvalidParameterValueException(
"Compute environment name does not match ^[A-Za-z0-9][A-Za-z0-9_-]{1,126}[A-Za-z0-9]$"
)
if self.get_compute_environment_by_name(compute_environment_name) is not None:
raise InvalidParameterValueException(
"A compute environment already exists with the name {0}".format(
compute_environment_name
)
)
# Look for IAM role
try:
self.iam_backend.get_role_by_arn(service_role)
except IAMNotFoundException:
raise InvalidParameterValueException(
"Could not find IAM role {0}".format(service_role)
)
if _type not in ("MANAGED", "UNMANAGED"):
raise InvalidParameterValueException(
"type {0} must be one of MANAGED | UNMANAGED".format(service_role)
)
if state is not None and state not in ("ENABLED", "DISABLED"):
raise InvalidParameterValueException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
if compute_resources is None and _type == "MANAGED":
raise InvalidParameterValueException(
"computeResources must be specified when creating a MANAGED environment".format(
state
)
)
elif compute_resources is not None:
self._validate_compute_resources(compute_resources)
# By here, all values except SPOT ones have been validated
new_comp_env = ComputeEnvironment(
compute_environment_name,
_type,
state,
compute_resources,
service_role,
region_name=self.region_name,
)
self._compute_environments[new_comp_env.arn] = new_comp_env
# Ok by this point, everything is legit, so if its Managed then start some instances
if _type == "MANAGED":
cpus = int(
compute_resources.get("desiredvCpus", compute_resources["minvCpus"])
)
instance_types = compute_resources["instanceTypes"]
needed_instance_types = self.find_min_instances_to_meet_vcpus(
instance_types, cpus
)
# Create instances
# Will loop over and over so we get decent subnet coverage
subnet_cycle = cycle(compute_resources["subnets"])
for instance_type in needed_instance_types:
reservation = self.ec2_backend.add_instances(
image_id="ami-ecs-optimised", # Todo import AMIs
count=1,
user_data=None,
security_group_names=[],
instance_type=instance_type,
region_name=self.region_name,
subnet_id=six.next(subnet_cycle),
key_name=compute_resources.get("ec2KeyPair", "AWS_OWNED"),
security_group_ids=compute_resources["securityGroupIds"],
)
new_comp_env.add_instance(reservation.instances[0])
# Create ECS cluster
# Should be of format P2OnDemand_Batch_UUID
cluster_name = "OnDemand_Batch_" + str(uuid.uuid4())
ecs_cluster = self.ecs_backend.create_cluster(cluster_name)
new_comp_env.set_ecs(ecs_cluster.arn, cluster_name)
return compute_environment_name, new_comp_env.arn
def _validate_compute_resources(self, cr):
"""
Checks contents of sub dictionary for managed clusters
:param cr: computeResources
:type cr: dict
"""
for param in (
"instanceRole",
"maxvCpus",
"minvCpus",
"instanceTypes",
"securityGroupIds",
"subnets",
"type",
):
if param not in cr:
raise InvalidParameterValueException(
"computeResources must contain {0}".format(param)
)
for profile in self.iam_backend.get_instance_profiles():
if profile.arn == cr["instanceRole"]:
break
else:
raise InvalidParameterValueException(
"could not find instanceRole {0}".format(cr["instanceRole"])
)
if cr["maxvCpus"] < 0:
raise InvalidParameterValueException("maxVCpus must be positive")
if cr["minvCpus"] < 0:
raise InvalidParameterValueException("minVCpus must be positive")
if cr["maxvCpus"] < cr["minvCpus"]:
raise InvalidParameterValueException(
"maxVCpus must be greater than minvCpus"
)
if len(cr["instanceTypes"]) == 0:
raise InvalidParameterValueException(
"At least 1 instance type must be provided"
)
for instance_type in cr["instanceTypes"]:
if instance_type == "optimal":
pass # Optimal should pick from latest of current gen
elif instance_type not in EC2_INSTANCE_TYPES:
raise InvalidParameterValueException(
"Instance type {0} does not exist".format(instance_type)
)
for sec_id in cr["securityGroupIds"]:
if self.ec2_backend.get_security_group_from_id(sec_id) is None:
raise InvalidParameterValueException(
"security group {0} does not exist".format(sec_id)
)
if len(cr["securityGroupIds"]) == 0:
raise InvalidParameterValueException(
"At least 1 security group must be provided"
)
for subnet_id in cr["subnets"]:
try:
self.ec2_backend.get_subnet(subnet_id)
except InvalidSubnetIdError:
raise InvalidParameterValueException(
"subnet {0} does not exist".format(subnet_id)
)
if len(cr["subnets"]) == 0:
raise InvalidParameterValueException("At least 1 subnet must be provided")
if cr["type"] not in ("EC2", "SPOT"):
raise InvalidParameterValueException(
"computeResources.type must be either EC2 | SPOT"
)
if cr["type"] == "SPOT":
raise InternalFailure("SPOT NOT SUPPORTED YET")
@staticmethod
def find_min_instances_to_meet_vcpus(instance_types, target):
"""
Finds the minimum needed instances to meed a vcpu target
:param instance_types: Instance types, like ['t2.medium', 't2.small']
:type instance_types: list of str
:param target: VCPU target
:type target: float
:return: List of instance types
:rtype: list of str
"""
# vcpus = [ (vcpus, instance_type), (vcpus, instance_type), ... ]
instance_vcpus = []
instances = []
for instance_type in instance_types:
if instance_type == "optimal":
instance_type = "m4.4xlarge"
instance_vcpus.append(
(EC2_INSTANCE_TYPES[instance_type]["vcpus"], instance_type)
)
instance_vcpus = sorted(instance_vcpus, key=lambda item: item[0], reverse=True)
# Loop through,
# if biggest instance type smaller than target, and len(instance_types)> 1, then use biggest type
# if biggest instance type bigger than target, and len(instance_types)> 1, then remove it and move on
# if biggest instance type bigger than target and len(instan_types) == 1 then add instance and finish
# if biggest instance type smaller than target and len(instan_types) == 1 then loop adding instances until target == 0
# ^^ boils down to keep adding last till target vcpus is negative
# #Algorithm ;-) ... Could probably be done better with some quality lambdas
while target > 0:
current_vcpu, current_instance = instance_vcpus[0]
if len(instance_vcpus) > 1:
if current_vcpu <= target:
target -= current_vcpu
instances.append(current_instance)
else:
# try next biggest instance
instance_vcpus.pop(0)
else:
# Were on the last instance
target -= current_vcpu
instances.append(current_instance)
return instances
def delete_compute_environment(self, compute_environment_name):
if compute_environment_name is None:
raise InvalidParameterValueException("Missing computeEnvironment parameter")
compute_env = self.get_compute_environment(compute_environment_name)
if compute_env is not None:
# Pop ComputeEnvironment
self._compute_environments.pop(compute_env.arn)
# Delete ECS cluster
self.ecs_backend.delete_cluster(compute_env.ecs_name)
if compute_env.env_type == "MANAGED":
# Delete compute environment
instance_ids = [instance.id for instance in compute_env.instances]
self.ec2_backend.terminate_instances(instance_ids)
def update_compute_environment(
self, compute_environment_name, state, compute_resources, service_role
):
# Validate
compute_env = self.get_compute_environment(compute_environment_name)
if compute_env is None:
raise ClientException("Compute environment {0} does not exist")
# Look for IAM role
if service_role is not None:
try:
role = self.iam_backend.get_role_by_arn(service_role)
except IAMNotFoundException:
raise InvalidParameterValueException(
"Could not find IAM role {0}".format(service_role)
)
compute_env.service_role = role
if state is not None:
if state not in ("ENABLED", "DISABLED"):
raise InvalidParameterValueException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
compute_env.state = state
if compute_resources is not None:
# TODO Implement resizing of instances based on changing vCpus
# compute_resources CAN contain desiredvCpus, maxvCpus, minvCpus, and can contain none of them.
pass
return compute_env.name, compute_env.arn
def create_job_queue(self, queue_name, priority, state, compute_env_order):
"""
Create a job queue
:param queue_name: Queue name
:type queue_name: str
:param priority: Queue priority
:type priority: int
:param state: Queue state
:type state: string
:param compute_env_order: Compute environment list
:type compute_env_order: list of dict
:return: Tuple of Name, ARN
:rtype: tuple of str
"""
for variable, var_name in (
(queue_name, "jobQueueName"),
(priority, "priority"),
(state, "state"),
(compute_env_order, "computeEnvironmentOrder"),
):
if variable is None:
raise ClientException("{0} must be provided".format(var_name))
if state not in ("ENABLED", "DISABLED"):
raise ClientException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
if self.get_job_queue_by_name(queue_name) is not None:
raise ClientException("Job queue {0} already exists".format(queue_name))
if len(compute_env_order) == 0:
raise ClientException("At least 1 compute environment must be provided")
try:
# orders and extracts computeEnvironment names
ordered_compute_environments = [
item["computeEnvironment"]
for item in sorted(compute_env_order, key=lambda x: x["order"])
]
env_objects = []
# Check each ARN exists, then make a list of compute env's
for arn in ordered_compute_environments:
env = self.get_compute_environment_by_arn(arn)
if env is None:
raise ClientException(
"Compute environment {0} does not exist".format(arn)
)
env_objects.append(env)
except Exception:
raise ClientException("computeEnvironmentOrder is malformed")
# Create new Job Queue
queue = JobQueue(
queue_name,
priority,
state,
env_objects,
compute_env_order,
self.region_name,
)
self._job_queues[queue.arn] = queue
return queue_name, queue.arn
def describe_job_queues(self, job_queues=None, max_results=None, next_token=None):
envs = set()
if job_queues is not None:
envs = set(job_queues)
result = []
for arn, job_queue in self._job_queues.items():
# Filter shortcut
if len(envs) > 0 and arn not in envs and job_queue.name not in envs:
continue
result.append(job_queue.describe())
return result
def update_job_queue(self, queue_name, priority, state, compute_env_order):
"""
Update a job queue
:param queue_name: Queue name
:type queue_name: str
:param priority: Queue priority
:type priority: int
:param state: Queue state
:type state: string
:param compute_env_order: Compute environment list
:type compute_env_order: list of dict
:return: Tuple of Name, ARN
:rtype: tuple of str
"""
if queue_name is None:
raise ClientException("jobQueueName must be provided")
job_queue = self.get_job_queue(queue_name)
if job_queue is None:
raise ClientException("Job queue {0} does not exist".format(queue_name))
if state is not None:
if state not in ("ENABLED", "DISABLED"):
raise ClientException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
job_queue.state = state
if compute_env_order is not None:
if len(compute_env_order) == 0:
raise ClientException("At least 1 compute environment must be provided")
try:
# orders and extracts computeEnvironment names
ordered_compute_environments = [
item["computeEnvironment"]
for item in sorted(compute_env_order, key=lambda x: x["order"])
]
env_objects = []
# Check each ARN exists, then make a list of compute env's
for arn in ordered_compute_environments:
env = self.get_compute_environment_by_arn(arn)
if env is None:
raise ClientException(
"Compute environment {0} does not exist".format(arn)
)
env_objects.append(env)
except Exception:
raise ClientException("computeEnvironmentOrder is malformed")
job_queue.env_order_json = compute_env_order
job_queue.environments = env_objects
if priority is not None:
job_queue.priority = priority
return queue_name, job_queue.arn
def delete_job_queue(self, queue_name):
job_queue = self.get_job_queue(queue_name)
if job_queue is not None:
del self._job_queues[job_queue.arn]
def register_job_definition(
self, def_name, parameters, _type, retry_strategy, container_properties
):
if def_name is None:
raise ClientException("jobDefinitionName must be provided")
job_def = self.get_job_definition_by_name(def_name)
if retry_strategy is not None:
try:
retry_strategy = retry_strategy["attempts"]
except Exception:
raise ClientException("retryStrategy is malformed")
if job_def is None:
job_def = JobDefinition(
def_name,
parameters,
_type,
container_properties,
region_name=self.region_name,
retry_strategy=retry_strategy,
)
else:
# Make new jobdef
job_def = job_def.update(
parameters, _type, container_properties, retry_strategy
)
self._job_definitions[job_def.arn] = job_def
return def_name, job_def.arn, job_def.revision
def deregister_job_definition(self, def_name):
job_def = self.get_job_definition_by_arn(def_name)
if job_def is None and ":" in def_name:
name, revision = def_name.split(":", 1)
job_def = self.get_job_definition_by_name_revision(name, revision)
if job_def is not None:
del self._job_definitions[job_def.arn]
def describe_job_definitions(
self,
job_def_name=None,
job_def_list=None,
status=None,
max_results=None,
next_token=None,
):
jobs = []
# As a job name can reference multiple revisions, we get a list of them
if job_def_name is not None:
job_def = self.get_job_definitions(job_def_name)
if job_def is not None:
jobs.extend(job_def)
elif job_def_list is not None:
for job in job_def_list:
job_def = self.get_job_definitions(job)
if job_def is not None:
jobs.extend(job_def)
else:
jobs.extend(self._job_definitions.values())
# Got all the job defs were after, filter then by status
if status is not None:
return [job for job in jobs if job.status == status]
return jobs
def submit_job(
self,
job_name,
job_def_id,
job_queue,
parameters=None,
retries=None,
depends_on=None,
container_overrides=None,
):
# TODO parameters, retries (which is a dict raw from request), job dependencies and container overrides are ignored for now
# Look for job definition
job_def = self.get_job_definition(job_def_id)
if job_def is None:
raise ClientException(
"Job definition {0} does not exist".format(job_def_id)
)
queue = self.get_job_queue(job_queue)
if queue is None:
raise ClientException("Job queue {0} does not exist".format(job_queue))
job = Job(
job_name,
job_def,
queue,
log_backend=self.logs_backend,
container_overrides=container_overrides,
)
self._jobs[job.job_id] = job
# Here comes the fun
job.start()
return job_name, job.job_id
def describe_jobs(self, jobs):
job_filter = set()
if jobs is not None:
job_filter = set(jobs)
result = []
for key, job in self._jobs.items():
if len(job_filter) > 0 and key not in job_filter:
continue
result.append(job.describe())
return result
def list_jobs(self, job_queue, job_status=None, max_results=None, next_token=None):
jobs = []
job_queue = self.get_job_queue(job_queue)
if job_queue is None:
raise ClientException("Job queue {0} does not exist".format(job_queue))
if job_status is not None and job_status not in (
"SUBMITTED",
"PENDING",
"RUNNABLE",
"STARTING",
"RUNNING",
"SUCCEEDED",
"FAILED",
):
raise ClientException(
"Job status is not one of SUBMITTED | PENDING | RUNNABLE | STARTING | RUNNING | SUCCEEDED | FAILED"
)
for job in job_queue.jobs:
if job_status is not None and job.job_state != job_status:
continue
jobs.append(job)
return jobs
def terminate_job(self, job_id, reason):
if job_id is None:
raise ClientException("Job ID does not exist")
if reason is None:
raise ClientException("Reason does not exist")
job = self.get_job_by_id(job_id)
if job is None:
raise ClientException("Job not found")
job.terminate(reason)
batch_backends = {}
for region in Session().get_available_regions("batch"):
batch_backends[region] = BatchBackend(region)
for region in Session().get_available_regions("batch", partition_name="aws-us-gov"):
batch_backends[region] = BatchBackend(region)
for region in Session().get_available_regions("batch", partition_name="aws-cn"):
batch_backends[region] = BatchBackend(region)
| 34.69494 | 131 | 0.578962 | from __future__ import unicode_literals
import re
from itertools import cycle
import six
import datetime
import time
import uuid
import logging
import docker
import threading
import dateutil.parser
from boto3 import Session
from moto.core import BaseBackend, BaseModel, CloudFormationModel
from moto.iam import iam_backends
from moto.ec2 import ec2_backends
from moto.ecs import ecs_backends
from moto.logs import logs_backends
from .exceptions import InvalidParameterValueException, InternalFailure, ClientException
from .utils import (
make_arn_for_compute_env,
make_arn_for_job_queue,
make_arn_for_task_def,
lowercase_first_key,
)
from moto.ec2.exceptions import InvalidSubnetIdError
from moto.ec2.models import INSTANCE_TYPES as EC2_INSTANCE_TYPES
from moto.iam.exceptions import IAMNotFoundException
from moto.core import ACCOUNT_ID as DEFAULT_ACCOUNT_ID
from moto.utilities.docker_utilities import DockerModel
logger = logging.getLogger(__name__)
COMPUTE_ENVIRONMENT_NAME_REGEX = re.compile(
r"^[A-Za-z0-9][A-Za-z0-9_-]{1,126}[A-Za-z0-9]$"
)
def datetime2int(date):
return int(time.mktime(date.timetuple()))
class ComputeEnvironment(CloudFormationModel):
def __init__(
self,
compute_environment_name,
_type,
state,
compute_resources,
service_role,
region_name,
):
self.name = compute_environment_name
self.env_type = _type
self.state = state
self.compute_resources = compute_resources
self.service_role = service_role
self.arn = make_arn_for_compute_env(
DEFAULT_ACCOUNT_ID, compute_environment_name, region_name
)
self.instances = []
self.ecs_arn = None
self.ecs_name = None
def add_instance(self, instance):
self.instances.append(instance)
def set_ecs(self, arn, name):
self.ecs_arn = arn
self.ecs_name = name
@property
def physical_resource_id(self):
return self.arn
@staticmethod
def cloudformation_name_type():
return "ComputeEnvironmentName"
@staticmethod
def cloudformation_type():
return "AWS::Batch::ComputeEnvironment"
@classmethod
def create_from_cloudformation_json(
cls, resource_name, cloudformation_json, region_name
):
backend = batch_backends[region_name]
properties = cloudformation_json["Properties"]
env = backend.create_compute_environment(
resource_name,
properties["Type"],
properties.get("State", "ENABLED"),
lowercase_first_key(properties["ComputeResources"]),
properties["ServiceRole"],
)
arn = env[1]
return backend.get_compute_environment_by_arn(arn)
class JobQueue(CloudFormationModel):
def __init__(
self, name, priority, state, environments, env_order_json, region_name
):
self.name = name
self.priority = priority
self.state = state
self.environments = environments
self.env_order_json = env_order_json
self.arn = make_arn_for_job_queue(DEFAULT_ACCOUNT_ID, name, region_name)
self.status = "VALID"
self.jobs = []
def describe(self):
result = {
"computeEnvironmentOrder": self.env_order_json,
"jobQueueArn": self.arn,
"jobQueueName": self.name,
"priority": self.priority,
"state": self.state,
"status": self.status,
}
return result
@property
def physical_resource_id(self):
return self.arn
@staticmethod
def cloudformation_name_type():
return "JobQueueName"
@staticmethod
def cloudformation_type():
return "AWS::Batch::JobQueue"
@classmethod
def create_from_cloudformation_json(
cls, resource_name, cloudformation_json, region_name
):
backend = batch_backends[region_name]
properties = cloudformation_json["Properties"]
compute_envs = [
lowercase_first_key(dict_item)
for dict_item in properties["ComputeEnvironmentOrder"]
]
queue = backend.create_job_queue(
queue_name=resource_name,
priority=properties["Priority"],
state=properties.get("State", "ENABLED"),
compute_env_order=compute_envs,
)
arn = queue[1]
return backend.get_job_queue_by_arn(arn)
class JobDefinition(CloudFormationModel):
def __init__(
self,
name,
parameters,
_type,
container_properties,
region_name,
revision=0,
retry_strategy=0,
):
self.name = name
self.retries = retry_strategy
self.type = _type
self.revision = revision
self._region = region_name
self.container_properties = container_properties
self.arn = None
self.status = "ACTIVE"
if parameters is None:
parameters = {}
self.parameters = parameters
self._validate()
self._update_arn()
def _update_arn(self):
self.revision += 1
self.arn = make_arn_for_task_def(
DEFAULT_ACCOUNT_ID, self.name, self.revision, self._region
)
def _validate(self):
if self.type not in ("container",):
raise ClientException('type must be one of "container"')
if self.type != "container":
raise NotImplementedError()
if not isinstance(self.parameters, dict):
raise ClientException("parameters must be a string to string map")
if "image" not in self.container_properties:
raise ClientException("containerProperties must contain image")
if "memory" not in self.container_properties:
raise ClientException("containerProperties must contain memory")
if self.container_properties["memory"] < 4:
raise ClientException("container memory limit must be greater than 4")
if "vcpus" not in self.container_properties:
raise ClientException("containerProperties must contain vcpus")
if self.container_properties["vcpus"] < 1:
raise ClientException("container vcpus limit must be greater than 0")
def update(self, parameters, _type, container_properties, retry_strategy):
if parameters is None:
parameters = self.parameters
if _type is None:
_type = self.type
if container_properties is None:
container_properties = self.container_properties
if retry_strategy is None:
retry_strategy = self.retries
return JobDefinition(
self.name,
parameters,
_type,
container_properties,
region_name=self._region,
revision=self.revision,
retry_strategy=retry_strategy,
)
def describe(self):
result = {
"jobDefinitionArn": self.arn,
"jobDefinitionName": self.name,
"parameters": self.parameters,
"revision": self.revision,
"status": self.status,
"type": self.type,
}
if self.container_properties is not None:
result["containerProperties"] = self.container_properties
if self.retries is not None and self.retries > 0:
result["retryStrategy"] = {"attempts": self.retries}
return result
@property
def physical_resource_id(self):
return self.arn
@staticmethod
def cloudformation_name_type():
return "JobDefinitionName"
@staticmethod
def cloudformation_type():
return "AWS::Batch::JobDefinition"
@classmethod
def create_from_cloudformation_json(
cls, resource_name, cloudformation_json, region_name
):
backend = batch_backends[region_name]
properties = cloudformation_json["Properties"]
res = backend.register_job_definition(
def_name=resource_name,
parameters=lowercase_first_key(properties.get("Parameters", {})),
_type="container",
retry_strategy=lowercase_first_key(properties["RetryStrategy"]),
container_properties=lowercase_first_key(properties["ContainerProperties"]),
)
arn = res[1]
return backend.get_job_definition_by_arn(arn)
class Job(threading.Thread, BaseModel, DockerModel):
def __init__(self, name, job_def, job_queue, log_backend, container_overrides):
threading.Thread.__init__(self)
DockerModel.__init__(self)
self.job_name = name
self.job_id = str(uuid.uuid4())
self.job_definition = job_def
self.container_overrides = container_overrides or {}
self.job_queue = job_queue
self.job_state = "SUBMITTED"
self.job_queue.jobs.append(self)
self.job_started_at = datetime.datetime(1970, 1, 1)
self.job_stopped_at = datetime.datetime(1970, 1, 1)
self.job_stopped = False
self.job_stopped_reason = None
self.stop = False
self.daemon = True
self.name = "MOTO-BATCH-" + self.job_id
self._log_backend = log_backend
self.log_stream_name = None
def describe(self):
result = {
"jobDefinition": self.job_definition.arn,
"jobId": self.job_id,
"jobName": self.job_name,
"jobQueue": self.job_queue.arn,
"status": self.job_state,
"dependsOn": [],
}
if result["status"] not in ["SUBMITTED", "PENDING", "RUNNABLE", "STARTING"]:
result["startedAt"] = datetime2int(self.job_started_at)
if self.job_stopped:
result["stoppedAt"] = datetime2int(self.job_stopped_at)
result["container"] = {}
result["container"]["command"] = [
'/bin/sh -c "for a in `seq 1 10`; do echo Hello World; sleep 1; done"'
]
result["container"]["privileged"] = False
result["container"]["readonlyRootFilesystem"] = False
result["container"]["ulimits"] = {}
result["container"]["vcpus"] = 1
result["container"]["volumes"] = ""
result["container"]["logStreamName"] = self.log_stream_name
if self.job_stopped_reason is not None:
result["statusReason"] = self.job_stopped_reason
return result
def _get_container_property(self, p, default):
return self.container_overrides.get(
p, self.job_definition.container_properties.get(p, default)
)
def run(self):
try:
self.job_state = "PENDING"
time.sleep(1)
image = self.job_definition.container_properties.get(
"image", "alpine:latest"
)
privileged = self.job_definition.container_properties.get(
"privileged", False
)
cmd = self._get_container_property(
"command",
'/bin/sh -c "for a in `seq 1 10`; do echo Hello World; sleep 1; done"',
)
environment = {
e["name"]: e["value"]
for e in self._get_container_property("environment", [])
}
volumes = {
v["name"]: v["host"]
for v in self._get_container_property("volumes", [])
}
mounts = [
docker.types.Mount(
m["containerPath"],
volumes[m["sourceVolume"]]["sourcePath"],
type="bind",
read_only=m["readOnly"],
)
for m in self._get_container_property("mountPoints", [])
]
name = "{0}-{1}".format(self.job_name, self.job_id)
self.job_state = "RUNNABLE"
time.sleep(1)
self.job_state = "STARTING"
log_config = docker.types.LogConfig(type=docker.types.LogConfig.types.JSON)
container = self.docker_client.containers.run(
image,
cmd,
detach=True,
name=name,
log_config=log_config,
environment=environment,
mounts=mounts,
privileged=privileged,
)
self.job_state = "RUNNING"
self.job_started_at = datetime.datetime.now()
try:
logs_stdout = []
logs_stderr = []
container.reload()
now = datetime.datetime.now()
i = 1
while container.status == "running" and not self.stop:
time.sleep(0.2)
if i % 5 == 0:
logs_stderr.extend(
container.logs(
stdout=False,
stderr=True,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
logs_stdout.extend(
container.logs(
stdout=True,
stderr=False,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
now = datetime.datetime.now()
container.reload()
i += 1
if container.status == "running":
container.kill()
self.job_stopped_at = datetime.datetime.now()
logs_stderr.extend(
container.logs(
stdout=False,
stderr=True,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
logs_stdout.extend(
container.logs(
stdout=True,
stderr=False,
timestamps=True,
since=datetime2int(now),
)
.decode()
.split("\n")
)
self.job_state = "SUCCEEDED" if not self.stop else "FAILED"
logs_stdout = [x for x in logs_stdout if len(x) > 0]
logs_stderr = [x for x in logs_stderr if len(x) > 0]
logs = []
for line in logs_stdout + logs_stderr:
date, line = line.split(" ", 1)
date = dateutil.parser.parse(date)
date = int(
(time.mktime(date.timetuple()) + date.microsecond / 1000000.0)
)
logs.append({"timestamp": date, "message": line.strip()})
log_group = "/aws/batch/job"
stream_name = "{0}/default/{1}".format(
self.job_definition.name, self.job_id
)
self.log_stream_name = stream_name
self._log_backend.ensure_log_group(log_group, None)
self._log_backend.create_log_stream(log_group, stream_name)
self._log_backend.put_log_events(log_group, stream_name, logs, None)
except Exception as err:
logger.error(
"Failed to run AWS Batch container {0}. Error {1}".format(
self.name, err
)
)
self.job_state = "FAILED"
container.kill()
finally:
container.remove()
except Exception as err:
logger.error(
"Failed to run AWS Batch container {0}. Error {1}".format(
self.name, err
)
)
self.job_state = "FAILED"
self.job_stopped = True
self.job_stopped_at = datetime.datetime.now()
def terminate(self, reason):
if not self.stop:
self.stop = True
self.job_stopped_reason = reason
class BatchBackend(BaseBackend):
def __init__(self, region_name=None):
super(BatchBackend, self).__init__()
self.region_name = region_name
self._compute_environments = {}
self._job_queues = {}
self._job_definitions = {}
self._jobs = {}
@property
def iam_backend(self):
return iam_backends["global"]
@property
def ec2_backend(self):
return ec2_backends[self.region_name]
@property
def ecs_backend(self):
return ecs_backends[self.region_name]
@property
def logs_backend(self):
return logs_backends[self.region_name]
def reset(self):
region_name = self.region_name
for job in self._jobs.values():
if job.job_state not in ("FAILED", "SUCCEEDED"):
job.stop = True
job.join(0.2)
self.__dict__ = {}
self.__init__(region_name)
def get_compute_environment_by_arn(self, arn):
return self._compute_environments.get(arn)
def get_compute_environment_by_name(self, name):
for comp_env in self._compute_environments.values():
if comp_env.name == name:
return comp_env
return None
def get_compute_environment(self, identifier):
env = self.get_compute_environment_by_arn(identifier)
if env is None:
env = self.get_compute_environment_by_name(identifier)
return env
def get_job_queue_by_arn(self, arn):
return self._job_queues.get(arn)
def get_job_queue_by_name(self, name):
for comp_env in self._job_queues.values():
if comp_env.name == name:
return comp_env
return None
def get_job_queue(self, identifier):
env = self.get_job_queue_by_arn(identifier)
if env is None:
env = self.get_job_queue_by_name(identifier)
return env
def get_job_definition_by_arn(self, arn):
return self._job_definitions.get(arn)
def get_job_definition_by_name(self, name):
latest_revision = -1
latest_job = None
for job_def in self._job_definitions.values():
if job_def.name == name and job_def.revision > latest_revision:
latest_job = job_def
latest_revision = job_def.revision
return latest_job
def get_job_definition_by_name_revision(self, name, revision):
for job_def in self._job_definitions.values():
if job_def.name == name and job_def.revision == revision:
return job_def
return None
def get_job_definition(self, identifier):
job_def = self.get_job_definition_by_arn(identifier)
if job_def is None:
if ":" in identifier:
job_def = self.get_job_definition_by_name_revision(
*identifier.split(":", 1)
)
else:
job_def = self.get_job_definition_by_name(identifier)
return job_def
def get_job_definitions(self, identifier):
result = []
env = self.get_job_definition_by_arn(identifier)
if env is not None:
result.append(env)
else:
for value in self._job_definitions.values():
if value.name == identifier:
result.append(value)
return result
def get_job_by_id(self, identifier):
try:
return self._jobs[identifier]
except KeyError:
return None
def describe_compute_environments(
self, environments=None, max_results=None, next_token=None
):
envs = set()
if environments is not None:
envs = set(environments)
result = []
for arn, environment in self._compute_environments.items():
if len(envs) > 0 and arn not in envs and environment.name not in envs:
continue
json_part = {
"computeEnvironmentArn": arn,
"computeEnvironmentName": environment.name,
"ecsClusterArn": environment.ecs_arn,
"serviceRole": environment.service_role,
"state": environment.state,
"type": environment.env_type,
"status": "VALID",
}
if environment.env_type == "MANAGED":
json_part["computeResources"] = environment.compute_resources
result.append(json_part)
return result
def create_compute_environment(
self, compute_environment_name, _type, state, compute_resources, service_role
):
if COMPUTE_ENVIRONMENT_NAME_REGEX.match(compute_environment_name) is None:
raise InvalidParameterValueException(
"Compute environment name does not match ^[A-Za-z0-9][A-Za-z0-9_-]{1,126}[A-Za-z0-9]$"
)
if self.get_compute_environment_by_name(compute_environment_name) is not None:
raise InvalidParameterValueException(
"A compute environment already exists with the name {0}".format(
compute_environment_name
)
)
try:
self.iam_backend.get_role_by_arn(service_role)
except IAMNotFoundException:
raise InvalidParameterValueException(
"Could not find IAM role {0}".format(service_role)
)
if _type not in ("MANAGED", "UNMANAGED"):
raise InvalidParameterValueException(
"type {0} must be one of MANAGED | UNMANAGED".format(service_role)
)
if state is not None and state not in ("ENABLED", "DISABLED"):
raise InvalidParameterValueException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
if compute_resources is None and _type == "MANAGED":
raise InvalidParameterValueException(
"computeResources must be specified when creating a MANAGED environment".format(
state
)
)
elif compute_resources is not None:
self._validate_compute_resources(compute_resources)
new_comp_env = ComputeEnvironment(
compute_environment_name,
_type,
state,
compute_resources,
service_role,
region_name=self.region_name,
)
self._compute_environments[new_comp_env.arn] = new_comp_env
if _type == "MANAGED":
cpus = int(
compute_resources.get("desiredvCpus", compute_resources["minvCpus"])
)
instance_types = compute_resources["instanceTypes"]
needed_instance_types = self.find_min_instances_to_meet_vcpus(
instance_types, cpus
)
subnet_cycle = cycle(compute_resources["subnets"])
for instance_type in needed_instance_types:
reservation = self.ec2_backend.add_instances(
image_id="ami-ecs-optimised",
count=1,
user_data=None,
security_group_names=[],
instance_type=instance_type,
region_name=self.region_name,
subnet_id=six.next(subnet_cycle),
key_name=compute_resources.get("ec2KeyPair", "AWS_OWNED"),
security_group_ids=compute_resources["securityGroupIds"],
)
new_comp_env.add_instance(reservation.instances[0])
cluster_name = "OnDemand_Batch_" + str(uuid.uuid4())
ecs_cluster = self.ecs_backend.create_cluster(cluster_name)
new_comp_env.set_ecs(ecs_cluster.arn, cluster_name)
return compute_environment_name, new_comp_env.arn
def _validate_compute_resources(self, cr):
for param in (
"instanceRole",
"maxvCpus",
"minvCpus",
"instanceTypes",
"securityGroupIds",
"subnets",
"type",
):
if param not in cr:
raise InvalidParameterValueException(
"computeResources must contain {0}".format(param)
)
for profile in self.iam_backend.get_instance_profiles():
if profile.arn == cr["instanceRole"]:
break
else:
raise InvalidParameterValueException(
"could not find instanceRole {0}".format(cr["instanceRole"])
)
if cr["maxvCpus"] < 0:
raise InvalidParameterValueException("maxVCpus must be positive")
if cr["minvCpus"] < 0:
raise InvalidParameterValueException("minVCpus must be positive")
if cr["maxvCpus"] < cr["minvCpus"]:
raise InvalidParameterValueException(
"maxVCpus must be greater than minvCpus"
)
if len(cr["instanceTypes"]) == 0:
raise InvalidParameterValueException(
"At least 1 instance type must be provided"
)
for instance_type in cr["instanceTypes"]:
if instance_type == "optimal":
pass
elif instance_type not in EC2_INSTANCE_TYPES:
raise InvalidParameterValueException(
"Instance type {0} does not exist".format(instance_type)
)
for sec_id in cr["securityGroupIds"]:
if self.ec2_backend.get_security_group_from_id(sec_id) is None:
raise InvalidParameterValueException(
"security group {0} does not exist".format(sec_id)
)
if len(cr["securityGroupIds"]) == 0:
raise InvalidParameterValueException(
"At least 1 security group must be provided"
)
for subnet_id in cr["subnets"]:
try:
self.ec2_backend.get_subnet(subnet_id)
except InvalidSubnetIdError:
raise InvalidParameterValueException(
"subnet {0} does not exist".format(subnet_id)
)
if len(cr["subnets"]) == 0:
raise InvalidParameterValueException("At least 1 subnet must be provided")
if cr["type"] not in ("EC2", "SPOT"):
raise InvalidParameterValueException(
"computeResources.type must be either EC2 | SPOT"
)
if cr["type"] == "SPOT":
raise InternalFailure("SPOT NOT SUPPORTED YET")
@staticmethod
def find_min_instances_to_meet_vcpus(instance_types, target):
instance_vcpus = []
instances = []
for instance_type in instance_types:
if instance_type == "optimal":
instance_type = "m4.4xlarge"
instance_vcpus.append(
(EC2_INSTANCE_TYPES[instance_type]["vcpus"], instance_type)
)
instance_vcpus = sorted(instance_vcpus, key=lambda item: item[0], reverse=True)
stance_vcpus[0]
if len(instance_vcpus) > 1:
if current_vcpu <= target:
target -= current_vcpu
instances.append(current_instance)
else:
instance_vcpus.pop(0)
else:
target -= current_vcpu
instances.append(current_instance)
return instances
def delete_compute_environment(self, compute_environment_name):
if compute_environment_name is None:
raise InvalidParameterValueException("Missing computeEnvironment parameter")
compute_env = self.get_compute_environment(compute_environment_name)
if compute_env is not None:
self._compute_environments.pop(compute_env.arn)
self.ecs_backend.delete_cluster(compute_env.ecs_name)
if compute_env.env_type == "MANAGED":
instance_ids = [instance.id for instance in compute_env.instances]
self.ec2_backend.terminate_instances(instance_ids)
def update_compute_environment(
self, compute_environment_name, state, compute_resources, service_role
):
compute_env = self.get_compute_environment(compute_environment_name)
if compute_env is None:
raise ClientException("Compute environment {0} does not exist")
if service_role is not None:
try:
role = self.iam_backend.get_role_by_arn(service_role)
except IAMNotFoundException:
raise InvalidParameterValueException(
"Could not find IAM role {0}".format(service_role)
)
compute_env.service_role = role
if state is not None:
if state not in ("ENABLED", "DISABLED"):
raise InvalidParameterValueException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
compute_env.state = state
if compute_resources is not None:
pass
return compute_env.name, compute_env.arn
def create_job_queue(self, queue_name, priority, state, compute_env_order):
for variable, var_name in (
(queue_name, "jobQueueName"),
(priority, "priority"),
(state, "state"),
(compute_env_order, "computeEnvironmentOrder"),
):
if variable is None:
raise ClientException("{0} must be provided".format(var_name))
if state not in ("ENABLED", "DISABLED"):
raise ClientException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
if self.get_job_queue_by_name(queue_name) is not None:
raise ClientException("Job queue {0} already exists".format(queue_name))
if len(compute_env_order) == 0:
raise ClientException("At least 1 compute environment must be provided")
try:
ordered_compute_environments = [
item["computeEnvironment"]
for item in sorted(compute_env_order, key=lambda x: x["order"])
]
env_objects = []
for arn in ordered_compute_environments:
env = self.get_compute_environment_by_arn(arn)
if env is None:
raise ClientException(
"Compute environment {0} does not exist".format(arn)
)
env_objects.append(env)
except Exception:
raise ClientException("computeEnvironmentOrder is malformed")
# Create new Job Queue
queue = JobQueue(
queue_name,
priority,
state,
env_objects,
compute_env_order,
self.region_name,
)
self._job_queues[queue.arn] = queue
return queue_name, queue.arn
def describe_job_queues(self, job_queues=None, max_results=None, next_token=None):
envs = set()
if job_queues is not None:
envs = set(job_queues)
result = []
for arn, job_queue in self._job_queues.items():
# Filter shortcut
if len(envs) > 0 and arn not in envs and job_queue.name not in envs:
continue
result.append(job_queue.describe())
return result
def update_job_queue(self, queue_name, priority, state, compute_env_order):
if queue_name is None:
raise ClientException("jobQueueName must be provided")
job_queue = self.get_job_queue(queue_name)
if job_queue is None:
raise ClientException("Job queue {0} does not exist".format(queue_name))
if state is not None:
if state not in ("ENABLED", "DISABLED"):
raise ClientException(
"state {0} must be one of ENABLED | DISABLED".format(state)
)
job_queue.state = state
if compute_env_order is not None:
if len(compute_env_order) == 0:
raise ClientException("At least 1 compute environment must be provided")
try:
# orders and extracts computeEnvironment names
ordered_compute_environments = [
item["computeEnvironment"]
for item in sorted(compute_env_order, key=lambda x: x["order"])
]
env_objects = []
# Check each ARN exists, then make a list of compute env's
for arn in ordered_compute_environments:
env = self.get_compute_environment_by_arn(arn)
if env is None:
raise ClientException(
"Compute environment {0} does not exist".format(arn)
)
env_objects.append(env)
except Exception:
raise ClientException("computeEnvironmentOrder is malformed")
job_queue.env_order_json = compute_env_order
job_queue.environments = env_objects
if priority is not None:
job_queue.priority = priority
return queue_name, job_queue.arn
def delete_job_queue(self, queue_name):
job_queue = self.get_job_queue(queue_name)
if job_queue is not None:
del self._job_queues[job_queue.arn]
def register_job_definition(
self, def_name, parameters, _type, retry_strategy, container_properties
):
if def_name is None:
raise ClientException("jobDefinitionName must be provided")
job_def = self.get_job_definition_by_name(def_name)
if retry_strategy is not None:
try:
retry_strategy = retry_strategy["attempts"]
except Exception:
raise ClientException("retryStrategy is malformed")
if job_def is None:
job_def = JobDefinition(
def_name,
parameters,
_type,
container_properties,
region_name=self.region_name,
retry_strategy=retry_strategy,
)
else:
job_def = job_def.update(
parameters, _type, container_properties, retry_strategy
)
self._job_definitions[job_def.arn] = job_def
return def_name, job_def.arn, job_def.revision
def deregister_job_definition(self, def_name):
job_def = self.get_job_definition_by_arn(def_name)
if job_def is None and ":" in def_name:
name, revision = def_name.split(":", 1)
job_def = self.get_job_definition_by_name_revision(name, revision)
if job_def is not None:
del self._job_definitions[job_def.arn]
def describe_job_definitions(
self,
job_def_name=None,
job_def_list=None,
status=None,
max_results=None,
next_token=None,
):
jobs = []
if job_def_name is not None:
job_def = self.get_job_definitions(job_def_name)
if job_def is not None:
jobs.extend(job_def)
elif job_def_list is not None:
for job in job_def_list:
job_def = self.get_job_definitions(job)
if job_def is not None:
jobs.extend(job_def)
else:
jobs.extend(self._job_definitions.values())
if status is not None:
return [job for job in jobs if job.status == status]
return jobs
def submit_job(
self,
job_name,
job_def_id,
job_queue,
parameters=None,
retries=None,
depends_on=None,
container_overrides=None,
):
job_def = self.get_job_definition(job_def_id)
if job_def is None:
raise ClientException(
"Job definition {0} does not exist".format(job_def_id)
)
queue = self.get_job_queue(job_queue)
if queue is None:
raise ClientException("Job queue {0} does not exist".format(job_queue))
job = Job(
job_name,
job_def,
queue,
log_backend=self.logs_backend,
container_overrides=container_overrides,
)
self._jobs[job.job_id] = job
job.start()
return job_name, job.job_id
def describe_jobs(self, jobs):
job_filter = set()
if jobs is not None:
job_filter = set(jobs)
result = []
for key, job in self._jobs.items():
if len(job_filter) > 0 and key not in job_filter:
continue
result.append(job.describe())
return result
def list_jobs(self, job_queue, job_status=None, max_results=None, next_token=None):
jobs = []
job_queue = self.get_job_queue(job_queue)
if job_queue is None:
raise ClientException("Job queue {0} does not exist".format(job_queue))
if job_status is not None and job_status not in (
"SUBMITTED",
"PENDING",
"RUNNABLE",
"STARTING",
"RUNNING",
"SUCCEEDED",
"FAILED",
):
raise ClientException(
"Job status is not one of SUBMITTED | PENDING | RUNNABLE | STARTING | RUNNING | SUCCEEDED | FAILED"
)
for job in job_queue.jobs:
if job_status is not None and job.job_state != job_status:
continue
jobs.append(job)
return jobs
def terminate_job(self, job_id, reason):
if job_id is None:
raise ClientException("Job ID does not exist")
if reason is None:
raise ClientException("Reason does not exist")
job = self.get_job_by_id(job_id)
if job is None:
raise ClientException("Job not found")
job.terminate(reason)
batch_backends = {}
for region in Session().get_available_regions("batch"):
batch_backends[region] = BatchBackend(region)
for region in Session().get_available_regions("batch", partition_name="aws-us-gov"):
batch_backends[region] = BatchBackend(region)
for region in Session().get_available_regions("batch", partition_name="aws-cn"):
batch_backends[region] = BatchBackend(region)
| true | true |
f72915e74f3b40b788a9b11a5ea95e96de60c1e3 | 3,057 | py | Python | dojo/tools/sslscan/parser.py | axelpavageau/django-DefectDojo | 00b425742b783ada0f432241c2812ac1257feb73 | [
"BSD-3-Clause"
] | 1,772 | 2018-01-22T23:32:15.000Z | 2022-03-31T14:49:33.000Z | dojo/tools/sslscan/parser.py | axelpavageau/django-DefectDojo | 00b425742b783ada0f432241c2812ac1257feb73 | [
"BSD-3-Clause"
] | 3,461 | 2018-01-20T19:12:28.000Z | 2022-03-31T17:14:39.000Z | dojo/tools/sslscan/parser.py | axelpavageau/django-DefectDojo | 00b425742b783ada0f432241c2812ac1257feb73 | [
"BSD-3-Clause"
] | 1,173 | 2018-01-23T07:10:23.000Z | 2022-03-31T14:40:43.000Z | import hashlib
from xml.dom import NamespaceErr
from defusedxml import ElementTree as ET
from dojo.models import Endpoint, Finding
__author__ = 'dr3dd589'
class SslscanParser(object):
def get_scan_types(self):
return ["Sslscan"]
def get_label_for_scan_types(self, scan_type):
return scan_type # no custom label for now
def get_description_for_scan_types(self, scan_type):
return "Import XML output of sslscan report."
def get_findings(self, file, test):
tree = ET.parse(file)
# get root of tree.
root = tree.getroot()
if 'document' not in root.tag:
raise NamespaceErr("This doesn't seem to be a valid sslscan xml file.")
dupes = dict()
for ssltest in root:
for target in ssltest:
title = ""
severity = ""
description = ""
severity = "Info"
host = ssltest.attrib['host']
port = int(ssltest.attrib['port'])
if target.tag == "heartbleed" and target.attrib['vulnerable'] == '1':
title = "heartbleed" + " | " + target.attrib['sslversion']
description = "**heartbleed** :" + "\n\n" + \
"**sslversion** : " + target.attrib['sslversion'] + "\n"
if target.tag == "cipher" and target.attrib['strength'] not in ['acceptable', 'strong']:
title = "cipher" + " | " + target.attrib['sslversion']
description = "**Cipher** : " + target.attrib['cipher'] + "\n\n" + \
"**Status** : " + target.attrib['status'] + "\n\n" + \
"**strength** : " + target.attrib['strength'] + "\n\n" + \
"**sslversion** : " + target.attrib['sslversion'] + "\n"
if title and description is not None:
dupe_key = hashlib.sha256(str(description + title).encode('utf-8')).hexdigest()
if dupe_key in dupes:
finding = dupes[dupe_key]
if finding.references:
finding.references = finding.references
dupes[dupe_key] = finding
else:
dupes[dupe_key] = True
finding = Finding(
title=title,
test=test,
description=description,
severity=severity,
dynamic_finding=True,)
finding.unsaved_endpoints = list()
dupes[dupe_key] = finding
if host is not None:
finding.unsaved_endpoints.append(Endpoint(
host=host,
port=port,
protocol='https' if port == 443 else None))
return dupes.values()
| 41.876712 | 104 | 0.47334 | import hashlib
from xml.dom import NamespaceErr
from defusedxml import ElementTree as ET
from dojo.models import Endpoint, Finding
__author__ = 'dr3dd589'
class SslscanParser(object):
def get_scan_types(self):
return ["Sslscan"]
def get_label_for_scan_types(self, scan_type):
return scan_type
def get_description_for_scan_types(self, scan_type):
return "Import XML output of sslscan report."
def get_findings(self, file, test):
tree = ET.parse(file)
root = tree.getroot()
if 'document' not in root.tag:
raise NamespaceErr("This doesn't seem to be a valid sslscan xml file.")
dupes = dict()
for ssltest in root:
for target in ssltest:
title = ""
severity = ""
description = ""
severity = "Info"
host = ssltest.attrib['host']
port = int(ssltest.attrib['port'])
if target.tag == "heartbleed" and target.attrib['vulnerable'] == '1':
title = "heartbleed" + " | " + target.attrib['sslversion']
description = "**heartbleed** :" + "\n\n" + \
"**sslversion** : " + target.attrib['sslversion'] + "\n"
if target.tag == "cipher" and target.attrib['strength'] not in ['acceptable', 'strong']:
title = "cipher" + " | " + target.attrib['sslversion']
description = "**Cipher** : " + target.attrib['cipher'] + "\n\n" + \
"**Status** : " + target.attrib['status'] + "\n\n" + \
"**strength** : " + target.attrib['strength'] + "\n\n" + \
"**sslversion** : " + target.attrib['sslversion'] + "\n"
if title and description is not None:
dupe_key = hashlib.sha256(str(description + title).encode('utf-8')).hexdigest()
if dupe_key in dupes:
finding = dupes[dupe_key]
if finding.references:
finding.references = finding.references
dupes[dupe_key] = finding
else:
dupes[dupe_key] = True
finding = Finding(
title=title,
test=test,
description=description,
severity=severity,
dynamic_finding=True,)
finding.unsaved_endpoints = list()
dupes[dupe_key] = finding
if host is not None:
finding.unsaved_endpoints.append(Endpoint(
host=host,
port=port,
protocol='https' if port == 443 else None))
return dupes.values()
| true | true |
f72917e6502cd6d63523cf16ccbe21e8f8e1821a | 2,629 | py | Python | numtypes/tests/test_nint32.py | WarrenWeckesser/numtypes | 4e46ac4a338ab46eec11cbacf9165827841ea4ff | [
"BSD-2-Clause"
] | 1 | 2020-02-20T15:01:47.000Z | 2020-02-20T15:01:47.000Z | numtypes/tests/test_nint32.py | WarrenWeckesser/numtypes | 4e46ac4a338ab46eec11cbacf9165827841ea4ff | [
"BSD-2-Clause"
] | 1 | 2020-04-20T13:04:32.000Z | 2020-04-20T13:04:32.000Z | numtypes/tests/test_nint32.py | WarrenWeckesser/numtypes | 4e46ac4a338ab46eec11cbacf9165827841ea4ff | [
"BSD-2-Clause"
] | 1 | 2020-04-02T09:12:36.000Z | 2020-04-02T09:12:36.000Z |
import pytest
import math
import numpy as np
from numpy.testing import assert_equal
from numtypes import nint32
def test_basic():
x = nint32(3)
assert x == 3
assert int(x) == 3
@pytest.mark.parametrize('typ', [np.int8, np.uint8, np.int16, np.uint16,
np.int32, np.uint32, np.int64, np.uint64])
def test_init_np_types(typ):
x = nint32(typ(123))
assert x == 123
def test_init_str_type():
x = nint32("123")
assert x == 123
def test_comparison():
x = nint32(100)
y = nint32(-500)
assert x > 0
assert x < 200
assert x < 123.4
assert x <= 200
assert 200 >= x
assert x == 100
assert x > y
assert x >= y
assert y < x
assert y <= x
assert x != y
def test_true_division():
x = nint32(20)
y = nint32(10)
z = x / y
assert isinstance(z, float)
assert z == 2.0
@pytest.mark.parametrize('nanstr', ['nan', '\t+NAN ', '-nAn'])
def test_nan_str(nanstr):
z = nint32(nanstr)
assert math.isnan(float(z))
assert math.isnan(z + 1.5)
def test_nan():
z = nint32(math.nan)
assert math.isnan(float(z))
assert z != z
def test_bool():
assert bool(nint32(123))
assert bool(nint32('nan'))
assert not bool(nint32(0))
def test_other():
z = 1.0 + 2.0j
a = nint32(2)
w = z / a
assert w == z/2
@pytest.mark.parametrize('value', [2**31, -2**31, 2**65])
def test_init_arg_too_big(value):
with pytest.raises(OverflowError, match='int too big to convert'):
nint32(value)
@pytest.mark.parametrize('arg', [2.5, None, 'abc'])
def test_init_bad_arg(arg):
with pytest.raises(TypeError, match='argument must be'):
nint32(arg)
@pytest.mark.parametrize('extreme_func, expected',
[(np.maximum, [20, 10, 18]),
(np.minimum, [10, -2, 10])])
def test_extreme_func(extreme_func, expected):
a = np.array([10, -2, 18], dtype=np.int32).astype(nint32)
b = np.array([20, 10, 10], dtype=np.int32).astype(nint32)
m = extreme_func(a, b)
assert m.dtype == nint32
assert_equal(m, expected)
@pytest.mark.parametrize('methodname, expected', [('min', -2), ('max', 18)])
def test_extreme_method(methodname, expected):
a = np.array([10, -2, 18], dtype=nint32)
m = getattr(a, methodname)()
assert m.dtype == nint32
assert m == expected
@pytest.mark.parametrize('methodname', ['min', 'max'])
def test_extreme_method_with_nan(methodname):
a = np.array([10, np.nan, -2, 18], dtype=nint32)
m = getattr(a, methodname)()
assert m.dtype == nint32
assert np.isnan(m)
| 23.061404 | 76 | 0.602891 |
import pytest
import math
import numpy as np
from numpy.testing import assert_equal
from numtypes import nint32
def test_basic():
x = nint32(3)
assert x == 3
assert int(x) == 3
@pytest.mark.parametrize('typ', [np.int8, np.uint8, np.int16, np.uint16,
np.int32, np.uint32, np.int64, np.uint64])
def test_init_np_types(typ):
x = nint32(typ(123))
assert x == 123
def test_init_str_type():
x = nint32("123")
assert x == 123
def test_comparison():
x = nint32(100)
y = nint32(-500)
assert x > 0
assert x < 200
assert x < 123.4
assert x <= 200
assert 200 >= x
assert x == 100
assert x > y
assert x >= y
assert y < x
assert y <= x
assert x != y
def test_true_division():
x = nint32(20)
y = nint32(10)
z = x / y
assert isinstance(z, float)
assert z == 2.0
@pytest.mark.parametrize('nanstr', ['nan', '\t+NAN ', '-nAn'])
def test_nan_str(nanstr):
z = nint32(nanstr)
assert math.isnan(float(z))
assert math.isnan(z + 1.5)
def test_nan():
z = nint32(math.nan)
assert math.isnan(float(z))
assert z != z
def test_bool():
assert bool(nint32(123))
assert bool(nint32('nan'))
assert not bool(nint32(0))
def test_other():
z = 1.0 + 2.0j
a = nint32(2)
w = z / a
assert w == z/2
@pytest.mark.parametrize('value', [2**31, -2**31, 2**65])
def test_init_arg_too_big(value):
with pytest.raises(OverflowError, match='int too big to convert'):
nint32(value)
@pytest.mark.parametrize('arg', [2.5, None, 'abc'])
def test_init_bad_arg(arg):
with pytest.raises(TypeError, match='argument must be'):
nint32(arg)
@pytest.mark.parametrize('extreme_func, expected',
[(np.maximum, [20, 10, 18]),
(np.minimum, [10, -2, 10])])
def test_extreme_func(extreme_func, expected):
a = np.array([10, -2, 18], dtype=np.int32).astype(nint32)
b = np.array([20, 10, 10], dtype=np.int32).astype(nint32)
m = extreme_func(a, b)
assert m.dtype == nint32
assert_equal(m, expected)
@pytest.mark.parametrize('methodname, expected', [('min', -2), ('max', 18)])
def test_extreme_method(methodname, expected):
a = np.array([10, -2, 18], dtype=nint32)
m = getattr(a, methodname)()
assert m.dtype == nint32
assert m == expected
@pytest.mark.parametrize('methodname', ['min', 'max'])
def test_extreme_method_with_nan(methodname):
a = np.array([10, np.nan, -2, 18], dtype=nint32)
m = getattr(a, methodname)()
assert m.dtype == nint32
assert np.isnan(m)
| true | true |
f72918b6f3095c4287cd002450044959007ad725 | 8,150 | py | Python | contrib/devtools/update-translations.py | ankerid/Anker | a20a6e911c35c480da222e0ed95b673922d8de5d | [
"MIT"
] | null | null | null | contrib/devtools/update-translations.py | ankerid/Anker | a20a6e911c35c480da222e0ed95b673922d8de5d | [
"MIT"
] | null | null | null | contrib/devtools/update-translations.py | ankerid/Anker | a20a6e911c35c480da222e0ed95b673922d8de5d | [
"MIT"
] | 1 | 2020-02-21T02:17:00.000Z | 2020-02-21T02:17:00.000Z | #!/usr/bin/env python
# Copyright (c) 2014 Wladimir J. van der Laan
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
'''
Run this script from the root of the repository to update all translations from
transifex.
It will do the following automatically:
- fetch all translations using the tx tool
- post-process them into valid and committable format
- remove invalid control characters
- remove location tags (makes diffs less noisy)
TODO:
- auto-add new translations to the build system according to the translation process
'''
from __future__ import division, print_function
import subprocess
import re
import sys
import os
import io
import xml.etree.ElementTree as ET
# Name of transifex tool
TX = 'tx'
# Name of source language file
SOURCE_LANG = 'anker_en.ts'
# Directory with locale files
LOCALE_DIR = 'src/qt/locale'
# Minimum number of messages for translation to be considered at all
MIN_NUM_MESSAGES = 10
def check_at_repository_root():
if not os.path.exists('.git'):
print('No .git directory found')
print('Execute this script at the root of the repository', file=sys.stderr)
exit(1)
def fetch_all_translations():
if subprocess.call([TX, 'pull', '-f', '-a']):
print('Error while fetching translations', file=sys.stderr)
exit(1)
def find_format_specifiers(s):
'''Find all format specifiers in a string.'''
pos = 0
specifiers = []
while True:
percent = s.find('%', pos)
if percent < 0:
break
try:
specifiers.append(s[percent+1])
except:
print('Failed to get specifier')
pos = percent+2
return specifiers
def split_format_specifiers(specifiers):
'''Split format specifiers between numeric (Qt) and others (strprintf)'''
numeric = []
other = []
for s in specifiers:
if s in {'1','2','3','4','5','6','7','8','9'}:
numeric.append(s)
else:
other.append(s)
# If both numeric format specifiers and "others" are used, assume we're dealing
# with a Qt-formatted message. In the case of Qt formatting (see https://doc.qt.io/qt-5/qstring.html#arg)
# only numeric formats are replaced at all. This means "(percentage: %1%)" is valid, without needing
# any kind of escaping that would be necessary for strprintf. Without this, this function
# would wrongly detect '%)' as a printf format specifier.
if numeric:
other = []
# numeric (Qt) can be present in any order, others (strprintf) must be in specified order
return set(numeric),other
def sanitize_string(s):
'''Sanitize string for printing'''
return s.replace('\n',' ')
def check_format_specifiers(source, translation, errors, numerus):
source_f = split_format_specifiers(find_format_specifiers(source))
# assert that no source messages contain both Qt and strprintf format specifiers
# if this fails, go change the source as this is hacky and confusing!
assert(not(source_f[0] and source_f[1]))
try:
translation_f = split_format_specifiers(find_format_specifiers(translation))
except IndexError:
errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation)))
return False
else:
if source_f != translation_f:
if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1:
# Allow numerus translations to omit %n specifier (usually when it only has one possible value)
return True
errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation)))
return False
return True
def all_ts_files(suffix=''):
for filename in os.listdir(LOCALE_DIR):
# process only language files, and do not process source language
if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix:
continue
if suffix: # remove provided suffix
filename = filename[0:-len(suffix)]
filepath = os.path.join(LOCALE_DIR, filename)
yield(filename, filepath)
FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]')
def remove_invalid_characters(s):
'''Remove invalid characters from translation string'''
return FIX_RE.sub(b'', s)
# Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for
# comparison, disable by default)
_orig_escape_cdata = None
def escape_cdata(text):
text = _orig_escape_cdata(text)
text = text.replace("'", ''')
text = text.replace('"', '"')
return text
def postprocess_translations(reduce_diff_hacks=False):
print('Checking and postprocessing...')
if reduce_diff_hacks:
global _orig_escape_cdata
_orig_escape_cdata = ET._escape_cdata
ET._escape_cdata = escape_cdata
for (filename,filepath) in all_ts_files():
os.rename(filepath, filepath+'.orig')
have_errors = False
for (filename,filepath) in all_ts_files('.orig'):
# pre-fixups to cope with transifex output
parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8'
with open(filepath + '.orig', 'rb') as f:
data = f.read()
# remove control characters; this must be done over the entire file otherwise the XML parser will fail
data = remove_invalid_characters(data)
tree = ET.parse(io.BytesIO(data), parser=parser)
# iterate over all messages in file
root = tree.getroot()
for context in root.findall('context'):
for message in context.findall('message'):
numerus = message.get('numerus') == 'yes'
source = message.find('source').text
translation_node = message.find('translation')
# pick all numerusforms
if numerus:
translations = [i.text for i in translation_node.findall('numerusform')]
else:
translations = [translation_node.text]
for translation in translations:
if translation is None:
continue
errors = []
valid = check_format_specifiers(source, translation, errors, numerus)
for error in errors:
print('%s: %s' % (filename, error))
if not valid: # set type to unfinished and clear string if invalid
translation_node.clear()
translation_node.set('type', 'unfinished')
have_errors = True
# Remove location tags
for location in message.findall('location'):
message.remove(location)
# Remove entire message if it is an unfinished translation
if translation_node.get('type') == 'unfinished':
context.remove(message)
# check if document is (virtually) empty, and remove it if so
num_messages = 0
for context in root.findall('context'):
for message in context.findall('message'):
num_messages += 1
if num_messages < MIN_NUM_MESSAGES:
print('Removing %s, as it contains only %i messages' % (filepath, num_messages))
continue
# write fixed-up tree
# if diff reduction requested, replace some XML to 'sanitize' to qt formatting
if reduce_diff_hacks:
out = io.BytesIO()
tree.write(out, encoding='utf-8')
out = out.getvalue()
out = out.replace(b' />', b'/>')
with open(filepath, 'wb') as f:
f.write(out)
else:
tree.write(filepath, encoding='utf-8')
return have_errors
if __name__ == '__main__':
check_at_repository_root()
fetch_all_translations()
postprocess_translations()
| 38.625592 | 124 | 0.633865 |
from __future__ import division, print_function
import subprocess
import re
import sys
import os
import io
import xml.etree.ElementTree as ET
TX = 'tx'
SOURCE_LANG = 'anker_en.ts'
LOCALE_DIR = 'src/qt/locale'
MIN_NUM_MESSAGES = 10
def check_at_repository_root():
if not os.path.exists('.git'):
print('No .git directory found')
print('Execute this script at the root of the repository', file=sys.stderr)
exit(1)
def fetch_all_translations():
if subprocess.call([TX, 'pull', '-f', '-a']):
print('Error while fetching translations', file=sys.stderr)
exit(1)
def find_format_specifiers(s):
pos = 0
specifiers = []
while True:
percent = s.find('%', pos)
if percent < 0:
break
try:
specifiers.append(s[percent+1])
except:
print('Failed to get specifier')
pos = percent+2
return specifiers
def split_format_specifiers(specifiers):
numeric = []
other = []
for s in specifiers:
if s in {'1','2','3','4','5','6','7','8','9'}:
numeric.append(s)
else:
other.append(s)
# with a Qt-formatted message. In the case of Qt formatting (see https://doc.qt.io/qt-5/qstring.html#arg)
# only numeric formats are replaced at all. This means "(percentage: %1%)" is valid, without needing
# any kind of escaping that would be necessary for strprintf. Without this, this function
# would wrongly detect '%)' as a printf format specifier.
if numeric:
other = []
# numeric (Qt) can be present in any order, others (strprintf) must be in specified order
return set(numeric),other
def sanitize_string(s):
return s.replace('\n',' ')
def check_format_specifiers(source, translation, errors, numerus):
source_f = split_format_specifiers(find_format_specifiers(source))
# assert that no source messages contain both Qt and strprintf format specifiers
# if this fails, go change the source as this is hacky and confusing!
assert(not(source_f[0] and source_f[1]))
try:
translation_f = split_format_specifiers(find_format_specifiers(translation))
except IndexError:
errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation)))
return False
else:
if source_f != translation_f:
if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1:
# Allow numerus translations to omit %n specifier (usually when it only has one possible value)
return True
errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation)))
return False
return True
def all_ts_files(suffix=''):
for filename in os.listdir(LOCALE_DIR):
# process only language files, and do not process source language
if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix:
continue
if suffix: # remove provided suffix
filename = filename[0:-len(suffix)]
filepath = os.path.join(LOCALE_DIR, filename)
yield(filename, filepath)
FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]')
def remove_invalid_characters(s):
return FIX_RE.sub(b'', s)
# Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for
_orig_escape_cdata = None
def escape_cdata(text):
text = _orig_escape_cdata(text)
text = text.replace("'", ''')
text = text.replace('"', '"')
return text
def postprocess_translations(reduce_diff_hacks=False):
print('Checking and postprocessing...')
if reduce_diff_hacks:
global _orig_escape_cdata
_orig_escape_cdata = ET._escape_cdata
ET._escape_cdata = escape_cdata
for (filename,filepath) in all_ts_files():
os.rename(filepath, filepath+'.orig')
have_errors = False
for (filename,filepath) in all_ts_files('.orig'):
# pre-fixups to cope with transifex output
parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8'
with open(filepath + '.orig', 'rb') as f:
data = f.read()
# remove control characters; this must be done over the entire file otherwise the XML parser will fail
data = remove_invalid_characters(data)
tree = ET.parse(io.BytesIO(data), parser=parser)
# iterate over all messages in file
root = tree.getroot()
for context in root.findall('context'):
for message in context.findall('message'):
numerus = message.get('numerus') == 'yes'
source = message.find('source').text
translation_node = message.find('translation')
# pick all numerusforms
if numerus:
translations = [i.text for i in translation_node.findall('numerusform')]
else:
translations = [translation_node.text]
for translation in translations:
if translation is None:
continue
errors = []
valid = check_format_specifiers(source, translation, errors, numerus)
for error in errors:
print('%s: %s' % (filename, error))
if not valid: # set type to unfinished and clear string if invalid
translation_node.clear()
translation_node.set('type', 'unfinished')
have_errors = True
# Remove location tags
for location in message.findall('location'):
message.remove(location)
# Remove entire message if it is an unfinished translation
if translation_node.get('type') == 'unfinished':
context.remove(message)
# check if document is (virtually) empty, and remove it if so
num_messages = 0
for context in root.findall('context'):
for message in context.findall('message'):
num_messages += 1
if num_messages < MIN_NUM_MESSAGES:
print('Removing %s, as it contains only %i messages' % (filepath, num_messages))
continue
# write fixed-up tree
# if diff reduction requested, replace some XML to 'sanitize' to qt formatting
if reduce_diff_hacks:
out = io.BytesIO()
tree.write(out, encoding='utf-8')
out = out.getvalue()
out = out.replace(b' />', b'/>')
with open(filepath, 'wb') as f:
f.write(out)
else:
tree.write(filepath, encoding='utf-8')
return have_errors
if __name__ == '__main__':
check_at_repository_root()
fetch_all_translations()
postprocess_translations()
| true | true |
f7291921a34c62df34fba28b8bbc84ec335d54e7 | 793 | py | Python | skrf/io/__init__.py | sdurant/scikit-rf | 09161b879c1a52a1bc2e2df89f2656c97136c39b | [
"BSD-3-Clause"
] | null | null | null | skrf/io/__init__.py | sdurant/scikit-rf | 09161b879c1a52a1bc2e2df89f2656c97136c39b | [
"BSD-3-Clause"
] | null | null | null | skrf/io/__init__.py | sdurant/scikit-rf | 09161b879c1a52a1bc2e2df89f2656c97136c39b | [
"BSD-3-Clause"
] | null | null | null |
'''
.. module:: skrf.io
========================================
io (:mod:`skrf.io`)
========================================
This Package provides functions and objects for input/output.
The general functions :func:`~general.read` and :func:`~general.write`
can be used to read and write [almost] any skrf object to disk, using the
:mod:`pickle` module.
Reading and writing touchstone files is supported through the
:class:`~touchstone.Touchstone` class, which can be more easily used
through the Network constructor, :func:`~skrf.network.Network.__init__`
.. automodule:: skrf.io.general
.. automodule:: skrf.io.touchstone
.. automodule:: skrf.io.csv
'''
import general
import csv
import touchstone
from general import *
from csv import *
from touchstone import *
| 22.657143 | 74 | 0.655738 |
import general
import csv
import touchstone
from general import *
from csv import *
from touchstone import *
| true | true |
f7291933a3cefebf2db62a049c100daa77684ddf | 3,116 | py | Python | tests/test_folder_layout.py | function2-llx/MONAI | 4cddaa830b61b88ec78e089bb5f21e05bb1a78f4 | [
"Apache-2.0"
] | 1 | 2022-02-15T09:32:28.000Z | 2022-02-15T09:32:28.000Z | tests/test_folder_layout.py | function2-llx/MONAI | 4cddaa830b61b88ec78e089bb5f21e05bb1a78f4 | [
"Apache-2.0"
] | null | null | null | tests/test_folder_layout.py | function2-llx/MONAI | 4cddaa830b61b88ec78e089bb5f21e05bb1a78f4 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from pathlib import Path
from parameterized import parameterized
from monai.data.folder_layout import FolderLayout
TEST_CASES = [
({"output_dir": ""}, {}, "subject"),
({"output_dir": Path(".")}, {}, "subject"),
({"output_dir": Path(".")}, {"idx": 1}, "subject_1"),
(dict(output_dir=Path("/test_run_1"), extension=".seg", makedirs=False), {}, "/test_run_1/subject.seg"),
(dict(output_dir=Path("/test_run_1"), extension=None, makedirs=False), {}, "/test_run_1/subject"),
(
dict(output_dir=Path("/test_run_1"), postfix="seg", extension=".test", makedirs=False),
{}, # using the default subject name
"/test_run_1/subject_seg.test",
),
(
dict(output_dir=Path("/test_run_1"), postfix="seg", extension=".test", makedirs=False),
{"subject": "test.abc"},
"/test_run_1/test_seg.test", # subject's extension is ignored
),
(
dict(output_dir=Path("/test_run_1/dest/test1/"), data_root_dir="/test_run", makedirs=False),
{"subject": "/test_run/source/test.abc"},
"/test_run_1/dest/test1/source/test", # preserves the structure from `subject`
),
(
dict(output_dir=Path("/test_run_1/dest/test1/"), makedirs=False),
{"subject": "/test_run/source/test.abc"},
"/test_run_1/dest/test1/test", # data_root_dir used
),
(
dict(output_dir=Path("/test_run_1/dest/test1/"), makedirs=False),
{"subject": "/test_run/source/test.abc", "key": "value"},
"/test_run_1/dest/test1/test_key-value", # data_root_dir used
),
(
dict(output_dir=Path("/test_run_1/"), postfix="seg", extension=".nii", makedirs=False),
dict(subject=Path("Sub-A"), idx="00", modality="T1"),
"/test_run_1/Sub-A_seg_00_modality-T1.nii", # test the code example
),
]
class TestFolderLayout(unittest.TestCase):
@parameterized.expand(TEST_CASES)
def test_value(self, con_params, f_params, expected):
fname = FolderLayout(**con_params).filename(**f_params)
self.assertEqual(Path(fname), Path(expected))
def test_mkdir(self):
"""mkdir=True should create the directory if it does not exist."""
with tempfile.TemporaryDirectory() as tempdir:
output_tmp = os.path.join(tempdir, "output")
FolderLayout(output_tmp, makedirs=True).filename("subject_test", "001")
self.assertTrue(os.path.exists(os.path.join(output_tmp)))
if __name__ == "__main__":
unittest.main()
| 41 | 108 | 0.661104 |
import os
import tempfile
import unittest
from pathlib import Path
from parameterized import parameterized
from monai.data.folder_layout import FolderLayout
TEST_CASES = [
({"output_dir": ""}, {}, "subject"),
({"output_dir": Path(".")}, {}, "subject"),
({"output_dir": Path(".")}, {"idx": 1}, "subject_1"),
(dict(output_dir=Path("/test_run_1"), extension=".seg", makedirs=False), {}, "/test_run_1/subject.seg"),
(dict(output_dir=Path("/test_run_1"), extension=None, makedirs=False), {}, "/test_run_1/subject"),
(
dict(output_dir=Path("/test_run_1"), postfix="seg", extension=".test", makedirs=False),
{},
"/test_run_1/subject_seg.test",
),
(
dict(output_dir=Path("/test_run_1"), postfix="seg", extension=".test", makedirs=False),
{"subject": "test.abc"},
"/test_run_1/test_seg.test",
),
(
dict(output_dir=Path("/test_run_1/dest/test1/"), data_root_dir="/test_run", makedirs=False),
{"subject": "/test_run/source/test.abc"},
"/test_run_1/dest/test1/source/test", # preserves the structure from `subject`
),
(
dict(output_dir=Path("/test_run_1/dest/test1/"), makedirs=False),
{"subject": "/test_run/source/test.abc"},
"/test_run_1/dest/test1/test", # data_root_dir used
),
(
dict(output_dir=Path("/test_run_1/dest/test1/"), makedirs=False),
{"subject": "/test_run/source/test.abc", "key": "value"},
"/test_run_1/dest/test1/test_key-value", # data_root_dir used
),
(
dict(output_dir=Path("/test_run_1/"), postfix="seg", extension=".nii", makedirs=False),
dict(subject=Path("Sub-A"), idx="00", modality="T1"),
"/test_run_1/Sub-A_seg_00_modality-T1.nii", # test the code example
),
]
class TestFolderLayout(unittest.TestCase):
@parameterized.expand(TEST_CASES)
def test_value(self, con_params, f_params, expected):
fname = FolderLayout(**con_params).filename(**f_params)
self.assertEqual(Path(fname), Path(expected))
def test_mkdir(self):
with tempfile.TemporaryDirectory() as tempdir:
output_tmp = os.path.join(tempdir, "output")
FolderLayout(output_tmp, makedirs=True).filename("subject_test", "001")
self.assertTrue(os.path.exists(os.path.join(output_tmp)))
if __name__ == "__main__":
unittest.main()
| true | true |
f729199ed548f4bc7e583b88a07ec65682e488b1 | 115 | py | Python | AITRexRush/Algorithm_1/readdata.py | 2360673637/AIGames | 7d149cc2cff8fa626ee1c9e1ad7c39e1a724a5bb | [
"MIT"
] | 2 | 2019-03-23T13:58:34.000Z | 2019-03-23T13:59:07.000Z | AITRexRush/Algorithm_1/readdata.py | 2360673637/AIGames | 7d149cc2cff8fa626ee1c9e1ad7c39e1a724a5bb | [
"MIT"
] | null | null | null | AITRexRush/Algorithm_1/readdata.py | 2360673637/AIGames | 7d149cc2cff8fa626ee1c9e1ad7c39e1a724a5bb | [
"MIT"
] | 1 | 2018-10-25T07:51:52.000Z | 2018-10-25T07:51:52.000Z | import pickle
f = open('E:\DQN__T-Rex Rush\data_1\Data_deque_0.pkl', 'rb')
data = pickle.load(f)
print(len(data)) | 19.166667 | 60 | 0.704348 | import pickle
f = open('E:\DQN__T-Rex Rush\data_1\Data_deque_0.pkl', 'rb')
data = pickle.load(f)
print(len(data)) | true | true |
f7291a292324e1720abca648dec64ace073b2fa8 | 2,897 | py | Python | pandas/tests/util/test_show_versions.py | PApostol/pandas | 578e918777f6f512f85a917dc34910df87f63e90 | [
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | pandas/tests/util/test_show_versions.py | PApostol/pandas | 578e918777f6f512f85a917dc34910df87f63e90 | [
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | pandas/tests/util/test_show_versions.py | PApostol/pandas | 578e918777f6f512f85a917dc34910df87f63e90 | [
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | import json
import os
import re
import pytest
from pandas.compat import (
IS64,
is_ci_environment,
)
from pandas.util._print_versions import (
_get_dependency_info,
_get_sys_info,
)
import pandas as pd
@pytest.mark.filterwarnings(
# openpyxl
"ignore:defusedxml.lxml is no longer supported:DeprecationWarning"
)
@pytest.mark.filterwarnings(
# html5lib
"ignore:Using or importing the ABCs from:DeprecationWarning"
)
@pytest.mark.filterwarnings(
# fastparquet
"ignore:pandas.core.index is deprecated:FutureWarning"
)
@pytest.mark.filterwarnings(
# pandas_datareader
"ignore:pandas.util.testing is deprecated:FutureWarning"
)
@pytest.mark.filterwarnings(
# https://github.com/pandas-dev/pandas/issues/35252
"ignore:Distutils:UserWarning"
)
@pytest.mark.filterwarnings("ignore:Setuptools is replacing distutils:UserWarning")
def test_show_versions(tmpdir):
# GH39701
as_json = os.path.join(tmpdir, "test_output.json")
pd.show_versions(as_json=as_json)
with open(as_json) as fd:
# check if file output is valid JSON, will raise an exception if not
result = json.load(fd)
# Basic check that each version element is found in output
expected = {
"system": _get_sys_info(),
"dependencies": _get_dependency_info(),
}
assert result == expected
def test_show_versions_console_json(capsys):
# GH39701
pd.show_versions(as_json=True)
stdout = capsys.readouterr().out
# check valid json is printed to the console if as_json is True
result = json.loads(stdout)
# Basic check that each version element is found in output
expected = {
"system": _get_sys_info(),
"dependencies": _get_dependency_info(),
}
assert result == expected
@pytest.mark.xfail(
is_ci_environment() and not IS64, reason="Failing on 32 bit Python CI job"
)
def test_show_versions_console(capsys):
# gh-32041
# gh-32041
pd.show_versions(as_json=False)
result = capsys.readouterr().out
# check header
assert "INSTALLED VERSIONS" in result
# check full commit hash
assert re.search(r"commit\s*:\s[0-9a-f]{40}\n", result)
# check required dependency
# 2020-12-09 npdev has "dirty" in the tag
# 2022-05-25 npdev released with RC wo/ "dirty".
# Just ensure we match [0-9]+\..* since npdev version is variable
assert re.search(r"numpy\s*:\s[0-9]+\..*\n", result)
# check optional dependency
assert re.search(r"pyarrow\s*:\s([0-9\.]+|None)\n", result)
def test_json_output_match(capsys, tmpdir):
# GH39701
pd.show_versions(as_json=True)
result_console = capsys.readouterr().out
out_path = os.path.join(tmpdir, "test_json.json")
pd.show_versions(as_json=out_path)
with open(out_path) as out_fd:
result_file = out_fd.read()
assert result_console == result_file
| 25.866071 | 83 | 0.696583 | import json
import os
import re
import pytest
from pandas.compat import (
IS64,
is_ci_environment,
)
from pandas.util._print_versions import (
_get_dependency_info,
_get_sys_info,
)
import pandas as pd
@pytest.mark.filterwarnings(
"ignore:defusedxml.lxml is no longer supported:DeprecationWarning"
)
@pytest.mark.filterwarnings(
"ignore:Using or importing the ABCs from:DeprecationWarning"
)
@pytest.mark.filterwarnings(
"ignore:pandas.core.index is deprecated:FutureWarning"
)
@pytest.mark.filterwarnings(
"ignore:pandas.util.testing is deprecated:FutureWarning"
)
@pytest.mark.filterwarnings(
"ignore:Distutils:UserWarning"
)
@pytest.mark.filterwarnings("ignore:Setuptools is replacing distutils:UserWarning")
def test_show_versions(tmpdir):
as_json = os.path.join(tmpdir, "test_output.json")
pd.show_versions(as_json=as_json)
with open(as_json) as fd:
result = json.load(fd)
expected = {
"system": _get_sys_info(),
"dependencies": _get_dependency_info(),
}
assert result == expected
def test_show_versions_console_json(capsys):
pd.show_versions(as_json=True)
stdout = capsys.readouterr().out
result = json.loads(stdout)
expected = {
"system": _get_sys_info(),
"dependencies": _get_dependency_info(),
}
assert result == expected
@pytest.mark.xfail(
is_ci_environment() and not IS64, reason="Failing on 32 bit Python CI job"
)
def test_show_versions_console(capsys):
pd.show_versions(as_json=False)
result = capsys.readouterr().out
assert "INSTALLED VERSIONS" in result
assert re.search(r"commit\s*:\s[0-9a-f]{40}\n", result)
assert re.search(r"numpy\s*:\s[0-9]+\..*\n", result)
assert re.search(r"pyarrow\s*:\s([0-9\.]+|None)\n", result)
def test_json_output_match(capsys, tmpdir):
pd.show_versions(as_json=True)
result_console = capsys.readouterr().out
out_path = os.path.join(tmpdir, "test_json.json")
pd.show_versions(as_json=out_path)
with open(out_path) as out_fd:
result_file = out_fd.read()
assert result_console == result_file
| true | true |
f7291a6cf9e74bacae40b852f118305493e09a49 | 124 | py | Python | src/bot.py | ali-m79/nashenas_telegram_bot | dd84e08f1767e6bd74699f8d05666b14dbadbf2b | [
"MIT"
] | null | null | null | src/bot.py | ali-m79/nashenas_telegram_bot | dd84e08f1767e6bd74699f8d05666b14dbadbf2b | [
"MIT"
] | null | null | null | src/bot.py | ali-m79/nashenas_telegram_bot | dd84e08f1767e6bd74699f8d05666b14dbadbf2b | [
"MIT"
] | null | null | null | import os
import telebot
# Initialize bot
bot = telebot.TeleBot(os.environ["TELEGRAM_BOT_TOKEN"], parse_mode="MARKDOWN")
| 15.5 | 78 | 0.774194 | import os
import telebot
bot = telebot.TeleBot(os.environ["TELEGRAM_BOT_TOKEN"], parse_mode="MARKDOWN")
| true | true |
f7291aa2570f6404d3f3d99822ef1c95ed97ad81 | 4,392 | py | Python | mask-detection/dwImages.py | danielvicentini/distancebot | 4ae2abeec6e29577d5f5c7bf3fdc974e6a187f13 | [
"BSD-Source-Code"
] | 1 | 2020-07-23T17:35:10.000Z | 2020-07-23T17:35:10.000Z | mask-detection/dwImages.py | danielvicentini/distancebot | 4ae2abeec6e29577d5f5c7bf3fdc974e6a187f13 | [
"BSD-Source-Code"
] | 18 | 2020-05-31T15:10:37.000Z | 2022-03-12T00:42:35.000Z | mask-detection/dwImages.py | danielvicentini/distancebot | 4ae2abeec6e29577d5f5c7bf3fdc974e6a187f13 | [
"BSD-Source-Code"
] | 2 | 2020-06-02T23:44:15.000Z | 2020-07-20T18:19:55.000Z |
import meraki, datetime, json, requests, os.path, shutil, time, threading, os, sys
from config_meraki import *
from video import *
from os import path
sys.path.append(os.path.abspath('..'))
from config_shared import meraki_api_key
#Local path to save Images
rooting = os.path.abspath(os.getcwd()) + "/mask-detection"
save_path = rooting + "/static/images/downloaded/"
imagesAnalized = "/static/images/images-analized/"
save_checked = rooting + imagesAnalized
save_checkedMasked = rooting + "/static/images/Masked/"
publishUrl = "http://159.89.238.176:10000"
publishPath = publishUrl + imagesAnalized
#Function to format the Time
def frmtTime(timeStamp):
for fmt in ("%Y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S-%f"):
if fmt in ("%Y-%m-%d %H:%M:%S.%f"):
timeStamp = datetime.datetime.strptime(timeStamp, "%Y-%m-%d %H:%M:%S.%f")
timeStamp = datetime.datetime.strftime(timeStamp, "%Y-%m-%dT%H:%M:%S-0300")
return(timeStamp)
elif fmt in ("%Y-%m-%dT%H:%M:%S-%f"):
timeStamp = datetime.datetime.strptime(timeStamp, "%Y-%m-%dT%H:%M:%S-%f")
timeStamp = datetime.datetime.strftime(timeStamp, "%Y-%m-%dT%H:%M:%S-0300")
return(timeStamp)
def loopVerification(network_id, mv_serial, statusfile):
while path.exists(statusfile):
eterno = getMerakiImage.main(network_id, mv_serial)
time.sleep(1)
#Function to save the image file
def saveFile(myFileName, snapShotUrl):
global myfileAndPath
myfileAndPath = os.path.join(save_path, myFileName)
time.sleep(5)
response = requests.get(snapShotUrl, stream = True)
response.raw.decode_content = True
with open(myfileAndPath,'wb') as f:
shutil.copyfileobj(response.raw, f)
checkMask(myfileAndPath)
return(myfileAndPath)
#Class to handle the POST
class getMerakiImage:
def main(network_id, mv_serial, timeStamp=None):
if timeStamp is None:
timeStamp = datetime.datetime.now() - datetime.timedelta(minutes=2)
timeStamp = frmtTime(str(timeStamp))
global dbTimeStamp
dbTimeStamp = timeStamp
myTime = datetime.datetime.strptime(timeStamp, "%Y-%m-%dT%H:%M:%S%z")
myTime = datetime.datetime.strftime(myTime, "%Y-%m-%d-%H-%M-%S")
dashboard = meraki.DashboardAPI(api_key=meraki_api_key,suppress_logging=True)
my_orgs = dashboard.organizations.getOrganizations()
try:
mySnap = dashboard.cameras.generateNetworkCameraSnapshot(network_id, mv_serial, timestamp=timeStamp )
snapShotUrl = mySnap["url"]
except:
print("No image available to this camera at this time.")
global notifyMV
notifyMV = mv_serial
global notifyNETID
notifyNETID = network_id
global myFileName
global myNoMaskImage
myFileName = "From-" + mv_serial + "-at-" + myTime + ".jpeg"
myNoMaskImage = publishPath + myFileName
try:
saveFile(myFileName, snapShotUrl)
except:
return ("File available at:" + myNoMaskImage)
def checkMask(myfileAndPath):
mySavedFile = save_checked + myFileName
mySavedFileMasked = save_checkedMasked + myFileName
checkImage = tagVideo(videopath=myfileAndPath, outputPath=mySavedFile, outputPathMask=mySavedFileMasked)
if checkImage == "No mask":
print ("Pessoa sem mascara detectada.")
notifyBot(checkImage)
print ("BOT Notified")
def notifyBot(checkImage):
body = {
"alarm": "distance-bot",
"data": {
"type": "01",
"message": "Person without mask detected.",
"who": notifyMV,
"image": myNoMaskImage
}
}
body = json.dumps(body)
headers = {
'Content-Type': 'application/json'
}
botAddress = "http://159.89.238.176:7000/"
response = requests.request("POST", url=botAddress, headers=headers, data=body)
payload={
"type":"sanitymask",
"local":"SALA-Log3",
"network":notifyNETID,
"serial":notifyMV,
"url":myNoMaskImage,
"time": dbTimeStamp
}
payload = json.dumps(payload)
#print (payload)
dbAddress = "http://127.0.0.1:8000/api/v1/banco"
dbresponse = requests.request("POST", url=dbAddress, headers=headers, data=payload)
#print (dbresponse.content)
if __name__ == '__main__':
getIt = getMerakiImage.main()
| 36.6 | 113 | 0.650956 |
import meraki, datetime, json, requests, os.path, shutil, time, threading, os, sys
from config_meraki import *
from video import *
from os import path
sys.path.append(os.path.abspath('..'))
from config_shared import meraki_api_key
rooting = os.path.abspath(os.getcwd()) + "/mask-detection"
save_path = rooting + "/static/images/downloaded/"
imagesAnalized = "/static/images/images-analized/"
save_checked = rooting + imagesAnalized
save_checkedMasked = rooting + "/static/images/Masked/"
publishUrl = "http://159.89.238.176:10000"
publishPath = publishUrl + imagesAnalized
def frmtTime(timeStamp):
for fmt in ("%Y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S-%f"):
if fmt in ("%Y-%m-%d %H:%M:%S.%f"):
timeStamp = datetime.datetime.strptime(timeStamp, "%Y-%m-%d %H:%M:%S.%f")
timeStamp = datetime.datetime.strftime(timeStamp, "%Y-%m-%dT%H:%M:%S-0300")
return(timeStamp)
elif fmt in ("%Y-%m-%dT%H:%M:%S-%f"):
timeStamp = datetime.datetime.strptime(timeStamp, "%Y-%m-%dT%H:%M:%S-%f")
timeStamp = datetime.datetime.strftime(timeStamp, "%Y-%m-%dT%H:%M:%S-0300")
return(timeStamp)
def loopVerification(network_id, mv_serial, statusfile):
while path.exists(statusfile):
eterno = getMerakiImage.main(network_id, mv_serial)
time.sleep(1)
def saveFile(myFileName, snapShotUrl):
global myfileAndPath
myfileAndPath = os.path.join(save_path, myFileName)
time.sleep(5)
response = requests.get(snapShotUrl, stream = True)
response.raw.decode_content = True
with open(myfileAndPath,'wb') as f:
shutil.copyfileobj(response.raw, f)
checkMask(myfileAndPath)
return(myfileAndPath)
class getMerakiImage:
def main(network_id, mv_serial, timeStamp=None):
if timeStamp is None:
timeStamp = datetime.datetime.now() - datetime.timedelta(minutes=2)
timeStamp = frmtTime(str(timeStamp))
global dbTimeStamp
dbTimeStamp = timeStamp
myTime = datetime.datetime.strptime(timeStamp, "%Y-%m-%dT%H:%M:%S%z")
myTime = datetime.datetime.strftime(myTime, "%Y-%m-%d-%H-%M-%S")
dashboard = meraki.DashboardAPI(api_key=meraki_api_key,suppress_logging=True)
my_orgs = dashboard.organizations.getOrganizations()
try:
mySnap = dashboard.cameras.generateNetworkCameraSnapshot(network_id, mv_serial, timestamp=timeStamp )
snapShotUrl = mySnap["url"]
except:
print("No image available to this camera at this time.")
global notifyMV
notifyMV = mv_serial
global notifyNETID
notifyNETID = network_id
global myFileName
global myNoMaskImage
myFileName = "From-" + mv_serial + "-at-" + myTime + ".jpeg"
myNoMaskImage = publishPath + myFileName
try:
saveFile(myFileName, snapShotUrl)
except:
return ("File available at:" + myNoMaskImage)
def checkMask(myfileAndPath):
mySavedFile = save_checked + myFileName
mySavedFileMasked = save_checkedMasked + myFileName
checkImage = tagVideo(videopath=myfileAndPath, outputPath=mySavedFile, outputPathMask=mySavedFileMasked)
if checkImage == "No mask":
print ("Pessoa sem mascara detectada.")
notifyBot(checkImage)
print ("BOT Notified")
def notifyBot(checkImage):
body = {
"alarm": "distance-bot",
"data": {
"type": "01",
"message": "Person without mask detected.",
"who": notifyMV,
"image": myNoMaskImage
}
}
body = json.dumps(body)
headers = {
'Content-Type': 'application/json'
}
botAddress = "http://159.89.238.176:7000/"
response = requests.request("POST", url=botAddress, headers=headers, data=body)
payload={
"type":"sanitymask",
"local":"SALA-Log3",
"network":notifyNETID,
"serial":notifyMV,
"url":myNoMaskImage,
"time": dbTimeStamp
}
payload = json.dumps(payload)
dbAddress = "http://127.0.0.1:8000/api/v1/banco"
dbresponse = requests.request("POST", url=dbAddress, headers=headers, data=payload)
if __name__ == '__main__':
getIt = getMerakiImage.main()
| true | true |
f7291bac5121574b1276aaabccdadb5a163284df | 205 | py | Python | basics/classes_obj/cls_obj.py | m-ymn/Python-Apps | 09d1ff8de554902a5fcdc34a868eab31df0c28f7 | [
"MIT"
] | 1 | 2020-03-09T18:16:13.000Z | 2020-03-09T18:16:13.000Z | basics/classes_obj/cls_obj.py | m-ymn/Python-Apps | 09d1ff8de554902a5fcdc34a868eab31df0c28f7 | [
"MIT"
] | null | null | null | basics/classes_obj/cls_obj.py | m-ymn/Python-Apps | 09d1ff8de554902a5fcdc34a868eab31df0c28f7 | [
"MIT"
] | null | null | null | from stu import Student
#stu1 = Student("jim", "business", 3.1, False)
stu1 = Student("oscar", "accounting", 3.0)
stu2 = Student("phylis", "business" , 3.8)
print(stu1.name)
print(stu1.on_honor_roll()) | 22.777778 | 46 | 0.678049 | from stu import Student
stu1 = Student("oscar", "accounting", 3.0)
stu2 = Student("phylis", "business" , 3.8)
print(stu1.name)
print(stu1.on_honor_roll()) | true | true |
f7291cc36a5c46779de6c224706e0344998ab17a | 978 | py | Python | instance/config.py | kirega/ireporter-ch3 | 964baf679ab99c657fa37473ee95ca5a93491a0f | [
"MIT"
] | null | null | null | instance/config.py | kirega/ireporter-ch3 | 964baf679ab99c657fa37473ee95ca5a93491a0f | [
"MIT"
] | null | null | null | instance/config.py | kirega/ireporter-ch3 | 964baf679ab99c657fa37473ee95ca5a93491a0f | [
"MIT"
] | null | null | null | import os
class Config():
DEBUG = False
TESTING = False
JWT_SECRET_KEY = 'jwt-secret-string'
JWT_BLACKLIST_ENABLED = True
JWT_BLACKLIST_TOKEN_CHECKS = ['access', 'refresh']
DB_HOST = os.environ.get('DB_HOST')
DB_USERNAME = os.environ.get('DB_USERNAME')
DB_PASS = os.environ.get('DB_PASS')
DB_NAME = os.environ.get('DB_NAME')
DB_PORT = os.environ.get('DB_PORT')
MAIL_DEFAULT_SENDER = 'info@ireporter.com'
MAIL_SERVER = os.environ.get('MAIL_SERVER')
MAIL_PORT = os.environ.get('MAIL_PORT')
MAIL_USE_TLS = os.environ.get('MAIL_USE_TLS')
MAIL_USERNAME = os.environ.get('MAIL_USERNAME')
MAIL_PASSWORD = os.environ.get('MAIL_PASSWORD')
class DevelopmentConfig(Config):
DEBUG = True
ENV = "development"
class TestingConfig(Config):
TESTING = True
DB_NAME = os.environ.get('DB_NAME_TEST')
settings = {
'development': DevelopmentConfig,
'testing': TestingConfig,
'production': Config,
}
| 25.736842 | 54 | 0.687117 | import os
class Config():
DEBUG = False
TESTING = False
JWT_SECRET_KEY = 'jwt-secret-string'
JWT_BLACKLIST_ENABLED = True
JWT_BLACKLIST_TOKEN_CHECKS = ['access', 'refresh']
DB_HOST = os.environ.get('DB_HOST')
DB_USERNAME = os.environ.get('DB_USERNAME')
DB_PASS = os.environ.get('DB_PASS')
DB_NAME = os.environ.get('DB_NAME')
DB_PORT = os.environ.get('DB_PORT')
MAIL_DEFAULT_SENDER = 'info@ireporter.com'
MAIL_SERVER = os.environ.get('MAIL_SERVER')
MAIL_PORT = os.environ.get('MAIL_PORT')
MAIL_USE_TLS = os.environ.get('MAIL_USE_TLS')
MAIL_USERNAME = os.environ.get('MAIL_USERNAME')
MAIL_PASSWORD = os.environ.get('MAIL_PASSWORD')
class DevelopmentConfig(Config):
DEBUG = True
ENV = "development"
class TestingConfig(Config):
TESTING = True
DB_NAME = os.environ.get('DB_NAME_TEST')
settings = {
'development': DevelopmentConfig,
'testing': TestingConfig,
'production': Config,
}
| true | true |
f7291d2841a5c0c749f073619541e093019aacb9 | 572 | py | Python | Django_Blog/06-User-Registration-Form/django_project/users/views.py | DSNR/snippets | 12006dd083be60c6444d8b5ca48fd917005e081b | [
"MIT"
] | null | null | null | Django_Blog/06-User-Registration-Form/django_project/users/views.py | DSNR/snippets | 12006dd083be60c6444d8b5ca48fd917005e081b | [
"MIT"
] | null | null | null | Django_Blog/06-User-Registration-Form/django_project/users/views.py | DSNR/snippets | 12006dd083be60c6444d8b5ca48fd917005e081b | [
"MIT"
] | null | null | null | from django.shortcuts import render, redirect
from django.contrib import messages
from .forms import UserRegisterForm
def register(request):
if request.method == 'POST':
form = UserRegisterForm(request.POST)
if form.is_valid():
form.save()
username = form.cleaned_data.get('username')
messages.success(request, f'Account created for {username}!')
return redirect('blog-home')
else:
form = UserRegisterForm()
return render(request, 'users/register.html', {'form': form})
| 33.647059 | 74 | 0.636364 | from django.shortcuts import render, redirect
from django.contrib import messages
from .forms import UserRegisterForm
def register(request):
if request.method == 'POST':
form = UserRegisterForm(request.POST)
if form.is_valid():
form.save()
username = form.cleaned_data.get('username')
messages.success(request, f'Account created for {username}!')
return redirect('blog-home')
else:
form = UserRegisterForm()
return render(request, 'users/register.html', {'form': form})
| true | true |
f7291d66f4ca7ed7e4e1c1771e6ff75f5f820c25 | 7,145 | py | Python | tools/c7n_azure/tests/test_deployment_units.py | scoleri/cloud-custodian | 706bc7557bffddaf676ec66bc6518ea175dd0d97 | [
"Apache-2.0"
] | null | null | null | tools/c7n_azure/tests/test_deployment_units.py | scoleri/cloud-custodian | 706bc7557bffddaf676ec66bc6518ea175dd0d97 | [
"Apache-2.0"
] | 79 | 2019-03-20T12:27:06.000Z | 2019-08-14T14:07:04.000Z | tools/c7n_azure/tests/test_deployment_units.py | scoleri/cloud-custodian | 706bc7557bffddaf676ec66bc6518ea175dd0d97 | [
"Apache-2.0"
] | 2 | 2019-04-22T15:20:23.000Z | 2019-08-27T12:37:51.000Z | # Copyright 2015-2018 Capital One Services, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import, division, print_function, unicode_literals
from azure_common import BaseTest, requires_arm_polling
from c7n_azure import constants
from c7n_azure.constants import FUNCTION_DOCKER_VERSION
from c7n_azure.functionapp_utils import FunctionAppUtilities
from c7n_azure.provisioning.app_insights import AppInsightsUnit
from c7n_azure.provisioning.app_service_plan import AppServicePlanUnit
from c7n_azure.provisioning.function_app import FunctionAppDeploymentUnit
from c7n_azure.provisioning.storage_account import StorageAccountUnit
from c7n_azure.session import Session
from msrestazure.azure_exceptions import CloudError
from c7n.utils import local_session
@requires_arm_polling
class DeploymentUnitsTest(BaseTest):
rg_name = 'cloud-custodian-test-deployment-units'
rg_location = 'westus'
@classmethod
def setUpClass(cls):
super(DeploymentUnitsTest, cls).setUpClass()
try:
cls.session = local_session(Session)
client = cls.session.client('azure.mgmt.resource.ResourceManagementClient')
client.resource_groups.create_or_update(cls.rg_name, {'location': cls.rg_location})
except CloudError:
pass
@classmethod
def tearDownClass(cls):
super(DeploymentUnitsTest, cls).tearDownClass()
try:
client = cls.session.client('azure.mgmt.resource.ResourceManagementClient')
client.resource_groups.delete(cls.rg_name)
except CloudError:
pass
def _validate(self, unit, params):
result = unit.provision(params)
self.assertNotEqual(result, None)
return result
def test_app_insights(self):
params = {'name': 'cloud-custodian-test',
'location': 'westus2',
'resource_group_name': self.rg_name}
unit = AppInsightsUnit()
self._validate(unit, params)
def test_storage_account(self):
params = {'name': 'custodianaccount47182745',
'location': self.rg_location,
'resource_group_name': self.rg_name}
unit = StorageAccountUnit()
self._validate(unit, params)
def test_service_plan(self):
params = {'name': 'cloud-custodian-test',
'location': self.rg_location,
'resource_group_name': self.rg_name,
'sku_tier': 'Basic',
'sku_name': 'B1'}
unit = AppServicePlanUnit()
self._validate(unit, params)
def test_app_service_plan_autoscale(self):
params = {'name': 'cloud-custodian-test-autoscale',
'location': self.rg_location,
'resource_group_name': self.rg_name,
'sku_tier': 'Basic',
'sku_name': 'B1',
'auto_scale': {
'enabled': True,
'min_capacity': 1,
'max_capacity': 2,
'default_capacity': 1}
}
unit = AppServicePlanUnit()
plan = self._validate(unit, params)
client = self.session.client('azure.mgmt.monitor.MonitorManagementClient')
rules = client.autoscale_settings.get(self.rg_name, constants.FUNCTION_AUTOSCALE_NAME)
self.assertEqual(rules.target_resource_uri, plan.id)
def test_function_app_consumption(self):
# provision storage account
sa_params = {
'name': 'custodianaccount47182748',
'location': self.rg_location,
'resource_group_name': self.rg_name}
storage_unit = StorageAccountUnit()
storage_account_id = storage_unit.provision(sa_params).id
conn_string = FunctionAppUtilities.get_storage_account_connection_string(storage_account_id)
# provision function app
func_params = {
'name': 'cc-consumption-47182748',
# Using different location due to http://go.microsoft.com/fwlink/?LinkId=825764
'location': 'eastus2',
'resource_group_name': self.rg_name,
'app_service_plan_id': None, # auto-provision a dynamic app plan
'app_insights_key': None,
'is_consumption_plan': True,
'storage_account_connection_string': conn_string
}
func_unit = FunctionAppDeploymentUnit()
func_app = self._validate(func_unit, func_params)
# verify settings are properly configured
self.assertEqual(func_app.kind, 'functionapp,linux')
self.assertTrue(func_app.reserved)
def test_function_app_dedicated(self):
# provision storage account
sa_params = {
'name': 'custodianaccount47182741',
'location': self.rg_location,
'resource_group_name': self.rg_name}
storage_unit = StorageAccountUnit()
storage_account_id = storage_unit.provision(sa_params).id
conn_string = FunctionAppUtilities.get_storage_account_connection_string(storage_account_id)
# provision app plan
app_plan_params = {
'name': 'cloud-custodian-test2',
'location': self.rg_location,
'resource_group_name': self.rg_name,
'sku_tier': 'Basic',
'sku_name': 'B1'}
app_plan_unit = AppServicePlanUnit()
app_plan = app_plan_unit.provision(app_plan_params)
# provision function app
func_app_name = 'cc-dedicated-47182748'
func_params = {
'name': func_app_name,
'location': self.rg_location,
'resource_group_name': self.rg_name,
'app_service_plan_id': app_plan.id,
'app_insights_key': None,
'is_consumption_plan': False,
'storage_account_connection_string': conn_string
}
func_unit = FunctionAppDeploymentUnit()
func_app = self._validate(func_unit, func_params)
# verify settings are properly configured
self.assertEqual(func_app.kind, 'functionapp,linux,container')
self.assertTrue(func_app.reserved)
wc = self.session.client('azure.mgmt.web.WebSiteManagementClient')
site_config = wc.web_apps.get_configuration(self.rg_name, func_app_name)
self.assertTrue(site_config.always_on)
self.assertEqual(site_config.linux_fx_version, FUNCTION_DOCKER_VERSION)
| 40.140449 | 101 | 0.645626 |
from __future__ import absolute_import, division, print_function, unicode_literals
from azure_common import BaseTest, requires_arm_polling
from c7n_azure import constants
from c7n_azure.constants import FUNCTION_DOCKER_VERSION
from c7n_azure.functionapp_utils import FunctionAppUtilities
from c7n_azure.provisioning.app_insights import AppInsightsUnit
from c7n_azure.provisioning.app_service_plan import AppServicePlanUnit
from c7n_azure.provisioning.function_app import FunctionAppDeploymentUnit
from c7n_azure.provisioning.storage_account import StorageAccountUnit
from c7n_azure.session import Session
from msrestazure.azure_exceptions import CloudError
from c7n.utils import local_session
@requires_arm_polling
class DeploymentUnitsTest(BaseTest):
rg_name = 'cloud-custodian-test-deployment-units'
rg_location = 'westus'
@classmethod
def setUpClass(cls):
super(DeploymentUnitsTest, cls).setUpClass()
try:
cls.session = local_session(Session)
client = cls.session.client('azure.mgmt.resource.ResourceManagementClient')
client.resource_groups.create_or_update(cls.rg_name, {'location': cls.rg_location})
except CloudError:
pass
@classmethod
def tearDownClass(cls):
super(DeploymentUnitsTest, cls).tearDownClass()
try:
client = cls.session.client('azure.mgmt.resource.ResourceManagementClient')
client.resource_groups.delete(cls.rg_name)
except CloudError:
pass
def _validate(self, unit, params):
result = unit.provision(params)
self.assertNotEqual(result, None)
return result
def test_app_insights(self):
params = {'name': 'cloud-custodian-test',
'location': 'westus2',
'resource_group_name': self.rg_name}
unit = AppInsightsUnit()
self._validate(unit, params)
def test_storage_account(self):
params = {'name': 'custodianaccount47182745',
'location': self.rg_location,
'resource_group_name': self.rg_name}
unit = StorageAccountUnit()
self._validate(unit, params)
def test_service_plan(self):
params = {'name': 'cloud-custodian-test',
'location': self.rg_location,
'resource_group_name': self.rg_name,
'sku_tier': 'Basic',
'sku_name': 'B1'}
unit = AppServicePlanUnit()
self._validate(unit, params)
def test_app_service_plan_autoscale(self):
params = {'name': 'cloud-custodian-test-autoscale',
'location': self.rg_location,
'resource_group_name': self.rg_name,
'sku_tier': 'Basic',
'sku_name': 'B1',
'auto_scale': {
'enabled': True,
'min_capacity': 1,
'max_capacity': 2,
'default_capacity': 1}
}
unit = AppServicePlanUnit()
plan = self._validate(unit, params)
client = self.session.client('azure.mgmt.monitor.MonitorManagementClient')
rules = client.autoscale_settings.get(self.rg_name, constants.FUNCTION_AUTOSCALE_NAME)
self.assertEqual(rules.target_resource_uri, plan.id)
def test_function_app_consumption(self):
sa_params = {
'name': 'custodianaccount47182748',
'location': self.rg_location,
'resource_group_name': self.rg_name}
storage_unit = StorageAccountUnit()
storage_account_id = storage_unit.provision(sa_params).id
conn_string = FunctionAppUtilities.get_storage_account_connection_string(storage_account_id)
func_params = {
'name': 'cc-consumption-47182748',
'location': 'eastus2',
'resource_group_name': self.rg_name,
'app_service_plan_id': None,
'app_insights_key': None,
'is_consumption_plan': True,
'storage_account_connection_string': conn_string
}
func_unit = FunctionAppDeploymentUnit()
func_app = self._validate(func_unit, func_params)
self.assertEqual(func_app.kind, 'functionapp,linux')
self.assertTrue(func_app.reserved)
def test_function_app_dedicated(self):
sa_params = {
'name': 'custodianaccount47182741',
'location': self.rg_location,
'resource_group_name': self.rg_name}
storage_unit = StorageAccountUnit()
storage_account_id = storage_unit.provision(sa_params).id
conn_string = FunctionAppUtilities.get_storage_account_connection_string(storage_account_id)
app_plan_params = {
'name': 'cloud-custodian-test2',
'location': self.rg_location,
'resource_group_name': self.rg_name,
'sku_tier': 'Basic',
'sku_name': 'B1'}
app_plan_unit = AppServicePlanUnit()
app_plan = app_plan_unit.provision(app_plan_params)
func_app_name = 'cc-dedicated-47182748'
func_params = {
'name': func_app_name,
'location': self.rg_location,
'resource_group_name': self.rg_name,
'app_service_plan_id': app_plan.id,
'app_insights_key': None,
'is_consumption_plan': False,
'storage_account_connection_string': conn_string
}
func_unit = FunctionAppDeploymentUnit()
func_app = self._validate(func_unit, func_params)
self.assertEqual(func_app.kind, 'functionapp,linux,container')
self.assertTrue(func_app.reserved)
wc = self.session.client('azure.mgmt.web.WebSiteManagementClient')
site_config = wc.web_apps.get_configuration(self.rg_name, func_app_name)
self.assertTrue(site_config.always_on)
self.assertEqual(site_config.linux_fx_version, FUNCTION_DOCKER_VERSION)
| true | true |
f7291d9a167eddfff3ff0f609ba4999ace509653 | 3,976 | py | Python | metrics/evaluation.py | riokt/video-paragraph | 2da3298819e73809af495457db2cf1dfffad712f | [
"MIT"
] | null | null | null | metrics/evaluation.py | riokt/video-paragraph | 2da3298819e73809af495457db2cf1dfffad712f | [
"MIT"
] | null | null | null | metrics/evaluation.py | riokt/video-paragraph | 2da3298819e73809af495457db2cf1dfffad712f | [
"MIT"
] | null | null | null | from cap_eval.bleu.bleu import Bleu
from cap_eval.cider.cider import Cider
from cap_eval.meteor.meteor import Meteor
import json
import numpy as np
# initialize the caption evaluators
meteor_scorer = Meteor()
cider_scorer = Cider()
bleu_scorer = Bleu(4)
def bleu_eval(refs, cands):
print ("calculating bleu_4 score...")
bleu, _ = bleu_scorer.compute_score(refs, cands)
return bleu
def cider_eval(refs, cands):
print ("calculating cider score...")
cider, _ = cider_scorer.compute_score(refs, cands)
return cider
def meteor_eval(refs, cands):
print ("calculating meteor score...")
meteor, _ = meteor_scorer.compute_score(refs, cands)
return meteor
def getNgrams(words_pred, unigrams, bigrams, trigrams, fourgrams):
# N=1
for w in words_pred:
if w not in unigrams:
unigrams[w] = 0
unigrams[w] += 1
# N=2
for i, w in enumerate(words_pred):
if i<len(words_pred)-1:
w_next = words_pred[i+1]
bigram = '%s_%s' % (w, w_next)
if bigram not in bigrams:
bigrams[bigram] = 0
bigrams[bigram] += 1
# N=3
for i, w in enumerate(words_pred):
if i<len(words_pred)-2:
w_next = words_pred[i + 1]
w_next_ = words_pred[i + 2]
tri = '%s_%s_%s' % (w, w_next, w_next_)
if tri not in trigrams:
trigrams[tri] = 0
trigrams[tri] += 1
# N=4
for i, w in enumerate(words_pred):
if i<len(words_pred)-3:
w_next = words_pred[i + 1]
w_next_ = words_pred[i + 2]
w_next__ = words_pred[i + 3]
four = '%s_%s_%s_%s' % (w, w_next, w_next_, w_next__)
if four not in fourgrams:
fourgrams[four] = 0
fourgrams[four] += 1
return unigrams, bigrams, trigrams, fourgrams
def diversity(data_pred):
div1, div2, re4 = [], [], []
try:
for i in range(len(data_pred)):
unigrams, bigrams, trigrams, fourgrams = {}, {}, {}, {}
if data_pred[i][-1] == '.':
para = data_pred[i].split('.')[:-1]
else:
para = data_pred[i].split('.')
for j, pred_sentence in enumerate(para):
if pred_sentence[-1] == '.':
pred_sentence = pred_sentence[:-1]
while len(pred_sentence) > 0 and pred_sentence[-1] == ' ':
pred_sentence = pred_sentence[:-1]
while len(pred_sentence) > 0 and pred_sentence[0] == ' ':
pred_sentence = pred_sentence[1:]
pred_sentence = pred_sentence.replace(',', ' ')
while ' ' in pred_sentence:
pred_sentence = pred_sentence.replace(' ', ' ')
words_pred = pred_sentence.split(' ')
unigrams, bigrams, trigrams, fourgrams = getNgrams(words_pred, unigrams, bigrams, trigrams, fourgrams)
sum_unigrams = sum([unigrams[un] for un in unigrams])
vid_div1 = float(len(unigrams)) / (float(sum_unigrams) + 1e-28)
vid_div2 = float(len(bigrams)) / (float(sum_unigrams) + 1e-28)
vid_re4 = float(sum([max(fourgrams[f]-1,0) for f in fourgrams])) / (float(sum([fourgrams[f] for f in fourgrams])) + 1e-28)
div1.append(vid_div1)
div2.append(vid_div2)
re4.append(vid_re4)
except Exception as e:
print(e)
pass
return np.mean(div1), np.mean(div2), np.mean(re4)
def compute(preds, names, refs):
refcaps = {}
candcaps = {}
for i in range(len(preds)):
candcaps[i] = [preds[i]]
refcaps[i] = refs[names[i]]
bleu = bleu_eval(refcaps, candcaps)
cider = cider_eval(refcaps, candcaps)
meteor = meteor_eval(refcaps, candcaps)
div1, div2, re4 = diversity(preds)
scores = {'bleu_4':bleu[3], 'bleu_3':bleu[2], 'bleu_2':bleu[1], 'bleu_1':bleu[0],
'cider':cider, 'meteor':meteor,
'div1':div1, 'div2':div2, 're4':re4}
return scores
if guess in word:
print("\nYes!", guess, "is in the word!")
# Create a new variable (so_far) to contain the guess
new = ""
i = 0
for i in range(len(word)):
if guess == word[i]:
new += guess
else:
new += so_far[i]
so_far = new
| 30.121212 | 128 | 0.613682 | from cap_eval.bleu.bleu import Bleu
from cap_eval.cider.cider import Cider
from cap_eval.meteor.meteor import Meteor
import json
import numpy as np
meteor_scorer = Meteor()
cider_scorer = Cider()
bleu_scorer = Bleu(4)
def bleu_eval(refs, cands):
print ("calculating bleu_4 score...")
bleu, _ = bleu_scorer.compute_score(refs, cands)
return bleu
def cider_eval(refs, cands):
print ("calculating cider score...")
cider, _ = cider_scorer.compute_score(refs, cands)
return cider
def meteor_eval(refs, cands):
print ("calculating meteor score...")
meteor, _ = meteor_scorer.compute_score(refs, cands)
return meteor
def getNgrams(words_pred, unigrams, bigrams, trigrams, fourgrams):
for w in words_pred:
if w not in unigrams:
unigrams[w] = 0
unigrams[w] += 1
for i, w in enumerate(words_pred):
if i<len(words_pred)-1:
w_next = words_pred[i+1]
bigram = '%s_%s' % (w, w_next)
if bigram not in bigrams:
bigrams[bigram] = 0
bigrams[bigram] += 1
for i, w in enumerate(words_pred):
if i<len(words_pred)-2:
w_next = words_pred[i + 1]
w_next_ = words_pred[i + 2]
tri = '%s_%s_%s' % (w, w_next, w_next_)
if tri not in trigrams:
trigrams[tri] = 0
trigrams[tri] += 1
for i, w in enumerate(words_pred):
if i<len(words_pred)-3:
w_next = words_pred[i + 1]
w_next_ = words_pred[i + 2]
w_next__ = words_pred[i + 3]
four = '%s_%s_%s_%s' % (w, w_next, w_next_, w_next__)
if four not in fourgrams:
fourgrams[four] = 0
fourgrams[four] += 1
return unigrams, bigrams, trigrams, fourgrams
def diversity(data_pred):
div1, div2, re4 = [], [], []
try:
for i in range(len(data_pred)):
unigrams, bigrams, trigrams, fourgrams = {}, {}, {}, {}
if data_pred[i][-1] == '.':
para = data_pred[i].split('.')[:-1]
else:
para = data_pred[i].split('.')
for j, pred_sentence in enumerate(para):
if pred_sentence[-1] == '.':
pred_sentence = pred_sentence[:-1]
while len(pred_sentence) > 0 and pred_sentence[-1] == ' ':
pred_sentence = pred_sentence[:-1]
while len(pred_sentence) > 0 and pred_sentence[0] == ' ':
pred_sentence = pred_sentence[1:]
pred_sentence = pred_sentence.replace(',', ' ')
while ' ' in pred_sentence:
pred_sentence = pred_sentence.replace(' ', ' ')
words_pred = pred_sentence.split(' ')
unigrams, bigrams, trigrams, fourgrams = getNgrams(words_pred, unigrams, bigrams, trigrams, fourgrams)
sum_unigrams = sum([unigrams[un] for un in unigrams])
vid_div1 = float(len(unigrams)) / (float(sum_unigrams) + 1e-28)
vid_div2 = float(len(bigrams)) / (float(sum_unigrams) + 1e-28)
vid_re4 = float(sum([max(fourgrams[f]-1,0) for f in fourgrams])) / (float(sum([fourgrams[f] for f in fourgrams])) + 1e-28)
div1.append(vid_div1)
div2.append(vid_div2)
re4.append(vid_re4)
except Exception as e:
print(e)
pass
return np.mean(div1), np.mean(div2), np.mean(re4)
def compute(preds, names, refs):
refcaps = {}
candcaps = {}
for i in range(len(preds)):
candcaps[i] = [preds[i]]
refcaps[i] = refs[names[i]]
bleu = bleu_eval(refcaps, candcaps)
cider = cider_eval(refcaps, candcaps)
meteor = meteor_eval(refcaps, candcaps)
div1, div2, re4 = diversity(preds)
scores = {'bleu_4':bleu[3], 'bleu_3':bleu[2], 'bleu_2':bleu[1], 'bleu_1':bleu[0],
'cider':cider, 'meteor':meteor,
'div1':div1, 'div2':div2, 're4':re4}
return scores
if guess in word:
print("\nYes!", guess, "is in the word!")
new = ""
i = 0
for i in range(len(word)):
if guess == word[i]:
new += guess
else:
new += so_far[i]
so_far = new
| true | true |
f7291dc38b29cc9779071f6ccb58bc60d287e347 | 15,714 | py | Python | BlackboardCrawler.py | ks-paul-lam/cuhk_blackboard_crawler | c03cdd82a7617a6a7441b65ad9b46b0ce15874a4 | [
"MIT"
] | null | null | null | BlackboardCrawler.py | ks-paul-lam/cuhk_blackboard_crawler | c03cdd82a7617a6a7441b65ad9b46b0ce15874a4 | [
"MIT"
] | null | null | null | BlackboardCrawler.py | ks-paul-lam/cuhk_blackboard_crawler | c03cdd82a7617a6a7441b65ad9b46b0ce15874a4 | [
"MIT"
] | null | null | null | import json
import time
import requests
import urllib
import re
import os
from functools import reduce
import uuid # in case the error happens
from getpass import getpass
# import xml.etree.ElementTree as ET
# XML parser is not used because the fucking –
import inspect # debugging purpose
import string
from sys import platform, stdout
from utils import mkdir, directory_flatten
class AuthenticationException(Exception):
pass
# Only developer should change it?
class BCFlags(object):
SLEEP_TIME=1
MAX_DEPTH=2
VERBOSE=True
IGNORE_SAME=True
# IGNORE_SAME: ignore same file
# Just a dict with support of middler & get/setattr
class BCPrefs(object):
@staticmethod
def default_dict():
return {'folder_prefix': 'blackboard', 'blackboard_url': 'https://blackboard.cuhk.edu.hk', 'email_suffix': '@link.cuhk.edu.hk', 'folder_name_style': 'CC_ONLY'}
no_verification = staticmethod(lambda x: True)
url_check = staticmethod(lambda x: re.match('https?://', str(x)) is not None)
email_check = staticmethod(lambda x: '@' in str(x))
folder_style = ['CC_ONLY', 'FULL', 'TERM_AND_CC']
folder_name_check = staticmethod(lambda x: x in BCPrefs.folder_style)
test = lambda x: '@' in x
keys = ['folder_prefix', 'blackboard_url', 'email_suffix', 'folder_name_style']
prefs_dict = {}
@staticmethod
def get_option_vals(key):
if(key is 'folder_name_style'):
return BCPrefs.folder_style
return []
@staticmethod
def get_pref_type(key):
if(key is 'folder_prefix'):
return 'text'
elif(key is 'blackboard_url'):
return 'text'
elif(key is 'email_suffix'):
return 'text'
elif(key is 'folder_name_style'):
return 'option'
else:
return 'text'
def __init__(self):
self['folder_prefix']='blackboard'
self['blackboard_url']='https://blackboard.cuhk.edu.hk'
self['email_suffix']='@link.cuhk.edu.hk'
self['folder_name_style']='CC_ONLY'
def __setitem__(self, key, value):
if(key not in self.keys):
# return (False,"{0} not found".format(key)) # Cannot return value in __setitem__...
raise KeyError("{0} not found".format(key))
if(key is 'blackboard_url' and not BCPrefs.url_check(value)):
# return (False,"{0} is not a url".format(value))
raise Exception("{0} is not a url".format(key))
if(key is 'email_suffix' and not BCPrefs.email_check(value)):
# return (False,"{0} is not a email".format(value))
raise Exception("{0} is not a email".format(value))
if(key is 'folder_name_style' and not BCPrefs.folder_name_check(value)):
raise Exception("{0} is not a folder style setting".format(value))
self.prefs_dict[key]=value
# return (True,'')
def __getitem__(self, key):
return self.prefs_dict[key]
def __delitem__(self, key):
del self.prefs_dict[key]
def __setattr__(self, key, value):
self.__setitem__(key, value)
def __getattr__(self, key):
return self.prefs_dict[key]
def __str__(self):
return self.prefs_dict.__str__()
def __repr__(self):
return self.prefs_dict.__str__()
class BlackboardCrawler:
flags = BCFlags()
prefs = BCPrefs()
def __init__(
self,
username,
password,
):
self.username = username
self.password = password
def updatePrefs(self, key, value):
self.prefs[key]=value
def log(self, s, t=0, coding='utf-8'):
if(self.flags.VERBOSE or t!=0):
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)
caller = calframe[1][3]
if(isinstance(s, str)):
print('{0}:{1}'.format(caller, s.encode(coding)))
else:
print('{0}:{1}'.format(caller, s))
def title_print(self, s):
s = '@ {0} @'.format(s)
print('@'*len(s))
print(s)
print('@'*len(s))
# return True/ False
def login(self):
self._init_bb_session()
self._login()
self.userid = self._get_bb_userid()
mkdir(self.prefs.folder_prefix)
# return list of courses
# [course_id, course_code, display_name]
def get_courses(self):
if(not self.userid):
raise AuthenticationException("Not logged in.")
self.log("getting courses...")
courses_resp = self.sess.get("{0}/learn/api/v1/users/{1}/memberships?expand=course.instructorsMembership,course.effectiveAvailability,course.permissions,courseRole&limit=10000&organization=false".format(self.prefs.blackboard_url, self.userid))
courses = courses_resp.json()['results']
courses_info = []
for course in courses:
if(course['course']['isAvailable']):
content_id = course['id']
course_id = course['course']['id']
course_code = course['course']['courseId']
display_name = course['course']['displayName']
courses_info.append((course_id, course_code, display_name))
self.log("finish getting courses...")
return courses_info #[course_id, course_code, course_name]
# download courses
def download(self, selected_courses_info):
# Un-enumerate it
selected_courses_info = map(lambda x: x[1], selected_courses_info)
self._download(selected_courses_info)
def get_metadata_from_file(self, file_name):
try:
if(not os.path.isfile(file_name)):
return {'size': -1}
size = os.path.getsize(file_name)
except Exception as inst:
self.log('err: {0}'.format(inst))
return {'size': -1}
return {'size': size}
# def get_metadata_from_url(self, url):
# try:
# resp = self.sess.get(url)
# headers = resp.headers
# self.log("headers: {0}".format(headers))
# size = int(headers['Content-Length'])
# except Exception as inst:
# self.log('url: {0}'.format(url))
# self.log('err: {0}'.format(inst))
# return {'size': -1}
# return {'size': size}
def file_same(self, file_name, file_size):
if(not self.flags.IGNORE_SAME):
return False
metadata_file = self.get_metadata_from_file(file_name)
# DEBUG: return True if size same, return False if not
# self.log(metadata_file)
# self.log(file_size)
metadata_file['size'] = int(metadata_file['size'])
file_size = int(file_size)
if(metadata_file['size']<0 or file_size<0):
return False
return (metadata_file['size'] == file_size)
def make_course_dir(self, course_info):
course_id, course_code, course_name = course_info
print("{0}; {1}; {2}".format(self.prefs.folder_prefix, course_code, os.path.join(self.prefs.folder_prefix, course_code)))
#course_code: 2018R1-CSCI4180
if(self.prefs.folder_name_style == 'CC_ONLY'):
course_code = course_code.split('-')[1]
if(not reduce(lambda x,y: x and y, map(lambda x: not x.isdigit(), course_code))):
while(not course_code[-1].isdigit()):
course_code = directory_flatten(course_code[:-1])
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_code))
elif(self.prefs.folder_name_style == 'FULL'):
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_name))
elif(self.prefs.folder_name_style == 'TERM_AND_CC'):
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_code))
else:
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_code))
return dir_name
def _download(self, courses_info):
for course_info in courses_info:
course_id, course_code, course_name = course_info
sections = self._get_course_sections(course_info)
dirname = self.make_course_dir(course_info)
# Ask if the user want to continue download if the folder exists?
for section in sections:
section_title = directory_flatten(section[1])
# print '---------------------'
# print dirname, section_title, os.path.join(dirname, section_title)
# print '---------------------'
path_prefix = os.path.join(dirname, section_title)
directories, files = self._get_item_from_section(path_prefix, section)
self._download_item_from_directories(path_prefix, directories, self.flags.MAX_DEPTH)
self._download_files(path_prefix, files)
time.sleep(self.flags.SLEEP_TIME)
def _download_file(self, url, path):
if(self.prefs.blackboard_url not in url):
url = self.prefs.blackboard_url+url
resp = self.sess.get(url, stream=True)
headers = resp.headers
url = urllib.parse.unquote(resp.url)
if(platform == "darwin"):
url = url.encode('latin1')
if(isinstance(path, str)):
self.log(u'path: {0}'.format(path))
else:
self.log('path: {0}'.format(path))
self.log('url: {0}'.format(url))
self.log("header: {0}".format(resp.headers))
try:
header_content = headers['Content-Disposition']
coding, local_filename = re.findall("[*]=(.+)''(.+)", header_content)[0]
except:
local_filename = url.split('/')[-1]
# self.log('local_filename1: {0}'.format(repr(header_content)))
# self.log('coding: {0}'.format(repr(coding)))
# self.log('repr local_filename2: {0}'.format(repr(local_filename)))
local_filename_unquoted = urllib.parse.unquote(local_filename)
self.debug = local_filename_unquoted
# self.log('local_filename3: {0}'.format(local_filename_unquoted))
# self.log('str local_filename3: {0}'.format(str(local_filename_unquoted)))
# self.log('repr local_filename3: {0}'.format(repr(local_filename_unquoted)))
# self.log('type local_filename3: {0}'.format(type(local_filename_unquoted)))
final_local_filename = local_filename_unquoted
# final_local_filename = local_filename_unquoted
# self.log(u'local_filename4: {0}'.format(final_local_filename))
# self.log(u'repr local_filename4: {0}'.format(repr(final_local_filename)))
try:
file_size = resp.headers['Content-Length']
except:
file_size = 0
# if(int(file_size)>=1024*1024*100):
# while(1):
# download = raw_input("The file {1} is around {0}MB, still download?(y/n)".format(int(file_size)/1024/1024, local_filename))
# if(download.lower() == 'y'):
# break
# elif(download.lower() == 'n'):
# return local_filename
# else:
# print("Please input only y or n!")
# NOTE the stream=True parameter
if(not self.file_same(os.path.join(path, final_local_filename), file_size)):
self.log(u"Downloading {0}".format(final_local_filename))
r = resp
with open(os.path.join(path, final_local_filename), 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
f.flush()
#f.flush() commented by recommendation from J.F.Sebastian
else:
self.log(u'File are found to be same: {0}'.format(final_local_filename))
return final_local_filename
def _download_files(self, path_prefix, files):
self.log(repr(files))
for f in files:
file_url, file_name = f
if(isinstance(file_name, str)):
self.log(u'url: {0} {1}'.format(file_url, file_name))
else:
self.log('url: {0} {1}'.format(file_url, file_name))
self._download_file(file_url, path_prefix)
def _download_item_from_directories(self, path_prefix, directories, depth):
if(depth<=0):
return
for directory in directories:
directory_url, directory_title = directory
directory_title = directory_flatten(directory_title[:64])
self.log(u'reading: {0} {1}'.format(directory_url, directory_title))
new_prefix = os.path.join(path_prefix, directory_title)
next_directories, files = self._get_item_from_section(new_prefix, directory)
self._download_files(new_prefix, files)
self._download_item_from_directories(new_prefix, next_directories, depth-1)
def _get_item_from_section(self, path_prefix, section):
section_url, section_name = section
section_name = section_name[:64]
if(isinstance(section_name, str)):
self.log(u'----reading sections: {0}'.format(section_name))
else:
self.log('----reading sections: {0}'.format(section_name))
dir_name = mkdir(path_prefix)
# path_prefix = dir_name
if(self.prefs.blackboard_url not in section_url):
section_url = self.prefs.blackboard_url+section_url
course_section_resp = self.sess.get(section_url)
directories = re.findall('<a href="(/webapps/blackboard/content/listContent.jsp?.+?)".+?><span style=".+?">(.+?)</span>', course_section_resp.text)
files = re.findall('<a(?:.+?|)href="(?:https://blackboard.cuhk.edu.hk|)(/bbcswebdav.+?)">(.+?)<', course_section_resp.text)
""" files type 1
<a href="/bbcswebdav/pid-2238145-dt-content-rid-8465171_1/xid-8465171_1" onClick="this.href='/webapps/blackboard/execute/content/file?cmd=view&content_id=_2238145_1&course_id=_87673_1'">
<span style="color:#000000;">lesson 11</span>
</a>
<a href="https://blackboard.cuhk.edu.hk/bbcswebdav/pid-2470832-dt-content-rid-13383234_1/xid-13383234_1">大綱</a>
"""
files2 = re.findall('<a href="(/bbcswebdav.+?)".+?">.+?">(.+)[^</span>]</a>', course_section_resp.text)
""" files type 2
<a href="/bbcswebdav/pid-2233230-dt-content-rid-8062745_1/xid-8062745_1" target="_blank">
<img src="https://d1e7kr0efngifs.cloudfront.net/3400.1.0-rel.35+67d71b7/images/ci/ng/cal_year_event.gif" alt="File">
第十課閱讀材料 (徐復觀).pdf
</a>
"""
time.sleep(self.flags.SLEEP_TIME)
return (directories, (files+files2)) # [(directory_url, directory_name), (file_url, file_name)
def _get_course_sections(self, course_info):
course_id, course_code, course_name = course_info
if(isinstance(course_name, str)):
self.log(u'reading course: {0}'.format(course_name))
else:
self.log('reading course: {0}'.format(course_name))
course_url = "{1}/webapps/blackboard/execute/courseMain?course_id={0}".format(course_id, self.prefs.blackboard_url)
course_url_resp = self.sess.get(course_url)
sections = re.findall('<a href="(/webapps/blackboard/content/listContent.jsp?.+?)".+?">.+?">(.+?)</span>', course_url_resp.text)
return sections
def _init_bb_session(self):
sess = requests.session()
# fake header, otherwise they wont care
sess.headers['User-Agent']='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36'
# go to starting page
blackboard_main_resp = sess.get(self.prefs.blackboard_url)
next_url_1 = re.findall('url=(.+)', blackboard_main_resp.text)[0]
# redirected to login page
next_url_1 = urllib.parse.unquote(next_url_1)
login_page_resp = sess.get(next_url_1)
self.login_page_url = login_page_resp.url
self.sess = sess
def _login(self):
if '@' not in self.username:
self.username = self.username + self.prefs.email_suffix
form_auth_payload={
'UserName': self.username,
'Password': self.password,
'AuthMethod': "FormsAuthentication"
}
self.log("logging in...")
logging_in_resp = self.sess.post(self.login_page_url, data=form_auth_payload)
time.sleep(self.flags.SLEEP_TIME)
next_url_3 = re.findall('action="(.+?)">',logging_in_resp.text)[0]
SAMLResponse = re.findall('name="SAMLResponse" value="(.+?)" />',logging_in_resp.text)[0]
resp4 = self.sess.post(next_url_3, data = {'SAMLResponse': SAMLResponse})
time.sleep(self.flags.SLEEP_TIME)
self.log("logged in...")
def _get_bb_userid(self):
course_url = '{0}/ultra/course'.format(self.prefs.blackboard_url)
course_resp = self.sess.get(course_url)
userid = re.findall('"id":"(.+?)"', course_resp.text)[0]
return userid
def set_auth(self, u, p):
self.username = u
self.password = p
def setFlags():
pass
| 40.709845 | 247 | 0.675894 | import json
import time
import requests
import urllib
import re
import os
from functools import reduce
import uuid
from getpass import getpass
import inspect
import string
from sys import platform, stdout
from utils import mkdir, directory_flatten
class AuthenticationException(Exception):
pass
class BCFlags(object):
SLEEP_TIME=1
MAX_DEPTH=2
VERBOSE=True
IGNORE_SAME=True
class BCPrefs(object):
@staticmethod
def default_dict():
return {'folder_prefix': 'blackboard', 'blackboard_url': 'https://blackboard.cuhk.edu.hk', 'email_suffix': '@link.cuhk.edu.hk', 'folder_name_style': 'CC_ONLY'}
no_verification = staticmethod(lambda x: True)
url_check = staticmethod(lambda x: re.match('https?://', str(x)) is not None)
email_check = staticmethod(lambda x: '@' in str(x))
folder_style = ['CC_ONLY', 'FULL', 'TERM_AND_CC']
folder_name_check = staticmethod(lambda x: x in BCPrefs.folder_style)
test = lambda x: '@' in x
keys = ['folder_prefix', 'blackboard_url', 'email_suffix', 'folder_name_style']
prefs_dict = {}
@staticmethod
def get_option_vals(key):
if(key is 'folder_name_style'):
return BCPrefs.folder_style
return []
@staticmethod
def get_pref_type(key):
if(key is 'folder_prefix'):
return 'text'
elif(key is 'blackboard_url'):
return 'text'
elif(key is 'email_suffix'):
return 'text'
elif(key is 'folder_name_style'):
return 'option'
else:
return 'text'
def __init__(self):
self['folder_prefix']='blackboard'
self['blackboard_url']='https://blackboard.cuhk.edu.hk'
self['email_suffix']='@link.cuhk.edu.hk'
self['folder_name_style']='CC_ONLY'
def __setitem__(self, key, value):
if(key not in self.keys):
ormat(key))
if(key is 'blackboard_url' and not BCPrefs.url_check(value)):
raise Exception("{0} is not a url".format(key))
if(key is 'email_suffix' and not BCPrefs.email_check(value)):
raise Exception("{0} is not a email".format(value))
if(key is 'folder_name_style' and not BCPrefs.folder_name_check(value)):
raise Exception("{0} is not a folder style setting".format(value))
self.prefs_dict[key]=value
def __getitem__(self, key):
return self.prefs_dict[key]
def __delitem__(self, key):
del self.prefs_dict[key]
def __setattr__(self, key, value):
self.__setitem__(key, value)
def __getattr__(self, key):
return self.prefs_dict[key]
def __str__(self):
return self.prefs_dict.__str__()
def __repr__(self):
return self.prefs_dict.__str__()
class BlackboardCrawler:
flags = BCFlags()
prefs = BCPrefs()
def __init__(
self,
username,
password,
):
self.username = username
self.password = password
def updatePrefs(self, key, value):
self.prefs[key]=value
def log(self, s, t=0, coding='utf-8'):
if(self.flags.VERBOSE or t!=0):
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)
caller = calframe[1][3]
if(isinstance(s, str)):
print('{0}:{1}'.format(caller, s.encode(coding)))
else:
print('{0}:{1}'.format(caller, s))
def title_print(self, s):
s = '@ {0} @'.format(s)
print('@'*len(s))
print(s)
print('@'*len(s))
def login(self):
self._init_bb_session()
self._login()
self.userid = self._get_bb_userid()
mkdir(self.prefs.folder_prefix)
def get_courses(self):
if(not self.userid):
raise AuthenticationException("Not logged in.")
self.log("getting courses...")
courses_resp = self.sess.get("{0}/learn/api/v1/users/{1}/memberships?expand=course.instructorsMembership,course.effectiveAvailability,course.permissions,courseRole&limit=10000&organization=false".format(self.prefs.blackboard_url, self.userid))
courses = courses_resp.json()['results']
courses_info = []
for course in courses:
if(course['course']['isAvailable']):
content_id = course['id']
course_id = course['course']['id']
course_code = course['course']['courseId']
display_name = course['course']['displayName']
courses_info.append((course_id, course_code, display_name))
self.log("finish getting courses...")
return courses_info
def download(self, selected_courses_info):
selected_courses_info = map(lambda x: x[1], selected_courses_info)
self._download(selected_courses_info)
def get_metadata_from_file(self, file_name):
try:
if(not os.path.isfile(file_name)):
return {'size': -1}
size = os.path.getsize(file_name)
except Exception as inst:
self.log('err: {0}'.format(inst))
return {'size': -1}
return {'size': size}
def file_same(self, file_name, file_size):
if(not self.flags.IGNORE_SAME):
return False
metadata_file = self.get_metadata_from_file(file_name)
metadata_file['size'] = int(metadata_file['size'])
file_size = int(file_size)
if(metadata_file['size']<0 or file_size<0):
return False
return (metadata_file['size'] == file_size)
def make_course_dir(self, course_info):
course_id, course_code, course_name = course_info
print("{0}; {1}; {2}".format(self.prefs.folder_prefix, course_code, os.path.join(self.prefs.folder_prefix, course_code)))
if(self.prefs.folder_name_style == 'CC_ONLY'):
course_code = course_code.split('-')[1]
if(not reduce(lambda x,y: x and y, map(lambda x: not x.isdigit(), course_code))):
while(not course_code[-1].isdigit()):
course_code = directory_flatten(course_code[:-1])
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_code))
elif(self.prefs.folder_name_style == 'FULL'):
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_name))
elif(self.prefs.folder_name_style == 'TERM_AND_CC'):
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_code))
else:
dir_name = mkdir(os.path.join(self.prefs.folder_prefix, course_code))
return dir_name
def _download(self, courses_info):
for course_info in courses_info:
course_id, course_code, course_name = course_info
sections = self._get_course_sections(course_info)
dirname = self.make_course_dir(course_info)
for section in sections:
section_title = directory_flatten(section[1])
path_prefix = os.path.join(dirname, section_title)
directories, files = self._get_item_from_section(path_prefix, section)
self._download_item_from_directories(path_prefix, directories, self.flags.MAX_DEPTH)
self._download_files(path_prefix, files)
time.sleep(self.flags.SLEEP_TIME)
def _download_file(self, url, path):
if(self.prefs.blackboard_url not in url):
url = self.prefs.blackboard_url+url
resp = self.sess.get(url, stream=True)
headers = resp.headers
url = urllib.parse.unquote(resp.url)
if(platform == "darwin"):
url = url.encode('latin1')
if(isinstance(path, str)):
self.log(u'path: {0}'.format(path))
else:
self.log('path: {0}'.format(path))
self.log('url: {0}'.format(url))
self.log("header: {0}".format(resp.headers))
try:
header_content = headers['Content-Disposition']
coding, local_filename = re.findall("[*]=(.+)''(.+)", header_content)[0]
except:
local_filename = url.split('/')[-1]
local_filename_unquoted = urllib.parse.unquote(local_filename)
self.debug = local_filename_unquoted
final_local_filename = local_filename_unquoted
try:
file_size = resp.headers['Content-Length']
except:
file_size = 0
if(not self.file_same(os.path.join(path, final_local_filename), file_size)):
self.log(u"Downloading {0}".format(final_local_filename))
r = resp
with open(os.path.join(path, final_local_filename), 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
f.flush()
else:
self.log(u'File are found to be same: {0}'.format(final_local_filename))
return final_local_filename
def _download_files(self, path_prefix, files):
self.log(repr(files))
for f in files:
file_url, file_name = f
if(isinstance(file_name, str)):
self.log(u'url: {0} {1}'.format(file_url, file_name))
else:
self.log('url: {0} {1}'.format(file_url, file_name))
self._download_file(file_url, path_prefix)
def _download_item_from_directories(self, path_prefix, directories, depth):
if(depth<=0):
return
for directory in directories:
directory_url, directory_title = directory
directory_title = directory_flatten(directory_title[:64])
self.log(u'reading: {0} {1}'.format(directory_url, directory_title))
new_prefix = os.path.join(path_prefix, directory_title)
next_directories, files = self._get_item_from_section(new_prefix, directory)
self._download_files(new_prefix, files)
self._download_item_from_directories(new_prefix, next_directories, depth-1)
def _get_item_from_section(self, path_prefix, section):
section_url, section_name = section
section_name = section_name[:64]
if(isinstance(section_name, str)):
self.log(u'----reading sections: {0}'.format(section_name))
else:
self.log('----reading sections: {0}'.format(section_name))
dir_name = mkdir(path_prefix)
if(self.prefs.blackboard_url not in section_url):
section_url = self.prefs.blackboard_url+section_url
course_section_resp = self.sess.get(section_url)
directories = re.findall('<a href="(/webapps/blackboard/content/listContent.jsp?.+?)".+?><span style=".+?">(.+?)</span>', course_section_resp.text)
files = re.findall('<a(?:.+?|)href="(?:https://blackboard.cuhk.edu.hk|)(/bbcswebdav.+?)">(.+?)<', course_section_resp.text)
files2 = re.findall('<a href="(/bbcswebdav.+?)".+?">.+?">(.+)[^</span>]</a>', course_section_resp.text)
time.sleep(self.flags.SLEEP_TIME)
return (directories, (files+files2))
def _get_course_sections(self, course_info):
course_id, course_code, course_name = course_info
if(isinstance(course_name, str)):
self.log(u'reading course: {0}'.format(course_name))
else:
self.log('reading course: {0}'.format(course_name))
course_url = "{1}/webapps/blackboard/execute/courseMain?course_id={0}".format(course_id, self.prefs.blackboard_url)
course_url_resp = self.sess.get(course_url)
sections = re.findall('<a href="(/webapps/blackboard/content/listContent.jsp?.+?)".+?">.+?">(.+?)</span>', course_url_resp.text)
return sections
def _init_bb_session(self):
sess = requests.session()
sess.headers['User-Agent']='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36'
blackboard_main_resp = sess.get(self.prefs.blackboard_url)
next_url_1 = re.findall('url=(.+)', blackboard_main_resp.text)[0]
next_url_1 = urllib.parse.unquote(next_url_1)
login_page_resp = sess.get(next_url_1)
self.login_page_url = login_page_resp.url
self.sess = sess
def _login(self):
if '@' not in self.username:
self.username = self.username + self.prefs.email_suffix
form_auth_payload={
'UserName': self.username,
'Password': self.password,
'AuthMethod': "FormsAuthentication"
}
self.log("logging in...")
logging_in_resp = self.sess.post(self.login_page_url, data=form_auth_payload)
time.sleep(self.flags.SLEEP_TIME)
next_url_3 = re.findall('action="(.+?)">',logging_in_resp.text)[0]
SAMLResponse = re.findall('name="SAMLResponse" value="(.+?)" />',logging_in_resp.text)[0]
resp4 = self.sess.post(next_url_3, data = {'SAMLResponse': SAMLResponse})
time.sleep(self.flags.SLEEP_TIME)
self.log("logged in...")
def _get_bb_userid(self):
course_url = '{0}/ultra/course'.format(self.prefs.blackboard_url)
course_resp = self.sess.get(course_url)
userid = re.findall('"id":"(.+?)"', course_resp.text)[0]
return userid
def set_auth(self, u, p):
self.username = u
self.password = p
def setFlags():
pass
| true | true |
f7291ddb80f03e8d4819f187dd1ba8ff24f104f3 | 573 | py | Python | stubs/micropython-v1_9_4-esp8266/ds18x20.py | mattytrentini/micropython-stubs | 4d596273823b69e9e5bcf5fa67f249c374ee0bbc | [
"MIT"
] | null | null | null | stubs/micropython-v1_9_4-esp8266/ds18x20.py | mattytrentini/micropython-stubs | 4d596273823b69e9e5bcf5fa67f249c374ee0bbc | [
"MIT"
] | null | null | null | stubs/micropython-v1_9_4-esp8266/ds18x20.py | mattytrentini/micropython-stubs | 4d596273823b69e9e5bcf5fa67f249c374ee0bbc | [
"MIT"
] | null | null | null | """
Module: 'ds18x20' on esp8266 v1.9.4
"""
# MCU: (sysname='esp8266', nodename='esp8266', release='2.2.0-dev(9422289)', version='v1.9.4-8-ga9a3caad0 on 2018-05-11', machine='ESP module with ESP8266')
# Stubber: 1.1.2 - updated
from typing import Any
class DS18X20:
""""""
def convert_temp(self, *argv) -> Any:
pass
def read_scratch(self, *argv) -> Any:
pass
def read_temp(self, *argv) -> Any:
pass
def scan(self, *argv) -> Any:
pass
def write_scratch(self, *argv) -> Any:
pass
def const():
pass
| 19.1 | 156 | 0.588133 |
from typing import Any
class DS18X20:
def convert_temp(self, *argv) -> Any:
pass
def read_scratch(self, *argv) -> Any:
pass
def read_temp(self, *argv) -> Any:
pass
def scan(self, *argv) -> Any:
pass
def write_scratch(self, *argv) -> Any:
pass
def const():
pass
| true | true |
f7291f2084206620bdfb545c8ab1a6dba8553523 | 2,837 | py | Python | eval.py | Noahs-ARK/PaLM | fe943bb0516d80b09f2b56de60dac9c54dc196e6 | [
"BSD-3-Clause"
] | 7 | 2020-01-10T06:25:27.000Z | 2021-11-27T15:29:45.000Z | eval.py | Noahs-ARK/PaLM | fe943bb0516d80b09f2b56de60dac9c54dc196e6 | [
"BSD-3-Clause"
] | null | null | null | eval.py | Noahs-ARK/PaLM | fe943bb0516d80b09f2b56de60dac9c54dc196e6 | [
"BSD-3-Clause"
] | null | null | null | import math
import numpy as np
import torch
import data
from torch.autograd import Variable
from utils import batchify, get_batch, repackage_hidden
import argparser
args = argparser.args()
from utils import Input
# Set the random seed manually for reproducibility.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
else:
torch.cuda.manual_seed(args.seed)
def model_load(fn):
global model, criterion, optimizer
with open(fn, 'rb') as f:
model, criterion, optimizer = torch.load(f)
import os
import hashlib
fn = 'corpus'
if os.path.exists(fn):
print('Loading cached dataset...')
corpus = torch.load(fn)
else:
print('Producing dataset...')
corpus = data.Corpus(args.data)
torch.save(corpus, fn)
eval_batch_size = 10
test_batch_size = 1
train_data, train_rps = batchify(corpus.train, corpus.train_rps, args.batch_size, args)
val_data, val_rps = batchify(corpus.valid, corpus.valid_rps, eval_batch_size, args)
test_data, test_rps = batchify(corpus.test, corpus.test_rps, test_batch_size, args)
print('Args:', args)
def evaluate(data_source, rps, batch_size=10):
# Turn on evaluation mode which disables dropout.
criterion = torch.nn.CrossEntropyLoss()
ntokens = len(corpus.dictionary)
model.eval()
if args.model == 'QRNN': model.reset()
total_loss = 0
hidden = model.init_hidden(batch_size)
with torch.no_grad():
for i in range(0, data_source.shape[1] - 1, args.bptt):
data, rp, targets = get_batch(
data_source, rps, i, batch_size=batch_size, args=args, evaluation=True)
input = Input(x=data, rp=rp)
output, hidden = model(input, hidden)
# total_loss += data.size(1) * criterion(model.decoder.weight, model.decoder.bias, output, targets).data
output = torch.nn.functional.linear(output, model.decoder.weight, bias=model.decoder.bias)
output = torch.nn.functional.log_softmax(output, dim=-1)
output_flat = output.view(-1, ntokens)
total_loss += data.size(1) * criterion(output_flat, targets).data
hidden = repackage_hidden(hidden)
return total_loss.item() / data_source.shape[1]
model_load(args.save)
test_loss = evaluate(val_data, val_rps, 10)
print('=' * 89)
print('| End of training | test loss {:5.2f} | test ppl {:8.2f} | test bpc {:8.3f}'.format(
val_loss, math.exp(test_loss), val_loss / math.log(2)))
print('=' * 89)
dsa
test_loss = evaluate(test_data, test_rps, 1)
print('=' * 89)
print('| End of training | test loss {:5.2f} | test ppl {:8.2f} | test bpc {:8.3f}'.format(
test_loss, math.exp(test_loss), test_loss / math.log(2)))
print('=' * 89)
| 35.911392 | 116 | 0.680649 | import math
import numpy as np
import torch
import data
from torch.autograd import Variable
from utils import batchify, get_batch, repackage_hidden
import argparser
args = argparser.args()
from utils import Input
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
else:
torch.cuda.manual_seed(args.seed)
def model_load(fn):
global model, criterion, optimizer
with open(fn, 'rb') as f:
model, criterion, optimizer = torch.load(f)
import os
import hashlib
fn = 'corpus'
if os.path.exists(fn):
print('Loading cached dataset...')
corpus = torch.load(fn)
else:
print('Producing dataset...')
corpus = data.Corpus(args.data)
torch.save(corpus, fn)
eval_batch_size = 10
test_batch_size = 1
train_data, train_rps = batchify(corpus.train, corpus.train_rps, args.batch_size, args)
val_data, val_rps = batchify(corpus.valid, corpus.valid_rps, eval_batch_size, args)
test_data, test_rps = batchify(corpus.test, corpus.test_rps, test_batch_size, args)
print('Args:', args)
def evaluate(data_source, rps, batch_size=10):
criterion = torch.nn.CrossEntropyLoss()
ntokens = len(corpus.dictionary)
model.eval()
if args.model == 'QRNN': model.reset()
total_loss = 0
hidden = model.init_hidden(batch_size)
with torch.no_grad():
for i in range(0, data_source.shape[1] - 1, args.bptt):
data, rp, targets = get_batch(
data_source, rps, i, batch_size=batch_size, args=args, evaluation=True)
input = Input(x=data, rp=rp)
output, hidden = model(input, hidden)
output = torch.nn.functional.linear(output, model.decoder.weight, bias=model.decoder.bias)
output = torch.nn.functional.log_softmax(output, dim=-1)
output_flat = output.view(-1, ntokens)
total_loss += data.size(1) * criterion(output_flat, targets).data
hidden = repackage_hidden(hidden)
return total_loss.item() / data_source.shape[1]
model_load(args.save)
test_loss = evaluate(val_data, val_rps, 10)
print('=' * 89)
print('| End of training | test loss {:5.2f} | test ppl {:8.2f} | test bpc {:8.3f}'.format(
val_loss, math.exp(test_loss), val_loss / math.log(2)))
print('=' * 89)
dsa
test_loss = evaluate(test_data, test_rps, 1)
print('=' * 89)
print('| End of training | test loss {:5.2f} | test ppl {:8.2f} | test bpc {:8.3f}'.format(
test_loss, math.exp(test_loss), test_loss / math.log(2)))
print('=' * 89)
| true | true |
f729201b4ab35a0773e4967ea692da05c632a7dc | 5,462 | py | Python | src/python/pants/util/contextutil.py | WamBamBoozle/pants | 98cadfa1a5d337146903eb66548cfe955f2627b3 | [
"Apache-2.0"
] | null | null | null | src/python/pants/util/contextutil.py | WamBamBoozle/pants | 98cadfa1a5d337146903eb66548cfe955f2627b3 | [
"Apache-2.0"
] | null | null | null | src/python/pants/util/contextutil.py | WamBamBoozle/pants | 98cadfa1a5d337146903eb66548cfe955f2627b3 | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
import shutil
import tarfile
import tempfile
import time
import uuid
import zipfile
from contextlib import closing, contextmanager
from six import string_types
from pants.util.dirutil import safe_delete
@contextmanager
def environment_as(**kwargs):
"""Update the environment to the supplied values, for example:
with environment_as(PYTHONPATH='foo:bar:baz',
PYTHON='/usr/bin/python2.6'):
subprocess.Popen(foo).wait()
"""
new_environment = kwargs
old_environment = {}
def setenv(key, val):
if val is not None:
os.environ[key] = val
else:
if key in os.environ:
del os.environ[key]
for key, val in new_environment.items():
old_environment[key] = os.environ.get(key)
setenv(key, val)
try:
yield
finally:
for key, val in old_environment.items():
setenv(key, val)
@contextmanager
def temporary_dir(root_dir=None, cleanup=True):
"""
A with-context that creates a temporary directory.
You may specify the following keyword args:
:param str root_dir: The parent directory to create the temporary directory.
:param bool cleanup: Whether or not to clean up the temporary directory.
"""
path = tempfile.mkdtemp(dir=root_dir)
try:
yield path
finally:
if cleanup:
shutil.rmtree(path, ignore_errors=True)
@contextmanager
def temporary_file_path(root_dir=None, cleanup=True):
"""
A with-context that creates a temporary file and returns its path.
You may specify the following keyword args:
:param str root_dir: The parent directory to create the temporary file.
:param bool cleanup: Whether or not to clean up the temporary file.
"""
with temporary_file(root_dir, cleanup) as fd:
fd.close()
yield fd.name
@contextmanager
def temporary_file(root_dir=None, cleanup=True, suffix=''):
"""
A with-context that creates a temporary file and returns a writeable file descriptor to it.
You may specify the following keyword args:
:param str root_dir: The parent directory to create the temporary file.
:param bool cleanup: Whether or not to clean up the temporary file.
:param str suffix: If suffix is specified, the file name will end with that suffix.
Otherwise there will be no suffix.
mkstemp() does not put a dot between the file name and the suffix;
if you need one, put it at the beginning of suffix.
See :py:class:`tempfile.NamedTemporaryFile`.
"""
with tempfile.NamedTemporaryFile(suffix=suffix, dir=root_dir, delete=False) as fd:
try:
yield fd
finally:
if cleanup:
safe_delete(fd.name)
@contextmanager
def safe_file(path, suffix=None, cleanup=True):
"""A with-context that copies a file, and copies the copy back to the original file on success.
This is useful for doing work on a file but only changing its state on success.
:param str suffix: Use this suffix to create the copy. Otherwise use a random string.
:param bool cleanup: Whether or not to clean up the copy.
"""
safe_path = '{0}.{1}'.format(path, suffix or uuid.uuid4())
if os.path.exists(path):
shutil.copy(path, safe_path)
try:
yield safe_path
if cleanup:
shutil.move(safe_path, path)
else:
shutil.copy(safe_path, path)
finally:
if cleanup:
safe_delete(safe_path)
@contextmanager
def pushd(directory):
"""
A with-context that encapsulates pushd/popd.
"""
cwd = os.getcwd()
os.chdir(directory)
try:
yield directory
finally:
os.chdir(cwd)
@contextmanager
def open_zip(path_or_file, *args, **kwargs):
"""
A with-context for zip files. Passes through positional and kwargs to zipfile.ZipFile.
"""
try:
allowZip64 = kwargs.pop('allowZip64', True)
zf = zipfile.ZipFile(path_or_file, *args, allowZip64=allowZip64, **kwargs)
except zipfile.BadZipfile as bze:
raise zipfile.BadZipfile("Bad Zipfile {0}: {1}".format(path_or_file, bze))
try:
yield zf
finally:
zf.close()
@contextmanager
def open_tar(path_or_file, *args, **kwargs):
"""
A with-context for tar files. Passes through positional and kwargs to tarfile.open.
If path_or_file is a file, caller must close it separately.
"""
(path, fileobj) = ((path_or_file, None) if isinstance(path_or_file, string_types)
else (None, path_or_file))
with closing(tarfile.open(path, *args, fileobj=fileobj, **kwargs)) as tar:
yield tar
class Timer(object):
"""Very basic with-context to time operations
Example usage:
>>> from pants.util.contextutil import Timer
>>> with Timer() as timer:
... time.sleep(2)
...
>>> timer.elapsed
2.0020849704742432
"""
def __init__(self, clock=time):
self._clock = clock
def __enter__(self):
self.start = self._clock.time()
self.finish = None
return self
@property
def elapsed(self):
if self.finish:
return self.finish - self.start
else:
return self._clock.time() - self.start
def __exit__(self, typ, val, traceback):
self.finish = self._clock.time()
| 27.585859 | 97 | 0.684182 |
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
import shutil
import tarfile
import tempfile
import time
import uuid
import zipfile
from contextlib import closing, contextmanager
from six import string_types
from pants.util.dirutil import safe_delete
@contextmanager
def environment_as(**kwargs):
new_environment = kwargs
old_environment = {}
def setenv(key, val):
if val is not None:
os.environ[key] = val
else:
if key in os.environ:
del os.environ[key]
for key, val in new_environment.items():
old_environment[key] = os.environ.get(key)
setenv(key, val)
try:
yield
finally:
for key, val in old_environment.items():
setenv(key, val)
@contextmanager
def temporary_dir(root_dir=None, cleanup=True):
path = tempfile.mkdtemp(dir=root_dir)
try:
yield path
finally:
if cleanup:
shutil.rmtree(path, ignore_errors=True)
@contextmanager
def temporary_file_path(root_dir=None, cleanup=True):
with temporary_file(root_dir, cleanup) as fd:
fd.close()
yield fd.name
@contextmanager
def temporary_file(root_dir=None, cleanup=True, suffix=''):
with tempfile.NamedTemporaryFile(suffix=suffix, dir=root_dir, delete=False) as fd:
try:
yield fd
finally:
if cleanup:
safe_delete(fd.name)
@contextmanager
def safe_file(path, suffix=None, cleanup=True):
safe_path = '{0}.{1}'.format(path, suffix or uuid.uuid4())
if os.path.exists(path):
shutil.copy(path, safe_path)
try:
yield safe_path
if cleanup:
shutil.move(safe_path, path)
else:
shutil.copy(safe_path, path)
finally:
if cleanup:
safe_delete(safe_path)
@contextmanager
def pushd(directory):
cwd = os.getcwd()
os.chdir(directory)
try:
yield directory
finally:
os.chdir(cwd)
@contextmanager
def open_zip(path_or_file, *args, **kwargs):
try:
allowZip64 = kwargs.pop('allowZip64', True)
zf = zipfile.ZipFile(path_or_file, *args, allowZip64=allowZip64, **kwargs)
except zipfile.BadZipfile as bze:
raise zipfile.BadZipfile("Bad Zipfile {0}: {1}".format(path_or_file, bze))
try:
yield zf
finally:
zf.close()
@contextmanager
def open_tar(path_or_file, *args, **kwargs):
(path, fileobj) = ((path_or_file, None) if isinstance(path_or_file, string_types)
else (None, path_or_file))
with closing(tarfile.open(path, *args, fileobj=fileobj, **kwargs)) as tar:
yield tar
class Timer(object):
def __init__(self, clock=time):
self._clock = clock
def __enter__(self):
self.start = self._clock.time()
self.finish = None
return self
@property
def elapsed(self):
if self.finish:
return self.finish - self.start
else:
return self._clock.time() - self.start
def __exit__(self, typ, val, traceback):
self.finish = self._clock.time()
| true | true |
f7292027ef65c83ec2873a3164a3805c28919e50 | 1,781 | py | Python | scapy-space-packets/packets_mm_tlm.py | solar-wine/tools-for-hack-a-sat-2020 | 8721173953b1649dea1004871269c944ef98eede | [
"BSD-3-Clause"
] | 58 | 2020-09-05T08:24:57.000Z | 2022-03-13T16:41:11.000Z | scapy-space-packets/packets_mm_tlm.py | solar-wine/tools-for-hack-a-sat-2020 | 8721173953b1649dea1004871269c944ef98eede | [
"BSD-3-Clause"
] | null | null | null | scapy-space-packets/packets_mm_tlm.py | solar-wine/tools-for-hack-a-sat-2020 | 8721173953b1649dea1004871269c944ef98eede | [
"BSD-3-Clause"
] | 10 | 2020-09-05T21:30:49.000Z | 2021-12-25T02:11:50.000Z | from scapy.all import *
from ccsds_base import CCSDSPacket
class MM_HK_TLM_PKT_TlmPkt(Packet):
"""Housekeeping Packet
app = MM
command = HK_TLM_PKT
msg_id = MM_HK_TLM_MID = 0x0887 = 0x0800 + 0x087
"""
name = "MM_HK_TLM_PKT_TlmPkt"
fields_desc = [
# APPEND_ITEM CMD_VALID_COUNT 8 UINT "Valid Command Count"
ByteField("CMD_VALID_COUNT", 0),
# APPEND_ITEM CMD_ERROR_COUNT 8 UINT "Error Command Count"
ByteField("CMD_ERROR_COUNT", 0),
# APPEND_ITEM LAST_ACTION 8 UINT "Last command action executed"
ByteField("LAST_ACTION", 0),
# STATE NONE 0
# STATE MEM_PEEK 1
# STATE MEM_POKE 2
# STATE LD_FR_FILE 3
# STATE LD_NO_INT 4
# STATE DMP_TO_FILE 5
# STATE DMP_TO_EVENT 6
# STATE MEM_FILL 7
# STATE SYM_LOOKUP 8
# STATE SYM_TO_FILE 9
# STATE EE_WR_ENA 10
# STATE EE_WR_DIS 11
# STATE NOOP 12
# STATE RESET 13
# APPEND_ITEM MEM_TYPE 8 UINT "Memory type for last command"
ByteField("MEM_TYPE", 0),
# APPEND_ITEM ADDRESS 32 UINT "Fully resolved address used for last command"
IntField("ADDRESS", 0),
# APPEND_ITEM FILL_PATTERN 32 UINT "Fill pattern used if memory fill command was issued"
IntField("FILL_PATTERN", 0),
# APPEND_ITEM BYTES_PROCESSED 32 UINT "Bytes processed for last command"
IntField("BYTES_PROCESSED", 0),
# APPEND_ITEM FILENAME 512 STRING "Name of the data file used for last command, where applicable"
StrFixedLenField("FILENAME", b"", 64),
]
bind_layers(CCSDSPacket, MM_HK_TLM_PKT_TlmPkt, pkttype=0, apid=135)
| 37.104167 | 105 | 0.617069 | from scapy.all import *
from ccsds_base import CCSDSPacket
class MM_HK_TLM_PKT_TlmPkt(Packet):
name = "MM_HK_TLM_PKT_TlmPkt"
fields_desc = [
ByteField("CMD_VALID_COUNT", 0),
ByteField("CMD_ERROR_COUNT", 0),
ByteField("LAST_ACTION", 0),
ByteField("MEM_TYPE", 0),
IntField("ADDRESS", 0),
IntField("FILL_PATTERN", 0),
IntField("BYTES_PROCESSED", 0),
StrFixedLenField("FILENAME", b"", 64),
]
bind_layers(CCSDSPacket, MM_HK_TLM_PKT_TlmPkt, pkttype=0, apid=135)
| true | true |
f72920db43e16615f154b87b7fa2ed4e591ba5f3 | 12,838 | py | Python | vector2d.py | toxicrecker/vector2d.py | 56b60c61184f2b055a168c0f56f9962888c83638 | [
"MIT"
] | 1 | 2021-11-10T22:38:43.000Z | 2021-11-10T22:38:43.000Z | vector2d.py | toxicrecker/vector2d.py | 56b60c61184f2b055a168c0f56f9962888c83638 | [
"MIT"
] | null | null | null | vector2d.py | toxicrecker/vector2d.py | 56b60c61184f2b055a168c0f56f9962888c83638 | [
"MIT"
] | 1 | 2022-02-01T22:22:18.000Z | 2022-02-01T22:22:18.000Z | import math
import random
class Vector2D(object):
def __init__(self, _x, _y):
self.x = _x
self.y = _y
@staticmethod
def UnitRandom():
return Vector2D(random.random(), random.random())
@staticmethod
def Zero():
return Vector2D(0,0)
@staticmethod
def One():
return Vector2D(1,1)
def __add__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x + other.x
newY = self.y + other.y
elif isinstance(other, (int, float)):
newX = self.x + other
newY = self.y + other
else:
return NotImplemented
return Vector2D(newX, newY)
def __radd__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x + self.x
newY = other.y + self.y
elif isinstance(other, (int, float)):
newX = other + self.x
newY = other + self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __iadd__(self, other):
if isinstance(other, Vector2D):
self.x += other.x
self.y += other.y
elif isinstance(other, (int, float)):
self.x += other
self.y += other
else:
return NotImplemented
return self
def __sub__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x - other.x
newY = self.y - other.y
elif isinstance(other, (int, float)):
newX = self.x - other
newY = self.y - other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rsub__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x - self.x
newY = other.y - self.y
elif isinstance(other, (int, float)):
newX = other - self.x
newY = other - self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __isub__(self, other):
if isinstance(other, Vector2D):
self.x -= other.x
self.y -= other.y
elif isinstance(other, (int, float)):
self.x -= other
self.y -= other
else:
return NotImplemented
return self
def __mul__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x * other.x
newY = self.y * other.y
elif isinstance(other, (int, float)):
newX = self.x * other
newY = self.y * other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rmul__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x * self.x
newY = other.y * self.y
elif isinstance(other, (int, float)):
newX = other * self.x
newY = other * self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __imul__(self, other):
if isinstance(other, Vector2D):
self.x *= other.x
self.y *= other.y
elif isinstance(other, (int, float)):
self.x *= other
self.y *= other
else:
return NotImplemented
return self
def __truediv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x / other.x
newY = self.y / other.y
elif isinstance(other, (int, float)):
newX = self.x / other
newY = self.y / other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rtruediv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x / self.x
newY = other.y / self.y
elif isinstance(other, (int, float)):
newX = other / self.x
newY = other / self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __floordiv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x // other.x
newY = self.y // other.y
elif isinstance(other, (int, float)):
newX = self.x // other
newY = self.y // other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rfloordiv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x // self.x
newY = other.y // self.y
elif isinstance(other, (int, float)):
newX = other // self.x
newY = other // self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __itruediv__(self, other):
if isinstance(other, Vector2D):
self.x /= other.x
self.y /= other.y
elif isinstance(other, (int, float)):
self.x /= other
self.y /= other
else:
return NotImplemented
return self
def __ifloordiv__(self, other):
if isinstance(other, Vector2D):
self.x //= other.x
self.y //= other.y
elif isinstance(other, (int, float)):
self.x //= other
self.y //= other
else:
return NotImplemented
return self
def __pow__(self, other):
newX = 0
newY = 0
if isinstance(other, (int, float)):
newX = self.x ** other
newY = self.y ** other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rpow__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x ** self.x
newY = other.y ** self.y
elif isinstance(other, (int, float)):
newX = other ** self.x
newY = other ** self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __neg__(self):
return Vector2D(-self.x, -self.y)
def __mod__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x % other.x
newY = self.y % other.y
elif isinstance(other, (int, float)):
newX = self.x % other
newY = self.y % other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rmod__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x % self.x
newY = other.y % self.y
elif isinstance(other, (int, float)):
newX = other % self.x
newY = other % self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __eq__(self, other):
if isinstance(other, Vector2D):
return self.x == other.x and self.y == other.y
else:
return NotImplemented
def __ne__(self, other):
if isinstance(other, Vector2D):
return self.x != other.x or self.y != other.y
else:
return NotImplemented
def __gt__(self, other):
if isinstance(other, Vector2D):
return self.length > other.length
else:
return NotImplemented
def __ge__(self, other):
if isinstance(other, Vector2D):
return self.length >= other.length
else:
return NotImplemented
def __lt__(self, other):
if isinstance(other, Vector2D):
return self.length < other.length
else:
return NotImplemented
def __le__(self, other):
if isinstance(other, Vector2D):
return self.length <= other.length
else:
return NotImplemented
def __neg__(self):
return Vector2D(-self.x, -self.y)
def __str__(self):
return "Vector2D {X:" + str(self.x) + ", Y:" + str(self.y) + "}"
@property
def length(self):
return math.sqrt((self.x ** 2) + (self.y ** 2))
__len__ = length.fget
def getNormalised(self):
length = self.length
# Prevent DBZ error
if length == 0:
return Vector2D.Zero()
return self / length
@staticmethod
def Distance(a, b):
# (b-a).length
return math.sqrt(((b.x-a.x)**2)+((b.y-a.y)**2))
@staticmethod
def Lerp(a, b, t):
return a + ((b-a).getNormalised() * (Vector2D.Distance(a, b) * t))
@staticmethod
def InverseLerp(a, b, v):
max = (b-a).length
vDist = (v-a).length
return vDist / max
@staticmethod
def DotProduct(a, b):
if (not isinstance(a, Vector2D)) or (not isinstance(b, Vector2D)):
raise RuntimeError(f"FVector2D.DotProduct() requires Vector2Ds as parameters.")
else:
return a.x * b.x + a.y * b.y
@staticmethod
def Project(a, b):
if (not isinstance(a, Vector2D)) or (not isinstance(b, Vector2D)):
raise RuntimeError(f"Vector2D.Project() requires Vector2Ds as parameters.")
else:
normB = b.getNormalised()
return normB * Vector2D.DotProduct(a, normB)
def AsInt(self):
return Vector2D(int(self.x), int(self.y))
@staticmethod
def isPointOnSegment(p1, p2, p):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p, Vector2D)):
raise RuntimeError(f"Vector2D.isPointOnSegment() requires Vector2Ds as parameters.")
else:
return min(p1.x, p2.x) <= p.x <= max(p1.x, p2.x) and min(p1.y, p2.y) <= p.y <= max(p1.y, p2.y)
@staticmethod
def CrossProduct(a, b):
if (not isinstance(a, Vector2D)) or (not isinstance(b, Vector2D)):
raise RuntimeError(f"Vector2D.CrossProduct() requires Vector2Ds as parameters.")
else:
return a.x*b.y - a.y*b.x
@staticmethod
def Direction(p1, p2, p3):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p3, Vector2D)):
raise RuntimeError(f"Vector2D.Direction() requires Vector2Ds as parameters.")
else:
return Vector2D.CrossProduct(p3 - p1, p2 - p1)
@staticmethod
def isIntersecting(p1, p2, p3, p4):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p3, Vector2D)) or (not isinstance(p4, Vector2D)):
raise RuntimeError(f"Vector2D.isIntersecting() requires Vector2Ds as parameters.")
else:
d1 = Vector2D.Direction(p3, p4, p1)
d2 = Vector2D.Direction(p3, p4, p2)
d3 = Vector2D.Direction(p1, p2, p3)
d4 = Vector2D.Direction(p1, p2, p4)
if ((d1 > 0 and d2 < 0) or (d1 < 0 and d2 > 0)) and ((d3 > 0 and d4 < 0) or (d3 < 0 and d4 > 0)):
return True
elif d1 == 0 and Vector2D.isPointOnSegment(p3, p4, p1):
return True
elif d2 == 0 and Vector2D.isPointOnSegment(p3, p4, p2):
return True
elif d3 == 0 and Vector2D.isPointOnSegment(p1, p2, p3):
return True
elif d4 == 0 and Vector2D.isPointOnSegment(p1, p2, p4):
return True
else:
return False
@staticmethod
def Intersection(p1, p2, p3, p4):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p3, Vector2D)) or (not isinstance(p4, Vector2D)):
raise RuntimeError(f"Vector2D.Intersection() requires Vector2Ds as parameters.")
else:
if Vector2D.isIntersecting(p1, p2, p3, p4):
# https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection
# Calculate intersection point on segments.
Tn = ((p1.x - p3.x) * (p3.y - p4.y)) - ((p1.y - p3.y) * (p3.x - p4.x))
Td = ((p1.x - p2.x) * (p3.y - p4.y)) - ((p1.y - p2.y) * (p3.x - p4.x))
if not Td == 0:
t = Tn / Td
x = p1.x + (t * (p2.x - p1.x))
y = p1.y + (t * (p2.y - p1.y))
return Vector2D(int(x),int(y))
else:
return None
else:
return None
| 30.712919 | 144 | 0.513164 | import math
import random
class Vector2D(object):
def __init__(self, _x, _y):
self.x = _x
self.y = _y
@staticmethod
def UnitRandom():
return Vector2D(random.random(), random.random())
@staticmethod
def Zero():
return Vector2D(0,0)
@staticmethod
def One():
return Vector2D(1,1)
def __add__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x + other.x
newY = self.y + other.y
elif isinstance(other, (int, float)):
newX = self.x + other
newY = self.y + other
else:
return NotImplemented
return Vector2D(newX, newY)
def __radd__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x + self.x
newY = other.y + self.y
elif isinstance(other, (int, float)):
newX = other + self.x
newY = other + self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __iadd__(self, other):
if isinstance(other, Vector2D):
self.x += other.x
self.y += other.y
elif isinstance(other, (int, float)):
self.x += other
self.y += other
else:
return NotImplemented
return self
def __sub__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x - other.x
newY = self.y - other.y
elif isinstance(other, (int, float)):
newX = self.x - other
newY = self.y - other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rsub__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x - self.x
newY = other.y - self.y
elif isinstance(other, (int, float)):
newX = other - self.x
newY = other - self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __isub__(self, other):
if isinstance(other, Vector2D):
self.x -= other.x
self.y -= other.y
elif isinstance(other, (int, float)):
self.x -= other
self.y -= other
else:
return NotImplemented
return self
def __mul__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x * other.x
newY = self.y * other.y
elif isinstance(other, (int, float)):
newX = self.x * other
newY = self.y * other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rmul__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x * self.x
newY = other.y * self.y
elif isinstance(other, (int, float)):
newX = other * self.x
newY = other * self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __imul__(self, other):
if isinstance(other, Vector2D):
self.x *= other.x
self.y *= other.y
elif isinstance(other, (int, float)):
self.x *= other
self.y *= other
else:
return NotImplemented
return self
def __truediv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x / other.x
newY = self.y / other.y
elif isinstance(other, (int, float)):
newX = self.x / other
newY = self.y / other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rtruediv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x / self.x
newY = other.y / self.y
elif isinstance(other, (int, float)):
newX = other / self.x
newY = other / self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __floordiv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x // other.x
newY = self.y // other.y
elif isinstance(other, (int, float)):
newX = self.x // other
newY = self.y // other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rfloordiv__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x // self.x
newY = other.y // self.y
elif isinstance(other, (int, float)):
newX = other // self.x
newY = other // self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __itruediv__(self, other):
if isinstance(other, Vector2D):
self.x /= other.x
self.y /= other.y
elif isinstance(other, (int, float)):
self.x /= other
self.y /= other
else:
return NotImplemented
return self
def __ifloordiv__(self, other):
if isinstance(other, Vector2D):
self.x //= other.x
self.y //= other.y
elif isinstance(other, (int, float)):
self.x //= other
self.y //= other
else:
return NotImplemented
return self
def __pow__(self, other):
newX = 0
newY = 0
if isinstance(other, (int, float)):
newX = self.x ** other
newY = self.y ** other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rpow__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x ** self.x
newY = other.y ** self.y
elif isinstance(other, (int, float)):
newX = other ** self.x
newY = other ** self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __neg__(self):
return Vector2D(-self.x, -self.y)
def __mod__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = self.x % other.x
newY = self.y % other.y
elif isinstance(other, (int, float)):
newX = self.x % other
newY = self.y % other
else:
return NotImplemented
return Vector2D(newX, newY)
def __rmod__(self, other):
newX = 0
newY = 0
if isinstance(other, Vector2D):
newX = other.x % self.x
newY = other.y % self.y
elif isinstance(other, (int, float)):
newX = other % self.x
newY = other % self.y
else:
return NotImplemented
return Vector2D(newX, newY)
def __eq__(self, other):
if isinstance(other, Vector2D):
return self.x == other.x and self.y == other.y
else:
return NotImplemented
def __ne__(self, other):
if isinstance(other, Vector2D):
return self.x != other.x or self.y != other.y
else:
return NotImplemented
def __gt__(self, other):
if isinstance(other, Vector2D):
return self.length > other.length
else:
return NotImplemented
def __ge__(self, other):
if isinstance(other, Vector2D):
return self.length >= other.length
else:
return NotImplemented
def __lt__(self, other):
if isinstance(other, Vector2D):
return self.length < other.length
else:
return NotImplemented
def __le__(self, other):
if isinstance(other, Vector2D):
return self.length <= other.length
else:
return NotImplemented
def __neg__(self):
return Vector2D(-self.x, -self.y)
def __str__(self):
return "Vector2D {X:" + str(self.x) + ", Y:" + str(self.y) + "}"
@property
def length(self):
return math.sqrt((self.x ** 2) + (self.y ** 2))
__len__ = length.fget
def getNormalised(self):
length = self.length
if length == 0:
return Vector2D.Zero()
return self / length
@staticmethod
def Distance(a, b):
return math.sqrt(((b.x-a.x)**2)+((b.y-a.y)**2))
@staticmethod
def Lerp(a, b, t):
return a + ((b-a).getNormalised() * (Vector2D.Distance(a, b) * t))
@staticmethod
def InverseLerp(a, b, v):
max = (b-a).length
vDist = (v-a).length
return vDist / max
@staticmethod
def DotProduct(a, b):
if (not isinstance(a, Vector2D)) or (not isinstance(b, Vector2D)):
raise RuntimeError(f"FVector2D.DotProduct() requires Vector2Ds as parameters.")
else:
return a.x * b.x + a.y * b.y
@staticmethod
def Project(a, b):
if (not isinstance(a, Vector2D)) or (not isinstance(b, Vector2D)):
raise RuntimeError(f"Vector2D.Project() requires Vector2Ds as parameters.")
else:
normB = b.getNormalised()
return normB * Vector2D.DotProduct(a, normB)
def AsInt(self):
return Vector2D(int(self.x), int(self.y))
@staticmethod
def isPointOnSegment(p1, p2, p):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p, Vector2D)):
raise RuntimeError(f"Vector2D.isPointOnSegment() requires Vector2Ds as parameters.")
else:
return min(p1.x, p2.x) <= p.x <= max(p1.x, p2.x) and min(p1.y, p2.y) <= p.y <= max(p1.y, p2.y)
@staticmethod
def CrossProduct(a, b):
if (not isinstance(a, Vector2D)) or (not isinstance(b, Vector2D)):
raise RuntimeError(f"Vector2D.CrossProduct() requires Vector2Ds as parameters.")
else:
return a.x*b.y - a.y*b.x
@staticmethod
def Direction(p1, p2, p3):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p3, Vector2D)):
raise RuntimeError(f"Vector2D.Direction() requires Vector2Ds as parameters.")
else:
return Vector2D.CrossProduct(p3 - p1, p2 - p1)
@staticmethod
def isIntersecting(p1, p2, p3, p4):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p3, Vector2D)) or (not isinstance(p4, Vector2D)):
raise RuntimeError(f"Vector2D.isIntersecting() requires Vector2Ds as parameters.")
else:
d1 = Vector2D.Direction(p3, p4, p1)
d2 = Vector2D.Direction(p3, p4, p2)
d3 = Vector2D.Direction(p1, p2, p3)
d4 = Vector2D.Direction(p1, p2, p4)
if ((d1 > 0 and d2 < 0) or (d1 < 0 and d2 > 0)) and ((d3 > 0 and d4 < 0) or (d3 < 0 and d4 > 0)):
return True
elif d1 == 0 and Vector2D.isPointOnSegment(p3, p4, p1):
return True
elif d2 == 0 and Vector2D.isPointOnSegment(p3, p4, p2):
return True
elif d3 == 0 and Vector2D.isPointOnSegment(p1, p2, p3):
return True
elif d4 == 0 and Vector2D.isPointOnSegment(p1, p2, p4):
return True
else:
return False
@staticmethod
def Intersection(p1, p2, p3, p4):
if (not isinstance(p1, Vector2D)) or (not isinstance(p2, Vector2D)) or (not isinstance(p3, Vector2D)) or (not isinstance(p4, Vector2D)):
raise RuntimeError(f"Vector2D.Intersection() requires Vector2Ds as parameters.")
else:
if Vector2D.isIntersecting(p1, p2, p3, p4):
Tn = ((p1.x - p3.x) * (p3.y - p4.y)) - ((p1.y - p3.y) * (p3.x - p4.x))
Td = ((p1.x - p2.x) * (p3.y - p4.y)) - ((p1.y - p2.y) * (p3.x - p4.x))
if not Td == 0:
t = Tn / Td
x = p1.x + (t * (p2.x - p1.x))
y = p1.y + (t * (p2.y - p1.y))
return Vector2D(int(x),int(y))
else:
return None
else:
return None
| true | true |
f7292608748e855f2440b3c114423a4eb879c8db | 3,466 | py | Python | src/bos_consensus/network/base.py | LuffyEMonkey/isaac-consensus-protocol | 806d967d56ef8862a477b2515c7854af289c10a0 | [
"Apache-2.0"
] | 1 | 2018-04-10T11:00:59.000Z | 2018-04-10T11:00:59.000Z | src/bos_consensus/network/base.py | LuffyEMonkey/isaac-consensus-protocol | 806d967d56ef8862a477b2515c7854af289c10a0 | [
"Apache-2.0"
] | null | null | null | src/bos_consensus/network/base.py | LuffyEMonkey/isaac-consensus-protocol | 806d967d56ef8862a477b2515c7854af289c10a0 | [
"Apache-2.0"
] | null | null | null | import urllib.parse
from ..util import (
get_module,
LoggingMixin,
)
def get_network_module(name):
return get_module('.' + name, package='bos_consensus.network')
class BaseTransport(LoggingMixin):
blockchain = None
config = None
message_received_callback = None
def __init__(self, **config):
super(BaseTransport, self).__init__()
self.config = config
self.set_requests()
def receive(self, data):
raise NotImplementedError()
def write(self, data):
raise NotImplementedError()
def send(self, addr, message):
raise NotImplementedError()
def set_requests(self):
raise NotImplementedError()
def start(self, blockchain, message_received_callback):
from ..blockchain.base import BaseBlockchain
assert isinstance(blockchain, BaseBlockchain)
self.set_logging('transport', node=blockchain.node_name)
self.blockchain = blockchain
self.message_received_callback = message_received_callback
self._start()
return
def _start(self):
raise NotImplementedError()
def stop(self):
raise NotImplementedError()
class BaseServer(LoggingMixin):
blockchain = None
config = None
def __init__(self, blockchain, **config):
super(BaseServer, self).__init__()
self.set_logging('network', node=blockchain.node_name)
self.blockchain = blockchain
self.config = config
def start(self):
self.blockchain.transport.start(self.blockchain, self.message_received_callback)
return
def message_received_callback(self):
raise NotImplementedError()
def stop(self):
self.blockchain.transport.stop()
return
class Endpoint:
scheme = None
host = None
port = None
extras = None
@classmethod
def from_uri(cls, uri):
parsed = urllib.parse.urlparse(uri)
assert len(parsed.scheme) > 0
assert len(parsed.hostname) > 0
assert type(parsed.port) in (int,)
return cls(
parsed.scheme,
parsed.hostname,
parsed.port,
**dict(
map(
lambda x: (x[0], x[1][0]),
urllib.parse.parse_qs(parsed.query).items()
)
)
)
def __init__(self, scheme, host, port, **extras):
assert type(host) in (str,)
assert type(port) in (int,)
self.scheme = scheme
self.host = host
self.port = port
self.extras = extras
@property
def uri_full(self):
return '%s%s' % (self.uri, self.get_extras())
def get_extras(self):
extras = ''
if self.extras:
extras = '?%s' % urllib.parse.urlencode(self.extras)
return extras
@property
def uri(self):
return '%(scheme)s://%(host)s:%(port)d' % self.__dict__
def __repr__(self):
return '<Endpoint: %s>' % self.uri_full
def __eq__(self, b):
assert isinstance(b, self.__class__)
return self.host == b.host and self.port == b.port
def update(self, **extras):
self.extras.update(extras)
return
def serialize(self):
return self.uri_full
def get(self, k, default=None):
return self.extras.get(k, default)
def join(self, u):
return urllib.parse.urljoin(self.uri, u) + self.get_extras()
| 22.506494 | 88 | 0.601558 | import urllib.parse
from ..util import (
get_module,
LoggingMixin,
)
def get_network_module(name):
return get_module('.' + name, package='bos_consensus.network')
class BaseTransport(LoggingMixin):
blockchain = None
config = None
message_received_callback = None
def __init__(self, **config):
super(BaseTransport, self).__init__()
self.config = config
self.set_requests()
def receive(self, data):
raise NotImplementedError()
def write(self, data):
raise NotImplementedError()
def send(self, addr, message):
raise NotImplementedError()
def set_requests(self):
raise NotImplementedError()
def start(self, blockchain, message_received_callback):
from ..blockchain.base import BaseBlockchain
assert isinstance(blockchain, BaseBlockchain)
self.set_logging('transport', node=blockchain.node_name)
self.blockchain = blockchain
self.message_received_callback = message_received_callback
self._start()
return
def _start(self):
raise NotImplementedError()
def stop(self):
raise NotImplementedError()
class BaseServer(LoggingMixin):
blockchain = None
config = None
def __init__(self, blockchain, **config):
super(BaseServer, self).__init__()
self.set_logging('network', node=blockchain.node_name)
self.blockchain = blockchain
self.config = config
def start(self):
self.blockchain.transport.start(self.blockchain, self.message_received_callback)
return
def message_received_callback(self):
raise NotImplementedError()
def stop(self):
self.blockchain.transport.stop()
return
class Endpoint:
scheme = None
host = None
port = None
extras = None
@classmethod
def from_uri(cls, uri):
parsed = urllib.parse.urlparse(uri)
assert len(parsed.scheme) > 0
assert len(parsed.hostname) > 0
assert type(parsed.port) in (int,)
return cls(
parsed.scheme,
parsed.hostname,
parsed.port,
**dict(
map(
lambda x: (x[0], x[1][0]),
urllib.parse.parse_qs(parsed.query).items()
)
)
)
def __init__(self, scheme, host, port, **extras):
assert type(host) in (str,)
assert type(port) in (int,)
self.scheme = scheme
self.host = host
self.port = port
self.extras = extras
@property
def uri_full(self):
return '%s%s' % (self.uri, self.get_extras())
def get_extras(self):
extras = ''
if self.extras:
extras = '?%s' % urllib.parse.urlencode(self.extras)
return extras
@property
def uri(self):
return '%(scheme)s://%(host)s:%(port)d' % self.__dict__
def __repr__(self):
return '<Endpoint: %s>' % self.uri_full
def __eq__(self, b):
assert isinstance(b, self.__class__)
return self.host == b.host and self.port == b.port
def update(self, **extras):
self.extras.update(extras)
return
def serialize(self):
return self.uri_full
def get(self, k, default=None):
return self.extras.get(k, default)
def join(self, u):
return urllib.parse.urljoin(self.uri, u) + self.get_extras()
| true | true |
f7292663511c82d1520e313dbfc23c098d684d1d | 125 | py | Python | tests/float/bytearray_construct.py | peterson79/pycom-micropython-sigfox | 3f93fc2c02567c96f18cff4af9125db8fd7a6fb4 | [
"MIT"
] | 303 | 2015-07-11T17:12:55.000Z | 2018-01-08T03:02:37.000Z | tests/float/bytearray_construct.py | peterson79/pycom-micropython-sigfox | 3f93fc2c02567c96f18cff4af9125db8fd7a6fb4 | [
"MIT"
] | 27 | 2015-01-02T16:17:37.000Z | 2015-09-07T19:21:26.000Z | tests/float/bytearray_construct.py | peterson79/pycom-micropython-sigfox | 3f93fc2c02567c96f18cff4af9125db8fd7a6fb4 | [
"MIT"
] | 26 | 2018-01-18T09:15:33.000Z | 2022-02-07T13:09:14.000Z | # test construction of bytearray from array with float type
from array import array
print(bytearray(array('f', [1, 2.3])))
| 20.833333 | 59 | 0.736 |
from array import array
print(bytearray(array('f', [1, 2.3])))
| true | true |
f72927a0a238ca762530566422979b22c243fee0 | 5,546 | bzl | Python | test_defs.bzl | Omrisnyk/dagger | 39456a4f54043e947f0b9268f9c7ab4ec9b6d8a1 | [
"Apache-2.0"
] | 1 | 2019-03-06T08:50:35.000Z | 2019-03-06T08:50:35.000Z | test_defs.bzl | Omrisnyk/dagger | 39456a4f54043e947f0b9268f9c7ab4ec9b6d8a1 | [
"Apache-2.0"
] | null | null | null | test_defs.bzl | Omrisnyk/dagger | 39456a4f54043e947f0b9268f9c7ab4ec9b6d8a1 | [
"Apache-2.0"
] | null | null | null | # Copyright (C) 2017 The Dagger 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.
# Defines a set of build variants and the list of extra javacopts to build with.
# The key will be appended to the generated test names to ensure uniqueness.
BUILD_VARIANTS = {
"FastInit": ["-Adagger.fastInit=enabled"],
"AheadOfTimeSubcomponents": ["-Adagger.experimentalAheadOfTimeSubcomponents=enabled"],
"FastInitAndAheadOfTimeSubcomponents": [
"-Adagger.fastInit=enabled",
"-Adagger.experimentalAheadOfTimeSubcomponents=enabled",
],
}
# TODO(ronshapiro): convert this to use bazel_common
# TODO(user): split into two functions for functional vs non-functional tests?
def GenJavaTests(
name,
srcs,
deps,
test_only_deps = None,
plugins = None,
javacopts = None,
lib_javacopts = None,
test_javacopts = None,
functional = True):
_GenTests(
native.java_library,
native.java_test,
name,
srcs,
deps,
test_only_deps,
plugins,
javacopts,
lib_javacopts,
test_javacopts,
functional,
)
def GenRobolectricTests(
name,
srcs,
deps,
test_only_deps = None,
plugins = None,
javacopts = None,
lib_javacopts = None,
test_javacopts = None,
functional = True,
manifest_values = None):
# TODO(ronshapiro): enable these with these instructions:
# https://docs.bazel.build/versions/master/be/android.html#android_local_test_examples
# We probably want to import all of Robolectric's dependencies into bazel-common because there
# are some differences (i.e. we both provide Guava).
pass
def _GenTests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps = None,
plugins = None,
javacopts = None,
lib_javacopts = None,
test_javacopts = None,
functional = True,
test_kwargs = {}):
_gen_tests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps,
plugins,
javacopts,
lib_javacopts,
test_javacopts,
functional,
test_kwargs = test_kwargs,
)
if functional:
for (variant_name, extra_javacopts) in BUILD_VARIANTS.items():
variant_javacopts = (javacopts or []) + extra_javacopts
_gen_tests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps,
plugins,
variant_javacopts,
lib_javacopts,
test_javacopts,
functional,
variant_name,
test_kwargs = test_kwargs,
)
def _gen_tests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps,
plugins,
javacopts,
lib_javacopts,
test_javacopts,
functional,
variant_name = None,
test_kwargs = {}):
if variant_name:
suffix = "_" + variant_name
tags = [variant_name]
# Add jvm_flags so that the mode can be accessed from within tests.
jvm_flags = ["-Ddagger.mode=" + variant_name]
else:
suffix = ""
tags = []
jvm_flags = []
test_files = []
supporting_files = []
for src in srcs:
if src.endswith("Test.java"):
test_files.append(src)
else:
supporting_files.append(src)
if not test_only_deps:
test_only_deps = []
test_deps = test_only_deps + deps
if supporting_files:
supporting_files_name = name + suffix + "_lib"
test_deps.append(":" + supporting_files_name)
library_rule_type(
name = supporting_files_name,
testonly = 1,
srcs = supporting_files,
javacopts = (javacopts or []) + (lib_javacopts or []),
plugins = plugins,
tags = tags,
deps = deps,
)
if functional:
_hjar_test(supporting_files_name, tags)
for test_file in test_files:
test_name = test_file.replace(".java", "")
prefix_path = "src/test/java/"
package_name = native.package_name()
if package_name.find("javatests/") != -1:
prefix_path = "javatests/"
test_class = (package_name + "/" + test_name).rpartition(prefix_path)[2].replace("/", ".")
test_rule_type(
name = test_name + suffix,
srcs = [test_file],
javacopts = (javacopts or []) + (test_javacopts or []),
jvm_flags = jvm_flags,
plugins = plugins,
tags = tags,
test_class = test_class,
deps = test_deps,
**test_kwargs
)
def _hjar_test(name, tags):
pass
| 28.885417 | 98 | 0.583844 |
BUILD_VARIANTS = {
"FastInit": ["-Adagger.fastInit=enabled"],
"AheadOfTimeSubcomponents": ["-Adagger.experimentalAheadOfTimeSubcomponents=enabled"],
"FastInitAndAheadOfTimeSubcomponents": [
"-Adagger.fastInit=enabled",
"-Adagger.experimentalAheadOfTimeSubcomponents=enabled",
],
}
def GenJavaTests(
name,
srcs,
deps,
test_only_deps = None,
plugins = None,
javacopts = None,
lib_javacopts = None,
test_javacopts = None,
functional = True):
_GenTests(
native.java_library,
native.java_test,
name,
srcs,
deps,
test_only_deps,
plugins,
javacopts,
lib_javacopts,
test_javacopts,
functional,
)
def GenRobolectricTests(
name,
srcs,
deps,
test_only_deps = None,
plugins = None,
javacopts = None,
lib_javacopts = None,
test_javacopts = None,
functional = True,
manifest_values = None):
nces (i.e. we both provide Guava).
pass
def _GenTests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps = None,
plugins = None,
javacopts = None,
lib_javacopts = None,
test_javacopts = None,
functional = True,
test_kwargs = {}):
_gen_tests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps,
plugins,
javacopts,
lib_javacopts,
test_javacopts,
functional,
test_kwargs = test_kwargs,
)
if functional:
for (variant_name, extra_javacopts) in BUILD_VARIANTS.items():
variant_javacopts = (javacopts or []) + extra_javacopts
_gen_tests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps,
plugins,
variant_javacopts,
lib_javacopts,
test_javacopts,
functional,
variant_name,
test_kwargs = test_kwargs,
)
def _gen_tests(
library_rule_type,
test_rule_type,
name,
srcs,
deps,
test_only_deps,
plugins,
javacopts,
lib_javacopts,
test_javacopts,
functional,
variant_name = None,
test_kwargs = {}):
if variant_name:
suffix = "_" + variant_name
tags = [variant_name]
# Add jvm_flags so that the mode can be accessed from within tests.
jvm_flags = ["-Ddagger.mode=" + variant_name]
else:
suffix = ""
tags = []
jvm_flags = []
test_files = []
supporting_files = []
for src in srcs:
if src.endswith("Test.java"):
test_files.append(src)
else:
supporting_files.append(src)
if not test_only_deps:
test_only_deps = []
test_deps = test_only_deps + deps
if supporting_files:
supporting_files_name = name + suffix + "_lib"
test_deps.append(":" + supporting_files_name)
library_rule_type(
name = supporting_files_name,
testonly = 1,
srcs = supporting_files,
javacopts = (javacopts or []) + (lib_javacopts or []),
plugins = plugins,
tags = tags,
deps = deps,
)
if functional:
_hjar_test(supporting_files_name, tags)
for test_file in test_files:
test_name = test_file.replace(".java", "")
prefix_path = "src/test/java/"
package_name = native.package_name()
if package_name.find("javatests/") != -1:
prefix_path = "javatests/"
test_class = (package_name + "/" + test_name).rpartition(prefix_path)[2].replace("/", ".")
test_rule_type(
name = test_name + suffix,
srcs = [test_file],
javacopts = (javacopts or []) + (test_javacopts or []),
jvm_flags = jvm_flags,
plugins = plugins,
tags = tags,
test_class = test_class,
deps = test_deps,
**test_kwargs
)
def _hjar_test(name, tags):
pass
| true | true |
f72927f72877de84433002b4955d3d5699d37e50 | 664 | py | Python | school/migrations/0005_auto_20200505_1038.py | Zulkarnain61630/School-management | 1fe801c61b803268c852eb2e8096c255ec158e4b | [
"MIT"
] | 331 | 2020-05-08T10:14:09.000Z | 2022-03-31T19:56:03.000Z | school/migrations/0005_auto_20200505_1038.py | aliffauzi/schoolmanagement | 6a4477af01df148404d1ff2941f74accb5717b09 | [
"MIT"
] | 16 | 2020-05-08T13:57:13.000Z | 2022-02-09T15:08:06.000Z | school/migrations/0005_auto_20200505_1038.py | aliffauzi/schoolmanagement | 6a4477af01df148404d1ff2941f74accb5717b09 | [
"MIT"
] | 231 | 2020-05-08T10:16:07.000Z | 2022-03-19T02:30:31.000Z | # Generated by Django 3.0.5 on 2020-05-05 05:08
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('school', '0004_auto_20200504_1753'),
]
operations = [
migrations.RemoveField(
model_name='attendance',
name='student_id',
),
migrations.AddField(
model_name='attendance',
name='roll',
field=models.CharField(max_length=10, null=True),
),
migrations.AlterField(
model_name='studentextra',
name='roll',
field=models.CharField(max_length=10),
),
]
| 23.714286 | 61 | 0.564759 |
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('school', '0004_auto_20200504_1753'),
]
operations = [
migrations.RemoveField(
model_name='attendance',
name='student_id',
),
migrations.AddField(
model_name='attendance',
name='roll',
field=models.CharField(max_length=10, null=True),
),
migrations.AlterField(
model_name='studentextra',
name='roll',
field=models.CharField(max_length=10),
),
]
| true | true |
f729290eab849378942b925f74eaba393164fa89 | 3,584 | py | Python | mimecast/komand_mimecast/actions/create_blocked_sender_policy/action.py | xhennessy-r7/insightconnect-plugins | 59268051313d67735b5dd3a30222eccb92aca8e9 | [
"MIT"
] | null | null | null | mimecast/komand_mimecast/actions/create_blocked_sender_policy/action.py | xhennessy-r7/insightconnect-plugins | 59268051313d67735b5dd3a30222eccb92aca8e9 | [
"MIT"
] | null | null | null | mimecast/komand_mimecast/actions/create_blocked_sender_policy/action.py | xhennessy-r7/insightconnect-plugins | 59268051313d67735b5dd3a30222eccb92aca8e9 | [
"MIT"
] | null | null | null | import komand
from .schema import CreateBlockedSenderPolicyInput, CreateBlockedSenderPolicyOutput, Input, Output
# Custom imports below
from komand_mimecast.util import util
class CreateBlockedSenderPolicy(komand.Action):
# URI for create blocked sender policy
_URI = '/api/policy/blockedsenders/create-policy'
def __init__(self):
super(self.__class__, self).__init__(
name='create_blocked_sender_policy',
description='Creates a blocked sender policy',
input=CreateBlockedSenderPolicyInput(),
output=CreateBlockedSenderPolicyOutput())
def run(self, params={}):
# Import variables from connection
url = self.connection.url
username = self.connection.username
password = self.connection.password
access_key = self.connection.access_key
secret_key = self.connection.secret_key
app_id = self.connection.app_id
app_key = self.connection.app_key
auth_type = self.connection.auth_type
source_ips = params.get(Input.SOURCE_IPS)
option = params.get(Input.OPTION)
data = {'option': option}
# Generate policy dictionary
policy = {}
for key, value in params.items():
temp = util.normalize(key, value)
policy.update(temp)
# Remove source_ips and option from policy as they should not be directly in that dictionary
if params.get(Input.SOURCE_IPS):
del policy['sourceIps']
del policy['option']
# Transform source_ips from comma delimited string to list
if params.get(Input.SOURCE_IPS):
source_ips = source_ips.split(',')
# Add conditions dic to policy
if source_ips:
policy['conditions'] = {'sourceIPs': source_ips}
# Add policy to data
data['policy'] = policy
# Mimecast request
mimecast_request = util.MimecastRequests()
response = mimecast_request.mimecast_post(url=url, uri=CreateBlockedSenderPolicy._URI, username=username,
password=password, auth_type=auth_type,
access_key=access_key, secret_key=secret_key,
app_id=app_id, app_key=app_key, data=data)
# Logout
logout = util.Authentication()
logout_result = logout.logout(url=url, username=username, password=password,
auth_type=auth_type, access_key=access_key,
secret_key=secret_key, app_id=app_id, app_key=app_key)
try:
# Test for logout fail
if logout_result['fail']:
self.logger.error(logout_result['fail'])
try:
raise Exception(
'Could not log out. Contact support for help. Status code is {}, see log for details'.format(
response['meta']['status']))
except KeyError:
self.logger.error(response)
raise Exception(
'Unknown error. The Mimecast server did not respond correctly, see log for details.')
except KeyError:
# Unknown key error
self.logger.error(logout_result)
raise Exception(
'Unknown error. The Mimecast server did not respond correctly, see log for details.')
return {Output.SENDER_POLICY: response['data']}
| 39.822222 | 117 | 0.594866 | import komand
from .schema import CreateBlockedSenderPolicyInput, CreateBlockedSenderPolicyOutput, Input, Output
from komand_mimecast.util import util
class CreateBlockedSenderPolicy(komand.Action):
_URI = '/api/policy/blockedsenders/create-policy'
def __init__(self):
super(self.__class__, self).__init__(
name='create_blocked_sender_policy',
description='Creates a blocked sender policy',
input=CreateBlockedSenderPolicyInput(),
output=CreateBlockedSenderPolicyOutput())
def run(self, params={}):
url = self.connection.url
username = self.connection.username
password = self.connection.password
access_key = self.connection.access_key
secret_key = self.connection.secret_key
app_id = self.connection.app_id
app_key = self.connection.app_key
auth_type = self.connection.auth_type
source_ips = params.get(Input.SOURCE_IPS)
option = params.get(Input.OPTION)
data = {'option': option}
policy = {}
for key, value in params.items():
temp = util.normalize(key, value)
policy.update(temp)
if params.get(Input.SOURCE_IPS):
del policy['sourceIps']
del policy['option']
if params.get(Input.SOURCE_IPS):
source_ips = source_ips.split(',')
if source_ips:
policy['conditions'] = {'sourceIPs': source_ips}
data['policy'] = policy
mimecast_request = util.MimecastRequests()
response = mimecast_request.mimecast_post(url=url, uri=CreateBlockedSenderPolicy._URI, username=username,
password=password, auth_type=auth_type,
access_key=access_key, secret_key=secret_key,
app_id=app_id, app_key=app_key, data=data)
logout = util.Authentication()
logout_result = logout.logout(url=url, username=username, password=password,
auth_type=auth_type, access_key=access_key,
secret_key=secret_key, app_id=app_id, app_key=app_key)
try:
if logout_result['fail']:
self.logger.error(logout_result['fail'])
try:
raise Exception(
'Could not log out. Contact support for help. Status code is {}, see log for details'.format(
response['meta']['status']))
except KeyError:
self.logger.error(response)
raise Exception(
'Unknown error. The Mimecast server did not respond correctly, see log for details.')
except KeyError:
self.logger.error(logout_result)
raise Exception(
'Unknown error. The Mimecast server did not respond correctly, see log for details.')
return {Output.SENDER_POLICY: response['data']}
| true | true |
f729295f47fd6fc7e0dbb6ec52f65e7a871ce49f | 2,604 | py | Python | myagents_tests.py | sguazt/connect4 | 631a2136096b38c8e0b8d60f9bb58518c8a7f8cc | [
"Apache-2.0"
] | null | null | null | myagents_tests.py | sguazt/connect4 | 631a2136096b38c8e0b8d60f9bb58518c8a7f8cc | [
"Apache-2.0"
] | null | null | null | myagents_tests.py | sguazt/connect4 | 631a2136096b38c8e0b8d60f9bb58518c8a7f8cc | [
"Apache-2.0"
] | null | null | null | # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# Copyright 2015 Marco Guazzone (marco.guazzone@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import upo.connect4.agents
import upo.connect4.game
import myagents
def do_test_evalfunc():
"""
board(7x6) = [ ] (row 0)
[ ] (row 1)
[ ] (row 2)
[ ] (row 3)
[ 1 ] (row 4)
[ 0 0 ] (row 5)
---------------
column: 0 1 2 3 4 5 6
board[column,row] -> token (a integer number >= 0, denoting the agent
who pushed to token)
"""
layout = (7, 6)
num_agents = 2
depth = 2*num_agents
verbose = 2
game_state = upo.connect4.game.GameState(layout, num_agents)
game_state.set_verbosity_level(verbose)
#[TODO]: Change the content of the board to setup a specific game state
# Fill 1st column
# Fill 2nd column
game_state.make_move(1, 1)
# Fill 3rd column
#game_state.make_move(0, 2)
#game_state.make_move(1, 2)
# Fill 4th column
game_state.make_move(0, 3)
game_state.make_move(1, 3)
# Fill 5th column
#game_state.make_move(0, 4)
# Fill 6th column
game_state.make_move(0, 5)
# Fill 7th column
#[/TODO]
# Setup first player (RED agent)
red_agent = upo.connect4.agents.AlphaBetaMinimaxComputerAgent(0, depth)
#red_agent.set_verbosity_level(verbose) # Uncomment if you need to know what RED agent is doing
# Setup second player (YELLOW agent)
yellow_agent = upo.connect4.agents.AlphaBetaMinimaxComputerAgent(1, depth, myagents.better_evaluation_function)
yellow_agent.set_verbosity_level(verbose)
print('Current State: ', game_state)
yellow_action = yellow_agent.get_action(game_state)
print('YELLOW moves: ', yellow_action)
game_state.make_move(yellow_agent.get_index(), yellow_action)
print('New State: ', game_state)
if __name__ == '__main__':
do_test_evalfunc()
| 32.962025 | 115 | 0.637865 |
import upo.connect4.agents
import upo.connect4.game
import myagents
def do_test_evalfunc():
layout = (7, 6)
num_agents = 2
depth = 2*num_agents
verbose = 2
game_state = upo.connect4.game.GameState(layout, num_agents)
game_state.set_verbosity_level(verbose)
game_state.make_move(1, 1)
game_state.make_move(0, 3)
game_state.make_move(1, 3)
game_state.make_move(0, 5)
red_agent = upo.connect4.agents.AlphaBetaMinimaxComputerAgent(0, depth)
inimaxComputerAgent(1, depth, myagents.better_evaluation_function)
yellow_agent.set_verbosity_level(verbose)
print('Current State: ', game_state)
yellow_action = yellow_agent.get_action(game_state)
print('YELLOW moves: ', yellow_action)
game_state.make_move(yellow_agent.get_index(), yellow_action)
print('New State: ', game_state)
if __name__ == '__main__':
do_test_evalfunc()
| true | true |
f7292c444a8914e9b83607ab794b4814e76b1c37 | 576 | py | Python | Trading arbitrage strategy/setup.py | iCoder333/Trading-arbitrage-strategy | af970c48027ab75a31a9d434373e620ceb38666b | [
"Apache-2.0"
] | 4 | 2018-08-20T12:30:13.000Z | 2018-08-31T08:43:55.000Z | Trading arbitrage strategy/setup.py | cristianver333/Trading-arbitrage-strategy | af970c48027ab75a31a9d434373e620ceb38666b | [
"Apache-2.0"
] | null | null | null | Trading arbitrage strategy/setup.py | cristianver333/Trading-arbitrage-strategy | af970c48027ab75a31a9d434373e620ceb38666b | [
"Apache-2.0"
] | 1 | 2018-08-22T03:10:54.000Z | 2018-08-22T03:10:54.000Z |
from setuptools import setup, find_packages
VERSION = "0.0.1"
setup(name="TraderBetty", version=VERSION, author="iuvbio",
author_email="",
url="https://github.com/iuvbio/traderbetty.git",
test_suite="", tests_require=[],
packages=find_packages(exclude=["data", "docs", "tests*"]),
install_requires=["ccxt", "pandas"],
description="Cryptocurrency portfolio manager and arbitrage trader",
license="MIT", classifiers=["Development Status :: 4 - Beta",
"Intended Audience :: Developers"],
)
| 32 | 74 | 0.626736 |
from setuptools import setup, find_packages
VERSION = "0.0.1"
setup(name="TraderBetty", version=VERSION, author="iuvbio",
author_email="",
url="https://github.com/iuvbio/traderbetty.git",
test_suite="", tests_require=[],
packages=find_packages(exclude=["data", "docs", "tests*"]),
install_requires=["ccxt", "pandas"],
description="Cryptocurrency portfolio manager and arbitrage trader",
license="MIT", classifiers=["Development Status :: 4 - Beta",
"Intended Audience :: Developers"],
)
| true | true |
f7292c569cb72015176166d04e41a8e845b781fd | 8,888 | py | Python | datashader/macros.py | goncaloperes/datashader | 1ae52b65ec8a79920e5db9c6c04487f254428553 | [
"BSD-3-Clause"
] | 706 | 2019-11-15T07:50:54.000Z | 2022-03-31T10:55:04.000Z | datashader/macros.py | goncaloperes/datashader | 1ae52b65ec8a79920e5db9c6c04487f254428553 | [
"BSD-3-Clause"
] | 223 | 2019-11-15T19:32:54.000Z | 2022-03-31T20:46:21.000Z | datashader/macros.py | goncaloperes/datashader | 1ae52b65ec8a79920e5db9c6c04487f254428553 | [
"BSD-3-Clause"
] | 106 | 2019-12-05T12:32:54.000Z | 2022-03-31T15:50:00.000Z | import re
import copy
import inspect
import ast
import textwrap
"""
Utilities for manipulating the Abstract Syntax Tree of Python constructs
"""
class NameVisitor(ast.NodeVisitor):
"""
NodeVisitor that builds a set of all of the named identifiers in an AST
"""
def __init__(self, *args, **kwargs):
super(NameVisitor, self).__init__(*args, **kwargs)
self.names = set()
def visit_Name(self, node):
self.names.add(node.id)
def visit_arg(self, node):
if hasattr(node, 'arg'):
self.names.add(node.arg)
elif hasattr(node, 'id'):
self.names.add(node.id)
def get_new_names(self, num_names):
"""
Returns a list of new names that are not already present in the AST.
New names will have the form _N, for N a non-negative integer. If the
AST has no existing identifiers of this form, then the returned names
will start at 0 ('_0', '_1', '_2'). If the AST already has identifiers
of this form, then the names returned will not include the existing
identifiers.
Parameters
----------
num_names: int
The number of new names to return
Returns
-------
list of str
"""
prop_re = re.compile(r"^_(\d+)$")
matching_names = [n for n in self.names if prop_re.match(n)]
if matching_names:
start_number = max([int(n[1:]) for n in matching_names]) + 1
else:
start_number = 0
return ["_" + str(n) for n in
range(start_number, start_number + num_names)]
class ExpandVarargTransformer(ast.NodeTransformer):
"""
Node transformer that replaces the starred use of a variable in an AST
with a collection of unstarred named variables.
"""
def __init__(self, starred_name, expand_names, *args, **kwargs):
"""
Parameters
----------
starred_name: str
The name of the starred variable to replace
expand_names: list of stf
List of the new names that should be used to replace the starred
variable
"""
super(ExpandVarargTransformer, self).__init__(*args, **kwargs)
self.starred_name = starred_name
self.expand_names = expand_names
class ExpandVarargTransformerStarred(ExpandVarargTransformer):
# Python 3
def visit_Starred(self, node):
if node.value.id == self.starred_name:
return [ast.Name(id=name, ctx=node.ctx) for name in
self.expand_names]
else:
return node
class ExpandVarargTransformerCallArg(ExpandVarargTransformer):
# Python 2
def visit_Call(self, node):
if getattr(node, 'starargs', None) and node.starargs.id == self.starred_name:
node.starargs = None
node.args.extend([ast.Name(id=name, ctx=ast.Load())
for name in self.expand_names])
return node
else:
return node
def function_to_ast(fn):
"""
Get the AST representation of a function
"""
# Get source code for function
# Dedent is needed if this is a nested function
fn_source = textwrap.dedent(inspect.getsource(fn))
# Parse function source code into an AST
fn_ast = ast.parse(fn_source)
# # The function will be the fist element of the module body
# fn_ast = module_ast.body[0]
return fn_ast
def ast_to_source(ast):
"""Convert AST to source code string using the astor package"""
import astor
return astor.to_source(ast)
def compile_function_ast(fn_ast):
"""
Compile function AST into a code object suitable for use in eval/exec
"""
assert isinstance(fn_ast, ast.Module)
fndef_ast = fn_ast.body[0]
assert isinstance(fndef_ast, ast.FunctionDef)
return compile(fn_ast, "<%s>" % fndef_ast.name, mode='exec')
def function_ast_to_function(fn_ast, stacklevel=1):
# Validate
assert isinstance(fn_ast, ast.Module)
fndef_ast = fn_ast.body[0]
assert isinstance(fndef_ast, ast.FunctionDef)
# Compile AST to code object
code = compile_function_ast(fn_ast)
# Evaluate the function in a scope that includes the globals and
# locals of desired frame.
current_frame = inspect.currentframe()
eval_frame = current_frame
for _ in range(stacklevel):
eval_frame = eval_frame.f_back
eval_locals = eval_frame.f_locals
eval_globals = eval_frame.f_globals
del current_frame
scope = copy.copy(eval_globals)
scope.update(eval_locals)
# Evaluate function in scope
eval(code, scope)
# Return the newly evaluated function from the scope
return scope[fndef_ast.name]
def _build_arg(name):
try:
# Python 3
return ast.arg(arg=name)
except AttributeError:
# Python 2
return ast.Name(id=name, ctx=ast.Param())
def expand_function_ast_varargs(fn_ast, expand_number):
"""
Given a function AST that use a variable length positional argument
(e.g. *args), return a function that replaces the use of this argument
with one or more fixed arguments.
To be supported, a function must have a starred argument in the function
signature, and it may only use this argument in starred form as the
input to other functions.
For example, suppose expand_number is 3 and fn_ast is an AST
representing this function...
def my_fn1(a, b, *args):
print(a, b)
other_fn(a, b, *args)
Then this function will return the AST of a function equivalent to...
def my_fn1(a, b, _0, _1, _2):
print(a, b)
other_fn(a, b, _0, _1, _2)
If the input function uses `args` for anything other than passing it to
other functions in starred form, an error will be raised.
Parameters
----------
fn_ast: ast.FunctionDef
expand_number: int
Returns
-------
ast.FunctionDef
"""
assert isinstance(fn_ast, ast.Module)
# Copy ast so we don't modify the input
fn_ast = copy.deepcopy(fn_ast)
# Extract function definition
fndef_ast = fn_ast.body[0]
assert isinstance(fndef_ast, ast.FunctionDef)
# Get function args
fn_args = fndef_ast.args
# Function variable arity argument
fn_vararg = fn_args.vararg
# Require vararg
if not fn_vararg:
raise ValueError("""\
Input function AST does not have a variable length positional argument
(e.g. *args) in the function signature""")
assert fn_vararg
# Get vararg name
if isinstance(fn_vararg, str):
vararg_name = fn_vararg
else:
vararg_name = fn_vararg.arg
# Compute new unique names to use in place of the variable argument
before_name_visitor = NameVisitor()
before_name_visitor.visit(fn_ast)
expand_names = before_name_visitor.get_new_names(expand_number)
# Replace use of *args in function body
if hasattr(ast, "Starred"):
# Python 3
expand_transformer = ExpandVarargTransformerStarred
else:
# Python 2
expand_transformer = ExpandVarargTransformerCallArg
new_fn_ast = expand_transformer(
vararg_name, expand_names
).visit(fn_ast)
new_fndef_ast = new_fn_ast.body[0]
# Replace vararg with additional args in function signature
new_fndef_ast.args.args.extend(
[_build_arg(name=name) for name in expand_names]
)
new_fndef_ast.args.vararg = None
# Run a new NameVistor an see if there were any other non-starred uses
# of the variable length argument. If so, raise an exception
after_name_visitor = NameVisitor()
after_name_visitor.visit(new_fn_ast)
if vararg_name in after_name_visitor.names:
raise ValueError("""\
The variable length positional argument {n} is used in an unsupported context
""".format(n=vararg_name))
# Remove decorators if present to avoid recursion
fndef_ast.decorator_list = []
# Add missing source code locations
ast.fix_missing_locations(new_fn_ast)
# Return result
return new_fn_ast
def expand_varargs(expand_number):
"""
Decorator to expand the variable length (starred) argument in a function
signature with a fixed number of arguments.
Parameters
----------
expand_number: int
The number of fixed arguments that should replace the variable length
argument
Returns
-------
function
Decorator Function
"""
if not isinstance(expand_number, int) or expand_number < 0:
raise ValueError("expand_number must be a non-negative integer")
def _expand_varargs(fn):
fn_ast = function_to_ast(fn)
fn_expanded_ast = expand_function_ast_varargs(fn_ast, expand_number)
return function_ast_to_function(fn_expanded_ast, stacklevel=2)
return _expand_varargs
| 29.333333 | 85 | 0.661679 | import re
import copy
import inspect
import ast
import textwrap
class NameVisitor(ast.NodeVisitor):
def __init__(self, *args, **kwargs):
super(NameVisitor, self).__init__(*args, **kwargs)
self.names = set()
def visit_Name(self, node):
self.names.add(node.id)
def visit_arg(self, node):
if hasattr(node, 'arg'):
self.names.add(node.arg)
elif hasattr(node, 'id'):
self.names.add(node.id)
def get_new_names(self, num_names):
prop_re = re.compile(r"^_(\d+)$")
matching_names = [n for n in self.names if prop_re.match(n)]
if matching_names:
start_number = max([int(n[1:]) for n in matching_names]) + 1
else:
start_number = 0
return ["_" + str(n) for n in
range(start_number, start_number + num_names)]
class ExpandVarargTransformer(ast.NodeTransformer):
def __init__(self, starred_name, expand_names, *args, **kwargs):
super(ExpandVarargTransformer, self).__init__(*args, **kwargs)
self.starred_name = starred_name
self.expand_names = expand_names
class ExpandVarargTransformerStarred(ExpandVarargTransformer):
def visit_Starred(self, node):
if node.value.id == self.starred_name:
return [ast.Name(id=name, ctx=node.ctx) for name in
self.expand_names]
else:
return node
class ExpandVarargTransformerCallArg(ExpandVarargTransformer):
def visit_Call(self, node):
if getattr(node, 'starargs', None) and node.starargs.id == self.starred_name:
node.starargs = None
node.args.extend([ast.Name(id=name, ctx=ast.Load())
for name in self.expand_names])
return node
else:
return node
def function_to_ast(fn):
fn_source = textwrap.dedent(inspect.getsource(fn))
fn_ast = ast.parse(fn_source)
ort astor
return astor.to_source(ast)
def compile_function_ast(fn_ast):
assert isinstance(fn_ast, ast.Module)
fndef_ast = fn_ast.body[0]
assert isinstance(fndef_ast, ast.FunctionDef)
return compile(fn_ast, "<%s>" % fndef_ast.name, mode='exec')
def function_ast_to_function(fn_ast, stacklevel=1):
assert isinstance(fn_ast, ast.Module)
fndef_ast = fn_ast.body[0]
assert isinstance(fndef_ast, ast.FunctionDef)
code = compile_function_ast(fn_ast)
current_frame = inspect.currentframe()
eval_frame = current_frame
for _ in range(stacklevel):
eval_frame = eval_frame.f_back
eval_locals = eval_frame.f_locals
eval_globals = eval_frame.f_globals
del current_frame
scope = copy.copy(eval_globals)
scope.update(eval_locals)
eval(code, scope)
return scope[fndef_ast.name]
def _build_arg(name):
try:
return ast.arg(arg=name)
except AttributeError:
return ast.Name(id=name, ctx=ast.Param())
def expand_function_ast_varargs(fn_ast, expand_number):
assert isinstance(fn_ast, ast.Module)
fn_ast = copy.deepcopy(fn_ast)
# Extract function definition
fndef_ast = fn_ast.body[0]
assert isinstance(fndef_ast, ast.FunctionDef)
# Get function args
fn_args = fndef_ast.args
# Function variable arity argument
fn_vararg = fn_args.vararg
# Require vararg
if not fn_vararg:
raise ValueError("""\
Input function AST does not have a variable length positional argument
(e.g. *args) in the function signature""")
assert fn_vararg
# Get vararg name
if isinstance(fn_vararg, str):
vararg_name = fn_vararg
else:
vararg_name = fn_vararg.arg
# Compute new unique names to use in place of the variable argument
before_name_visitor = NameVisitor()
before_name_visitor.visit(fn_ast)
expand_names = before_name_visitor.get_new_names(expand_number)
# Replace use of *args in function body
if hasattr(ast, "Starred"):
# Python 3
expand_transformer = ExpandVarargTransformerStarred
else:
# Python 2
expand_transformer = ExpandVarargTransformerCallArg
new_fn_ast = expand_transformer(
vararg_name, expand_names
).visit(fn_ast)
new_fndef_ast = new_fn_ast.body[0]
# Replace vararg with additional args in function signature
new_fndef_ast.args.args.extend(
[_build_arg(name=name) for name in expand_names]
)
new_fndef_ast.args.vararg = None
# Run a new NameVistor an see if there were any other non-starred uses
# of the variable length argument. If so, raise an exception
after_name_visitor = NameVisitor()
after_name_visitor.visit(new_fn_ast)
if vararg_name in after_name_visitor.names:
raise ValueError("""\
The variable length positional argument {n} is used in an unsupported context
""".format(n=vararg_name))
# Remove decorators if present to avoid recursion
fndef_ast.decorator_list = []
# Add missing source code locations
ast.fix_missing_locations(new_fn_ast)
# Return result
return new_fn_ast
def expand_varargs(expand_number):
if not isinstance(expand_number, int) or expand_number < 0:
raise ValueError("expand_number must be a non-negative integer")
def _expand_varargs(fn):
fn_ast = function_to_ast(fn)
fn_expanded_ast = expand_function_ast_varargs(fn_ast, expand_number)
return function_ast_to_function(fn_expanded_ast, stacklevel=2)
return _expand_varargs
| true | true |
f7292d17656c29b94c4d7a7e66797e9a545cc35e | 229 | py | Python | newslister/proxy/__init__.py | dang3r/nl | f0f70ef6fc5bcf7eef44dab8f6e1388c929f179d | [
"MIT"
] | 1 | 2018-12-12T10:40:23.000Z | 2018-12-12T10:40:23.000Z | newslister/proxy/__init__.py | dang3r/nl | f0f70ef6fc5bcf7eef44dab8f6e1388c929f179d | [
"MIT"
] | null | null | null | newslister/proxy/__init__.py | dang3r/nl | f0f70ef6fc5bcf7eef44dab8f6e1388c929f179d | [
"MIT"
] | null | null | null | from newslister.proxy.aws import *
from .aws import ProxyPool
def factory(cloud, count, **kwargs):
if cloud == 'aws':
group = ProxyPool(count, **kwargs)
return group
raise Exception('No factory exists!') | 25.444444 | 42 | 0.659389 | from newslister.proxy.aws import *
from .aws import ProxyPool
def factory(cloud, count, **kwargs):
if cloud == 'aws':
group = ProxyPool(count, **kwargs)
return group
raise Exception('No factory exists!') | true | true |
f7292da0f02b6f9a514918ef639000b76c9120ef | 699 | py | Python | tests/unit/test_url.py | javiergodoy/pandas-profiling | 0bed133520b9982263ed8cbc1af6b8f5a511bf0d | [
"MIT"
] | 2 | 2020-01-30T15:01:18.000Z | 2020-01-30T15:01:19.000Z | tests/unit/test_url.py | javiergodoy/pandas-profiling | 0bed133520b9982263ed8cbc1af6b8f5a511bf0d | [
"MIT"
] | null | null | null | tests/unit/test_url.py | javiergodoy/pandas-profiling | 0bed133520b9982263ed8cbc1af6b8f5a511bf0d | [
"MIT"
] | 1 | 2020-06-12T00:02:15.000Z | 2020-06-12T00:02:15.000Z | import pandas as pd
import numpy as np
import pandas_profiling
def test_urls(get_data_file):
file_name = get_data_file(
"whitelist_urls.csv",
"https://raw.githubusercontent.com/openeventdata/scraper/master/whitelist_urls.csv",
)
df = pd.read_csv(
file_name, header=None, names=["source", "url", "reach", "language"]
)
# Add ~10% missing values
df = df.mask(np.random.random(df.shape) < 0.1)
profile = df.profile_report(
title="DataFrame with URL column", samples={"head": 0, "tail": 0}
)
assert "<small>URL</small>" in profile.to_html(), "URL not detected"
assert "<th>URL</th>" in profile.to_html(), "URL not detected"
| 26.884615 | 92 | 0.649499 | import pandas as pd
import numpy as np
import pandas_profiling
def test_urls(get_data_file):
file_name = get_data_file(
"whitelist_urls.csv",
"https://raw.githubusercontent.com/openeventdata/scraper/master/whitelist_urls.csv",
)
df = pd.read_csv(
file_name, header=None, names=["source", "url", "reach", "language"]
)
df = df.mask(np.random.random(df.shape) < 0.1)
profile = df.profile_report(
title="DataFrame with URL column", samples={"head": 0, "tail": 0}
)
assert "<small>URL</small>" in profile.to_html(), "URL not detected"
assert "<th>URL</th>" in profile.to_html(), "URL not detected"
| true | true |
f7292ddb8ee78736309198ad7b3bdd680ec2f155 | 10,993 | py | Python | chemdataextractor_batteries/tests/test_doc_table.py | MB9991/test_demo- | ca3df4ecf20f7a26a621f68caf668f2e726a737d | [
"MIT"
] | 199 | 2016-10-07T06:55:23.000Z | 2022-03-29T09:50:03.000Z | tests/test_doc_table.py | qingtong00/ChemDataExtractor | 349a3bea965f2073141d62043b89319222e46af1 | [
"MIT"
] | 29 | 2016-10-04T08:56:05.000Z | 2022-03-06T19:36:55.000Z | tests/test_doc_table.py | qingtong00/ChemDataExtractor | 349a3bea965f2073141d62043b89319222e46af1 | [
"MIT"
] | 95 | 2016-10-10T14:24:27.000Z | 2022-03-16T18:30:00.000Z | # -*- coding: utf-8 -*-
"""
test_doc_table
~~~~~~~~~~~~~~
Test the Table Document element.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import unittest
from chemdataextractor.doc.table import Table, Cell
from chemdataextractor.doc.text import Caption
logging.basicConfig(level=logging.DEBUG)
log = logging.getLogger(__name__)
class TestTable(unittest.TestCase):
"""Simple Table instantiation tests."""
maxDiff = None
def test_uvvis_table(self):
""""""
t = Table(
caption=Caption('Selected photophysical properties of biarylsubstituted pyrazoles 5–8 and 1-methyl-3,5-diphenylpyrazole (9) at room temperature'),
headings=[
[
Cell('Compound'),
Cell('Absorption maxima λmax,abs (ε) [nm] (L cm−1 mol−1)'),
Cell('Emission maxima λmax,em (Φf) [nm] (a.u.)'),
Cell('Stokes-shift Δṽ [cm−1]')
]
],
rows=[
[Cell(' 5a '), Cell('273.5 (40 100)'), Cell('357.0 (0.77)'), Cell('9400')],
[Cell(' 5b '), Cell('268.5 (36 700)'), Cell('359.0 (0.77)'), Cell('8600')],
[Cell('Coumarin 343'), Cell('263.0 (38 400)'), Cell('344.5 (0.67)'), Cell('9000')],
[Cell(' 5d '), Cell('281.0 (34 200)'), Cell('351.5 (0.97)'), Cell('7100')],
[Cell(' 5e '), Cell('285.0 (44 000)'), Cell('382.0 (0.35)'), Cell('8900')],
[Cell(' 5f '), Cell('289.0 (43 300)'), Cell('363.0 (0.80)'), Cell('7100')],
[Cell(' 5g '), Cell('285.0 (42 000)'), Cell('343.5 (0.86)'), Cell('6000')],
[Cell(' 6a '), Cell('283.5 (35 600)'), Cell('344.5 (0.49)'), Cell('6300')],
[Cell(' 6b '), Cell('267.5 (35 800)'), Cell('338.5 (0.83)'), Cell('7800')],
[Cell(' 6c '), Cell('286.0 (33 000)'), Cell('347.0 (0.27)'), Cell('6200')],
[Cell(' 6d '), Cell('306.5 (36 600)'), Cell('384.0 (0.10)'), Cell('6600')],
[Cell(' 7 '), Cell('288.5 (62 500)'), Cell('367.0 (0.07)'), Cell('7400')],
[Cell('Compound 8a '), Cell('257.0 (36 300), 293.0 sh (25 000)'), Cell('385.0 (0.41)'), Cell('8200')],
[Cell(' 8b '), Cell('257.0 (32 000), 296.0 sh (23000)'), Cell('388.0 (0.33)'), Cell('8000')],
[Cell(' 8c '), Cell('257.0 (27 400), 307.5 (18900)'), Cell('387.0 (0.12)'), Cell('6700')],
[Cell(' 8d '), Cell('268.5 (29 500)'), Cell('385.0 (0.29)'), Cell('11 300')],
[Cell('Dye 8e '), Cell('261.5 (39 900), 288.0 sh (29 600), 311.0 sh (20 500)'), Cell('386.5 (0.37)'), Cell('6300')],
[Cell(' 8f '), Cell('256.5 (27 260), 296.0 (28404)'), Cell('388.5 (0.35)'), Cell('8000')],
[Cell(' 8g '), Cell('272.5 (39 600)'), Cell('394.0 (0.30)'), Cell('11 300')],
[Cell(' 8h '), Cell('286.0 (22 900)'), Cell('382.5 (0.33)'), Cell('8800')],
[Cell(' 9 '), Cell('254.0 (28 800)'), Cell('338.5 (0.40)'), Cell('9800')]]
)
gold = [
{'labels': [u'5a'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'40100', 'value': u'273.5'}]}], 'quantum_yields': [{'value': u'0.77'}]},
{'labels': [u'5b'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'36700', 'value': u'268.5'}]}], 'quantum_yields': [{'value': u'0.77'}]},
{'names': [u'Coumarin 343'], 'quantum_yields': [{'value': u'0.67'}], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'38400', 'value': u'263.0'}]}]},
{'labels': [u'5d'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'34200', 'value': u'281.0'}]}], 'quantum_yields': [{'value': u'0.97'}]},
{'labels': [u'5e'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'44000', 'value': u'285.0'}]}], 'quantum_yields': [{'value': u'0.35'}]},
{'labels': [u'5f'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'43300', 'value': u'289.0'}]}], 'quantum_yields': [{'value': u'0.80'}]},
{'labels': [u'5g'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'42000', 'value': u'285.0'}]}], 'quantum_yields': [{'value': u'0.86'}]},
{'labels': [u'6a'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'35600', 'value': u'283.5'}]}], 'quantum_yields': [{'value': u'0.49'}]},
{'labels': [u'6b'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'35800', 'value': u'267.5'}]}], 'quantum_yields': [{'value': u'0.83'}]},
{'labels': [u'6c'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'33000', 'value': u'286.0'}]}], 'quantum_yields': [{'value': u'0.27'}]},
{'labels': [u'6d'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'36600', 'value': u'306.5'}]}], 'quantum_yields': [{'value': u'0.10'}]},
{'labels': [u'7'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'62500', 'value': u'288.5'}]}], 'quantum_yields': [{'value': u'0.07'}]},
{'labels': [u'8a'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'36300', 'value': u'257.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'25000', 'value': u'293.0'}]}], 'quantum_yields': [{'value': u'0.41'}]},
{'labels': [u'8b'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'32000', 'value': u'257.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'23000', 'value': u'296.0'}]}], 'quantum_yields': [{'value': u'0.33'}]},
{'labels': [u'8c'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'27400', 'value': u'257.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'18900', 'value': u'307.5'}]}], 'quantum_yields': [{'value': u'0.12'}]},
{'labels': [u'8d'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'29500', 'value': u'268.5'}]}], 'quantum_yields': [{'value': u'0.29'}]},
{'labels': [u'8e'], 'quantum_yields': [{'value': u'0.37'}], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'39900', 'value': u'261.5'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'29600', 'value': u'288.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'20500', 'value': u'311.0'}]}]},
{'labels': [u'8f'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'27260', 'value': u'256.5'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'28404', 'value': u'296.0'}]}], 'quantum_yields': [{'value': u'0.35'}]},
{'labels': [u'8g'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'39600', 'value': u'272.5'}]}], 'quantum_yields': [{'value': u'0.30'}]},
{'labels': [u'8h'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'22900', 'value': u'286.0'}]}], 'quantum_yields': [{'value': u'0.33'}]},
{'labels': [u'9'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'28800', 'value': u'254.0'}]}], 'quantum_yields': [{'value': u'0.40'}]},
]
for record in t.records:
print(record.serialize())
self.assertEqual(gold, [record.serialize() for record in t.records])
def test_spectroscopic_table(self):
""""""
t = Table(
caption=Caption('Spectroscopic properties of Coumarins in acetonitrile at 298 K.'),
headings=[
[
Cell(''), # Blank compound heading
Cell('λmax (nm)'),
Cell('ε (M–1 cm–1)'),
Cell('λem (nm)'),
Cell('ϕ')
]
],
rows=[
[Cell('Coumarin 343'), Cell('398'), Cell('40 800'), Cell('492'), Cell('0.52')],
[Cell('C144'), Cell('429'), Cell('9500'), Cell('601'), Cell('N/A')],
[Cell('Coumarin 34'), Cell('269'), Cell('-'), Cell('435'), Cell('<0.01')],
]
)
# for record in t.caption.records:
# print(record.to_primitive())
# print(record.is_contextual)
gold = [
{'names': ['Coumarin 343'], 'quantum_yields': [{'type': '\u03d5', 'solvent': 'acetonitrile', 'value': '0.52', 'temperature': '298', 'temperature_units': 'K'}], 'uvvis_spectra': [{'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'units': 'nm', 'value': '398'}]}, {'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'extinction': '40800', 'extinction_units': 'M \u2013 1 cm \u2013 1'}]} ]},
{'labels': ['C144'], 'uvvis_spectra': [{'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'units': 'nm', 'value': '429'}]}, {'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'extinction': '9500', 'extinction_units': 'M \u2013 1 cm \u2013 1'}]}]},
{'names': ['Coumarin 34'], 'quantum_yields': [{'type': '\u03d5', 'solvent': 'acetonitrile', 'value': '<0.01', 'temperature': '298', 'temperature_units': 'K'}], 'uvvis_spectra': [{'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'units': 'nm', 'value': '269'}]}]},
{'names': ['Coumarins']},
{'names': ['acetonitrile']}
]
# for record in t.records:
# print(record.to_primitive())
self.assertEqual(gold, [record.serialize() for record in t.records])
if __name__ == '__main__':
unittest.main()
| 83.280303 | 472 | 0.528063 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import unittest
from chemdataextractor.doc.table import Table, Cell
from chemdataextractor.doc.text import Caption
logging.basicConfig(level=logging.DEBUG)
log = logging.getLogger(__name__)
class TestTable(unittest.TestCase):
maxDiff = None
def test_uvvis_table(self):
t = Table(
caption=Caption('Selected photophysical properties of biarylsubstituted pyrazoles 5–8 and 1-methyl-3,5-diphenylpyrazole (9) at room temperature'),
headings=[
[
Cell('Compound'),
Cell('Absorption maxima λmax,abs (ε) [nm] (L cm−1 mol−1)'),
Cell('Emission maxima λmax,em (Φf) [nm] (a.u.)'),
Cell('Stokes-shift Δṽ [cm−1]')
]
],
rows=[
[Cell(' 5a '), Cell('273.5 (40 100)'), Cell('357.0 (0.77)'), Cell('9400')],
[Cell(' 5b '), Cell('268.5 (36 700)'), Cell('359.0 (0.77)'), Cell('8600')],
[Cell('Coumarin 343'), Cell('263.0 (38 400)'), Cell('344.5 (0.67)'), Cell('9000')],
[Cell(' 5d '), Cell('281.0 (34 200)'), Cell('351.5 (0.97)'), Cell('7100')],
[Cell(' 5e '), Cell('285.0 (44 000)'), Cell('382.0 (0.35)'), Cell('8900')],
[Cell(' 5f '), Cell('289.0 (43 300)'), Cell('363.0 (0.80)'), Cell('7100')],
[Cell(' 5g '), Cell('285.0 (42 000)'), Cell('343.5 (0.86)'), Cell('6000')],
[Cell(' 6a '), Cell('283.5 (35 600)'), Cell('344.5 (0.49)'), Cell('6300')],
[Cell(' 6b '), Cell('267.5 (35 800)'), Cell('338.5 (0.83)'), Cell('7800')],
[Cell(' 6c '), Cell('286.0 (33 000)'), Cell('347.0 (0.27)'), Cell('6200')],
[Cell(' 6d '), Cell('306.5 (36 600)'), Cell('384.0 (0.10)'), Cell('6600')],
[Cell(' 7 '), Cell('288.5 (62 500)'), Cell('367.0 (0.07)'), Cell('7400')],
[Cell('Compound 8a '), Cell('257.0 (36 300), 293.0 sh (25 000)'), Cell('385.0 (0.41)'), Cell('8200')],
[Cell(' 8b '), Cell('257.0 (32 000), 296.0 sh (23000)'), Cell('388.0 (0.33)'), Cell('8000')],
[Cell(' 8c '), Cell('257.0 (27 400), 307.5 (18900)'), Cell('387.0 (0.12)'), Cell('6700')],
[Cell(' 8d '), Cell('268.5 (29 500)'), Cell('385.0 (0.29)'), Cell('11 300')],
[Cell('Dye 8e '), Cell('261.5 (39 900), 288.0 sh (29 600), 311.0 sh (20 500)'), Cell('386.5 (0.37)'), Cell('6300')],
[Cell(' 8f '), Cell('256.5 (27 260), 296.0 (28404)'), Cell('388.5 (0.35)'), Cell('8000')],
[Cell(' 8g '), Cell('272.5 (39 600)'), Cell('394.0 (0.30)'), Cell('11 300')],
[Cell(' 8h '), Cell('286.0 (22 900)'), Cell('382.5 (0.33)'), Cell('8800')],
[Cell(' 9 '), Cell('254.0 (28 800)'), Cell('338.5 (0.40)'), Cell('9800')]]
)
gold = [
{'labels': [u'5a'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'40100', 'value': u'273.5'}]}], 'quantum_yields': [{'value': u'0.77'}]},
{'labels': [u'5b'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'36700', 'value': u'268.5'}]}], 'quantum_yields': [{'value': u'0.77'}]},
{'names': [u'Coumarin 343'], 'quantum_yields': [{'value': u'0.67'}], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'38400', 'value': u'263.0'}]}]},
{'labels': [u'5d'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'34200', 'value': u'281.0'}]}], 'quantum_yields': [{'value': u'0.97'}]},
{'labels': [u'5e'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'44000', 'value': u'285.0'}]}], 'quantum_yields': [{'value': u'0.35'}]},
{'labels': [u'5f'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'43300', 'value': u'289.0'}]}], 'quantum_yields': [{'value': u'0.80'}]},
{'labels': [u'5g'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'42000', 'value': u'285.0'}]}], 'quantum_yields': [{'value': u'0.86'}]},
{'labels': [u'6a'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'35600', 'value': u'283.5'}]}], 'quantum_yields': [{'value': u'0.49'}]},
{'labels': [u'6b'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'35800', 'value': u'267.5'}]}], 'quantum_yields': [{'value': u'0.83'}]},
{'labels': [u'6c'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'33000', 'value': u'286.0'}]}], 'quantum_yields': [{'value': u'0.27'}]},
{'labels': [u'6d'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'36600', 'value': u'306.5'}]}], 'quantum_yields': [{'value': u'0.10'}]},
{'labels': [u'7'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'62500', 'value': u'288.5'}]}], 'quantum_yields': [{'value': u'0.07'}]},
{'labels': [u'8a'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'36300', 'value': u'257.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'25000', 'value': u'293.0'}]}], 'quantum_yields': [{'value': u'0.41'}]},
{'labels': [u'8b'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'32000', 'value': u'257.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'23000', 'value': u'296.0'}]}], 'quantum_yields': [{'value': u'0.33'}]},
{'labels': [u'8c'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'27400', 'value': u'257.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'18900', 'value': u'307.5'}]}], 'quantum_yields': [{'value': u'0.12'}]},
{'labels': [u'8d'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'29500', 'value': u'268.5'}]}], 'quantum_yields': [{'value': u'0.29'}]},
{'labels': [u'8e'], 'quantum_yields': [{'value': u'0.37'}], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'39900', 'value': u'261.5'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'29600', 'value': u'288.0'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'shape': u'sh', 'extinction': u'20500', 'value': u'311.0'}]}]},
{'labels': [u'8f'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'27260', 'value': u'256.5'}, {'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'28404', 'value': u'296.0'}]}], 'quantum_yields': [{'value': u'0.35'}]},
{'labels': [u'8g'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'39600', 'value': u'272.5'}]}], 'quantum_yields': [{'value': u'0.30'}]},
{'labels': [u'8h'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'22900', 'value': u'286.0'}]}], 'quantum_yields': [{'value': u'0.33'}]},
{'labels': [u'9'], 'uvvis_spectra': [{'peaks': [{'units': u'nm', 'extinction_units': u'L cm \u2212 1 mol \u2212 1', 'extinction': u'28800', 'value': u'254.0'}]}], 'quantum_yields': [{'value': u'0.40'}]},
]
for record in t.records:
print(record.serialize())
self.assertEqual(gold, [record.serialize() for record in t.records])
def test_spectroscopic_table(self):
t = Table(
caption=Caption('Spectroscopic properties of Coumarins in acetonitrile at 298 K.'),
headings=[
[
Cell(''),
Cell('λmax (nm)'),
Cell('ε (M–1 cm–1)'),
Cell('λem (nm)'),
Cell('ϕ')
]
],
rows=[
[Cell('Coumarin 343'), Cell('398'), Cell('40 800'), Cell('492'), Cell('0.52')],
[Cell('C144'), Cell('429'), Cell('9500'), Cell('601'), Cell('N/A')],
[Cell('Coumarin 34'), Cell('269'), Cell('-'), Cell('435'), Cell('<0.01')],
]
)
gold = [
{'names': ['Coumarin 343'], 'quantum_yields': [{'type': '\u03d5', 'solvent': 'acetonitrile', 'value': '0.52', 'temperature': '298', 'temperature_units': 'K'}], 'uvvis_spectra': [{'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'units': 'nm', 'value': '398'}]}, {'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'extinction': '40800', 'extinction_units': 'M \u2013 1 cm \u2013 1'}]} ]},
{'labels': ['C144'], 'uvvis_spectra': [{'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'units': 'nm', 'value': '429'}]}, {'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'extinction': '9500', 'extinction_units': 'M \u2013 1 cm \u2013 1'}]}]},
{'names': ['Coumarin 34'], 'quantum_yields': [{'type': '\u03d5', 'solvent': 'acetonitrile', 'value': '<0.01', 'temperature': '298', 'temperature_units': 'K'}], 'uvvis_spectra': [{'temperature': '298', 'temperature_units': 'K', 'solvent': 'acetonitrile', 'peaks': [{'units': 'nm', 'value': '269'}]}]},
{'names': ['Coumarins']},
{'names': ['acetonitrile']}
]
self.assertEqual(gold, [record.serialize() for record in t.records])
if __name__ == '__main__':
unittest.main()
| true | true |
f729303c685f926a5098329355067c0fc0fd2cd5 | 10,711 | py | Python | tests/components/recorder/test_init.py | lupin-de-mid/home-assistant | 35f6dbc9dc0fb12d1d04837acbf09dabb325f4fe | [
"Apache-2.0"
] | 1 | 2021-01-02T14:13:46.000Z | 2021-01-02T14:13:46.000Z | tests/components/recorder/test_init.py | lupin-de-mid/home-assistant | 35f6dbc9dc0fb12d1d04837acbf09dabb325f4fe | [
"Apache-2.0"
] | null | null | null | tests/components/recorder/test_init.py | lupin-de-mid/home-assistant | 35f6dbc9dc0fb12d1d04837acbf09dabb325f4fe | [
"Apache-2.0"
] | null | null | null | """The tests for the Recorder component."""
# pylint: disable=protected-access
import json
from datetime import datetime, timedelta
import unittest
from unittest.mock import patch, call
import pytest
from homeassistant.core import callback
from homeassistant.const import MATCH_ALL
from homeassistant.components import recorder
from homeassistant.bootstrap import setup_component
from tests.common import get_test_home_assistant
class TestRecorder(unittest.TestCase):
"""Test the recorder module."""
def setUp(self): # pylint: disable=invalid-name
"""Setup things to be run when tests are started."""
self.hass = get_test_home_assistant()
db_uri = 'sqlite://' # In memory DB
setup_component(self.hass, recorder.DOMAIN, {
recorder.DOMAIN: {recorder.CONF_DB_URL: db_uri}})
self.hass.start()
recorder._verify_instance()
self.session = recorder.Session()
recorder._INSTANCE.block_till_done()
def tearDown(self): # pylint: disable=invalid-name
"""Stop everything that was started."""
recorder._INSTANCE.shutdown(None)
self.hass.stop()
assert recorder._INSTANCE is None
def _add_test_states(self):
"""Add multiple states to the db for testing."""
now = datetime.now()
five_days_ago = now - timedelta(days=5)
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
for event_id in range(5):
if event_id < 3:
timestamp = five_days_ago
state = 'purgeme'
else:
timestamp = now
state = 'dontpurgeme'
self.session.add(recorder.get_model('States')(
entity_id='test.recorder2',
domain='sensor',
state=state,
attributes=json.dumps(attributes),
last_changed=timestamp,
last_updated=timestamp,
created=timestamp,
event_id=event_id + 1000
))
self.session.commit()
def _add_test_events(self):
"""Add a few events for testing."""
now = datetime.now()
five_days_ago = now - timedelta(days=5)
event_data = {'test_attr': 5, 'test_attr_10': 'nice'}
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
for event_id in range(5):
if event_id < 2:
timestamp = five_days_ago
event_type = 'EVENT_TEST_PURGE'
else:
timestamp = now
event_type = 'EVENT_TEST'
self.session.add(recorder.get_model('Events')(
event_type=event_type,
event_data=json.dumps(event_data),
origin='LOCAL',
created=timestamp,
time_fired=timestamp,
))
def test_saving_state(self):
"""Test saving and restoring a state."""
entity_id = 'test.recorder'
state = 'restoring_from_db'
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
self.hass.states.set(entity_id, state, attributes)
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
db_states = recorder.query('States')
states = recorder.execute(db_states)
assert db_states[0].event_id is not None
self.assertEqual(1, len(states))
self.assertEqual(self.hass.states.get(entity_id), states[0])
def test_saving_event(self):
"""Test saving and restoring an event."""
event_type = 'EVENT_TEST'
event_data = {'test_attr': 5, 'test_attr_10': 'nice'}
events = []
@callback
def event_listener(event):
"""Record events from eventbus."""
if event.event_type == event_type:
events.append(event)
self.hass.bus.listen(MATCH_ALL, event_listener)
self.hass.bus.fire(event_type, event_data)
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
db_events = recorder.execute(
recorder.query('Events').filter_by(
event_type=event_type))
assert len(events) == 1
assert len(db_events) == 1
event = events[0]
db_event = db_events[0]
assert event.event_type == db_event.event_type
assert event.data == db_event.data
assert event.origin == db_event.origin
# Recorder uses SQLite and stores datetimes as integer unix timestamps
assert event.time_fired.replace(microsecond=0) == \
db_event.time_fired.replace(microsecond=0)
def test_purge_old_states(self):
"""Test deleting old states."""
self._add_test_states()
# make sure we start with 5 states
states = recorder.query('States')
self.assertEqual(states.count(), 5)
# run purge_old_data()
recorder._INSTANCE.purge_days = 4
recorder._INSTANCE._purge_old_data()
# we should only have 2 states left after purging
self.assertEqual(states.count(), 2)
def test_purge_old_events(self):
"""Test deleting old events."""
self._add_test_events()
events = recorder.query('Events').filter(
recorder.get_model('Events').event_type.like("EVENT_TEST%"))
self.assertEqual(events.count(), 5)
# run purge_old_data()
recorder._INSTANCE.purge_days = 4
recorder._INSTANCE._purge_old_data()
# now we should only have 3 events left
self.assertEqual(events.count(), 3)
def test_purge_disabled(self):
"""Test leaving purge_days disabled."""
self._add_test_states()
self._add_test_events()
# make sure we start with 5 states and events
states = recorder.query('States')
events = recorder.query('Events').filter(
recorder.get_model('Events').event_type.like("EVENT_TEST%"))
self.assertEqual(states.count(), 5)
self.assertEqual(events.count(), 5)
# run purge_old_data()
recorder._INSTANCE.purge_days = None
recorder._INSTANCE._purge_old_data()
# we should have all of our states still
self.assertEqual(states.count(), 5)
self.assertEqual(events.count(), 5)
def test_schema_no_recheck(self):
"""Test that schema is not double-checked when up-to-date."""
with patch.object(recorder._INSTANCE, '_apply_update') as update, \
patch.object(recorder._INSTANCE, '_inspect_schema_version') \
as inspect:
recorder._INSTANCE._migrate_schema()
self.assertEqual(update.call_count, 0)
self.assertEqual(inspect.call_count, 0)
def test_invalid_update(self):
"""Test that an invalid new version raises an exception."""
with self.assertRaises(ValueError):
recorder._INSTANCE._apply_update(-1)
def test_schema_update_calls(self):
"""Test that schema migrations occurr in correct order."""
test_version = recorder.models.SchemaChanges(schema_version=0)
self.session.add(test_version)
with patch.object(recorder._INSTANCE, '_apply_update') as update:
recorder._INSTANCE._migrate_schema()
update.assert_has_calls([call(version+1) for version in range(
0, recorder.models.SCHEMA_VERSION)])
@pytest.fixture
def hass_recorder():
"""HASS fixture with in-memory recorder."""
hass = get_test_home_assistant()
def setup_recorder(config):
"""Setup with params."""
db_uri = 'sqlite://' # In memory DB
conf = {recorder.CONF_DB_URL: db_uri}
conf.update(config)
assert setup_component(hass, recorder.DOMAIN, {recorder.DOMAIN: conf})
hass.start()
hass.block_till_done()
recorder._verify_instance()
recorder._INSTANCE.block_till_done()
return hass
yield setup_recorder
hass.stop()
def _add_entities(hass, entity_ids):
"""Add entities."""
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
for idx, entity_id in enumerate(entity_ids):
hass.states.set(entity_id, 'state{}'.format(idx), attributes)
hass.block_till_done()
recorder._INSTANCE.block_till_done()
db_states = recorder.query('States')
states = recorder.execute(db_states)
assert db_states[0].event_id is not None
return states
# pylint: disable=redefined-outer-name,invalid-name
def test_saving_state_include_domains(hass_recorder):
"""Test saving and restoring a state."""
hass = hass_recorder({'include': {'domains': 'test2'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_incl_entities(hass_recorder):
"""Test saving and restoring a state."""
hass = hass_recorder({'include': {'entities': 'test2.recorder'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_exclude_domains(hass_recorder):
"""Test saving and restoring a state."""
hass = hass_recorder({'exclude': {'domains': 'test'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_exclude_entities(hass_recorder):
"""Test saving and restoring a state."""
hass = hass_recorder({'exclude': {'entities': 'test.recorder'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_exclude_domain_include_entity(hass_recorder):
"""Test saving and restoring a state."""
hass = hass_recorder({
'include': {'entities': 'test.recorder'},
'exclude': {'domains': 'test'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 2
def test_saving_state_include_domain_exclude_entity(hass_recorder):
"""Test saving and restoring a state."""
hass = hass_recorder({
'exclude': {'entities': 'test.recorder'},
'include': {'domains': 'test'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder',
'test.ok'])
assert len(states) == 1
assert hass.states.get('test.ok') == states[0]
assert hass.states.get('test.ok').state == 'state2'
| 35.233553 | 78 | 0.632341 |
import json
from datetime import datetime, timedelta
import unittest
from unittest.mock import patch, call
import pytest
from homeassistant.core import callback
from homeassistant.const import MATCH_ALL
from homeassistant.components import recorder
from homeassistant.bootstrap import setup_component
from tests.common import get_test_home_assistant
class TestRecorder(unittest.TestCase):
def setUp(self):
self.hass = get_test_home_assistant()
db_uri = 'sqlite://'
setup_component(self.hass, recorder.DOMAIN, {
recorder.DOMAIN: {recorder.CONF_DB_URL: db_uri}})
self.hass.start()
recorder._verify_instance()
self.session = recorder.Session()
recorder._INSTANCE.block_till_done()
def tearDown(self):
recorder._INSTANCE.shutdown(None)
self.hass.stop()
assert recorder._INSTANCE is None
def _add_test_states(self):
now = datetime.now()
five_days_ago = now - timedelta(days=5)
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
for event_id in range(5):
if event_id < 3:
timestamp = five_days_ago
state = 'purgeme'
else:
timestamp = now
state = 'dontpurgeme'
self.session.add(recorder.get_model('States')(
entity_id='test.recorder2',
domain='sensor',
state=state,
attributes=json.dumps(attributes),
last_changed=timestamp,
last_updated=timestamp,
created=timestamp,
event_id=event_id + 1000
))
self.session.commit()
def _add_test_events(self):
now = datetime.now()
five_days_ago = now - timedelta(days=5)
event_data = {'test_attr': 5, 'test_attr_10': 'nice'}
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
for event_id in range(5):
if event_id < 2:
timestamp = five_days_ago
event_type = 'EVENT_TEST_PURGE'
else:
timestamp = now
event_type = 'EVENT_TEST'
self.session.add(recorder.get_model('Events')(
event_type=event_type,
event_data=json.dumps(event_data),
origin='LOCAL',
created=timestamp,
time_fired=timestamp,
))
def test_saving_state(self):
entity_id = 'test.recorder'
state = 'restoring_from_db'
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
self.hass.states.set(entity_id, state, attributes)
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
db_states = recorder.query('States')
states = recorder.execute(db_states)
assert db_states[0].event_id is not None
self.assertEqual(1, len(states))
self.assertEqual(self.hass.states.get(entity_id), states[0])
def test_saving_event(self):
event_type = 'EVENT_TEST'
event_data = {'test_attr': 5, 'test_attr_10': 'nice'}
events = []
@callback
def event_listener(event):
if event.event_type == event_type:
events.append(event)
self.hass.bus.listen(MATCH_ALL, event_listener)
self.hass.bus.fire(event_type, event_data)
self.hass.block_till_done()
recorder._INSTANCE.block_till_done()
db_events = recorder.execute(
recorder.query('Events').filter_by(
event_type=event_type))
assert len(events) == 1
assert len(db_events) == 1
event = events[0]
db_event = db_events[0]
assert event.event_type == db_event.event_type
assert event.data == db_event.data
assert event.origin == db_event.origin
assert event.time_fired.replace(microsecond=0) == \
db_event.time_fired.replace(microsecond=0)
def test_purge_old_states(self):
self._add_test_states()
states = recorder.query('States')
self.assertEqual(states.count(), 5)
recorder._INSTANCE.purge_days = 4
recorder._INSTANCE._purge_old_data()
self.assertEqual(states.count(), 2)
def test_purge_old_events(self):
self._add_test_events()
events = recorder.query('Events').filter(
recorder.get_model('Events').event_type.like("EVENT_TEST%"))
self.assertEqual(events.count(), 5)
recorder._INSTANCE.purge_days = 4
recorder._INSTANCE._purge_old_data()
self.assertEqual(events.count(), 3)
def test_purge_disabled(self):
self._add_test_states()
self._add_test_events()
states = recorder.query('States')
events = recorder.query('Events').filter(
recorder.get_model('Events').event_type.like("EVENT_TEST%"))
self.assertEqual(states.count(), 5)
self.assertEqual(events.count(), 5)
recorder._INSTANCE.purge_days = None
recorder._INSTANCE._purge_old_data()
self.assertEqual(states.count(), 5)
self.assertEqual(events.count(), 5)
def test_schema_no_recheck(self):
with patch.object(recorder._INSTANCE, '_apply_update') as update, \
patch.object(recorder._INSTANCE, '_inspect_schema_version') \
as inspect:
recorder._INSTANCE._migrate_schema()
self.assertEqual(update.call_count, 0)
self.assertEqual(inspect.call_count, 0)
def test_invalid_update(self):
with self.assertRaises(ValueError):
recorder._INSTANCE._apply_update(-1)
def test_schema_update_calls(self):
test_version = recorder.models.SchemaChanges(schema_version=0)
self.session.add(test_version)
with patch.object(recorder._INSTANCE, '_apply_update') as update:
recorder._INSTANCE._migrate_schema()
update.assert_has_calls([call(version+1) for version in range(
0, recorder.models.SCHEMA_VERSION)])
@pytest.fixture
def hass_recorder():
hass = get_test_home_assistant()
def setup_recorder(config):
db_uri = 'sqlite://'
conf = {recorder.CONF_DB_URL: db_uri}
conf.update(config)
assert setup_component(hass, recorder.DOMAIN, {recorder.DOMAIN: conf})
hass.start()
hass.block_till_done()
recorder._verify_instance()
recorder._INSTANCE.block_till_done()
return hass
yield setup_recorder
hass.stop()
def _add_entities(hass, entity_ids):
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
for idx, entity_id in enumerate(entity_ids):
hass.states.set(entity_id, 'state{}'.format(idx), attributes)
hass.block_till_done()
recorder._INSTANCE.block_till_done()
db_states = recorder.query('States')
states = recorder.execute(db_states)
assert db_states[0].event_id is not None
return states
def test_saving_state_include_domains(hass_recorder):
hass = hass_recorder({'include': {'domains': 'test2'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_incl_entities(hass_recorder):
hass = hass_recorder({'include': {'entities': 'test2.recorder'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_exclude_domains(hass_recorder):
hass = hass_recorder({'exclude': {'domains': 'test'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_exclude_entities(hass_recorder):
hass = hass_recorder({'exclude': {'entities': 'test.recorder'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 1
assert hass.states.get('test2.recorder') == states[0]
def test_saving_state_exclude_domain_include_entity(hass_recorder):
hass = hass_recorder({
'include': {'entities': 'test.recorder'},
'exclude': {'domains': 'test'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder'])
assert len(states) == 2
def test_saving_state_include_domain_exclude_entity(hass_recorder):
hass = hass_recorder({
'exclude': {'entities': 'test.recorder'},
'include': {'domains': 'test'}})
states = _add_entities(hass, ['test.recorder', 'test2.recorder',
'test.ok'])
assert len(states) == 1
assert hass.states.get('test.ok') == states[0]
assert hass.states.get('test.ok').state == 'state2'
| true | true |
f72930c9fae6786588aa3d075c729ae46c002f3f | 1,811 | py | Python | sunpy/gui/__init__.py | astrofrog/sunpy | c658f7013a300b78e4cbf2146c2bcb98db32d788 | [
"BSD-2-Clause"
] | 1 | 2015-11-08T06:57:15.000Z | 2015-11-08T06:57:15.000Z | sunpy/gui/__init__.py | astrofrog/sunpy | c658f7013a300b78e4cbf2146c2bcb98db32d788 | [
"BSD-2-Clause"
] | null | null | null | sunpy/gui/__init__.py | astrofrog/sunpy | c658f7013a300b78e4cbf2146c2bcb98db32d788 | [
"BSD-2-Clause"
] | null | null | null | #-*- coding: utf-8 -*-
# Author: Matt Earnshaw <matt@earnshaw.org.uk>
from __future__ import absolute_import
import os
import sys
import sunpy
from PyQt4.QtGui import QApplication
from sunpy.gui.mainwindow import MainWindow
from sunpy.io import UnrecognizedFileTypeError
class Plotman(object):
""" Wraps a MainWindow so PlotMan instances can be created via the CLI.
Examples
--------
from sunpy.gui import Plotman
plots = Plotman("data/examples")
plots.show()
"""
def __init__(self, *paths):
""" *paths: directories containing FITS paths
or FITS paths to be opened in PlotMan """
self.app = QApplication(sys.argv)
self.main = MainWindow()
self.open_files(paths)
def open_files(self, inputs):
VALID_EXTENSIONS = [".jp2", ".fits", ".fts"]
to_open = []
# Determine files to process
for input_ in inputs:
if os.path.isfile(input_):
to_open.append(input_)
elif os.path.isdir(input_):
for file_ in os.listdir(input_):
to_open.append(file_)
else:
raise IOError("Path " + input_ + " does not exist.")
# Load files
for filepath in to_open:
name, ext = os.path.splitext(filepath) #pylint: disable=W0612
if ext.lower() in VALID_EXTENSIONS:
try:
self.main.add_tab(filepath, os.path.basename(filepath))
except UnrecognizedFileTypeError:
pass
def show(self):
self.main.show()
self.app.exec_()
if __name__=="__main__":
from sunpy.gui import Plotman
plots = Plotman(sunpy.AIA_171_IMAGE)
plots.show()
| 29.209677 | 75 | 0.578686 |
from __future__ import absolute_import
import os
import sys
import sunpy
from PyQt4.QtGui import QApplication
from sunpy.gui.mainwindow import MainWindow
from sunpy.io import UnrecognizedFileTypeError
class Plotman(object):
def __init__(self, *paths):
self.app = QApplication(sys.argv)
self.main = MainWindow()
self.open_files(paths)
def open_files(self, inputs):
VALID_EXTENSIONS = [".jp2", ".fits", ".fts"]
to_open = []
for input_ in inputs:
if os.path.isfile(input_):
to_open.append(input_)
elif os.path.isdir(input_):
for file_ in os.listdir(input_):
to_open.append(file_)
else:
raise IOError("Path " + input_ + " does not exist.")
for filepath in to_open:
name, ext = os.path.splitext(filepath)
if ext.lower() in VALID_EXTENSIONS:
try:
self.main.add_tab(filepath, os.path.basename(filepath))
except UnrecognizedFileTypeError:
pass
def show(self):
self.main.show()
self.app.exec_()
if __name__=="__main__":
from sunpy.gui import Plotman
plots = Plotman(sunpy.AIA_171_IMAGE)
plots.show()
| true | true |
f72933a040a9144612baa882c52f9c07c03beaef | 1,196 | py | Python | tests_functional/test_metadata.py | locriandev/doozer | 375eafccc15aadfd18dc50061eccb9a917d31b0b | [
"Apache-2.0"
] | 16 | 2018-11-06T16:49:03.000Z | 2021-11-07T19:48:49.000Z | tests_functional/test_metadata.py | locriandev/doozer | 375eafccc15aadfd18dc50061eccb9a917d31b0b | [
"Apache-2.0"
] | 479 | 2018-11-15T15:37:49.000Z | 2022-03-31T08:39:44.000Z | tests_functional/test_metadata.py | locriandev/doozer | 375eafccc15aadfd18dc50061eccb9a917d31b0b | [
"Apache-2.0"
] | 38 | 2018-11-07T14:33:15.000Z | 2021-12-13T13:59:12.000Z | #!/usr/bin/env python3
import unittest
from mock import MagicMock
from tests_functional import DoozerRunnerTestCase
from doozerlib import metadata
class TestMetadata(DoozerRunnerTestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def setUp(self):
super().setUp()
def tearDown(self):
super().tearDown()
def test_cgit_atom(self):
data_obj = MagicMock(key="cluster-etcd-operator", filename="cluster-etcd-operator.yml", data={"name": "cluster-etcd-operator"})
runtime = MagicMock()
runtime.group_config.urls.cgit = "http://pkgs.devel.redhat.com/cgit"
meta = metadata.Metadata("image", runtime, data_obj)
entry_list = meta.cgit_atom_feed(commit_hash='35ecfa4436139442edc19585c1c81ebfaca18550')
entry = entry_list[0]
self.assertEqual(entry.updated, '2019-07-09T18:01:53+00:00')
self.assertEqual(entry.id, '81597b027a3cb2c38865273b13ab320b361feca6')
entry_list = meta.cgit_atom_feed(branch='rhaos-4.8-rhel-8')
self.assertTrue(len(entry_list) > 1)
self.assertIsNotNone(entry_list[0].id)
if __name__ == "__main__":
unittest.main()
| 30.666667 | 135 | 0.690635 |
import unittest
from mock import MagicMock
from tests_functional import DoozerRunnerTestCase
from doozerlib import metadata
class TestMetadata(DoozerRunnerTestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def setUp(self):
super().setUp()
def tearDown(self):
super().tearDown()
def test_cgit_atom(self):
data_obj = MagicMock(key="cluster-etcd-operator", filename="cluster-etcd-operator.yml", data={"name": "cluster-etcd-operator"})
runtime = MagicMock()
runtime.group_config.urls.cgit = "http://pkgs.devel.redhat.com/cgit"
meta = metadata.Metadata("image", runtime, data_obj)
entry_list = meta.cgit_atom_feed(commit_hash='35ecfa4436139442edc19585c1c81ebfaca18550')
entry = entry_list[0]
self.assertEqual(entry.updated, '2019-07-09T18:01:53+00:00')
self.assertEqual(entry.id, '81597b027a3cb2c38865273b13ab320b361feca6')
entry_list = meta.cgit_atom_feed(branch='rhaos-4.8-rhel-8')
self.assertTrue(len(entry_list) > 1)
self.assertIsNotNone(entry_list[0].id)
if __name__ == "__main__":
unittest.main()
| true | true |
f72933d68cca1cb46357e2f0ee1e7da63a8a27d6 | 28,079 | py | Python | python/ray/util/client/server/server.py | sumanthratna/ray | 78b776942f9aada4a3e9832b48c6b270054f59b5 | [
"Apache-2.0"
] | 1 | 2020-05-30T21:01:17.000Z | 2020-05-30T21:01:17.000Z | python/ray/util/client/server/server.py | sumanthratna/ray | 78b776942f9aada4a3e9832b48c6b270054f59b5 | [
"Apache-2.0"
] | 66 | 2021-02-23T19:49:27.000Z | 2022-03-05T08:04:38.000Z | python/ray/util/client/server/server.py | sumanthratna/ray | 78b776942f9aada4a3e9832b48c6b270054f59b5 | [
"Apache-2.0"
] | null | null | null | import logging
from concurrent import futures
import grpc
import base64
from collections import defaultdict
from dataclasses import dataclass
import os
import sys
import threading
from typing import Any
from typing import List
from typing import Dict
from typing import Set
from typing import Optional
from typing import Callable
from ray import cloudpickle
from ray.job_config import JobConfig
import ray
import ray.state
import ray.core.generated.ray_client_pb2 as ray_client_pb2
import ray.core.generated.ray_client_pb2_grpc as ray_client_pb2_grpc
import time
import inspect
import json
from ray.util.client.common import (GRPC_MAX_MESSAGE_SIZE,
CLIENT_SERVER_MAX_THREADS)
from ray.util.client.server.server_pickler import convert_from_arg
from ray.util.client.server.server_pickler import dumps_from_server
from ray.util.client.server.server_pickler import loads_from_client
from ray.util.client.server.dataservicer import DataServicer
from ray.util.client.server.logservicer import LogstreamServicer
from ray.util.client.server.server_stubs import current_server
from ray.util.placement_group import PlacementGroup
from ray._private.client_mode_hook import disable_client_hook
logger = logging.getLogger(__name__)
class RayletServicer(ray_client_pb2_grpc.RayletDriverServicer):
def __init__(self, ray_connect_handler: Callable):
"""Construct a raylet service
Args:
ray_connect_handler (Callable): Function to connect to ray cluster
"""
# Stores client_id -> (ref_id -> ObjectRef)
self.object_refs: Dict[str, Dict[bytes, ray.ObjectRef]] = defaultdict(
dict)
# Stores client_id -> (client_ref_id -> ref_id (in self.object_refs))
self.client_side_ref_map: Dict[str, Dict[bytes, bytes]] = defaultdict(
dict)
self.function_refs = {}
self.actor_refs: Dict[bytes, ray.ActorHandle] = {}
self.actor_owners: Dict[str, Set[bytes]] = defaultdict(set)
self.registered_actor_classes = {}
self.named_actors = set()
self.state_lock = threading.Lock()
self.ray_connect_handler = ray_connect_handler
def Init(self, request, context=None) -> ray_client_pb2.InitResponse:
import pickle
if request.job_config:
job_config = pickle.loads(request.job_config)
job_config.client_job = True
else:
job_config = None
current_job_config = None
with disable_client_hook():
if ray.is_initialized():
worker = ray.worker.global_worker
current_job_config = worker.core_worker.get_job_config()
else:
self.ray_connect_handler(job_config)
if job_config is None:
return ray_client_pb2.InitResponse()
job_config = job_config.get_proto_job_config()
# If the server has been initialized, we need to compare whether the
# runtime env is compatible.
if current_job_config and set(job_config.runtime_env.uris) != set(
current_job_config.runtime_env.uris):
return ray_client_pb2.InitResponse(
ok=False,
msg="Runtime environment doesn't match "
f"request one {job_config.runtime_env.uris} "
f"current one {current_job_config.runtime_env.uris}")
return ray_client_pb2.InitResponse(ok=True)
def PrepRuntimeEnv(self, request,
context=None) -> ray_client_pb2.PrepRuntimeEnvResponse:
job_config = ray.worker.global_worker.core_worker.get_job_config()
missing_uris = self._prepare_runtime_env(job_config.runtime_env)
if len(missing_uris) != 0:
raise grpc.RpcError(f"Missing uris: {missing_uris}")
return ray_client_pb2.PrepRuntimeEnvResponse()
def KVPut(self, request, context=None) -> ray_client_pb2.KVPutResponse:
with disable_client_hook():
already_exists = ray.experimental.internal_kv._internal_kv_put(
request.key, request.value, overwrite=request.overwrite)
return ray_client_pb2.KVPutResponse(already_exists=already_exists)
def KVGet(self, request, context=None) -> ray_client_pb2.KVGetResponse:
with disable_client_hook():
value = ray.experimental.internal_kv._internal_kv_get(request.key)
return ray_client_pb2.KVGetResponse(value=value)
def KVDel(self, request, context=None) -> ray_client_pb2.KVDelResponse:
with disable_client_hook():
ray.experimental.internal_kv._internal_kv_del(request.key)
return ray_client_pb2.KVDelResponse()
def KVList(self, request, context=None) -> ray_client_pb2.KVListResponse:
with disable_client_hook():
keys = ray.experimental.internal_kv._internal_kv_list(
request.prefix)
return ray_client_pb2.KVListResponse(keys=keys)
def KVExists(self, request,
context=None) -> ray_client_pb2.KVExistsResponse:
with disable_client_hook():
exists = ray.experimental.internal_kv._internal_kv_exists(
request.key)
return ray_client_pb2.KVExistsResponse(exists=exists)
def ClusterInfo(self, request,
context=None) -> ray_client_pb2.ClusterInfoResponse:
resp = ray_client_pb2.ClusterInfoResponse()
resp.type = request.type
if request.type == ray_client_pb2.ClusterInfoType.CLUSTER_RESOURCES:
with disable_client_hook():
resources = ray.cluster_resources()
# Normalize resources into floats
# (the function may return values that are ints)
float_resources = {k: float(v) for k, v in resources.items()}
resp.resource_table.CopyFrom(
ray_client_pb2.ClusterInfoResponse.ResourceTable(
table=float_resources))
elif request.type == \
ray_client_pb2.ClusterInfoType.AVAILABLE_RESOURCES:
with disable_client_hook():
resources = ray.available_resources()
# Normalize resources into floats
# (the function may return values that are ints)
float_resources = {k: float(v) for k, v in resources.items()}
resp.resource_table.CopyFrom(
ray_client_pb2.ClusterInfoResponse.ResourceTable(
table=float_resources))
elif request.type == ray_client_pb2.ClusterInfoType.RUNTIME_CONTEXT:
ctx = ray_client_pb2.ClusterInfoResponse.RuntimeContext()
with disable_client_hook():
rtc = ray.get_runtime_context()
ctx.job_id = rtc.job_id.binary()
ctx.node_id = rtc.node_id.binary()
ctx.capture_client_tasks = \
rtc.should_capture_child_tasks_in_placement_group
resp.runtime_context.CopyFrom(ctx)
else:
with disable_client_hook():
resp.json = self._return_debug_cluster_info(request, context)
return resp
def _return_debug_cluster_info(self, request, context=None) -> str:
"""Handle ClusterInfo requests that only return a json blob."""
data = None
if request.type == ray_client_pb2.ClusterInfoType.NODES:
data = ray.nodes()
elif request.type == ray_client_pb2.ClusterInfoType.IS_INITIALIZED:
data = ray.is_initialized()
elif request.type == ray_client_pb2.ClusterInfoType.TIMELINE:
data = ray.timeline()
else:
raise TypeError("Unsupported cluster info type")
return json.dumps(data)
def release(self, client_id: str, id: bytes) -> bool:
with self.state_lock:
if client_id in self.object_refs:
if id in self.object_refs[client_id]:
logger.debug(
f"Releasing object {id.hex()} for {client_id}")
del self.object_refs[client_id][id]
return True
if client_id in self.actor_owners:
if id in self.actor_owners[client_id]:
logger.debug(f"Releasing actor {id.hex()} for {client_id}")
self.actor_owners[client_id].remove(id)
if self._can_remove_actor_ref(id):
logger.debug(f"Deleting reference to actor {id.hex()}")
del self.actor_refs[id]
return True
return False
def release_all(self, client_id):
with self.state_lock:
self._release_objects(client_id)
self._release_actors(client_id)
def _can_remove_actor_ref(self, actor_id_bytes):
no_owner = not any(actor_id_bytes in actor_list
for actor_list in self.actor_owners.values())
return no_owner and actor_id_bytes not in self.named_actors
def _release_objects(self, client_id):
if client_id not in self.object_refs:
logger.debug(f"Releasing client with no references: {client_id}")
return
count = len(self.object_refs[client_id])
del self.object_refs[client_id]
if client_id in self.client_side_ref_map:
del self.client_side_ref_map[client_id]
logger.debug(f"Released all {count} objects for client {client_id}")
def _release_actors(self, client_id):
if client_id not in self.actor_owners:
logger.debug(f"Releasing client with no actors: {client_id}")
return
count = 0
actors_to_remove = self.actor_owners.pop(client_id)
for id_bytes in actors_to_remove:
count += 1
if self._can_remove_actor_ref(id_bytes):
logger.debug(f"Deleting reference to actor {id_bytes.hex()}")
del self.actor_refs[id_bytes]
logger.debug(f"Released all {count} actors for client: {client_id}")
def Terminate(self, req, context=None):
if req.WhichOneof("terminate_type") == "task_object":
try:
object_ref = \
self.object_refs[req.client_id][req.task_object.id]
with disable_client_hook():
ray.cancel(
object_ref,
force=req.task_object.force,
recursive=req.task_object.recursive)
except Exception as e:
return_exception_in_context(e, context)
elif req.WhichOneof("terminate_type") == "actor":
try:
actor_ref = self.actor_refs[req.actor.id]
with disable_client_hook():
ray.kill(actor_ref, no_restart=req.actor.no_restart)
except Exception as e:
return_exception_in_context(e, context)
else:
raise RuntimeError(
"Client requested termination without providing a valid "
"terminate_type")
return ray_client_pb2.TerminateResponse(ok=True)
def GetObject(self, request, context=None):
return self._get_object(request, "", context)
def _get_object(self, request, client_id: str, context=None):
if request.id not in self.object_refs[client_id]:
return ray_client_pb2.GetResponse(valid=False)
objectref = self.object_refs[client_id][request.id]
logger.debug("get: %s" % objectref)
try:
with disable_client_hook():
item = ray.get(objectref, timeout=request.timeout)
except Exception as e:
return ray_client_pb2.GetResponse(
valid=False, error=cloudpickle.dumps(e))
item_ser = dumps_from_server(item, client_id, self)
return ray_client_pb2.GetResponse(valid=True, data=item_ser)
def PutObject(self, request: ray_client_pb2.PutRequest,
context=None) -> ray_client_pb2.PutResponse:
"""gRPC entrypoint for unary PutObject
"""
return self._put_object(request, "", context)
def _put_object(self,
request: ray_client_pb2.PutRequest,
client_id: str,
context=None):
"""Put an object in the cluster with ray.put() via gRPC.
Args:
request: PutRequest with pickled data.
client_id: The client who owns this data, for tracking when to
delete this reference.
context: gRPC context.
"""
try:
obj = loads_from_client(request.data, self)
with disable_client_hook():
objectref = ray.put(obj)
except Exception as e:
logger.exception("Put failed:")
return ray_client_pb2.PutResponse(
id=b"", valid=False, error=cloudpickle.dumps(e))
self.object_refs[client_id][objectref.binary()] = objectref
if len(request.client_ref_id) > 0:
self.client_side_ref_map[client_id][
request.client_ref_id] = objectref.binary()
logger.debug("put: %s" % objectref)
return ray_client_pb2.PutResponse(id=objectref.binary(), valid=True)
def WaitObject(self, request, context=None) -> ray_client_pb2.WaitResponse:
object_refs = []
for id in request.object_ids:
if id not in self.object_refs[request.client_id]:
raise Exception(
"Asking for a ref not associated with this client: %s" %
str(id))
object_refs.append(self.object_refs[request.client_id][id])
num_returns = request.num_returns
timeout = request.timeout
try:
with disable_client_hook():
ready_object_refs, remaining_object_refs = ray.wait(
object_refs,
num_returns=num_returns,
timeout=timeout if timeout != -1 else None,
)
except Exception as e:
# TODO(ameer): improve exception messages.
logger.error(f"Exception {e}")
return ray_client_pb2.WaitResponse(valid=False)
logger.debug("wait: %s %s" % (str(ready_object_refs),
str(remaining_object_refs)))
ready_object_ids = [
ready_object_ref.binary() for ready_object_ref in ready_object_refs
]
remaining_object_ids = [
remaining_object_ref.binary()
for remaining_object_ref in remaining_object_refs
]
return ray_client_pb2.WaitResponse(
valid=True,
ready_object_ids=ready_object_ids,
remaining_object_ids=remaining_object_ids)
def Schedule(self, task, context=None) -> ray_client_pb2.ClientTaskTicket:
logger.debug(
"schedule: %s %s" % (task.name,
ray_client_pb2.ClientTask.RemoteExecType.Name(
task.type)))
try:
with disable_client_hook():
if task.type == ray_client_pb2.ClientTask.FUNCTION:
result = self._schedule_function(task, context)
elif task.type == ray_client_pb2.ClientTask.ACTOR:
result = self._schedule_actor(task, context)
elif task.type == ray_client_pb2.ClientTask.METHOD:
result = self._schedule_method(task, context)
elif task.type == ray_client_pb2.ClientTask.NAMED_ACTOR:
result = self._schedule_named_actor(task, context)
else:
raise NotImplementedError(
"Unimplemented Schedule task type: %s" %
ray_client_pb2.ClientTask.RemoteExecType.Name(
task.type))
result.valid = True
return result
except Exception as e:
logger.debug(f"Caught schedule exception, returning: {e}")
return ray_client_pb2.ClientTaskTicket(
valid=False, error=cloudpickle.dumps(e))
def _schedule_method(self, task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
actor_handle = self.actor_refs.get(task.payload_id)
if actor_handle is None:
raise Exception(
"Can't run an actor the server doesn't have a handle for")
arglist, kwargs = self._convert_args(task.args, task.kwargs)
method = getattr(actor_handle, task.name)
opts = decode_options(task.options)
if opts is not None:
method = method.options(**opts)
output = method.remote(*arglist, **kwargs)
ids = self.unify_and_track_outputs(output, task.client_id)
return ray_client_pb2.ClientTaskTicket(return_ids=ids)
def _schedule_actor(self, task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
remote_class = self.lookup_or_register_actor(
task.payload_id, task.client_id,
decode_options(task.baseline_options))
arglist, kwargs = self._convert_args(task.args, task.kwargs)
opts = decode_options(task.options)
if opts is not None:
remote_class = remote_class.options(**opts)
with current_server(self):
actor = remote_class.remote(*arglist, **kwargs)
self.actor_refs[actor._actor_id.binary()] = actor
self.actor_owners[task.client_id].add(actor._actor_id.binary())
return ray_client_pb2.ClientTaskTicket(
return_ids=[actor._actor_id.binary()])
def _schedule_function(self, task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
remote_func = self.lookup_or_register_func(
task.payload_id, task.client_id,
decode_options(task.baseline_options))
arglist, kwargs = self._convert_args(task.args, task.kwargs)
opts = decode_options(task.options)
if opts is not None:
remote_func = remote_func.options(**opts)
with current_server(self):
output = remote_func.remote(*arglist, **kwargs)
ids = self.unify_and_track_outputs(output, task.client_id)
return ray_client_pb2.ClientTaskTicket(return_ids=ids)
def _schedule_named_actor(self,
task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
assert len(task.payload_id) == 0
actor = ray.get_actor(task.name)
bin_actor_id = actor._actor_id.binary()
self.actor_refs[bin_actor_id] = actor
self.actor_owners[task.client_id].add(bin_actor_id)
self.named_actors.add(bin_actor_id)
return ray_client_pb2.ClientTaskTicket(
return_ids=[actor._actor_id.binary()])
def _convert_args(self, arg_list, kwarg_map):
argout = []
for arg in arg_list:
t = convert_from_arg(arg, self)
argout.append(t)
kwargout = {}
for k in kwarg_map:
kwargout[k] = convert_from_arg(kwarg_map[k], self)
return argout, kwargout
def _prepare_runtime_env(self, job_runtime_env) -> List[str]:
"""Download runtime environment to local node"""
missing_uris = []
uris = job_runtime_env.uris
from ray._private import runtime_env
with disable_client_hook():
for uri in uris:
try:
working_dir = runtime_env.fetch_package(uri)
if working_dir:
os.chdir(str(working_dir))
sys.path.insert(0, str(working_dir))
except IOError:
missing_uris.append(uri)
return missing_uris
def lookup_or_register_func(
self, id: bytes, client_id: str,
options: Optional[Dict]) -> ray.remote_function.RemoteFunction:
with disable_client_hook():
if id not in self.function_refs:
funcref = self.object_refs[client_id][id]
func = ray.get(funcref)
if not inspect.isfunction(func):
raise Exception("Attempting to register function that "
"isn't a function.")
if options is None or len(options) == 0:
self.function_refs[id] = ray.remote(func)
else:
self.function_refs[id] = ray.remote(**options)(func)
return self.function_refs[id]
def lookup_or_register_actor(self, id: bytes, client_id: str,
options: Optional[Dict]):
with disable_client_hook():
if id not in self.registered_actor_classes:
actor_class_ref = self.object_refs[client_id][id]
actor_class = ray.get(actor_class_ref)
if not inspect.isclass(actor_class):
raise Exception("Attempting to schedule actor that "
"isn't a class.")
if options is None or len(options) == 0:
reg_class = ray.remote(actor_class)
else:
reg_class = ray.remote(**options)(actor_class)
self.registered_actor_classes[id] = reg_class
return self.registered_actor_classes[id]
def unify_and_track_outputs(self, output, client_id):
if output is None:
outputs = []
elif isinstance(output, list):
outputs = output
else:
outputs = [output]
for out in outputs:
if out.binary() in self.object_refs[client_id]:
logger.warning(f"Already saw object_ref {out}")
self.object_refs[client_id][out.binary()] = out
return [out.binary() for out in outputs]
def return_exception_in_context(err, context):
if context is not None:
context.set_details(encode_exception(err))
context.set_code(grpc.StatusCode.INTERNAL)
def encode_exception(exception) -> str:
data = cloudpickle.dumps(exception)
return base64.standard_b64encode(data).decode()
def decode_options(
options: ray_client_pb2.TaskOptions) -> Optional[Dict[str, Any]]:
if options.json_options == "":
return None
opts = json.loads(options.json_options)
assert isinstance(opts, dict)
if opts.get("placement_group", None):
# Placement groups in Ray client options are serialized as dicts.
# Convert the dict to a PlacementGroup.
opts["placement_group"] = PlacementGroup.from_dict(
opts["placement_group"])
return opts
@dataclass
class ClientServerHandle:
"""Holds the handles to the registered gRPC servicers and their server."""
task_servicer: RayletServicer
data_servicer: DataServicer
logs_servicer: LogstreamServicer
grpc_server: grpc.Server
# Add a hook for all the cases that previously
# expected simply a gRPC server
def __getattr__(self, attr):
return getattr(self.grpc_server, attr)
def serve(connection_str, ray_connect_handler=None):
def default_connect_handler(job_config: JobConfig = None):
with disable_client_hook():
if not ray.is_initialized():
return ray.init(job_config=job_config)
ray_connect_handler = ray_connect_handler or default_connect_handler
server = grpc.server(
futures.ThreadPoolExecutor(max_workers=CLIENT_SERVER_MAX_THREADS),
options=[
("grpc.max_send_message_length", GRPC_MAX_MESSAGE_SIZE),
("grpc.max_receive_message_length", GRPC_MAX_MESSAGE_SIZE),
])
task_servicer = RayletServicer(ray_connect_handler)
data_servicer = DataServicer(task_servicer)
logs_servicer = LogstreamServicer()
ray_client_pb2_grpc.add_RayletDriverServicer_to_server(
task_servicer, server)
ray_client_pb2_grpc.add_RayletDataStreamerServicer_to_server(
data_servicer, server)
ray_client_pb2_grpc.add_RayletLogStreamerServicer_to_server(
logs_servicer, server)
server.add_insecure_port(connection_str)
current_handle = ClientServerHandle(
task_servicer=task_servicer,
data_servicer=data_servicer,
logs_servicer=logs_servicer,
grpc_server=server,
)
server.start()
return current_handle
def init_and_serve(connection_str, *args, **kwargs):
with disable_client_hook():
# Disable client mode inside the worker's environment
info = ray.init(*args, **kwargs)
def ray_connect_handler(job_config=None):
# Ray client will disconnect from ray when
# num_clients == 0.
if ray.is_initialized():
return info
else:
return ray.init(job_config=job_config, *args, **kwargs)
server_handle = serve(
connection_str, ray_connect_handler=ray_connect_handler)
return (server_handle, info)
def shutdown_with_server(server, _exiting_interpreter=False):
server.stop(1)
with disable_client_hook():
ray.shutdown(_exiting_interpreter)
def create_ray_handler(redis_address, redis_password):
def ray_connect_handler(job_config: JobConfig = None):
if redis_address:
if redis_password:
ray.init(
address=redis_address,
_redis_password=redis_password,
job_config=job_config)
else:
ray.init(address=redis_address, job_config=job_config)
else:
ray.init(job_config=job_config)
return ray_connect_handler
def try_create_redis_client(args):
if "redis-address" not in args:
possible = ray._private.services.find_redis_address()
if len(possible) != 1:
return None
address = possible.pop()
else:
address = args["redis-address"]
if args.redis_password is None:
password = ray.ray_constants.REDIS_DEFAULT_PASSWORD
else:
password = args.redis_password
return ray._private.services.create_redis_client(address, password)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--host", type=str, default="0.0.0.0", help="Host IP to bind to")
parser.add_argument(
"-p", "--port", type=int, default=50051, help="Port to bind to")
parser.add_argument(
"--redis-address",
required=False,
type=str,
help="Address to use to connect to Ray")
parser.add_argument(
"--redis-password",
required=False,
type=str,
help="Password for connecting to Redis")
args = parser.parse_args()
logging.basicConfig(level="INFO")
# This redis client is used for health checking. We can't use `internal_kv`
# because it requires `ray.init` to be called, which only connect handlers
# should do.
redis_client = None
ray_connect_handler = create_ray_handler(args.redis_address,
args.redis_password)
hostport = "%s:%d" % (args.host, args.port)
logger.info(f"Starting Ray Client server on {hostport}")
server = serve(hostport, ray_connect_handler)
try:
while True:
health_report = {
"time": time.time(),
}
try:
if not redis_client:
redis_client = try_create_redis_client(args)
redis_client.hset("healthcheck:ray_client_server", "value",
json.dumps(health_report))
except Exception as e:
logger.error("Failed to put health check.")
logger.exception(e)
time.sleep(1)
except KeyboardInterrupt:
server.stop(0)
if __name__ == "__main__":
main()
| 41.111274 | 79 | 0.627408 | import logging
from concurrent import futures
import grpc
import base64
from collections import defaultdict
from dataclasses import dataclass
import os
import sys
import threading
from typing import Any
from typing import List
from typing import Dict
from typing import Set
from typing import Optional
from typing import Callable
from ray import cloudpickle
from ray.job_config import JobConfig
import ray
import ray.state
import ray.core.generated.ray_client_pb2 as ray_client_pb2
import ray.core.generated.ray_client_pb2_grpc as ray_client_pb2_grpc
import time
import inspect
import json
from ray.util.client.common import (GRPC_MAX_MESSAGE_SIZE,
CLIENT_SERVER_MAX_THREADS)
from ray.util.client.server.server_pickler import convert_from_arg
from ray.util.client.server.server_pickler import dumps_from_server
from ray.util.client.server.server_pickler import loads_from_client
from ray.util.client.server.dataservicer import DataServicer
from ray.util.client.server.logservicer import LogstreamServicer
from ray.util.client.server.server_stubs import current_server
from ray.util.placement_group import PlacementGroup
from ray._private.client_mode_hook import disable_client_hook
logger = logging.getLogger(__name__)
class RayletServicer(ray_client_pb2_grpc.RayletDriverServicer):
def __init__(self, ray_connect_handler: Callable):
self.object_refs: Dict[str, Dict[bytes, ray.ObjectRef]] = defaultdict(
dict)
self.client_side_ref_map: Dict[str, Dict[bytes, bytes]] = defaultdict(
dict)
self.function_refs = {}
self.actor_refs: Dict[bytes, ray.ActorHandle] = {}
self.actor_owners: Dict[str, Set[bytes]] = defaultdict(set)
self.registered_actor_classes = {}
self.named_actors = set()
self.state_lock = threading.Lock()
self.ray_connect_handler = ray_connect_handler
def Init(self, request, context=None) -> ray_client_pb2.InitResponse:
import pickle
if request.job_config:
job_config = pickle.loads(request.job_config)
job_config.client_job = True
else:
job_config = None
current_job_config = None
with disable_client_hook():
if ray.is_initialized():
worker = ray.worker.global_worker
current_job_config = worker.core_worker.get_job_config()
else:
self.ray_connect_handler(job_config)
if job_config is None:
return ray_client_pb2.InitResponse()
job_config = job_config.get_proto_job_config()
if current_job_config and set(job_config.runtime_env.uris) != set(
current_job_config.runtime_env.uris):
return ray_client_pb2.InitResponse(
ok=False,
msg="Runtime environment doesn't match "
f"request one {job_config.runtime_env.uris} "
f"current one {current_job_config.runtime_env.uris}")
return ray_client_pb2.InitResponse(ok=True)
def PrepRuntimeEnv(self, request,
context=None) -> ray_client_pb2.PrepRuntimeEnvResponse:
job_config = ray.worker.global_worker.core_worker.get_job_config()
missing_uris = self._prepare_runtime_env(job_config.runtime_env)
if len(missing_uris) != 0:
raise grpc.RpcError(f"Missing uris: {missing_uris}")
return ray_client_pb2.PrepRuntimeEnvResponse()
def KVPut(self, request, context=None) -> ray_client_pb2.KVPutResponse:
with disable_client_hook():
already_exists = ray.experimental.internal_kv._internal_kv_put(
request.key, request.value, overwrite=request.overwrite)
return ray_client_pb2.KVPutResponse(already_exists=already_exists)
def KVGet(self, request, context=None) -> ray_client_pb2.KVGetResponse:
with disable_client_hook():
value = ray.experimental.internal_kv._internal_kv_get(request.key)
return ray_client_pb2.KVGetResponse(value=value)
def KVDel(self, request, context=None) -> ray_client_pb2.KVDelResponse:
with disable_client_hook():
ray.experimental.internal_kv._internal_kv_del(request.key)
return ray_client_pb2.KVDelResponse()
def KVList(self, request, context=None) -> ray_client_pb2.KVListResponse:
with disable_client_hook():
keys = ray.experimental.internal_kv._internal_kv_list(
request.prefix)
return ray_client_pb2.KVListResponse(keys=keys)
def KVExists(self, request,
context=None) -> ray_client_pb2.KVExistsResponse:
with disable_client_hook():
exists = ray.experimental.internal_kv._internal_kv_exists(
request.key)
return ray_client_pb2.KVExistsResponse(exists=exists)
def ClusterInfo(self, request,
context=None) -> ray_client_pb2.ClusterInfoResponse:
resp = ray_client_pb2.ClusterInfoResponse()
resp.type = request.type
if request.type == ray_client_pb2.ClusterInfoType.CLUSTER_RESOURCES:
with disable_client_hook():
resources = ray.cluster_resources()
# Normalize resources into floats
# (the function may return values that are ints)
float_resources = {k: float(v) for k, v in resources.items()}
resp.resource_table.CopyFrom(
ray_client_pb2.ClusterInfoResponse.ResourceTable(
table=float_resources))
elif request.type == \
ray_client_pb2.ClusterInfoType.AVAILABLE_RESOURCES:
with disable_client_hook():
resources = ray.available_resources()
# Normalize resources into floats
# (the function may return values that are ints)
float_resources = {k: float(v) for k, v in resources.items()}
resp.resource_table.CopyFrom(
ray_client_pb2.ClusterInfoResponse.ResourceTable(
table=float_resources))
elif request.type == ray_client_pb2.ClusterInfoType.RUNTIME_CONTEXT:
ctx = ray_client_pb2.ClusterInfoResponse.RuntimeContext()
with disable_client_hook():
rtc = ray.get_runtime_context()
ctx.job_id = rtc.job_id.binary()
ctx.node_id = rtc.node_id.binary()
ctx.capture_client_tasks = \
rtc.should_capture_child_tasks_in_placement_group
resp.runtime_context.CopyFrom(ctx)
else:
with disable_client_hook():
resp.json = self._return_debug_cluster_info(request, context)
return resp
def _return_debug_cluster_info(self, request, context=None) -> str:
data = None
if request.type == ray_client_pb2.ClusterInfoType.NODES:
data = ray.nodes()
elif request.type == ray_client_pb2.ClusterInfoType.IS_INITIALIZED:
data = ray.is_initialized()
elif request.type == ray_client_pb2.ClusterInfoType.TIMELINE:
data = ray.timeline()
else:
raise TypeError("Unsupported cluster info type")
return json.dumps(data)
def release(self, client_id: str, id: bytes) -> bool:
with self.state_lock:
if client_id in self.object_refs:
if id in self.object_refs[client_id]:
logger.debug(
f"Releasing object {id.hex()} for {client_id}")
del self.object_refs[client_id][id]
return True
if client_id in self.actor_owners:
if id in self.actor_owners[client_id]:
logger.debug(f"Releasing actor {id.hex()} for {client_id}")
self.actor_owners[client_id].remove(id)
if self._can_remove_actor_ref(id):
logger.debug(f"Deleting reference to actor {id.hex()}")
del self.actor_refs[id]
return True
return False
def release_all(self, client_id):
with self.state_lock:
self._release_objects(client_id)
self._release_actors(client_id)
def _can_remove_actor_ref(self, actor_id_bytes):
no_owner = not any(actor_id_bytes in actor_list
for actor_list in self.actor_owners.values())
return no_owner and actor_id_bytes not in self.named_actors
def _release_objects(self, client_id):
if client_id not in self.object_refs:
logger.debug(f"Releasing client with no references: {client_id}")
return
count = len(self.object_refs[client_id])
del self.object_refs[client_id]
if client_id in self.client_side_ref_map:
del self.client_side_ref_map[client_id]
logger.debug(f"Released all {count} objects for client {client_id}")
def _release_actors(self, client_id):
if client_id not in self.actor_owners:
logger.debug(f"Releasing client with no actors: {client_id}")
return
count = 0
actors_to_remove = self.actor_owners.pop(client_id)
for id_bytes in actors_to_remove:
count += 1
if self._can_remove_actor_ref(id_bytes):
logger.debug(f"Deleting reference to actor {id_bytes.hex()}")
del self.actor_refs[id_bytes]
logger.debug(f"Released all {count} actors for client: {client_id}")
def Terminate(self, req, context=None):
if req.WhichOneof("terminate_type") == "task_object":
try:
object_ref = \
self.object_refs[req.client_id][req.task_object.id]
with disable_client_hook():
ray.cancel(
object_ref,
force=req.task_object.force,
recursive=req.task_object.recursive)
except Exception as e:
return_exception_in_context(e, context)
elif req.WhichOneof("terminate_type") == "actor":
try:
actor_ref = self.actor_refs[req.actor.id]
with disable_client_hook():
ray.kill(actor_ref, no_restart=req.actor.no_restart)
except Exception as e:
return_exception_in_context(e, context)
else:
raise RuntimeError(
"Client requested termination without providing a valid "
"terminate_type")
return ray_client_pb2.TerminateResponse(ok=True)
def GetObject(self, request, context=None):
return self._get_object(request, "", context)
def _get_object(self, request, client_id: str, context=None):
if request.id not in self.object_refs[client_id]:
return ray_client_pb2.GetResponse(valid=False)
objectref = self.object_refs[client_id][request.id]
logger.debug("get: %s" % objectref)
try:
with disable_client_hook():
item = ray.get(objectref, timeout=request.timeout)
except Exception as e:
return ray_client_pb2.GetResponse(
valid=False, error=cloudpickle.dumps(e))
item_ser = dumps_from_server(item, client_id, self)
return ray_client_pb2.GetResponse(valid=True, data=item_ser)
def PutObject(self, request: ray_client_pb2.PutRequest,
context=None) -> ray_client_pb2.PutResponse:
return self._put_object(request, "", context)
def _put_object(self,
request: ray_client_pb2.PutRequest,
client_id: str,
context=None):
try:
obj = loads_from_client(request.data, self)
with disable_client_hook():
objectref = ray.put(obj)
except Exception as e:
logger.exception("Put failed:")
return ray_client_pb2.PutResponse(
id=b"", valid=False, error=cloudpickle.dumps(e))
self.object_refs[client_id][objectref.binary()] = objectref
if len(request.client_ref_id) > 0:
self.client_side_ref_map[client_id][
request.client_ref_id] = objectref.binary()
logger.debug("put: %s" % objectref)
return ray_client_pb2.PutResponse(id=objectref.binary(), valid=True)
def WaitObject(self, request, context=None) -> ray_client_pb2.WaitResponse:
object_refs = []
for id in request.object_ids:
if id not in self.object_refs[request.client_id]:
raise Exception(
"Asking for a ref not associated with this client: %s" %
str(id))
object_refs.append(self.object_refs[request.client_id][id])
num_returns = request.num_returns
timeout = request.timeout
try:
with disable_client_hook():
ready_object_refs, remaining_object_refs = ray.wait(
object_refs,
num_returns=num_returns,
timeout=timeout if timeout != -1 else None,
)
except Exception as e:
# TODO(ameer): improve exception messages.
logger.error(f"Exception {e}")
return ray_client_pb2.WaitResponse(valid=False)
logger.debug("wait: %s %s" % (str(ready_object_refs),
str(remaining_object_refs)))
ready_object_ids = [
ready_object_ref.binary() for ready_object_ref in ready_object_refs
]
remaining_object_ids = [
remaining_object_ref.binary()
for remaining_object_ref in remaining_object_refs
]
return ray_client_pb2.WaitResponse(
valid=True,
ready_object_ids=ready_object_ids,
remaining_object_ids=remaining_object_ids)
def Schedule(self, task, context=None) -> ray_client_pb2.ClientTaskTicket:
logger.debug(
"schedule: %s %s" % (task.name,
ray_client_pb2.ClientTask.RemoteExecType.Name(
task.type)))
try:
with disable_client_hook():
if task.type == ray_client_pb2.ClientTask.FUNCTION:
result = self._schedule_function(task, context)
elif task.type == ray_client_pb2.ClientTask.ACTOR:
result = self._schedule_actor(task, context)
elif task.type == ray_client_pb2.ClientTask.METHOD:
result = self._schedule_method(task, context)
elif task.type == ray_client_pb2.ClientTask.NAMED_ACTOR:
result = self._schedule_named_actor(task, context)
else:
raise NotImplementedError(
"Unimplemented Schedule task type: %s" %
ray_client_pb2.ClientTask.RemoteExecType.Name(
task.type))
result.valid = True
return result
except Exception as e:
logger.debug(f"Caught schedule exception, returning: {e}")
return ray_client_pb2.ClientTaskTicket(
valid=False, error=cloudpickle.dumps(e))
def _schedule_method(self, task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
actor_handle = self.actor_refs.get(task.payload_id)
if actor_handle is None:
raise Exception(
"Can't run an actor the server doesn't have a handle for")
arglist, kwargs = self._convert_args(task.args, task.kwargs)
method = getattr(actor_handle, task.name)
opts = decode_options(task.options)
if opts is not None:
method = method.options(**opts)
output = method.remote(*arglist, **kwargs)
ids = self.unify_and_track_outputs(output, task.client_id)
return ray_client_pb2.ClientTaskTicket(return_ids=ids)
def _schedule_actor(self, task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
remote_class = self.lookup_or_register_actor(
task.payload_id, task.client_id,
decode_options(task.baseline_options))
arglist, kwargs = self._convert_args(task.args, task.kwargs)
opts = decode_options(task.options)
if opts is not None:
remote_class = remote_class.options(**opts)
with current_server(self):
actor = remote_class.remote(*arglist, **kwargs)
self.actor_refs[actor._actor_id.binary()] = actor
self.actor_owners[task.client_id].add(actor._actor_id.binary())
return ray_client_pb2.ClientTaskTicket(
return_ids=[actor._actor_id.binary()])
def _schedule_function(self, task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
remote_func = self.lookup_or_register_func(
task.payload_id, task.client_id,
decode_options(task.baseline_options))
arglist, kwargs = self._convert_args(task.args, task.kwargs)
opts = decode_options(task.options)
if opts is not None:
remote_func = remote_func.options(**opts)
with current_server(self):
output = remote_func.remote(*arglist, **kwargs)
ids = self.unify_and_track_outputs(output, task.client_id)
return ray_client_pb2.ClientTaskTicket(return_ids=ids)
def _schedule_named_actor(self,
task: ray_client_pb2.ClientTask,
context=None) -> ray_client_pb2.ClientTaskTicket:
assert len(task.payload_id) == 0
actor = ray.get_actor(task.name)
bin_actor_id = actor._actor_id.binary()
self.actor_refs[bin_actor_id] = actor
self.actor_owners[task.client_id].add(bin_actor_id)
self.named_actors.add(bin_actor_id)
return ray_client_pb2.ClientTaskTicket(
return_ids=[actor._actor_id.binary()])
def _convert_args(self, arg_list, kwarg_map):
argout = []
for arg in arg_list:
t = convert_from_arg(arg, self)
argout.append(t)
kwargout = {}
for k in kwarg_map:
kwargout[k] = convert_from_arg(kwarg_map[k], self)
return argout, kwargout
def _prepare_runtime_env(self, job_runtime_env) -> List[str]:
missing_uris = []
uris = job_runtime_env.uris
from ray._private import runtime_env
with disable_client_hook():
for uri in uris:
try:
working_dir = runtime_env.fetch_package(uri)
if working_dir:
os.chdir(str(working_dir))
sys.path.insert(0, str(working_dir))
except IOError:
missing_uris.append(uri)
return missing_uris
def lookup_or_register_func(
self, id: bytes, client_id: str,
options: Optional[Dict]) -> ray.remote_function.RemoteFunction:
with disable_client_hook():
if id not in self.function_refs:
funcref = self.object_refs[client_id][id]
func = ray.get(funcref)
if not inspect.isfunction(func):
raise Exception("Attempting to register function that "
"isn't a function.")
if options is None or len(options) == 0:
self.function_refs[id] = ray.remote(func)
else:
self.function_refs[id] = ray.remote(**options)(func)
return self.function_refs[id]
def lookup_or_register_actor(self, id: bytes, client_id: str,
options: Optional[Dict]):
with disable_client_hook():
if id not in self.registered_actor_classes:
actor_class_ref = self.object_refs[client_id][id]
actor_class = ray.get(actor_class_ref)
if not inspect.isclass(actor_class):
raise Exception("Attempting to schedule actor that "
"isn't a class.")
if options is None or len(options) == 0:
reg_class = ray.remote(actor_class)
else:
reg_class = ray.remote(**options)(actor_class)
self.registered_actor_classes[id] = reg_class
return self.registered_actor_classes[id]
def unify_and_track_outputs(self, output, client_id):
if output is None:
outputs = []
elif isinstance(output, list):
outputs = output
else:
outputs = [output]
for out in outputs:
if out.binary() in self.object_refs[client_id]:
logger.warning(f"Already saw object_ref {out}")
self.object_refs[client_id][out.binary()] = out
return [out.binary() for out in outputs]
def return_exception_in_context(err, context):
if context is not None:
context.set_details(encode_exception(err))
context.set_code(grpc.StatusCode.INTERNAL)
def encode_exception(exception) -> str:
data = cloudpickle.dumps(exception)
return base64.standard_b64encode(data).decode()
def decode_options(
options: ray_client_pb2.TaskOptions) -> Optional[Dict[str, Any]]:
if options.json_options == "":
return None
opts = json.loads(options.json_options)
assert isinstance(opts, dict)
if opts.get("placement_group", None):
# Placement groups in Ray client options are serialized as dicts.
# Convert the dict to a PlacementGroup.
opts["placement_group"] = PlacementGroup.from_dict(
opts["placement_group"])
return opts
@dataclass
class ClientServerHandle:
task_servicer: RayletServicer
data_servicer: DataServicer
logs_servicer: LogstreamServicer
grpc_server: grpc.Server
# Add a hook for all the cases that previously
# expected simply a gRPC server
def __getattr__(self, attr):
return getattr(self.grpc_server, attr)
def serve(connection_str, ray_connect_handler=None):
def default_connect_handler(job_config: JobConfig = None):
with disable_client_hook():
if not ray.is_initialized():
return ray.init(job_config=job_config)
ray_connect_handler = ray_connect_handler or default_connect_handler
server = grpc.server(
futures.ThreadPoolExecutor(max_workers=CLIENT_SERVER_MAX_THREADS),
options=[
("grpc.max_send_message_length", GRPC_MAX_MESSAGE_SIZE),
("grpc.max_receive_message_length", GRPC_MAX_MESSAGE_SIZE),
])
task_servicer = RayletServicer(ray_connect_handler)
data_servicer = DataServicer(task_servicer)
logs_servicer = LogstreamServicer()
ray_client_pb2_grpc.add_RayletDriverServicer_to_server(
task_servicer, server)
ray_client_pb2_grpc.add_RayletDataStreamerServicer_to_server(
data_servicer, server)
ray_client_pb2_grpc.add_RayletLogStreamerServicer_to_server(
logs_servicer, server)
server.add_insecure_port(connection_str)
current_handle = ClientServerHandle(
task_servicer=task_servicer,
data_servicer=data_servicer,
logs_servicer=logs_servicer,
grpc_server=server,
)
server.start()
return current_handle
def init_and_serve(connection_str, *args, **kwargs):
with disable_client_hook():
# Disable client mode inside the worker's environment
info = ray.init(*args, **kwargs)
def ray_connect_handler(job_config=None):
if ray.is_initialized():
return info
else:
return ray.init(job_config=job_config, *args, **kwargs)
server_handle = serve(
connection_str, ray_connect_handler=ray_connect_handler)
return (server_handle, info)
def shutdown_with_server(server, _exiting_interpreter=False):
server.stop(1)
with disable_client_hook():
ray.shutdown(_exiting_interpreter)
def create_ray_handler(redis_address, redis_password):
def ray_connect_handler(job_config: JobConfig = None):
if redis_address:
if redis_password:
ray.init(
address=redis_address,
_redis_password=redis_password,
job_config=job_config)
else:
ray.init(address=redis_address, job_config=job_config)
else:
ray.init(job_config=job_config)
return ray_connect_handler
def try_create_redis_client(args):
if "redis-address" not in args:
possible = ray._private.services.find_redis_address()
if len(possible) != 1:
return None
address = possible.pop()
else:
address = args["redis-address"]
if args.redis_password is None:
password = ray.ray_constants.REDIS_DEFAULT_PASSWORD
else:
password = args.redis_password
return ray._private.services.create_redis_client(address, password)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--host", type=str, default="0.0.0.0", help="Host IP to bind to")
parser.add_argument(
"-p", "--port", type=int, default=50051, help="Port to bind to")
parser.add_argument(
"--redis-address",
required=False,
type=str,
help="Address to use to connect to Ray")
parser.add_argument(
"--redis-password",
required=False,
type=str,
help="Password for connecting to Redis")
args = parser.parse_args()
logging.basicConfig(level="INFO")
# because it requires `ray.init` to be called, which only connect handlers
# should do.
redis_client = None
ray_connect_handler = create_ray_handler(args.redis_address,
args.redis_password)
hostport = "%s:%d" % (args.host, args.port)
logger.info(f"Starting Ray Client server on {hostport}")
server = serve(hostport, ray_connect_handler)
try:
while True:
health_report = {
"time": time.time(),
}
try:
if not redis_client:
redis_client = try_create_redis_client(args)
redis_client.hset("healthcheck:ray_client_server", "value",
json.dumps(health_report))
except Exception as e:
logger.error("Failed to put health check.")
logger.exception(e)
time.sleep(1)
except KeyboardInterrupt:
server.stop(0)
if __name__ == "__main__":
main()
| true | true |
f729353149eb39923fbab7eeaa37a5be66fd809d | 679 | py | Python | migrations/versions/cce9d107c21a_add_status_as_of_column.py | ourresearch/journalsdb | 169feb9be684eac59f3294dccdb319eb10fe1958 | [
"MIT"
] | 8 | 2021-02-01T21:00:20.000Z | 2022-01-25T09:51:24.000Z | migrations/versions/cce9d107c21a_add_status_as_of_column.py | ourresearch/journalsdb | 169feb9be684eac59f3294dccdb319eb10fe1958 | [
"MIT"
] | 43 | 2021-04-28T00:20:53.000Z | 2022-03-09T00:39:56.000Z | migrations/versions/cce9d107c21a_add_status_as_of_column.py | ourresearch/journalsdb | 169feb9be684eac59f3294dccdb319eb10fe1958 | [
"MIT"
] | null | null | null | """add status_as_of column
Revision ID: cce9d107c21a
Revises: 9f8c327dd506
Create Date: 2021-07-08 19:16:15.975527
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "cce9d107c21a"
down_revision = "9f8c327dd506"
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column("journals", sa.Column("status_as_of", sa.DateTime(), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column("journals", "status_as_of")
# ### end Alembic commands ###
| 23.413793 | 86 | 0.701031 | from alembic import op
import sqlalchemy as sa
revision = "cce9d107c21a"
down_revision = "9f8c327dd506"
branch_labels = None
depends_on = None
def upgrade():
| true | true |
f7293603000d5152866e1493476863a150c34abe | 567 | py | Python | Math/Leetcode49.py | Rylie-W/LeetRecord | 623c4efe88b3af54b8a65f6ec23db850b8c6f46f | [
"Apache-2.0"
] | null | null | null | Math/Leetcode49.py | Rylie-W/LeetRecord | 623c4efe88b3af54b8a65f6ec23db850b8c6f46f | [
"Apache-2.0"
] | null | null | null | Math/Leetcode49.py | Rylie-W/LeetRecord | 623c4efe88b3af54b8a65f6ec23db850b8c6f46f | [
"Apache-2.0"
] | null | null | null | class Solution:
def groupAnagrams(self, strs):
if not strs or len(strs)==0:
return strs
memo=dict()
for i in strs:
temp=self.sortString(i)
if memo.get(temp):
memo[temp].append(i)
else: memo[temp]=[i]
return memo.values()
def sortString(self,s):
sorted_s=sorted(s)
return ''.join(sorted_s)
if __name__ == '__main__':
sol=Solution()
# strs = ["eat", "tea", "tan", "ate", "nat", "bat"]
strs=['','']
print(sol.groupAnagrams(strs))
| 23.625 | 55 | 0.513228 | class Solution:
def groupAnagrams(self, strs):
if not strs or len(strs)==0:
return strs
memo=dict()
for i in strs:
temp=self.sortString(i)
if memo.get(temp):
memo[temp].append(i)
else: memo[temp]=[i]
return memo.values()
def sortString(self,s):
sorted_s=sorted(s)
return ''.join(sorted_s)
if __name__ == '__main__':
sol=Solution()
strs=['','']
print(sol.groupAnagrams(strs))
| true | true |
f72936579a668b3181430356252546d44be9c15c | 915 | py | Python | 1.5/15.py | marcelox5/python-course | b0848ee41b51d8f550431bdcbbbe9c38cfa9e58c | [
"MIT"
] | null | null | null | 1.5/15.py | marcelox5/python-course | b0848ee41b51d8f550431bdcbbbe9c38cfa9e58c | [
"MIT"
] | null | null | null | 1.5/15.py | marcelox5/python-course | b0848ee41b51d8f550431bdcbbbe9c38cfa9e58c | [
"MIT"
] | null | null | null | #!C:\Python34
print("print(2+4)=", 2+4)
print("_+__+__+__+_")
print("\n")
print("print(3-5)", 3-5)
print("_+__+__+__+_")
print("\n")
print("print(2--5)", 2--5)
print("_+__+__+__+_")
print("\n")
print("print(+2-5)", +2-5)
print("_+__+__+__+_")
print("\n")
print("print(3*8)=", 3*8)
print("_+__+__+__+_")
print("\n")
print("print(2**4)=", 2**4)
print("_+__+__+__+_")
print("\n")
print("print(7 / 3)=", 7 / 3)
print("_+__+__+__+_")
print("\n")
print("print(7.0 / 3)=", 7.0 / 3)
print("_+__+__+__+_")
print("\n")
print("print(7 // 3)=", 7 // 3)
print("_+__+__+__+_")
print("\n")
print("print(7.0 // 3)=", 7.0 // 3)
print("_+__+__+__+_")
print("\n")
print("print(0 // 3)=", 0 // 3)
print("_+__+__+__+_")
print("\n")
print("print(0 / 3)=", 0 / 3)
print("_+__+__+__+_")
print("\n")
print("print(8 % 5)=", 8 % 5)
print("_+__+__+__+_")
print("\n")
print("print(8 % 4)=", 8 % 4)
print("_+__+__+__+_")
print("\n") | 16.339286 | 35 | 0.519126 |
print("print(2+4)=", 2+4)
print("_+__+__+__+_")
print("\n")
print("print(3-5)", 3-5)
print("_+__+__+__+_")
print("\n")
print("print(2--5)", 2--5)
print("_+__+__+__+_")
print("\n")
print("print(+2-5)", +2-5)
print("_+__+__+__+_")
print("\n")
print("print(3*8)=", 3*8)
print("_+__+__+__+_")
print("\n")
print("print(2**4)=", 2**4)
print("_+__+__+__+_")
print("\n")
print("print(7 / 3)=", 7 / 3)
print("_+__+__+__+_")
print("\n")
print("print(7.0 / 3)=", 7.0 / 3)
print("_+__+__+__+_")
print("\n")
print("print(7 // 3)=", 7 // 3)
print("_+__+__+__+_")
print("\n")
print("print(7.0 // 3)=", 7.0 // 3)
print("_+__+__+__+_")
print("\n")
print("print(0 // 3)=", 0 // 3)
print("_+__+__+__+_")
print("\n")
print("print(0 / 3)=", 0 / 3)
print("_+__+__+__+_")
print("\n")
print("print(8 % 5)=", 8 % 5)
print("_+__+__+__+_")
print("\n")
print("print(8 % 4)=", 8 % 4)
print("_+__+__+__+_")
print("\n") | true | true |
f72937da28c924d792b6d61ba3209ed0c4d40f71 | 1,017 | py | Python | vindinium/models/map.py | renatopp/vindinium-python | d6cb7fd08eb9af8a8ca03214b9a42a2e8e9d7c2a | [
"MIT"
] | 6 | 2015-09-30T06:29:36.000Z | 2019-04-07T11:43:47.000Z | vindinium/models/map.py | renatopp/vindinium-python | d6cb7fd08eb9af8a8ca03214b9a42a2e8e9d7c2a | [
"MIT"
] | null | null | null | vindinium/models/map.py | renatopp/vindinium-python | d6cb7fd08eb9af8a8ca03214b9a42a2e8e9d7c2a | [
"MIT"
] | 10 | 2015-06-20T21:03:14.000Z | 2019-04-07T11:43:30.000Z | __all__ = ['Map']
class Map(object):
'''Represents static elements in the game, such as walls, paths, taverns,
mines and spawn points.
Attributes:
size (int): the board size (in a single axis).
'''
def __init__(self, size):
'''Constructor.
Args:
size (int): the board size.
'''
self.size = size
self.__board = [[0 for i in xrange(size)] for j in xrange(size)]
def __getitem__(self, key):
'''Returns an item in the map.'''
return self.__board[key[0]][key[1]]
def __setitem__(self, key, value):
'''Sets an item in the map.'''
self.__board[key[0]][key[1]] = value
def __str__(self):
'''Pretty map.'''
s = ' '
s += '-'*(self.size) + '\n'
for y in xrange(self.size):
s += '|'
for x in xrange(self.size):
s += str(self[x, y] or ' ')
s += '|\n'
s += ' ' + '-'*(self.size)
return s | 26.763158 | 78 | 0.481809 | __all__ = ['Map']
class Map(object):
def __init__(self, size):
self.size = size
self.__board = [[0 for i in xrange(size)] for j in xrange(size)]
def __getitem__(self, key):
return self.__board[key[0]][key[1]]
def __setitem__(self, key, value):
self.__board[key[0]][key[1]] = value
def __str__(self):
s = ' '
s += '-'*(self.size) + '\n'
for y in xrange(self.size):
s += '|'
for x in xrange(self.size):
s += str(self[x, y] or ' ')
s += '|\n'
s += ' ' + '-'*(self.size)
return s | true | true |
f729388c6f3f9d33692a0b71c6bf0abe64b5a73d | 34,804 | py | Python | boltons/dictutils.py | bogdan-kulynych/boltons | a065dc576c1dc01e9539d973dfb961591ecee427 | [
"BSD-3-Clause"
] | 1 | 2020-12-08T18:37:42.000Z | 2020-12-08T18:37:42.000Z | boltons/dictutils.py | bogdan-kulynych/boltons | a065dc576c1dc01e9539d973dfb961591ecee427 | [
"BSD-3-Clause"
] | null | null | null | boltons/dictutils.py | bogdan-kulynych/boltons | a065dc576c1dc01e9539d973dfb961591ecee427 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""Python has a very powerful mapping type at its core: the :class:`dict`
type. While versatile and featureful, the :class:`dict` prioritizes
simplicity and performance. As a result, it does not retain the order
of item insertion [1]_, nor does it store multiple values per key. It
is a fast, unordered 1:1 mapping.
The :class:`OrderedMultiDict` contrasts to the built-in :class:`dict`,
as a relatively maximalist, ordered 1:n subtype of
:class:`dict`. Virtually every feature of :class:`dict` has been
retooled to be intuitive in the face of this added
complexity. Additional methods have been added, such as
:class:`collections.Counter`-like functionality.
A prime advantage of the :class:`OrderedMultiDict` (OMD) is its
non-destructive nature. Data can be added to an :class:`OMD` without being
rearranged or overwritten. The property can allow the developer to
work more freely with the data, as well as make more assumptions about
where input data will end up in the output, all without any extra
work.
One great example of this is the :meth:`OMD.inverted()` method, which
returns a new OMD with the values as keys and the keys as values. All
the data and the respective order is still represented in the inverted
form, all from an operation which would be outright wrong and reckless
with a built-in :class:`dict` or :class:`collections.OrderedDict`.
The OMD has been performance tuned to be suitable for a wide range of
usages, including as a basic unordered MultiDict. Special
thanks to `Mark Williams`_ for all his help.
.. [1] As of 2015, `basic dicts on PyPy are ordered
<http://morepypy.blogspot.com/2015/01/faster-more-memory-efficient-and-more.html>`_,
and as of December 2017, `basic dicts in CPython 3 are now ordered
<https://mail.python.org/pipermail/python-dev/2017-December/151283.html>`_, as
well.
.. _Mark Williams: https://github.com/markrwilliams
"""
try:
from collections.abc import KeysView, ValuesView, ItemsView
except ImportError:
from collections import KeysView, ValuesView, ItemsView
import itertools
try:
from itertools import izip_longest
except ImportError:
from itertools import zip_longest as izip_longest
try:
from typeutils import make_sentinel
_MISSING = make_sentinel(var_name='_MISSING')
except ImportError:
_MISSING = object()
PREV, NEXT, KEY, VALUE, SPREV, SNEXT = range(6)
__all__ = ['MultiDict', 'OMD', 'OrderedMultiDict', 'OneToOne', 'ManyToMany', 'subdict', 'FrozenDict']
try:
profile
except NameError:
profile = lambda x: x
class OrderedMultiDict(dict):
"""A MultiDict is a dictionary that can have multiple values per key
and the OrderedMultiDict (OMD) is a MultiDict that retains
original insertion order. Common use cases include:
* handling query strings parsed from URLs
* inverting a dictionary to create a reverse index (values to keys)
* stacking data from multiple dictionaries in a non-destructive way
The OrderedMultiDict constructor is identical to the built-in
:class:`dict`, and overall the API constitutes an intuitive
superset of the built-in type:
>>> omd = OrderedMultiDict()
>>> omd['a'] = 1
>>> omd['b'] = 2
>>> omd.add('a', 3)
>>> omd.get('a')
3
>>> omd.getlist('a')
[1, 3]
Some non-:class:`dict`-like behaviors also make an appearance,
such as support for :func:`reversed`:
>>> list(reversed(omd))
['b', 'a']
Note that unlike some other MultiDicts, this OMD gives precedence
to the most recent value added. ``omd['a']`` refers to ``3``, not
``1``.
>>> omd
OrderedMultiDict([('a', 1), ('b', 2), ('a', 3)])
>>> omd.poplast('a')
3
>>> omd
OrderedMultiDict([('a', 1), ('b', 2)])
>>> omd.pop('a')
1
>>> omd
OrderedMultiDict([('b', 2)])
If you want a safe-to-modify or flat dictionary, use
:meth:`OrderedMultiDict.todict()`.
>>> from pprint import pprint as pp # preserve printed ordering
>>> omd = OrderedMultiDict([('a', 1), ('b', 2), ('a', 3)])
>>> pp(omd.todict())
{'a': 3, 'b': 2}
>>> pp(omd.todict(multi=True))
{'a': [1, 3], 'b': [2]}
With ``multi=False``, items appear with the keys in to original
insertion order, alongside the most-recently inserted value for
that key.
>>> OrderedMultiDict([('a', 1), ('b', 2), ('a', 3)]).items(multi=False)
[('a', 3), ('b', 2)]
.. warning::
``dict(omd)`` changed behavior `in Python 3.7
<https://bugs.python.org/issue34320>`_ due to changes made to
support the transition from :class:`collections.OrderedDict` to
the built-in dictionary being ordered. Before 3.7, the result
would be a new dictionary, with values that were lists, similar
to ``omd.todict(multi=True)`` (but only shallow-copy; the lists
were direct references to OMD internal structures). From 3.7
onward, the values became singular, like
``omd.todict(multi=False)``. For reliable cross-version
behavior, just use :meth:`~OrderedMultiDict.todict()`.
"""
def __init__(self, *args, **kwargs):
if len(args) > 1:
raise TypeError('%s expected at most 1 argument, got %s'
% (self.__class__.__name__, len(args)))
super(OrderedMultiDict, self).__init__()
self._clear_ll()
if args:
self.update_extend(args[0])
if kwargs:
self.update(kwargs)
def _clear_ll(self):
try:
_map = self._map
except AttributeError:
_map = self._map = {}
self.root = []
_map.clear()
self.root[:] = [self.root, self.root, None]
def _insert(self, k, v):
root = self.root
cells = self._map.setdefault(k, [])
last = root[PREV]
cell = [last, root, k, v]
last[NEXT] = root[PREV] = cell
cells.append(cell)
def add(self, k, v):
"""Add a single value *v* under a key *k*. Existing values under *k*
are preserved.
"""
values = super(OrderedMultiDict, self).setdefault(k, [])
self._insert(k, v)
values.append(v)
def addlist(self, k, v):
"""Add an iterable of values underneath a specific key, preserving
any values already under that key.
>>> omd = OrderedMultiDict([('a', -1)])
>>> omd.addlist('a', range(3))
>>> omd
OrderedMultiDict([('a', -1), ('a', 0), ('a', 1), ('a', 2)])
Called ``addlist`` for consistency with :meth:`getlist`, but
tuples and other sequences and iterables work.
"""
self_insert = self._insert
values = super(OrderedMultiDict, self).setdefault(k, [])
for subv in v:
self_insert(k, subv)
values.extend(v)
def get(self, k, default=None):
"""Return the value for key *k* if present in the dictionary, else
*default*. If *default* is not given, ``None`` is returned.
This method never raises a :exc:`KeyError`.
To get all values under a key, use :meth:`OrderedMultiDict.getlist`.
"""
return super(OrderedMultiDict, self).get(k, [default])[-1]
def getlist(self, k, default=_MISSING):
"""Get all values for key *k* as a list, if *k* is in the
dictionary, else *default*. The list returned is a copy and
can be safely mutated. If *default* is not given, an empty
:class:`list` is returned.
"""
try:
return super(OrderedMultiDict, self).__getitem__(k)[:]
except KeyError:
if default is _MISSING:
return []
return default
def clear(self):
"Empty the dictionary."
super(OrderedMultiDict, self).clear()
self._clear_ll()
def setdefault(self, k, default=_MISSING):
"""If key *k* is in the dictionary, return its value. If not, insert
*k* with a value of *default* and return *default*. *default*
defaults to ``None``. See :meth:`dict.setdefault` for more
information.
"""
if not super(OrderedMultiDict, self).__contains__(k):
self[k] = None if default is _MISSING else default
return self[k]
def copy(self):
"Return a shallow copy of the dictionary."
return self.__class__(self.iteritems(multi=True))
@classmethod
def fromkeys(cls, keys, default=None):
"""Create a dictionary from a list of keys, with all the values
set to *default*, or ``None`` if *default* is not set.
"""
return cls([(k, default) for k in keys])
def update(self, E, **F):
"""Add items from a dictionary or iterable (and/or keyword arguments),
overwriting values under an existing key. See
:meth:`dict.update` for more details.
"""
# E and F are throwback names to the dict() __doc__
if E is self:
return
self_add = self.add
if isinstance(E, OrderedMultiDict):
for k in E:
if k in self:
del self[k]
for k, v in E.iteritems(multi=True):
self_add(k, v)
elif callable(getattr(E, 'keys', None)):
for k in E.keys():
self[k] = E[k]
else:
seen = set()
seen_add = seen.add
for k, v in E:
if k not in seen and k in self:
del self[k]
seen_add(k)
self_add(k, v)
for k in F:
self[k] = F[k]
return
def update_extend(self, E, **F):
"""Add items from a dictionary, iterable, and/or keyword
arguments without overwriting existing items present in the
dictionary. Like :meth:`update`, but adds to existing keys
instead of overwriting them.
"""
if E is self:
iterator = iter(E.items())
elif isinstance(E, OrderedMultiDict):
iterator = E.iteritems(multi=True)
elif hasattr(E, 'keys'):
iterator = ((k, E[k]) for k in E.keys())
else:
iterator = E
self_add = self.add
for k, v in iterator:
self_add(k, v)
def __setitem__(self, k, v):
if super(OrderedMultiDict, self).__contains__(k):
self._remove_all(k)
self._insert(k, v)
super(OrderedMultiDict, self).__setitem__(k, [v])
def __getitem__(self, k):
return super(OrderedMultiDict, self).__getitem__(k)[-1]
def __delitem__(self, k):
super(OrderedMultiDict, self).__delitem__(k)
self._remove_all(k)
def __eq__(self, other):
if self is other:
return True
try:
if len(other) != len(self):
return False
except TypeError:
return False
if isinstance(other, OrderedMultiDict):
selfi = self.iteritems(multi=True)
otheri = other.iteritems(multi=True)
zipped_items = izip_longest(selfi, otheri, fillvalue=(None, None))
for (selfk, selfv), (otherk, otherv) in zipped_items:
if selfk != otherk or selfv != otherv:
return False
if not(next(selfi, _MISSING) is _MISSING
and next(otheri, _MISSING) is _MISSING):
# leftovers (TODO: watch for StopIteration?)
return False
return True
elif hasattr(other, 'keys'):
for selfk in self:
try:
other[selfk] == self[selfk]
except KeyError:
return False
return True
return False
def __ne__(self, other):
return not (self == other)
def pop(self, k, default=_MISSING):
"""Remove all values under key *k*, returning the most-recently
inserted value. Raises :exc:`KeyError` if the key is not
present and no *default* is provided.
"""
try:
return self.popall(k)[-1]
except KeyError:
if default is _MISSING:
raise KeyError(k)
return default
def popall(self, k, default=_MISSING):
"""Remove all values under key *k*, returning them in the form of
a list. Raises :exc:`KeyError` if the key is not present and no
*default* is provided.
"""
super_self = super(OrderedMultiDict, self)
if super_self.__contains__(k):
self._remove_all(k)
if default is _MISSING:
return super_self.pop(k)
return super_self.pop(k, default)
def poplast(self, k=_MISSING, default=_MISSING):
"""Remove and return the most-recently inserted value under the key
*k*, or the most-recently inserted key if *k* is not
provided. If no values remain under *k*, it will be removed
from the OMD. Raises :exc:`KeyError` if *k* is not present in
the dictionary, or the dictionary is empty.
"""
if k is _MISSING:
if self:
k = self.root[PREV][KEY]
else:
raise KeyError('empty %r' % type(self))
try:
self._remove(k)
except KeyError:
if default is _MISSING:
raise KeyError(k)
return default
values = super(OrderedMultiDict, self).__getitem__(k)
v = values.pop()
if not values:
super(OrderedMultiDict, self).__delitem__(k)
return v
def _remove(self, k):
values = self._map[k]
cell = values.pop()
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
if not values:
del self._map[k]
def _remove_all(self, k):
values = self._map[k]
while values:
cell = values.pop()
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
del self._map[k]
def iteritems(self, multi=False):
"""Iterate over the OMD's items in insertion order. By default,
yields only the most-recently inserted value for each key. Set
*multi* to ``True`` to get all inserted items.
"""
root = self.root
curr = root[NEXT]
if multi:
while curr is not root:
yield curr[KEY], curr[VALUE]
curr = curr[NEXT]
else:
for key in self.iterkeys():
yield key, self[key]
def iterkeys(self, multi=False):
"""Iterate over the OMD's keys in insertion order. By default, yields
each key once, according to the most recent insertion. Set
*multi* to ``True`` to get all keys, including duplicates, in
insertion order.
"""
root = self.root
curr = root[NEXT]
if multi:
while curr is not root:
yield curr[KEY]
curr = curr[NEXT]
else:
yielded = set()
yielded_add = yielded.add
while curr is not root:
k = curr[KEY]
if k not in yielded:
yielded_add(k)
yield k
curr = curr[NEXT]
def itervalues(self, multi=False):
"""Iterate over the OMD's values in insertion order. By default,
yields the most-recently inserted value per unique key. Set
*multi* to ``True`` to get all values according to insertion
order.
"""
for k, v in self.iteritems(multi=multi):
yield v
def todict(self, multi=False):
"""Gets a basic :class:`dict` of the items in this dictionary. Keys
are the same as the OMD, values are the most recently inserted
values for each key.
Setting the *multi* arg to ``True`` is yields the same
result as calling :class:`dict` on the OMD, except that all the
value lists are copies that can be safely mutated.
"""
if multi:
return dict([(k, self.getlist(k)) for k in self])
return dict([(k, self[k]) for k in self])
def sorted(self, key=None, reverse=False):
"""Similar to the built-in :func:`sorted`, except this method returns
a new :class:`OrderedMultiDict` sorted by the provided key
function, optionally reversed.
Args:
key (callable): A callable to determine the sort key of
each element. The callable should expect an **item**
(key-value pair tuple).
reverse (bool): Set to ``True`` to reverse the ordering.
>>> omd = OrderedMultiDict(zip(range(3), range(3)))
>>> omd.sorted(reverse=True)
OrderedMultiDict([(2, 2), (1, 1), (0, 0)])
Note that the key function receives an **item** (key-value
tuple), so the recommended signature looks like:
>>> omd = OrderedMultiDict(zip('hello', 'world'))
>>> omd.sorted(key=lambda i: i[1]) # i[0] is the key, i[1] is the val
OrderedMultiDict([('o', 'd'), ('l', 'l'), ('e', 'o'), ('h', 'w')])
"""
cls = self.__class__
return cls(sorted(self.iteritems(), key=key, reverse=reverse))
def sortedvalues(self, key=None, reverse=False):
"""Returns a copy of the :class:`OrderedMultiDict` with the same keys
in the same order as the original OMD, but the values within
each keyspace have been sorted according to *key* and
*reverse*.
Args:
key (callable): A single-argument callable to determine
the sort key of each element. The callable should expect
an **item** (key-value pair tuple).
reverse (bool): Set to ``True`` to reverse the ordering.
>>> omd = OrderedMultiDict()
>>> omd.addlist('even', [6, 2])
>>> omd.addlist('odd', [1, 5])
>>> omd.add('even', 4)
>>> omd.add('odd', 3)
>>> somd = omd.sortedvalues()
>>> somd.getlist('even')
[2, 4, 6]
>>> somd.keys(multi=True) == omd.keys(multi=True)
True
>>> omd == somd
False
>>> somd
OrderedMultiDict([('even', 2), ('even', 4), ('odd', 1), ('odd', 3), ('even', 6), ('odd', 5)])
As demonstrated above, contents and key order are
retained. Only value order changes.
"""
try:
superself_iteritems = super(OrderedMultiDict, self).iteritems()
except AttributeError:
superself_iteritems = super(OrderedMultiDict, self).items()
# (not reverse) because they pop off in reverse order for reinsertion
sorted_val_map = dict([(k, sorted(v, key=key, reverse=(not reverse)))
for k, v in superself_iteritems])
ret = self.__class__()
for k in self.iterkeys(multi=True):
ret.add(k, sorted_val_map[k].pop())
return ret
def inverted(self):
"""Returns a new :class:`OrderedMultiDict` with values and keys
swapped, like creating dictionary transposition or reverse
index. Insertion order is retained and all keys and values
are represented in the output.
>>> omd = OMD([(0, 2), (1, 2)])
>>> omd.inverted().getlist(2)
[0, 1]
Inverting twice yields a copy of the original:
>>> omd.inverted().inverted()
OrderedMultiDict([(0, 2), (1, 2)])
"""
return self.__class__((v, k) for k, v in self.iteritems(multi=True))
def counts(self):
"""Returns a mapping from key to number of values inserted under that
key. Like :py:class:`collections.Counter`, but returns a new
:class:`OrderedMultiDict`.
"""
# Returns an OMD because Counter/OrderedDict may not be
# available, and neither Counter nor dict maintain order.
super_getitem = super(OrderedMultiDict, self).__getitem__
return self.__class__((k, len(super_getitem(k))) for k in self)
def keys(self, multi=False):
"""Returns a list containing the output of :meth:`iterkeys`. See
that method's docs for more details.
"""
return list(self.iterkeys(multi=multi))
def values(self, multi=False):
"""Returns a list containing the output of :meth:`itervalues`. See
that method's docs for more details.
"""
return list(self.itervalues(multi=multi))
def items(self, multi=False):
"""Returns a list containing the output of :meth:`iteritems`. See
that method's docs for more details.
"""
return list(self.iteritems(multi=multi))
def __iter__(self):
return self.iterkeys()
def __reversed__(self):
root = self.root
curr = root[PREV]
lengths = {}
lengths_sd = lengths.setdefault
get_values = super(OrderedMultiDict, self).__getitem__
while curr is not root:
k = curr[KEY]
vals = get_values(k)
if lengths_sd(k, 1) == len(vals):
yield k
lengths[k] += 1
curr = curr[PREV]
def __repr__(self):
cn = self.__class__.__name__
kvs = ', '.join([repr((k, v)) for k, v in self.iteritems(multi=True)])
return '%s([%s])' % (cn, kvs)
def viewkeys(self):
"OMD.viewkeys() -> a set-like object providing a view on OMD's keys"
return KeysView(self)
def viewvalues(self):
"OMD.viewvalues() -> an object providing a view on OMD's values"
return ValuesView(self)
def viewitems(self):
"OMD.viewitems() -> a set-like object providing a view on OMD's items"
return ItemsView(self)
# A couple of convenient aliases
OMD = OrderedMultiDict
MultiDict = OrderedMultiDict
class FastIterOrderedMultiDict(OrderedMultiDict):
"""An OrderedMultiDict backed by a skip list. Iteration over keys
is faster and uses constant memory but adding duplicate key-value
pairs is slower. Brainchild of Mark Williams.
"""
def _clear_ll(self):
# TODO: always reset objects? (i.e., no else block below)
try:
_map = self._map
except AttributeError:
_map = self._map = {}
self.root = []
_map.clear()
self.root[:] = [self.root, self.root,
None, None,
self.root, self.root]
def _insert(self, k, v):
root = self.root
empty = []
cells = self._map.setdefault(k, empty)
last = root[PREV]
if cells is empty:
cell = [last, root,
k, v,
last, root]
# was the last one skipped?
if last[SPREV][SNEXT] is root:
last[SPREV][SNEXT] = cell
last[NEXT] = last[SNEXT] = root[PREV] = root[SPREV] = cell
cells.append(cell)
else:
# if the previous was skipped, go back to the cell that
# skipped it
sprev = last[SPREV] if (last[SPREV][SNEXT] is not last) else last
cell = [last, root,
k, v,
sprev, root]
# skip me
last[SNEXT] = root
last[NEXT] = root[PREV] = root[SPREV] = cell
cells.append(cell)
def _remove(self, k):
cells = self._map[k]
cell = cells.pop()
if not cells:
del self._map[k]
cell[PREV][SNEXT] = cell[SNEXT]
if cell[PREV][SPREV][SNEXT] is cell:
cell[PREV][SPREV][SNEXT] = cell[NEXT]
elif cell[SNEXT] is cell[NEXT]:
cell[SPREV][SNEXT], cell[SNEXT][SPREV] = cell[SNEXT], cell[SPREV]
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
def _remove_all(self, k):
cells = self._map.pop(k)
while cells:
cell = cells.pop()
if cell[PREV][SPREV][SNEXT] is cell:
cell[PREV][SPREV][SNEXT] = cell[NEXT]
elif cell[SNEXT] is cell[NEXT]:
cell[SPREV][SNEXT], cell[SNEXT][SPREV] = cell[SNEXT], cell[SPREV]
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
cell[PREV][SNEXT] = cell[SNEXT]
def iteritems(self, multi=False):
next_link = NEXT if multi else SNEXT
root = self.root
curr = root[next_link]
while curr is not root:
yield curr[KEY], curr[VALUE]
curr = curr[next_link]
def iterkeys(self, multi=False):
next_link = NEXT if multi else SNEXT
root = self.root
curr = root[next_link]
while curr is not root:
yield curr[KEY]
curr = curr[next_link]
def __reversed__(self):
root = self.root
curr = root[PREV]
while curr is not root:
if curr[SPREV][SNEXT] is not curr:
curr = curr[SPREV]
if curr is root:
break
yield curr[KEY]
curr = curr[PREV]
_SELF_INIT_MARKER = object()
class OneToOne(dict):
"""Implements a one-to-one mapping dictionary. In addition to
inheriting from and behaving exactly like the builtin
:class:`dict`, all values are automatically added as keys on a
reverse mapping, available as the `inv` attribute. This
arrangement keeps key and value namespaces distinct.
Basic operations are intuitive:
>>> oto = OneToOne({'a': 1, 'b': 2})
>>> print(oto['a'])
1
>>> print(oto.inv[1])
a
>>> len(oto)
2
Overwrites happens in both directions:
>>> oto.inv[1] = 'c'
>>> print(oto.get('a'))
None
>>> len(oto)
2
For a very similar project, with even more one-to-one
functionality, check out `bidict <https://github.com/jab/bidict>`_.
"""
__slots__ = ('inv')
def __init__(self, *a, **kw):
if a and a[0] is _SELF_INIT_MARKER:
self.inv = a[1]
dict.__init__(self, [(v, k) for k, v in self.inv.items()])
else:
dict.__init__(self, *a, **kw)
self.inv = self.__class__(_SELF_INIT_MARKER, self)
def __setitem__(self, key, val):
hash(val) # ensure val is a valid key
if key in self:
dict.__delitem__(self.inv, self[key])
if val in self.inv:
del self.inv[val]
dict.__setitem__(self, key, val)
dict.__setitem__(self.inv, val, key)
def __delitem__(self, key):
dict.__delitem__(self.inv, self[key])
dict.__delitem__(self, key)
def clear(self):
dict.clear(self)
dict.clear(self.inv)
def copy(self):
return self.__class__(self)
def pop(self, key, default=_MISSING):
if key in self:
dict.__delitem__(self.inv, self[key])
return dict.pop(self, key)
if default is not _MISSING:
return default
raise KeyError()
def popitem(self):
key, val = dict.popitem(self)
dict.__delitem__(self.inv, val)
return key, val
def setdefault(self, key, default=None):
if key not in self:
self[key] = default
return self[key]
def update(self, dict_or_iterable, **kw):
if isinstance(dict_or_iterable, dict):
for val in dict_or_iterable.values():
hash(val)
keys_vals = list(dict_or_iterable.items())
else:
for key, val in dict_or_iterable:
hash(key)
hash(val)
keys_vals = list(dict_or_iterable)
for val in kw.values():
hash(val)
keys_vals.extend(kw.items())
for key, val in keys_vals:
self[key] = val
def __repr__(self):
cn = self.__class__.__name__
dict_repr = dict.__repr__(self)
return "%s(%s)" % (cn, dict_repr)
# marker for the secret handshake used internally to set up the invert ManyToMany
_PAIRING = object()
class ManyToMany(object):
"""
a dict-like entity that represents a many-to-many relationship
between two groups of objects
behaves like a dict-of-tuples; also has .inv which is kept
up to date which is a dict-of-tuples in the other direction
also, can be used as a directed graph among hashable python objects
"""
def __init__(self, items=None):
self.data = {}
if type(items) is tuple and items and items[0] is _PAIRING:
self.inv = items[1]
else:
self.inv = self.__class__((_PAIRING, self))
if items:
self.update(items)
return
def get(self, key, default=frozenset()):
try:
return self[key]
except KeyError:
return default
def __getitem__(self, key):
return frozenset(self.data[key])
def __setitem__(self, key, vals):
vals = set(vals)
if key in self:
to_remove = self.data[key] - vals
vals -= self.data[key]
for val in to_remove:
self.remove(key, val)
for val in vals:
self.add(key, val)
def __delitem__(self, key):
for val in self.data.pop(key):
self.inv.data[val].remove(key)
if not self.inv.data[val]:
del self.inv.data[val]
def update(self, iterable):
"""given an iterable of (key, val), add them all"""
if type(iterable) is type(self):
other = iterable
for k in other.data:
if k not in self.data:
self.data[k] = other.data[k]
else:
self.data[k].update(other.data[k])
for k in other.inv.data:
if k not in self.inv.data:
self.inv.data[k] = other.inv.data[k]
else:
self.inv.data[k].update(other.inv.data[k])
elif callable(getattr(iterable, 'keys', None)):
for k in iterable.keys():
self.add(k, iterable[k])
else:
for key, val in iterable:
self.add(key, val)
return
def add(self, key, val):
if key not in self.data:
self.data[key] = set()
self.data[key].add(val)
if val not in self.inv.data:
self.inv.data[val] = set()
self.inv.data[val].add(key)
def remove(self, key, val):
self.data[key].remove(val)
if not self.data[key]:
del self.data[key]
self.inv.data[val].remove(key)
if not self.inv.data[val]:
del self.inv.data[val]
def replace(self, key, newkey):
"""
replace instances of key by newkey
"""
if key not in self.data:
return
self.data[newkey] = fwdset = self.data.pop(key)
for val in fwdset:
revset = self.inv.data[val]
revset.remove(key)
revset.add(newkey)
def iteritems(self):
for key in self.data:
for val in self.data[key]:
yield key, val
def keys(self):
return self.data.keys()
def __contains__(self, key):
return key in self.data
def __iter__(self):
return self.data.__iter__()
def __len__(self):
return self.data.__len__()
def __eq__(self, other):
return type(self) == type(other) and self.data == other.data
def __repr__(self):
cn = self.__class__.__name__
return '%s(%r)' % (cn, list(self.iteritems()))
def subdict(d, keep=None, drop=None):
"""Compute the "subdictionary" of a dict, *d*.
A subdict is to a dict what a subset is a to set. If *A* is a
subdict of *B*, that means that all keys of *A* are present in
*B*.
Returns a new dict with any keys in *drop* removed, and any keys
in *keep* still present, provided they were in the original
dict. *keep* defaults to all keys, *drop* defaults to empty, so
without one of these arguments, calling this function is
equivalent to calling ``dict()``.
>>> from pprint import pprint as pp
>>> pp(subdict({'a': 1, 'b': 2}))
{'a': 1, 'b': 2}
>>> subdict({'a': 1, 'b': 2, 'c': 3}, drop=['b', 'c'])
{'a': 1}
>>> pp(subdict({'a': 1, 'b': 2, 'c': 3}, keep=['a', 'c']))
{'a': 1, 'c': 3}
"""
if keep is None:
keep = d.keys()
if drop is None:
drop = []
keys = set(keep) - set(drop)
return type(d)([(k, v) for k, v in d.items() if k in keys])
class FrozenHashError(TypeError):
pass
class FrozenDict(dict):
"""An immutable dict subtype that is hashable and can itself be used
as a :class:`dict` key or :class:`set` entry. What
:class:`frozenset` is to :class:`set`, FrozenDict is to
:class:`dict`.
There was once an attempt to introduce such a type to the standard
library, but it was rejected: `PEP 416 <https://www.python.org/dev/peps/pep-0416/>`_.
Because FrozenDict is a :class:`dict` subtype, it automatically
works everywhere a dict would, including JSON serialization.
"""
__slots__ = ('_hash',)
def updated(self, *a, **kw):
"""Make a copy and add items from a dictionary or iterable (and/or
keyword arguments), overwriting values under an existing
key. See :meth:`dict.update` for more details.
"""
data = dict(self)
data.update(*a, **kw)
return type(self)(data)
@classmethod
def fromkeys(cls, keys, value=None):
# one of the lesser known and used/useful dict methods
return cls(dict.fromkeys(keys, value))
def __repr__(self):
cn = self.__class__.__name__
return '%s(%s)' % (cn, dict.__repr__(self))
def __reduce_ex__(self, protocol):
return type(self), (dict(self),)
def __hash__(self):
try:
ret = self._hash
except AttributeError:
try:
ret = self._hash = hash(frozenset(self.items()))
except Exception as e:
ret = self._hash = FrozenHashError(e)
if ret.__class__ is FrozenHashError:
raise ret
return ret
def __copy__(self):
return self # immutable types don't copy, see tuple's behavior
# block everything else
def _raise_frozen_typeerror(self, *a, **kw):
"raises a TypeError, because FrozenDicts are immutable"
raise TypeError('%s object is immutable' % self.__class__.__name__)
__setitem__ = __delitem__ = update = _raise_frozen_typeerror
setdefault = pop = popitem = clear = _raise_frozen_typeerror
del _raise_frozen_typeerror
# end dictutils.py
| 33.497594 | 101 | 0.575509 |
try:
from collections.abc import KeysView, ValuesView, ItemsView
except ImportError:
from collections import KeysView, ValuesView, ItemsView
import itertools
try:
from itertools import izip_longest
except ImportError:
from itertools import zip_longest as izip_longest
try:
from typeutils import make_sentinel
_MISSING = make_sentinel(var_name='_MISSING')
except ImportError:
_MISSING = object()
PREV, NEXT, KEY, VALUE, SPREV, SNEXT = range(6)
__all__ = ['MultiDict', 'OMD', 'OrderedMultiDict', 'OneToOne', 'ManyToMany', 'subdict', 'FrozenDict']
try:
profile
except NameError:
profile = lambda x: x
class OrderedMultiDict(dict):
def __init__(self, *args, **kwargs):
if len(args) > 1:
raise TypeError('%s expected at most 1 argument, got %s'
% (self.__class__.__name__, len(args)))
super(OrderedMultiDict, self).__init__()
self._clear_ll()
if args:
self.update_extend(args[0])
if kwargs:
self.update(kwargs)
def _clear_ll(self):
try:
_map = self._map
except AttributeError:
_map = self._map = {}
self.root = []
_map.clear()
self.root[:] = [self.root, self.root, None]
def _insert(self, k, v):
root = self.root
cells = self._map.setdefault(k, [])
last = root[PREV]
cell = [last, root, k, v]
last[NEXT] = root[PREV] = cell
cells.append(cell)
def add(self, k, v):
values = super(OrderedMultiDict, self).setdefault(k, [])
self._insert(k, v)
values.append(v)
def addlist(self, k, v):
self_insert = self._insert
values = super(OrderedMultiDict, self).setdefault(k, [])
for subv in v:
self_insert(k, subv)
values.extend(v)
def get(self, k, default=None):
return super(OrderedMultiDict, self).get(k, [default])[-1]
def getlist(self, k, default=_MISSING):
try:
return super(OrderedMultiDict, self).__getitem__(k)[:]
except KeyError:
if default is _MISSING:
return []
return default
def clear(self):
super(OrderedMultiDict, self).clear()
self._clear_ll()
def setdefault(self, k, default=_MISSING):
if not super(OrderedMultiDict, self).__contains__(k):
self[k] = None if default is _MISSING else default
return self[k]
def copy(self):
return self.__class__(self.iteritems(multi=True))
@classmethod
def fromkeys(cls, keys, default=None):
return cls([(k, default) for k in keys])
def update(self, E, **F):
if E is self:
return
self_add = self.add
if isinstance(E, OrderedMultiDict):
for k in E:
if k in self:
del self[k]
for k, v in E.iteritems(multi=True):
self_add(k, v)
elif callable(getattr(E, 'keys', None)):
for k in E.keys():
self[k] = E[k]
else:
seen = set()
seen_add = seen.add
for k, v in E:
if k not in seen and k in self:
del self[k]
seen_add(k)
self_add(k, v)
for k in F:
self[k] = F[k]
return
def update_extend(self, E, **F):
if E is self:
iterator = iter(E.items())
elif isinstance(E, OrderedMultiDict):
iterator = E.iteritems(multi=True)
elif hasattr(E, 'keys'):
iterator = ((k, E[k]) for k in E.keys())
else:
iterator = E
self_add = self.add
for k, v in iterator:
self_add(k, v)
def __setitem__(self, k, v):
if super(OrderedMultiDict, self).__contains__(k):
self._remove_all(k)
self._insert(k, v)
super(OrderedMultiDict, self).__setitem__(k, [v])
def __getitem__(self, k):
return super(OrderedMultiDict, self).__getitem__(k)[-1]
def __delitem__(self, k):
super(OrderedMultiDict, self).__delitem__(k)
self._remove_all(k)
def __eq__(self, other):
if self is other:
return True
try:
if len(other) != len(self):
return False
except TypeError:
return False
if isinstance(other, OrderedMultiDict):
selfi = self.iteritems(multi=True)
otheri = other.iteritems(multi=True)
zipped_items = izip_longest(selfi, otheri, fillvalue=(None, None))
for (selfk, selfv), (otherk, otherv) in zipped_items:
if selfk != otherk or selfv != otherv:
return False
if not(next(selfi, _MISSING) is _MISSING
and next(otheri, _MISSING) is _MISSING):
return False
return True
elif hasattr(other, 'keys'):
for selfk in self:
try:
other[selfk] == self[selfk]
except KeyError:
return False
return True
return False
def __ne__(self, other):
return not (self == other)
def pop(self, k, default=_MISSING):
try:
return self.popall(k)[-1]
except KeyError:
if default is _MISSING:
raise KeyError(k)
return default
def popall(self, k, default=_MISSING):
super_self = super(OrderedMultiDict, self)
if super_self.__contains__(k):
self._remove_all(k)
if default is _MISSING:
return super_self.pop(k)
return super_self.pop(k, default)
def poplast(self, k=_MISSING, default=_MISSING):
if k is _MISSING:
if self:
k = self.root[PREV][KEY]
else:
raise KeyError('empty %r' % type(self))
try:
self._remove(k)
except KeyError:
if default is _MISSING:
raise KeyError(k)
return default
values = super(OrderedMultiDict, self).__getitem__(k)
v = values.pop()
if not values:
super(OrderedMultiDict, self).__delitem__(k)
return v
def _remove(self, k):
values = self._map[k]
cell = values.pop()
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
if not values:
del self._map[k]
def _remove_all(self, k):
values = self._map[k]
while values:
cell = values.pop()
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
del self._map[k]
def iteritems(self, multi=False):
root = self.root
curr = root[NEXT]
if multi:
while curr is not root:
yield curr[KEY], curr[VALUE]
curr = curr[NEXT]
else:
for key in self.iterkeys():
yield key, self[key]
def iterkeys(self, multi=False):
root = self.root
curr = root[NEXT]
if multi:
while curr is not root:
yield curr[KEY]
curr = curr[NEXT]
else:
yielded = set()
yielded_add = yielded.add
while curr is not root:
k = curr[KEY]
if k not in yielded:
yielded_add(k)
yield k
curr = curr[NEXT]
def itervalues(self, multi=False):
for k, v in self.iteritems(multi=multi):
yield v
def todict(self, multi=False):
if multi:
return dict([(k, self.getlist(k)) for k in self])
return dict([(k, self[k]) for k in self])
def sorted(self, key=None, reverse=False):
cls = self.__class__
return cls(sorted(self.iteritems(), key=key, reverse=reverse))
def sortedvalues(self, key=None, reverse=False):
try:
superself_iteritems = super(OrderedMultiDict, self).iteritems()
except AttributeError:
superself_iteritems = super(OrderedMultiDict, self).items()
sorted_val_map = dict([(k, sorted(v, key=key, reverse=(not reverse)))
for k, v in superself_iteritems])
ret = self.__class__()
for k in self.iterkeys(multi=True):
ret.add(k, sorted_val_map[k].pop())
return ret
def inverted(self):
return self.__class__((v, k) for k, v in self.iteritems(multi=True))
def counts(self):
super_getitem = super(OrderedMultiDict, self).__getitem__
return self.__class__((k, len(super_getitem(k))) for k in self)
def keys(self, multi=False):
return list(self.iterkeys(multi=multi))
def values(self, multi=False):
return list(self.itervalues(multi=multi))
def items(self, multi=False):
return list(self.iteritems(multi=multi))
def __iter__(self):
return self.iterkeys()
def __reversed__(self):
root = self.root
curr = root[PREV]
lengths = {}
lengths_sd = lengths.setdefault
get_values = super(OrderedMultiDict, self).__getitem__
while curr is not root:
k = curr[KEY]
vals = get_values(k)
if lengths_sd(k, 1) == len(vals):
yield k
lengths[k] += 1
curr = curr[PREV]
def __repr__(self):
cn = self.__class__.__name__
kvs = ', '.join([repr((k, v)) for k, v in self.iteritems(multi=True)])
return '%s([%s])' % (cn, kvs)
def viewkeys(self):
return KeysView(self)
def viewvalues(self):
return ValuesView(self)
def viewitems(self):
return ItemsView(self)
OMD = OrderedMultiDict
MultiDict = OrderedMultiDict
class FastIterOrderedMultiDict(OrderedMultiDict):
def _clear_ll(self):
try:
_map = self._map
except AttributeError:
_map = self._map = {}
self.root = []
_map.clear()
self.root[:] = [self.root, self.root,
None, None,
self.root, self.root]
def _insert(self, k, v):
root = self.root
empty = []
cells = self._map.setdefault(k, empty)
last = root[PREV]
if cells is empty:
cell = [last, root,
k, v,
last, root]
if last[SPREV][SNEXT] is root:
last[SPREV][SNEXT] = cell
last[NEXT] = last[SNEXT] = root[PREV] = root[SPREV] = cell
cells.append(cell)
else:
sprev = last[SPREV] if (last[SPREV][SNEXT] is not last) else last
cell = [last, root,
k, v,
sprev, root]
last[SNEXT] = root
last[NEXT] = root[PREV] = root[SPREV] = cell
cells.append(cell)
def _remove(self, k):
cells = self._map[k]
cell = cells.pop()
if not cells:
del self._map[k]
cell[PREV][SNEXT] = cell[SNEXT]
if cell[PREV][SPREV][SNEXT] is cell:
cell[PREV][SPREV][SNEXT] = cell[NEXT]
elif cell[SNEXT] is cell[NEXT]:
cell[SPREV][SNEXT], cell[SNEXT][SPREV] = cell[SNEXT], cell[SPREV]
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
def _remove_all(self, k):
cells = self._map.pop(k)
while cells:
cell = cells.pop()
if cell[PREV][SPREV][SNEXT] is cell:
cell[PREV][SPREV][SNEXT] = cell[NEXT]
elif cell[SNEXT] is cell[NEXT]:
cell[SPREV][SNEXT], cell[SNEXT][SPREV] = cell[SNEXT], cell[SPREV]
cell[PREV][NEXT], cell[NEXT][PREV] = cell[NEXT], cell[PREV]
cell[PREV][SNEXT] = cell[SNEXT]
def iteritems(self, multi=False):
next_link = NEXT if multi else SNEXT
root = self.root
curr = root[next_link]
while curr is not root:
yield curr[KEY], curr[VALUE]
curr = curr[next_link]
def iterkeys(self, multi=False):
next_link = NEXT if multi else SNEXT
root = self.root
curr = root[next_link]
while curr is not root:
yield curr[KEY]
curr = curr[next_link]
def __reversed__(self):
root = self.root
curr = root[PREV]
while curr is not root:
if curr[SPREV][SNEXT] is not curr:
curr = curr[SPREV]
if curr is root:
break
yield curr[KEY]
curr = curr[PREV]
_SELF_INIT_MARKER = object()
class OneToOne(dict):
__slots__ = ('inv')
def __init__(self, *a, **kw):
if a and a[0] is _SELF_INIT_MARKER:
self.inv = a[1]
dict.__init__(self, [(v, k) for k, v in self.inv.items()])
else:
dict.__init__(self, *a, **kw)
self.inv = self.__class__(_SELF_INIT_MARKER, self)
def __setitem__(self, key, val):
hash(val)
if key in self:
dict.__delitem__(self.inv, self[key])
if val in self.inv:
del self.inv[val]
dict.__setitem__(self, key, val)
dict.__setitem__(self.inv, val, key)
def __delitem__(self, key):
dict.__delitem__(self.inv, self[key])
dict.__delitem__(self, key)
def clear(self):
dict.clear(self)
dict.clear(self.inv)
def copy(self):
return self.__class__(self)
def pop(self, key, default=_MISSING):
if key in self:
dict.__delitem__(self.inv, self[key])
return dict.pop(self, key)
if default is not _MISSING:
return default
raise KeyError()
def popitem(self):
key, val = dict.popitem(self)
dict.__delitem__(self.inv, val)
return key, val
def setdefault(self, key, default=None):
if key not in self:
self[key] = default
return self[key]
def update(self, dict_or_iterable, **kw):
if isinstance(dict_or_iterable, dict):
for val in dict_or_iterable.values():
hash(val)
keys_vals = list(dict_or_iterable.items())
else:
for key, val in dict_or_iterable:
hash(key)
hash(val)
keys_vals = list(dict_or_iterable)
for val in kw.values():
hash(val)
keys_vals.extend(kw.items())
for key, val in keys_vals:
self[key] = val
def __repr__(self):
cn = self.__class__.__name__
dict_repr = dict.__repr__(self)
return "%s(%s)" % (cn, dict_repr)
_PAIRING = object()
class ManyToMany(object):
def __init__(self, items=None):
self.data = {}
if type(items) is tuple and items and items[0] is _PAIRING:
self.inv = items[1]
else:
self.inv = self.__class__((_PAIRING, self))
if items:
self.update(items)
return
def get(self, key, default=frozenset()):
try:
return self[key]
except KeyError:
return default
def __getitem__(self, key):
return frozenset(self.data[key])
def __setitem__(self, key, vals):
vals = set(vals)
if key in self:
to_remove = self.data[key] - vals
vals -= self.data[key]
for val in to_remove:
self.remove(key, val)
for val in vals:
self.add(key, val)
def __delitem__(self, key):
for val in self.data.pop(key):
self.inv.data[val].remove(key)
if not self.inv.data[val]:
del self.inv.data[val]
def update(self, iterable):
if type(iterable) is type(self):
other = iterable
for k in other.data:
if k not in self.data:
self.data[k] = other.data[k]
else:
self.data[k].update(other.data[k])
for k in other.inv.data:
if k not in self.inv.data:
self.inv.data[k] = other.inv.data[k]
else:
self.inv.data[k].update(other.inv.data[k])
elif callable(getattr(iterable, 'keys', None)):
for k in iterable.keys():
self.add(k, iterable[k])
else:
for key, val in iterable:
self.add(key, val)
return
def add(self, key, val):
if key not in self.data:
self.data[key] = set()
self.data[key].add(val)
if val not in self.inv.data:
self.inv.data[val] = set()
self.inv.data[val].add(key)
def remove(self, key, val):
self.data[key].remove(val)
if not self.data[key]:
del self.data[key]
self.inv.data[val].remove(key)
if not self.inv.data[val]:
del self.inv.data[val]
def replace(self, key, newkey):
if key not in self.data:
return
self.data[newkey] = fwdset = self.data.pop(key)
for val in fwdset:
revset = self.inv.data[val]
revset.remove(key)
revset.add(newkey)
def iteritems(self):
for key in self.data:
for val in self.data[key]:
yield key, val
def keys(self):
return self.data.keys()
def __contains__(self, key):
return key in self.data
def __iter__(self):
return self.data.__iter__()
def __len__(self):
return self.data.__len__()
def __eq__(self, other):
return type(self) == type(other) and self.data == other.data
def __repr__(self):
cn = self.__class__.__name__
return '%s(%r)' % (cn, list(self.iteritems()))
def subdict(d, keep=None, drop=None):
if keep is None:
keep = d.keys()
if drop is None:
drop = []
keys = set(keep) - set(drop)
return type(d)([(k, v) for k, v in d.items() if k in keys])
class FrozenHashError(TypeError):
pass
class FrozenDict(dict):
__slots__ = ('_hash',)
def updated(self, *a, **kw):
data = dict(self)
data.update(*a, **kw)
return type(self)(data)
@classmethod
def fromkeys(cls, keys, value=None):
return cls(dict.fromkeys(keys, value))
def __repr__(self):
cn = self.__class__.__name__
return '%s(%s)' % (cn, dict.__repr__(self))
def __reduce_ex__(self, protocol):
return type(self), (dict(self),)
def __hash__(self):
try:
ret = self._hash
except AttributeError:
try:
ret = self._hash = hash(frozenset(self.items()))
except Exception as e:
ret = self._hash = FrozenHashError(e)
if ret.__class__ is FrozenHashError:
raise ret
return ret
def __copy__(self):
return self
def _raise_frozen_typeerror(self, *a, **kw):
raise TypeError('%s object is immutable' % self.__class__.__name__)
__setitem__ = __delitem__ = update = _raise_frozen_typeerror
setdefault = pop = popitem = clear = _raise_frozen_typeerror
del _raise_frozen_typeerror
| true | true |
f729389fe933234c11c91329270793893d994ccb | 1,884 | py | Python | datasets/Part 2 - Regression/Section 6 - Polynomial Regression/polinomial_regression.py | Lguiller/machinelearning-az | 7c062302944b91131783fe663e1cff21e5956ca2 | [
"MIT"
] | null | null | null | datasets/Part 2 - Regression/Section 6 - Polynomial Regression/polinomial_regression.py | Lguiller/machinelearning-az | 7c062302944b91131783fe663e1cff21e5956ca2 | [
"MIT"
] | null | null | null | datasets/Part 2 - Regression/Section 6 - Polynomial Regression/polinomial_regression.py | Lguiller/machinelearning-az | 7c062302944b91131783fe663e1cff21e5956ca2 | [
"MIT"
] | 1 | 2019-06-11T14:54:37.000Z | 2019-06-11T14:54:37.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 5 12:45:44 2019
@author: juangabriel
"""
# Regresión polinómica
# Cómo importar las librerías
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importar el data set
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
# Dividir el data set en conjunto de entrenamiento y conjunto de testing
"""
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
"""
# Escalado de variables
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)"""
# Ajustar la regresión lineal con el dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
# Ajustar la regresión polinómica con el dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
# Visualización de los resultados del Modelo Lineal
plt.scatter(X, y, color = "red")
plt.plot(X, lin_reg.predict(X), color = "blue")
plt.title("Modelo de Regresión Lineal")
plt.xlabel("Posición del empleado")
plt.ylabel("Sueldo (en $)")
plt.show()
# Visualización de los resultados del Modelo Polinómico
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape(len(X_grid), 1)
plt.scatter(X, y, color = "red")
plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = "blue")
plt.title("Modelo de Regresión Polinómica")
plt.xlabel("Posición del empleado")
plt.ylabel("Sueldo (en $)")
plt.show()
# Predicción de nuestros modelos
lin_reg.predict(6.5)
lin_reg_2.predict(poly_reg.fit_transform(6.5))
| 25.808219 | 92 | 0.751592 |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
plt.scatter(X, y, color = "red")
plt.plot(X, lin_reg.predict(X), color = "blue")
plt.title("Modelo de Regresión Lineal")
plt.xlabel("Posición del empleado")
plt.ylabel("Sueldo (en $)")
plt.show()
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape(len(X_grid), 1)
plt.scatter(X, y, color = "red")
plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = "blue")
plt.title("Modelo de Regresión Polinómica")
plt.xlabel("Posición del empleado")
plt.ylabel("Sueldo (en $)")
plt.show()
lin_reg.predict(6.5)
lin_reg_2.predict(poly_reg.fit_transform(6.5))
| true | true |
f729390d1f962ea09fcd5caafeaf6d3c52877f77 | 1,295 | py | Python | netattacker/__init__.py | venkat-abhi/netattacker | d1b4eadfa2fa065cbd0287f78b137427463920d6 | [
"Apache-2.0"
] | null | null | null | netattacker/__init__.py | venkat-abhi/netattacker | d1b4eadfa2fa065cbd0287f78b137427463920d6 | [
"Apache-2.0"
] | null | null | null | netattacker/__init__.py | venkat-abhi/netattacker | d1b4eadfa2fa065cbd0287f78b137427463920d6 | [
"Apache-2.0"
] | null | null | null | from importlib import import_module
from inspect import getmembers, isclass
from netattacker.attacker import AttackerBaseClass
base_classes = ['AttackerBaseClass', 'ScannerBaseClass']
def grab(attack_module:str, *args, **kwargs):
try:
# Need to figure out a better way
if ('modules' not in attack_module):
module_name = "netattacker.modules." + attack_module
# for example poisoners.arp, or poisoners.apr.ArpPoisoner is passed
if (1 == attack_module.count('.')):
imported_module = import_module(module_name)
for name, obj in getmembers(imported_module, isclass):
if (issubclass(obj, AttackerBaseClass) and name not in base_classes):
class_name = name
break
if (2 <= attack_module.count('.')):
module_name, class_name = attack_module.rsplit(".", 1)
imported_module = import_module(module_name)
attack_class = getattr(imported_module, class_name)
instance = attack_class(*args, **kwargs)
except (AttributeError, AssertionError, ImportError):
raise ImportError('[#] {} is not part of any submodules!'.format(attack_module))
else:
if not issubclass(attack_class, AttackerBaseClass):
raise ImportError("[#] The project currently doesn't have {}, but you are welcome to send in the request for it!".format(attack_class))
return instance
| 34.078947 | 138 | 0.744402 | from importlib import import_module
from inspect import getmembers, isclass
from netattacker.attacker import AttackerBaseClass
base_classes = ['AttackerBaseClass', 'ScannerBaseClass']
def grab(attack_module:str, *args, **kwargs):
try:
if ('modules' not in attack_module):
module_name = "netattacker.modules." + attack_module
if (1 == attack_module.count('.')):
imported_module = import_module(module_name)
for name, obj in getmembers(imported_module, isclass):
if (issubclass(obj, AttackerBaseClass) and name not in base_classes):
class_name = name
break
if (2 <= attack_module.count('.')):
module_name, class_name = attack_module.rsplit(".", 1)
imported_module = import_module(module_name)
attack_class = getattr(imported_module, class_name)
instance = attack_class(*args, **kwargs)
except (AttributeError, AssertionError, ImportError):
raise ImportError('[#] {} is not part of any submodules!'.format(attack_module))
else:
if not issubclass(attack_class, AttackerBaseClass):
raise ImportError("[#] The project currently doesn't have {}, but you are welcome to send in the request for it!".format(attack_class))
return instance
| true | true |
f729392f2684debd3a927c2ff1121a500d446369 | 2,943 | py | Python | writeups/closed/files/2020-uiuctf/coelacanth_vault.py | shawnduong/zero-to-hero-hacking | eec6768674b9908177c74925c6c5bd1b2da91369 | [
"MIT"
] | 8 | 2020-08-07T23:51:04.000Z | 2022-02-17T02:27:30.000Z | writeups/closed/files/2020-uiuctf/coelacanth_vault.py | shawnduong/zero-to-hero-hacking | eec6768674b9908177c74925c6c5bd1b2da91369 | [
"MIT"
] | null | null | null | writeups/closed/files/2020-uiuctf/coelacanth_vault.py | shawnduong/zero-to-hero-hacking | eec6768674b9908177c74925c6c5bd1b2da91369 | [
"MIT"
] | 3 | 2020-08-07T23:52:10.000Z | 2021-12-30T07:07:09.000Z | #!/usr/bin/python
import random
from Crypto.Util import number
from functools import reduce
TOTAL = 15
THRESHOLD = 10
MAX_COELACANTH = 9
NUM_LOCKS = 5
NUM_TRIES = 250
# substitute for math.prod
prod = lambda n: reduce(lambda x, y: x*y, n)
def create_key(t, n, size=8):
while True:
seq = sorted([number.getPrime(size) for _ in range(TOTAL)])
if len(set(seq)) != len(seq):
continue
alpha = prod(seq[:t])
beta = prod(seq[-t + 1:])
if alpha > beta:
secret = random.randint(beta, alpha)
shares = [(secret % num, num) for num in seq]
return secret, shares
def construct_key(shares):
glue = lambda A, n, s=1, t=0, N=0: (n < 2 and t % N or glue(n, A % n, t, s - A//n * t, N or n), -1)[n < 1]
mod = prod([m for s, m in shares])
secret = sum([s * glue(mod//m, m) * mod//m for s, m in shares]) % mod
return secret
if __name__ == "__main__":
print("Hi, and welcome to the virtual Animal Crossing: New Horizon coelacanth vault!")
print("There are {} different cryptolocks that must be unlocked in order to open the vault.".format(NUM_LOCKS))
print("You get one portion of each code for each coelacanth you have caught, and will need to use them to reconstruct the key for each lock.")
print("Unfortunately, it appears you have not caught enough coelacanths, and thus will need to find another way into the vault.")
print("Be warned, these locks have protection against brute force; too many wrong attempts and you will have to start over!")
print("")
num_shares = abs(int(input("How many coelacanth have you caught? ")))
if num_shares > MAX_COELACANTH:
print("Don't be silly! You definitely haven't caught more than {} coelacanth!".format(MAX_COELACANTH))
print("Come back when you decide to stop lying.")
quit()
for lock_num in range(NUM_LOCKS):
print("Generating key for lock {}, please wait...".format(lock_num))
secret, shares = create_key(THRESHOLD, TOTAL)
r_secret = construct_key(random.sample(shares, THRESHOLD))
assert(secret == r_secret)
print("Generated!")
print("Here are your key portions:")
print(random.sample(shares, num_shares))
for num_attempts in range(NUM_TRIES):
n_secret = abs(int(input("Please input the key: ")))
if secret == n_secret:
print("Lock {} unlocked with {} failed attempts!".format(lock_num, num_attempts))
break
else:
print("Key incorrect. You have {} tries remaining for this lock.".format(NUM_TRIES - num_attempts))
else:
print("BRUTE FORCE DETECTED. LOCKS SHUT DOWN.")
print("Get out. You don't deserve what's in this vault.")
quit()
print("Opening vault...")
with open("flag.txt", "r") as f:
print(f.read())
| 39.77027 | 146 | 0.621814 |
import random
from Crypto.Util import number
from functools import reduce
TOTAL = 15
THRESHOLD = 10
MAX_COELACANTH = 9
NUM_LOCKS = 5
NUM_TRIES = 250
prod = lambda n: reduce(lambda x, y: x*y, n)
def create_key(t, n, size=8):
while True:
seq = sorted([number.getPrime(size) for _ in range(TOTAL)])
if len(set(seq)) != len(seq):
continue
alpha = prod(seq[:t])
beta = prod(seq[-t + 1:])
if alpha > beta:
secret = random.randint(beta, alpha)
shares = [(secret % num, num) for num in seq]
return secret, shares
def construct_key(shares):
glue = lambda A, n, s=1, t=0, N=0: (n < 2 and t % N or glue(n, A % n, t, s - A//n * t, N or n), -1)[n < 1]
mod = prod([m for s, m in shares])
secret = sum([s * glue(mod//m, m) * mod//m for s, m in shares]) % mod
return secret
if __name__ == "__main__":
print("Hi, and welcome to the virtual Animal Crossing: New Horizon coelacanth vault!")
print("There are {} different cryptolocks that must be unlocked in order to open the vault.".format(NUM_LOCKS))
print("You get one portion of each code for each coelacanth you have caught, and will need to use them to reconstruct the key for each lock.")
print("Unfortunately, it appears you have not caught enough coelacanths, and thus will need to find another way into the vault.")
print("Be warned, these locks have protection against brute force; too many wrong attempts and you will have to start over!")
print("")
num_shares = abs(int(input("How many coelacanth have you caught? ")))
if num_shares > MAX_COELACANTH:
print("Don't be silly! You definitely haven't caught more than {} coelacanth!".format(MAX_COELACANTH))
print("Come back when you decide to stop lying.")
quit()
for lock_num in range(NUM_LOCKS):
print("Generating key for lock {}, please wait...".format(lock_num))
secret, shares = create_key(THRESHOLD, TOTAL)
r_secret = construct_key(random.sample(shares, THRESHOLD))
assert(secret == r_secret)
print("Generated!")
print("Here are your key portions:")
print(random.sample(shares, num_shares))
for num_attempts in range(NUM_TRIES):
n_secret = abs(int(input("Please input the key: ")))
if secret == n_secret:
print("Lock {} unlocked with {} failed attempts!".format(lock_num, num_attempts))
break
else:
print("Key incorrect. You have {} tries remaining for this lock.".format(NUM_TRIES - num_attempts))
else:
print("BRUTE FORCE DETECTED. LOCKS SHUT DOWN.")
print("Get out. You don't deserve what's in this vault.")
quit()
print("Opening vault...")
with open("flag.txt", "r") as f:
print(f.read())
| true | true |
f7293b124cfe6a600bee645fddac463b0a401a67 | 424 | py | Python | energagement/myapp/migrations/0008_auto_20150701_1012.py | mpetyx/energagement- | 8053d433057327eaff7b1fe8f03ad77700755b49 | [
"MIT"
] | null | null | null | energagement/myapp/migrations/0008_auto_20150701_1012.py | mpetyx/energagement- | 8053d433057327eaff7b1fe8f03ad77700755b49 | [
"MIT"
] | 12 | 2015-06-02T15:27:36.000Z | 2015-09-17T22:05:00.000Z | energagement/myapp/migrations/0008_auto_20150701_1012.py | mpetyx/energagement- | 8053d433057327eaff7b1fe8f03ad77700755b49 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('myapp', '0007_auto_20150629_1643'),
]
operations = [
migrations.AlterField(
model_name='value',
name='timestamp',
field=models.DateField(),
preserve_default=True,
),
]
| 20.190476 | 45 | 0.591981 |
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('myapp', '0007_auto_20150629_1643'),
]
operations = [
migrations.AlterField(
model_name='value',
name='timestamp',
field=models.DateField(),
preserve_default=True,
),
]
| true | true |
f7293b5e110b7e9317cb9bb3ad658066507ecbf7 | 8,013 | py | Python | darling_ansible/python_venv/lib/python3.7/site-packages/oci/apigateway/models/api_specification_route.py | revnav/sandbox | f9c8422233d093b76821686b6c249417502cf61d | [
"Apache-2.0"
] | null | null | null | darling_ansible/python_venv/lib/python3.7/site-packages/oci/apigateway/models/api_specification_route.py | revnav/sandbox | f9c8422233d093b76821686b6c249417502cf61d | [
"Apache-2.0"
] | null | null | null | darling_ansible/python_venv/lib/python3.7/site-packages/oci/apigateway/models/api_specification_route.py | revnav/sandbox | f9c8422233d093b76821686b6c249417502cf61d | [
"Apache-2.0"
] | 1 | 2020-06-25T03:12:58.000Z | 2020-06-25T03:12:58.000Z | # coding: utf-8
# Copyright (c) 2016, 2020, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
@init_model_state_from_kwargs
class ApiSpecificationRoute(object):
"""
A single route that forwards requests to a particular backend and may contain some additional policies.
"""
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "ANY"
METHODS_ANY = "ANY"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "HEAD"
METHODS_HEAD = "HEAD"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "GET"
METHODS_GET = "GET"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "POST"
METHODS_POST = "POST"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "PUT"
METHODS_PUT = "PUT"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "PATCH"
METHODS_PATCH = "PATCH"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "DELETE"
METHODS_DELETE = "DELETE"
#: A constant which can be used with the methods property of a ApiSpecificationRoute.
#: This constant has a value of "OPTIONS"
METHODS_OPTIONS = "OPTIONS"
def __init__(self, **kwargs):
"""
Initializes a new ApiSpecificationRoute object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param path:
The value to assign to the path property of this ApiSpecificationRoute.
:type path: str
:param methods:
The value to assign to the methods property of this ApiSpecificationRoute.
Allowed values for items in this list are: "ANY", "HEAD", "GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS", 'UNKNOWN_ENUM_VALUE'.
Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.
:type methods: list[str]
:param request_policies:
The value to assign to the request_policies property of this ApiSpecificationRoute.
:type request_policies: ApiSpecificationRouteRequestPolicies
:param logging_policies:
The value to assign to the logging_policies property of this ApiSpecificationRoute.
:type logging_policies: ApiSpecificationLoggingPolicies
:param backend:
The value to assign to the backend property of this ApiSpecificationRoute.
:type backend: ApiSpecificationRouteBackend
"""
self.swagger_types = {
'path': 'str',
'methods': 'list[str]',
'request_policies': 'ApiSpecificationRouteRequestPolicies',
'logging_policies': 'ApiSpecificationLoggingPolicies',
'backend': 'ApiSpecificationRouteBackend'
}
self.attribute_map = {
'path': 'path',
'methods': 'methods',
'request_policies': 'requestPolicies',
'logging_policies': 'loggingPolicies',
'backend': 'backend'
}
self._path = None
self._methods = None
self._request_policies = None
self._logging_policies = None
self._backend = None
@property
def path(self):
"""
**[Required]** Gets the path of this ApiSpecificationRoute.
A URL path pattern that must be matched on this route. The path pattern may contain a subset of RFC 6570 identifiers
to allow wildcard and parameterized matching.
:return: The path of this ApiSpecificationRoute.
:rtype: str
"""
return self._path
@path.setter
def path(self, path):
"""
Sets the path of this ApiSpecificationRoute.
A URL path pattern that must be matched on this route. The path pattern may contain a subset of RFC 6570 identifiers
to allow wildcard and parameterized matching.
:param path: The path of this ApiSpecificationRoute.
:type: str
"""
self._path = path
@property
def methods(self):
"""
Gets the methods of this ApiSpecificationRoute.
A list of allowed methods on this route.
Allowed values for items in this list are: "ANY", "HEAD", "GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS", 'UNKNOWN_ENUM_VALUE'.
Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.
:return: The methods of this ApiSpecificationRoute.
:rtype: list[str]
"""
return self._methods
@methods.setter
def methods(self, methods):
"""
Sets the methods of this ApiSpecificationRoute.
A list of allowed methods on this route.
:param methods: The methods of this ApiSpecificationRoute.
:type: list[str]
"""
allowed_values = ["ANY", "HEAD", "GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"]
if methods:
methods[:] = ['UNKNOWN_ENUM_VALUE' if not value_allowed_none_or_none_sentinel(x, allowed_values) else x for x in methods]
self._methods = methods
@property
def request_policies(self):
"""
Gets the request_policies of this ApiSpecificationRoute.
:return: The request_policies of this ApiSpecificationRoute.
:rtype: ApiSpecificationRouteRequestPolicies
"""
return self._request_policies
@request_policies.setter
def request_policies(self, request_policies):
"""
Sets the request_policies of this ApiSpecificationRoute.
:param request_policies: The request_policies of this ApiSpecificationRoute.
:type: ApiSpecificationRouteRequestPolicies
"""
self._request_policies = request_policies
@property
def logging_policies(self):
"""
Gets the logging_policies of this ApiSpecificationRoute.
:return: The logging_policies of this ApiSpecificationRoute.
:rtype: ApiSpecificationLoggingPolicies
"""
return self._logging_policies
@logging_policies.setter
def logging_policies(self, logging_policies):
"""
Sets the logging_policies of this ApiSpecificationRoute.
:param logging_policies: The logging_policies of this ApiSpecificationRoute.
:type: ApiSpecificationLoggingPolicies
"""
self._logging_policies = logging_policies
@property
def backend(self):
"""
**[Required]** Gets the backend of this ApiSpecificationRoute.
:return: The backend of this ApiSpecificationRoute.
:rtype: ApiSpecificationRouteBackend
"""
return self._backend
@backend.setter
def backend(self, backend):
"""
Sets the backend of this ApiSpecificationRoute.
:param backend: The backend of this ApiSpecificationRoute.
:type: ApiSpecificationRouteBackend
"""
self._backend = backend
def __repr__(self):
return formatted_flat_dict(self)
def __eq__(self, other):
if other is None:
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other
| 35.613333 | 245 | 0.665668 |
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel
from oci.decorators import init_model_state_from_kwargs
@init_model_state_from_kwargs
class ApiSpecificationRoute(object):
METHODS_ANY = "ANY"
METHODS_HEAD = "HEAD"
METHODS_GET = "GET"
METHODS_POST = "POST"
METHODS_PUT = "PUT"
METHODS_PATCH = "PATCH"
METHODS_DELETE = "DELETE"
METHODS_OPTIONS = "OPTIONS"
def __init__(self, **kwargs):
self.swagger_types = {
'path': 'str',
'methods': 'list[str]',
'request_policies': 'ApiSpecificationRouteRequestPolicies',
'logging_policies': 'ApiSpecificationLoggingPolicies',
'backend': 'ApiSpecificationRouteBackend'
}
self.attribute_map = {
'path': 'path',
'methods': 'methods',
'request_policies': 'requestPolicies',
'logging_policies': 'loggingPolicies',
'backend': 'backend'
}
self._path = None
self._methods = None
self._request_policies = None
self._logging_policies = None
self._backend = None
@property
def path(self):
return self._path
@path.setter
def path(self, path):
self._path = path
@property
def methods(self):
return self._methods
@methods.setter
def methods(self, methods):
allowed_values = ["ANY", "HEAD", "GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"]
if methods:
methods[:] = ['UNKNOWN_ENUM_VALUE' if not value_allowed_none_or_none_sentinel(x, allowed_values) else x for x in methods]
self._methods = methods
@property
def request_policies(self):
return self._request_policies
@request_policies.setter
def request_policies(self, request_policies):
self._request_policies = request_policies
@property
def logging_policies(self):
return self._logging_policies
@logging_policies.setter
def logging_policies(self, logging_policies):
self._logging_policies = logging_policies
@property
def backend(self):
return self._backend
@backend.setter
def backend(self, backend):
self._backend = backend
def __repr__(self):
return formatted_flat_dict(self)
def __eq__(self, other):
if other is None:
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other
| true | true |
f7293badfc9fb26b41b3748c1ecff37b57616082 | 3,534 | py | Python | mars/tensor/reduction/nanargmin.py | sighingnow/mars | c7897fbd144d230fff5edabc1494fb3ff44aa0d2 | [
"Apache-2.0"
] | null | null | null | mars/tensor/reduction/nanargmin.py | sighingnow/mars | c7897fbd144d230fff5edabc1494fb3ff44aa0d2 | [
"Apache-2.0"
] | null | null | null | mars/tensor/reduction/nanargmin.py | sighingnow/mars | c7897fbd144d230fff5edabc1494fb3ff44aa0d2 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2018 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...serialize import Int64Field, TupleField
from .core import TensorReduction, TensorArgReductionMixin, TensorArgMapMixin, TensorArgCombineMixin
class TensorNanArgminMap(TensorReduction, TensorArgMapMixin):
_op_type_ = OperandDef.NANARGMIN_CHUNK
_offset = Int64Field('offset')
_total_shape = TupleField('total_shape')
_func_name = 'nanargmin'
_agg_func_name = 'nanmin'
def __init__(self, axis=None, dtype=np.dtype(int), combine_size=None,
offset=None, total_shape=None,**kw):
super(TensorNanArgminMap, self).__init__(_axis=axis, _dtype=dtype, _combine_size=combine_size,
_offset=offset, _total_shape=total_shape, **kw)
@property
def offset(self):
return getattr(self, '_offset', None)
@property
def total_shape(self):
return getattr(self, '_total_shape', None)
class TensorNanArgminCombine(TensorReduction, TensorArgCombineMixin):
_op_type_ = OperandDef.NANARGMIN_COMBINE
_func_name = 'nanargmin'
def __init__(self, axis=None, dtype=np.dtype(int), combine_size=None, **kw):
super(TensorNanArgminCombine, self).__init__(_axis=axis, _dtype=dtype,
_combine_size=combine_size, **kw)
class TensorNanArgmin(TensorReduction, TensorArgReductionMixin):
_op_type_ = OperandDef.NANARGMIN
_func_name = 'nanargmin'
def __init__(self, axis=None, dtype=np.dtype(int), combine_size=None, **kw):
super(TensorNanArgmin, self).__init__(_axis=axis, _dtype=dtype, _combine_size=combine_size, **kw)
@staticmethod
def _get_op_types():
return TensorNanArgminMap, TensorNanArgmin, TensorNanArgminCombine
def nanargmin(a, axis=None, out=None, combine_size=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
index_array : Tensor
A tensor of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([[mt.nan, 4], [2, 3]])
>>> mt.argmin(a).execute()
0
>>> mt.nanargmin(a).execute()
2
>>> mt.nanargmin(a, axis=0).execute()
array([1, 1])
>>> mt.nanargmin(a, axis=1).execute()
array([1, 0])
"""
op = TensorNanArgmin(axis=axis, dtype=np.dtype(int), combine_size=combine_size)
return op(a, out=out)
| 32.422018 | 105 | 0.675156 |
import numpy as np
from ... import opcodes as OperandDef
from ...serialize import Int64Field, TupleField
from .core import TensorReduction, TensorArgReductionMixin, TensorArgMapMixin, TensorArgCombineMixin
class TensorNanArgminMap(TensorReduction, TensorArgMapMixin):
_op_type_ = OperandDef.NANARGMIN_CHUNK
_offset = Int64Field('offset')
_total_shape = TupleField('total_shape')
_func_name = 'nanargmin'
_agg_func_name = 'nanmin'
def __init__(self, axis=None, dtype=np.dtype(int), combine_size=None,
offset=None, total_shape=None,**kw):
super(TensorNanArgminMap, self).__init__(_axis=axis, _dtype=dtype, _combine_size=combine_size,
_offset=offset, _total_shape=total_shape, **kw)
@property
def offset(self):
return getattr(self, '_offset', None)
@property
def total_shape(self):
return getattr(self, '_total_shape', None)
class TensorNanArgminCombine(TensorReduction, TensorArgCombineMixin):
_op_type_ = OperandDef.NANARGMIN_COMBINE
_func_name = 'nanargmin'
def __init__(self, axis=None, dtype=np.dtype(int), combine_size=None, **kw):
super(TensorNanArgminCombine, self).__init__(_axis=axis, _dtype=dtype,
_combine_size=combine_size, **kw)
class TensorNanArgmin(TensorReduction, TensorArgReductionMixin):
_op_type_ = OperandDef.NANARGMIN
_func_name = 'nanargmin'
def __init__(self, axis=None, dtype=np.dtype(int), combine_size=None, **kw):
super(TensorNanArgmin, self).__init__(_axis=axis, _dtype=dtype, _combine_size=combine_size, **kw)
@staticmethod
def _get_op_types():
return TensorNanArgminMap, TensorNanArgmin, TensorNanArgminCombine
def nanargmin(a, axis=None, out=None, combine_size=None):
op = TensorNanArgmin(axis=axis, dtype=np.dtype(int), combine_size=combine_size)
return op(a, out=out)
| true | true |
f7293c82577b4455cadc028d56cf912f14a2d0f3 | 2,522 | py | Python | app/problem_register/migrations/0001_initial.py | 3ANov/spbauto_map | 3f1c3dcb5f370a9e04d5d496b18a3e6ab3dbe7a1 | [
"MIT"
] | null | null | null | app/problem_register/migrations/0001_initial.py | 3ANov/spbauto_map | 3f1c3dcb5f370a9e04d5d496b18a3e6ab3dbe7a1 | [
"MIT"
] | 17 | 2020-02-15T16:57:38.000Z | 2021-09-17T19:33:25.000Z | app/problem_register/migrations/0001_initial.py | 3ANov/spbauto_map | 3f1c3dcb5f370a9e04d5d496b18a3e6ab3dbe7a1 | [
"MIT"
] | null | null | null | # Generated by Django 3.2 on 2021-05-04 07:35
import colorfield.fields
from django.conf import settings
import django.contrib.gis.db.models.fields
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
('places', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='ProblemStatus',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=20)),
('color', colorfield.fields.ColorField(default='#FFFFFF', max_length=18)),
],
),
migrations.CreateModel(
name='ProblemLabel',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('description', models.CharField(max_length=500, verbose_name='описание проблемы')),
('created_date', models.DateTimeField(default=django.utils.timezone.now, verbose_name='время добавления')),
('house_number', models.CharField(blank=True, max_length=50, verbose_name='номер дома')),
('geom', django.contrib.gis.db.models.fields.PointField(srid=4326)),
('author', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='добавлено пользователем')),
('county', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.county', verbose_name='район области')),
('place', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.place', verbose_name='населённый пункт')),
('road', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.road', verbose_name='дорога')),
('state_district', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.statedistrict', verbose_name='район города')),
('status', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='problem_register.problemstatus', verbose_name='статус проблемы')),
],
),
]
| 54.826087 | 184 | 0.666931 |
import colorfield.fields
from django.conf import settings
import django.contrib.gis.db.models.fields
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
('places', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='ProblemStatus',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=20)),
('color', colorfield.fields.ColorField(default='#FFFFFF', max_length=18)),
],
),
migrations.CreateModel(
name='ProblemLabel',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('description', models.CharField(max_length=500, verbose_name='описание проблемы')),
('created_date', models.DateTimeField(default=django.utils.timezone.now, verbose_name='время добавления')),
('house_number', models.CharField(blank=True, max_length=50, verbose_name='номер дома')),
('geom', django.contrib.gis.db.models.fields.PointField(srid=4326)),
('author', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='добавлено пользователем')),
('county', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.county', verbose_name='район области')),
('place', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.place', verbose_name='населённый пункт')),
('road', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.road', verbose_name='дорога')),
('state_district', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='places.statedistrict', verbose_name='район города')),
('status', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='problem_register.problemstatus', verbose_name='статус проблемы')),
],
),
]
| true | true |
f7293d67e1c31733c637eac8626a9cea639c2578 | 1,737 | py | Python | nate/svonet/svo_offsets.py | UWNETLAB/nelanna | 9029670d5804f478cac2e83d77ff86ff2a7266c2 | [
"MIT"
] | 8 | 2020-02-09T15:39:49.000Z | 2022-02-06T14:35:08.000Z | nate/svonet/svo_offsets.py | UWNETLAB/nlpnet | 9029670d5804f478cac2e83d77ff86ff2a7266c2 | [
"MIT"
] | 11 | 2020-03-13T18:46:46.000Z | 2020-04-02T18:58:57.000Z | nate/svonet/svo_offsets.py | UWNETLAB/nlpnet | 9029670d5804f478cac2e83d77ff86ff2a7266c2 | [
"MIT"
] | 1 | 2020-08-05T19:08:50.000Z | 2020-08-05T19:08:50.000Z | """Generates the offset dictionary for the SVO pipeline."""
from time import time as marktime
from typing import List
from itertools import groupby
from collections import defaultdict
def generate_svo_offsets(svo_list: List, time: List, minimum_offsets):
"""Creates offset dictionary and int-to-string lookup for SVO format."""
print("Generating Offsets:")
start = marktime()
svo_dict = defaultdict(list)
for i, svo in enumerate(svo_list):
svo_dict[svo].append(time[i])
svo_int_dict, lookup = text_to_int(svo_dict)
# prune SVOs, excluding those with fewer occurrences than specified by minimum_offsets
offsets = {
k: v for k, v in svo_int_dict.items() if len(v) >= minimum_offsets
}
print("Finished offset generation in {} seconds".format(
round(marktime() - start)))
print("Commencing timestamp deduplication...")
# increment simultaneous occurrences by 1 millisecond to satisfy Kleinberg requirements
for item in offsets.keys():
offsets[item].sort()
offsets[item] = [
g + i * 0.001
for k, group in groupby(offsets[item])
for i, g in enumerate(group)
]
print("finished timestamp deduplication in {} seconds".format(
round(marktime() - start)))
print("Finished Generating Offsets. Returning offset dictionary.")
return offsets, lookup
def text_to_int(svo_dict):
"""Converts SVO terms to integers, and generates a lookup dictionary."""
svo_int_dict = defaultdict(list)
lookup_dict = defaultdict(tuple)
i = 0
for k, v in svo_dict.items():
svo_int_dict[i] = v
lookup_dict[i] = k
i = i + 1
return svo_int_dict, lookup_dict
| 30.473684 | 91 | 0.670697 | from time import time as marktime
from typing import List
from itertools import groupby
from collections import defaultdict
def generate_svo_offsets(svo_list: List, time: List, minimum_offsets):
print("Generating Offsets:")
start = marktime()
svo_dict = defaultdict(list)
for i, svo in enumerate(svo_list):
svo_dict[svo].append(time[i])
svo_int_dict, lookup = text_to_int(svo_dict)
offsets = {
k: v for k, v in svo_int_dict.items() if len(v) >= minimum_offsets
}
print("Finished offset generation in {} seconds".format(
round(marktime() - start)))
print("Commencing timestamp deduplication...")
for item in offsets.keys():
offsets[item].sort()
offsets[item] = [
g + i * 0.001
for k, group in groupby(offsets[item])
for i, g in enumerate(group)
]
print("finished timestamp deduplication in {} seconds".format(
round(marktime() - start)))
print("Finished Generating Offsets. Returning offset dictionary.")
return offsets, lookup
def text_to_int(svo_dict):
svo_int_dict = defaultdict(list)
lookup_dict = defaultdict(tuple)
i = 0
for k, v in svo_dict.items():
svo_int_dict[i] = v
lookup_dict[i] = k
i = i + 1
return svo_int_dict, lookup_dict
| true | true |
f7293d96cfc10e9d38e19644eab6c50924cd6563 | 429 | py | Python | rl/core/online_learners/online_learner.py | gtrll/librl | 39709c3e485e232865b3e08b7211cd9d871c666a | [
"MIT"
] | 5 | 2020-07-14T23:01:53.000Z | 2020-12-09T08:11:29.000Z | rl/core/online_learners/online_learner.py | chinganc/mamba | e8adf0cf91660aed2c025508137a14f9d062248c | [
"MIT"
] | 1 | 2022-03-27T04:43:31.000Z | 2022-03-27T04:43:31.000Z | rl/core/online_learners/online_learner.py | chinganc/mamba | e8adf0cf91660aed2c025508137a14f9d062248c | [
"MIT"
] | 4 | 2020-08-05T14:13:26.000Z | 2022-02-26T00:46:03.000Z | # Copyright (c) 2019 Georgia Tech Robot Learning Lab
# Licensed under the MIT License.
from abc import ABC, abstractmethod
class OnlineLearner(ABC):
""" An abstract interface of iterative algorithms. """
@abstractmethod
def update(self, *args, **kwargs):
""" Update the state given feedback. """
@property
@abstractmethod
def decision(self):
""" Return the (stochastic) decision. """
| 23.833333 | 58 | 0.666667 |
from abc import ABC, abstractmethod
class OnlineLearner(ABC):
@abstractmethod
def update(self, *args, **kwargs):
@property
@abstractmethod
def decision(self):
| true | true |
f7293edc1b5a4be9bc88b63b859c48a069551969 | 4,914 | py | Python | Chef.py | joaocps/cd-drive-through-p2p | 86db0bf4701c37e02db2ea1f6472c9649465ea48 | [
"MIT"
] | null | null | null | Chef.py | joaocps/cd-drive-through-p2p | 86db0bf4701c37e02db2ea1f6472c9649465ea48 | [
"MIT"
] | null | null | null | Chef.py | joaocps/cd-drive-through-p2p | 86db0bf4701c37e02db2ea1f6472c9649465ea48 | [
"MIT"
] | null | null | null | # coding: utf-8
import time
import pickle
import socket
import random
import logging
import argparse
import threading
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M:%S')
logger = logging.getLogger('Chef')
def contains_successor(identification, successor, node):
if identification < node <= successor:
return True
elif successor < identification and (node > identification or node < successor):
return True
return False
class Chef(threading.Thread):
def __init__(self, port=5002, ide=2, ring_address=5000):
threading.Thread.__init__(self)
self.name = "CHEF"
self.id = ide
self.port = port
self.ring_address = ring_address
self.ring_completed = False
self.ring_ids_dict = {'RESTAURANT': None, 'WAITER': None, 'CHEF': self.id, 'CLERK': None}
if ring_address is None:
self.successor_id = self.id
self.successor_port = self.port
self.inside_ring = True
else:
self.successor_id = None
self.successor_port = None
self.inside_ring = False
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.logger = logging.getLogger("Node {}".format(self.id))
def send(self, port, o):
p = pickle.dumps(o)
self.socket.sendto(p, ('localhost', port))
def recv(self):
try:
p, port = self.socket.recvfrom(1024)
except socket.timeout:
return None, None
else:
if len(p) == 0:
return None, port
else:
return p, port
def node_join(self, args):
self.logger.debug('Node join: %s', args)
port = args['addr']
identification = args['id']
if self.id == self.successor_id:
self.successor_id = identification
self.successor_port = port
args = {'successor_id': self.id, 'successor_port': self.port}
self.send(port, {'method': 'JOIN_REP', 'args': args})
elif contains_successor(self.id, self.successor_id, identification):
args = {'successor_id': self.successor_id, 'successor_port': self.successor_port}
self.successor_id = identification
self.successor_port = port
self.send(port, {'method': 'JOIN_REP', 'args': args})
else:
self.logger.debug('Find Successor(%d)', args['id'])
self.send(self.successor_port, {'method': 'JOIN_RING', 'args': args})
self.logger.info(self)
def node_discovery(self, args):
if self.ring_ids_dict['RESTAURANT'] is None and args['RESTAURANT'] is not None:
self.ring_ids_dict['RESTAURANT'] = args['RESTAURANT']
if self.ring_ids_dict['WAITER'] is None and args['WAITER'] is not None:
self.ring_ids_dict['WAITER'] = args['WAITER']
if self.ring_ids_dict['CLERK'] is None and args['CLERK'] is not None:
self.ring_ids_dict['CLERK'] = args['CLERK']
self.send(self.successor_port, {'method': 'NODE_DISCOVERY', 'args': self.ring_ids_dict})
def __str__(self):
return 'Node ID: {}; Ring Address: {}; Successor_id: {}; Successor_port: {};' \
.format(self.id, self.ring_address, self.successor_id, self.successor_port)
def __repr__(self):
return self.__str__()
def run(self):
self.socket.bind(('localhost', self.port))
while not self.inside_ring:
o = {'method': 'JOIN_RING', 'args': {'addr': self.port, 'id': self.id}}
self.send(self.ring_address, o)
p, addr = self.recv()
if p is not None:
o = pickle.loads(p)
self.logger.debug('O: %s', o)
if o['method'] == 'JOIN_REP':
args = o['args']
self.successor_id = args['successor_id']
self.successor_port = args['successor_port']
self.inside_ring = True
self.logger.info(self)
done = False
while not done:
p, addr = self.recv()
if p is not None:
o = pickle.loads(p)
self.logger.info('O: %s', o)
if o['method'] == 'JOIN_RING':
self.node_join(o['args'])
elif o['method'] == 'NODE_DISCOVERY':
self.node_discovery(o['args'])
elif o['method'] == 'START':
self.send(self.successor_port, {'method': 'COOK', 'args': o['args']})
elif o['method'] == 'COOKED':
self.send(self.successor_port,{'method': 'DONE', 'args': o['args']})
else:
self.send(self.successor_port,o)
| 34.605634 | 97 | 0.556777 |
import time
import pickle
import socket
import random
import logging
import argparse
import threading
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M:%S')
logger = logging.getLogger('Chef')
def contains_successor(identification, successor, node):
if identification < node <= successor:
return True
elif successor < identification and (node > identification or node < successor):
return True
return False
class Chef(threading.Thread):
def __init__(self, port=5002, ide=2, ring_address=5000):
threading.Thread.__init__(self)
self.name = "CHEF"
self.id = ide
self.port = port
self.ring_address = ring_address
self.ring_completed = False
self.ring_ids_dict = {'RESTAURANT': None, 'WAITER': None, 'CHEF': self.id, 'CLERK': None}
if ring_address is None:
self.successor_id = self.id
self.successor_port = self.port
self.inside_ring = True
else:
self.successor_id = None
self.successor_port = None
self.inside_ring = False
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.logger = logging.getLogger("Node {}".format(self.id))
def send(self, port, o):
p = pickle.dumps(o)
self.socket.sendto(p, ('localhost', port))
def recv(self):
try:
p, port = self.socket.recvfrom(1024)
except socket.timeout:
return None, None
else:
if len(p) == 0:
return None, port
else:
return p, port
def node_join(self, args):
self.logger.debug('Node join: %s', args)
port = args['addr']
identification = args['id']
if self.id == self.successor_id:
self.successor_id = identification
self.successor_port = port
args = {'successor_id': self.id, 'successor_port': self.port}
self.send(port, {'method': 'JOIN_REP', 'args': args})
elif contains_successor(self.id, self.successor_id, identification):
args = {'successor_id': self.successor_id, 'successor_port': self.successor_port}
self.successor_id = identification
self.successor_port = port
self.send(port, {'method': 'JOIN_REP', 'args': args})
else:
self.logger.debug('Find Successor(%d)', args['id'])
self.send(self.successor_port, {'method': 'JOIN_RING', 'args': args})
self.logger.info(self)
def node_discovery(self, args):
if self.ring_ids_dict['RESTAURANT'] is None and args['RESTAURANT'] is not None:
self.ring_ids_dict['RESTAURANT'] = args['RESTAURANT']
if self.ring_ids_dict['WAITER'] is None and args['WAITER'] is not None:
self.ring_ids_dict['WAITER'] = args['WAITER']
if self.ring_ids_dict['CLERK'] is None and args['CLERK'] is not None:
self.ring_ids_dict['CLERK'] = args['CLERK']
self.send(self.successor_port, {'method': 'NODE_DISCOVERY', 'args': self.ring_ids_dict})
def __str__(self):
return 'Node ID: {}; Ring Address: {}; Successor_id: {}; Successor_port: {};' \
.format(self.id, self.ring_address, self.successor_id, self.successor_port)
def __repr__(self):
return self.__str__()
def run(self):
self.socket.bind(('localhost', self.port))
while not self.inside_ring:
o = {'method': 'JOIN_RING', 'args': {'addr': self.port, 'id': self.id}}
self.send(self.ring_address, o)
p, addr = self.recv()
if p is not None:
o = pickle.loads(p)
self.logger.debug('O: %s', o)
if o['method'] == 'JOIN_REP':
args = o['args']
self.successor_id = args['successor_id']
self.successor_port = args['successor_port']
self.inside_ring = True
self.logger.info(self)
done = False
while not done:
p, addr = self.recv()
if p is not None:
o = pickle.loads(p)
self.logger.info('O: %s', o)
if o['method'] == 'JOIN_RING':
self.node_join(o['args'])
elif o['method'] == 'NODE_DISCOVERY':
self.node_discovery(o['args'])
elif o['method'] == 'START':
self.send(self.successor_port, {'method': 'COOK', 'args': o['args']})
elif o['method'] == 'COOKED':
self.send(self.successor_port,{'method': 'DONE', 'args': o['args']})
else:
self.send(self.successor_port,o)
| true | true |
f7293f910233b1a53743fbb23de7be2d8f6d7a41 | 5,065 | py | Python | glue/viewers/image/composite_array.py | nabobalis/glue | 1c718378b5527e64d85cc6a6f9a0330652e5cf4b | [
"BSD-3-Clause"
] | null | null | null | glue/viewers/image/composite_array.py | nabobalis/glue | 1c718378b5527e64d85cc6a6f9a0330652e5cf4b | [
"BSD-3-Clause"
] | null | null | null | glue/viewers/image/composite_array.py | nabobalis/glue | 1c718378b5527e64d85cc6a6f9a0330652e5cf4b | [
"BSD-3-Clause"
] | null | null | null | # This artist can be used to deal with the sampling of the data as well as any
# RGB blending.
import numpy as np
from matplotlib.colors import ColorConverter, Colormap
from astropy.visualization import (LinearStretch, SqrtStretch, AsinhStretch,
LogStretch, ManualInterval, ContrastBiasStretch)
__all__ = ['CompositeArray']
COLOR_CONVERTER = ColorConverter()
STRETCHES = {
'linear': LinearStretch,
'sqrt': SqrtStretch,
'arcsinh': AsinhStretch,
'log': LogStretch
}
class CompositeArray(object):
def __init__(self, **kwargs):
# We keep a dictionary of layers. The key should be the UUID of the
# layer artist, and the values should be dictionaries that contain
# 'zorder', 'visible', 'array', 'color', and 'alpha'.
self.layers = {}
self._first = True
def allocate(self, uuid):
self.layers[uuid] = {'zorder': 0,
'visible': True,
'array': None,
'shape': None,
'color': '0.5',
'alpha': 1,
'clim': (0, 1),
'contrast': 1,
'bias': 0.5,
'stretch': 'linear'}
def deallocate(self, uuid):
self.layers.pop(uuid)
def set(self, uuid, **kwargs):
for key, value in kwargs.items():
if key not in self.layers[uuid]:
raise KeyError("Unknown key: {0}".format(key))
else:
self.layers[uuid][key] = value
@property
def shape(self):
for layer in self.layers.values():
if callable(layer['shape']):
shape = layer['shape']()
elif layer['shape'] is not None:
shape = layer['shape']
elif callable(layer['array']):
array = layer['array']()
if array is None:
return None
else:
shape = array.shape
else:
shape = layer['array'].shape
if shape is not None:
return shape
return None
def __getitem__(self, item):
return self()[item]
def __call__(self, bounds=None):
img = None
visible_layers = 0
for uuid in sorted(self.layers, key=lambda x: self.layers[x]['zorder']):
layer = self.layers[uuid]
if not layer['visible']:
continue
interval = ManualInterval(*layer['clim'])
contrast_bias = ContrastBiasStretch(layer['contrast'], layer['bias'])
if callable(layer['array']):
array = layer['array'](bounds=bounds)
else:
array = layer['array']
if array is None:
continue
if np.isscalar(array):
scalar = True
array = np.atleast_2d(array)
else:
scalar = False
data = STRETCHES[layer['stretch']]()(contrast_bias(interval(array)))
data[np.isnan(data)] = 0
if isinstance(layer['color'], Colormap):
if img is None:
img = np.ones(data.shape + (4,))
# Compute colormapped image
plane = layer['color'](data)
alpha_plane = layer['alpha'] * plane[:, :, 3]
# Use traditional alpha compositing
plane[:, :, 0] = plane[:, :, 0] * alpha_plane
plane[:, :, 1] = plane[:, :, 1] * alpha_plane
plane[:, :, 2] = plane[:, :, 2] * alpha_plane
img[:, :, 0] *= (1 - alpha_plane)
img[:, :, 1] *= (1 - alpha_plane)
img[:, :, 2] *= (1 - alpha_plane)
img[:, :, 3] = 1
else:
if img is None:
img = np.zeros(data.shape + (4,))
# Get color and pre-multiply by alpha values
color = COLOR_CONVERTER.to_rgba_array(layer['color'])[0]
color *= layer['alpha']
# We should treat NaN values as zero (post-stretch), which means
# that those pixels don't contribute towards the final image.
reset = np.isnan(data)
if np.any(reset):
data[reset] = 0.
plane = data[:, :, np.newaxis] * color
plane[:, :, 3] = 1
visible_layers += 1
if scalar:
plane = plane[0, 0]
img += plane
if img is None:
return None
else:
img = np.clip(img, 0, 1)
return img
@property
def dtype(self):
return np.dtype(float)
@property
def ndim(self):
return 2
@property
def size(self):
return np.product(self.shape)
def __contains__(self, item):
return item in self.layers
| 28.778409 | 83 | 0.476802 |
import numpy as np
from matplotlib.colors import ColorConverter, Colormap
from astropy.visualization import (LinearStretch, SqrtStretch, AsinhStretch,
LogStretch, ManualInterval, ContrastBiasStretch)
__all__ = ['CompositeArray']
COLOR_CONVERTER = ColorConverter()
STRETCHES = {
'linear': LinearStretch,
'sqrt': SqrtStretch,
'arcsinh': AsinhStretch,
'log': LogStretch
}
class CompositeArray(object):
def __init__(self, **kwargs):
self.layers = {}
self._first = True
def allocate(self, uuid):
self.layers[uuid] = {'zorder': 0,
'visible': True,
'array': None,
'shape': None,
'color': '0.5',
'alpha': 1,
'clim': (0, 1),
'contrast': 1,
'bias': 0.5,
'stretch': 'linear'}
def deallocate(self, uuid):
self.layers.pop(uuid)
def set(self, uuid, **kwargs):
for key, value in kwargs.items():
if key not in self.layers[uuid]:
raise KeyError("Unknown key: {0}".format(key))
else:
self.layers[uuid][key] = value
@property
def shape(self):
for layer in self.layers.values():
if callable(layer['shape']):
shape = layer['shape']()
elif layer['shape'] is not None:
shape = layer['shape']
elif callable(layer['array']):
array = layer['array']()
if array is None:
return None
else:
shape = array.shape
else:
shape = layer['array'].shape
if shape is not None:
return shape
return None
def __getitem__(self, item):
return self()[item]
def __call__(self, bounds=None):
img = None
visible_layers = 0
for uuid in sorted(self.layers, key=lambda x: self.layers[x]['zorder']):
layer = self.layers[uuid]
if not layer['visible']:
continue
interval = ManualInterval(*layer['clim'])
contrast_bias = ContrastBiasStretch(layer['contrast'], layer['bias'])
if callable(layer['array']):
array = layer['array'](bounds=bounds)
else:
array = layer['array']
if array is None:
continue
if np.isscalar(array):
scalar = True
array = np.atleast_2d(array)
else:
scalar = False
data = STRETCHES[layer['stretch']]()(contrast_bias(interval(array)))
data[np.isnan(data)] = 0
if isinstance(layer['color'], Colormap):
if img is None:
img = np.ones(data.shape + (4,))
plane = layer['color'](data)
alpha_plane = layer['alpha'] * plane[:, :, 3]
plane[:, :, 0] = plane[:, :, 0] * alpha_plane
plane[:, :, 1] = plane[:, :, 1] * alpha_plane
plane[:, :, 2] = plane[:, :, 2] * alpha_plane
img[:, :, 0] *= (1 - alpha_plane)
img[:, :, 1] *= (1 - alpha_plane)
img[:, :, 2] *= (1 - alpha_plane)
img[:, :, 3] = 1
else:
if img is None:
img = np.zeros(data.shape + (4,))
color = COLOR_CONVERTER.to_rgba_array(layer['color'])[0]
color *= layer['alpha']
reset = np.isnan(data)
if np.any(reset):
data[reset] = 0.
plane = data[:, :, np.newaxis] * color
plane[:, :, 3] = 1
visible_layers += 1
if scalar:
plane = plane[0, 0]
img += plane
if img is None:
return None
else:
img = np.clip(img, 0, 1)
return img
@property
def dtype(self):
return np.dtype(float)
@property
def ndim(self):
return 2
@property
def size(self):
return np.product(self.shape)
def __contains__(self, item):
return item in self.layers
| true | true |
f7293fb10151a0afd2415399bbc15a21c5dea227 | 2,097 | py | Python | assignment-14/unused.py | faizanahemad/eva | cca4666f5953bbe040f52b91b9085ac38f2bc8e1 | [
"MIT"
] | 3 | 2019-07-22T15:55:07.000Z | 2019-12-02T13:18:50.000Z | assignment-14/unused.py | faizanahemad/eva | cca4666f5953bbe040f52b91b9085ac38f2bc8e1 | [
"MIT"
] | null | null | null | assignment-14/unused.py | faizanahemad/eva | cca4666f5953bbe040f52b91b9085ac38f2bc8e1 | [
"MIT"
] | 2 | 2019-11-16T05:31:10.000Z | 2020-01-06T08:34:08.000Z | def replace_slice(input_: tf.Tensor, replacement, begin) -> tf.Tensor:
inp_shape = tf.shape(input_)
size = tf.shape(replacement)
padding = tf.stack([begin, inp_shape - (begin + size)], axis=1)
replacement_pad = tf.pad(replacement, padding)
mask = tf.pad(tf.ones_like(replacement, dtype=tf.bool), padding)
return tf.where(mask, replacement_pad, input_)
def tf_cutout(x: tf.Tensor, area: int = 81, c: int = 3) -> tf.Tensor:
"""
Cutout data augmentation. Randomly cuts a h by w whole in the image, and fill the whole with zeros.
:param x: Input image.
:param h: Height of the hole.
:param w: Width of the hole
:param c: Number of color channels in the image. Default: 3 (RGB).
:return: Transformed image.
"""
minimum = tf.cast(tf.reduce_min(x),dtype=tf.int32)
maximum = tf.cast(tf.reduce_max(x),dtype=tf.int32)
h = np.random.randint(3,np.sqrt(area)+1)
w = int(np.ceil(area/h))
shape = tf.shape(x)
x0 = tf.random.uniform([], 0, shape[0] + 1 - h, dtype=tf.int32)
y0 = tf.random.uniform([], 0, shape[1] + 1 - w, dtype=tf.int32)
slic = tf.cast(tf.random.uniform([h, w, c],minval=minimum,maxval=maximum, dtype=tf.int32),dtype=tf.uint8)
x = replace_slice(x,slic, [x0, y0, 0])
return x
def get_tf_cutout_eraser(area=81):
def cutout_mapper(x,y):
return tf_cutout(x,area,3),y
return cutout_mapper
def batch_cut(imgs,cutout_proba=1.0, copy=False):
cutout_fn = get_cutout_eraser(p=1.0, pixel_level=True)
if copy:
imgs = np.copy(imgs)
for i,im in enumerate(imgs):
imgs[i] = cutout_fn(im, proba=cutout_proba)
return imgs
def cifar10_augs(imgs,cifar10_proba=1.0,policy_idx=None,magnitude_idx=None):
policy = CIFAR10Policy(fillcolor=tuple(train_mean.astype(int)),log=True)
new_images = []
for i,im in enumerate(imgs):
p_1 = np.random.rand()
if p_1 < cifar10_proba:
im = policy(im,policy_idx=policy_idx,magnitude_idx=magnitude_idx)
new_images.append(im)
new_images = np.stack(new_images)
return new_images | 38.833333 | 109 | 0.662852 | def replace_slice(input_: tf.Tensor, replacement, begin) -> tf.Tensor:
inp_shape = tf.shape(input_)
size = tf.shape(replacement)
padding = tf.stack([begin, inp_shape - (begin + size)], axis=1)
replacement_pad = tf.pad(replacement, padding)
mask = tf.pad(tf.ones_like(replacement, dtype=tf.bool), padding)
return tf.where(mask, replacement_pad, input_)
def tf_cutout(x: tf.Tensor, area: int = 81, c: int = 3) -> tf.Tensor:
minimum = tf.cast(tf.reduce_min(x),dtype=tf.int32)
maximum = tf.cast(tf.reduce_max(x),dtype=tf.int32)
h = np.random.randint(3,np.sqrt(area)+1)
w = int(np.ceil(area/h))
shape = tf.shape(x)
x0 = tf.random.uniform([], 0, shape[0] + 1 - h, dtype=tf.int32)
y0 = tf.random.uniform([], 0, shape[1] + 1 - w, dtype=tf.int32)
slic = tf.cast(tf.random.uniform([h, w, c],minval=minimum,maxval=maximum, dtype=tf.int32),dtype=tf.uint8)
x = replace_slice(x,slic, [x0, y0, 0])
return x
def get_tf_cutout_eraser(area=81):
def cutout_mapper(x,y):
return tf_cutout(x,area,3),y
return cutout_mapper
def batch_cut(imgs,cutout_proba=1.0, copy=False):
cutout_fn = get_cutout_eraser(p=1.0, pixel_level=True)
if copy:
imgs = np.copy(imgs)
for i,im in enumerate(imgs):
imgs[i] = cutout_fn(im, proba=cutout_proba)
return imgs
def cifar10_augs(imgs,cifar10_proba=1.0,policy_idx=None,magnitude_idx=None):
policy = CIFAR10Policy(fillcolor=tuple(train_mean.astype(int)),log=True)
new_images = []
for i,im in enumerate(imgs):
p_1 = np.random.rand()
if p_1 < cifar10_proba:
im = policy(im,policy_idx=policy_idx,magnitude_idx=magnitude_idx)
new_images.append(im)
new_images = np.stack(new_images)
return new_images | true | true |
f7294193cf63cfe94ee995a6946fbd3309eb571e | 5,337 | py | Python | rkivacc/report.py | spycrab/rkivacc | 90238b6aa12cc6747579c28d2a5012bcf7602b30 | [
"MIT"
] | 1 | 2021-01-04T22:43:15.000Z | 2021-01-04T22:43:15.000Z | rkivacc/report.py | spycrab/rkivacc | 90238b6aa12cc6747579c28d2a5012bcf7602b30 | [
"MIT"
] | null | null | null | rkivacc/report.py | spycrab/rkivacc | 90238b6aa12cc6747579c28d2a5012bcf7602b30 | [
"MIT"
] | null | null | null | import rkivacc
import os
import tempfile
import email.utils
import requests
import openpyxl
class RKIReport:
def __get_column(row, map, column):
if not column in map:
return None
value = row[map[column]].value
if isinstance(value, str):
return RKIReport.__strip_stars(value)
else:
return value
def __extract_row(row, map, state = True):
data = {}
if state:
data["state"] = RKIReport.__get_column(row, map, "state")
data.update({
"vaccinations": RKIReport.__get_column(row, map, "vaccinations"),
"delta": RKIReport.__get_column(row, map, "delta"),
"vaccinations_per_capita": RKIReport.__get_column(row, map, "vaccinations_per_capita"),
"reasons": {
"age": RKIReport.__get_column(row, map, "reasons_age"),
"profession": RKIReport.__get_column(row, map, "reasons_profession"),
"medical": RKIReport.__get_column(row, map, "reasons_medical"),
"nursing_home": RKIReport.__get_column(row, map, "reasons_nursing_home")
}
})
return data
def __strip_stars(str):
while str[-1] == "*":
str = str[0:-1]
return str
def __init__(self, report_xlsx_path, modified = None):
workbook = openpyxl.load_workbook(filename = report_xlsx_path, data_only = True)
sheet = workbook.worksheets[rkivacc.WORKSHEET_INDEX]
self._states = {}
self._modified = modified
table_rows = sheet.iter_rows(min_row=rkivacc.TABLE_FIRST_ROW - 1,
max_row=rkivacc.TABLE_FIRST_ROW + rkivacc.TABLE_LENGTH - 1,
max_col=10)
header_row = next(table_rows)
known_rows = {
"RS": None,
"Bundesland": "state",
"Impfungen kumulativ": "vaccinations",
"Differenz zum Vortag": "delta",
"Impfungen pro 1.000 Einwohner": "vaccinations_per_capita",
"Indikation nach Alter": "reasons_age",
"Berufliche Indikation": "reasons_profession",
"Medizinische Indikation": "reasons_medical",
"Pflegeheim-bewohnerIn": "reasons_nursing_home",
}
map = {}
for cellIndex in range(0, len(header_row)):
value = header_row[cellIndex].value
if value is None:
continue
value = RKIReport.__strip_stars(value)
if not value in known_rows:
print("Warning: Found unknown header item '{}'. This might be bad news.".format(value))
continue
key = known_rows[value]
if key is None:
continue
map[key] = cellIndex
total_row = next(sheet.iter_rows(min_row=rkivacc.TABLE_FIRST_ROW + rkivacc.TABLE_LENGTH,
max_row=rkivacc.TABLE_FIRST_ROW + rkivacc.TABLE_LENGTH,
max_col=10))
for row in table_rows:
self._states[RKIReport.__strip_stars(row[map["state"]].value)] = RKIReport.__extract_row(row, map)
self._total = RKIReport.__extract_row(total_row, map, state = False)
def states(self):
return list(self._states.keys())
def state(self, name):
return self._states[name]
def all_states(self):
return self._states.values()
def total(self):
return self._total
def modified(self):
return self._modified
def has_per_capita(self):
return (self._total["vaccinations_per_capita"] is not None)
def calculate_per_capita(self):
if self.has_per_capita():
print("Per capita data already present! Not overwriting.")
return
for state in self.states():
if not state in rkivacc.POPULATION_STATS:
raise RuntimeError("State '{}' not in population stats!".format(state))
self._states[state]["vaccinations_per_capita"] = (self._states[state]["vaccinations"] / rkivacc.POPULATION_STATS[state]) * 1000
self._total["vaccinations_per_capita"] = (self._total["vaccinations"] / rkivacc.POPULATION_STATS["total"]) * 1000
def download(target_path):
try:
response = requests.get(rkivacc.URL)
except RequestException:
raise RuntimeError("Failed to obtain statistics from server")
if not response.ok:
raise RuntimeError("Unexpected status code from server: {}".format(response.status_code))
with open(target_path, "wb") as file:
file.write(response.content)
return email.utils.parsedate_to_datetime(response.headers['Last-Modified'])
def obtain():
temp_dir = tempfile.TemporaryDirectory()
temp_file = os.path.join(temp_dir.name, "rkiReport.xlsx")
last_modified = RKIReport.download(temp_file)
report = RKIReport(temp_file, last_modified)
temp_dir.cleanup()
return report
| 37.0625 | 139 | 0.572794 | import rkivacc
import os
import tempfile
import email.utils
import requests
import openpyxl
class RKIReport:
def __get_column(row, map, column):
if not column in map:
return None
value = row[map[column]].value
if isinstance(value, str):
return RKIReport.__strip_stars(value)
else:
return value
def __extract_row(row, map, state = True):
data = {}
if state:
data["state"] = RKIReport.__get_column(row, map, "state")
data.update({
"vaccinations": RKIReport.__get_column(row, map, "vaccinations"),
"delta": RKIReport.__get_column(row, map, "delta"),
"vaccinations_per_capita": RKIReport.__get_column(row, map, "vaccinations_per_capita"),
"reasons": {
"age": RKIReport.__get_column(row, map, "reasons_age"),
"profession": RKIReport.__get_column(row, map, "reasons_profession"),
"medical": RKIReport.__get_column(row, map, "reasons_medical"),
"nursing_home": RKIReport.__get_column(row, map, "reasons_nursing_home")
}
})
return data
def __strip_stars(str):
while str[-1] == "*":
str = str[0:-1]
return str
def __init__(self, report_xlsx_path, modified = None):
workbook = openpyxl.load_workbook(filename = report_xlsx_path, data_only = True)
sheet = workbook.worksheets[rkivacc.WORKSHEET_INDEX]
self._states = {}
self._modified = modified
table_rows = sheet.iter_rows(min_row=rkivacc.TABLE_FIRST_ROW - 1,
max_row=rkivacc.TABLE_FIRST_ROW + rkivacc.TABLE_LENGTH - 1,
max_col=10)
header_row = next(table_rows)
known_rows = {
"RS": None,
"Bundesland": "state",
"Impfungen kumulativ": "vaccinations",
"Differenz zum Vortag": "delta",
"Impfungen pro 1.000 Einwohner": "vaccinations_per_capita",
"Indikation nach Alter": "reasons_age",
"Berufliche Indikation": "reasons_profession",
"Medizinische Indikation": "reasons_medical",
"Pflegeheim-bewohnerIn": "reasons_nursing_home",
}
map = {}
for cellIndex in range(0, len(header_row)):
value = header_row[cellIndex].value
if value is None:
continue
value = RKIReport.__strip_stars(value)
if not value in known_rows:
print("Warning: Found unknown header item '{}'. This might be bad news.".format(value))
continue
key = known_rows[value]
if key is None:
continue
map[key] = cellIndex
total_row = next(sheet.iter_rows(min_row=rkivacc.TABLE_FIRST_ROW + rkivacc.TABLE_LENGTH,
max_row=rkivacc.TABLE_FIRST_ROW + rkivacc.TABLE_LENGTH,
max_col=10))
for row in table_rows:
self._states[RKIReport.__strip_stars(row[map["state"]].value)] = RKIReport.__extract_row(row, map)
self._total = RKIReport.__extract_row(total_row, map, state = False)
def states(self):
return list(self._states.keys())
def state(self, name):
return self._states[name]
def all_states(self):
return self._states.values()
def total(self):
return self._total
def modified(self):
return self._modified
def has_per_capita(self):
return (self._total["vaccinations_per_capita"] is not None)
def calculate_per_capita(self):
if self.has_per_capita():
print("Per capita data already present! Not overwriting.")
return
for state in self.states():
if not state in rkivacc.POPULATION_STATS:
raise RuntimeError("State '{}' not in population stats!".format(state))
self._states[state]["vaccinations_per_capita"] = (self._states[state]["vaccinations"] / rkivacc.POPULATION_STATS[state]) * 1000
self._total["vaccinations_per_capita"] = (self._total["vaccinations"] / rkivacc.POPULATION_STATS["total"]) * 1000
def download(target_path):
try:
response = requests.get(rkivacc.URL)
except RequestException:
raise RuntimeError("Failed to obtain statistics from server")
if not response.ok:
raise RuntimeError("Unexpected status code from server: {}".format(response.status_code))
with open(target_path, "wb") as file:
file.write(response.content)
return email.utils.parsedate_to_datetime(response.headers['Last-Modified'])
def obtain():
temp_dir = tempfile.TemporaryDirectory()
temp_file = os.path.join(temp_dir.name, "rkiReport.xlsx")
last_modified = RKIReport.download(temp_file)
report = RKIReport(temp_file, last_modified)
temp_dir.cleanup()
return report
| true | true |
f7294311b09039d93476d2accfb997483c517f56 | 6,680 | py | Python | src/gennav_ros/conversions.py | SuhrudhSarathy/gennav_ros | 471c47431b020ad411f08c2c4d2ee38d702a5a3b | [
"MIT"
] | null | null | null | src/gennav_ros/conversions.py | SuhrudhSarathy/gennav_ros | 471c47431b020ad411f08c2c4d2ee38d702a5a3b | [
"MIT"
] | null | null | null | src/gennav_ros/conversions.py | SuhrudhSarathy/gennav_ros | 471c47431b020ad411f08c2c4d2ee38d702a5a3b | [
"MIT"
] | null | null | null | import math
import tf
from gennav import utils as utils
from gennav.utils import RobotState, Trajectory
from gennav.utils.common import Velocity
from geometry_msgs.msg import Point, Quaternion, Transform, Twist, Vector3
from trajectory_msgs.msg import MultiDOFJointTrajectory, MultiDOFJointTrajectoryPoint
def traj_to_msg(traj):
"""Converts the trajectory planned by the planner to a publishable ROS message
Args:
traj (gennav.utils.Trajectory): Trajectory planned by the planner
Returns:
geometry_msgs/MultiDOFJointTrajectory.msg: A publishable ROS message
"""
traj_msg = MultiDOFJointTrajectory(points=[], joint_names=None, header=None)
for state, timestamp in zip(traj.path, traj.timestamps):
quaternion = tf.transformations.quaternion_from_euler(
state.orientation.roll,
state.orientation.pitch,
state.orientation.yaw,
)
velocity = Twist()
acceleration = Twist()
transforms = Transform(
translation=Point(state.position.x, state.position.y, state.position.z),
rotation=Quaternion(
quaternion[0], quaternion[1], quaternion[2], quaternion[3]
),
)
traj_point = MultiDOFJointTrajectoryPoint(
transforms=[transforms],
velocities=[velocity],
accelerations=[acceleration],
time_from_start=timestamp,
)
traj_msg.points.append(traj_point)
return traj_msg
def msg_to_traj(msg):
"""Converts the trajectory containing ROS message to gennav.utils.Trajectory data type
Args:
msg (geometry_msgs/MultiDOFJointTrajectory.msg): The ROS message
Returns:
gennav.utils.Trajectory : Object containing the trajectory data
"""
path = []
timestamps = []
for point in msg.points:
timestamps.append(point.time_from_start)
position = utils.common.Point(
x=point.transforms[0].translation.x,
y=point.transforms[0].translation.y,
z=point.transforms[0].translation.z,
)
rotation = tf.transformations.euler_from_quaternion(
[
point.transforms[0].rotation.x,
point.transforms[0].rotation.y,
point.transforms[0].rotation.z,
point.transforms[0].rotation.w,
]
)
rotation = utils.geometry.OrientationRPY(
roll=rotation[0], pitch=rotation[1], yaw=rotation[2]
)
linear_vel = utils.geometry.Vector3D(
x=point.velocities[0].linear.x,
y=point.velocities[0].linear.y,
z=point.velocities[0].linear.z,
)
angular_vel = utils.geometry.Vector3D(
x=point.velocities[0].angular.x,
y=point.velocities[0].angular.y,
z=point.velocities[0].linear.z,
)
velocity = Velocity(linear=linear_vel, angular=angular_vel)
state = RobotState(position=position, orientation=rotation, velocity=velocity)
path.append(state)
traj = Trajectory(path=path, timestamps=timestamps)
return traj
def Odom_to_RobotState(msg):
"""Converts a ROS message of type nav_msgs/Odometry.msg to an object of type gennav.utils.RobotState
Args:
msg (nav_msgs/Odometry.msg): ROS message containing the pose and velocity of the robot
Returns:
gennav.utils.RobotState: A RobotState() object which can be passed to the controller
"""
position = utils.common.Point(
x=msg.pose.pose.position.x,
y=msg.pose.pose.position.y,
z=msg.pose.pose.position.z,
)
orientation = tf.transformations.euler_from_quaternion(
[
msg.pose.pose.orientation.x,
msg.pose.pose.orientation.y,
msg.pose.pose.orientation.z,
msg.pose.pose.orientation.w,
]
)
orientation = utils.geometry.OrientationRPY(
roll=orientation[0], pitch=orientation[1], yaw=orientation[2]
)
linear_vel = utils.geometry.Vector3D(
x=msg.twist.twist.linear.x,
y=msg.twist.twist.linear.y,
z=msg.twist.twist.linear.z,
)
angular_vel = utils.geometry.Vector3D(
x=msg.twist.twist.angular.x,
y=msg.twist.twist.angular.y,
z=msg.twist.twist.angular.z,
)
velocity = Velocity(linear=linear_vel, angular=angular_vel)
state = RobotState(position=position, orientation=orientation, velocity=velocity)
return state
def Velocity_to_Twist(velocity):
"""Converts an object of type gennav.utils.common.Velocity to a ROS message (Twist) publishable on cmd_vel
Args:
velocity (gennav.utils.Velociy): Velocity to be sent to the robot
Returns:
geometry_msgs/Twist.msg: ROS message which can be sent to the robot via the cmd_vel topic
"""
linear = Vector3(x=velocity.linear.x, y=velocity.linear.y, z=velocity.linear.z)
angular = Vector3(x=velocity.angular.x, y=velocity.angular.y, z=velocity.angular.z)
msg = Twist(linear=linear, angular=angular)
return msg
def LaserScan_to_polygons(scan_data, threshold, angle_deviation=0):
"""Converts data of sensor_msgs/LaserScan ROS message type to polygons
Args:
scan_data (sensor_msgs.msg.LaserScan): Data to be converted
threshold (int):Threshold for deciding when to start a new obstacle
angle_deviation: Deviation of the zero ofthe lidar from zero of the bot(in radians)
Returns:
list[list[tuple[float, float, float]]]: Corresponding polygons
"""
obstacle_1D = [[scan_data.ranges[0]]]
current_obstacle_index = 0
for i in range(len(scan_data.ranges) - 1):
if abs(scan_data.ranges[i] - scan_data.ranges[i + 1]) > threshold:
obstacle_1D.append([])
current_obstacle_index += 1
obstacle_1D[current_obstacle_index].append(scan_data.ranges[i + 1])
obstacle_list = []
least_angle = 2 * math.pi / len(scan_data.ranges)
pt_count = 0
for i in range(len(obstacle_1D)):
for j in range(len(obstacle_1D[i])):
obstacle_1D[i][j] = (
obstacle_1D[i][j],
angle_deviation + pt_count * least_angle,
)
pt_count = pt_count + 1
point_list = []
for obstacle in obstacle_1D:
for point_rtheta in obstacle:
point = (
(point_rtheta[0] * math.cos(point_rtheta[1])),
(point_rtheta[0] * math.sin(point_rtheta[1])),
0,
)
point_list.append(point)
obstacle_list.append(point_list)
return obstacle_list
| 33.737374 | 110 | 0.642216 | import math
import tf
from gennav import utils as utils
from gennav.utils import RobotState, Trajectory
from gennav.utils.common import Velocity
from geometry_msgs.msg import Point, Quaternion, Transform, Twist, Vector3
from trajectory_msgs.msg import MultiDOFJointTrajectory, MultiDOFJointTrajectoryPoint
def traj_to_msg(traj):
traj_msg = MultiDOFJointTrajectory(points=[], joint_names=None, header=None)
for state, timestamp in zip(traj.path, traj.timestamps):
quaternion = tf.transformations.quaternion_from_euler(
state.orientation.roll,
state.orientation.pitch,
state.orientation.yaw,
)
velocity = Twist()
acceleration = Twist()
transforms = Transform(
translation=Point(state.position.x, state.position.y, state.position.z),
rotation=Quaternion(
quaternion[0], quaternion[1], quaternion[2], quaternion[3]
),
)
traj_point = MultiDOFJointTrajectoryPoint(
transforms=[transforms],
velocities=[velocity],
accelerations=[acceleration],
time_from_start=timestamp,
)
traj_msg.points.append(traj_point)
return traj_msg
def msg_to_traj(msg):
path = []
timestamps = []
for point in msg.points:
timestamps.append(point.time_from_start)
position = utils.common.Point(
x=point.transforms[0].translation.x,
y=point.transforms[0].translation.y,
z=point.transforms[0].translation.z,
)
rotation = tf.transformations.euler_from_quaternion(
[
point.transforms[0].rotation.x,
point.transforms[0].rotation.y,
point.transforms[0].rotation.z,
point.transforms[0].rotation.w,
]
)
rotation = utils.geometry.OrientationRPY(
roll=rotation[0], pitch=rotation[1], yaw=rotation[2]
)
linear_vel = utils.geometry.Vector3D(
x=point.velocities[0].linear.x,
y=point.velocities[0].linear.y,
z=point.velocities[0].linear.z,
)
angular_vel = utils.geometry.Vector3D(
x=point.velocities[0].angular.x,
y=point.velocities[0].angular.y,
z=point.velocities[0].linear.z,
)
velocity = Velocity(linear=linear_vel, angular=angular_vel)
state = RobotState(position=position, orientation=rotation, velocity=velocity)
path.append(state)
traj = Trajectory(path=path, timestamps=timestamps)
return traj
def Odom_to_RobotState(msg):
position = utils.common.Point(
x=msg.pose.pose.position.x,
y=msg.pose.pose.position.y,
z=msg.pose.pose.position.z,
)
orientation = tf.transformations.euler_from_quaternion(
[
msg.pose.pose.orientation.x,
msg.pose.pose.orientation.y,
msg.pose.pose.orientation.z,
msg.pose.pose.orientation.w,
]
)
orientation = utils.geometry.OrientationRPY(
roll=orientation[0], pitch=orientation[1], yaw=orientation[2]
)
linear_vel = utils.geometry.Vector3D(
x=msg.twist.twist.linear.x,
y=msg.twist.twist.linear.y,
z=msg.twist.twist.linear.z,
)
angular_vel = utils.geometry.Vector3D(
x=msg.twist.twist.angular.x,
y=msg.twist.twist.angular.y,
z=msg.twist.twist.angular.z,
)
velocity = Velocity(linear=linear_vel, angular=angular_vel)
state = RobotState(position=position, orientation=orientation, velocity=velocity)
return state
def Velocity_to_Twist(velocity):
linear = Vector3(x=velocity.linear.x, y=velocity.linear.y, z=velocity.linear.z)
angular = Vector3(x=velocity.angular.x, y=velocity.angular.y, z=velocity.angular.z)
msg = Twist(linear=linear, angular=angular)
return msg
def LaserScan_to_polygons(scan_data, threshold, angle_deviation=0):
obstacle_1D = [[scan_data.ranges[0]]]
current_obstacle_index = 0
for i in range(len(scan_data.ranges) - 1):
if abs(scan_data.ranges[i] - scan_data.ranges[i + 1]) > threshold:
obstacle_1D.append([])
current_obstacle_index += 1
obstacle_1D[current_obstacle_index].append(scan_data.ranges[i + 1])
obstacle_list = []
least_angle = 2 * math.pi / len(scan_data.ranges)
pt_count = 0
for i in range(len(obstacle_1D)):
for j in range(len(obstacle_1D[i])):
obstacle_1D[i][j] = (
obstacle_1D[i][j],
angle_deviation + pt_count * least_angle,
)
pt_count = pt_count + 1
point_list = []
for obstacle in obstacle_1D:
for point_rtheta in obstacle:
point = (
(point_rtheta[0] * math.cos(point_rtheta[1])),
(point_rtheta[0] * math.sin(point_rtheta[1])),
0,
)
point_list.append(point)
obstacle_list.append(point_list)
return obstacle_list
| true | true |
f7294364ac852b57cc57abd82f03e5f9110e4046 | 1,897 | py | Python | tempest/api/compute/limits/test_absolute_limits_negative.py | pcaruana/tempest | 907ed711f265305316fdc80acd16e9657cb2c0b4 | [
"Apache-2.0"
] | null | null | null | tempest/api/compute/limits/test_absolute_limits_negative.py | pcaruana/tempest | 907ed711f265305316fdc80acd16e9657cb2c0b4 | [
"Apache-2.0"
] | null | null | null | tempest/api/compute/limits/test_absolute_limits_negative.py | pcaruana/tempest | 907ed711f265305316fdc80acd16e9657cb2c0b4 | [
"Apache-2.0"
] | 1 | 2019-02-14T23:36:55.000Z | 2019-02-14T23:36:55.000Z | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from tempest.api.compute import base
from tempest import exceptions
from tempest import test
class AbsoluteLimitsNegativeTestJSON(base.BaseV2ComputeTest):
@classmethod
def resource_setup(cls):
super(AbsoluteLimitsNegativeTestJSON, cls).resource_setup()
cls.client = cls.limits_client
cls.server_client = cls.servers_client
@test.attr(type=['negative', 'gate'])
def test_max_image_meta_exceed_limit(self):
# We should not create vm with image meta over maxImageMeta limit
# Get max limit value
max_meta = self.client.get_specific_absolute_limit('maxImageMeta')
# Create server should fail, since we are passing > metadata Limit!
max_meta_data = int(max_meta) + 1
meta_data = {}
for xx in range(max_meta_data):
meta_data[str(xx)] = str(xx)
# A 403 Forbidden or 413 Overlimit (old behaviour) exception
# will be raised when out of quota
self.assertRaises((exceptions.Unauthorized, exceptions.OverLimit),
self.server_client.create_server,
name='test', meta=meta_data,
flavor_ref=self.flavor_ref,
image_ref=self.image_ref)
| 38.714286 | 78 | 0.678967 |
from tempest.api.compute import base
from tempest import exceptions
from tempest import test
class AbsoluteLimitsNegativeTestJSON(base.BaseV2ComputeTest):
@classmethod
def resource_setup(cls):
super(AbsoluteLimitsNegativeTestJSON, cls).resource_setup()
cls.client = cls.limits_client
cls.server_client = cls.servers_client
@test.attr(type=['negative', 'gate'])
def test_max_image_meta_exceed_limit(self):
max_meta = self.client.get_specific_absolute_limit('maxImageMeta')
max_meta_data = int(max_meta) + 1
meta_data = {}
for xx in range(max_meta_data):
meta_data[str(xx)] = str(xx)
self.assertRaises((exceptions.Unauthorized, exceptions.OverLimit),
self.server_client.create_server,
name='test', meta=meta_data,
flavor_ref=self.flavor_ref,
image_ref=self.image_ref)
| true | true |
f729436be71e8b9271ea6d70e2e79ea138dfc0ff | 1,525 | py | Python | modules/action/crackPasswordHashJohnTR.py | Marx314/apt2 | f0782ec121057bd5d8c0c5fa438d0a13dc4dd448 | [
"MIT"
] | 2 | 2017-05-11T22:08:47.000Z | 2020-04-07T06:31:26.000Z | modules/action/crackPasswordHashJohnTR.py | padfoot999/apt2 | e473246cb273f2e2c60e2e43663535caaab8add3 | [
"MIT"
] | null | null | null | modules/action/crackPasswordHashJohnTR.py | padfoot999/apt2 | e473246cb273f2e2c60e2e43663535caaab8add3 | [
"MIT"
] | 1 | 2020-04-07T06:31:27.000Z | 2020-04-07T06:31:27.000Z | from core.actionModule import actionModule
from core.keystore import KeyStore as kb
class crackPasswordHashJohnTR(actionModule):
def __init__(self, config, display, lock):
super(crackPasswordHashJohnTR, self).__init__(config, display, lock)
self.title = "Attempt to crack any password hashes"
self.shortName = "CrackPasswordHashJTR"
self.description = "execute john the ripper on each hash"
self.requirements = ["john"]
self.triggers = ["newPasswordHash"]
self.types = ["hashcrack"]
self.safeLevel = 5
def getTargets(self):
self.targets = kb.get('user/*/passwordhash')
def process(self):
# load any targets we are interested in
self.getTargets()
# loop over each target
for t in self.targets:
# TODO
# get list of all password hashes for current user
# loop over each hash
if not self.seentarget(t + h):
# add the new IP to the already seen list
self.addseentarget(t + h)
# get the type of each hash (ntlm, lm, netntlmv2, etc..)
# write each hach out to a temp file seperated by type (i.e. one file for all ntlm, one file for all
# netntlmv2, etc...)
# if any hashes were written to files
# for each file
# execute john on file
# parse output of john and update kb with newly cracked passwords
return
| 36.309524 | 116 | 0.598033 | from core.actionModule import actionModule
from core.keystore import KeyStore as kb
class crackPasswordHashJohnTR(actionModule):
def __init__(self, config, display, lock):
super(crackPasswordHashJohnTR, self).__init__(config, display, lock)
self.title = "Attempt to crack any password hashes"
self.shortName = "CrackPasswordHashJTR"
self.description = "execute john the ripper on each hash"
self.requirements = ["john"]
self.triggers = ["newPasswordHash"]
self.types = ["hashcrack"]
self.safeLevel = 5
def getTargets(self):
self.targets = kb.get('user/*/passwordhash')
def process(self):
self.getTargets()
for t in self.targets:
if not self.seentarget(t + h):
self.addseentarget(t + h)
return
| true | true |
f72944a80e90f20fbd5a29de2e52ad3e58553fcb | 15,017 | py | Python | pptx/oxml/__init__.py | fos4X/python-pptx | 2202a15dab204b7ae80860f7f10636e0cea0a766 | [
"MIT"
] | null | null | null | pptx/oxml/__init__.py | fos4X/python-pptx | 2202a15dab204b7ae80860f7f10636e0cea0a766 | [
"MIT"
] | null | null | null | pptx/oxml/__init__.py | fos4X/python-pptx | 2202a15dab204b7ae80860f7f10636e0cea0a766 | [
"MIT"
] | null | null | null | # encoding: utf-8
"""Initializes lxml parser, particularly the custom element classes.
Also makes available a handful of functions that wrap its typical uses.
"""
from __future__ import (
absolute_import, division, print_function, unicode_literals
)
import os
from lxml import etree
from .ns import NamespacePrefixedTag
# configure etree XML parser -------------------------------
element_class_lookup = etree.ElementNamespaceClassLookup()
oxml_parser = etree.XMLParser(remove_blank_text=True, resolve_entities=False)
oxml_parser.set_element_class_lookup(element_class_lookup)
def parse_from_template(template_name):
"""
Return an element loaded from the XML in the template file identified by
*template_name*.
"""
thisdir = os.path.split(__file__)[0]
filename = os.path.join(
thisdir, '..', 'templates', '%s.xml' % template_name
)
with open(filename, 'rb') as f:
xml = f.read()
return parse_xml(xml)
def parse_xml(xml):
"""
Return root lxml element obtained by parsing XML character string in
*xml*, which can be either a Python 2.x string or unicode.
"""
root_element = etree.fromstring(xml, oxml_parser)
return root_element
def register_element_cls(nsptagname, cls):
"""
Register *cls* to be constructed when the oxml parser encounters an
element having name *nsptag_name*. *nsptag_name* is a string of the form
``nspfx:tagroot``, e.g. ``'w:document'``.
"""
nsptag = NamespacePrefixedTag(nsptagname)
namespace = element_class_lookup.get_namespace(nsptag.nsuri)
namespace[nsptag.local_part] = cls
from .action import CT_Hyperlink # noqa: E402
register_element_cls('a:hlinkClick', CT_Hyperlink)
register_element_cls('a:hlinkHover', CT_Hyperlink)
from .chart.axis import ( # noqa: E402
CT_AxisUnit, CT_CatAx, CT_ChartLines, CT_Crosses, CT_DateAx,
CT_LblOffset, CT_Scaling, CT_TickLblPos, CT_TickMark, CT_ValAx
)
register_element_cls('c:catAx', CT_CatAx)
register_element_cls('c:crosses', CT_Crosses)
register_element_cls('c:dateAx', CT_DateAx)
register_element_cls('c:lblOffset', CT_LblOffset)
register_element_cls('c:majorGridlines', CT_ChartLines)
register_element_cls('c:majorTickMark', CT_TickMark)
register_element_cls('c:majorUnit', CT_AxisUnit)
register_element_cls('c:minorTickMark', CT_TickMark)
register_element_cls('c:minorUnit', CT_AxisUnit)
register_element_cls('c:scaling', CT_Scaling)
register_element_cls('c:tickLblPos', CT_TickLblPos)
register_element_cls('c:valAx', CT_ValAx)
from .chart.chart import ( # noqa: E402
CT_Chart, CT_ChartSpace, CT_ExternalData, CT_PlotArea, CT_Style
)
register_element_cls('c:chart', CT_Chart)
register_element_cls('c:chartSpace', CT_ChartSpace)
register_element_cls('c:externalData', CT_ExternalData)
register_element_cls('c:plotArea', CT_PlotArea)
register_element_cls('c:style', CT_Style)
from .chart.datalabel import CT_DLbl, CT_DLblPos, CT_DLbls # noqa: E402
register_element_cls('c:dLbl', CT_DLbl)
register_element_cls('c:dLblPos', CT_DLblPos)
register_element_cls('c:dLbls', CT_DLbls)
from .chart.legend import CT_Legend, CT_LegendPos # noqa: E402
register_element_cls('c:legend', CT_Legend)
register_element_cls('c:legendPos', CT_LegendPos)
from .chart.marker import ( # noqa: E402
CT_Marker, CT_MarkerSize, CT_MarkerStyle
)
register_element_cls('c:marker', CT_Marker)
register_element_cls('c:size', CT_MarkerSize)
register_element_cls('c:symbol', CT_MarkerStyle)
from .chart.plot import ( # noqa: E402
CT_Area3DChart, CT_AreaChart, CT_BarChart, CT_BarDir, CT_BubbleChart,
CT_BubbleScale, CT_DoughnutChart, CT_GapAmount, CT_Grouping,
CT_LineChart, CT_Overlap, CT_PieChart, CT_RadarChart, CT_ScatterChart
)
register_element_cls('c:area3DChart', CT_Area3DChart)
register_element_cls('c:areaChart', CT_AreaChart)
register_element_cls('c:barChart', CT_BarChart)
register_element_cls('c:barDir', CT_BarDir)
register_element_cls('c:bubbleChart', CT_BubbleChart)
register_element_cls('c:bubbleScale', CT_BubbleScale)
register_element_cls('c:doughnutChart', CT_DoughnutChart)
register_element_cls('c:gapWidth', CT_GapAmount)
register_element_cls('c:grouping', CT_Grouping)
register_element_cls('c:lineChart', CT_LineChart)
register_element_cls('c:overlap', CT_Overlap)
register_element_cls('c:pieChart', CT_PieChart)
register_element_cls('c:radarChart', CT_RadarChart)
register_element_cls('c:scatterChart', CT_ScatterChart)
from .chart.series import ( # noqa: E402
CT_AxDataSource, CT_DPt, CT_Lvl, CT_NumDataSource, CT_SeriesComposite,
CT_StrVal_NumVal_Composite
)
register_element_cls('c:bubbleSize', CT_NumDataSource)
register_element_cls('c:cat', CT_AxDataSource)
register_element_cls('c:dPt', CT_DPt)
register_element_cls('c:lvl', CT_Lvl)
register_element_cls('c:pt', CT_StrVal_NumVal_Composite)
register_element_cls('c:ser', CT_SeriesComposite)
register_element_cls('c:val', CT_NumDataSource)
register_element_cls('c:xVal', CT_NumDataSource)
register_element_cls('c:yVal', CT_NumDataSource)
from .chart.shared import ( # noqa: E402
CT_Boolean, CT_Boolean_Explicit, CT_Double, CT_Layout, CT_LayoutMode,
CT_ManualLayout, CT_NumFmt, CT_Title, CT_Tx, CT_UnsignedInt
)
register_element_cls('c:autoUpdate', CT_Boolean)
register_element_cls('c:bubble3D', CT_Boolean)
register_element_cls('c:crossAx', CT_UnsignedInt)
register_element_cls('c:crossesAt', CT_Double)
register_element_cls('c:date1904', CT_Boolean)
register_element_cls('c:delete', CT_Boolean)
register_element_cls('c:idx', CT_UnsignedInt)
register_element_cls('c:invertIfNegative', CT_Boolean_Explicit)
register_element_cls('c:layout', CT_Layout)
register_element_cls('c:manualLayout', CT_ManualLayout)
register_element_cls('c:max', CT_Double)
register_element_cls('c:min', CT_Double)
register_element_cls('c:numFmt', CT_NumFmt)
register_element_cls('c:order', CT_UnsignedInt)
register_element_cls('c:overlay', CT_Boolean_Explicit)
register_element_cls('c:ptCount', CT_UnsignedInt)
register_element_cls('c:showLegendKey', CT_Boolean_Explicit)
register_element_cls('c:showVal', CT_Boolean_Explicit)
register_element_cls('c:smooth', CT_Boolean)
register_element_cls('c:title', CT_Title)
register_element_cls('c:tx', CT_Tx)
register_element_cls('c:varyColors', CT_Boolean)
register_element_cls('c:x', CT_Double)
register_element_cls('c:xMode', CT_LayoutMode)
from .coreprops import CT_CoreProperties # noqa: E402
register_element_cls('cp:coreProperties', CT_CoreProperties)
from .dml.color import ( # noqa: E402
CT_Color, CT_HslColor, CT_Percentage, CT_PresetColor, CT_SchemeColor,
CT_ScRgbColor, CT_SRgbColor, CT_SystemColor
)
register_element_cls('a:bgClr', CT_Color)
register_element_cls('a:fgClr', CT_Color)
register_element_cls('a:hslClr', CT_HslColor)
register_element_cls('a:lumMod', CT_Percentage)
register_element_cls('a:lumOff', CT_Percentage)
register_element_cls('a:prstClr', CT_PresetColor)
register_element_cls('a:schemeClr', CT_SchemeColor)
register_element_cls('a:scrgbClr', CT_ScRgbColor)
register_element_cls('a:srgbClr', CT_SRgbColor)
register_element_cls('a:sysClr', CT_SystemColor)
from .dml.fill import ( # noqa: E402
CT_Blip, CT_BlipFillProperties, CT_GradientFillProperties,
CT_GroupFillProperties, CT_NoFillProperties, CT_PatternFillProperties,
CT_RelativeRect, CT_SolidColorFillProperties
)
register_element_cls('a:blip', CT_Blip)
register_element_cls('a:blipFill', CT_BlipFillProperties)
register_element_cls('a:gradFill', CT_GradientFillProperties)
register_element_cls('a:grpFill', CT_GroupFillProperties)
register_element_cls('a:noFill', CT_NoFillProperties)
register_element_cls('a:pattFill', CT_PatternFillProperties)
register_element_cls('a:solidFill', CT_SolidColorFillProperties)
register_element_cls('a:srcRect', CT_RelativeRect)
from .dml.line import CT_PresetLineDashProperties # noqa: E402
register_element_cls('a:prstDash', CT_PresetLineDashProperties)
from .presentation import ( # noqa: E402
CT_Presentation, CT_SlideId, CT_SlideIdList, CT_SlideMasterIdList,
CT_SlideMasterIdListEntry, CT_SlideSize
)
register_element_cls('p:presentation', CT_Presentation)
register_element_cls('p:sldId', CT_SlideId)
register_element_cls('p:sldIdLst', CT_SlideIdList)
register_element_cls('p:sldMasterId', CT_SlideMasterIdListEntry)
register_element_cls('p:sldMasterIdLst', CT_SlideMasterIdList)
register_element_cls('p:sldSz', CT_SlideSize)
from .shapes.autoshape import ( # noqa: E402
CT_AdjPoint2D, CT_CustomGeometry2D, CT_GeomGuide, CT_GeomGuideList,
CT_NonVisualDrawingShapeProps, CT_Path2D, CT_Path2DClose,
CT_Path2DLineTo, CT_Path2DList, CT_Path2DMoveTo, CT_PresetGeometry2D,
CT_Shape, CT_ShapeNonVisual
)
register_element_cls('a:avLst', CT_GeomGuideList)
register_element_cls('a:custGeom', CT_CustomGeometry2D)
register_element_cls('a:gd', CT_GeomGuide)
register_element_cls('a:close', CT_Path2DClose)
register_element_cls('a:lnTo', CT_Path2DLineTo)
register_element_cls('a:moveTo', CT_Path2DMoveTo)
register_element_cls('a:path', CT_Path2D)
register_element_cls('a:pathLst', CT_Path2DList)
register_element_cls('a:prstGeom', CT_PresetGeometry2D)
register_element_cls('a:pt', CT_AdjPoint2D)
register_element_cls('p:cNvSpPr', CT_NonVisualDrawingShapeProps)
register_element_cls('p:nvSpPr', CT_ShapeNonVisual)
register_element_cls('p:sp', CT_Shape)
from .shapes.connector import ( # noqa: E402
CT_Connection, CT_Connector, CT_ConnectorNonVisual,
CT_NonVisualConnectorProperties
)
register_element_cls('a:endCxn', CT_Connection)
register_element_cls('a:stCxn', CT_Connection)
register_element_cls('p:cNvCxnSpPr', CT_NonVisualConnectorProperties)
register_element_cls('p:cxnSp', CT_Connector)
register_element_cls('p:nvCxnSpPr', CT_ConnectorNonVisual)
from .shapes.graphfrm import ( # noqa: E402
CT_GraphicalObject, CT_GraphicalObjectData, CT_GraphicalObjectFrame,
CT_GraphicalObjectFrameNonVisual
)
register_element_cls('a:graphic', CT_GraphicalObject)
register_element_cls('a:graphicData', CT_GraphicalObjectData)
register_element_cls('p:graphicFrame', CT_GraphicalObjectFrame)
register_element_cls('p:nvGraphicFramePr', CT_GraphicalObjectFrameNonVisual)
from .shapes.groupshape import ( # noqa: E402
CT_GroupShape, CT_GroupShapeNonVisual, CT_GroupShapeProperties
)
register_element_cls('p:grpSp', CT_GroupShape)
register_element_cls('p:grpSpPr', CT_GroupShapeProperties)
register_element_cls('p:nvGrpSpPr', CT_GroupShapeNonVisual)
register_element_cls('p:spTree', CT_GroupShape)
from .shapes.picture import CT_Picture, CT_PictureNonVisual # noqa: E402
register_element_cls('p:blipFill', CT_BlipFillProperties)
register_element_cls('p:nvPicPr', CT_PictureNonVisual)
register_element_cls('p:pic', CT_Picture)
from .shapes.shared import ( # noqa: E402
CT_ApplicationNonVisualDrawingProps, CT_LineProperties,
CT_NonVisualDrawingProps, CT_Placeholder, CT_Point2D, CT_PositiveSize2D,
CT_ShapeProperties, CT_Transform2D
)
register_element_cls('a:ext', CT_PositiveSize2D)
register_element_cls('a:ln', CT_LineProperties)
register_element_cls('a:off', CT_Point2D)
register_element_cls('a:xfrm', CT_Transform2D)
register_element_cls('c:spPr', CT_ShapeProperties)
register_element_cls('p:cNvPr', CT_NonVisualDrawingProps)
register_element_cls('p:nvPr', CT_ApplicationNonVisualDrawingProps)
register_element_cls('p:ph', CT_Placeholder)
register_element_cls('p:spPr', CT_ShapeProperties)
register_element_cls('p:xfrm', CT_Transform2D)
from .shapes.table import ( # noqa: E402
CT_Table, CT_TableCell, CT_TableCellProperties, CT_TableCol,
CT_TableGrid, CT_TableProperties, CT_TableRow
)
register_element_cls('a:gridCol', CT_TableCol)
register_element_cls('a:tbl', CT_Table)
register_element_cls('a:tblGrid', CT_TableGrid)
register_element_cls('a:tblPr', CT_TableProperties)
register_element_cls('a:tc', CT_TableCell)
register_element_cls('a:tcPr', CT_TableCellProperties)
register_element_cls('a:tr', CT_TableRow)
from .slide import ( # noqa: E402
CT_CommonSlideData, CT_NotesMaster, CT_NotesSlide, CT_Slide,
CT_SlideLayout, CT_SlideLayoutIdList, CT_SlideLayoutIdListEntry,
CT_SlideMaster, CT_SlideTiming, CT_TimeNodeList, CT_TLMediaNodeVideo
)
register_element_cls('p:childTnLst', CT_TimeNodeList)
register_element_cls('p:cSld', CT_CommonSlideData)
register_element_cls('p:notes', CT_NotesSlide)
register_element_cls('p:notesMaster', CT_NotesMaster)
register_element_cls('p:sld', CT_Slide)
register_element_cls('p:sldLayout', CT_SlideLayout)
register_element_cls('p:sldLayoutId', CT_SlideLayoutIdListEntry)
register_element_cls('p:sldLayoutIdLst', CT_SlideLayoutIdList)
register_element_cls('p:sldMaster', CT_SlideMaster)
register_element_cls('p:timing', CT_SlideTiming)
register_element_cls('p:video', CT_TLMediaNodeVideo)
from .text import ( # noqa: E402
CT_RegularTextRun, CT_TextBody, CT_TextBodyProperties,
CT_TextCharacterProperties, CT_TextField, CT_TextFont, CT_TextLineBreak,
CT_TextNormalAutofit, CT_TextParagraph, CT_TextParagraphProperties,
CT_TextSpacing, CT_TextSpacingPercent, CT_TextSpacingPoint
)
register_element_cls('a:bodyPr', CT_TextBodyProperties)
register_element_cls('a:br', CT_TextLineBreak)
register_element_cls('a:defRPr', CT_TextCharacterProperties)
register_element_cls('a:endParaRPr', CT_TextCharacterProperties)
register_element_cls('a:fld', CT_TextField)
register_element_cls('a:latin', CT_TextFont)
register_element_cls('a:lnSpc', CT_TextSpacing)
register_element_cls('a:normAutofit', CT_TextNormalAutofit)
register_element_cls('a:r', CT_RegularTextRun)
register_element_cls('a:p', CT_TextParagraph)
register_element_cls('a:pPr', CT_TextParagraphProperties)
register_element_cls('c:rich', CT_TextBody)
register_element_cls('a:rPr', CT_TextCharacterProperties)
register_element_cls('a:spcAft', CT_TextSpacing)
register_element_cls('a:spcBef', CT_TextSpacing)
register_element_cls('a:spcPct', CT_TextSpacingPercent)
register_element_cls('a:spcPts', CT_TextSpacingPoint)
register_element_cls('a:txBody', CT_TextBody)
register_element_cls('c:txPr', CT_TextBody)
register_element_cls('p:txBody', CT_TextBody)
from .theme import CT_OfficeStyleSheet # noqa: E402
register_element_cls('a:theme', CT_OfficeStyleSheet)
| 41.369146 | 77 | 0.767796 |
from __future__ import (
absolute_import, division, print_function, unicode_literals
)
import os
from lxml import etree
from .ns import NamespacePrefixedTag
element_class_lookup = etree.ElementNamespaceClassLookup()
oxml_parser = etree.XMLParser(remove_blank_text=True, resolve_entities=False)
oxml_parser.set_element_class_lookup(element_class_lookup)
def parse_from_template(template_name):
thisdir = os.path.split(__file__)[0]
filename = os.path.join(
thisdir, '..', 'templates', '%s.xml' % template_name
)
with open(filename, 'rb') as f:
xml = f.read()
return parse_xml(xml)
def parse_xml(xml):
root_element = etree.fromstring(xml, oxml_parser)
return root_element
def register_element_cls(nsptagname, cls):
nsptag = NamespacePrefixedTag(nsptagname)
namespace = element_class_lookup.get_namespace(nsptag.nsuri)
namespace[nsptag.local_part] = cls
from .action import CT_Hyperlink
register_element_cls('a:hlinkClick', CT_Hyperlink)
register_element_cls('a:hlinkHover', CT_Hyperlink)
from .chart.axis import (
CT_AxisUnit, CT_CatAx, CT_ChartLines, CT_Crosses, CT_DateAx,
CT_LblOffset, CT_Scaling, CT_TickLblPos, CT_TickMark, CT_ValAx
)
register_element_cls('c:catAx', CT_CatAx)
register_element_cls('c:crosses', CT_Crosses)
register_element_cls('c:dateAx', CT_DateAx)
register_element_cls('c:lblOffset', CT_LblOffset)
register_element_cls('c:majorGridlines', CT_ChartLines)
register_element_cls('c:majorTickMark', CT_TickMark)
register_element_cls('c:majorUnit', CT_AxisUnit)
register_element_cls('c:minorTickMark', CT_TickMark)
register_element_cls('c:minorUnit', CT_AxisUnit)
register_element_cls('c:scaling', CT_Scaling)
register_element_cls('c:tickLblPos', CT_TickLblPos)
register_element_cls('c:valAx', CT_ValAx)
from .chart.chart import (
CT_Chart, CT_ChartSpace, CT_ExternalData, CT_PlotArea, CT_Style
)
register_element_cls('c:chart', CT_Chart)
register_element_cls('c:chartSpace', CT_ChartSpace)
register_element_cls('c:externalData', CT_ExternalData)
register_element_cls('c:plotArea', CT_PlotArea)
register_element_cls('c:style', CT_Style)
from .chart.datalabel import CT_DLbl, CT_DLblPos, CT_DLbls
register_element_cls('c:dLbl', CT_DLbl)
register_element_cls('c:dLblPos', CT_DLblPos)
register_element_cls('c:dLbls', CT_DLbls)
from .chart.legend import CT_Legend, CT_LegendPos
register_element_cls('c:legend', CT_Legend)
register_element_cls('c:legendPos', CT_LegendPos)
from .chart.marker import (
CT_Marker, CT_MarkerSize, CT_MarkerStyle
)
register_element_cls('c:marker', CT_Marker)
register_element_cls('c:size', CT_MarkerSize)
register_element_cls('c:symbol', CT_MarkerStyle)
from .chart.plot import (
CT_Area3DChart, CT_AreaChart, CT_BarChart, CT_BarDir, CT_BubbleChart,
CT_BubbleScale, CT_DoughnutChart, CT_GapAmount, CT_Grouping,
CT_LineChart, CT_Overlap, CT_PieChart, CT_RadarChart, CT_ScatterChart
)
register_element_cls('c:area3DChart', CT_Area3DChart)
register_element_cls('c:areaChart', CT_AreaChart)
register_element_cls('c:barChart', CT_BarChart)
register_element_cls('c:barDir', CT_BarDir)
register_element_cls('c:bubbleChart', CT_BubbleChart)
register_element_cls('c:bubbleScale', CT_BubbleScale)
register_element_cls('c:doughnutChart', CT_DoughnutChart)
register_element_cls('c:gapWidth', CT_GapAmount)
register_element_cls('c:grouping', CT_Grouping)
register_element_cls('c:lineChart', CT_LineChart)
register_element_cls('c:overlap', CT_Overlap)
register_element_cls('c:pieChart', CT_PieChart)
register_element_cls('c:radarChart', CT_RadarChart)
register_element_cls('c:scatterChart', CT_ScatterChart)
from .chart.series import (
CT_AxDataSource, CT_DPt, CT_Lvl, CT_NumDataSource, CT_SeriesComposite,
CT_StrVal_NumVal_Composite
)
register_element_cls('c:bubbleSize', CT_NumDataSource)
register_element_cls('c:cat', CT_AxDataSource)
register_element_cls('c:dPt', CT_DPt)
register_element_cls('c:lvl', CT_Lvl)
register_element_cls('c:pt', CT_StrVal_NumVal_Composite)
register_element_cls('c:ser', CT_SeriesComposite)
register_element_cls('c:val', CT_NumDataSource)
register_element_cls('c:xVal', CT_NumDataSource)
register_element_cls('c:yVal', CT_NumDataSource)
from .chart.shared import (
CT_Boolean, CT_Boolean_Explicit, CT_Double, CT_Layout, CT_LayoutMode,
CT_ManualLayout, CT_NumFmt, CT_Title, CT_Tx, CT_UnsignedInt
)
register_element_cls('c:autoUpdate', CT_Boolean)
register_element_cls('c:bubble3D', CT_Boolean)
register_element_cls('c:crossAx', CT_UnsignedInt)
register_element_cls('c:crossesAt', CT_Double)
register_element_cls('c:date1904', CT_Boolean)
register_element_cls('c:delete', CT_Boolean)
register_element_cls('c:idx', CT_UnsignedInt)
register_element_cls('c:invertIfNegative', CT_Boolean_Explicit)
register_element_cls('c:layout', CT_Layout)
register_element_cls('c:manualLayout', CT_ManualLayout)
register_element_cls('c:max', CT_Double)
register_element_cls('c:min', CT_Double)
register_element_cls('c:numFmt', CT_NumFmt)
register_element_cls('c:order', CT_UnsignedInt)
register_element_cls('c:overlay', CT_Boolean_Explicit)
register_element_cls('c:ptCount', CT_UnsignedInt)
register_element_cls('c:showLegendKey', CT_Boolean_Explicit)
register_element_cls('c:showVal', CT_Boolean_Explicit)
register_element_cls('c:smooth', CT_Boolean)
register_element_cls('c:title', CT_Title)
register_element_cls('c:tx', CT_Tx)
register_element_cls('c:varyColors', CT_Boolean)
register_element_cls('c:x', CT_Double)
register_element_cls('c:xMode', CT_LayoutMode)
from .coreprops import CT_CoreProperties
register_element_cls('cp:coreProperties', CT_CoreProperties)
from .dml.color import (
CT_Color, CT_HslColor, CT_Percentage, CT_PresetColor, CT_SchemeColor,
CT_ScRgbColor, CT_SRgbColor, CT_SystemColor
)
register_element_cls('a:bgClr', CT_Color)
register_element_cls('a:fgClr', CT_Color)
register_element_cls('a:hslClr', CT_HslColor)
register_element_cls('a:lumMod', CT_Percentage)
register_element_cls('a:lumOff', CT_Percentage)
register_element_cls('a:prstClr', CT_PresetColor)
register_element_cls('a:schemeClr', CT_SchemeColor)
register_element_cls('a:scrgbClr', CT_ScRgbColor)
register_element_cls('a:srgbClr', CT_SRgbColor)
register_element_cls('a:sysClr', CT_SystemColor)
from .dml.fill import (
CT_Blip, CT_BlipFillProperties, CT_GradientFillProperties,
CT_GroupFillProperties, CT_NoFillProperties, CT_PatternFillProperties,
CT_RelativeRect, CT_SolidColorFillProperties
)
register_element_cls('a:blip', CT_Blip)
register_element_cls('a:blipFill', CT_BlipFillProperties)
register_element_cls('a:gradFill', CT_GradientFillProperties)
register_element_cls('a:grpFill', CT_GroupFillProperties)
register_element_cls('a:noFill', CT_NoFillProperties)
register_element_cls('a:pattFill', CT_PatternFillProperties)
register_element_cls('a:solidFill', CT_SolidColorFillProperties)
register_element_cls('a:srcRect', CT_RelativeRect)
from .dml.line import CT_PresetLineDashProperties
register_element_cls('a:prstDash', CT_PresetLineDashProperties)
from .presentation import (
CT_Presentation, CT_SlideId, CT_SlideIdList, CT_SlideMasterIdList,
CT_SlideMasterIdListEntry, CT_SlideSize
)
register_element_cls('p:presentation', CT_Presentation)
register_element_cls('p:sldId', CT_SlideId)
register_element_cls('p:sldIdLst', CT_SlideIdList)
register_element_cls('p:sldMasterId', CT_SlideMasterIdListEntry)
register_element_cls('p:sldMasterIdLst', CT_SlideMasterIdList)
register_element_cls('p:sldSz', CT_SlideSize)
from .shapes.autoshape import (
CT_AdjPoint2D, CT_CustomGeometry2D, CT_GeomGuide, CT_GeomGuideList,
CT_NonVisualDrawingShapeProps, CT_Path2D, CT_Path2DClose,
CT_Path2DLineTo, CT_Path2DList, CT_Path2DMoveTo, CT_PresetGeometry2D,
CT_Shape, CT_ShapeNonVisual
)
register_element_cls('a:avLst', CT_GeomGuideList)
register_element_cls('a:custGeom', CT_CustomGeometry2D)
register_element_cls('a:gd', CT_GeomGuide)
register_element_cls('a:close', CT_Path2DClose)
register_element_cls('a:lnTo', CT_Path2DLineTo)
register_element_cls('a:moveTo', CT_Path2DMoveTo)
register_element_cls('a:path', CT_Path2D)
register_element_cls('a:pathLst', CT_Path2DList)
register_element_cls('a:prstGeom', CT_PresetGeometry2D)
register_element_cls('a:pt', CT_AdjPoint2D)
register_element_cls('p:cNvSpPr', CT_NonVisualDrawingShapeProps)
register_element_cls('p:nvSpPr', CT_ShapeNonVisual)
register_element_cls('p:sp', CT_Shape)
from .shapes.connector import (
CT_Connection, CT_Connector, CT_ConnectorNonVisual,
CT_NonVisualConnectorProperties
)
register_element_cls('a:endCxn', CT_Connection)
register_element_cls('a:stCxn', CT_Connection)
register_element_cls('p:cNvCxnSpPr', CT_NonVisualConnectorProperties)
register_element_cls('p:cxnSp', CT_Connector)
register_element_cls('p:nvCxnSpPr', CT_ConnectorNonVisual)
from .shapes.graphfrm import (
CT_GraphicalObject, CT_GraphicalObjectData, CT_GraphicalObjectFrame,
CT_GraphicalObjectFrameNonVisual
)
register_element_cls('a:graphic', CT_GraphicalObject)
register_element_cls('a:graphicData', CT_GraphicalObjectData)
register_element_cls('p:graphicFrame', CT_GraphicalObjectFrame)
register_element_cls('p:nvGraphicFramePr', CT_GraphicalObjectFrameNonVisual)
from .shapes.groupshape import (
CT_GroupShape, CT_GroupShapeNonVisual, CT_GroupShapeProperties
)
register_element_cls('p:grpSp', CT_GroupShape)
register_element_cls('p:grpSpPr', CT_GroupShapeProperties)
register_element_cls('p:nvGrpSpPr', CT_GroupShapeNonVisual)
register_element_cls('p:spTree', CT_GroupShape)
from .shapes.picture import CT_Picture, CT_PictureNonVisual
register_element_cls('p:blipFill', CT_BlipFillProperties)
register_element_cls('p:nvPicPr', CT_PictureNonVisual)
register_element_cls('p:pic', CT_Picture)
from .shapes.shared import (
CT_ApplicationNonVisualDrawingProps, CT_LineProperties,
CT_NonVisualDrawingProps, CT_Placeholder, CT_Point2D, CT_PositiveSize2D,
CT_ShapeProperties, CT_Transform2D
)
register_element_cls('a:ext', CT_PositiveSize2D)
register_element_cls('a:ln', CT_LineProperties)
register_element_cls('a:off', CT_Point2D)
register_element_cls('a:xfrm', CT_Transform2D)
register_element_cls('c:spPr', CT_ShapeProperties)
register_element_cls('p:cNvPr', CT_NonVisualDrawingProps)
register_element_cls('p:nvPr', CT_ApplicationNonVisualDrawingProps)
register_element_cls('p:ph', CT_Placeholder)
register_element_cls('p:spPr', CT_ShapeProperties)
register_element_cls('p:xfrm', CT_Transform2D)
from .shapes.table import (
CT_Table, CT_TableCell, CT_TableCellProperties, CT_TableCol,
CT_TableGrid, CT_TableProperties, CT_TableRow
)
register_element_cls('a:gridCol', CT_TableCol)
register_element_cls('a:tbl', CT_Table)
register_element_cls('a:tblGrid', CT_TableGrid)
register_element_cls('a:tblPr', CT_TableProperties)
register_element_cls('a:tc', CT_TableCell)
register_element_cls('a:tcPr', CT_TableCellProperties)
register_element_cls('a:tr', CT_TableRow)
from .slide import (
CT_CommonSlideData, CT_NotesMaster, CT_NotesSlide, CT_Slide,
CT_SlideLayout, CT_SlideLayoutIdList, CT_SlideLayoutIdListEntry,
CT_SlideMaster, CT_SlideTiming, CT_TimeNodeList, CT_TLMediaNodeVideo
)
register_element_cls('p:childTnLst', CT_TimeNodeList)
register_element_cls('p:cSld', CT_CommonSlideData)
register_element_cls('p:notes', CT_NotesSlide)
register_element_cls('p:notesMaster', CT_NotesMaster)
register_element_cls('p:sld', CT_Slide)
register_element_cls('p:sldLayout', CT_SlideLayout)
register_element_cls('p:sldLayoutId', CT_SlideLayoutIdListEntry)
register_element_cls('p:sldLayoutIdLst', CT_SlideLayoutIdList)
register_element_cls('p:sldMaster', CT_SlideMaster)
register_element_cls('p:timing', CT_SlideTiming)
register_element_cls('p:video', CT_TLMediaNodeVideo)
from .text import (
CT_RegularTextRun, CT_TextBody, CT_TextBodyProperties,
CT_TextCharacterProperties, CT_TextField, CT_TextFont, CT_TextLineBreak,
CT_TextNormalAutofit, CT_TextParagraph, CT_TextParagraphProperties,
CT_TextSpacing, CT_TextSpacingPercent, CT_TextSpacingPoint
)
register_element_cls('a:bodyPr', CT_TextBodyProperties)
register_element_cls('a:br', CT_TextLineBreak)
register_element_cls('a:defRPr', CT_TextCharacterProperties)
register_element_cls('a:endParaRPr', CT_TextCharacterProperties)
register_element_cls('a:fld', CT_TextField)
register_element_cls('a:latin', CT_TextFont)
register_element_cls('a:lnSpc', CT_TextSpacing)
register_element_cls('a:normAutofit', CT_TextNormalAutofit)
register_element_cls('a:r', CT_RegularTextRun)
register_element_cls('a:p', CT_TextParagraph)
register_element_cls('a:pPr', CT_TextParagraphProperties)
register_element_cls('c:rich', CT_TextBody)
register_element_cls('a:rPr', CT_TextCharacterProperties)
register_element_cls('a:spcAft', CT_TextSpacing)
register_element_cls('a:spcBef', CT_TextSpacing)
register_element_cls('a:spcPct', CT_TextSpacingPercent)
register_element_cls('a:spcPts', CT_TextSpacingPoint)
register_element_cls('a:txBody', CT_TextBody)
register_element_cls('c:txPr', CT_TextBody)
register_element_cls('p:txBody', CT_TextBody)
from .theme import CT_OfficeStyleSheet
register_element_cls('a:theme', CT_OfficeStyleSheet)
| true | true |
f72944e008e528745fb7dd9aba4f8c4bfe65192b | 2,089 | py | Python | gcode-object-monitor/gcodeServer.py | llalls/Gcode_Monitor_Docker | 878b81fa691d52f08ad2b7cfab23449b67ce8764 | [
"MIT"
] | null | null | null | gcode-object-monitor/gcodeServer.py | llalls/Gcode_Monitor_Docker | 878b81fa691d52f08ad2b7cfab23449b67ce8764 | [
"MIT"
] | null | null | null | gcode-object-monitor/gcodeServer.py | llalls/Gcode_Monitor_Docker | 878b81fa691d52f08ad2b7cfab23449b67ce8764 | [
"MIT"
] | null | null | null | import io
import os, sys
import threading
import numpy as np
import base64, string
from PIL import Image
from threading import Lock
from flask import url_for, Flask, request, redirect, render_template, send_from_directory, jsonify
import gcodeCompare
app = Flask(__name__, template_folder='templates')
lock = Lock()
@app.route('/')
def web_form():
return render_template('web_form.html')
@app.route('/GcodeViewer_server_version.html')
def gcodeViewer():
return render_template('GcodeViewer_server_version.html')
@app.route('/camera1_server_version.html')
def camera1():
return render_template('camera1_server_version.html')
@app.route('/camera2_server_version.html')
def camera2():
return render_template('camera2_server_version.html')
@app.route('/upload', methods=['POST'])
def upload():
imageURL = request.form.get('dataURL', '')
rotate_frequency = request.form.get('rotate_frequency', '')
image_position_data_x = request.form.get('camera_position_x', '')
image_position_data_y = request.form.get('camera_position_y', '')
image_position_data_z = request.form.get('camera_position_z', '')
pil_img = Image.open(io.BytesIO(base64.b64decode(imageURL)))
lock.acquire()
gcodeCompare.image_compare(pil_img, rotate_frequency, image_position_data_x, image_position_data_y, image_position_data_z)
lock.release()
return('upload success!')
@app.route('/rotate', methods=['GET'])
def rotate():
camera_position_list = gcodeCompare.get_image_compare_list()
print('rotate flask: ' + str(camera_position_list))
rotate_data_flask = {
'rotate_time': camera_position_list[0][0],
'position_x' : camera_position_list[0][1],
'position_y' : camera_position_list[0][2],
'position_z' : camera_position_list[0][3]
}
return jsonify(rotate_data_flask)
def gcodeImage_filename(upload_Time):
filename = 'gcodeImage_' + str(upload_Time) + '.jpg'
return filename
if __name__ == "__main__":
app.run(debug=True, host='0.0.0.0', port=8888)
lock = threading.Lock() | 28.22973 | 126 | 0.722834 | import io
import os, sys
import threading
import numpy as np
import base64, string
from PIL import Image
from threading import Lock
from flask import url_for, Flask, request, redirect, render_template, send_from_directory, jsonify
import gcodeCompare
app = Flask(__name__, template_folder='templates')
lock = Lock()
@app.route('/')
def web_form():
return render_template('web_form.html')
@app.route('/GcodeViewer_server_version.html')
def gcodeViewer():
return render_template('GcodeViewer_server_version.html')
@app.route('/camera1_server_version.html')
def camera1():
return render_template('camera1_server_version.html')
@app.route('/camera2_server_version.html')
def camera2():
return render_template('camera2_server_version.html')
@app.route('/upload', methods=['POST'])
def upload():
imageURL = request.form.get('dataURL', '')
rotate_frequency = request.form.get('rotate_frequency', '')
image_position_data_x = request.form.get('camera_position_x', '')
image_position_data_y = request.form.get('camera_position_y', '')
image_position_data_z = request.form.get('camera_position_z', '')
pil_img = Image.open(io.BytesIO(base64.b64decode(imageURL)))
lock.acquire()
gcodeCompare.image_compare(pil_img, rotate_frequency, image_position_data_x, image_position_data_y, image_position_data_z)
lock.release()
return('upload success!')
@app.route('/rotate', methods=['GET'])
def rotate():
camera_position_list = gcodeCompare.get_image_compare_list()
print('rotate flask: ' + str(camera_position_list))
rotate_data_flask = {
'rotate_time': camera_position_list[0][0],
'position_x' : camera_position_list[0][1],
'position_y' : camera_position_list[0][2],
'position_z' : camera_position_list[0][3]
}
return jsonify(rotate_data_flask)
def gcodeImage_filename(upload_Time):
filename = 'gcodeImage_' + str(upload_Time) + '.jpg'
return filename
if __name__ == "__main__":
app.run(debug=True, host='0.0.0.0', port=8888)
lock = threading.Lock() | true | true |
f72945b47d67acc2ed74ec645c6eaf015a73bd05 | 4,214 | py | Python | nicos_mlz/sans1/setups/tisane_multifg.py | mlz-ictrl/nicos | a6de0bc194ba42e3dc04a033713b41b5499ba8e1 | [
"CC-BY-3.0",
"Apache-2.0",
"CC-BY-4.0"
] | 12 | 2019-11-06T15:40:36.000Z | 2022-01-01T16:23:00.000Z | nicos_mlz/sans1/setups/tisane_multifg.py | ess-dmsc/nicos | 755d61d403ff7123f804c45fc80c7ff4d762993b | [
"CC-BY-3.0",
"Apache-2.0",
"CC-BY-4.0"
] | 91 | 2020-08-18T09:20:26.000Z | 2022-02-01T11:07:14.000Z | nicos_mlz/sans1/setups/tisane_multifg.py | mlz-ictrl/nicos | a6de0bc194ba42e3dc04a033713b41b5499ba8e1 | [
"CC-BY-3.0",
"Apache-2.0",
"CC-BY-4.0"
] | 6 | 2020-01-11T10:52:30.000Z | 2022-02-25T12:35:23.000Z | description = 'frequency counter, fg1 and fg2'
excludes = ['frequency']
# group = 'lowlevel'
tango_base = 'tango://sans1hw.sans1.frm2:10000/sans1/tisane'
ARMING_STRING_FC = (
':FUNC "FREQ";'
':CALC:AVER 1;'
':CALC:SMO:RESP FAST;'
':CALC:SMO 1;'
':INP:COUP AC;'
':INP:FILT 0;'
':INP:IMP +1.00000000E+006;'
':INP:LEV -1.80000000E+000;'
':INP:LEV:REL +50;'
':INP:LEV:AUTO 0;'
':INP:NREJ 1;'
':INP:PROB +1;'
':INP:RANG +5.00000000E+000;'
':INP:SLOP POS;'
':MMEM:CDIR "INT:\\";'
':OUTP:POL NORM;'
':OUTP 0;'
':SAMP:COUN +1;'
':FREQ:GATE:POL NEG;'
':FREQ:GATE:SOUR TIME;'
':FREQ:GATE:TIME +1.00000000000000E-001;'
':TRIG:COUN +1;'
':TRIG:DEL +0.00000000000000E+000;'
':TRIG:SLOP NEG;'
':TRIG:SOUR IMM;'
)
ARMING_STRING = (
'*RST;'
':SOUR1:FUNC:SHAP SQU;'
':SOUR1:FREQ 5;'
':SOUR1:VOLT 2.4;'
':SOUR1:VOLT:UNIT VPP;'
':SOUR1:VOLT:OFFS 1.3;'
':SOUR1:FUNCtion:SQU:DCYCle 50;'
':SOUR1:AM:STATe OFF;'
':SOUR1:SWEep:STATe OFF;'
':SOUR1:BURSt:MODE TRIG;'
':OUTP1:LOAD 50;'
':OUTP1:POL NORM;'
':TRIG1:SOUR EXT;'
':SOUR1:BURSt:NCYCles 9.9E37;'
':SOUR2:FUNC:SHAP SQU;'
':SOUR2:FREQ 10;'
':SOUR2:VOLT 5;'
':SOUR2:VOLT:UNIT VPP;'
':SOUR2:VOLT:OFFS 1.3;'
':SOUR2:FUNCtion:SQU:DCYCle 50;'
':SOUR2:AM:STATe OFF;'
':SOUR2:SWEep:STATe OFF;'
':SOUR2:BURSt:MODE TRIG;'
':OUTP2:LOAD 50;'
':OUTP2:POL NORM;'
':TRIG2:SOUR EXT;'
':SOUR2:BURSt:NCYCles 9.9E37;'
':SOUR1:BURSt:STATe ON;'
':SOUR2:BURSt:STATe ON;'
':OUTP1 ON;'
':OUTP2 ON;'
)
OFF_STRING = (
':OUTP1 OFF;'
':OUTP2 OFF;'
':SOUR1:BURSt:STATe OFF;'
':SOUR2:BURSt:STATe OFF;'
)
devices = dict(
tisane_fc = device('nicos.devices.entangle.Sensor',
description = "Frequency counter for chopper signal",
tangodevice = "%s/fc1_frequency" % tango_base,
unit = "Hz",
pollinterval = 1,
fmtstr = '%.6f',
),
tisane_fc_trigger = device('nicos_mlz.devices.io_trigger.Trigger',
description = "String blasting device",
tangodevice = "%s/fc1_io" % tango_base,
lowlevel = True,
safesetting = 'idle',
strings = {'idle' : '',
'arm' : ARMING_STRING_FC,
}
),
# tisane_fg1_sample = device('nicos_mlz.sans1.devices.tisane.Burst',
# description = "Signal-generator for sample tisane signal",
# tangodevice = "%s_multifg/ch1_burst" % tango_base,
# frequency = 1000,
# amplitude = 2.5,
# offset = 1.3,
# shape = 'square',
# duty = 50,
# mapping = dict(On = 1, Off = 0),
# ),
# tisane_fg2_det = device('nicos_mlz.sans1.devices.tisane.Burst',
# description = "Signal-generator for detector tisane signal",
# tangodevice = "%s_multifg/ch2_burst" % tango_base,
# frequency = 1000,
# amplitude = 5.0,
# offset = 1.3,
# shape = 'square',
# duty = 50,
# mapping = dict(On = 1, Off = 0),
# ),
tisane_fg_multi = device('nicos_mlz.devices.io_trigger.Trigger',
description = "String blasting device",
tangodevice = "%s_multifg/io" % tango_base,
safesetting = 'idle',
strings = {'idle' : '',
'arm' : ARMING_STRING,
'off' : OFF_STRING,
}
),
)
| 32.921875 | 71 | 0.44803 | description = 'frequency counter, fg1 and fg2'
excludes = ['frequency']
tango_base = 'tango://sans1hw.sans1.frm2:10000/sans1/tisane'
ARMING_STRING_FC = (
':FUNC "FREQ";'
':CALC:AVER 1;'
':CALC:SMO:RESP FAST;'
':CALC:SMO 1;'
':INP:COUP AC;'
':INP:FILT 0;'
':INP:IMP +1.00000000E+006;'
':INP:LEV -1.80000000E+000;'
':INP:LEV:REL +50;'
':INP:LEV:AUTO 0;'
':INP:NREJ 1;'
':INP:PROB +1;'
':INP:RANG +5.00000000E+000;'
':INP:SLOP POS;'
':MMEM:CDIR "INT:\\";'
':OUTP:POL NORM;'
':OUTP 0;'
':SAMP:COUN +1;'
':FREQ:GATE:POL NEG;'
':FREQ:GATE:SOUR TIME;'
':FREQ:GATE:TIME +1.00000000000000E-001;'
':TRIG:COUN +1;'
':TRIG:DEL +0.00000000000000E+000;'
':TRIG:SLOP NEG;'
':TRIG:SOUR IMM;'
)
ARMING_STRING = (
'*RST;'
':SOUR1:FUNC:SHAP SQU;'
':SOUR1:FREQ 5;'
':SOUR1:VOLT 2.4;'
':SOUR1:VOLT:UNIT VPP;'
':SOUR1:VOLT:OFFS 1.3;'
':SOUR1:FUNCtion:SQU:DCYCle 50;'
':SOUR1:AM:STATe OFF;'
':SOUR1:SWEep:STATe OFF;'
':SOUR1:BURSt:MODE TRIG;'
':OUTP1:LOAD 50;'
':OUTP1:POL NORM;'
':TRIG1:SOUR EXT;'
':SOUR1:BURSt:NCYCles 9.9E37;'
':SOUR2:FUNC:SHAP SQU;'
':SOUR2:FREQ 10;'
':SOUR2:VOLT 5;'
':SOUR2:VOLT:UNIT VPP;'
':SOUR2:VOLT:OFFS 1.3;'
':SOUR2:FUNCtion:SQU:DCYCle 50;'
':SOUR2:AM:STATe OFF;'
':SOUR2:SWEep:STATe OFF;'
':SOUR2:BURSt:MODE TRIG;'
':OUTP2:LOAD 50;'
':OUTP2:POL NORM;'
':TRIG2:SOUR EXT;'
':SOUR2:BURSt:NCYCles 9.9E37;'
':SOUR1:BURSt:STATe ON;'
':SOUR2:BURSt:STATe ON;'
':OUTP1 ON;'
':OUTP2 ON;'
)
OFF_STRING = (
':OUTP1 OFF;'
':OUTP2 OFF;'
':SOUR1:BURSt:STATe OFF;'
':SOUR2:BURSt:STATe OFF;'
)
devices = dict(
tisane_fc = device('nicos.devices.entangle.Sensor',
description = "Frequency counter for chopper signal",
tangodevice = "%s/fc1_frequency" % tango_base,
unit = "Hz",
pollinterval = 1,
fmtstr = '%.6f',
),
tisane_fc_trigger = device('nicos_mlz.devices.io_trigger.Trigger',
description = "String blasting device",
tangodevice = "%s/fc1_io" % tango_base,
lowlevel = True,
safesetting = 'idle',
strings = {'idle' : '',
'arm' : ARMING_STRING_FC,
}
),
tisane_fg_multi = device('nicos_mlz.devices.io_trigger.Trigger',
description = "String blasting device",
tangodevice = "%s_multifg/io" % tango_base,
safesetting = 'idle',
strings = {'idle' : '',
'arm' : ARMING_STRING,
'off' : OFF_STRING,
}
),
)
| true | true |
f72945db8c9c7fdc2f5f44205c18d4ae471405d9 | 18,663 | py | Python | gamestonk_terminal/portfolio/portfolio_view.py | minhhoang1023/GamestonkTerminal | 195dc19b491052df080178c0cc6a9d535a91a704 | [
"MIT"
] | 1 | 2022-02-18T04:02:52.000Z | 2022-02-18T04:02:52.000Z | gamestonk_terminal/portfolio/portfolio_view.py | minhhoang1023/GamestonkTerminal | 195dc19b491052df080178c0cc6a9d535a91a704 | [
"MIT"
] | null | null | null | gamestonk_terminal/portfolio/portfolio_view.py | minhhoang1023/GamestonkTerminal | 195dc19b491052df080178c0cc6a9d535a91a704 | [
"MIT"
] | null | null | null | """Portfolio View"""
__docformat__ = "numpy"
import logging
from typing import List, Optional
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from gamestonk_terminal.config_terminal import theme
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.portfolio import (
portfolio_model,
)
from gamestonk_terminal.helper_funcs import (
plot_autoscale,
export_data,
)
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.rich_config import console
# from reportlab.lib.pagesizes import letter
# from reportlab.pdfgen import canvas
# from reportlab.lib.utils import ImageReader
# from gamestonk_terminal.portfolio import reportlab_helpers
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def load_info():
"""Prints instructions to load a CSV
Returns
----------
text : str
Information on how to load a csv
"""
text = """
In order to load a CSV do the following:
1. Add headers to the first row, below is data for each column:\n
\t1. Identifier for the asset (such as a stock ticker)
\t2. Type of asset (stock, bond, option, crypto)
\t3. The volume of the asset transacted
\t4. The buy date in yyyy/mm/dd
\t5. The Price paid for the asset
\t6. Any fees paid during the transaction
\t7. A premium paid or received if this was an option
\t8. Whether the asset was bought (covered) or sold (shorted)\n
2. Place this file in gamestonk_terminal/portfolio/portfolios\n
"""
console.print(text)
@log_start_end(log=logger)
def display_returns_vs_bench(
portfolio: portfolio_model.Portfolio,
benchmark: str = "SPY",
external_axes: Optional[plt.Axes] = None,
):
"""Display portfolio returns vs benchmark
Parameters
----------
portfolio: Portfolio
Custom portfolio object with trade list
benchmark: str
Symbol for benchmark. Defaults to SPY
external_axes: plt.Axes
Optional axes to display plot on
"""
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes
portfolio.generate_holdings_from_trades()
portfolio.add_benchmark(benchmark)
cumulative_returns = (1 + portfolio.returns).cumprod()
benchmark_c_returns = (1 + portfolio.benchmark_returns).cumprod()
ax.plot(cumulative_returns.index, cumulative_returns, label="Portfolio")
ax.plot(benchmark_c_returns.index, benchmark_c_returns, label="Benchmark")
ax.set_ylabel("Cumulative Returns")
ax.legend(loc="upper left")
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
@log_start_end(log=logger)
def display_allocation(
portfolio: portfolio_model.Portfolio,
export: str = "",
external_axes: Optional[plt.Axes] = None,
):
"""Display allocation of assets vs time
Parameters
----------
portfolio: Portfolio
Portfolio object with trades loaded
export: str
Format to export plot
external_axes: plt.Axes
Optional axes to display plot on
"""
portfolio.generate_holdings_from_trades()
all_holdings = pd.concat(
[
portfolio.portfolio["StockHoldings"],
portfolio.portfolio["ETFHoldings"],
portfolio.portfolio["CryptoHoldings"],
],
axis=1,
)
all_holdings = all_holdings.drop(columns=["temp"])
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes
all_holdings.plot(ax=ax)
ax.set_title("Individual Asset Holdings")
ax.legend(loc="upper left")
ax.set_ylabel("Holdings ($)")
theme.style_primary_axis(ax)
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"rolling",
)
@log_start_end(log=logger)
def display_rolling_stats(
portfolio: portfolio_model.Portfolio,
length: int = 60,
benchmark: str = "SPY",
risk_free_rate: float = 0,
external_axes: Optional[List[plt.Axes]] = None,
export: str = "",
):
"""Display portfolio returns vs benchmark
Parameters
----------
portfolio: Portfolio
Custom portfolio object with trade list
length: int
Length of rolling window
benchmark: str
Symbol for benchmark. Defaults to SPY
risk_free_rate: float
Value to use for risk free rate in sharpe/other calculations
external_axes: Optional[List[plt.Axes]]
Optional axes to display plot on
export: str
Export to file
"""
portfolio.generate_holdings_from_trades()
portfolio.add_benchmark(benchmark)
portfolio.add_rf(risk_free_rate)
if external_axes is None:
_, ax = plt.subplots(4, 1, figsize=(8, 8), dpi=PLOT_DPI, sharex=True)
else:
if len(external_axes) != 4:
console.print("[red]4 axes expected./n[/red]")
return
ax = external_axes
rolling_volatility = portfolio.returns.rolling(length).std()
rolling_volatility_bench = portfolio.benchmark_returns.rolling(length).std()
rolling_sharpe = portfolio.returns.rolling(length).apply(
lambda x: (x.mean() - risk_free_rate) / x.std()
)
rolling_sharpe_bench = portfolio.benchmark_returns.rolling(length).apply(
lambda x: (x.mean() - risk_free_rate) / x.std()
)
rolling_volatility.plot(ax=ax[1])
rolling_volatility_bench.plot(ax=ax[1])
ax[1].set_title("Rolling Volatility")
rolling_sharpe.plot(ax=ax[2])
rolling_sharpe_bench.plot(ax=ax[2])
ax[2].set_title("Rolling Sharpe Ratio")
# Rolling beta is defined as Cov(Port,Bench)/var(Bench)
covs = (
pd.DataFrame(
{"Portfolio": portfolio.returns, "Benchmark": portfolio.benchmark_returns}
)
.dropna(axis=0)
.rolling(length)
.cov()
.unstack()
.dropna()
)
rolling_beta = covs["Portfolio"]["Benchmark"] / covs["Benchmark"]["Benchmark"]
rolling_beta.plot(ax=ax[3])
ax[3].set_title("Rolling Beta to Benchmark")
c_returns = (1 + portfolio.returns).cumprod()
bench_c_rets = (1 + portfolio.benchmark_returns).cumprod()
ax[0].plot(c_returns.index, c_returns)
ax[0].plot(bench_c_rets.index, bench_c_rets)
ax[0].set_title("Cumulative Returns")
if external_axes is None:
for a in ax[0], ax[1], ax[2]:
a.legend(["Portfolio", "Benchmark"], loc="upper left")
for a in ax[0], ax[1], ax[2], ax[3]:
a.set_xlim(portfolio.returns.index[0], portfolio.returns.index[-1])
a.set_xlabel([])
a.grid("on")
theme.style_primary_axis(a)
ax[3].set_xlabel("Date")
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"rolling",
)
@log_start_end(log=logger)
def display_drawdown(
holdings: pd.DataFrame,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
):
"""Display drawdown curve
Parameters
----------
holdings: pd.DataFrame
Dataframe of holdings vs time
export: str
Format to export data
external_axes: plt.Axes
Optional axes to display plot on
"""
drawdown = portfolio_model.calculate_drawdown(holdings)
if external_axes is None:
_, ax = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI, sharex=True)
else:
ax = external_axes
ax[0].plot(holdings.index, holdings)
ax[0].set_title("Holdings")
ax[1].plot(holdings.index, drawdown)
ax[1].fill_between(holdings.index, np.asarray(drawdown), alpha=0.4)
ax[1].set_title("Portfolio Drawdown")
theme.style_primary_axis(ax[1])
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"dd",
)
#
# @log_start_end(log=logger)
# def plot_overall_return(
# comb: pd.DataFrame, m_tick: str, plot: bool = False
# ) -> ImageReader:
# """Generates overall return graph
#
# Parameters
# ----------
# comb : pd.DataFrame
# Dataframe with returns
# m_tick : str
# The ticker for the market asset
# plot : bool
# Whether to plot the graph or return it for PDF
#
# Returns
# ----------
# img : ImageReader
# Overal return graph
# """
# fig, ax = plt.subplots(figsize=(10, 5))
# ax.plot(comb.index, comb["return"], color="tab:blue", label="Portfolio")
# ax.plot(comb.index, comb[("Market", "Return")], color="orange", label=m_tick)
#
# ax.set_ylabel("", fontweight="bold", fontsize=12, color="black")
# ax.set_xlabel("")
# ax.yaxis.set_label_coords(-0.1, 0.5)
# ax.grid(True)
# ax.spines["top"].set_visible(False)
# ax.spines["right"].set_visible(False)
# ax.spines["bottom"].set_visible(False)
# ax.spines["left"].set_visible(False)
# fig.suptitle(
# "Cumulative Performance", y=0.99, fontweight="bold", fontsize=14, color="black"
# )
# ax.axhline(0, ls="-", lw=1, color="gray", zorder=1)
# ax.axhline(0, ls="--", lw=1, color="black", zorder=2)
# fig.set_facecolor("white")
# ax.set_title(
# f'{comb.index[:1][0].strftime("%Y/%m/%d")} - {comb.index[-1:][0].strftime("%Y/%m/%d")}',
# fontsize=12,
# color="gray",
# )
# ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
# ax.set_facecolor("white")
# ax.legend()
# fig.autofmt_xdate()
# if plot:
# plt.show()
# console.print("")
# return None
# imgdata = BytesIO()
# fig.savefig(imgdata, format="png")
# plt.close("all")
# imgdata.seek(0)
# return ImageReader(imgdata)
#
#
# @log_start_end(log=logger)
# def plot_rolling_beta(df: pd.DataFrame) -> ImageReader:
# """Returns a chart with the portfolio's rolling beta
#
# Parameters
# ----------
# df : pd.DataFrame
# The dataframe to be analyzed
#
# Returns
# ----------
# img : ImageReader
# Rolling beta graph
# """
#
# fig, ax = plt.subplots(figsize=(10, 5))
# ax.plot(
# df.index,
# df["total"],
# color="tab:blue",
# )
#
# ax.set_ylabel("", fontweight="bold", fontsize=12, color="black")
# ax.set_xlabel("")
# ax.yaxis.set_label_coords(-0.1, 0.5)
# ax.grid(True)
# ax.spines["top"].set_visible(False)
# ax.spines["right"].set_visible(False)
# ax.spines["bottom"].set_visible(False)
# ax.spines["left"].set_visible(False)
# fig.suptitle(
# "Rolling Beta of Stocks", y=0.99, fontweight="bold", fontsize=14, color="black"
# )
# ax.axhline(0, ls="-", lw=1, color="gray", zorder=1)
# ax.axhline(0, ls="--", lw=1, color="black", zorder=2)
# fig.set_facecolor("white")
# ax.set_title(
# f'{df.index[:1][0].strftime("%Y-%m-%d")} - {df.index[-1:][0].strftime("%Y-%m-%d")}',
# color="gray",
# )
# ax.set_facecolor("white")
# fig.autofmt_xdate()
# imgdata = BytesIO()
# fig.savefig(imgdata, format="png")
# plt.close("all")
# imgdata.seek(0)
# return ImageReader(imgdata)
#
#
# @log_start_end(log=logger)
# def plot_ef(
# stocks: List[str],
# variance: float,
# per_ret: float,
# rf_rate: float,
# period: str = "3mo",
# n_portfolios: int = 300,
# risk_free: bool = False,
# ):
# """Display efficient frontier
#
# Parameters
# ----------
# stocks : List[str]
# List of the stocks to be included in the weights
# variance : float
# The variance for the portfolio
# per_ret : float
# The portfolio's return for the portfolio
# rf_rate : float
# The risk free rate
# period : str
# The period to track
# n_portfolios : int
# The number of portfolios to generate
# risk_free : bool
# Include the risk-free asset
# """
# fig, ax = plt.subplots(figsize=(10, 5), dpi=PLOT_DPI)
# ef, rets, stds = optimizer_model.generate_random_portfolios(
# [x.upper() for x in stocks], period, n_portfolios
# )
# sharpes = rets / stds
# ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
# plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)
# # Find the tangency portfolio
# ret_sharpe, std_sharpe, _ = ef.portfolio_performance(risk_free_rate=rf_rate)
# ax.scatter(std_sharpe, ret_sharpe, marker="*", s=100, c="r", label="Max Sharpe")
# plt.plot(variance, per_ret, "ro", label="Portfolio")
# # Add risk free line
# if risk_free:
# y = ret_sharpe * 1.2
# m = (ret_sharpe - rf_rate) / std_sharpe
# x2 = (y - rf_rate) / m
# x = [0, x2]
# y = [rf_rate, y]
# line = Line2D(x, y, color="#FF0000", label="Capital Allocation Line")
# ax.set_xlim(xmin=min(stds) * 0.8)
# ax.add_line(line)
# ax.set_title(f"Efficient Frontier simulating {n_portfolios} portfolios")
# ax.legend()
# fig.tight_layout()
# ax.grid(b=True, which="major", color="#666666", linestyle="-")
#
# if gtff.USE_ION:
# plt.ion()
#
# imgdata = BytesIO()
# fig.savefig(imgdata, format="png")
# plt.close("all")
# imgdata.seek(0)
# return ImageReader(imgdata)
# @log_start_end(log=logger)
# def display_allocation2(data: pd.DataFrame, graph: bool):
# """Displays allocation
# Parameters
# ----------
# data: pd.DataFrame
# The portfolio allocation dataframe
# graph: bool
# If pie chart shall be displayed with table"""
#
# print_rich_table(data, headers=list(data.columns), title="Allocation")
# console.print("")
#
# if graph:
# graph_data = data[data["pct_allocation"] >= 5].copy()
# if not graph_data.empty:
# graph_data.loc["Other"] = [
# "NA",
# data["value"].sum() - graph_data["value"].sum(),
# 100 - graph_data["value"].sum(),
# ]
# labels = graph_data.index.values
# sizes = graph_data["value"].to_list()
# else:
# labels = data.index.values
# sizes = data["value"].to_list()
# fig, ax = plt.subplots()
# ax.pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
# ax.axis("equal")
# ax.set_title("Portfolio Allocation")
# fig.set_tight_layout(True)
#
# plt.show()
#
# class Report:
# @log_start_end(log=logger)
# def __init__(self, df: pd.DataFrame, hist: pd.DataFrame, m_tick: str):
# """Generate financial reports.
# Financial reports allow users to show the how they have been performing in
# trades. This allows for a simple way to show progress and analyze metrics
# that track portfolio performance
#
# Parameters
# ----------
# df : pd.DataFrame
# The dataframe with previous holdings information
# hist : pd.DataFrame
# The dataframe with previous prices for stocks in the portfolio
# df_m : pd.DataFrame
# Dataframe of benchmark
# n : int
# The number of days to analyze
#
# Attributes
# ----------
# generate_report : None
# Generates a report with the given parameters
# generate_pg1 : None
# Creates the first page of the PDF report
# generate_pg2 : None
# Creates the second page of the PDF report
#
# """
# self.df = df
# self.hist = hist
# self.m_tick = m_tick
# self.df_m = yfinance_model.get_market(self.df.index[0], self.m_tick)
# # self.returns, self.variance = portfolio_model.get_return(df, self.df_m, n)
# self.returns = pd.DataFrame()
# self.rf = get_rf()
# self.betas = portfolio_model.get_rolling_beta(
# self.df, self.hist, self.df_m, 365
# )
#
# @log_start_end(log=logger)
# def generate_report(self) -> None:
# d = path.dirname(path.abspath(__file__)).replace(
# "gamestonk_terminal", "exports"
# )
# loc = path.abspath(
# path.join(
# d,
# f"ar_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
# )
# )
# report = canvas.Canvas(loc, pagesize=letter)
# reportlab_helpers.base_format(report, "Overview")
# self.generate_pg1(report)
# self.generate_pg2(report)
# report.save()
# console.print("File save in:\n", loc, "\n")
#
# @log_start_end(log=logger)
# def generate_pg1(self, report: canvas.Canvas) -> None:
# report.drawImage(
# plot_overall_return(self.returns, self.m_tick, False), 15, 400, 600, 300
# )
# main_text = portfolio_model.get_main_text(self.returns)
# reportlab_helpers.draw_paragraph(report, main_text, 30, 410, 550, 200)
# current_return = self.returns["return"][-1]
# beta = self.betas["total"][-1]
# market_return = self.returns[("Market", "Return")][-1]
# sharpe = f"{(current_return - self.rf)/ np.std(self.returns['return']):.2f}"
# treynor = f"{(current_return - self.rf)/ beta:.2f}" if beta > 0 else "N/A"
# alpha = f"{current_return - (self.rf + beta * (market_return - self.rf)):.2f}"
# information = (
# f"{float(alpha)/ (np.std(self.returns['return'] - market_return)):.2f}"
# )
# perf = [
# ["Sharpe", sharpe],
# ["Treynor", treynor],
# ["Alpha", alpha],
# ["Information", information],
# ]
# reportlab_helpers.draw_table(report, "Performance", 540, 300, 30, perf)
# reportlab_helpers.draw_paragraph(
# report, portfolio_model.performance_text, 140, 290, 460, 200
# )
# report.showPage()
#
# @log_start_end(log=logger)
# def generate_pg2(self, report: canvas.Canvas) -> None:
# reportlab_helpers.base_format(report, "Portfolio Analysis")
# if "Holding" in self.df.columns:
# report.drawImage(plot_rolling_beta(self.betas), 15, 400, 600, 300)
# main_t = portfolio_model.get_beta_text(self.betas)
# reportlab_helpers.draw_paragraph(report, main_t, 30, 410, 550, 200)
# # report.drawImage(plot_ef(uniques, self.variance, self.returns["return"][-1], self.rf), 15, 65, 600, 300)
| 32.177586 | 120 | 0.610191 | __docformat__ = "numpy"
import logging
from typing import List, Optional
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from gamestonk_terminal.config_terminal import theme
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.portfolio import (
portfolio_model,
)
from gamestonk_terminal.helper_funcs import (
plot_autoscale,
export_data,
)
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.rich_config import console
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def load_info():
text = """
In order to load a CSV do the following:
1. Add headers to the first row, below is data for each column:\n
\t1. Identifier for the asset (such as a stock ticker)
\t2. Type of asset (stock, bond, option, crypto)
\t3. The volume of the asset transacted
\t4. The buy date in yyyy/mm/dd
\t5. The Price paid for the asset
\t6. Any fees paid during the transaction
\t7. A premium paid or received if this was an option
\t8. Whether the asset was bought (covered) or sold (shorted)\n
2. Place this file in gamestonk_terminal/portfolio/portfolios\n
"""
console.print(text)
@log_start_end(log=logger)
def display_returns_vs_bench(
portfolio: portfolio_model.Portfolio,
benchmark: str = "SPY",
external_axes: Optional[plt.Axes] = None,
):
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes
portfolio.generate_holdings_from_trades()
portfolio.add_benchmark(benchmark)
cumulative_returns = (1 + portfolio.returns).cumprod()
benchmark_c_returns = (1 + portfolio.benchmark_returns).cumprod()
ax.plot(cumulative_returns.index, cumulative_returns, label="Portfolio")
ax.plot(benchmark_c_returns.index, benchmark_c_returns, label="Benchmark")
ax.set_ylabel("Cumulative Returns")
ax.legend(loc="upper left")
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
@log_start_end(log=logger)
def display_allocation(
portfolio: portfolio_model.Portfolio,
export: str = "",
external_axes: Optional[plt.Axes] = None,
):
portfolio.generate_holdings_from_trades()
all_holdings = pd.concat(
[
portfolio.portfolio["StockHoldings"],
portfolio.portfolio["ETFHoldings"],
portfolio.portfolio["CryptoHoldings"],
],
axis=1,
)
all_holdings = all_holdings.drop(columns=["temp"])
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes
all_holdings.plot(ax=ax)
ax.set_title("Individual Asset Holdings")
ax.legend(loc="upper left")
ax.set_ylabel("Holdings ($)")
theme.style_primary_axis(ax)
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"rolling",
)
@log_start_end(log=logger)
def display_rolling_stats(
portfolio: portfolio_model.Portfolio,
length: int = 60,
benchmark: str = "SPY",
risk_free_rate: float = 0,
external_axes: Optional[List[plt.Axes]] = None,
export: str = "",
):
portfolio.generate_holdings_from_trades()
portfolio.add_benchmark(benchmark)
portfolio.add_rf(risk_free_rate)
if external_axes is None:
_, ax = plt.subplots(4, 1, figsize=(8, 8), dpi=PLOT_DPI, sharex=True)
else:
if len(external_axes) != 4:
console.print("[red]4 axes expected./n[/red]")
return
ax = external_axes
rolling_volatility = portfolio.returns.rolling(length).std()
rolling_volatility_bench = portfolio.benchmark_returns.rolling(length).std()
rolling_sharpe = portfolio.returns.rolling(length).apply(
lambda x: (x.mean() - risk_free_rate) / x.std()
)
rolling_sharpe_bench = portfolio.benchmark_returns.rolling(length).apply(
lambda x: (x.mean() - risk_free_rate) / x.std()
)
rolling_volatility.plot(ax=ax[1])
rolling_volatility_bench.plot(ax=ax[1])
ax[1].set_title("Rolling Volatility")
rolling_sharpe.plot(ax=ax[2])
rolling_sharpe_bench.plot(ax=ax[2])
ax[2].set_title("Rolling Sharpe Ratio")
covs = (
pd.DataFrame(
{"Portfolio": portfolio.returns, "Benchmark": portfolio.benchmark_returns}
)
.dropna(axis=0)
.rolling(length)
.cov()
.unstack()
.dropna()
)
rolling_beta = covs["Portfolio"]["Benchmark"] / covs["Benchmark"]["Benchmark"]
rolling_beta.plot(ax=ax[3])
ax[3].set_title("Rolling Beta to Benchmark")
c_returns = (1 + portfolio.returns).cumprod()
bench_c_rets = (1 + portfolio.benchmark_returns).cumprod()
ax[0].plot(c_returns.index, c_returns)
ax[0].plot(bench_c_rets.index, bench_c_rets)
ax[0].set_title("Cumulative Returns")
if external_axes is None:
for a in ax[0], ax[1], ax[2]:
a.legend(["Portfolio", "Benchmark"], loc="upper left")
for a in ax[0], ax[1], ax[2], ax[3]:
a.set_xlim(portfolio.returns.index[0], portfolio.returns.index[-1])
a.set_xlabel([])
a.grid("on")
theme.style_primary_axis(a)
ax[3].set_xlabel("Date")
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"rolling",
)
@log_start_end(log=logger)
def display_drawdown(
holdings: pd.DataFrame,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
):
drawdown = portfolio_model.calculate_drawdown(holdings)
if external_axes is None:
_, ax = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI, sharex=True)
else:
ax = external_axes
ax[0].plot(holdings.index, holdings)
ax[0].set_title("Holdings")
ax[1].plot(holdings.index, drawdown)
ax[1].fill_between(holdings.index, np.asarray(drawdown), alpha=0.4)
ax[1].set_title("Portfolio Drawdown")
theme.style_primary_axis(ax[1])
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"dd",
)
#
# Parameters
# ----------
# comb : pd.DataFrame
# Dataframe with returns
# m_tick : str
# The ticker for the market asset
# plot : bool
# Whether to plot the graph or return it for PDF
#
# Returns
# ----------
# img : ImageReader
# Overal return graph
# """
#
# Parameters
# ----------
# df : pd.DataFrame
# The dataframe to be analyzed
#
# Returns
# ----------
# img : ImageReader
# Rolling beta graph
# """
#
# fig, ax = plt.subplots(figsize=(10, 5))
# ax.plot(
# df.index,
# df["total"],
# color="tab:blue",
# )
#
# ax.set_ylabel("", fontweight="bold", fontsize=12, color="black")
# ax.set_xlabel("")
# ax.yaxis.set_label_coords(-0.1, 0.5)
# ax.grid(True)
# ax.spines["top"].set_visible(False)
# ax.spines["right"].set_visible(False)
# ax.spines["bottom"].set_visible(False)
# ax.spines["left"].set_visible(False)
# fig.suptitle(
# "Rolling Beta of Stocks", y=0.99, fontweight="bold", fontsize=14, color="black"
# )
# ax.axhline(0, ls="-", lw=1, color="gray", zorder=1)
# ax.axhline(0, ls="--", lw=1, color="black", zorder=2)
# fig.set_facecolor("white")
# ax.set_title(
# f'{df.index[:1][0].strftime("%Y-%m-%d")} - {df.index[-1:][0].strftime("%Y-%m-%d")}',
# color="gray",
# )
# ax.set_facecolor("white")
# fig.autofmt_xdate()
# imgdata = BytesIO()
# fig.savefig(imgdata, format="png")
# plt.close("all")
# imgdata.seek(0)
# return ImageReader(imgdata)
#
#
# @log_start_end(log=logger)
# def plot_ef(
# stocks: List[str],
# variance: float,
# per_ret: float,
# rf_rate: float,
# period: str = "3mo",
# n_portfolios: int = 300,
# risk_free: bool = False,
# ):
# """Display efficient frontier
#
# Parameters
# ----------
# stocks : List[str]
# List of the stocks to be included in the weights
# variance : float
# The variance for the portfolio
# per_ret : float
# The portfolio's return for the portfolio
# rf_rate : float
# The risk free rate
# period : str
# The period to track
# n_portfolios : int
# The number of portfolios to generate
# risk_free : bool
# Include the risk-free asset
# """
# ----------
# data: pd.DataFrame
# The portfolio allocation dataframe
# graph: bool
# If pie chart shall be displayed with table"""
# Financial reports allow users to show the how they have been performing in
# trades. This allows for a simple way to show progress and analyze metrics
# that track portfolio performance
#
# Parameters
# ----------
# df : pd.DataFrame
# The dataframe with previous holdings information
# hist : pd.DataFrame
# The dataframe with previous prices for stocks in the portfolio
# df_m : pd.DataFrame
# Dataframe of benchmark
# n : int
# The number of days to analyze
#
# Attributes
# ----------
# generate_report : None
# Generates a report with the given parameters
# generate_pg1 : None
# Creates the first page of the PDF report
# generate_pg2 : None
# Creates the second page of the PDF report
#
# """
| true | true |
f729468046e62f854295c7f77bca8258e271ead4 | 817 | py | Python | tutorial/basic/ex3.py | SalAlba/matplotlib | f73ff4e77074152fb9abc400d66f56111e656687 | [
"MIT"
] | null | null | null | tutorial/basic/ex3.py | SalAlba/matplotlib | f73ff4e77074152fb9abc400d66f56111e656687 | [
"MIT"
] | null | null | null | tutorial/basic/ex3.py | SalAlba/matplotlib | f73ff4e77074152fb9abc400d66f56111e656687 | [
"MIT"
] | null | null | null | import numpy as np
import matplotlib.pyplot as plt
from sal_timer import timer
def plot_1():
# ...
data = {
'a': np.arange(50),
'c': np.random.randint(0, 50, 50),
'd': np.random.randn(50)
}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100
# ...
# x : x
# y : y
# c : color
# s : size
plt.scatter(x='a', y='b', c='c', s='d', data=data)
# ...
plt.xlabel('entry a')
plt.ylabel('entry b')
plt.show()
@timer
def main():
plot_1()
if __name__ == '__main__':
print('========================================== START ==========================================')
#...
main()
print('========================================== END ============================================') | 19 | 104 | 0.376989 | import numpy as np
import matplotlib.pyplot as plt
from sal_timer import timer
def plot_1():
data = {
'a': np.arange(50),
'c': np.random.randint(0, 50, 50),
'd': np.random.randn(50)
}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100
plt.scatter(x='a', y='b', c='c', s='d', data=data)
plt.xlabel('entry a')
plt.ylabel('entry b')
plt.show()
@timer
def main():
plot_1()
if __name__ == '__main__':
print('========================================== START ==========================================')
main()
print('========================================== END ============================================') | true | true |
f72947347cb1c119a762d738eaa1b76c322be864 | 20,672 | py | Python | Adelphi Academic Calendar/skill/skill_env/Lib/site-packages/boto3/resources/model.py | EnriqueGambra/Amazon-Alexa-Skill | 198ed51bef555eee006041fef0bcbf5c955142d5 | [
"MIT"
] | null | null | null | Adelphi Academic Calendar/skill/skill_env/Lib/site-packages/boto3/resources/model.py | EnriqueGambra/Amazon-Alexa-Skill | 198ed51bef555eee006041fef0bcbf5c955142d5 | [
"MIT"
] | null | null | null | Adelphi Academic Calendar/skill/skill_env/Lib/site-packages/boto3/resources/model.py | EnriqueGambra/Amazon-Alexa-Skill | 198ed51bef555eee006041fef0bcbf5c955142d5 | [
"MIT"
] | 1 | 2019-10-11T17:15:20.000Z | 2019-10-11T17:15:20.000Z | # Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""
The models defined in this file represent the resource JSON description
format and provide a layer of abstraction from the raw JSON. The advantages
of this are:
* Pythonic interface (e.g. ``action.request.operation``)
* Consumers need not change for minor JSON changes (e.g. renamed field)
These models are used both by the resource factory to generate resource
classes as well as by the documentation generator.
"""
import logging
from botocore import xform_name
logger = logging.getLogger(__name__)
class Identifier(object):
"""
A resource identifier, given by its name.
:type name: string
:param name: The name of the identifier
"""
def __init__(self, name, member_name=None):
#: (``string``) The name of the identifier
self.name = name
self.member_name = member_name
class Action(object):
"""
A service operation action.
:type name: string
:param name: The name of the action
:type definition: dict
:param definition: The JSON definition
:type resource_defs: dict
:param resource_defs: All resources defined in the service
"""
def __init__(self, name, definition, resource_defs):
self._definition = definition
#: (``string``) The name of the action
self.name = name
#: (:py:class:`Request`) This action's request or ``None``
self.request = None
if 'request' in definition:
self.request = Request(definition.get('request', {}))
#: (:py:class:`ResponseResource`) This action's resource or ``None``
self.resource = None
if 'resource' in definition:
self.resource = ResponseResource(definition.get('resource', {}),
resource_defs)
#: (``string``) The JMESPath search path or ``None``
self.path = definition.get('path')
class DefinitionWithParams(object):
"""
An item which has parameters exposed via the ``params`` property.
A request has an operation and parameters, while a waiter has
a name, a low-level waiter name and parameters.
:type definition: dict
:param definition: The JSON definition
"""
def __init__(self, definition):
self._definition = definition
@property
def params(self):
"""
Get a list of auto-filled parameters for this request.
:type: list(:py:class:`Parameter`)
"""
params = []
for item in self._definition.get('params', []):
params.append(Parameter(**item))
return params
class Parameter(object):
"""
An auto-filled parameter which has a source and target. For example,
the ``QueueUrl`` may be auto-filled from a resource's ``url`` identifier
when making calls to ``queue.receive_messages``.
:type target: string
:param target: The destination parameter name, e.g. ``QueueUrl``
:type source_type: string
:param source_type: Where the source is defined.
:type source: string
:param source: The source name, e.g. ``Url``
"""
def __init__(self, target, source, name=None, path=None, value=None,
**kwargs):
#: (``string``) The destination parameter name
self.target = target
#: (``string``) Where the source is defined
self.source = source
#: (``string``) The name of the source, if given
self.name = name
#: (``string``) The JMESPath query of the source
self.path = path
#: (``string|int|float|bool``) The source constant value
self.value = value
# Complain if we encounter any unknown values.
if kwargs:
logger.warning('Unknown parameter options found: %s', kwargs)
class Request(DefinitionWithParams):
"""
A service operation action request.
:type definition: dict
:param definition: The JSON definition
"""
def __init__(self, definition):
super(Request, self).__init__(definition)
#: (``string``) The name of the low-level service operation
self.operation = definition.get('operation')
class Waiter(DefinitionWithParams):
"""
An event waiter specification.
:type name: string
:param name: Name of the waiter
:type definition: dict
:param definition: The JSON definition
"""
PREFIX = 'WaitUntil'
def __init__(self, name, definition):
super(Waiter, self).__init__(definition)
#: (``string``) The name of this waiter
self.name = name
#: (``string``) The name of the underlying event waiter
self.waiter_name = definition.get('waiterName')
class ResponseResource(object):
"""
A resource response to create after performing an action.
:type definition: dict
:param definition: The JSON definition
:type resource_defs: dict
:param resource_defs: All resources defined in the service
"""
def __init__(self, definition, resource_defs):
self._definition = definition
self._resource_defs = resource_defs
#: (``string``) The name of the response resource type
self.type = definition.get('type')
#: (``string``) The JMESPath search query or ``None``
self.path = definition.get('path')
@property
def identifiers(self):
"""
A list of resource identifiers.
:type: list(:py:class:`Identifier`)
"""
identifiers = []
for item in self._definition.get('identifiers', []):
identifiers.append(
Parameter(**item))
return identifiers
@property
def model(self):
"""
Get the resource model for the response resource.
:type: :py:class:`ResourceModel`
"""
return ResourceModel(self.type, self._resource_defs[self.type],
self._resource_defs)
class Collection(Action):
"""
A group of resources. See :py:class:`Action`.
:type name: string
:param name: The name of the collection
:type definition: dict
:param definition: The JSON definition
:type resource_defs: dict
:param resource_defs: All resources defined in the service
"""
@property
def batch_actions(self):
"""
Get a list of batch actions supported by the resource type
contained in this action. This is a shortcut for accessing
the same information through the resource model.
:rtype: list(:py:class:`Action`)
"""
return self.resource.model.batch_actions
class ResourceModel(object):
"""
A model representing a resource, defined via a JSON description
format. A resource has identifiers, attributes, actions,
sub-resources, references and collections. For more information
on resources, see :ref:`guide_resources`.
:type name: string
:param name: The name of this resource, e.g. ``sqs`` or ``Queue``
:type definition: dict
:param definition: The JSON definition
:type resource_defs: dict
:param resource_defs: All resources defined in the service
"""
def __init__(self, name, definition, resource_defs):
self._definition = definition
self._resource_defs = resource_defs
self._renamed = {}
#: (``string``) The name of this resource
self.name = name
#: (``string``) The service shape name for this resource or ``None``
self.shape = definition.get('shape')
def load_rename_map(self, shape=None):
"""
Load a name translation map given a shape. This will set
up renamed values for any collisions, e.g. if the shape,
an action, and a subresource all are all named ``foo``
then the resource will have an action ``foo``, a subresource
named ``Foo`` and a property named ``foo_attribute``.
This is the order of precedence, from most important to
least important:
* Load action (resource.load)
* Identifiers
* Actions
* Subresources
* References
* Collections
* Waiters
* Attributes (shape members)
Batch actions are only exposed on collections, so do not
get modified here. Subresources use upper camel casing, so
are unlikely to collide with anything but other subresources.
Creates a structure like this::
renames = {
('action', 'id'): 'id_action',
('collection', 'id'): 'id_collection',
('attribute', 'id'): 'id_attribute'
}
# Get the final name for an action named 'id'
name = renames.get(('action', 'id'), 'id')
:type shape: botocore.model.Shape
:param shape: The underlying shape for this resource.
"""
# Meta is a reserved name for resources
names = set(['meta'])
self._renamed = {}
if self._definition.get('load'):
names.add('load')
for item in self._definition.get('identifiers', []):
self._load_name_with_category(names, item['name'], 'identifier')
for name in self._definition.get('actions', {}):
self._load_name_with_category(names, name, 'action')
for name, ref in self._get_has_definition().items():
# Subresources require no tmp members, just typically
# identifiers and user input.
data_required = False
for identifier in ref['resource']['identifiers']:
if identifier['source'] == 'tmp':
data_required = True
break
if not data_required:
self._load_name_with_category(names, name, 'subresource',
snake_case=False)
else:
self._load_name_with_category(names, name, 'reference')
for name in self._definition.get('hasMany', {}):
self._load_name_with_category(names, name, 'collection')
for name in self._definition.get('waiters', {}):
self._load_name_with_category(names, Waiter.PREFIX + name,
'waiter')
if shape is not None:
for name in shape.members.keys():
self._load_name_with_category(names, name, 'attribute')
def _load_name_with_category(self, names, name, category,
snake_case=True):
"""
Load a name with a given category, possibly renaming it
if that name is already in use. The name will be stored
in ``names`` and possibly be set up in ``self._renamed``.
:type names: set
:param names: Existing names (Python attributes, properties, or
methods) on the resource.
:type name: string
:param name: The original name of the value.
:type category: string
:param category: The value type, such as 'identifier' or 'action'
:type snake_case: bool
:param snake_case: True (default) if the name should be snake cased.
"""
if snake_case:
name = xform_name(name)
if name in names:
logger.debug('Renaming %s %s %s' % (self.name, category, name))
self._renamed[(category, name)] = name + '_' + category
name += '_' + category
if name in names:
# This isn't good, let's raise instead of trying to keep
# renaming this value.
raise ValueError('Problem renaming {0} {1} to {2}!'.format(
self.name, category, name))
names.add(name)
def _get_name(self, category, name, snake_case=True):
"""
Get a possibly renamed value given a category and name. This
uses the rename map set up in ``load_rename_map``, so that
method must be called once first.
:type category: string
:param category: The value type, such as 'identifier' or 'action'
:type name: string
:param name: The original name of the value
:type snake_case: bool
:param snake_case: True (default) if the name should be snake cased.
:rtype: string
:return: Either the renamed value if it is set, otherwise the
original name.
"""
if snake_case:
name = xform_name(name)
return self._renamed.get((category, name), name)
def get_attributes(self, shape):
"""
Get a dictionary of attribute names to original name and shape
models that represent the attributes of this resource. Looks
like the following:
{
'some_name': ('SomeName', <Shape...>)
}
:type shape: botocore.model.Shape
:param shape: The underlying shape for this resource.
:rtype: dict
:return: Mapping of resource attributes.
"""
attributes = {}
identifier_names = [i.name for i in self.identifiers]
for name, member in shape.members.items():
snake_cased = xform_name(name)
if snake_cased in identifier_names:
# Skip identifiers, these are set through other means
continue
snake_cased = self._get_name('attribute', snake_cased,
snake_case=False)
attributes[snake_cased] = (name, member)
return attributes
@property
def identifiers(self):
"""
Get a list of resource identifiers.
:type: list(:py:class:`Identifier`)
"""
identifiers = []
for item in self._definition.get('identifiers', []):
name = self._get_name('identifier', item['name'])
member_name = item.get('memberName', None)
if member_name:
member_name = self._get_name('attribute', member_name)
identifiers.append(Identifier(name, member_name))
return identifiers
@property
def load(self):
"""
Get the load action for this resource, if it is defined.
:type: :py:class:`Action` or ``None``
"""
action = self._definition.get('load')
if action is not None:
action = Action('load', action, self._resource_defs)
return action
@property
def actions(self):
"""
Get a list of actions for this resource.
:type: list(:py:class:`Action`)
"""
actions = []
for name, item in self._definition.get('actions', {}).items():
name = self._get_name('action', name)
actions.append(Action(name, item, self._resource_defs))
return actions
@property
def batch_actions(self):
"""
Get a list of batch actions for this resource.
:type: list(:py:class:`Action`)
"""
actions = []
for name, item in self._definition.get('batchActions', {}).items():
name = self._get_name('batch_action', name)
actions.append(Action(name, item, self._resource_defs))
return actions
def _get_has_definition(self):
"""
Get a ``has`` relationship definition from a model, where the
service resource model is treated special in that it contains
a relationship to every resource defined for the service. This
allows things like ``s3.Object('bucket-name', 'key')`` to
work even though the JSON doesn't define it explicitly.
:rtype: dict
:return: Mapping of names to subresource and reference
definitions.
"""
if self.name not in self._resource_defs:
# This is the service resource, so let us expose all of
# the defined resources as subresources.
definition = {}
for name, resource_def in self._resource_defs.items():
# It's possible for the service to have renamed a
# resource or to have defined multiple names that
# point to the same resource type, so we need to
# take that into account.
found = False
has_items = self._definition.get('has', {}).items()
for has_name, has_def in has_items:
if has_def.get('resource', {}).get('type') == name:
definition[has_name] = has_def
found = True
if not found:
# Create a relationship definition and attach it
# to the model, such that all identifiers must be
# supplied by the user. It will look something like:
#
# {
# 'resource': {
# 'type': 'ResourceName',
# 'identifiers': [
# {'target': 'Name1', 'source': 'input'},
# {'target': 'Name2', 'source': 'input'},
# ...
# ]
# }
# }
#
fake_has = {
'resource': {
'type': name,
'identifiers': []
}
}
for identifier in resource_def.get('identifiers', []):
fake_has['resource']['identifiers'].append({
'target': identifier['name'], 'source': 'input'
})
definition[name] = fake_has
else:
definition = self._definition.get('has', {})
return definition
def _get_related_resources(self, subresources):
"""
Get a list of sub-resources or references.
:type subresources: bool
:param subresources: ``True`` to get sub-resources, ``False`` to
get references.
:rtype: list(:py:class:`ResponseResource`)
"""
resources = []
for name, definition in self._get_has_definition().items():
if subresources:
name = self._get_name('subresource', name, snake_case=False)
else:
name = self._get_name('reference', name)
action = Action(name, definition, self._resource_defs)
data_required = False
for identifier in action.resource.identifiers:
if identifier.source == 'tmp':
data_required = True
break
if subresources and not data_required:
resources.append(action)
elif not subresources and data_required:
resources.append(action)
return resources
@property
def subresources(self):
"""
Get a list of sub-resources.
:type: list(:py:class:`ResponseResource`)
"""
return self._get_related_resources(True)
@property
def references(self):
"""
Get a list of reference resources.
:type: list(:py:class:`ResponseResource`)
"""
return self._get_related_resources(False)
@property
def collections(self):
"""
Get a list of collections for this resource.
:type: list(:py:class:`Collection`)
"""
collections = []
for name, item in self._definition.get('hasMany', {}).items():
name = self._get_name('collection', name)
collections.append(Collection(name, item, self._resource_defs))
return collections
@property
def waiters(self):
"""
Get a list of waiters for this resource.
:type: list(:py:class:`Waiter`)
"""
waiters = []
for name, item in self._definition.get('waiters', {}).items():
name = self._get_name('waiter', Waiter.PREFIX + name)
waiters.append(Waiter(name, item))
return waiters
| 33.18138 | 76 | 0.580786 |
import logging
from botocore import xform_name
logger = logging.getLogger(__name__)
class Identifier(object):
def __init__(self, name, member_name=None):
self.name = name
self.member_name = member_name
class Action(object):
def __init__(self, name, definition, resource_defs):
self._definition = definition
self.name = name
self.request = None
if 'request' in definition:
self.request = Request(definition.get('request', {}))
#: (:py:class:`ResponseResource`) This action's resource or ``None``
self.resource = None
if 'resource' in definition:
self.resource = ResponseResource(definition.get('resource', {}),
resource_defs)
self.path = definition.get('path')
class DefinitionWithParams(object):
def __init__(self, definition):
self._definition = definition
@property
def params(self):
params = []
for item in self._definition.get('params', []):
params.append(Parameter(**item))
return params
class Parameter(object):
def __init__(self, target, source, name=None, path=None, value=None,
**kwargs):
self.target = target
self.source = source
self.name = name
self.path = path
self.value = value
if kwargs:
logger.warning('Unknown parameter options found: %s', kwargs)
class Request(DefinitionWithParams):
def __init__(self, definition):
super(Request, self).__init__(definition)
self.operation = definition.get('operation')
class Waiter(DefinitionWithParams):
PREFIX = 'WaitUntil'
def __init__(self, name, definition):
super(Waiter, self).__init__(definition)
self.name = name
self.waiter_name = definition.get('waiterName')
class ResponseResource(object):
def __init__(self, definition, resource_defs):
self._definition = definition
self._resource_defs = resource_defs
self.type = definition.get('type')
self.path = definition.get('path')
@property
def identifiers(self):
identifiers = []
for item in self._definition.get('identifiers', []):
identifiers.append(
Parameter(**item))
return identifiers
@property
def model(self):
return ResourceModel(self.type, self._resource_defs[self.type],
self._resource_defs)
class Collection(Action):
@property
def batch_actions(self):
return self.resource.model.batch_actions
class ResourceModel(object):
def __init__(self, name, definition, resource_defs):
self._definition = definition
self._resource_defs = resource_defs
self._renamed = {}
self.name = name
self.shape = definition.get('shape')
def load_rename_map(self, shape=None):
names = set(['meta'])
self._renamed = {}
if self._definition.get('load'):
names.add('load')
for item in self._definition.get('identifiers', []):
self._load_name_with_category(names, item['name'], 'identifier')
for name in self._definition.get('actions', {}):
self._load_name_with_category(names, name, 'action')
for name, ref in self._get_has_definition().items():
data_required = False
for identifier in ref['resource']['identifiers']:
if identifier['source'] == 'tmp':
data_required = True
break
if not data_required:
self._load_name_with_category(names, name, 'subresource',
snake_case=False)
else:
self._load_name_with_category(names, name, 'reference')
for name in self._definition.get('hasMany', {}):
self._load_name_with_category(names, name, 'collection')
for name in self._definition.get('waiters', {}):
self._load_name_with_category(names, Waiter.PREFIX + name,
'waiter')
if shape is not None:
for name in shape.members.keys():
self._load_name_with_category(names, name, 'attribute')
def _load_name_with_category(self, names, name, category,
snake_case=True):
if snake_case:
name = xform_name(name)
if name in names:
logger.debug('Renaming %s %s %s' % (self.name, category, name))
self._renamed[(category, name)] = name + '_' + category
name += '_' + category
if name in names:
raise ValueError('Problem renaming {0} {1} to {2}!'.format(
self.name, category, name))
names.add(name)
def _get_name(self, category, name, snake_case=True):
if snake_case:
name = xform_name(name)
return self._renamed.get((category, name), name)
def get_attributes(self, shape):
attributes = {}
identifier_names = [i.name for i in self.identifiers]
for name, member in shape.members.items():
snake_cased = xform_name(name)
if snake_cased in identifier_names:
continue
snake_cased = self._get_name('attribute', snake_cased,
snake_case=False)
attributes[snake_cased] = (name, member)
return attributes
@property
def identifiers(self):
identifiers = []
for item in self._definition.get('identifiers', []):
name = self._get_name('identifier', item['name'])
member_name = item.get('memberName', None)
if member_name:
member_name = self._get_name('attribute', member_name)
identifiers.append(Identifier(name, member_name))
return identifiers
@property
def load(self):
action = self._definition.get('load')
if action is not None:
action = Action('load', action, self._resource_defs)
return action
@property
def actions(self):
actions = []
for name, item in self._definition.get('actions', {}).items():
name = self._get_name('action', name)
actions.append(Action(name, item, self._resource_defs))
return actions
@property
def batch_actions(self):
actions = []
for name, item in self._definition.get('batchActions', {}).items():
name = self._get_name('batch_action', name)
actions.append(Action(name, item, self._resource_defs))
return actions
def _get_has_definition(self):
if self.name not in self._resource_defs:
definition = {}
for name, resource_def in self._resource_defs.items():
# resource or to have defined multiple names that
# point to the same resource type, so we need to
# take that into account.
found = False
has_items = self._definition.get('has', {}).items()
for has_name, has_def in has_items:
if has_def.get('resource', {}).get('type') == name:
definition[has_name] = has_def
found = True
if not found:
# Create a relationship definition and attach it
# to the model, such that all identifiers must be
# supplied by the user. It will look something like:
#
# {
# 'resource': {
# 'type': 'ResourceName',
# 'identifiers': [
# {'target': 'Name1', 'source': 'input'},
# {'target': 'Name2', 'source': 'input'},
# ...
# ]
# }
# }
#
fake_has = {
'resource': {
'type': name,
'identifiers': []
}
}
for identifier in resource_def.get('identifiers', []):
fake_has['resource']['identifiers'].append({
'target': identifier['name'], 'source': 'input'
})
definition[name] = fake_has
else:
definition = self._definition.get('has', {})
return definition
def _get_related_resources(self, subresources):
resources = []
for name, definition in self._get_has_definition().items():
if subresources:
name = self._get_name('subresource', name, snake_case=False)
else:
name = self._get_name('reference', name)
action = Action(name, definition, self._resource_defs)
data_required = False
for identifier in action.resource.identifiers:
if identifier.source == 'tmp':
data_required = True
break
if subresources and not data_required:
resources.append(action)
elif not subresources and data_required:
resources.append(action)
return resources
@property
def subresources(self):
return self._get_related_resources(True)
@property
def references(self):
return self._get_related_resources(False)
@property
def collections(self):
collections = []
for name, item in self._definition.get('hasMany', {}).items():
name = self._get_name('collection', name)
collections.append(Collection(name, item, self._resource_defs))
return collections
@property
def waiters(self):
waiters = []
for name, item in self._definition.get('waiters', {}).items():
name = self._get_name('waiter', Waiter.PREFIX + name)
waiters.append(Waiter(name, item))
return waiters
| true | true |
f72947ac9f07322c8993ddf9e0c52af9e4ac8187 | 330,339 | py | Python | app/config/avatarinfo.py | xiaomi2019/lolita_son | 8205dff0d423aaedfa7fca8790d1d6fe50213e6e | [
"MIT"
] | null | null | null | app/config/avatarinfo.py | xiaomi2019/lolita_son | 8205dff0d423aaedfa7fca8790d1d6fe50213e6e | [
"MIT"
] | null | null | null | app/config/avatarinfo.py | xiaomi2019/lolita_son | 8205dff0d423aaedfa7fca8790d1d6fe50213e6e | [
"MIT"
] | 2 | 2019-02-18T03:49:52.000Z | 2020-03-03T16:42:02.000Z | # -*- coding: utf-8 -*-
'''
Author: Hannibal
Data:
Desc: local data config
NOTE: Don't modify this file, it's build by xml-to-python!!!
'''
avatarinfo_map = {};
avatarinfo_map[1] = {"id":1,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":8,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/human/run/all.atlas","total":8,"speed":8,"prefix":"role/human/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":6,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/human/attack/all.atlas","total":7,"speed":8,"prefix":"role/human/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/human/attacked/all.atlas","total":2,"speed":8,"prefix":"role/human/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/human/die/all.atlas","total":6,"speed":8,"prefix":"role/human/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/human/skill/all.atlas","total":7,"speed":8,"prefix":"role/human/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/skill/skill.lk",},],};
avatarinfo_map[102] = {"id":102,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/girl/stand/all.atlas","total":8,"speed":8,"prefix":"role/girl/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/girl/run/all.atlas","total":8,"speed":8,"prefix":"role/girl/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/girl/stand/all.atlas","total":8,"speed":8,"prefix":"role/girl/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/girl/attack/all.atlas","total":7,"speed":8,"prefix":"role/girl/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/girl/attacked/all.atlas","total":2,"speed":8,"prefix":"role/girl/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/girl/die/all.atlas","total":6,"speed":8,"prefix":"role/girl/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/girl/skill/all.atlas","total":7,"speed":8,"prefix":"role/girl/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/skill/skill.lk",},],};
avatarinfo_map[101] = {"id":101,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":8,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/human/run/all.atlas","total":8,"speed":8,"prefix":"role/human/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":8,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/human/attack/all.atlas","total":7,"speed":8,"prefix":"role/human/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/human/attacked/all.atlas","total":2,"speed":8,"prefix":"role/human/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/human/die/all.atlas","total":6,"speed":8,"prefix":"role/human/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/human/skill/all.atlas","total":7,"speed":8,"prefix":"role/human/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/skill/skill.lk",},],};
avatarinfo_map[201] = {"id":201,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/marryh/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/marryh/run/all.atlas","total":8,"speed":8,"prefix":"role/marryh/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/marryh/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/marryh/attack/all.atlas","total":7,"speed":8,"prefix":"role/marryh/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/marryh/attacked/all.atlas","total":2,"speed":8,"prefix":"role/marryh/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/marryh/die/all.atlas","total":6,"speed":8,"prefix":"role/marryh/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/marryh/skill/all.atlas","total":7,"speed":8,"prefix":"role/marryh/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/skill/skill.lk",},],};
avatarinfo_map[202] = {"id":202,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/marryg/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryg/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/marryg/run/all.atlas","total":8,"speed":8,"prefix":"role/marryg/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/marryg/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryg/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/marryg/attack/all.atlas","total":7,"speed":8,"prefix":"role/marryg/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/marryg/attacked/all.atlas","total":2,"speed":8,"prefix":"role/marryg/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/marryg/die/all.atlas","total":6,"speed":8,"prefix":"role/marryg/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/marryg/skill/all.atlas","total":7,"speed":8,"prefix":"role/marryg/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/skill/skill.lk",},],};
avatarinfo_map[211] = {"id":211,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/lvbuorigh/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/lvbuorigh/run/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigh/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/lvbuorigh/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/lvbuorigh/attack/all.atlas","total":7,"speed":8,"prefix":"role/lvbuorigh/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/lvbuorigh/attacked/all.atlas","total":2,"speed":8,"prefix":"role/lvbuorigh/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/lvbuorigh/die/all.atlas","total":6,"speed":8,"prefix":"role/lvbuorigh/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/lvbuorigh/skill/all.atlas","total":7,"speed":8,"prefix":"role/lvbuorigh/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/skill/skill.lk",},],};
avatarinfo_map[212] = {"id":212,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/lvbuorigg/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigg/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/lvbuorigg/run/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigg/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/lvbuorigg/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigg/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/lvbuorigg/attack/all.atlas","total":7,"speed":8,"prefix":"role/lvbuorigg/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/lvbuorigg/attacked/all.atlas","total":2,"speed":8,"prefix":"role/lvbuorigg/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/lvbuorigg/die/all.atlas","total":6,"speed":8,"prefix":"role/lvbuorigg/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/lvbuorigg/skill/all.atlas","total":7,"speed":8,"prefix":"role/lvbuorigg/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigg/skill/skill.lk",},],};
avatarinfo_map[221] = {"id":221,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/lvbublueh/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbublueh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/lvbublueh/run/all.atlas","total":8,"speed":8,"prefix":"role/lvbublueh/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/lvbublueh/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbublueh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/lvbublueh/attack/all.atlas","total":7,"speed":8,"prefix":"role/lvbublueh/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/lvbublueh/attacked/all.atlas","total":2,"speed":8,"prefix":"role/lvbublueh/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/lvbublueh/die/all.atlas","total":6,"speed":8,"prefix":"role/lvbublueh/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/lvbublueh/skill/all.atlas","total":7,"speed":8,"prefix":"role/lvbublueh/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbublueh/skill/skill.lk",},],};
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avatarinfo_map[50072] = {"id":50072,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul9/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul9/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul9/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul9/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul9/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul9/stand/stand.link",},],};
avatarinfo_map[50073] = {"id":50073,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul10/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul10/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul10/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul10/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul10/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul10/stand/stand.link",},],};
avatarinfo_map[50074] = {"id":50074,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul11/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul11/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul11/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul11/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul11/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul11/stand/stand.link",},],};
avatarinfo_map[50075] = {"id":50075,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul12/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul12/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul12/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul12/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul12/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul12/stand/stand.link",},],};
avatarinfo_map[50076] = {"id":50076,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul13/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul13/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul13/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul13/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul13/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul13/stand/stand.link",},],};
avatarinfo_map[50077] = {"id":50077,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul14/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul14/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul14/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul14/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul14/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul14/stand/stand.link",},],};
avatarinfo_map[50078] = {"id":50078,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul15/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul15/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul15/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul15/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul15/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul15/stand/stand.link",},],};
avatarinfo_map[60001] = {"id":60001,"info_data":[{"aid":0,"w":512,"h":512,"path":"zhuque/stand.atlas","total":10,"speed":8,"prefix":"zhuque/stand/","dir":"0,1,2,3,4,3,2,1","mirror":1,"dir4":0,"link":"",},{"aid":1,"w":512,"h":512,"path":"zhuque/run.atlas","total":10,"speed":8,"prefix":"zhuque/run/","dir":"0,1,2,3,4,3,2,1","mirror":1,"dir4":0,"link":"",},{"aid":2,"w":512,"h":512,"path":"zhuque/stand.atlas","total":10,"speed":8,"prefix":"zhuque/stand/","dir":"0,1,2,3,4,3,2,1","mirror":1,"dir4":0,"link":"",},{"aid":3,"w":512,"h":512,"path":"zhuque/attack.atlas","total":13,"speed":8,"prefix":"zhuque/attack/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},{"aid":4,"w":512,"h":512,"path":"zhuque/attacked.atlas","total":2,"speed":8,"prefix":"zhuque/attacked/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},{"aid":5,"w":512,"h":512,"path":"zhuque/die.atlas","total":15,"speed":8,"prefix":"zhuque/die/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},{"aid":7,"w":512,"h":512,"path":"zhuque/skill.atlas","total":14,"speed":8,"prefix":"zhuque/skill/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},],};
class Avatarinfo:
def __init__(self, key):
config = avatarinfo_map.get(key);
for k, v in config.items():
setattr(self, k, v);
return
def create_Avatarinfo(key):
config = avatarinfo_map.get(key);
if not config:
return
return Avatarinfo(key)
| 1,241.87594 | 1,940 | 0.650093 |
avatarinfo_map = {};
avatarinfo_map[1] = {"id":1,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":8,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/human/run/all.atlas","total":8,"speed":8,"prefix":"role/human/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":6,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/human/attack/all.atlas","total":7,"speed":8,"prefix":"role/human/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/human/attacked/all.atlas","total":2,"speed":8,"prefix":"role/human/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/human/die/all.atlas","total":6,"speed":8,"prefix":"role/human/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/human/skill/all.atlas","total":7,"speed":8,"prefix":"role/human/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/skill/skill.lk",},],};
avatarinfo_map[102] = {"id":102,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/girl/stand/all.atlas","total":8,"speed":8,"prefix":"role/girl/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/girl/run/all.atlas","total":8,"speed":8,"prefix":"role/girl/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/girl/stand/all.atlas","total":8,"speed":8,"prefix":"role/girl/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/girl/attack/all.atlas","total":7,"speed":8,"prefix":"role/girl/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/girl/attacked/all.atlas","total":2,"speed":8,"prefix":"role/girl/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/girl/die/all.atlas","total":6,"speed":8,"prefix":"role/girl/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/girl/skill/all.atlas","total":7,"speed":8,"prefix":"role/girl/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/girl/skill/skill.lk",},],};
avatarinfo_map[101] = {"id":101,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":8,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/human/run/all.atlas","total":8,"speed":8,"prefix":"role/human/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/human/stand/all.atlas","total":8,"speed":8,"prefix":"role/human/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/human/attack/all.atlas","total":7,"speed":8,"prefix":"role/human/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/human/attacked/all.atlas","total":2,"speed":8,"prefix":"role/human/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/human/die/all.atlas","total":6,"speed":8,"prefix":"role/human/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/human/skill/all.atlas","total":7,"speed":8,"prefix":"role/human/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/human/skill/skill.lk",},],};
avatarinfo_map[201] = {"id":201,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/marryh/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/marryh/run/all.atlas","total":8,"speed":8,"prefix":"role/marryh/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/marryh/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/marryh/attack/all.atlas","total":7,"speed":8,"prefix":"role/marryh/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/marryh/attacked/all.atlas","total":2,"speed":8,"prefix":"role/marryh/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/marryh/die/all.atlas","total":6,"speed":8,"prefix":"role/marryh/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/marryh/skill/all.atlas","total":7,"speed":8,"prefix":"role/marryh/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryh/skill/skill.lk",},],};
avatarinfo_map[202] = {"id":202,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/marryg/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryg/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/marryg/run/all.atlas","total":8,"speed":8,"prefix":"role/marryg/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/marryg/stand/all.atlas","total":8,"speed":8,"prefix":"role/marryg/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/marryg/attack/all.atlas","total":7,"speed":8,"prefix":"role/marryg/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/marryg/attacked/all.atlas","total":2,"speed":8,"prefix":"role/marryg/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/marryg/die/all.atlas","total":6,"speed":8,"prefix":"role/marryg/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/marryg/skill/all.atlas","total":7,"speed":8,"prefix":"role/marryg/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/marryg/skill/skill.lk",},],};
avatarinfo_map[211] = {"id":211,"info_data":[{"aid":0,"w":512,"h":512,"path":"role/lvbuorigh/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/stand/stand.lk",},{"aid":1,"w":512,"h":512,"path":"role/lvbuorigh/run/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigh/run/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/run/run.lk",},{"aid":2,"w":512,"h":512,"path":"role/lvbuorigh/stand/all.atlas","total":8,"speed":8,"prefix":"role/lvbuorigh/stand/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/stand/stand.lk",},{"aid":3,"w":512,"h":512,"path":"role/lvbuorigh/attack/all.atlas","total":7,"speed":8,"prefix":"role/lvbuorigh/attack/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/attack/attack.lk",},{"aid":4,"w":512,"h":512,"path":"role/lvbuorigh/attacked/all.atlas","total":2,"speed":8,"prefix":"role/lvbuorigh/attacked/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/attacked/attacked.lk",},{"aid":5,"w":512,"h":512,"path":"role/lvbuorigh/die/all.atlas","total":6,"speed":8,"prefix":"role/lvbuorigh/die/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/die/die.lk",},{"aid":7,"w":512,"h":512,"path":"role/lvbuorigh/skill/all.atlas","total":7,"speed":8,"prefix":"role/lvbuorigh/skill/all/","dir":"7,7,3,3,3,3,7,7","mirror":1,"dir4":0,"link":"role/lvbuorigh/skill/skill.lk",},],};
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avatarinfo_map[50067] = {"id":50067,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul4/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul4/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul4/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul4/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul4/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul4/stand/stand.link",},],};
avatarinfo_map[50068] = {"id":50068,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul5/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul5/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul5/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul5/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul5/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul5/stand/stand.link",},],};
avatarinfo_map[50069] = {"id":50069,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul6/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul6/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul6/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul6/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul6/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul6/stand/stand.link",},],};
avatarinfo_map[50070] = {"id":50070,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul7/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul7/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul7/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul7/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul7/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul7/stand/stand.link",},],};
avatarinfo_map[50071] = {"id":50071,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul8/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul8/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul8/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul8/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul8/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul8/stand/stand.link",},],};
avatarinfo_map[50072] = {"id":50072,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul9/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul9/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul9/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul9/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul9/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul9/stand/stand.link",},],};
avatarinfo_map[50073] = {"id":50073,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul10/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul10/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul10/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul10/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul10/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul10/stand/stand.link",},],};
avatarinfo_map[50074] = {"id":50074,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul11/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul11/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul11/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul11/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul11/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul11/stand/stand.link",},],};
avatarinfo_map[50075] = {"id":50075,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul12/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul12/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul12/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul12/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul12/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul12/stand/stand.link",},],};
avatarinfo_map[50076] = {"id":50076,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul13/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul13/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul13/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul13/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul13/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul13/stand/stand.link",},],};
avatarinfo_map[50077] = {"id":50077,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul14/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul14/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul14/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul14/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul14/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul14/stand/stand.link",},],};
avatarinfo_map[50078] = {"id":50078,"info_data":[{"aid":0,"w":512,"h":512,"path":"fairy/soul15/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul15/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul15/stand/stand.link",},{"aid":1,"w":512,"h":512,"path":"fairy/soul15/stand/all.atlas","total":6,"speed":8,"prefix":"fairy/soul15/stand/all/","dir":"0,0,0,0,0,0,0,0","mirror":1,"dir4":0,"link":"fairy/soul15/stand/stand.link",},],};
avatarinfo_map[60001] = {"id":60001,"info_data":[{"aid":0,"w":512,"h":512,"path":"zhuque/stand.atlas","total":10,"speed":8,"prefix":"zhuque/stand/","dir":"0,1,2,3,4,3,2,1","mirror":1,"dir4":0,"link":"",},{"aid":1,"w":512,"h":512,"path":"zhuque/run.atlas","total":10,"speed":8,"prefix":"zhuque/run/","dir":"0,1,2,3,4,3,2,1","mirror":1,"dir4":0,"link":"",},{"aid":2,"w":512,"h":512,"path":"zhuque/stand.atlas","total":10,"speed":8,"prefix":"zhuque/stand/","dir":"0,1,2,3,4,3,2,1","mirror":1,"dir4":0,"link":"",},{"aid":3,"w":512,"h":512,"path":"zhuque/attack.atlas","total":13,"speed":8,"prefix":"zhuque/attack/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},{"aid":4,"w":512,"h":512,"path":"zhuque/attacked.atlas","total":2,"speed":8,"prefix":"zhuque/attacked/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},{"aid":5,"w":512,"h":512,"path":"zhuque/die.atlas","total":15,"speed":8,"prefix":"zhuque/die/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},{"aid":7,"w":512,"h":512,"path":"zhuque/skill.atlas","total":14,"speed":8,"prefix":"zhuque/skill/","dir":"0,0,0,3,0,0,0,7","mirror":1,"dir4":0,"link":"",},],};
class Avatarinfo:
def __init__(self, key):
config = avatarinfo_map.get(key);
for k, v in config.items():
setattr(self, k, v);
return
def create_Avatarinfo(key):
config = avatarinfo_map.get(key);
if not config:
return
return Avatarinfo(key)
| true | true |
f72947e8280ccd02ea7e175d8af516ba5d7a2c08 | 18,043 | py | Python | vis_utils/graphics/geometry/splines.py | eherr/vis_utils | b757b01f42e6da02ad62130c3b0e61e9eaa3886f | [
"MIT"
] | 4 | 2020-05-20T03:55:19.000Z | 2020-12-24T06:33:40.000Z | vis_utils/graphics/geometry/splines.py | eherr/vis_utils | b757b01f42e6da02ad62130c3b0e61e9eaa3886f | [
"MIT"
] | 1 | 2020-05-18T11:21:35.000Z | 2020-07-07T21:25:57.000Z | vis_utils/graphics/geometry/splines.py | eherr/vis_utils | b757b01f42e6da02ad62130c3b0e61e9eaa3886f | [
"MIT"
] | 1 | 2020-07-20T06:57:13.000Z | 2020-07-20T06:57:13.000Z | #!/usr/bin/env python
#
# Copyright 2019 DFKI GmbH.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the
# following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
# USE OR OTHER DEALINGS IN THE SOFTWARE.
# -*- coding: utf-8 -*-
import numpy as np
import scipy.interpolate as si
import math
from .utils import closestLowerValueBinarySearch
B_SPLINE_DEGREE=3
class BSplineWrapper(object):
def __init__(self, points, degree=B_SPLINE_DEGREE, domain=None):
self.points = np.array(points)
if isinstance(points[0], (int, float, complex)):
self.dimensions = 1
else:
self.dimensions = len(points[0])
self.degree = degree
if domain is not None:
self.domain = domain
else:
self.domain = (0.0, 1.0)
self.initiated = True
self.spline_def = []
points_t = np.array(points).T
t_func = np.linspace(self.domain[0], self.domain[1], len(points)).tolist()
for d in range(len(points_t)):
#print d, self.dimensions
self.spline_def.append(si.splrep(t_func, points_t[d], w=None, k=3))
def _initiate_control_points(self):
return
def clear(self):
return
def queryPoint(self, u):
"""
"""
point = []
for d in range(self.dimensions):
point.append(si.splev(u, self.spline_def[d]))
return np.array(point)
def get_last_control_point(self):
return self.points[-1]
class BSpline(object):
"""
http://demonstrations.wolfram.com/GeneratingABSplineCurveByTheCoxDeBoorAlgorithm/
http://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-basis.html
"""
def __init__(self, points, degree=3, domain=None):
self.points = np.array(points)
if isinstance(points[0], (int, float, complex)):
self.dimensions = 1
else:
self.dimensions = len(points[0])
self.degree = degree
if domain is not None:
self.domain = domain
else:
self.domain = (0.0, 1.0)
self.knots = None
self.initiated = False
self._create_knots()
def _initiate_control_points(self):
return
def clear(self):
return
def get_last_control_point(self):
return self.points[-1]
def _create_knots(self):
"""
http://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-curve.html
#To change the shape of a B-spline curve, one can modify one or more of
#these control parameters:
#the positions of control points, the positions of knots, and the degree of the curve.
# given n+1 control points and m+1 knots the following property must be true
#m = n + p + 1. // p+1 = m-n
# for a campled curve the last knot must be of multiplicity p+1
If you have n+1 control points (n=9) and p = 3.
Then, m must be 13 so that the knot vector has 14 knots
The remaining 14 - (4 + 4) = 6 knots can be anywhere in the domain.
U = { 0, 0, 0, 0, 0.14, 0.28, 0.42, 0.57, 0.71, 0.85, 1, 1, 1, 1 }.
how do find the knot points C(ui).
"""
outer_knots = self.degree+1
print("multiplicity", outer_knots)
n = len(self.points) - 1
print("control points", len(self.points))
print("n", n)
p = self.degree
m = n + p + 1
n_knots = m + 1
inner_knots = n_knots-(outer_knots*2 - 2)
print("knots", n_knots)
print("free knots", inner_knots)
print("domain", self.domain)
#print np.linspace(0.0, 1.0, 4)
knots = np.linspace(self.domain[0], self.domain[1], inner_knots).tolist()
#print self.knots
self.knots = knots[:1] * (outer_knots-1) + knots +\
knots[-1:] * (outer_knots-1)
print(self.knots)
print(len(self.knots))
self.initiated = True
def queryPoint(self, u):
"""
"""
return self.evaluate(u, algorithm="deboor")
def evaluate(self, u, algorithm="standard"):
#print "evaluate", u
if self.domain[0] < u < self.domain[1]:
if algorithm == "standard":
value = 0.0#np.zeros(self.dim)
n = len(self.points)
w_list = []
for i in range(n):
#i+=self.degree
#print "iteration",i, self.basis(u, i, self.degree)
#i = self.get_begin_of_knot_range(u)
w = self.basis(u, i, self.degree)
w_list.append(w)
#print temp
value += w * self.points[i]
#print sum(w_list)
return value
elif algorithm == "deboor":
i = self.get_begin_of_knot_range(u)
#print u
return self.deboor(self.degree, self.degree, u, i)
elif u >= self.domain[1]:
return self.points[-1]
elif u <= self.domain[0]:
return self.points[0]
def basis(self, u, i, p):
"""http://devosaurus.blogspot.de/2013/10/exploring-b-splines-in-python.html
"""
if p == 0:
if self.knots[i] <= u < self.knots[i+1]:
return 1.0
else:
return 0.0
elif p >= 1:
#print i+p
#print "knot interval", i, i+p, self.knots[i+p]
out = 0.0
w_nom = (u-self.knots[i])
w_denom = (self.knots[i+p]-self.knots[i])
if w_denom > 0.0:
w = w_nom / w_denom
out += w * self.basis(u, i, p-1)
w_inv_nom = (self.knots[i+p+1] - u)
w_inv_denom = (self.knots[i+p+1] - self.knots[i+1])
if w_inv_denom > 0.0:
w_inv = w_inv_nom / w_inv_denom
out += w_inv * self.basis(u, i+1, p-1)
return out
def get_begin_of_knot_range(self, u):
begin_of_range = 0
for i, u_i in enumerate(self.knots):
if u_i < u:
begin_of_range = i
else:
break
#print "begin", begin_of_range
return begin_of_range
def deboor(self, k, p, u, i):
"""
https://chi3x10.wordpress.com/2009/10/18/de-boor-algorithm-in-c/
"""
if k == 0:
return self.points[i]
elif k >= 1:
denom = (self.knots[i+p+1-k] - self.knots[i])
if denom >0:
alpha = (u-self.knots[i])/denom
return (1-alpha) * self.deboor(k-1, p, u, i-1) \
+ (alpha * self.deboor(k-1, p, u, i))
else:
return np.zeros(self.dimensions)
class CatmullRomSpline():
'''
spline that goes through control points with arc length mapping used by motion planning
implemented using the following resources and examples:
#http://www.cs.cmu.edu/~462/projects/assn2/assn2/catmullRom.pdf
#http://algorithmist.net/docs/catmullrom.pdf
#http://www.mvps.org/directx/articles/catmull/
#http://hawkesy.blogspot.de/2010/05/catmull-rom-spline-curve-implementation.html
#http://pages.cpsc.ucalgary.ca/~jungle/587/pdf/5-interpolation.pdf
'''
def __init__(self,controlPoints, dimensions, granularity=100):
self.granularity = granularity
#http://algorithmist.net/docs/catmullrom.pdf
#base matrix to calculate one component of a point on the spline based on the influence of control points
self.catmullRomBaseMatrix = np.array([[-1.0, 3.0, -3.0, 1.0],
[2.0, -5.0, 4.0, -1.0],
[-1.0, 0.0, 1.0, 0.0],
[0.0, 2.0, 0.0, 0.0]])
self.dimensions = dimensions
self.fullArcLength = 0
self.initiated = False
self.controlPoints = []
self.numberOfSegments = 0
if len (controlPoints) >0:
self.initiateControlPoints(controlPoints)
self.initiated = True
def initiateControlPoints(self,controlPoints):
'''
@param controlPoints array of class accessible by controlPoints[index][dimension]
'''
self.numberOfSegments = len(controlPoints)-1
self.controlPoints = [controlPoints[0]]+controlPoints+[controlPoints[-1],controlPoints[-1]]#as a workaround add multiple points at the end instead of one
print("length of control point list ",len(self.controlPoints))
print("number of segments ",self.numberOfSegments)
print("number of dimensions",self.dimensions)
self.updateArcLengthMappingTable()
return
def addPoint(self,point):
#add point replace auxiliary control points
if self.initiated:
del self.controlPoints[-2:]
self.numberOfSegments = len(self.controlPoints)-1#"-2 + 1
self.controlPoints += [point,point,point]
print(self.controlPoints)
#update arc length mapping
self.updateArcLengthMappingTable()
else:
self.initiateControlPoints([point,])
self.initiated = True
def clear(self):
self.controlPoints = []
self.initiated = False
self.fullArcLength = 0
self.numberOfSegments = 0
self.arcLengthMap = []
def transformByMatrix(self,matrix):
'''
matrix nxn transformation matrix where n is the number of dimensions of the catmull rom spline
'''
if self.dimensions < matrix.shape[0]:
for i in range(len(self.controlPoints)):
self.controlPoints[i] = np.dot(matrix, self.controlPoints[i])
else:
print("failed",matrix.shape)
return
def updateArcLengthMappingTable(self):
'''
creates a table that maps from parameter space of query point to relative arc length based on the given granularity in the constructor of the catmull rom spline
http://pages.cpsc.ucalgary.ca/~jungle/587/pdf/5-interpolation.pdf
'''
self.fullArcLength = 0
granularity = self.granularity
u = np.arange(granularity+1) / float(granularity)
lastPoint = None
numberOfEvalulations = 0
self.arcLengthMap = []
for i in u:
point = self.queryPoint(i)
if lastPoint is not None:
delta = []
d = 0
while d < self.dimensions:
delta.append(math.sqrt((point[d]-lastPoint[d])**2))
d += 1
self.fullArcLength += np.sum(delta)#(point-lastPoint).length()
#print self.fullArcLength
self.arcLengthMap.append([i,self.fullArcLength])
numberOfEvalulations+=1
lastPoint= point
# self.fullArcLength = arcLength
#normalize values
if self.fullArcLength > 0 :
for i in range(numberOfEvalulations):
self.arcLengthMap[i][1] /= self.fullArcLength
def getFullArcLength(self, granularity = 100):
#granularity = self.granularity
u = np.arange(granularity+1) / float(granularity)
arcLength = 0.0
lastPoint = None
for i in u:
print("sample",i)
point = self.queryPoint(i)
if lastPoint != None:
arcLength += np.linalg.norm(point-lastPoint)#(point-lastPoint).length()
lastPoint= point
print(point)
return arcLength
def getDistanceToPath(self,absoluteArcLength, position):
'''
evaluates a point with absoluteArcLength on self to get a point on the path
then the distance between the given position and the point on the path is returned
'''
pointOnPath = self.getPointAtAbsoluteArcLength(absoluteArcLength)
return np.linalg.norm(position-pointOnPath)
def getLastControlPoint(self):
if len(self.controlPoints)> 0:
return self.controlPoints[-1]
else:
return [0,0,0]
def getArcLengthForParameter(self,t):
stepSize = 1/self.granularity
tableIndex = int(t/stepSize)
return self.arcLengthMap[tableIndex][1]*self.fullArcLength
def getPointAtAbsoluteArcLength(self,absoluteArcLength):
point = np.zeros((1,self.dimensions))#source of bug
if absoluteArcLength <= self.fullArcLength:
# parameterize curve by arc length
relativeArcLength = absoluteArcLength/self.fullArcLength
point = self.queryPointByRelativeArcLength(relativeArcLength)
else:
return None
# else:
# raise ValueError('%f exceeded arc length %f' % (absoluteArcLength,self.fullArcLength))
return point
def findClosestValuesInArcLengthMap(self,relativeArcLength):
'''
- given a relative arc length between 0 and 1 it uses closestLowerValueBinarySearch from the Generic Algorithms module to search the self.arcLengthMap for the values bounding the searched value
- returns floor parameter, ceiling parameter, floor arc length, ceiling arc length and a bool if the exact value was found
'''
foundExactValue = True
result = closestLowerValueBinarySearch(self.arcLengthMap,0,len(self.arcLengthMap)-1,relativeArcLength, getter = lambda A,i: A[i][1])#returns the index and a flag value, requires a getter for the array
index = result[0]
if result[1] == 0:#found exact value
floorP, ceilP = self.arcLengthMap[index][0],self.arcLengthMap[index][0]
floorL, ceilL = self.arcLengthMap[index][1],self.arcLengthMap[index][1]
foundExactValue = True
elif result[1] ==1:#found lower value
floorP = self.arcLengthMap[index][0]
floorL = self.arcLengthMap[index][1]
if index <len(self.arcLengthMap):#check array bounds
ceilP = self.arcLengthMap[index+1][0]
ceilL = self.arcLengthMap[index+1][1]
foundExactValue = False
else:
foundExactValue = True
ceilP= floorP
ceilL = floorL
elif result[1] ==2:#value smaller than smallest element in the array
ceilP = self.arcLengthMap[index][0]
floorL = self.arcLengthMap[index][1]
floorP = ceilP
ceilL = floorL
foundExactValue = True
elif result[1] ==3:#value larger than largest element in the array
ceilP = self.arcLengthMap[index][0]
ceilL = self.arcLengthMap[index][1]
floorP = ceilP
floorL = ceilL
foundExactValue = True
#print relativeArcLength,floorL,ceilL,foundExactValue
return floorP,ceilP,floorL,ceilL,foundExactValue
#see slide 30 of http://pages.cpsc.ucalgary.ca/~jungle/587/pdf/5-interpolation.pdf
#note it does a binary search so it is rather expensive to be called at every frame
def queryPointByRelativeArcLength(self,relativeArcLength):
floorP,ceilP,floorL,ceilL,foundExactValue = self.findClosestValuesInArcLengthMap(relativeArcLength)
if not foundExactValue:
alpha = (relativeArcLength-floorL)/(ceilL-floorL)#can be reused a-
#t = floorL+alpha*(ceilL-floorL)
t = floorP+alpha*(ceilP-floorP)
else:
t = floorP
#t = relativeArcLength#todo add correct mapping
return self.queryPoint(t)
def mapToSegment(self,t):
i = min(math.floor( self.numberOfSegments *t),self.numberOfSegments)#the part of t before i
localT =(self.numberOfSegments*t) -math.floor( self.numberOfSegments *t)#the rest, e.g. N = 10 and t = 0.62 => i = 6 and the rest is 0.02
#i = min(i,self.numberOfSegments)
return i+1,localT#increment i by 1 to ignore the first auxiliary control point
def getControlPointVectors(self,i):
i = int(i)
#if i<=self.numberOfSegments-2:
d = 0
vectors = []
while d < self.dimensions:
v = [float(self.controlPoints[i-1][d]),float(self.controlPoints[i][d]),float(self.controlPoints[i+1][d]),float(self.controlPoints[i+2][d])]
vectors.append(np.array(v))
d+=1
return vectors
#
def queryPoint(self, t):
i,localT = self.mapToSegment(t)
weightVector = np.array([localT**3,localT**2,localT,1])
controlPointVectors = self.getControlPointVectors(i)
point =[]
d =0
while d < self.dimensions:
point.append(self.queryValue(weightVector, controlPointVectors[d]))
d += 1
return np.array(point)
def queryValue(self, weightVector, controllPointVector):
v = np.dot(self.catmullRomBaseMatrix, controllPointVector)
v = np.dot(weightVector, v)
return 0.5 * v
| 38.307856 | 208 | 0.592529 |
import numpy as np
import scipy.interpolate as si
import math
from .utils import closestLowerValueBinarySearch
B_SPLINE_DEGREE=3
class BSplineWrapper(object):
def __init__(self, points, degree=B_SPLINE_DEGREE, domain=None):
self.points = np.array(points)
if isinstance(points[0], (int, float, complex)):
self.dimensions = 1
else:
self.dimensions = len(points[0])
self.degree = degree
if domain is not None:
self.domain = domain
else:
self.domain = (0.0, 1.0)
self.initiated = True
self.spline_def = []
points_t = np.array(points).T
t_func = np.linspace(self.domain[0], self.domain[1], len(points)).tolist()
for d in range(len(points_t)):
self.spline_def.append(si.splrep(t_func, points_t[d], w=None, k=3))
def _initiate_control_points(self):
return
def clear(self):
return
def queryPoint(self, u):
point = []
for d in range(self.dimensions):
point.append(si.splev(u, self.spline_def[d]))
return np.array(point)
def get_last_control_point(self):
return self.points[-1]
class BSpline(object):
def __init__(self, points, degree=3, domain=None):
self.points = np.array(points)
if isinstance(points[0], (int, float, complex)):
self.dimensions = 1
else:
self.dimensions = len(points[0])
self.degree = degree
if domain is not None:
self.domain = domain
else:
self.domain = (0.0, 1.0)
self.knots = None
self.initiated = False
self._create_knots()
def _initiate_control_points(self):
return
def clear(self):
return
def get_last_control_point(self):
return self.points[-1]
def _create_knots(self):
outer_knots = self.degree+1
print("multiplicity", outer_knots)
n = len(self.points) - 1
print("control points", len(self.points))
print("n", n)
p = self.degree
m = n + p + 1
n_knots = m + 1
inner_knots = n_knots-(outer_knots*2 - 2)
print("knots", n_knots)
print("free knots", inner_knots)
print("domain", self.domain)
knots = np.linspace(self.domain[0], self.domain[1], inner_knots).tolist()
self.knots = knots[:1] * (outer_knots-1) + knots +\
knots[-1:] * (outer_knots-1)
print(self.knots)
print(len(self.knots))
self.initiated = True
def queryPoint(self, u):
return self.evaluate(u, algorithm="deboor")
def evaluate(self, u, algorithm="standard"):
if self.domain[0] < u < self.domain[1]:
if algorithm == "standard":
value = 0.0
n = len(self.points)
w_list = []
for i in range(n):
w = self.basis(u, i, self.degree)
w_list.append(w)
value += w * self.points[i]
return value
elif algorithm == "deboor":
i = self.get_begin_of_knot_range(u)
return self.deboor(self.degree, self.degree, u, i)
elif u >= self.domain[1]:
return self.points[-1]
elif u <= self.domain[0]:
return self.points[0]
def basis(self, u, i, p):
if p == 0:
if self.knots[i] <= u < self.knots[i+1]:
return 1.0
else:
return 0.0
elif p >= 1:
out = 0.0
w_nom = (u-self.knots[i])
w_denom = (self.knots[i+p]-self.knots[i])
if w_denom > 0.0:
w = w_nom / w_denom
out += w * self.basis(u, i, p-1)
w_inv_nom = (self.knots[i+p+1] - u)
w_inv_denom = (self.knots[i+p+1] - self.knots[i+1])
if w_inv_denom > 0.0:
w_inv = w_inv_nom / w_inv_denom
out += w_inv * self.basis(u, i+1, p-1)
return out
def get_begin_of_knot_range(self, u):
begin_of_range = 0
for i, u_i in enumerate(self.knots):
if u_i < u:
begin_of_range = i
else:
break
return begin_of_range
def deboor(self, k, p, u, i):
if k == 0:
return self.points[i]
elif k >= 1:
denom = (self.knots[i+p+1-k] - self.knots[i])
if denom >0:
alpha = (u-self.knots[i])/denom
return (1-alpha) * self.deboor(k-1, p, u, i-1) \
+ (alpha * self.deboor(k-1, p, u, i))
else:
return np.zeros(self.dimensions)
class CatmullRomSpline():
def __init__(self,controlPoints, dimensions, granularity=100):
self.granularity = granularity
self.catmullRomBaseMatrix = np.array([[-1.0, 3.0, -3.0, 1.0],
[2.0, -5.0, 4.0, -1.0],
[-1.0, 0.0, 1.0, 0.0],
[0.0, 2.0, 0.0, 0.0]])
self.dimensions = dimensions
self.fullArcLength = 0
self.initiated = False
self.controlPoints = []
self.numberOfSegments = 0
if len (controlPoints) >0:
self.initiateControlPoints(controlPoints)
self.initiated = True
def initiateControlPoints(self,controlPoints):
self.numberOfSegments = len(controlPoints)-1
self.controlPoints = [controlPoints[0]]+controlPoints+[controlPoints[-1],controlPoints[-1]]
print("length of control point list ",len(self.controlPoints))
print("number of segments ",self.numberOfSegments)
print("number of dimensions",self.dimensions)
self.updateArcLengthMappingTable()
return
def addPoint(self,point):
if self.initiated:
del self.controlPoints[-2:]
self.numberOfSegments = len(self.controlPoints)-1
self.controlPoints += [point,point,point]
print(self.controlPoints)
#update arc length mapping
self.updateArcLengthMappingTable()
else:
self.initiateControlPoints([point,])
self.initiated = True
def clear(self):
self.controlPoints = []
self.initiated = False
self.fullArcLength = 0
self.numberOfSegments = 0
self.arcLengthMap = []
def transformByMatrix(self,matrix):
if self.dimensions < matrix.shape[0]:
for i in range(len(self.controlPoints)):
self.controlPoints[i] = np.dot(matrix, self.controlPoints[i])
else:
print("failed",matrix.shape)
return
def updateArcLengthMappingTable(self):
self.fullArcLength = 0
granularity = self.granularity
u = np.arange(granularity+1) / float(granularity)
lastPoint = None
numberOfEvalulations = 0
self.arcLengthMap = []
for i in u:
point = self.queryPoint(i)
if lastPoint is not None:
delta = []
d = 0
while d < self.dimensions:
delta.append(math.sqrt((point[d]-lastPoint[d])**2))
d += 1
self.fullArcLength += np.sum(delta)#(point-lastPoint).length()
#print self.fullArcLength
self.arcLengthMap.append([i,self.fullArcLength])
numberOfEvalulations+=1
lastPoint= point
# self.fullArcLength = arcLength
#normalize values
if self.fullArcLength > 0 :
for i in range(numberOfEvalulations):
self.arcLengthMap[i][1] /= self.fullArcLength
def getFullArcLength(self, granularity = 100):
#granularity = self.granularity
u = np.arange(granularity+1) / float(granularity)
arcLength = 0.0
lastPoint = None
for i in u:
print("sample",i)
point = self.queryPoint(i)
if lastPoint != None:
arcLength += np.linalg.norm(point-lastPoint)#(point-lastPoint).length()
lastPoint= point
print(point)
return arcLength
def getDistanceToPath(self,absoluteArcLength, position):
pointOnPath = self.getPointAtAbsoluteArcLength(absoluteArcLength)
return np.linalg.norm(position-pointOnPath)
def getLastControlPoint(self):
if len(self.controlPoints)> 0:
return self.controlPoints[-1]
else:
return [0,0,0]
def getArcLengthForParameter(self,t):
stepSize = 1/self.granularity
tableIndex = int(t/stepSize)
return self.arcLengthMap[tableIndex][1]*self.fullArcLength
def getPointAtAbsoluteArcLength(self,absoluteArcLength):
point = np.zeros((1,self.dimensions))#source of bug
if absoluteArcLength <= self.fullArcLength:
# parameterize curve by arc length
relativeArcLength = absoluteArcLength/self.fullArcLength
point = self.queryPointByRelativeArcLength(relativeArcLength)
else:
return None
# else:
# raise ValueError('%f exceeded arc length %f' % (absoluteArcLength,self.fullArcLength))
return point
def findClosestValuesInArcLengthMap(self,relativeArcLength):
foundExactValue = True
result = closestLowerValueBinarySearch(self.arcLengthMap,0,len(self.arcLengthMap)-1,relativeArcLength, getter = lambda A,i: A[i][1])#returns the index and a flag value, requires a getter for the array
index = result[0]
if result[1] == 0:#found exact value
floorP, ceilP = self.arcLengthMap[index][0],self.arcLengthMap[index][0]
floorL, ceilL = self.arcLengthMap[index][1],self.arcLengthMap[index][1]
foundExactValue = True
elif result[1] ==1:#found lower value
floorP = self.arcLengthMap[index][0]
floorL = self.arcLengthMap[index][1]
if index <len(self.arcLengthMap):#check array bounds
ceilP = self.arcLengthMap[index+1][0]
ceilL = self.arcLengthMap[index+1][1]
foundExactValue = False
else:
foundExactValue = True
ceilP= floorP
ceilL = floorL
elif result[1] ==2:#value smaller than smallest element in the array
ceilP = self.arcLengthMap[index][0]
floorL = self.arcLengthMap[index][1]
floorP = ceilP
ceilL = floorL
foundExactValue = True
elif result[1] ==3:#value larger than largest element in the array
ceilP = self.arcLengthMap[index][0]
ceilL = self.arcLengthMap[index][1]
floorP = ceilP
floorL = ceilL
foundExactValue = True
#print relativeArcLength,floorL,ceilL,foundExactValue
return floorP,ceilP,floorL,ceilL,foundExactValue
#see slide 30 of http://pages.cpsc.ucalgary.ca/~jungle/587/pdf/5-interpolation.pdf
#note it does a binary search so it is rather expensive to be called at every frame
def queryPointByRelativeArcLength(self,relativeArcLength):
floorP,ceilP,floorL,ceilL,foundExactValue = self.findClosestValuesInArcLengthMap(relativeArcLength)
if not foundExactValue:
alpha = (relativeArcLength-floorL)/(ceilL-floorL)#can be reused a-
#t = floorL+alpha*(ceilL-floorL)
t = floorP+alpha*(ceilP-floorP)
else:
t = floorP
#t = relativeArcLength#todo add correct mapping
return self.queryPoint(t)
def mapToSegment(self,t):
i = min(math.floor( self.numberOfSegments *t),self.numberOfSegments)#the part of t before i
localT =(self.numberOfSegments*t) -math.floor( self.numberOfSegments *t)#the rest, e.g. N = 10 and t = 0.62 => i = 6 and the rest is 0.02
#i = min(i,self.numberOfSegments)
return i+1,localT#increment i by 1 to ignore the first auxiliary control point
def getControlPointVectors(self,i):
i = int(i)
#if i<=self.numberOfSegments-2:
d = 0
vectors = []
while d < self.dimensions:
v = [float(self.controlPoints[i-1][d]),float(self.controlPoints[i][d]),float(self.controlPoints[i+1][d]),float(self.controlPoints[i+2][d])]
vectors.append(np.array(v))
d+=1
return vectors
#
def queryPoint(self, t):
i,localT = self.mapToSegment(t)
weightVector = np.array([localT**3,localT**2,localT,1])
controlPointVectors = self.getControlPointVectors(i)
point =[]
d =0
while d < self.dimensions:
point.append(self.queryValue(weightVector, controlPointVectors[d]))
d += 1
return np.array(point)
def queryValue(self, weightVector, controllPointVector):
v = np.dot(self.catmullRomBaseMatrix, controllPointVector)
v = np.dot(weightVector, v)
return 0.5 * v
| true | true |
f729480afa55183b14180203d863eeaa07548e19 | 777 | py | Python | image_email_car.py | vibhorkrishna/SHADY | 2882b079d47d9e6b628820ccdb9246ef25288bc1 | [
"MIT"
] | 16 | 2020-10-17T15:24:04.000Z | 2022-03-25T19:33:36.000Z | image_email_car.py | yugantsingh/S.H.A.D.Y | 2882b079d47d9e6b628820ccdb9246ef25288bc1 | [
"MIT"
] | 5 | 2020-10-17T15:28:18.000Z | 2021-08-08T03:54:24.000Z | image_email_car.py | yugantsingh/S.H.A.D.Y | 2882b079d47d9e6b628820ccdb9246ef25288bc1 | [
"MIT"
] | 7 | 2021-04-19T18:12:29.000Z | 2022-03-20T12:34:51.000Z | import os
import smtplib
from email.mime.text import MIMEText
from email.mime.image import MIMEImage
from email.mime.multipart import MIMEMultipart
def SendMail(ImgFileName):
img_data = open(ImgFileName, 'rb').read()
msg = MIMEMultipart()
msg['Subject'] = 'Crash Alert'
msg['From'] = 'sample@gmail.com.cc'
msg['To'] = 'sample@gmail.com.cc'
text = MIMEText("Vehicle Crash Detected. Send HELP")
msg.attach(text)
image = MIMEImage(img_data, name=os.path.basename(ImgFileName))
msg.attach(image)
s = smtplib.SMTP('smtp.gmail.com', 587)
s.ehlo()
s.starttls()
s.ehlo()
s.login("sample_sender@gmail.com", "sample_password")
s.sendmail("sample_sender@gmail.com", "sample_receiver@gmail.com", msg.as_string())
s.quit() | 29.884615 | 87 | 0.685972 | import os
import smtplib
from email.mime.text import MIMEText
from email.mime.image import MIMEImage
from email.mime.multipart import MIMEMultipart
def SendMail(ImgFileName):
img_data = open(ImgFileName, 'rb').read()
msg = MIMEMultipart()
msg['Subject'] = 'Crash Alert'
msg['From'] = 'sample@gmail.com.cc'
msg['To'] = 'sample@gmail.com.cc'
text = MIMEText("Vehicle Crash Detected. Send HELP")
msg.attach(text)
image = MIMEImage(img_data, name=os.path.basename(ImgFileName))
msg.attach(image)
s = smtplib.SMTP('smtp.gmail.com', 587)
s.ehlo()
s.starttls()
s.ehlo()
s.login("sample_sender@gmail.com", "sample_password")
s.sendmail("sample_sender@gmail.com", "sample_receiver@gmail.com", msg.as_string())
s.quit() | true | true |
f729488ad6693c34933ba3d23d7e87235a0fa288 | 6,951 | py | Python | tensorflow_federated/python/aggregators/quantile_estimation_test.py | truthiswill/federated | d25eeac036dfc2a485120a195fd904223cfc823a | [
"Apache-2.0"
] | 1 | 2022-02-08T01:11:14.000Z | 2022-02-08T01:11:14.000Z | tensorflow_federated/python/aggregators/quantile_estimation_test.py | truthiswill/federated | d25eeac036dfc2a485120a195fd904223cfc823a | [
"Apache-2.0"
] | null | null | null | tensorflow_federated/python/aggregators/quantile_estimation_test.py | truthiswill/federated | d25eeac036dfc2a485120a195fd904223cfc823a | [
"Apache-2.0"
] | null | null | null | # Copyright 2020, The TensorFlow Federated 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.
import collections
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
import tensorflow_privacy as tfp
from tensorflow_federated.python.aggregators import quantile_estimation
from tensorflow_federated.python.core.api import test_case
from tensorflow_federated.python.core.backends.test import execution_contexts
from tensorflow_federated.python.core.impl.types import computation_types
from tensorflow_federated.python.core.impl.types import placements
from tensorflow_federated.python.core.impl.types import type_conversions
from tensorflow_federated.python.core.templates import estimation_process
from tensorflow_federated.python.core.test import static_assert
QEProcess = quantile_estimation.PrivateQuantileEstimationProcess
class PrivateQEComputationTest(test_case.TestCase, parameterized.TestCase):
@parameterized.named_parameters(('private', True), ('non_private', False))
def test_process_type_signature(self, private):
if private:
quantile_estimator_query = tfp.QuantileEstimatorQuery(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
below_estimate_stddev=0.5,
expected_num_records=100,
geometric_update=True)
else:
quantile_estimator_query = tfp.NoPrivacyQuantileEstimatorQuery(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
geometric_update=True)
process = QEProcess(quantile_estimator_query)
query_state = quantile_estimator_query.initial_global_state()
sum_process_state = ()
server_state_type = computation_types.FederatedType(
type_conversions.type_from_tensors((query_state, sum_process_state)),
placements.SERVER)
self.assertEqual(
computation_types.FunctionType(
parameter=None, result=server_state_type),
process.initialize.type_signature)
estimate_type = computation_types.FederatedType(tf.float32,
placements.SERVER)
self.assertEqual(
computation_types.FunctionType(
parameter=server_state_type, result=estimate_type),
process.report.type_signature)
client_value_type = computation_types.FederatedType(tf.float32,
placements.CLIENTS)
self.assertTrue(
process.next.type_signature.is_equivalent_to(
computation_types.FunctionType(
parameter=collections.OrderedDict(
state=server_state_type, value=client_value_type),
result=server_state_type)))
def test_bad_query(self):
non_quantile_estimator_query = tfp.GaussianSumQuery(
l2_norm_clip=1.0, stddev=1.0)
with self.assertRaises(TypeError):
QEProcess(non_quantile_estimator_query)
def test_bad_aggregation_factory(self):
quantile_estimator_query = tfp.NoPrivacyQuantileEstimatorQuery(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
geometric_update=True)
with self.assertRaises(TypeError):
QEProcess(
quantile_estimator_query=quantile_estimator_query,
record_aggregation_factory="I'm not a record_aggregation_factory.")
class PrivateQEExecutionTest(test_case.TestCase, parameterized.TestCase):
@parameterized.named_parameters(('arithmetic', False), ('geometric', True))
def test_adaptation(self, geometric_update):
initial_estimate = 3.14159
target_quantile = 0.61803
learning_rate = 2.71828
quantile_estimator_query = tfp.NoPrivacyQuantileEstimatorQuery(
initial_estimate=initial_estimate,
target_quantile=target_quantile,
learning_rate=learning_rate,
geometric_update=geometric_update)
process = QEProcess(quantile_estimator_query)
state = process.initialize()
self.assertAllClose(process.report(state), initial_estimate)
# Run on two records greater than estimate.
state = process.next(state, [initial_estimate + 1, initial_estimate + 2])
if geometric_update:
expected_estimate = (
initial_estimate * np.exp(learning_rate * target_quantile))
else:
expected_estimate = initial_estimate + learning_rate * target_quantile
self.assertAllClose(process.report(state), expected_estimate)
def test_no_noise_cls(self):
process = QEProcess.no_noise(
initial_estimate=1.0, target_quantile=0.5, learning_rate=1.0)
self.assertIsInstance(process, QEProcess)
state = process.initialize()
self.assertEqual(process.report(state), 1.0)
def test_no_noise_affine_cls(self):
process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
multiplier=2.0,
increment=1.0)
self.assertIsInstance(process, estimation_process.EstimationProcess)
state = process.initialize()
self.assertEqual(process.report(state), 3.0)
def test_no_noise_secure_true_false_equal_results(self):
simple_process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
secure_estimation=False)
secure_process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
secure_estimation=True)
data = [0.5, 1.5, 2.5] # 2 bigger than the initial estimate 1.0, 1 smaller.
simple_state = simple_process.initialize()
secure_state = secure_process.initialize()
for _ in range(3):
simple_state = simple_process.next(simple_state, data)
secure_state = secure_process.next(secure_state, data)
self.assertAllClose(
simple_process.report(simple_state),
secure_process.report(secure_state))
def test_secure_estimation_true_only_contains_secure_aggregation(self):
secure_process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
secure_estimation=True)
try:
static_assert.assert_not_contains_unsecure_aggregation(
secure_process.next)
except: # pylint: disable=bare-except
self.fail('Computation contains non-secure aggregation.')
if __name__ == '__main__':
execution_contexts.set_test_execution_context()
test_case.main()
| 36.39267 | 80 | 0.727233 |
import collections
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
import tensorflow_privacy as tfp
from tensorflow_federated.python.aggregators import quantile_estimation
from tensorflow_federated.python.core.api import test_case
from tensorflow_federated.python.core.backends.test import execution_contexts
from tensorflow_federated.python.core.impl.types import computation_types
from tensorflow_federated.python.core.impl.types import placements
from tensorflow_federated.python.core.impl.types import type_conversions
from tensorflow_federated.python.core.templates import estimation_process
from tensorflow_federated.python.core.test import static_assert
QEProcess = quantile_estimation.PrivateQuantileEstimationProcess
class PrivateQEComputationTest(test_case.TestCase, parameterized.TestCase):
@parameterized.named_parameters(('private', True), ('non_private', False))
def test_process_type_signature(self, private):
if private:
quantile_estimator_query = tfp.QuantileEstimatorQuery(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
below_estimate_stddev=0.5,
expected_num_records=100,
geometric_update=True)
else:
quantile_estimator_query = tfp.NoPrivacyQuantileEstimatorQuery(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
geometric_update=True)
process = QEProcess(quantile_estimator_query)
query_state = quantile_estimator_query.initial_global_state()
sum_process_state = ()
server_state_type = computation_types.FederatedType(
type_conversions.type_from_tensors((query_state, sum_process_state)),
placements.SERVER)
self.assertEqual(
computation_types.FunctionType(
parameter=None, result=server_state_type),
process.initialize.type_signature)
estimate_type = computation_types.FederatedType(tf.float32,
placements.SERVER)
self.assertEqual(
computation_types.FunctionType(
parameter=server_state_type, result=estimate_type),
process.report.type_signature)
client_value_type = computation_types.FederatedType(tf.float32,
placements.CLIENTS)
self.assertTrue(
process.next.type_signature.is_equivalent_to(
computation_types.FunctionType(
parameter=collections.OrderedDict(
state=server_state_type, value=client_value_type),
result=server_state_type)))
def test_bad_query(self):
non_quantile_estimator_query = tfp.GaussianSumQuery(
l2_norm_clip=1.0, stddev=1.0)
with self.assertRaises(TypeError):
QEProcess(non_quantile_estimator_query)
def test_bad_aggregation_factory(self):
quantile_estimator_query = tfp.NoPrivacyQuantileEstimatorQuery(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
geometric_update=True)
with self.assertRaises(TypeError):
QEProcess(
quantile_estimator_query=quantile_estimator_query,
record_aggregation_factory="I'm not a record_aggregation_factory.")
class PrivateQEExecutionTest(test_case.TestCase, parameterized.TestCase):
@parameterized.named_parameters(('arithmetic', False), ('geometric', True))
def test_adaptation(self, geometric_update):
initial_estimate = 3.14159
target_quantile = 0.61803
learning_rate = 2.71828
quantile_estimator_query = tfp.NoPrivacyQuantileEstimatorQuery(
initial_estimate=initial_estimate,
target_quantile=target_quantile,
learning_rate=learning_rate,
geometric_update=geometric_update)
process = QEProcess(quantile_estimator_query)
state = process.initialize()
self.assertAllClose(process.report(state), initial_estimate)
# Run on two records greater than estimate.
state = process.next(state, [initial_estimate + 1, initial_estimate + 2])
if geometric_update:
expected_estimate = (
initial_estimate * np.exp(learning_rate * target_quantile))
else:
expected_estimate = initial_estimate + learning_rate * target_quantile
self.assertAllClose(process.report(state), expected_estimate)
def test_no_noise_cls(self):
process = QEProcess.no_noise(
initial_estimate=1.0, target_quantile=0.5, learning_rate=1.0)
self.assertIsInstance(process, QEProcess)
state = process.initialize()
self.assertEqual(process.report(state), 1.0)
def test_no_noise_affine_cls(self):
process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
multiplier=2.0,
increment=1.0)
self.assertIsInstance(process, estimation_process.EstimationProcess)
state = process.initialize()
self.assertEqual(process.report(state), 3.0)
def test_no_noise_secure_true_false_equal_results(self):
simple_process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
secure_estimation=False)
secure_process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
secure_estimation=True)
data = [0.5, 1.5, 2.5] # 2 bigger than the initial estimate 1.0, 1 smaller.
simple_state = simple_process.initialize()
secure_state = secure_process.initialize()
for _ in range(3):
simple_state = simple_process.next(simple_state, data)
secure_state = secure_process.next(secure_state, data)
self.assertAllClose(
simple_process.report(simple_state),
secure_process.report(secure_state))
def test_secure_estimation_true_only_contains_secure_aggregation(self):
secure_process = QEProcess.no_noise(
initial_estimate=1.0,
target_quantile=0.5,
learning_rate=1.0,
secure_estimation=True)
try:
static_assert.assert_not_contains_unsecure_aggregation(
secure_process.next)
except: # pylint: disable=bare-except
self.fail('Computation contains non-secure aggregation.')
if __name__ == '__main__':
execution_contexts.set_test_execution_context()
test_case.main()
| true | true |
f7294897198f440d03f45b003bcec4d8b534ebe2 | 518 | py | Python | server/cria_banco.py | henriquechavesm/hotels-api-flask | e17b66b53b27e57d41ca5d3d4f315faa9bc1169f | [
"Unlicense"
] | null | null | null | server/cria_banco.py | henriquechavesm/hotels-api-flask | e17b66b53b27e57d41ca5d3d4f315faa9bc1169f | [
"Unlicense"
] | null | null | null | server/cria_banco.py | henriquechavesm/hotels-api-flask | e17b66b53b27e57d41ca5d3d4f315faa9bc1169f | [
"Unlicense"
] | null | null | null | import sqlite3
connection = sqlite3.connect('banco.db')
cursor = connection.cursor()
cria_tabela = """ CREATE TABLE IF NOT EXISTS hoteis
(hotel_id text PRIMARY KEY,
nome text,
estrelas real,
diaria real,
cidade text) """
cria_hotel = """INSERT INTO hoteis VALUES
('alpha', 'Alpha Hotel', 4.3, 420.30, 'Rio de Janeiro')"""
cursor.execute(cria_tabela)
cursor.execute(cria_hotel)
connection.commit()
connection.close()
| 25.9 | 74 | 0.600386 | import sqlite3
connection = sqlite3.connect('banco.db')
cursor = connection.cursor()
cria_tabela = """ CREATE TABLE IF NOT EXISTS hoteis
(hotel_id text PRIMARY KEY,
nome text,
estrelas real,
diaria real,
cidade text) """
cria_hotel = """INSERT INTO hoteis VALUES
('alpha', 'Alpha Hotel', 4.3, 420.30, 'Rio de Janeiro')"""
cursor.execute(cria_tabela)
cursor.execute(cria_hotel)
connection.commit()
connection.close()
| true | true |
f72949945e04b9a725eeea351dfa6ae431ff5dc2 | 2,706 | py | Python | chemdataextractor/relex/pattern.py | cjcourt/cdesnowball | ed08637a06d72d3ef83296bfb686a682df0d4443 | [
"MIT"
] | 13 | 2018-06-25T14:07:07.000Z | 2022-02-16T17:00:11.000Z | chemdataextractor/relex/pattern.py | cjcourt/cdesnowball | ed08637a06d72d3ef83296bfb686a682df0d4443 | [
"MIT"
] | null | null | null | chemdataextractor/relex/pattern.py | cjcourt/cdesnowball | ed08637a06d72d3ef83296bfb686a682df0d4443 | [
"MIT"
] | 4 | 2020-02-26T08:34:26.000Z | 2021-03-18T17:33:36.000Z | # -*- coding: utf-8 -*-
"""
chemdataextractor.relex.pattern.py
Extraction pattern object
"""
"""
Modify generate_cde_element() function to adapt the changes of phrase.py.
If any prefix/middle/suffix are empty (blank), do not add it to the resulting phrase.
Modified by jz449
"""
import re
from ..parse.elements import I, W, R, Any, And, Start, OneOrMore, Group
class Pattern:
""" Pattern object, fundamentally the same as a phrase except assigned a confidence"""
def __init__(self, entities=None,
elements=None,
label=None,
sentences=None,
order=None,
relations=None, confidence=0):
self.cluster_label = label
self.elements = elements
self.entities = entities
self.number_of_entities = len(order)
self.order = order
self.relations = relations
self.confidence = confidence
self.parse_expression = self.generate_cde_parse_expression()
def __repr__(self):
return self.to_string()
def to_string(self):
output_string = ''
output_string += ' '.join(self.elements['prefix']['tokens']) + ' '
output_string += self.entities[0].tag + ' '
for i in range(0, self.number_of_entities - 1):
output_string += ' '.join(self.elements['middle_' + str(i+1)]['tokens']) + ' '
output_string += self.entities[i + 1].tag + ' '
output_string = output_string
output_string += ' '.join(self.elements['suffix']['tokens'])
return output_string
def generate_cde_parse_expression(self):
"""Create a CDE parse expression for this extraction pattern
"""
elements = []
prefix_tokens = self.elements['prefix']['tokens']
for token in prefix_tokens:
if token == '<Blank>':
continue
elements.append(I(token))
elements.append(self.entities[0].parse_expression)
for middle in range(0, self.number_of_entities -1):
middle_tokens = self.elements['middle_' + str(middle+1)]['tokens']
for token in middle_tokens:
if token == '<Blank>':
continue
elements.append(I(token))
elements.append(self.entities[middle+1].parse_expression)
suffix_tokens = self.elements['suffix']['tokens']
for token in suffix_tokens:
if token == '<Blank>':
continue
elements.append(I(token))
final_phrase = And(exprs=elements)
parse_expression = (final_phrase)('phrase')
return parse_expression
| 33.407407 | 90 | 0.589061 |
import re
from ..parse.elements import I, W, R, Any, And, Start, OneOrMore, Group
class Pattern:
def __init__(self, entities=None,
elements=None,
label=None,
sentences=None,
order=None,
relations=None, confidence=0):
self.cluster_label = label
self.elements = elements
self.entities = entities
self.number_of_entities = len(order)
self.order = order
self.relations = relations
self.confidence = confidence
self.parse_expression = self.generate_cde_parse_expression()
def __repr__(self):
return self.to_string()
def to_string(self):
output_string = ''
output_string += ' '.join(self.elements['prefix']['tokens']) + ' '
output_string += self.entities[0].tag + ' '
for i in range(0, self.number_of_entities - 1):
output_string += ' '.join(self.elements['middle_' + str(i+1)]['tokens']) + ' '
output_string += self.entities[i + 1].tag + ' '
output_string = output_string
output_string += ' '.join(self.elements['suffix']['tokens'])
return output_string
def generate_cde_parse_expression(self):
elements = []
prefix_tokens = self.elements['prefix']['tokens']
for token in prefix_tokens:
if token == '<Blank>':
continue
elements.append(I(token))
elements.append(self.entities[0].parse_expression)
for middle in range(0, self.number_of_entities -1):
middle_tokens = self.elements['middle_' + str(middle+1)]['tokens']
for token in middle_tokens:
if token == '<Blank>':
continue
elements.append(I(token))
elements.append(self.entities[middle+1].parse_expression)
suffix_tokens = self.elements['suffix']['tokens']
for token in suffix_tokens:
if token == '<Blank>':
continue
elements.append(I(token))
final_phrase = And(exprs=elements)
parse_expression = (final_phrase)('phrase')
return parse_expression
| true | true |
f7294b4c8ab15cf32eb5a8981f5001dc4da902e2 | 879 | py | Python | isi_sdk_9_0_0/test/test_network_pools.py | mohitjain97/isilon_sdk_python | a371f438f542568edb8cda35e929e6b300b1177c | [
"Unlicense"
] | 24 | 2018-06-22T14:13:23.000Z | 2022-03-23T01:21:26.000Z | isi_sdk_9_0_0/test/test_network_pools.py | mohitjain97/isilon_sdk_python | a371f438f542568edb8cda35e929e6b300b1177c | [
"Unlicense"
] | 46 | 2018-04-30T13:28:22.000Z | 2022-03-21T21:11:07.000Z | isi_sdk_9_0_0/test/test_network_pools.py | mohitjain97/isilon_sdk_python | a371f438f542568edb8cda35e929e6b300b1177c | [
"Unlicense"
] | 29 | 2018-06-19T00:14:04.000Z | 2022-02-08T17:51:19.000Z | # coding: utf-8
"""
Isilon SDK
Isilon SDK - Language bindings for the OneFS API # noqa: E501
OpenAPI spec version: 10
Contact: sdk@isilon.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import isi_sdk_9_0_0
from isi_sdk_9_0_0.models.network_pools import NetworkPools # noqa: E501
from isi_sdk_9_0_0.rest import ApiException
class TestNetworkPools(unittest.TestCase):
"""NetworkPools unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testNetworkPools(self):
"""Test NetworkPools"""
# FIXME: construct object with mandatory attributes with example values
# model = isi_sdk_9_0_0.models.network_pools.NetworkPools() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| 21.439024 | 81 | 0.697383 |
from __future__ import absolute_import
import unittest
import isi_sdk_9_0_0
from isi_sdk_9_0_0.models.network_pools import NetworkPools
from isi_sdk_9_0_0.rest import ApiException
class TestNetworkPools(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def testNetworkPools(self):
s
if __name__ == '__main__':
unittest.main()
| true | true |
f7294b570e2d193311252cd752179c1f4eb9c82e | 2,706 | py | Python | test/builddir/gyptest-all.py | cxm95/GYP-r1831 | 027f09366443871acb43d0dbbbb33ad5dec5b4e2 | [
"BSD-3-Clause"
] | 34 | 2015-01-14T03:21:08.000Z | 2020-04-26T10:06:56.000Z | core/deps/gyp/test/builddir/gyptest-all.py | K-Constantine/Amaraki | e8736e4754af62a8510c3a5db8a72df48f7681a7 | [
"MIT"
] | 11 | 2015-01-02T19:56:56.000Z | 2018-07-27T12:34:02.000Z | core/deps/gyp/test/builddir/gyptest-all.py | K-Constantine/Amaraki | e8736e4754af62a8510c3a5db8a72df48f7681a7 | [
"MIT"
] | 29 | 2015-02-13T00:18:53.000Z | 2021-02-10T23:38:58.000Z | #!/usr/bin/env python
# Copyright (c) 2012 Google Inc. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""
Verify the settings that cause a set of programs to be created in
a specific build directory, and that no intermediate built files
get created outside of that build directory hierarchy even when
referred to with deeply-nested ../../.. paths.
"""
import TestGyp
# TODO(mmoss): Make only supports (theoretically) a single, global build
# directory (through GYP_GENERATOR_FLAGS 'output_dir'), rather than
# gyp-file-specific settings (e.g. the stuff in builddir.gypi) that the other
# generators support, so this doesn't work yet for make.
# TODO(mmoss) Make also has the issue that the top-level Makefile is written to
# the "--depth" location, which is one level above 'src', but then this test
# moves 'src' somewhere else, leaving the Makefile behind, so make can't find
# its sources. I'm not sure if make is wrong for writing outside the current
# directory, or if the test is wrong for assuming everything generated is under
# the current directory.
# Android, Ninja, and CMake do not support setting the build directory.
test = TestGyp.TestGyp(formats=['!make', '!ninja', '!android', '!cmake'])
test.run_gyp('prog1.gyp', '--depth=..', chdir='src')
if test.format == 'msvs':
if test.uses_msbuild:
test.must_contain('src/prog1.vcxproj',
'<OutDir>..\\builddir\\Default\\</OutDir>')
else:
test.must_contain('src/prog1.vcproj',
'OutputDirectory="..\\builddir\\Default\\"')
test.relocate('src', 'relocate/src')
test.subdir('relocate/builddir')
# Make sure that all the built ../../etc. files only get put under builddir,
# by making all of relocate read-only and then making only builddir writable.
test.writable('relocate', False)
test.writable('relocate/builddir', True)
# Suppress the test infrastructure's setting SYMROOT on the command line.
test.build('prog1.gyp', test.ALL, SYMROOT=None, chdir='relocate/src')
expect1 = """\
Hello from prog1.c
Hello from func1.c
"""
expect2 = """\
Hello from subdir2/prog2.c
Hello from func2.c
"""
expect3 = """\
Hello from subdir2/subdir3/prog3.c
Hello from func3.c
"""
expect4 = """\
Hello from subdir2/subdir3/subdir4/prog4.c
Hello from func4.c
"""
expect5 = """\
Hello from subdir2/subdir3/subdir4/subdir5/prog5.c
Hello from func5.c
"""
def run_builddir(prog, expect):
dir = 'relocate/builddir/Default/'
test.run(program=test.workpath(dir + prog), stdout=expect)
run_builddir('prog1', expect1)
run_builddir('prog2', expect2)
run_builddir('prog3', expect3)
run_builddir('prog4', expect4)
run_builddir('prog5', expect5)
test.pass_test()
| 31.465116 | 79 | 0.730599 |
import TestGyp
# TODO(mmoss) Make also has the issue that the top-level Makefile is written to
# the "--depth" location, which is one level above 'src', but then this test
# moves 'src' somewhere else, leaving the Makefile behind, so make can't find
# directory, or if the test is wrong for assuming everything generated is under
# the current directory.
# Android, Ninja, and CMake do not support setting the build directory.
test = TestGyp.TestGyp(formats=['!make', '!ninja', '!android', '!cmake'])
test.run_gyp('prog1.gyp', '--depth=..', chdir='src')
if test.format == 'msvs':
if test.uses_msbuild:
test.must_contain('src/prog1.vcxproj',
'<OutDir>..\\builddir\\Default\\</OutDir>')
else:
test.must_contain('src/prog1.vcproj',
'OutputDirectory="..\\builddir\\Default\\"')
test.relocate('src', 'relocate/src')
test.subdir('relocate/builddir')
# Make sure that all the built ../../etc. files only get put under builddir,
# by making all of relocate read-only and then making only builddir writable.
test.writable('relocate', False)
test.writable('relocate/builddir', True)
# Suppress the test infrastructure's setting SYMROOT on the command line.
test.build('prog1.gyp', test.ALL, SYMROOT=None, chdir='relocate/src')
expect1 = """\
Hello from prog1.c
Hello from func1.c
"""
expect2 = """\
Hello from subdir2/prog2.c
Hello from func2.c
"""
expect3 = """\
Hello from subdir2/subdir3/prog3.c
Hello from func3.c
"""
expect4 = """\
Hello from subdir2/subdir3/subdir4/prog4.c
Hello from func4.c
"""
expect5 = """\
Hello from subdir2/subdir3/subdir4/subdir5/prog5.c
Hello from func5.c
"""
def run_builddir(prog, expect):
dir = 'relocate/builddir/Default/'
test.run(program=test.workpath(dir + prog), stdout=expect)
run_builddir('prog1', expect1)
run_builddir('prog2', expect2)
run_builddir('prog3', expect3)
run_builddir('prog4', expect4)
run_builddir('prog5', expect5)
test.pass_test()
| true | true |
f7294b77d995638db74a6d560aad123bfb7def06 | 15,973 | py | Python | autobahn/wamp/test/test_user_handler_errors.py | rapyuta-robotics/autobahn-python | c08e9e352d526a7fd0885bb94706366a432ada1a | [
"MIT"
] | 1,670 | 2015-10-12T15:46:22.000Z | 2022-03-30T22:12:53.000Z | autobahn/wamp/test/test_user_handler_errors.py | rapyuta-robotics/autobahn-python | c08e9e352d526a7fd0885bb94706366a432ada1a | [
"MIT"
] | 852 | 2015-10-16T22:11:03.000Z | 2022-03-27T07:57:01.000Z | autobahn/wamp/test/test_user_handler_errors.py | rapyuta-robotics/autobahn-python | c08e9e352d526a7fd0885bb94706366a432ada1a | [
"MIT"
] | 790 | 2015-10-15T08:46:12.000Z | 2022-03-30T12:22:13.000Z | ###############################################################################
#
# The MIT License (MIT)
#
# Copyright (c) Crossbar.io Technologies GmbH
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
###############################################################################
import os
if os.environ.get('USE_TWISTED', False):
from twisted.trial import unittest
from twisted.internet import defer
from twisted.python.failure import Failure
from autobahn.wamp import message, role
from autobahn.wamp.exception import ProtocolError
from autobahn.twisted.wamp import ApplicationSession
class MockTransport:
def __init__(self):
self.messages = []
def send(self, msg):
self.messages.append(msg)
def close(self, *args, **kw):
pass
class MockApplicationSession(ApplicationSession):
'''
This is used by tests, which typically attach their own handler to
on*() methods. This just collects any errors from onUserError
'''
def __init__(self, *args, **kw):
ApplicationSession.__init__(self, *args, **kw)
self.errors = []
self._realm = 'dummy'
self._transport = MockTransport()
def onUserError(self, e, msg):
self.errors.append((e.value, msg))
def exception_raiser(exc):
'''
Create a method that takes any args and always raises the given
Exception instance.
'''
assert isinstance(exc, Exception), "Must derive from Exception"
def method(*args, **kw):
raise exc
return method
def async_exception_raiser(exc):
'''
Create a method that takes any args, and always returns a Deferred
that has failed.
'''
assert isinstance(exc, Exception), "Must derive from Exception"
def method(*args, **kw):
try:
raise exc
except:
return defer.fail(Failure())
return method
def create_mock_welcome():
return message.Welcome(
1234,
{
'broker': role.RoleBrokerFeatures(),
},
)
class TestSessionCallbacks(unittest.TestCase):
'''
These test that callbacks on user-overridden ApplicationSession
methods that produce errors are handled correctly.
XXX should do state-diagram documenting where we are when each
of these cases arises :/
'''
# XXX sure would be nice to use py.test @fixture to do the
# async/sync exception-raising stuff (i.e. make each test run
# twice)...but that would mean switching all test-running over
# to py-test
skip = True
def test_on_join(self):
session = MockApplicationSession()
exception = RuntimeError("blammo")
session.onJoin = exception_raiser(exception)
msg = create_mock_welcome()
# give the sesion a WELCOME, from which it should call onJoin
session.onMessage(msg)
# make sure we got the right error out of onUserError
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_join_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("blammo")
session.onJoin = async_exception_raiser(exception)
msg = create_mock_welcome()
# give the sesion a WELCOME, from which it should call onJoin
session.onMessage(msg)
# make sure we got the right error out of onUserError
# import traceback
# traceback.print_exception(*session.errors[0][:3])
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_leave(self):
session = MockApplicationSession()
exception = RuntimeError("boom")
session.onLeave = exception_raiser(exception)
msg = message.Abort("testing")
# we haven't done anything, so this is "abort before we've
# connected"
session.onMessage(msg)
# make sure we got the right error out of onUserError
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_leave_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("boom")
session.onLeave = async_exception_raiser(exception)
msg = message.Abort("testing")
# we haven't done anything, so this is "abort before we've
# connected"
session.onMessage(msg)
# make sure we got the right error out of onUserError
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_leave_valid_session(self):
'''
cover when onLeave called after we have a valid session
'''
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onLeave = exception_raiser(exception)
# we have to get to an established connection first...
session.onMessage(create_mock_welcome())
self.assertTrue(session._session_id is not None)
# okay we have a session ("because ._session_id is not None")
msg = message.Goodbye()
session.onMessage(msg)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_leave_valid_session_deferred(self):
'''
cover when onLeave called after we have a valid session
'''
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onLeave = async_exception_raiser(exception)
# we have to get to an established connection first...
session.onMessage(create_mock_welcome())
self.assertTrue(session._session_id is not None)
# okay we have a session ("because ._session_id is not None")
msg = message.Goodbye()
session.onMessage(msg)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_leave_after_bad_challenge(self):
'''
onLeave raises error after onChallenge fails
'''
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onLeave = exception_raiser(exception)
session.onChallenge = exception_raiser(exception)
# make a challenge (which will fail, and then the
# subsequent onLeave will also fail)
msg = message.Challenge("foo")
session.onMessage(msg)
self.assertEqual(2, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_via_close(self):
session = MockApplicationSession()
exception = RuntimeError("sideways")
session.onDisconnect = exception_raiser(exception)
# we short-cut the whole state-machine traversal here by
# just calling onClose directly, which would normally be
# called via a Protocol, e.g.,
# autobahn.wamp.websocket.WampWebSocketProtocol
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_via_close_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("sideways")
session.onDisconnect = async_exception_raiser(exception)
# we short-cut the whole state-machine traversal here by
# just calling onClose directly, which would normally be
# called via a Protocol, e.g.,
# autobahn.wamp.websocket.WampWebSocketProtocol
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
# XXX FIXME Probably more ways to call onLeave!
def test_on_challenge(self):
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onChallenge = exception_raiser(exception)
msg = message.Challenge("foo")
# execute
session.onMessage(msg)
# we already handle any onChallenge errors as "abort the
# connection". So make sure our error showed up in the
# fake-transport.
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
self.assertEqual(1, len(session._transport.messages))
reply = session._transport.messages[0]
self.assertIsInstance(reply, message.Abort)
self.assertEqual("such challenge", reply.message)
def test_on_challenge_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onChallenge = async_exception_raiser(exception)
msg = message.Challenge("foo")
# execute
session.onMessage(msg)
# we already handle any onChallenge errors as "abort the
# connection". So make sure our error showed up in the
# fake-transport.
self.assertEqual(1, len(session.errors))
self.assertEqual(session.errors[0][0], exception)
self.assertEqual(1, len(session._transport.messages))
reply = session._transport.messages[0]
self.assertIsInstance(reply, message.Abort)
self.assertEqual("such challenge", reply.message)
def test_no_session(self):
'''
test "all other cases" when we don't yet have a session
established, which should all raise ProtocolErrors and
*not* go through the onUserError handler. We cheat and
just test one.
'''
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onConnect = exception_raiser(exception)
for msg in [message.Goodbye()]:
self.assertRaises(ProtocolError, session.onMessage, (msg,))
self.assertEqual(0, len(session.errors))
def test_on_disconnect(self):
session = MockApplicationSession()
exception = RuntimeError("oh sadness")
session.onDisconnect = exception_raiser(exception)
# we short-cut the whole state-machine traversal here by
# just calling onClose directly, which would normally be
# called via a Protocol, e.g.,
# autobahn.wamp.websocket.WampWebSocketProtocol
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("oh sadness")
session.onDisconnect = async_exception_raiser(exception)
# we short-cut the whole state-machine traversal here by
# just calling onClose directly, which would normally be
# called via a Protocol, e.g.,
# autobahn.wamp.websocket.WampWebSocketProtocol
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_with_session(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onDisconnect = exception_raiser(exception)
# create a valid session
session.onMessage(create_mock_welcome())
# we short-cut the whole state-machine traversal here by
# just calling onClose directly, which would normally be
# called via a Protocol, e.g.,
# autobahn.wamp.websocket.WampWebSocketProtocol
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_with_session_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onDisconnect = async_exception_raiser(exception)
# create a valid session
session.onMessage(create_mock_welcome())
# we short-cut the whole state-machine traversal here by
# just calling onClose directly, which would normally be
# called via a Protocol, e.g.,
# autobahn.wamp.websocket.WampWebSocketProtocol
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_connect(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onConnect = exception_raiser(exception)
trans = MockTransport()
# normally would be called from a Protocol?
session.onOpen(trans)
# shouldn't have done the .join()
self.assertEqual(0, len(trans.messages))
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_connect_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onConnect = async_exception_raiser(exception)
trans = MockTransport()
# normally would be called from a Protocol?
session.onOpen(trans)
# shouldn't have done the .join()
self.assertEqual(0, len(trans.messages))
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
# XXX likely missing other ways to invoke the above. need to
# cover, for sure:
#
# onChallenge
# onJoin
# onLeave
# onDisconnect
#
# what about other ISession ones?
# onConnect
# onDisconnect
# NOTE: for Event stuff, that is publish() handlers,
# test_publish_callback_exception in test_protocol.py already
# covers exceptions coming from user-code.
| 39.832918 | 79 | 0.615476 | rror showed up in the
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
self.assertEqual(1, len(session._transport.messages))
reply = session._transport.messages[0]
self.assertIsInstance(reply, message.Abort)
self.assertEqual("such challenge", reply.message)
def test_on_challenge_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onChallenge = async_exception_raiser(exception)
msg = message.Challenge("foo")
session.onMessage(msg)
# connection". So make sure our error showed up in the
self.assertEqual(1, len(session.errors))
self.assertEqual(session.errors[0][0], exception)
self.assertEqual(1, len(session._transport.messages))
reply = session._transport.messages[0]
self.assertIsInstance(reply, message.Abort)
self.assertEqual("such challenge", reply.message)
def test_no_session(self):
session = MockApplicationSession()
exception = RuntimeError("such challenge")
session.onConnect = exception_raiser(exception)
for msg in [message.Goodbye()]:
self.assertRaises(ProtocolError, session.onMessage, (msg,))
self.assertEqual(0, len(session.errors))
def test_on_disconnect(self):
session = MockApplicationSession()
exception = RuntimeError("oh sadness")
session.onDisconnect = exception_raiser(exception)
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("oh sadness")
session.onDisconnect = async_exception_raiser(exception)
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_with_session(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onDisconnect = exception_raiser(exception)
session.onMessage(create_mock_welcome())
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_disconnect_with_session_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onDisconnect = async_exception_raiser(exception)
session.onMessage(create_mock_welcome())
session.onClose(False)
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_connect(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onConnect = exception_raiser(exception)
trans = MockTransport()
session.onOpen(trans)
self.assertEqual(0, len(trans.messages))
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
def test_on_connect_deferred(self):
session = MockApplicationSession()
exception = RuntimeError("the pain runs deep")
session.onConnect = async_exception_raiser(exception)
trans = MockTransport()
# normally would be called from a Protocol?
session.onOpen(trans)
# shouldn't have done the .join()
self.assertEqual(0, len(trans.messages))
self.assertEqual(1, len(session.errors))
self.assertEqual(exception, session.errors[0][0])
| true | true |
f7294c405e804b007ba7b92b289de9b8c65cad1c | 11,133 | py | Python | platformio/project/options.py | FastEmbedded/platformio-core | a481a5dedad8c45fa5fe362b47eb1e9876938bc5 | [
"Apache-2.0"
] | null | null | null | platformio/project/options.py | FastEmbedded/platformio-core | a481a5dedad8c45fa5fe362b47eb1e9876938bc5 | [
"Apache-2.0"
] | null | null | null | platformio/project/options.py | FastEmbedded/platformio-core | a481a5dedad8c45fa5fe362b47eb1e9876938bc5 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2014-present PlatformIO <contact@platformio.org>
#
# 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.
# pylint: disable=redefined-builtin, too-many-arguments
import os
from collections import OrderedDict, namedtuple
import click
ConfigOptionClass = namedtuple(
"ConfigOption",
[
"scope",
"name",
"type",
"multiple",
"sysenvvar",
"buildenvvar",
"oldnames",
"default",
],
)
def ConfigOption(
scope,
name,
type=str,
multiple=False,
sysenvvar=None,
buildenvvar=None,
oldnames=None,
default=None,
):
return ConfigOptionClass(
scope, name, type, multiple, sysenvvar, buildenvvar, oldnames, default
)
def ConfigPlatformioOption(*args, **kwargs):
return ConfigOption("platformio", *args, **kwargs)
def ConfigEnvOption(*args, **kwargs):
return ConfigOption("env", *args, **kwargs)
ProjectOptions = OrderedDict(
[
("%s.%s" % (option.scope, option.name), option)
for option in [
#
# [platformio]
#
ConfigPlatformioOption(name="description"),
ConfigPlatformioOption(
name="default_envs",
oldnames=["env_default"],
multiple=True,
sysenvvar="PLATFORMIO_DEFAULT_ENVS",
),
ConfigPlatformioOption(name="extra_configs", multiple=True),
# Dirs
ConfigPlatformioOption(
name="core_dir",
oldnames=["home_dir"],
sysenvvar="PLATFORMIO_CORE_DIR",
default=os.path.join(os.path.expanduser("~"), ".platformio"),
),
ConfigPlatformioOption(
name="globallib_dir",
sysenvvar="PLATFORMIO_GLOBALLIB_DIR",
default=os.path.join("$PROJECT_CORE_DIR", "lib"),
),
ConfigPlatformioOption(
name="platforms_dir",
sysenvvar="PLATFORMIO_PLATFORMS_DIR",
default=os.path.join("$PROJECT_CORE_DIR", "platforms"),
),
ConfigPlatformioOption(
name="packages_dir",
sysenvvar="PLATFORMIO_PACKAGES_DIR",
default=os.path.join("$PROJECT_CORE_DIR", "packages"),
),
ConfigPlatformioOption(
name="cache_dir",
sysenvvar="PLATFORMIO_CACHE_DIR",
default=os.path.join("$PROJECT_CORE_DIR", ".cache"),
),
ConfigPlatformioOption(
name="build_cache_dir", sysenvvar="PLATFORMIO_BUILD_CACHE_DIR"
),
ConfigPlatformioOption(
name="workspace_dir",
sysenvvar="PLATFORMIO_WORKSPACE_DIR",
default=os.path.join("$PROJECT_DIR", ".pio"),
),
ConfigPlatformioOption(
name="build_dir",
sysenvvar="PLATFORMIO_BUILD_DIR",
default=os.path.join("$PROJECT_WORKSPACE_DIR", "build"),
),
ConfigPlatformioOption(
name="libdeps_dir",
sysenvvar="PLATFORMIO_LIBDEPS_DIR",
default=os.path.join("$PROJECT_WORKSPACE_DIR", "libdeps"),
),
ConfigPlatformioOption(
name="lib_dir",
sysenvvar="PLATFORMIO_LIB_DIR",
default=os.path.join("$PROJECT_DIR", "lib"),
),
ConfigPlatformioOption(
name="include_dir",
sysenvvar="PLATFORMIO_INCLUDE_DIR",
default=os.path.join("$PROJECT_DIR", "include"),
),
ConfigPlatformioOption(
name="src_dir",
sysenvvar="PLATFORMIO_SRC_DIR",
default=os.path.join("$PROJECT_DIR", "src"),
),
ConfigPlatformioOption(
name="test_dir",
sysenvvar="PLATFORMIO_TEST_DIR",
default=os.path.join("$PROJECT_DIR", "test"),
),
ConfigPlatformioOption(
name="boards_dir",
sysenvvar="PLATFORMIO_BOARDS_DIR",
default=os.path.join("$PROJECT_DIR", "boards"),
),
ConfigPlatformioOption(
name="data_dir",
sysenvvar="PLATFORMIO_DATA_DIR",
default=os.path.join("$PROJECT_DIR", "data"),
),
ConfigPlatformioOption(
name="shared_dir",
sysenvvar="PLATFORMIO_SHARED_DIR",
default=os.path.join("$PROJECT_DIR", "shared"),
),
#
# [env]
#
ConfigEnvOption(name="extends", multiple=True),
# Generic
ConfigEnvOption(name="platform", buildenvvar="PIOPLATFORM"),
ConfigEnvOption(name="platform_packages", multiple=True),
ConfigEnvOption(
name="framework", multiple=True, buildenvvar="PIOFRAMEWORK"
),
# Board
ConfigEnvOption(name="board", buildenvvar="BOARD"),
ConfigEnvOption(
name="board_build.mcu", oldnames=["board_mcu"], buildenvvar="BOARD_MCU"
),
ConfigEnvOption(
name="board_build.f_cpu",
oldnames=["board_f_cpu"],
buildenvvar="BOARD_F_CPU",
),
ConfigEnvOption(
name="board_build.f_flash",
oldnames=["board_f_flash"],
buildenvvar="BOARD_F_FLASH",
),
ConfigEnvOption(
name="board_build.flash_mode",
oldnames=["board_flash_mode"],
buildenvvar="BOARD_FLASH_MODE",
),
# Build
ConfigEnvOption(name="build_type", type=click.Choice(["release", "debug"])),
ConfigEnvOption(
name="build_flags",
multiple=True,
sysenvvar="PLATFORMIO_BUILD_FLAGS",
buildenvvar="BUILD_FLAGS",
),
ConfigEnvOption(
name="src_build_flags",
multiple=True,
sysenvvar="PLATFORMIO_SRC_BUILD_FLAGS",
buildenvvar="SRC_BUILD_FLAGS",
),
ConfigEnvOption(
name="build_unflags",
multiple=True,
sysenvvar="PLATFORMIO_BUILD_UNFLAGS",
buildenvvar="BUILD_UNFLAGS",
),
ConfigEnvOption(
name="src_filter",
multiple=True,
sysenvvar="PLATFORMIO_SRC_FILTER",
buildenvvar="SRC_FILTER",
),
ConfigEnvOption(name="targets", multiple=True),
# Upload
ConfigEnvOption(
name="upload_port",
sysenvvar="PLATFORMIO_UPLOAD_PORT",
buildenvvar="UPLOAD_PORT",
),
ConfigEnvOption(name="upload_protocol", buildenvvar="UPLOAD_PROTOCOL"),
ConfigEnvOption(
name="upload_speed", type=click.INT, buildenvvar="UPLOAD_SPEED"
),
ConfigEnvOption(
name="upload_flags",
multiple=True,
sysenvvar="PLATFORMIO_UPLOAD_FLAGS",
buildenvvar="UPLOAD_FLAGS",
),
ConfigEnvOption(
name="upload_resetmethod", buildenvvar="UPLOAD_RESETMETHOD"
),
ConfigEnvOption(name="upload_command", buildenvvar="UPLOADCMD"),
# Monitor
ConfigEnvOption(name="monitor_port"),
ConfigEnvOption(name="monitor_speed", oldnames=["monitor_baud"]),
ConfigEnvOption(name="monitor_rts", type=click.IntRange(0, 1)),
ConfigEnvOption(name="monitor_dtr", type=click.IntRange(0, 1)),
ConfigEnvOption(name="monitor_flags", multiple=True),
# Library
ConfigEnvOption(
name="lib_deps",
oldnames=["lib_use", "lib_force", "lib_install"],
multiple=True,
),
ConfigEnvOption(name="lib_ignore", multiple=True),
ConfigEnvOption(
name="lib_extra_dirs",
multiple=True,
sysenvvar="PLATFORMIO_LIB_EXTRA_DIRS",
),
ConfigEnvOption(
name="lib_ldf_mode",
type=click.Choice(["off", "chain", "deep", "chain+", "deep+"]),
),
ConfigEnvOption(
name="lib_compat_mode", type=click.Choice(["off", "soft", "strict"])
),
ConfigEnvOption(name="lib_archive", type=click.BOOL),
# Test
ConfigEnvOption(name="test_filter", multiple=True),
ConfigEnvOption(name="test_ignore", multiple=True),
ConfigEnvOption(name="test_port"),
ConfigEnvOption(name="test_speed", type=click.INT),
ConfigEnvOption(name="test_transport"),
ConfigEnvOption(name="test_build_project_src", type=click.BOOL),
# Debug
ConfigEnvOption(name="debug_tool"),
ConfigEnvOption(name="debug_init_break"),
ConfigEnvOption(name="debug_init_cmds", multiple=True),
ConfigEnvOption(name="debug_extra_cmds", multiple=True),
ConfigEnvOption(
name="debug_load_cmds", oldnames=["debug_load_cmd"], multiple=True
),
ConfigEnvOption(
name="debug_load_mode",
type=click.Choice(["always", "modified", "manual"]),
),
ConfigEnvOption(name="debug_server", multiple=True),
ConfigEnvOption(name="debug_port"),
ConfigEnvOption(
name="debug_svd_path",
type=click.Path(exists=True, file_okay=True, dir_okay=False),
),
# Check
ConfigEnvOption(name="check_tool", multiple=True),
ConfigEnvOption(name="check_filter", multiple=True),
ConfigEnvOption(name="check_flags", multiple=True),
ConfigEnvOption(
name="check_severity",
multiple=True,
type=click.Choice(["low", "medium", "high"]),
),
# Other
ConfigEnvOption(
name="extra_scripts",
oldnames=["extra_script"],
multiple=True,
sysenvvar="PLATFORMIO_EXTRA_SCRIPTS",
),
]
]
)
| 36.864238 | 88 | 0.536783 |
import os
from collections import OrderedDict, namedtuple
import click
ConfigOptionClass = namedtuple(
"ConfigOption",
[
"scope",
"name",
"type",
"multiple",
"sysenvvar",
"buildenvvar",
"oldnames",
"default",
],
)
def ConfigOption(
scope,
name,
type=str,
multiple=False,
sysenvvar=None,
buildenvvar=None,
oldnames=None,
default=None,
):
return ConfigOptionClass(
scope, name, type, multiple, sysenvvar, buildenvvar, oldnames, default
)
def ConfigPlatformioOption(*args, **kwargs):
return ConfigOption("platformio", *args, **kwargs)
def ConfigEnvOption(*args, **kwargs):
return ConfigOption("env", *args, **kwargs)
ProjectOptions = OrderedDict(
[
("%s.%s" % (option.scope, option.name), option)
for option in [
ConfigPlatformioOption(name="description"),
ConfigPlatformioOption(
name="default_envs",
oldnames=["env_default"],
multiple=True,
sysenvvar="PLATFORMIO_DEFAULT_ENVS",
),
ConfigPlatformioOption(name="extra_configs", multiple=True),
ConfigPlatformioOption(
name="core_dir",
oldnames=["home_dir"],
sysenvvar="PLATFORMIO_CORE_DIR",
default=os.path.join(os.path.expanduser("~"), ".platformio"),
),
ConfigPlatformioOption(
name="globallib_dir",
sysenvvar="PLATFORMIO_GLOBALLIB_DIR",
default=os.path.join("$PROJECT_CORE_DIR", "lib"),
),
ConfigPlatformioOption(
name="platforms_dir",
sysenvvar="PLATFORMIO_PLATFORMS_DIR",
default=os.path.join("$PROJECT_CORE_DIR", "platforms"),
),
ConfigPlatformioOption(
name="packages_dir",
sysenvvar="PLATFORMIO_PACKAGES_DIR",
default=os.path.join("$PROJECT_CORE_DIR", "packages"),
),
ConfigPlatformioOption(
name="cache_dir",
sysenvvar="PLATFORMIO_CACHE_DIR",
default=os.path.join("$PROJECT_CORE_DIR", ".cache"),
),
ConfigPlatformioOption(
name="build_cache_dir", sysenvvar="PLATFORMIO_BUILD_CACHE_DIR"
),
ConfigPlatformioOption(
name="workspace_dir",
sysenvvar="PLATFORMIO_WORKSPACE_DIR",
default=os.path.join("$PROJECT_DIR", ".pio"),
),
ConfigPlatformioOption(
name="build_dir",
sysenvvar="PLATFORMIO_BUILD_DIR",
default=os.path.join("$PROJECT_WORKSPACE_DIR", "build"),
),
ConfigPlatformioOption(
name="libdeps_dir",
sysenvvar="PLATFORMIO_LIBDEPS_DIR",
default=os.path.join("$PROJECT_WORKSPACE_DIR", "libdeps"),
),
ConfigPlatformioOption(
name="lib_dir",
sysenvvar="PLATFORMIO_LIB_DIR",
default=os.path.join("$PROJECT_DIR", "lib"),
),
ConfigPlatformioOption(
name="include_dir",
sysenvvar="PLATFORMIO_INCLUDE_DIR",
default=os.path.join("$PROJECT_DIR", "include"),
),
ConfigPlatformioOption(
name="src_dir",
sysenvvar="PLATFORMIO_SRC_DIR",
default=os.path.join("$PROJECT_DIR", "src"),
),
ConfigPlatformioOption(
name="test_dir",
sysenvvar="PLATFORMIO_TEST_DIR",
default=os.path.join("$PROJECT_DIR", "test"),
),
ConfigPlatformioOption(
name="boards_dir",
sysenvvar="PLATFORMIO_BOARDS_DIR",
default=os.path.join("$PROJECT_DIR", "boards"),
),
ConfigPlatformioOption(
name="data_dir",
sysenvvar="PLATFORMIO_DATA_DIR",
default=os.path.join("$PROJECT_DIR", "data"),
),
ConfigPlatformioOption(
name="shared_dir",
sysenvvar="PLATFORMIO_SHARED_DIR",
default=os.path.join("$PROJECT_DIR", "shared"),
),
ConfigEnvOption(name="extends", multiple=True),
ConfigEnvOption(name="platform", buildenvvar="PIOPLATFORM"),
ConfigEnvOption(name="platform_packages", multiple=True),
ConfigEnvOption(
name="framework", multiple=True, buildenvvar="PIOFRAMEWORK"
),
ConfigEnvOption(name="board", buildenvvar="BOARD"),
ConfigEnvOption(
name="board_build.mcu", oldnames=["board_mcu"], buildenvvar="BOARD_MCU"
),
ConfigEnvOption(
name="board_build.f_cpu",
oldnames=["board_f_cpu"],
buildenvvar="BOARD_F_CPU",
),
ConfigEnvOption(
name="board_build.f_flash",
oldnames=["board_f_flash"],
buildenvvar="BOARD_F_FLASH",
),
ConfigEnvOption(
name="board_build.flash_mode",
oldnames=["board_flash_mode"],
buildenvvar="BOARD_FLASH_MODE",
),
ConfigEnvOption(name="build_type", type=click.Choice(["release", "debug"])),
ConfigEnvOption(
name="build_flags",
multiple=True,
sysenvvar="PLATFORMIO_BUILD_FLAGS",
buildenvvar="BUILD_FLAGS",
),
ConfigEnvOption(
name="src_build_flags",
multiple=True,
sysenvvar="PLATFORMIO_SRC_BUILD_FLAGS",
buildenvvar="SRC_BUILD_FLAGS",
),
ConfigEnvOption(
name="build_unflags",
multiple=True,
sysenvvar="PLATFORMIO_BUILD_UNFLAGS",
buildenvvar="BUILD_UNFLAGS",
),
ConfigEnvOption(
name="src_filter",
multiple=True,
sysenvvar="PLATFORMIO_SRC_FILTER",
buildenvvar="SRC_FILTER",
),
ConfigEnvOption(name="targets", multiple=True),
ConfigEnvOption(
name="upload_port",
sysenvvar="PLATFORMIO_UPLOAD_PORT",
buildenvvar="UPLOAD_PORT",
),
ConfigEnvOption(name="upload_protocol", buildenvvar="UPLOAD_PROTOCOL"),
ConfigEnvOption(
name="upload_speed", type=click.INT, buildenvvar="UPLOAD_SPEED"
),
ConfigEnvOption(
name="upload_flags",
multiple=True,
sysenvvar="PLATFORMIO_UPLOAD_FLAGS",
buildenvvar="UPLOAD_FLAGS",
),
ConfigEnvOption(
name="upload_resetmethod", buildenvvar="UPLOAD_RESETMETHOD"
),
ConfigEnvOption(name="upload_command", buildenvvar="UPLOADCMD"),
ConfigEnvOption(name="monitor_port"),
ConfigEnvOption(name="monitor_speed", oldnames=["monitor_baud"]),
ConfigEnvOption(name="monitor_rts", type=click.IntRange(0, 1)),
ConfigEnvOption(name="monitor_dtr", type=click.IntRange(0, 1)),
ConfigEnvOption(name="monitor_flags", multiple=True),
ConfigEnvOption(
name="lib_deps",
oldnames=["lib_use", "lib_force", "lib_install"],
multiple=True,
),
ConfigEnvOption(name="lib_ignore", multiple=True),
ConfigEnvOption(
name="lib_extra_dirs",
multiple=True,
sysenvvar="PLATFORMIO_LIB_EXTRA_DIRS",
),
ConfigEnvOption(
name="lib_ldf_mode",
type=click.Choice(["off", "chain", "deep", "chain+", "deep+"]),
),
ConfigEnvOption(
name="lib_compat_mode", type=click.Choice(["off", "soft", "strict"])
),
ConfigEnvOption(name="lib_archive", type=click.BOOL),
ConfigEnvOption(name="test_filter", multiple=True),
ConfigEnvOption(name="test_ignore", multiple=True),
ConfigEnvOption(name="test_port"),
ConfigEnvOption(name="test_speed", type=click.INT),
ConfigEnvOption(name="test_transport"),
ConfigEnvOption(name="test_build_project_src", type=click.BOOL),
ConfigEnvOption(name="debug_tool"),
ConfigEnvOption(name="debug_init_break"),
ConfigEnvOption(name="debug_init_cmds", multiple=True),
ConfigEnvOption(name="debug_extra_cmds", multiple=True),
ConfigEnvOption(
name="debug_load_cmds", oldnames=["debug_load_cmd"], multiple=True
),
ConfigEnvOption(
name="debug_load_mode",
type=click.Choice(["always", "modified", "manual"]),
),
ConfigEnvOption(name="debug_server", multiple=True),
ConfigEnvOption(name="debug_port"),
ConfigEnvOption(
name="debug_svd_path",
type=click.Path(exists=True, file_okay=True, dir_okay=False),
),
ConfigEnvOption(name="check_tool", multiple=True),
ConfigEnvOption(name="check_filter", multiple=True),
ConfigEnvOption(name="check_flags", multiple=True),
ConfigEnvOption(
name="check_severity",
multiple=True,
type=click.Choice(["low", "medium", "high"]),
),
ConfigEnvOption(
name="extra_scripts",
oldnames=["extra_script"],
multiple=True,
sysenvvar="PLATFORMIO_EXTRA_SCRIPTS",
),
]
]
)
| true | true |
f7294e0ad4c7650c40bcba9faef45623896783b6 | 498 | py | Python | plotly/validators/violin/hoverlabel/font/_familysrc.py | gnestor/plotly.py | a8ae062795ddbf9867b8578fe6d9e244948c15ff | [
"MIT"
] | 12 | 2020-04-18T18:10:22.000Z | 2021-12-06T10:11:15.000Z | plotly/validators/violin/hoverlabel/font/_familysrc.py | gnestor/plotly.py | a8ae062795ddbf9867b8578fe6d9e244948c15ff | [
"MIT"
] | 27 | 2020-04-28T21:23:12.000Z | 2021-06-25T15:36:38.000Z | plotly/validators/violin/hoverlabel/font/_familysrc.py | gnestor/plotly.py | a8ae062795ddbf9867b8578fe6d9e244948c15ff | [
"MIT"
] | 6 | 2020-04-18T23:07:08.000Z | 2021-11-18T07:53:06.000Z | import _plotly_utils.basevalidators
class FamilysrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name='familysrc',
parent_name='violin.hoverlabel.font',
**kwargs
):
super(FamilysrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop('edit_type', 'none'),
role=kwargs.pop('role', 'info'),
**kwargs
)
| 26.210526 | 68 | 0.610442 | import _plotly_utils.basevalidators
class FamilysrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name='familysrc',
parent_name='violin.hoverlabel.font',
**kwargs
):
super(FamilysrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop('edit_type', 'none'),
role=kwargs.pop('role', 'info'),
**kwargs
)
| true | true |
f7294e7f641f154afe0e379c73bf47c73bcb2ef9 | 5,005 | py | Python | cme/modules/adcs.py | jim-fun/CrackMapExec | 25686f4271b87f32bbef5701125245fd1eb8575c | [
"BSD-2-Clause"
] | null | null | null | cme/modules/adcs.py | jim-fun/CrackMapExec | 25686f4271b87f32bbef5701125245fd1eb8575c | [
"BSD-2-Clause"
] | 5 | 2021-07-27T11:31:49.000Z | 2022-03-19T00:09:41.000Z | cme/modules/adcs.py | jim-fun/CrackMapExec | 25686f4271b87f32bbef5701125245fd1eb8575c | [
"BSD-2-Clause"
] | null | null | null | import re
from impacket.ldap import ldap, ldapasn1
from impacket.ldap.ldap import LDAPSearchError
class CMEModule:
'''
Find PKI Enrollment Services in Active Directory and Certificate Templates Names.
Module by Tobias Neitzel (@qtc_de) and Sam Freeside (@snovvcrash)
'''
name = 'adcs'
description = 'Find PKI Enrollment Services in Active Directory and Certificate Templates Names'
supported_protocols = ['ldap']
opsec_safe= True
multiple_hosts = True
def options(self, context, module_options):
'''
SERVER PKI Enrollment Server to enumerate templates for. Default is None.
'''
self.context = context
self.regex = re.compile('(https?://.+)')
self.server = None
if module_options and 'SERVER' in module_options:
self.server = module_options['SERVER']
def on_login(self, context, connection):
'''
On a successful LDAP login we perform a search for all PKI Enrollment Server or Certificate Templates Names.
'''
if self.server is None:
search_filter = '(objectClass=pKIEnrollmentService)'
else:
search_filter = '(distinguishedName=CN={},CN=Enrollment Services,CN=Public Key Services,CN=Services,CN=Configuration,'.format(self.server)
self.context.log.highlight('Using PKI Enrollment Server: {}'.format(self.server))
context.log.debug("Starting LDAP search with search filter '{}'".format(search_filter))
try:
sc = ldap.SimplePagedResultsControl()
if self.server is None:
resp = connection.ldapConnection.search(searchFilter=search_filter,
attributes=['dNSHostName', 'msPKI-Enrollment-Servers'],
sizeLimit=0, searchControls=[sc],
perRecordCallback=self.process_servers,
searchBase='CN=Configuration,' + connection.ldapConnection._baseDN)
else:
resp = connection.ldapConnection.search(searchFilter=search_filter + connection.ldapConnection._baseDN + ')',
attributes=['certificateTemplates'],
sizeLimit=0, searchControls=[sc],
perRecordCallback=self.process_templates,
searchBase='CN=Configuration,' + connection.ldapConnection._baseDN)
except LDAPSearchError as e:
context.log.error('Obtained unexpected exception: {}'.format(str(e)))
def process_servers(self, item):
'''
Function that is called to process the items obtain by the LDAP search when listing PKI Enrollment Servers.
'''
if not isinstance(item, ldapasn1.SearchResultEntry):
return
urls = []
host_name = None
try:
for attribute in item['attributes']:
if str(attribute['type']) == 'dNSHostName':
host_name = attribute['vals'][0].asOctets().decode('utf-8')
elif str(attribute['type']) == 'msPKI-Enrollment-Servers':
values = attribute['vals']
for value in values:
value = value.asOctets().decode('utf-8')
match = self.regex.search(value)
if match:
urls.append(match.group(1))
except Exception as e:
entry = host_name or 'item'
self.context.log.error("Skipping {}, cannot process LDAP entry due to error: '{}'".format(entry, str(e)))
if host_name:
self.context.log.highlight('Found PKI Enrollment Server: {}'.format(host_name))
for url in urls:
self.context.log.highlight('Found PKI Enrollment WebService: {}'.format(url))
def process_templates(self, item):
'''
Function that is called to process the items obtain by the LDAP search when listing Certificate Templates Names for a specific PKI Enrollment Server.
'''
if not isinstance(item, ldapasn1.SearchResultEntry):
return
templates = []
template_name = None
try:
for attribute in item['attributes']:
if str(attribute['type']) == 'certificateTemplates':
for val in attribute['vals']:
template_name = val.asOctets().decode('utf-8')
templates.append(template_name)
except Exception as e:
entry = template_name or 'item'
self.context.log.error("Skipping {}, cannot process LDAP entry due to error: '{}'".format(entry, str(e)))
if templates:
for t in templates:
self.context.log.highlight('Found Certificate Template: {}'.format(t))
| 40.04 | 157 | 0.575824 | import re
from impacket.ldap import ldap, ldapasn1
from impacket.ldap.ldap import LDAPSearchError
class CMEModule:
name = 'adcs'
description = 'Find PKI Enrollment Services in Active Directory and Certificate Templates Names'
supported_protocols = ['ldap']
opsec_safe= True
multiple_hosts = True
def options(self, context, module_options):
self.context = context
self.regex = re.compile('(https?://.+)')
self.server = None
if module_options and 'SERVER' in module_options:
self.server = module_options['SERVER']
def on_login(self, context, connection):
if self.server is None:
search_filter = '(objectClass=pKIEnrollmentService)'
else:
search_filter = '(distinguishedName=CN={},CN=Enrollment Services,CN=Public Key Services,CN=Services,CN=Configuration,'.format(self.server)
self.context.log.highlight('Using PKI Enrollment Server: {}'.format(self.server))
context.log.debug("Starting LDAP search with search filter '{}'".format(search_filter))
try:
sc = ldap.SimplePagedResultsControl()
if self.server is None:
resp = connection.ldapConnection.search(searchFilter=search_filter,
attributes=['dNSHostName', 'msPKI-Enrollment-Servers'],
sizeLimit=0, searchControls=[sc],
perRecordCallback=self.process_servers,
searchBase='CN=Configuration,' + connection.ldapConnection._baseDN)
else:
resp = connection.ldapConnection.search(searchFilter=search_filter + connection.ldapConnection._baseDN + ')',
attributes=['certificateTemplates'],
sizeLimit=0, searchControls=[sc],
perRecordCallback=self.process_templates,
searchBase='CN=Configuration,' + connection.ldapConnection._baseDN)
except LDAPSearchError as e:
context.log.error('Obtained unexpected exception: {}'.format(str(e)))
def process_servers(self, item):
if not isinstance(item, ldapasn1.SearchResultEntry):
return
urls = []
host_name = None
try:
for attribute in item['attributes']:
if str(attribute['type']) == 'dNSHostName':
host_name = attribute['vals'][0].asOctets().decode('utf-8')
elif str(attribute['type']) == 'msPKI-Enrollment-Servers':
values = attribute['vals']
for value in values:
value = value.asOctets().decode('utf-8')
match = self.regex.search(value)
if match:
urls.append(match.group(1))
except Exception as e:
entry = host_name or 'item'
self.context.log.error("Skipping {}, cannot process LDAP entry due to error: '{}'".format(entry, str(e)))
if host_name:
self.context.log.highlight('Found PKI Enrollment Server: {}'.format(host_name))
for url in urls:
self.context.log.highlight('Found PKI Enrollment WebService: {}'.format(url))
def process_templates(self, item):
if not isinstance(item, ldapasn1.SearchResultEntry):
return
templates = []
template_name = None
try:
for attribute in item['attributes']:
if str(attribute['type']) == 'certificateTemplates':
for val in attribute['vals']:
template_name = val.asOctets().decode('utf-8')
templates.append(template_name)
except Exception as e:
entry = template_name or 'item'
self.context.log.error("Skipping {}, cannot process LDAP entry due to error: '{}'".format(entry, str(e)))
if templates:
for t in templates:
self.context.log.highlight('Found Certificate Template: {}'.format(t))
| true | true |
f7294f27a6283273ffe98b0239f510ab3c7e0687 | 7,024 | py | Python | src/python/scripts/rsao/reverbObjectBinauralisation_flexible.py | s3a-spatialaudio/VISR | 55f6289bc5058d4898106f3520e1a60644ffb3ab | [
"ISC"
] | 17 | 2019-03-12T14:52:22.000Z | 2021-11-09T01:16:23.000Z | src/python/scripts/rsao/reverbObjectBinauralisation_flexible.py | s3a-spatialaudio/VISR | 55f6289bc5058d4898106f3520e1a60644ffb3ab | [
"ISC"
] | null | null | null | src/python/scripts/rsao/reverbObjectBinauralisation_flexible.py | s3a-spatialaudio/VISR | 55f6289bc5058d4898106f3520e1a60644ffb3ab | [
"ISC"
] | 2 | 2019-08-11T12:53:07.000Z | 2021-06-22T10:08:08.000Z | # -*- coding: utf-8 -*-
"""
Created on Tue Feb 14 15:59:11 2017
@author: af5u13
"""
# Usage for debugging from raw Python console
#exec(open("/Users/af5u13/dev/visr/src/python/scripts/rsao/reverbObjectBinauralisation.py").read())
import visr
import signalflows
import panning
import pml
import rbbl
import rcl
import rrl
#import objectmodel as om
import h5py
import numpy as np;
import matplotlib.pyplot as plt
import os
class ReverbToBinaural( visr.CompositeComponent ):
def __init__( self, context, name, parent,
loudspeakerConfig,
numberOfInputs,
rendererOutputs,
interpolationPeriod,
diffusionFilters,
trackingConfiguration,
brirRouting,
brirFilters,
scenePort = 4242,
reverbConfiguration=''):
super(ReverbToBinaural,self).__init__( context, name, parent )
self.coreRenderer = signalflows.BaselineRenderer( ctxt, 'renderer', self,
loudspeakerConfig=loudspeakerConfig,
numberOfInputs=numberOfInputs,
numberOfOutputs=rendererOutputs,
interpolationPeriod=interpolationPeriod,
diffusionFilters=diffusionFilters,
reverbConfig=reverbConfiguration,
sceneReceiverPort=scenePort,
trackingConfiguration=trackingConfiguration
)
numFilters = brirFilters.numberOfRows
firLength = brirFilters.numberOfColumns
numRoutings = brirRouting.size
self.convolver = rcl.FirFilterMatrix( ctxt, 'convolver', self,
numberOfInputs=rendererOutputs,
numberOfOutputs=2,
maxFilters=numFilters,
filterLength=firLength,
maxRoutings=numRoutings,
filters=brirFilters,
routings=brirRouting,
controlInputs=rcl.FirFilterMatrix.ControlPortConfig.NoInputs
)
self.audioIn = visr.AudioInputFloat( "audioIn", self, numberOfInputs )
self.audioOut = visr.AudioOutputFloat( "audioOut", self, 2 )
self.audioConnection( self.audioIn, self.coreRenderer.audioPort("input"))
self.audioConnection( self.coreRenderer.audioPort("output"),
self.convolver.audioPort("in"))
self.audioConnection( self.convolver.audioPort("out"), self.audioOut )
if len(trackingConfiguration) > 0:
self.posIn = visr.ParameterInput( "posIn", self,
pml.ListenerPosition.staticType,
pml.DoubleBufferingProtocol.staticType,
pml.EmptyParameterConfig() )
self.parameterConnection( self.posIn, self.coreRenderer.parameterPort("trackingPositionInput") )
# Get VISR base directory from rsao subdirectory.
visrBaseDirectory = os.path.normpath(os.path.join( os.getcwd(), '../../../..' )).replace('\\','/')
blockSize = 1024
samplingFrequency = 48000
parameterUpdatePeriod = 1024
numBlocks = 8
signalLength = blockSize * numBlocks
t = 1.0/samplingFrequency * np.arange(0,signalLength)
numObjects = 1;
ctxt = visr.SignalFlowContext( blockSize, samplingFrequency)
lspConfigFile = os.path.join( visrBaseDirectory, 'config/bbc/bs2051-4+5+0.xml').replace('\\','/')
# lspConfigFile = os.path.join( visrBaseDirectory, 'config/isvr/audiolab_39speakers_1subwoofer.xml' )
lc = panning.LoudspeakerArray( lspConfigFile )
numOutputChannels = np.max( lc.channelIndices() + lc.subwooferChannelIndices() ) +1
numLoudspeakers = lc.numberOfRegularLoudspeakers
diffFilterFile = os.path.join( visrBaseDirectory, 'config/filters/random_phase_allpass_64ch_512taps.wav')
diffFiltersRaw = np.array(pml.MatrixParameterFloat.fromAudioFile( diffFilterFile ),
dtype = np.float32 )
diffFilters = pml.MatrixParameterFloat( diffFiltersRaw[ np.array(lc.channelIndices() )-1,: ] )
reverbConfigStr = '{ "numReverbObjects": %i, "discreteReflectionsPerObject": 20, "lateReverbFilterLength": 2.0, "lateReverbDecorrelationFilters": "%s/config/filters/random_phase_allpass_64ch_1024taps.wav" }' % (numObjects, visrBaseDirectory )
## Load the BBC BRIR dataset
brirFile = os.path.join( os.getcwd(), 'BBC_BRIR.mat' )
brirMat = h5py.File( brirFile )
brirFull = np.array( brirMat['h_sweetspot'], dtype=np.float32 ).copy('C')
# Scalefactor to compensate for the very low amplitudes of the BBC BRIRs
brirScaleFactor = 500;
brirFlat = brirScaleFactor * np.concatenate( (brirFull[:,0,:], brirFull[:,1,:] ) )
brirFilterParam = pml.MatrixParameterFloat( brirFlat, 16 )
numBrirSpeakers = brirFull.shape[0]
# Define the routing for the binaural convolver such that it matches the organisation of the
# flat BRIR matrix.
filterRouting = rbbl.FilterRoutingList()
for idx in range(0, numBrirSpeakers ):
filterRouting.addRouting( idx, 0, idx, 1.0 )
filterRouting.addRouting( idx, 1, idx+numBrirSpeakers, 1.0 )
renderer = ReverbToBinaural( ctxt, 'top', None,
loudspeakerConfig=lc,
numberOfInputs=numObjects,
rendererOutputs=numOutputChannels,
interpolationPeriod=parameterUpdatePeriod,
diffusionFilters=diffFilters,
trackingConfiguration='',
brirFilters = brirFilterParam,
brirRouting = filterRouting,
reverbConfiguration=reverbConfigStr,
scenePort = 4242
)
print( 'Created renderer.' )
flow = rrl.AudioSignalFlow( renderer )
## Non-realtime code
#inputSignal = np.zeros( (numObjects, signalLength ), dtype=np.float32 )
## inputSignal[0,:] = 0.75*np.sin( 2.0*np.pi*440 * t )
#inputSignal[ 0, 100 ] = 1
#
#outputSignal = np.zeros( (2, signalLength ), dtype=np.float32 )
#
#for blockIdx in range(0,numBlocks):
## if blockIdx % (parameterUpdatePeriod/blockSize) == 0:
## ov = paramInput.data()
## ov.clear()
## ov.set( ro.objectId, ro )
## paramInput.swapBuffers()
#
# inputBlock = inputSignal[:, blockIdx*blockSize:(blockIdx+1)*blockSize]
# outputBlock = flow.process( inputBlock )
# outputSignal[:, blockIdx*blockSize:(blockIdx+1)*blockSize] = outputBlock
#
#
#plt.figure(1)
#plt.plot( t, outputSignal[0,:], 'bo-', t, outputSignal[1,:], 'rx-' )
#plt.show( block = False )
| 43.9 | 242 | 0.604784 |
import visr
import signalflows
import panning
import pml
import rbbl
import rcl
import rrl
import h5py
import numpy as np;
import matplotlib.pyplot as plt
import os
class ReverbToBinaural( visr.CompositeComponent ):
def __init__( self, context, name, parent,
loudspeakerConfig,
numberOfInputs,
rendererOutputs,
interpolationPeriod,
diffusionFilters,
trackingConfiguration,
brirRouting,
brirFilters,
scenePort = 4242,
reverbConfiguration=''):
super(ReverbToBinaural,self).__init__( context, name, parent )
self.coreRenderer = signalflows.BaselineRenderer( ctxt, 'renderer', self,
loudspeakerConfig=loudspeakerConfig,
numberOfInputs=numberOfInputs,
numberOfOutputs=rendererOutputs,
interpolationPeriod=interpolationPeriod,
diffusionFilters=diffusionFilters,
reverbConfig=reverbConfiguration,
sceneReceiverPort=scenePort,
trackingConfiguration=trackingConfiguration
)
numFilters = brirFilters.numberOfRows
firLength = brirFilters.numberOfColumns
numRoutings = brirRouting.size
self.convolver = rcl.FirFilterMatrix( ctxt, 'convolver', self,
numberOfInputs=rendererOutputs,
numberOfOutputs=2,
maxFilters=numFilters,
filterLength=firLength,
maxRoutings=numRoutings,
filters=brirFilters,
routings=brirRouting,
controlInputs=rcl.FirFilterMatrix.ControlPortConfig.NoInputs
)
self.audioIn = visr.AudioInputFloat( "audioIn", self, numberOfInputs )
self.audioOut = visr.AudioOutputFloat( "audioOut", self, 2 )
self.audioConnection( self.audioIn, self.coreRenderer.audioPort("input"))
self.audioConnection( self.coreRenderer.audioPort("output"),
self.convolver.audioPort("in"))
self.audioConnection( self.convolver.audioPort("out"), self.audioOut )
if len(trackingConfiguration) > 0:
self.posIn = visr.ParameterInput( "posIn", self,
pml.ListenerPosition.staticType,
pml.DoubleBufferingProtocol.staticType,
pml.EmptyParameterConfig() )
self.parameterConnection( self.posIn, self.coreRenderer.parameterPort("trackingPositionInput") )
visrBaseDirectory = os.path.normpath(os.path.join( os.getcwd(), '../../../..' )).replace('\\','/')
blockSize = 1024
samplingFrequency = 48000
parameterUpdatePeriod = 1024
numBlocks = 8
signalLength = blockSize * numBlocks
t = 1.0/samplingFrequency * np.arange(0,signalLength)
numObjects = 1;
ctxt = visr.SignalFlowContext( blockSize, samplingFrequency)
lspConfigFile = os.path.join( visrBaseDirectory, 'config/bbc/bs2051-4+5+0.xml').replace('\\','/')
lc = panning.LoudspeakerArray( lspConfigFile )
numOutputChannels = np.max( lc.channelIndices() + lc.subwooferChannelIndices() ) +1
numLoudspeakers = lc.numberOfRegularLoudspeakers
diffFilterFile = os.path.join( visrBaseDirectory, 'config/filters/random_phase_allpass_64ch_512taps.wav')
diffFiltersRaw = np.array(pml.MatrixParameterFloat.fromAudioFile( diffFilterFile ),
dtype = np.float32 )
diffFilters = pml.MatrixParameterFloat( diffFiltersRaw[ np.array(lc.channelIndices() )-1,: ] )
reverbConfigStr = '{ "numReverbObjects": %i, "discreteReflectionsPerObject": 20, "lateReverbFilterLength": 2.0, "lateReverbDecorrelationFilters": "%s/config/filters/random_phase_allpass_64ch_1024taps.wav" }' % (numObjects, visrBaseDirectory )
s.getcwd(), 'BBC_BRIR.mat' )
brirMat = h5py.File( brirFile )
brirFull = np.array( brirMat['h_sweetspot'], dtype=np.float32 ).copy('C')
brirScaleFactor = 500;
brirFlat = brirScaleFactor * np.concatenate( (brirFull[:,0,:], brirFull[:,1,:] ) )
brirFilterParam = pml.MatrixParameterFloat( brirFlat, 16 )
numBrirSpeakers = brirFull.shape[0]
filterRouting = rbbl.FilterRoutingList()
for idx in range(0, numBrirSpeakers ):
filterRouting.addRouting( idx, 0, idx, 1.0 )
filterRouting.addRouting( idx, 1, idx+numBrirSpeakers, 1.0 )
renderer = ReverbToBinaural( ctxt, 'top', None,
loudspeakerConfig=lc,
numberOfInputs=numObjects,
rendererOutputs=numOutputChannels,
interpolationPeriod=parameterUpdatePeriod,
diffusionFilters=diffFilters,
trackingConfiguration='',
brirFilters = brirFilterParam,
brirRouting = filterRouting,
reverbConfiguration=reverbConfigStr,
scenePort = 4242
)
print( 'Created renderer.' )
flow = rrl.AudioSignalFlow( renderer )
| true | true |
f7294fc1c05c8e2ec65a218654cd6859e059a4d9 | 1,410 | py | Python | stasians_help/urls.py | stanmain/stasians_help | 155b17c3323e71e38739402186e0d9501ab9d831 | [
"MIT"
] | null | null | null | stasians_help/urls.py | stanmain/stasians_help | 155b17c3323e71e38739402186e0d9501ab9d831 | [
"MIT"
] | null | null | null | stasians_help/urls.py | stanmain/stasians_help | 155b17c3323e71e38739402186e0d9501ab9d831 | [
"MIT"
] | null | null | null | """stasians_help URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.conf import settings
from django.conf.urls.static import static
from django.contrib import admin
from django.urls import path, include
from profiles.views import RegistrationView
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('main.urls')),
path('', include('django.contrib.auth.urls')),
path('pages/', include('pages.urls')),
path('registration/', RegistrationView.as_view(), name='registration'),
path('subjects/', include('subjects.urls')),
path('news/', include('news.urls')),
path('profile/', include('profiles.urls')),
path('orders/', include('orders.urls')),
path('comments/', include('django_comments.urls')),
]
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
| 36.153846 | 77 | 0.703546 |
from django.conf import settings
from django.conf.urls.static import static
from django.contrib import admin
from django.urls import path, include
from profiles.views import RegistrationView
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('main.urls')),
path('', include('django.contrib.auth.urls')),
path('pages/', include('pages.urls')),
path('registration/', RegistrationView.as_view(), name='registration'),
path('subjects/', include('subjects.urls')),
path('news/', include('news.urls')),
path('profile/', include('profiles.urls')),
path('orders/', include('orders.urls')),
path('comments/', include('django_comments.urls')),
]
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
| true | true |
f72951655290a417050aa4a7bd1e7df075ef3d8e | 2,724 | py | Python | cloudify_nsx/security/group_dynamic_member.py | cloudify-cosmo/cloudify-nsx-plugin | d7b2abbe384e55aaf47b2c8474ab07f622eb83b5 | [
"Apache-2.0"
] | 2 | 2017-03-08T21:44:54.000Z | 2019-01-16T06:00:16.000Z | cloudify_nsx/security/group_dynamic_member.py | cloudify-cosmo/cloudify-nsx-plugin | d7b2abbe384e55aaf47b2c8474ab07f622eb83b5 | [
"Apache-2.0"
] | 3 | 2017-01-26T13:26:31.000Z | 2017-02-03T14:51:45.000Z | cloudify_nsx/security/group_dynamic_member.py | cloudify-cosmo/cloudify-nsx-plugin | d7b2abbe384e55aaf47b2c8474ab07f622eb83b5 | [
"Apache-2.0"
] | 5 | 2016-12-28T15:26:02.000Z | 2017-01-30T08:46:10.000Z | ########
# Copyright (c) 2016 GigaSpaces Technologies Ltd. All rights reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# * See the License for the specific language governing permissions and
# * limitations under the License.
from cloudify import ctx
from cloudify.decorators import operation
import cloudify_nsx.library.nsx_security_group as nsx_security_group
import cloudify_nsx.library.nsx_common as common
@operation
def create(**kwargs):
kwargs = common.get_properties_update(
'dynamic_member', "security_group_id", kwargs,
target_relationship="cloudify.nsx.relationships.contained_in",
target_property="resource_id"
)
validation_rules = {
"security_group_id": {
"required": True
},
# dynamic member definition
"dynamic_set": {
"required": True
}
}
use_existing, dynamic_member = common.get_properties_and_validate(
'dynamic_member', kwargs, validation_rules
)
resource_id = ctx.instance.runtime_properties.get('resource_id')
if resource_id:
ctx.logger.info("Reused %s" % resource_id)
return
# credentials
client_session = common.nsx_login(kwargs)
resource_id = nsx_security_group.set_dynamic_member(
client_session,
dynamic_member['security_group_id'],
dynamic_member['dynamic_set']
)
ctx.instance.runtime_properties['resource_id'] = resource_id
ctx.logger.info("created %s" % (
resource_id
))
@operation
def delete(**kwargs):
use_existing, dynamic_member = common.get_properties(
'dynamic_member', kwargs
)
if use_existing:
common.remove_properties('dynamic_member')
ctx.logger.info("Used existed")
return
resource_id = ctx.instance.runtime_properties.get('resource_id')
if not resource_id:
common.remove_properties('dynamic_member')
ctx.logger.info("Not fully created, skip")
return
# credentials
client_session = common.nsx_login(kwargs)
common.attempt_with_rerun(
nsx_security_group.del_dynamic_member,
client_session=client_session,
security_group_id=resource_id
)
ctx.logger.info("delete %s" % resource_id)
common.remove_properties('dynamic_member')
| 29.608696 | 79 | 0.694934 | import ctx
from cloudify.decorators import operation
import cloudify_nsx.library.nsx_security_group as nsx_security_group
import cloudify_nsx.library.nsx_common as common
@operation
def create(**kwargs):
kwargs = common.get_properties_update(
'dynamic_member', "security_group_id", kwargs,
target_relationship="cloudify.nsx.relationships.contained_in",
target_property="resource_id"
)
validation_rules = {
"security_group_id": {
"required": True
},
"dynamic_set": {
"required": True
}
}
use_existing, dynamic_member = common.get_properties_and_validate(
'dynamic_member', kwargs, validation_rules
)
resource_id = ctx.instance.runtime_properties.get('resource_id')
if resource_id:
ctx.logger.info("Reused %s" % resource_id)
return
client_session = common.nsx_login(kwargs)
resource_id = nsx_security_group.set_dynamic_member(
client_session,
dynamic_member['security_group_id'],
dynamic_member['dynamic_set']
)
ctx.instance.runtime_properties['resource_id'] = resource_id
ctx.logger.info("created %s" % (
resource_id
))
@operation
def delete(**kwargs):
use_existing, dynamic_member = common.get_properties(
'dynamic_member', kwargs
)
if use_existing:
common.remove_properties('dynamic_member')
ctx.logger.info("Used existed")
return
resource_id = ctx.instance.runtime_properties.get('resource_id')
if not resource_id:
common.remove_properties('dynamic_member')
ctx.logger.info("Not fully created, skip")
return
client_session = common.nsx_login(kwargs)
common.attempt_with_rerun(
nsx_security_group.del_dynamic_member,
client_session=client_session,
security_group_id=resource_id
)
ctx.logger.info("delete %s" % resource_id)
common.remove_properties('dynamic_member')
| true | true |
f72952743caf8d0c5c27e3e7e4c081a234332a37 | 3,477 | py | Python | zql-backend/gateway/server.py | Zubdano/zql | 2e1ec981da60cfd41ad743bc38950c97e506c2c8 | [
"MIT"
] | null | null | null | zql-backend/gateway/server.py | Zubdano/zql | 2e1ec981da60cfd41ad743bc38950c97e506c2c8 | [
"MIT"
] | 4 | 2017-09-25T04:35:01.000Z | 2017-12-18T04:15:04.000Z | zql-backend/gateway/server.py | Zubdano/zql | 2e1ec981da60cfd41ad743bc38950c97e506c2c8 | [
"MIT"
] | null | null | null | from functools import wraps
import os
from client import AuthDecorator, HttpClient
from flask import Flask, jsonify, request, g
from flask_pymongo import PyMongo
from auth import create_user, get_user_by_token, login_user
from errors import APIError
app = Flask(__name__)
mongo_uri = 'mongodb://{}:{}@ds259325.mlab.com:59325/zql'
app.config['MONGO_URI'] = mongo_uri.format(os.environ['ZQL_MONGO_USER'], os.environ['ZQL_MONGO_PASS'])
mongo = PyMongo(app)
# Initialize clients for all of our services.
grammar = AuthDecorator(HttpClient('http://127.0.0.1:2666/'))
interpreter = AuthDecorator(HttpClient('http://127.0.0.1:2020/'))
postprocessing = AuthDecorator(HttpClient('http://127.0.0.1:2015/'))
mapping = {
# Interpreter Service forwarders
'interpret': interpreter,
'keywords': interpreter,
# Grammar Service forwarders
'grammar': grammar,
'grammars': grammar,
# Postprocessing Service forwarders
'event': postprocessing,
'events': postprocessing,
}
def copy_headers():
headers = {}
for header in request.headers.keys():
headers[header] = request.headers.get(header)
return headers
# TBH idk why we need this, but doesn't work without it when testing on localhost
# For now, just add this to POST request handlers
def access_control(func):
@wraps(func)
def wrapper(*args, **kwargs):
if request.method == 'OPTIONS':
resp = Flask.make_default_options_response(app)
resp.headers['Access-Control-Allow-Headers'] = 'Content-Type,token'
resp.headers['Content-Type'] = 'application/json'
else:
resp = func(*args, **kwargs)
resp.headers['Access-Control-Allow-Origin'] = '*'
return resp
return wrapper
@app.route('/login', methods=['POST', 'OPTIONS'])
@access_control
def login():
username = request.json.get('username')
password = request.json.get('password')
if username is None or password is None:
raise APIError('Missing username or password', status_code=409)
user = login_user(mongo, username, password)
if not user:
raise APIError('Incorrect username/password', status_code=409)
return jsonify(user)
@app.route('/create_user', methods=['POST', 'OPTIONS'])
@access_control
def new_user():
username = request.json.get('username')
password = request.json.get('password')
permission = request.json.get('permission') # value of 0-2
if username is None or password is None or permission is None:
raise APIError('Missing username, password or permission', status_code=409)
try:
user = create_user(mongo, username, password, permission)
except AssertionError as e:
raise APIError(e.args[0])
return jsonify(user)
@app.route('/', defaults={'path': ''})
@app.route('/<path:path>', methods=['GET', 'POST', 'PUT', 'DELETE', 'OPTIONS'])
@access_control
def forward(path):
app.logger.info('Forwarding route: %s method: %s' % (path, request.method))
forwarder = path.split('/')[0]
if forwarder not in mapping:
raise APIError('Path not found in mapping', 401)
res, code = mapping[forwarder].make_request(
path, request.method, request.data, copy_headers())
resp = jsonify(res)
resp.status_code = code
return resp
@app.errorhandler(APIError)
def handle_api_error(e):
response = jsonify(e.to_dict())
response.status_code = e.status_code
return response
| 28.975 | 102 | 0.681622 | from functools import wraps
import os
from client import AuthDecorator, HttpClient
from flask import Flask, jsonify, request, g
from flask_pymongo import PyMongo
from auth import create_user, get_user_by_token, login_user
from errors import APIError
app = Flask(__name__)
mongo_uri = 'mongodb://{}:{}@ds259325.mlab.com:59325/zql'
app.config['MONGO_URI'] = mongo_uri.format(os.environ['ZQL_MONGO_USER'], os.environ['ZQL_MONGO_PASS'])
mongo = PyMongo(app)
grammar = AuthDecorator(HttpClient('http://127.0.0.1:2666/'))
interpreter = AuthDecorator(HttpClient('http://127.0.0.1:2020/'))
postprocessing = AuthDecorator(HttpClient('http://127.0.0.1:2015/'))
mapping = {
'interpret': interpreter,
'keywords': interpreter,
'grammar': grammar,
'grammars': grammar,
'event': postprocessing,
'events': postprocessing,
}
def copy_headers():
headers = {}
for header in request.headers.keys():
headers[header] = request.headers.get(header)
return headers
# For now, just add this to POST request handlers
def access_control(func):
@wraps(func)
def wrapper(*args, **kwargs):
if request.method == 'OPTIONS':
resp = Flask.make_default_options_response(app)
resp.headers['Access-Control-Allow-Headers'] = 'Content-Type,token'
resp.headers['Content-Type'] = 'application/json'
else:
resp = func(*args, **kwargs)
resp.headers['Access-Control-Allow-Origin'] = '*'
return resp
return wrapper
@app.route('/login', methods=['POST', 'OPTIONS'])
@access_control
def login():
username = request.json.get('username')
password = request.json.get('password')
if username is None or password is None:
raise APIError('Missing username or password', status_code=409)
user = login_user(mongo, username, password)
if not user:
raise APIError('Incorrect username/password', status_code=409)
return jsonify(user)
@app.route('/create_user', methods=['POST', 'OPTIONS'])
@access_control
def new_user():
username = request.json.get('username')
password = request.json.get('password')
permission = request.json.get('permission') # value of 0-2
if username is None or password is None or permission is None:
raise APIError('Missing username, password or permission', status_code=409)
try:
user = create_user(mongo, username, password, permission)
except AssertionError as e:
raise APIError(e.args[0])
return jsonify(user)
@app.route('/', defaults={'path': ''})
@app.route('/<path:path>', methods=['GET', 'POST', 'PUT', 'DELETE', 'OPTIONS'])
@access_control
def forward(path):
app.logger.info('Forwarding route: %s method: %s' % (path, request.method))
forwarder = path.split('/')[0]
if forwarder not in mapping:
raise APIError('Path not found in mapping', 401)
res, code = mapping[forwarder].make_request(
path, request.method, request.data, copy_headers())
resp = jsonify(res)
resp.status_code = code
return resp
@app.errorhandler(APIError)
def handle_api_error(e):
response = jsonify(e.to_dict())
response.status_code = e.status_code
return response
| true | true |
f72954110ae79bca59ed580f3fd05ba732181949 | 912 | py | Python | test/test_devices_location.py | metacore-io/metacore-api-client-python | 37d6127442e7a56deeedb88bf50b83e5d24fa7b0 | [
"MIT"
] | null | null | null | test/test_devices_location.py | metacore-io/metacore-api-client-python | 37d6127442e7a56deeedb88bf50b83e5d24fa7b0 | [
"MIT"
] | null | null | null | test/test_devices_location.py | metacore-io/metacore-api-client-python | 37d6127442e7a56deeedb88bf50b83e5d24fa7b0 | [
"MIT"
] | null | null | null | # coding: utf-8
"""
Metacore IoT Object Storage API
Metacore Object Storage - IOT Core Services # noqa: E501
OpenAPI spec version: 1.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import metacore_api_python_cli
from models.devices_location import DevicesLocation # noqa: E501
from metacore_api_python_cli.rest import ApiException
class TestDevicesLocation(unittest.TestCase):
"""DevicesLocation unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testDevicesLocation(self):
"""Test DevicesLocation"""
# FIXME: construct object with mandatory attributes with example values
# model = metacore_api_python_cli.models.devices_location.DevicesLocation() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| 22.8 | 97 | 0.712719 |
from __future__ import absolute_import
import unittest
import metacore_api_python_cli
from models.devices_location import DevicesLocation
from metacore_api_python_cli.rest import ApiException
class TestDevicesLocation(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def testDevicesLocation(self):
s
if __name__ == '__main__':
unittest.main()
| true | true |
f72955f215fe61b946fdd28a4d4aa254ec154e6f | 12,150 | py | Python | sdks/python/client/argo_workflows/model/github_com_argoproj_labs_argo_dataflow_api_v1alpha1_http_sink.py | roofurmston/argo-workflows | 79a95f223396ecab408d831781ab2d38d1fa6de0 | [
"Apache-2.0"
] | 7,643 | 2017-08-22T22:10:45.000Z | 2021-02-09T17:13:12.000Z | sdks/python/client/argo_workflows/model/github_com_argoproj_labs_argo_dataflow_api_v1alpha1_http_sink.py | roofurmston/argo-workflows | 79a95f223396ecab408d831781ab2d38d1fa6de0 | [
"Apache-2.0"
] | 4,183 | 2017-08-22T22:45:29.000Z | 2021-02-09T17:41:29.000Z | sdks/python/client/argo_workflows/model/github_com_argoproj_labs_argo_dataflow_api_v1alpha1_http_sink.py | isubasinghe/argo-workflows | 1a6e94f1d490e2265c977514d698a1ca70e14fe3 | [
"Apache-2.0"
] | 1,524 | 2017-08-22T22:10:53.000Z | 2021-02-09T16:26:16.000Z | """
Argo Server API
You can get examples of requests and responses by using the CLI with `--gloglevel=9`, e.g. `argo list --gloglevel=9` # noqa: E501
The version of the OpenAPI document: VERSION
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from argo_workflows.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
from ..model_utils import OpenApiModel
from argo_workflows.exceptions import ApiAttributeError
def lazy_import():
from argo_workflows.model.github_com_argoproj_labs_argo_dataflow_api_v1alpha1_http_header import GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader
globals()['GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader'] = GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader
class GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPSink(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'headers': ([GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader],), # noqa: E501
'insecure_skip_verify': (bool,), # noqa: E501
'url': (str,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'headers': 'headers', # noqa: E501
'insecure_skip_verify': 'insecureSkipVerify', # noqa: E501
'url': 'url', # noqa: E501
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs): # noqa: E501
"""GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPSink - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
headers ([GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader]): [optional] # noqa: E501
insecure_skip_verify (bool): [optional] # noqa: E501
url (str): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPSink - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
headers ([GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader]): [optional] # noqa: E501
insecure_skip_verify (bool): [optional] # noqa: E501
url (str): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes.")
| 45 | 155 | 0.586091 |
import re
import sys
from argo_workflows.model_utils import (
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
from ..model_utils import OpenApiModel
from argo_workflows.exceptions import ApiAttributeError
def lazy_import():
from argo_workflows.model.github_com_argoproj_labs_argo_dataflow_api_v1alpha1_http_header import GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader
globals()['GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader'] = GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader
class GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPSink(ModelNormal):
allowed_values = {
}
validations = {
}
@cached_property
def additional_properties_type():
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,)
_nullable = False
@cached_property
def openapi_types():
lazy_import()
return {
'headers': ([GithubComArgoprojLabsArgoDataflowApiV1alpha1HTTPHeader],),
'insecure_skip_verify': (bool,),
'url': (str,),
}
@cached_property
def discriminator():
return None
attribute_map = {
'headers': 'headers',
'insecure_skip_verify': 'insecureSkipVerify',
'url': 'url',
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs):
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
continue
setattr(self, var_name, var_value)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes.")
| true | true |
f729566fe91c9de71b375817c0d66671b3a74995 | 1,517 | py | Python | tagger/build_data.py | jtraviesor/alfred-tf-trainer | 9747d24bef418415a31abfe0c9982d2f1d9d8298 | [
"MIT"
] | 3 | 2017-11-18T11:41:46.000Z | 2020-02-13T19:22:28.000Z | tagger/build_data.py | jtraviesor/alfred-tf-trainer | 9747d24bef418415a31abfe0c9982d2f1d9d8298 | [
"MIT"
] | 4 | 2017-09-01T05:28:49.000Z | 2017-11-20T05:27:49.000Z | tagger/build_data.py | jtraviesor/alfred-tf-trainer | 9747d24bef418415a31abfe0c9982d2f1d9d8298 | [
"MIT"
] | 1 | 2018-06-08T03:55:32.000Z | 2018-06-08T03:55:32.000Z | from config import config
from data_utils import CoNLLDataset, get_vocabs, UNK, NUM, \
get_glove_vocab, write_vocab, load_vocab, get_char_vocab, \
export_trimmed_glove_vectors, get_processing_word
def build_data(config):
"""
Procedure to build data
Args:
config: defines attributes needed in the function
Returns:
creates vocab files from the datasets
creates a npz embedding file from trimmed glove vectors
"""
processing_word = get_processing_word(lowercase=config.lowercase)
# Generators
dev = CoNLLDataset(config.dev_filename, processing_word)
test = CoNLLDataset(config.test_filename, processing_word)
train = CoNLLDataset(config.train_filename, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.glove_filename)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.words_filename)
write_vocab(vocab_tags, config.tags_filename)
# Trim GloVe Vectors
vocab = load_vocab(config.words_filename)
export_trimmed_glove_vectors(vocab, config.glove_filename,
config.trimmed_filename, config.dim)
# Build and save char vocab
train = CoNLLDataset(config.train_filename)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.chars_filename)
if __name__ == "__main__":
build_data(config) | 31.604167 | 69 | 0.723797 | from config import config
from data_utils import CoNLLDataset, get_vocabs, UNK, NUM, \
get_glove_vocab, write_vocab, load_vocab, get_char_vocab, \
export_trimmed_glove_vectors, get_processing_word
def build_data(config):
processing_word = get_processing_word(lowercase=config.lowercase)
dev = CoNLLDataset(config.dev_filename, processing_word)
test = CoNLLDataset(config.test_filename, processing_word)
train = CoNLLDataset(config.train_filename, processing_word)
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.glove_filename)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
write_vocab(vocab, config.words_filename)
write_vocab(vocab_tags, config.tags_filename)
vocab = load_vocab(config.words_filename)
export_trimmed_glove_vectors(vocab, config.glove_filename,
config.trimmed_filename, config.dim)
train = CoNLLDataset(config.train_filename)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.chars_filename)
if __name__ == "__main__":
build_data(config) | true | true |
f72958c3092e5fbc2e266cb6c554d971f5957e0b | 204 | py | Python | hls4ml/writer/__init__.py | ngpaladi/hls4ml | 54861d0f611ee72f779a8f739b5fac571f3e79c2 | [
"Apache-2.0"
] | 1 | 2022-02-05T14:20:22.000Z | 2022-02-05T14:20:22.000Z | hls4ml/writer/__init__.py | ngpaladi/hls4ml | 54861d0f611ee72f779a8f739b5fac571f3e79c2 | [
"Apache-2.0"
] | 2 | 2020-12-04T15:38:30.000Z | 2021-04-27T13:54:43.000Z | hls4ml/writer/__init__.py | ngpaladi/hls4ml | 54861d0f611ee72f779a8f739b5fac571f3e79c2 | [
"Apache-2.0"
] | 2 | 2021-02-16T21:15:53.000Z | 2021-04-01T03:51:52.000Z | from __future__ import absolute_import
from hls4ml.writer.writers import Writer, register_writer, get_writer
from hls4ml.writer.vivado_writer import VivadoWriter
register_writer('Vivado', VivadoWriter)
| 29.142857 | 69 | 0.857843 | from __future__ import absolute_import
from hls4ml.writer.writers import Writer, register_writer, get_writer
from hls4ml.writer.vivado_writer import VivadoWriter
register_writer('Vivado', VivadoWriter)
| true | true |
f7295938f7a5c8eb813df1e080d84cb28c749497 | 1,628 | py | Python | rally/cli/commands/info.py | LorenzoBianconi/rally | 2bbd7ee590cca048fb4ad6a8eefc484989979ff8 | [
"Apache-2.0"
] | 1 | 2021-09-29T02:16:09.000Z | 2021-09-29T02:16:09.000Z | rally/cli/commands/info.py | noah8713/rally-ovs | 2434787c2cf4ca267108966c4ddc55ded3c333d9 | [
"Apache-2.0"
] | 1 | 2020-07-14T11:29:31.000Z | 2020-07-14T11:29:31.000Z | rally/cli/commands/info.py | noah8713/rally-ovs | 2434787c2cf4ca267108966c4ddc55ded3c333d9 | [
"Apache-2.0"
] | 1 | 2020-07-02T01:33:48.000Z | 2020-07-02T01:33:48.000Z | # Copyright 2014: Mirantis Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from __future__ import print_function
from rally.cli import cliutils
from rally.cli.commands import plugin
class InfoCommands(object):
"""[Deprecated since 0.1.1] Allows you to get quick doc of rally entities.
"""
@cliutils.args("--query", dest="query", type=str, help="Search query.")
def find(self, query):
"""Search for an entity that matches the query and print info about it.
:param query: search query.
"""
print("This command was deprecated, and will be removed in 0.2.0 use:")
print("rally plugin show %s" % query)
plugin.PluginCommands().show(query)
return 1
def list(self):
"""List main entities in Rally for which rally info find works.
Lists task scenario groups, deploy engines and server providers.
"""
print("This command was deprecated, and will be removed in 0.2.0 use:")
print("rally plugin list")
plugin.PluginCommands().list()
return 1
| 33.22449 | 79 | 0.675676 |
from __future__ import print_function
from rally.cli import cliutils
from rally.cli.commands import plugin
class InfoCommands(object):
@cliutils.args("--query", dest="query", type=str, help="Search query.")
def find(self, query):
print("This command was deprecated, and will be removed in 0.2.0 use:")
print("rally plugin show %s" % query)
plugin.PluginCommands().show(query)
return 1
def list(self):
print("This command was deprecated, and will be removed in 0.2.0 use:")
print("rally plugin list")
plugin.PluginCommands().list()
return 1
| true | true |
f7295961f8b3fe13c736ae75dbcee162064cec07 | 6,096 | py | Python | qa/rpc-tests/proxy_test.py | andimion666/OBX | fa1405ff9af31514cc1e126beeba56bb9655615f | [
"MIT"
] | null | null | null | qa/rpc-tests/proxy_test.py | andimion666/OBX | fa1405ff9af31514cc1e126beeba56bb9655615f | [
"MIT"
] | null | null | null | qa/rpc-tests/proxy_test.py | andimion666/OBX | fa1405ff9af31514cc1e126beeba56bb9655615f | [
"MIT"
] | null | null | null | #!/usr/bin/env python2
# Copyright (c) 2015 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
import socket
import traceback, sys
from binascii import hexlify
import time, os
from socks5 import Socks5Configuration, Socks5Command, Socks5Server, AddressType
from test_framework import BitcoinTestFramework
from util import *
'''
Test plan:
- Start bitcoind's with different proxy configurations
- Use addnode to initiate connections
- Verify that proxies are connected to, and the right connection command is given
- Proxy configurations to test on bitcoind side:
- `-proxy` (proxy everything)
- `-onion` (proxy just onions)
- `-proxyrandomize` Circuit randomization
- Proxy configurations to test on proxy side,
- support no authentication (other proxy)
- support no authentication + user/pass authentication (Tor)
- proxy on IPv6
- Create various proxies (as threads)
- Create bitcoinds that connect to them
- Manipulate the bitcoinds using addnode (onetry) an observe effects
addnode connect to IPv4
addnode connect to IPv6
addnode connect to onion
addnode connect to generic DNS name
'''
class ProxyTest(BitcoinTestFramework):
def __init__(self):
# Create two proxies on different ports
# ... one unauthenticated
self.conf1 = Socks5Configuration()
self.conf1.addr = ('127.0.0.1', 13000 + (os.getpid() % 1000))
self.conf1.unauth = True
self.conf1.auth = False
# ... one supporting authenticated and unauthenticated (Tor)
self.conf2 = Socks5Configuration()
self.conf2.addr = ('127.0.0.1', 14000 + (os.getpid() % 1000))
self.conf2.unauth = True
self.conf2.auth = True
# ... one on IPv6 with similar configuration
self.conf3 = Socks5Configuration()
self.conf3.af = socket.AF_INET6
self.conf3.addr = ('::1', 15000 + (os.getpid() % 1000))
self.conf3.unauth = True
self.conf3.auth = True
self.serv1 = Socks5Server(self.conf1)
self.serv1.start()
self.serv2 = Socks5Server(self.conf2)
self.serv2.start()
self.serv3 = Socks5Server(self.conf3)
self.serv3.start()
def setup_nodes(self):
# Note: proxies are not used to connect to local nodes
# this is because the proxy to use is based on CService.GetNetwork(), which return NET_UNROUTABLE for localhost
return start_nodes(4, self.options.tmpdir, extra_args=[
['-listen', '-debug=net', '-debug=proxy', '-proxy=%s:%i' % (self.conf1.addr),'-proxyrandomize=1'],
['-listen', '-debug=net', '-debug=proxy', '-proxy=%s:%i' % (self.conf1.addr),'-onion=%s:%i' % (self.conf2.addr),'-proxyrandomize=0'],
['-listen', '-debug=net', '-debug=proxy', '-proxy=%s:%i' % (self.conf2.addr),'-proxyrandomize=1'],
['-listen', '-debug=net', '-debug=proxy', '-proxy=[%s]:%i' % (self.conf3.addr),'-proxyrandomize=0']
])
def node_test(self, node, proxies, auth):
rv = []
# Test: outgoing IPv4 connection through node
node.addnode("15.61.23.23:1234", "onetry")
cmd = proxies[0].queue.get()
assert(isinstance(cmd, Socks5Command))
# Note: bitcoind's SOCKS5 implementation only sends atyp DOMAINNAME, even if connecting directly to IPv4/IPv6
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "15.61.23.23")
assert_equal(cmd.port, 1234)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
# Test: outgoing IPv6 connection through node
node.addnode("[1233:3432:2434:2343:3234:2345:6546:4534]:5443", "onetry")
cmd = proxies[1].queue.get()
assert(isinstance(cmd, Socks5Command))
# Note: bitcoind's SOCKS5 implementation only sends atyp DOMAINNAME, even if connecting directly to IPv4/IPv6
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "1233:3432:2434:2343:3234:2345:6546:4534")
assert_equal(cmd.port, 5443)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
# Test: outgoing onion connection through node
node.addnode("Orboxvj7kcklujarx.onion:9116", "onetry")
cmd = proxies[2].queue.get()
assert(isinstance(cmd, Socks5Command))
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "Orboxvj7kcklujarx.onion")
assert_equal(cmd.port, 9116)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
# Test: outgoing DNS name connection through node
node.addnode("node.noumenon:8333", "onetry")
cmd = proxies[3].queue.get()
assert(isinstance(cmd, Socks5Command))
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "node.noumenon")
assert_equal(cmd.port, 8333)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
return rv
def run_test(self):
# basic -proxy
self.node_test(self.nodes[0], [self.serv1, self.serv1, self.serv1, self.serv1], False)
# -proxy plus -onion
self.node_test(self.nodes[1], [self.serv1, self.serv1, self.serv2, self.serv1], False)
# -proxy plus -onion, -proxyrandomize
rv = self.node_test(self.nodes[2], [self.serv2, self.serv2, self.serv2, self.serv2], True)
# Check that credentials as used for -proxyrandomize connections are unique
credentials = set((x.username,x.password) for x in rv)
assert_equal(len(credentials), 4)
# proxy on IPv6 localhost
self.node_test(self.nodes[3], [self.serv3, self.serv3, self.serv3, self.serv3], False)
if __name__ == '__main__':
ProxyTest().main()
| 41.753425 | 145 | 0.652887 |
import socket
import traceback, sys
from binascii import hexlify
import time, os
from socks5 import Socks5Configuration, Socks5Command, Socks5Server, AddressType
from test_framework import BitcoinTestFramework
from util import *
class ProxyTest(BitcoinTestFramework):
def __init__(self):
self.conf1 = Socks5Configuration()
self.conf1.addr = ('127.0.0.1', 13000 + (os.getpid() % 1000))
self.conf1.unauth = True
self.conf1.auth = False
self.conf2 = Socks5Configuration()
self.conf2.addr = ('127.0.0.1', 14000 + (os.getpid() % 1000))
self.conf2.unauth = True
self.conf2.auth = True
self.conf3 = Socks5Configuration()
self.conf3.af = socket.AF_INET6
self.conf3.addr = ('::1', 15000 + (os.getpid() % 1000))
self.conf3.unauth = True
self.conf3.auth = True
self.serv1 = Socks5Server(self.conf1)
self.serv1.start()
self.serv2 = Socks5Server(self.conf2)
self.serv2.start()
self.serv3 = Socks5Server(self.conf3)
self.serv3.start()
def setup_nodes(self):
return start_nodes(4, self.options.tmpdir, extra_args=[
['-listen', '-debug=net', '-debug=proxy', '-proxy=%s:%i' % (self.conf1.addr),'-proxyrandomize=1'],
['-listen', '-debug=net', '-debug=proxy', '-proxy=%s:%i' % (self.conf1.addr),'-onion=%s:%i' % (self.conf2.addr),'-proxyrandomize=0'],
['-listen', '-debug=net', '-debug=proxy', '-proxy=%s:%i' % (self.conf2.addr),'-proxyrandomize=1'],
['-listen', '-debug=net', '-debug=proxy', '-proxy=[%s]:%i' % (self.conf3.addr),'-proxyrandomize=0']
])
def node_test(self, node, proxies, auth):
rv = []
node.addnode("15.61.23.23:1234", "onetry")
cmd = proxies[0].queue.get()
assert(isinstance(cmd, Socks5Command))
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "15.61.23.23")
assert_equal(cmd.port, 1234)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
# Test: outgoing IPv6 connection through node
node.addnode("[1233:3432:2434:2343:3234:2345:6546:4534]:5443", "onetry")
cmd = proxies[1].queue.get()
assert(isinstance(cmd, Socks5Command))
# Note: bitcoind's SOCKS5 implementation only sends atyp DOMAINNAME, even if connecting directly to IPv4/IPv6
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "1233:3432:2434:2343:3234:2345:6546:4534")
assert_equal(cmd.port, 5443)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
node.addnode("Orboxvj7kcklujarx.onion:9116", "onetry")
cmd = proxies[2].queue.get()
assert(isinstance(cmd, Socks5Command))
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "Orboxvj7kcklujarx.onion")
assert_equal(cmd.port, 9116)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
node.addnode("node.noumenon:8333", "onetry")
cmd = proxies[3].queue.get()
assert(isinstance(cmd, Socks5Command))
assert_equal(cmd.atyp, AddressType.DOMAINNAME)
assert_equal(cmd.addr, "node.noumenon")
assert_equal(cmd.port, 8333)
if not auth:
assert_equal(cmd.username, None)
assert_equal(cmd.password, None)
rv.append(cmd)
return rv
def run_test(self):
self.node_test(self.nodes[0], [self.serv1, self.serv1, self.serv1, self.serv1], False)
self.node_test(self.nodes[1], [self.serv1, self.serv1, self.serv2, self.serv1], False)
rv = self.node_test(self.nodes[2], [self.serv2, self.serv2, self.serv2, self.serv2], True)
credentials = set((x.username,x.password) for x in rv)
assert_equal(len(credentials), 4)
self.node_test(self.nodes[3], [self.serv3, self.serv3, self.serv3, self.serv3], False)
if __name__ == '__main__':
ProxyTest().main()
| true | true |
f7295a30c9b7f3ca58b9c2c3b7b2714e4c9b5bfd | 1,670 | py | Python | paradocx/util.py | yougov/paradocx | e525b3cdb54af6896fc941ef03c2be4275443bcf | [
"MIT"
] | 3 | 2018-07-24T19:17:14.000Z | 2020-02-09T23:49:03.000Z | paradocx/util.py | jaraco/paradocx | 123405b97fc82f60f4bd47cae465802f8063024b | [
"MIT"
] | 4 | 2018-03-03T17:45:17.000Z | 2021-07-06T02:51:30.000Z | paradocx/util.py | jaraco/paradocx | 123405b97fc82f60f4bd47cae465802f8063024b | [
"MIT"
] | 2 | 2021-02-13T17:21:24.000Z | 2022-03-26T07:43:00.000Z | from lxml.etree import fromstring, tostring
from lxml import builder
from openpack.basepack import ooxml_namespaces
docx_namespaces = {
'w': "http://schemas.openxmlformats.org/wordprocessingml/2006/main",
'r': 'http://schemas.openxmlformats.org/officeDocument/2006/relationships',
've': 'http://schemas.openxmlformats.org/markup-compatibility/2006',
'wp': ('http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing'),
'v': 'urn:schemas-microsoft-com:vml',
}
docx_namespaces.update(ooxml_namespaces)
def expand_namespace(tag):
"""
>>> expand_namespace('w:document')
'{http://schemas.openxmlformats.org/wordprocessingml/2006/main}document'
"""
namespace, sep, tag = tag.rpartition(':')
fmt = '{%(namespace)s}%(tag)s' if namespace else '%(tag)s'
namespace = docx_namespaces[namespace]
return fmt % vars()
class ElementMaker(builder.ElementMaker):
def __getitem__(self, name):
return "%s%s" % (self._namespace, name)
w = ElementMaker(namespace=docx_namespaces['w'], nsmap=docx_namespaces)
dcterms = ElementMaker(namespace=docx_namespaces['dcterms'], nsmap=docx_namespaces)
class TemplateSource(object):
template = None
def __init__(self, variables, encoding='utf-8'):
self.variables = variables
self.xml = self._from_template()
self.element = self._to_element()
def _from_template(self):
"""Use self.template and self.variables to generate XML content."""
raise NotImplementedError
def _to_element(self):
return fromstring(self.xml)
def __str__(self):
return tostring(self.element, encoding=self.encoding)
| 31.509434 | 85 | 0.705988 | from lxml.etree import fromstring, tostring
from lxml import builder
from openpack.basepack import ooxml_namespaces
docx_namespaces = {
'w': "http://schemas.openxmlformats.org/wordprocessingml/2006/main",
'r': 'http://schemas.openxmlformats.org/officeDocument/2006/relationships',
've': 'http://schemas.openxmlformats.org/markup-compatibility/2006',
'wp': ('http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing'),
'v': 'urn:schemas-microsoft-com:vml',
}
docx_namespaces.update(ooxml_namespaces)
def expand_namespace(tag):
namespace, sep, tag = tag.rpartition(':')
fmt = '{%(namespace)s}%(tag)s' if namespace else '%(tag)s'
namespace = docx_namespaces[namespace]
return fmt % vars()
class ElementMaker(builder.ElementMaker):
def __getitem__(self, name):
return "%s%s" % (self._namespace, name)
w = ElementMaker(namespace=docx_namespaces['w'], nsmap=docx_namespaces)
dcterms = ElementMaker(namespace=docx_namespaces['dcterms'], nsmap=docx_namespaces)
class TemplateSource(object):
template = None
def __init__(self, variables, encoding='utf-8'):
self.variables = variables
self.xml = self._from_template()
self.element = self._to_element()
def _from_template(self):
raise NotImplementedError
def _to_element(self):
return fromstring(self.xml)
def __str__(self):
return tostring(self.element, encoding=self.encoding)
| true | true |
f7295a49a03b25e52668055172a342ad19df66e4 | 5,865 | py | Python | tests/base/test_multitenancy.py | santhoshpkumar/rasa-nul-ramsi | a9334e11400ac75d6eb4dee6bafe55ccb3c51f68 | [
"Apache-2.0"
] | 1,304 | 2017-11-15T08:33:44.000Z | 2022-03-30T09:05:12.000Z | tests/base/test_multitenancy.py | siddas27/rasa_nlu | 91d874e834dda3be91bdfaba944ac772192f207e | [
"Apache-2.0"
] | 94 | 2017-11-15T11:44:50.000Z | 2021-12-19T11:48:11.000Z | tests/base/test_multitenancy.py | siddas27/rasa_nlu | 91d874e834dda3be91bdfaba944ac772192f207e | [
"Apache-2.0"
] | 397 | 2017-11-11T09:02:01.000Z | 2022-03-18T08:04:38.000Z | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import tempfile
import pytest
from treq.testing import StubTreq
from rasa_nlu import config
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.data_router import DataRouter
from rasa_nlu.model import Trainer
from rasa_nlu.server import RasaNLU
from tests.utilities import ResponseTest
@pytest.fixture(scope="module")
def app(component_builder):
"""Use IResource interface of Klein to mock Rasa HTTP server.
:param component_builder:
:return:
"""
if "TRAVIS_BUILD_DIR" in os.environ:
root_dir = os.environ["TRAVIS_BUILD_DIR"]
else:
root_dir = os.getcwd()
_, nlu_log_file = tempfile.mkstemp(suffix="_rasa_nlu_logs.json")
train_models(component_builder,
os.path.join(root_dir, "data/examples/rasa/demo-rasa.json"))
router = DataRouter(os.path.join(root_dir, "test_projects"))
rasa = RasaNLU(router, logfile=nlu_log_file, testing=True)
return StubTreq(rasa.app.resource())
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse?q=food&project=test_project_mitie",
{"entities": [], "intent": "affirm", "text": "food"}
),
ResponseTest(
"http://dummy-uri/parse?q=food&project=test_project_mitie_sklearn",
{"entities": [], "intent": "restaurant_search", "text": "food"}
),
ResponseTest(
"http://dummy-uri/parse?q=food&project=test_project_spacy_sklearn",
{"entities": [], "intent": "restaurant_search", "text": "food"}
),
])
@pytest.inlineCallbacks
def test_get_parse(app, response_test):
response = yield app.get(response_test.endpoint)
rjs = yield response.json()
assert response.code == 200
assert all(prop in rjs for prop in ['entities', 'intent', 'text'])
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse?q=food",
{"error": "No project found with name 'default'."}
),
ResponseTest(
"http://dummy-uri/parse?q=food&project=umpalumpa",
{"error": "No project found with name 'umpalumpa'."}
)
])
@pytest.inlineCallbacks
def test_get_parse_invalid_model(app, response_test):
response = yield app.get(response_test.endpoint)
rjs = yield response.json()
assert response.code == 404
assert rjs.get("error").startswith(response_test.expected_response["error"])
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse",
{"entities": [], "intent": "affirm", "text": "food"},
payload={"q": "food", "project": "test_project_mitie"}
),
ResponseTest(
"http://dummy-uri/parse",
{"entities": [], "intent": "restaurant_search", "text": "food"},
payload={"q": "food", "project": "test_project_mitie_sklearn"}
),
ResponseTest(
"http://dummy-uri/parse",
{"entities": [], "intent": "restaurant_search", "text": "food"},
payload={"q": "food", "project": "test_project_spacy_sklearn"}
),
])
@pytest.inlineCallbacks
def test_post_parse(app, response_test):
response = yield app.post(response_test.endpoint, json=response_test.payload)
rjs = yield response.json()
assert response.code == 200
assert all(prop in rjs for prop in ['entities', 'intent', 'text'])
@pytest.inlineCallbacks
def test_post_parse_specific_model(app):
status = yield app.get("http://dummy-uri/status")
sjs = yield status.json()
project = sjs["available_projects"]["test_project_spacy_sklearn"]
model = project["available_models"][0]
query = ResponseTest("http://dummy-uri/parse",
{"entities": [], "intent": "affirm", "text": "food"},
payload={"q": "food",
"project": "test_project_spacy_sklearn",
"model": model})
response = yield app.post(query.endpoint, json=query.payload)
assert response.code == 200
# check that that model now is loaded in the server
status = yield app.get("http://dummy-uri/status")
sjs = yield status.json()
project = sjs["available_projects"]["test_project_spacy_sklearn"]
assert model in project["loaded_models"]
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse",
{"error": "No project found with name 'default'."},
payload={"q": "food"}
),
ResponseTest(
"http://dummy-uri/parse",
{"error": "No project found with name 'umpalumpa'."},
payload={"q": "food", "project": "umpalumpa"}
),
])
@pytest.inlineCallbacks
def test_post_parse_invalid_model(app, response_test):
response = yield app.post(response_test.endpoint, json=response_test.payload)
rjs = yield response.json()
assert response.code == 404
assert rjs.get("error").startswith(response_test.expected_response["error"])
def train_models(component_builder, data):
# Retrain different multitenancy models
def train(cfg_name, project_name):
from rasa_nlu.train import create_persistor
from rasa_nlu import training_data
cfg = config.load(cfg_name)
trainer = Trainer(cfg, component_builder)
training_data = training_data.load_data(data)
trainer.train(training_data)
trainer.persist("test_projects", project_name=project_name)
train("sample_configs/config_spacy.yml", "test_project_spacy_sklearn")
train("sample_configs/config_mitie.yml", "test_project_mitie")
train("sample_configs/config_mitie_sklearn.yml", "test_project_mitie_sklearn")
| 34.5 | 82 | 0.656777 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import tempfile
import pytest
from treq.testing import StubTreq
from rasa_nlu import config
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.data_router import DataRouter
from rasa_nlu.model import Trainer
from rasa_nlu.server import RasaNLU
from tests.utilities import ResponseTest
@pytest.fixture(scope="module")
def app(component_builder):
if "TRAVIS_BUILD_DIR" in os.environ:
root_dir = os.environ["TRAVIS_BUILD_DIR"]
else:
root_dir = os.getcwd()
_, nlu_log_file = tempfile.mkstemp(suffix="_rasa_nlu_logs.json")
train_models(component_builder,
os.path.join(root_dir, "data/examples/rasa/demo-rasa.json"))
router = DataRouter(os.path.join(root_dir, "test_projects"))
rasa = RasaNLU(router, logfile=nlu_log_file, testing=True)
return StubTreq(rasa.app.resource())
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse?q=food&project=test_project_mitie",
{"entities": [], "intent": "affirm", "text": "food"}
),
ResponseTest(
"http://dummy-uri/parse?q=food&project=test_project_mitie_sklearn",
{"entities": [], "intent": "restaurant_search", "text": "food"}
),
ResponseTest(
"http://dummy-uri/parse?q=food&project=test_project_spacy_sklearn",
{"entities": [], "intent": "restaurant_search", "text": "food"}
),
])
@pytest.inlineCallbacks
def test_get_parse(app, response_test):
response = yield app.get(response_test.endpoint)
rjs = yield response.json()
assert response.code == 200
assert all(prop in rjs for prop in ['entities', 'intent', 'text'])
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse?q=food",
{"error": "No project found with name 'default'."}
),
ResponseTest(
"http://dummy-uri/parse?q=food&project=umpalumpa",
{"error": "No project found with name 'umpalumpa'."}
)
])
@pytest.inlineCallbacks
def test_get_parse_invalid_model(app, response_test):
response = yield app.get(response_test.endpoint)
rjs = yield response.json()
assert response.code == 404
assert rjs.get("error").startswith(response_test.expected_response["error"])
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse",
{"entities": [], "intent": "affirm", "text": "food"},
payload={"q": "food", "project": "test_project_mitie"}
),
ResponseTest(
"http://dummy-uri/parse",
{"entities": [], "intent": "restaurant_search", "text": "food"},
payload={"q": "food", "project": "test_project_mitie_sklearn"}
),
ResponseTest(
"http://dummy-uri/parse",
{"entities": [], "intent": "restaurant_search", "text": "food"},
payload={"q": "food", "project": "test_project_spacy_sklearn"}
),
])
@pytest.inlineCallbacks
def test_post_parse(app, response_test):
response = yield app.post(response_test.endpoint, json=response_test.payload)
rjs = yield response.json()
assert response.code == 200
assert all(prop in rjs for prop in ['entities', 'intent', 'text'])
@pytest.inlineCallbacks
def test_post_parse_specific_model(app):
status = yield app.get("http://dummy-uri/status")
sjs = yield status.json()
project = sjs["available_projects"]["test_project_spacy_sklearn"]
model = project["available_models"][0]
query = ResponseTest("http://dummy-uri/parse",
{"entities": [], "intent": "affirm", "text": "food"},
payload={"q": "food",
"project": "test_project_spacy_sklearn",
"model": model})
response = yield app.post(query.endpoint, json=query.payload)
assert response.code == 200
status = yield app.get("http://dummy-uri/status")
sjs = yield status.json()
project = sjs["available_projects"]["test_project_spacy_sklearn"]
assert model in project["loaded_models"]
@pytest.mark.parametrize("response_test", [
ResponseTest(
"http://dummy-uri/parse",
{"error": "No project found with name 'default'."},
payload={"q": "food"}
),
ResponseTest(
"http://dummy-uri/parse",
{"error": "No project found with name 'umpalumpa'."},
payload={"q": "food", "project": "umpalumpa"}
),
])
@pytest.inlineCallbacks
def test_post_parse_invalid_model(app, response_test):
response = yield app.post(response_test.endpoint, json=response_test.payload)
rjs = yield response.json()
assert response.code == 404
assert rjs.get("error").startswith(response_test.expected_response["error"])
def train_models(component_builder, data):
def train(cfg_name, project_name):
from rasa_nlu.train import create_persistor
from rasa_nlu import training_data
cfg = config.load(cfg_name)
trainer = Trainer(cfg, component_builder)
training_data = training_data.load_data(data)
trainer.train(training_data)
trainer.persist("test_projects", project_name=project_name)
train("sample_configs/config_spacy.yml", "test_project_spacy_sklearn")
train("sample_configs/config_mitie.yml", "test_project_mitie")
train("sample_configs/config_mitie_sklearn.yml", "test_project_mitie_sklearn")
| true | true |
f7295b2e8f0955e1ef2f661ebd9f5ac7666ac0d0 | 11,869 | py | Python | mayan/apps/document_indexing/apps.py | bonitobonita24/Mayan-EDMS | 7845fe0e1e83c81f5d227a16116397a3d3883b85 | [
"Apache-2.0"
] | 343 | 2015-01-05T14:19:35.000Z | 2018-12-10T19:07:48.000Z | mayan/apps/document_indexing/apps.py | bonitobonita24/Mayan-EDMS | 7845fe0e1e83c81f5d227a16116397a3d3883b85 | [
"Apache-2.0"
] | 191 | 2015-01-03T00:48:19.000Z | 2018-11-30T09:10:25.000Z | mayan/apps/document_indexing/apps.py | bonitobonita24/Mayan-EDMS | 7845fe0e1e83c81f5d227a16116397a3d3883b85 | [
"Apache-2.0"
] | 114 | 2015-01-08T20:21:05.000Z | 2018-12-10T19:07:53.000Z | from django.apps import apps
from django.db.models.signals import post_delete, post_save, pre_delete
from django.utils.translation import ugettext_lazy as _
from mayan.apps.acls.classes import ModelPermission
from mayan.apps.acls.permissions import permission_acl_edit, permission_acl_view
from mayan.apps.common.apps import MayanAppConfig
from mayan.apps.common.classes import ModelCopy
from mayan.apps.common.menus import (
menu_list_facet, menu_main, menu_object, menu_related, menu_secondary,
menu_setup, menu_tools
)
from mayan.apps.documents.links.document_type_links import link_document_type_list
from mayan.apps.documents.signals import signal_post_initial_document_type
from mayan.apps.events.classes import EventModelRegistry, ModelEventType
from mayan.apps.navigation.classes import SourceColumn
from mayan.apps.rest_api.fields import DynamicSerializerField
from mayan.apps.views.html_widgets import TwoStateWidget
from .events import event_index_template_created, event_index_template_edited
from .handlers import (
handler_create_default_document_index, handler_delete_empty,
handler_index_document, handler_remove_document
)
from .html_widgets import (
get_instance_link, index_instance_item_link, node_level
)
from .links import (
link_document_index_instance_list, link_document_type_index_templates,
link_index_instance_menu, link_index_instance_rebuild,
link_index_instances_reset, link_index_template_setup,
link_index_template_create, link_index_template_document_types,
link_index_template_delete, link_index_template_edit,
link_index_template_list, link_index_template_node_tree_view,
link_index_instances_rebuild, link_index_template_node_create,
link_index_template_node_delete, link_index_template_node_edit
)
from .permissions import (
permission_index_template_delete, permission_index_template_edit,
permission_index_instance_view,
permission_index_template_rebuild, permission_index_template_view
)
class DocumentIndexingApp(MayanAppConfig):
app_namespace = 'indexing'
app_url = 'indexing'
has_rest_api = True
has_tests = True
name = 'mayan.apps.document_indexing'
verbose_name = _('Document indexing')
def ready(self):
super().ready()
Document = apps.get_model(
app_label='documents', model_name='Document'
)
DocumentType = apps.get_model(
app_label='documents', model_name='DocumentType'
)
DocumentIndexInstanceNode = self.get_model(
model_name='DocumentIndexInstanceNode'
)
IndexInstance = self.get_model(model_name='IndexInstance')
IndexInstanceNode = self.get_model(model_name='IndexInstanceNode')
IndexInstanceNodeSearchResult = self.get_model(
model_name='IndexInstanceNodeSearchResult'
)
IndexTemplate = self.get_model(model_name='IndexTemplate')
IndexTemplateNode = self.get_model(model_name='IndexTemplateNode')
DynamicSerializerField.add_serializer(
klass=IndexTemplate,
serializer_class='mayan.apps.document_indexing.serializers.IndexTemplateSerializer'
)
EventModelRegistry.register(model=IndexTemplate)
ModelCopy(
model=IndexTemplateNode, excludes={'parent__isnull': False},
extra_kwargs={'get_or_create': True}
).add_fields(
field_names=(
'index', 'expression', 'enabled', 'link_documents'
),
)
ModelCopy(
model=IndexTemplate, bind_link=True, register_permission=True
).add_fields(
field_names=(
'label', 'slug', 'enabled', 'document_types',
'index_template_nodes'
),
)
ModelEventType.register(
event_types=(
event_index_template_created, event_index_template_edited
), model=IndexTemplate
)
ModelPermission.register(
model=Document, permissions=(
permission_index_instance_view,
)
)
ModelPermission.register(
model=IndexTemplate, permissions=(
permission_acl_edit, permission_acl_view,
permission_index_template_delete,
permission_index_template_edit,
permission_index_instance_view,
permission_index_template_rebuild,
permission_index_template_view,
)
)
ModelPermission.register_inheritance(
model=IndexTemplateNode, related='index'
)
ModelPermission.register_inheritance(
model=IndexInstanceNode, related='index_template_node__index'
)
# Document Index Instance Node
SourceColumn(
func=lambda context: get_instance_link(
index_instance_node=context['object'],
), include_label=True, is_sortable=True, label=_('Level'),
sort_field='value', source=DocumentIndexInstanceNode
)
# Index instance
SourceColumn(
attribute='get_level_count', include_label=True,
label=_('Depth'), source=IndexInstance
)
SourceColumn(
attribute='get_descendants_count', include_label=True,
label=_('Total nodes'), source=IndexInstance
)
SourceColumn(
func=lambda context: context[
'object'
].get_descendants_document_count(
user=context['request'].user
), include_label=True, label=_('Total documents'),
help_text=_(
'Number of unique documents this item contains.'
), source=IndexInstance
)
# Index instance node
column_index_instance_node_level = SourceColumn(
func=lambda context: index_instance_item_link(context['object']),
is_identifier=True, is_sortable=True, label=_('Level'),
sort_field='value', source=IndexInstanceNode
)
column_index_instance_node_level.add_exclude(
source=DocumentIndexInstanceNode
)
column_index_instance_node_level_count = SourceColumn(
attribute='get_level_count', include_label=True,
label=_('Depth'), source=IndexInstanceNode
)
column_index_instance_node_level_count.add_exclude(
source=DocumentIndexInstanceNode
)
column_index_instance_node_count = SourceColumn(
attribute='get_descendants_count',
include_label=True, label=_('Nodes'), source=IndexInstanceNode
)
column_index_instance_node_count.add_exclude(
source=DocumentIndexInstanceNode
)
column_index_instance_node_document_count = SourceColumn(
func=lambda context: context[
'object'
].get_descendants_document_count(
user=context['request'].user
), include_label=True, label=_('Documents'),
help_text=_(
'Number of unique documents this item contains.'
), source=IndexInstanceNode
)
column_index_instance_node_document_count.add_exclude(
source=DocumentIndexInstanceNode
)
SourceColumn(
func=lambda context: index_instance_item_link(context['object']),
is_identifier=True, is_sortable=True, label=_('Level'),
sort_field='value', source=IndexInstanceNodeSearchResult
)
SourceColumn(
attribute='get_full_path', source=IndexInstanceNodeSearchResult
)
# Index template
column_index_label = SourceColumn(
attribute='label', is_identifier=True, is_sortable=True,
source=IndexTemplate
)
column_index_label.add_exclude(source=IndexInstance)
SourceColumn(
attribute='label', is_object_absolute_url=True,
is_identifier=True, is_sortable=True, source=IndexInstance
)
column_index_slug = SourceColumn(
attribute='slug', include_label=True, is_sortable=True,
source=IndexTemplate
)
column_index_slug.add_exclude(source=IndexInstance)
column_index_enabled = SourceColumn(
attribute='enabled', include_label=True, is_sortable=True,
source=IndexTemplate, widget=TwoStateWidget
)
column_index_enabled.add_exclude(source=IndexInstance)
# Index template node
SourceColumn(
func=lambda context: node_level(context['object']),
include_label=True, is_identifier=True, label=_('Level'),
source=IndexTemplateNode
)
SourceColumn(
attribute='enabled', include_label=True, is_sortable=True,
source=IndexTemplateNode, widget=TwoStateWidget
)
SourceColumn(
attribute='link_documents', include_label=True, is_sortable=True,
source=IndexTemplateNode, widget=TwoStateWidget
)
menu_list_facet.bind_links(
links=(
link_document_index_instance_list,
), sources=(Document,)
)
menu_list_facet.bind_links(
links=(link_document_type_index_templates,),
sources=(DocumentType,)
)
menu_list_facet.bind_links(
links=(
link_index_template_document_types,
link_index_template_node_tree_view
), sources=(IndexTemplate,)
)
menu_object.bind_links(
links=(
link_index_template_delete, link_index_template_edit,
link_index_instance_rebuild
), sources=(IndexTemplate,)
)
menu_object.bind_links(
links=(
link_index_template_node_create, link_index_template_node_edit,
link_index_template_node_delete
), sources=(IndexTemplateNode,)
)
menu_main.bind_links(links=(link_index_instance_menu,), position=50)
menu_related.bind_links(
links=(link_index_template_list,),
sources=(
DocumentType, 'documents:document_type_list',
'documents:document_type_create'
)
)
menu_related.bind_links(
links=(link_document_type_list,),
sources=(
IndexTemplate, 'indexing:index_template_list',
'indexing:index_template_create'
)
)
menu_secondary.bind_links(
links=(link_index_template_list, link_index_template_create),
sources=(
IndexTemplate, 'indexing:index_template_list',
'indexing:index_template_create'
)
)
menu_setup.bind_links(links=(link_index_template_setup,))
menu_tools.bind_links(
links=(link_index_instances_rebuild, link_index_instances_reset)
)
post_save.connect(
dispatch_uid='document_indexing_handler_index_document',
receiver=handler_index_document,
sender=Document
)
post_delete.connect(
dispatch_uid='document_indexing_handler_delete_empty',
receiver=handler_delete_empty,
sender=Document
)
pre_delete.connect(
dispatch_uid='document_indexing_handler_remove_document',
receiver=handler_remove_document,
sender=Document
)
signal_post_initial_document_type.connect(
dispatch_uid='document_indexing_handler_create_default_document_index',
receiver=handler_create_default_document_index,
sender=DocumentType
)
| 36.975078 | 95 | 0.656669 | from django.apps import apps
from django.db.models.signals import post_delete, post_save, pre_delete
from django.utils.translation import ugettext_lazy as _
from mayan.apps.acls.classes import ModelPermission
from mayan.apps.acls.permissions import permission_acl_edit, permission_acl_view
from mayan.apps.common.apps import MayanAppConfig
from mayan.apps.common.classes import ModelCopy
from mayan.apps.common.menus import (
menu_list_facet, menu_main, menu_object, menu_related, menu_secondary,
menu_setup, menu_tools
)
from mayan.apps.documents.links.document_type_links import link_document_type_list
from mayan.apps.documents.signals import signal_post_initial_document_type
from mayan.apps.events.classes import EventModelRegistry, ModelEventType
from mayan.apps.navigation.classes import SourceColumn
from mayan.apps.rest_api.fields import DynamicSerializerField
from mayan.apps.views.html_widgets import TwoStateWidget
from .events import event_index_template_created, event_index_template_edited
from .handlers import (
handler_create_default_document_index, handler_delete_empty,
handler_index_document, handler_remove_document
)
from .html_widgets import (
get_instance_link, index_instance_item_link, node_level
)
from .links import (
link_document_index_instance_list, link_document_type_index_templates,
link_index_instance_menu, link_index_instance_rebuild,
link_index_instances_reset, link_index_template_setup,
link_index_template_create, link_index_template_document_types,
link_index_template_delete, link_index_template_edit,
link_index_template_list, link_index_template_node_tree_view,
link_index_instances_rebuild, link_index_template_node_create,
link_index_template_node_delete, link_index_template_node_edit
)
from .permissions import (
permission_index_template_delete, permission_index_template_edit,
permission_index_instance_view,
permission_index_template_rebuild, permission_index_template_view
)
class DocumentIndexingApp(MayanAppConfig):
app_namespace = 'indexing'
app_url = 'indexing'
has_rest_api = True
has_tests = True
name = 'mayan.apps.document_indexing'
verbose_name = _('Document indexing')
def ready(self):
super().ready()
Document = apps.get_model(
app_label='documents', model_name='Document'
)
DocumentType = apps.get_model(
app_label='documents', model_name='DocumentType'
)
DocumentIndexInstanceNode = self.get_model(
model_name='DocumentIndexInstanceNode'
)
IndexInstance = self.get_model(model_name='IndexInstance')
IndexInstanceNode = self.get_model(model_name='IndexInstanceNode')
IndexInstanceNodeSearchResult = self.get_model(
model_name='IndexInstanceNodeSearchResult'
)
IndexTemplate = self.get_model(model_name='IndexTemplate')
IndexTemplateNode = self.get_model(model_name='IndexTemplateNode')
DynamicSerializerField.add_serializer(
klass=IndexTemplate,
serializer_class='mayan.apps.document_indexing.serializers.IndexTemplateSerializer'
)
EventModelRegistry.register(model=IndexTemplate)
ModelCopy(
model=IndexTemplateNode, excludes={'parent__isnull': False},
extra_kwargs={'get_or_create': True}
).add_fields(
field_names=(
'index', 'expression', 'enabled', 'link_documents'
),
)
ModelCopy(
model=IndexTemplate, bind_link=True, register_permission=True
).add_fields(
field_names=(
'label', 'slug', 'enabled', 'document_types',
'index_template_nodes'
),
)
ModelEventType.register(
event_types=(
event_index_template_created, event_index_template_edited
), model=IndexTemplate
)
ModelPermission.register(
model=Document, permissions=(
permission_index_instance_view,
)
)
ModelPermission.register(
model=IndexTemplate, permissions=(
permission_acl_edit, permission_acl_view,
permission_index_template_delete,
permission_index_template_edit,
permission_index_instance_view,
permission_index_template_rebuild,
permission_index_template_view,
)
)
ModelPermission.register_inheritance(
model=IndexTemplateNode, related='index'
)
ModelPermission.register_inheritance(
model=IndexInstanceNode, related='index_template_node__index'
)
SourceColumn(
func=lambda context: get_instance_link(
index_instance_node=context['object'],
), include_label=True, is_sortable=True, label=_('Level'),
sort_field='value', source=DocumentIndexInstanceNode
)
SourceColumn(
attribute='get_level_count', include_label=True,
label=_('Depth'), source=IndexInstance
)
SourceColumn(
attribute='get_descendants_count', include_label=True,
label=_('Total nodes'), source=IndexInstance
)
SourceColumn(
func=lambda context: context[
'object'
].get_descendants_document_count(
user=context['request'].user
), include_label=True, label=_('Total documents'),
help_text=_(
'Number of unique documents this item contains.'
), source=IndexInstance
)
column_index_instance_node_level = SourceColumn(
func=lambda context: index_instance_item_link(context['object']),
is_identifier=True, is_sortable=True, label=_('Level'),
sort_field='value', source=IndexInstanceNode
)
column_index_instance_node_level.add_exclude(
source=DocumentIndexInstanceNode
)
column_index_instance_node_level_count = SourceColumn(
attribute='get_level_count', include_label=True,
label=_('Depth'), source=IndexInstanceNode
)
column_index_instance_node_level_count.add_exclude(
source=DocumentIndexInstanceNode
)
column_index_instance_node_count = SourceColumn(
attribute='get_descendants_count',
include_label=True, label=_('Nodes'), source=IndexInstanceNode
)
column_index_instance_node_count.add_exclude(
source=DocumentIndexInstanceNode
)
column_index_instance_node_document_count = SourceColumn(
func=lambda context: context[
'object'
].get_descendants_document_count(
user=context['request'].user
), include_label=True, label=_('Documents'),
help_text=_(
'Number of unique documents this item contains.'
), source=IndexInstanceNode
)
column_index_instance_node_document_count.add_exclude(
source=DocumentIndexInstanceNode
)
SourceColumn(
func=lambda context: index_instance_item_link(context['object']),
is_identifier=True, is_sortable=True, label=_('Level'),
sort_field='value', source=IndexInstanceNodeSearchResult
)
SourceColumn(
attribute='get_full_path', source=IndexInstanceNodeSearchResult
)
column_index_label = SourceColumn(
attribute='label', is_identifier=True, is_sortable=True,
source=IndexTemplate
)
column_index_label.add_exclude(source=IndexInstance)
SourceColumn(
attribute='label', is_object_absolute_url=True,
is_identifier=True, is_sortable=True, source=IndexInstance
)
column_index_slug = SourceColumn(
attribute='slug', include_label=True, is_sortable=True,
source=IndexTemplate
)
column_index_slug.add_exclude(source=IndexInstance)
column_index_enabled = SourceColumn(
attribute='enabled', include_label=True, is_sortable=True,
source=IndexTemplate, widget=TwoStateWidget
)
column_index_enabled.add_exclude(source=IndexInstance)
SourceColumn(
func=lambda context: node_level(context['object']),
include_label=True, is_identifier=True, label=_('Level'),
source=IndexTemplateNode
)
SourceColumn(
attribute='enabled', include_label=True, is_sortable=True,
source=IndexTemplateNode, widget=TwoStateWidget
)
SourceColumn(
attribute='link_documents', include_label=True, is_sortable=True,
source=IndexTemplateNode, widget=TwoStateWidget
)
menu_list_facet.bind_links(
links=(
link_document_index_instance_list,
), sources=(Document,)
)
menu_list_facet.bind_links(
links=(link_document_type_index_templates,),
sources=(DocumentType,)
)
menu_list_facet.bind_links(
links=(
link_index_template_document_types,
link_index_template_node_tree_view
), sources=(IndexTemplate,)
)
menu_object.bind_links(
links=(
link_index_template_delete, link_index_template_edit,
link_index_instance_rebuild
), sources=(IndexTemplate,)
)
menu_object.bind_links(
links=(
link_index_template_node_create, link_index_template_node_edit,
link_index_template_node_delete
), sources=(IndexTemplateNode,)
)
menu_main.bind_links(links=(link_index_instance_menu,), position=50)
menu_related.bind_links(
links=(link_index_template_list,),
sources=(
DocumentType, 'documents:document_type_list',
'documents:document_type_create'
)
)
menu_related.bind_links(
links=(link_document_type_list,),
sources=(
IndexTemplate, 'indexing:index_template_list',
'indexing:index_template_create'
)
)
menu_secondary.bind_links(
links=(link_index_template_list, link_index_template_create),
sources=(
IndexTemplate, 'indexing:index_template_list',
'indexing:index_template_create'
)
)
menu_setup.bind_links(links=(link_index_template_setup,))
menu_tools.bind_links(
links=(link_index_instances_rebuild, link_index_instances_reset)
)
post_save.connect(
dispatch_uid='document_indexing_handler_index_document',
receiver=handler_index_document,
sender=Document
)
post_delete.connect(
dispatch_uid='document_indexing_handler_delete_empty',
receiver=handler_delete_empty,
sender=Document
)
pre_delete.connect(
dispatch_uid='document_indexing_handler_remove_document',
receiver=handler_remove_document,
sender=Document
)
signal_post_initial_document_type.connect(
dispatch_uid='document_indexing_handler_create_default_document_index',
receiver=handler_create_default_document_index,
sender=DocumentType
)
| true | true |
f7295b40d74fc0ff213abaf939f429fe1b3a169a | 1,016 | py | Python | cpa.py | joyousprakhar/DialogWAE | 5d3a32f131ac80d0c7e25d24735933264703346d | [
"Unlicense"
] | null | null | null | cpa.py | joyousprakhar/DialogWAE | 5d3a32f131ac80d0c7e25d24735933264703346d | [
"Unlicense"
] | null | null | null | cpa.py | joyousprakhar/DialogWAE | 5d3a32f131ac80d0c7e25d24735933264703346d | [
"Unlicense"
] | null | null | null | import models, experiments, configs, data
print("haha")
config = getattr(configs, 'config_'+'DialogWAE_GMP')()
args.dataset='DailyDial'
args.data_path='./data'
data_path=data_path+dataset+'/'
print("haha")
corpus = getattr(data, dataset+'Corpus')(data_path, wordvec_path='/media/prakhar/Local Disk/glove/'+'glove.twitter.27B.200d.txt', wordvec_dim=config['emb_size'])
dials = corpus.get_dialogs()
metas = corpus.get_metas()
train_dial, valid_dial, test_dial = dials.get("train"), dials.get("valid"), dials.get("test")
train_meta, valid_meta, test_meta = metas.get("train"), metas.get("valid"), metas.get("test")
train_loader = getattr(data, dataset+'DataLoader')("Train", train_dial, train_meta, config['maxlen'])
valid_loader = getattr(data, dataset+'DataLoader')("Valid", valid_dial, valid_meta, config['maxlen'])
test_loader = getattr(data, adataset+'DataLoader')("Test", test_dial, test_meta, config['maxlen'])
vocab = corpus.ivocab
ivocab = corpus.vocab
n_tokens = len(ivocab)
metrics=Metrics(corpus.word2vec) | 48.380952 | 161 | 0.749016 | import models, experiments, configs, data
print("haha")
config = getattr(configs, 'config_'+'DialogWAE_GMP')()
args.dataset='DailyDial'
args.data_path='./data'
data_path=data_path+dataset+'/'
print("haha")
corpus = getattr(data, dataset+'Corpus')(data_path, wordvec_path='/media/prakhar/Local Disk/glove/'+'glove.twitter.27B.200d.txt', wordvec_dim=config['emb_size'])
dials = corpus.get_dialogs()
metas = corpus.get_metas()
train_dial, valid_dial, test_dial = dials.get("train"), dials.get("valid"), dials.get("test")
train_meta, valid_meta, test_meta = metas.get("train"), metas.get("valid"), metas.get("test")
train_loader = getattr(data, dataset+'DataLoader')("Train", train_dial, train_meta, config['maxlen'])
valid_loader = getattr(data, dataset+'DataLoader')("Valid", valid_dial, valid_meta, config['maxlen'])
test_loader = getattr(data, adataset+'DataLoader')("Test", test_dial, test_meta, config['maxlen'])
vocab = corpus.ivocab
ivocab = corpus.vocab
n_tokens = len(ivocab)
metrics=Metrics(corpus.word2vec) | true | true |
f7295b50c4c66a23ceae1f918df8e7b8086720db | 46 | py | Python | gradslam/version.py | glebshevchukk/gradslam | ce38c98537e1521b5e0a780f1c53f26924df7dab | [
"MIT"
] | 1,048 | 2020-11-06T03:49:27.000Z | 2022-03-28T17:57:05.000Z | gradslam/version.py | glebshevchukk/gradslam | ce38c98537e1521b5e0a780f1c53f26924df7dab | [
"MIT"
] | 32 | 2020-11-08T09:19:21.000Z | 2022-03-14T20:36:25.000Z | gradslam/version.py | glebshevchukk/gradslam | ce38c98537e1521b5e0a780f1c53f26924df7dab | [
"MIT"
] | 124 | 2020-11-08T07:29:57.000Z | 2022-03-16T11:40:56.000Z | # Export version number
__version__ = "0.1.0"
| 15.333333 | 23 | 0.717391 |
__version__ = "0.1.0"
| true | true |
f7295bce721b3d0cb3aa2aeb4ce6d113b18dc2aa | 2,084 | py | Python | NiaPy/benchmarks/ridge.py | lukapecnik/NiaPy | a40ac08a4c06a13019ec5e39cc137461884928b0 | [
"MIT"
] | 1 | 2020-03-16T11:15:43.000Z | 2020-03-16T11:15:43.000Z | NiaPy/benchmarks/ridge.py | lukapecnik/NiaPy | a40ac08a4c06a13019ec5e39cc137461884928b0 | [
"MIT"
] | null | null | null | NiaPy/benchmarks/ridge.py | lukapecnik/NiaPy | a40ac08a4c06a13019ec5e39cc137461884928b0 | [
"MIT"
] | 1 | 2020-03-25T16:20:36.000Z | 2020-03-25T16:20:36.000Z | # encoding=utf8
"""Implementation of Ridge benchmark."""
import math
from NiaPy.benchmarks.benchmark import Benchmark
__all__ = ["Ridge"]
class Ridge(Benchmark):
r"""Implementation of Ridge function.
Date: 2018
Author: Lucija Brezočnik
License: MIT
Function: **Ridge function**
:math:`f(\mathbf{x}) = \sum_{i=1}^D (\sum_{j=1}^i x_j)^2`
**Input domain:**
The function can be defined on any input domain but it is usually
evaluated on the hypercube :math:`x_i ∈ [-64, 64]`, for all :math:`i = 1, 2,..., D`.
**Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (0,...,0)`
LaTeX formats:
Inline:
$f(\mathbf{x}) = \sum_{i=1}^D (\sum_{j=1}^i x_j)^2 $
Equation:
\begin{equation} f(\mathbf{x}) =
\sum_{i=1}^D (\sum_{j=1}^i x_j)^2 \end{equation}
Domain:
$-64 \leq x_i \leq 64$
Reference: http://www.cs.unm.edu/~neal.holts/dga/benchmarkFunction/ridge.html
"""
Name = ["Ridge"]
def __init__(self, Lower=-64.0, Upper=64.0):
"""Initialize Ridge benchmark.
Args:
Lower (Optional[float]): Lower bound of problem.
Upper (Optional[float]): Upper bound of problem.
See Also:
:func:`NiaPy.benchmarks.Benchmark.__init__`
"""
Benchmark.__init__(self, Lower, Upper)
@staticmethod
def latex_code():
"""Return the latex code of the problem.
Returns:
[str] -- latex code.
"""
return r'''$f(\mathbf{x}) = \sum_{i=1}^D (\sum_{j=1}^i x_j)^2 $'''
@classmethod
def function(cls):
"""Return benchmark evaluation function.
Returns:
[fun] -- Evaluation function.
"""
def evaluate(D, sol):
val = 0.0
for i in range(D):
val1 = 0.0
for j in range(i + 1):
val1 += sol[j]
val += math.pow(val1, 2)
return val
return evaluate
| 22.170213 | 92 | 0.516315 |
import math
from NiaPy.benchmarks.benchmark import Benchmark
__all__ = ["Ridge"]
class Ridge(Benchmark):
Name = ["Ridge"]
def __init__(self, Lower=-64.0, Upper=64.0):
Benchmark.__init__(self, Lower, Upper)
@staticmethod
def latex_code():
return r'''$f(\mathbf{x}) = \sum_{i=1}^D (\sum_{j=1}^i x_j)^2 $'''
@classmethod
def function(cls):
def evaluate(D, sol):
val = 0.0
for i in range(D):
val1 = 0.0
for j in range(i + 1):
val1 += sol[j]
val += math.pow(val1, 2)
return val
return evaluate
| true | true |
f7295c3f9b37e8e0cfb6e3e5eac7c40d078fa1d6 | 490 | py | Python | config/config.py | morethanoneanimal/Nagel-Schreckenberg-simulation | 2af5cd2e68060114d5c0fd3f5f936c3313542e0b | [
"MIT"
] | 23 | 2016-03-17T13:19:26.000Z | 2021-05-26T02:03:07.000Z | config/config.py | morethanoneanimal/Nagel-Schreckenberg-simulation | 2af5cd2e68060114d5c0fd3f5f936c3313542e0b | [
"MIT"
] | 2 | 2018-06-26T07:32:00.000Z | 2021-10-07T18:25:38.000Z | config/config.py | morethanoneanimal/Nagel-Schreckenberg-simulation | 2af5cd2e68060114d5c0fd3f5f936c3313542e0b | [
"MIT"
] | 11 | 2016-02-10T02:16:19.000Z | 2021-10-04T01:13:18.000Z | from simulation.speedLimits import *
from simulation.trafficGenerators import *
maxFps= 40
size = width, heigth = 1280, 500
# in miliseconds
updateFrame = 500
seed = None
lanes = 2
length = 200
maxSpeed = 5
maxLength = 10000
speedLimits = [ SpeedLimit( range=((100,1),(100,1)), limit=0, ticks=0, active=False),
SpeedLimit( range=((130, 0), (170,0)), limit=0, ticks=0)
]
trafficGenerator = SimpleTrafficGenerator()
slowDownProbability, laneChangeProbability = 0.5, 0.5
| 22.272727 | 85 | 0.704082 | from simulation.speedLimits import *
from simulation.trafficGenerators import *
maxFps= 40
size = width, heigth = 1280, 500
updateFrame = 500
seed = None
lanes = 2
length = 200
maxSpeed = 5
maxLength = 10000
speedLimits = [ SpeedLimit( range=((100,1),(100,1)), limit=0, ticks=0, active=False),
SpeedLimit( range=((130, 0), (170,0)), limit=0, ticks=0)
]
trafficGenerator = SimpleTrafficGenerator()
slowDownProbability, laneChangeProbability = 0.5, 0.5
| true | true |
f7295d92eab502b61133988532484ecbe17f21c7 | 42,301 | py | Python | corehq/apps/hqwebapp/views.py | kkrampa/commcare-hq | d64d7cad98b240325ad669ccc7effb07721b4d44 | [
"BSD-3-Clause"
] | 1 | 2020-05-05T13:10:01.000Z | 2020-05-05T13:10:01.000Z | corehq/apps/hqwebapp/views.py | kkrampa/commcare-hq | d64d7cad98b240325ad669ccc7effb07721b4d44 | [
"BSD-3-Clause"
] | 1 | 2019-12-09T14:00:14.000Z | 2019-12-09T14:00:14.000Z | corehq/apps/hqwebapp/views.py | MaciejChoromanski/commcare-hq | fd7f65362d56d73b75a2c20d2afeabbc70876867 | [
"BSD-3-Clause"
] | 5 | 2015-11-30T13:12:45.000Z | 2019-07-01T19:27:07.000Z | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
import functools
import json
import logging
import os
import re
import sys
import traceback
import uuid
from datetime import datetime
from django.utils import html
from six.moves.urllib.parse import urlparse
from django.conf import settings
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from django.contrib.auth.forms import AdminPasswordChangeForm
from django.contrib.auth.models import User
from django.contrib.auth.views import logout as django_logout
from django.core import cache
from django.core.mail.message import EmailMessage
from django.http import HttpResponseRedirect, HttpResponse, Http404, \
HttpResponseServerError, HttpResponseNotFound, HttpResponseBadRequest, \
HttpResponseForbidden, HttpResponsePermanentRedirect
from django.shortcuts import redirect, render
from django.template import loader
from django.template.loader import render_to_string
from django.template.response import TemplateResponse
from django.urls import resolve
from django.utils.decorators import method_decorator
from django.utils.translation import ugettext as _, ugettext_noop, LANGUAGE_SESSION_KEY
from django.views.decorators.debug import sensitive_post_parameters
from django.views.decorators.http import require_GET, require_POST
from django.views.generic import TemplateView
from django.views.generic.base import View
from djangular.views.mixins import JSONResponseMixin
import httpagentparser
from couchdbkit import ResourceNotFound
from two_factor.views import LoginView
from two_factor.forms import AuthenticationTokenForm, BackupTokenForm
from corehq.apps.analytics import ab_tests
from corehq.apps.hqadmin.service_checks import CHECKS, run_checks
from corehq.apps.users.landing_pages import get_redirect_url, get_cloudcare_urlname
from corehq.apps.users.models import CouchUser
from corehq.form_processor.utils.general import should_use_sql_backend
from dimagi.utils.couch.cache.cache_core import get_redis_default_cache
from dimagi.utils.couch.database import get_db
from memoized import memoized
from dimagi.utils.django.request import mutable_querydict
from dimagi.utils.logging import notify_exception
from dimagi.utils.parsing import string_to_datetime
from dimagi.utils.web import get_url_base, json_response, get_site_domain
from no_exceptions.exceptions import Http403
from soil import DownloadBase
from soil import views as soil_views
from corehq.apps.accounting.models import Subscription
from corehq.apps.domain.decorators import require_superuser, login_and_domain_required, two_factor_exempt, \
track_domain_request
from corehq.apps.domain.models import Domain
from corehq.apps.domain.utils import normalize_domain_name, get_domain_from_url
from corehq.apps.dropbox.decorators import require_dropbox_session
from corehq.apps.dropbox.exceptions import DropboxUploadAlreadyInProgress, DropboxInvalidToken
from corehq.apps.dropbox.models import DropboxUploadHelper
from corehq.apps.dropbox.views import DROPBOX_ACCESS_TOKEN, DropboxAuthInitiate
from corehq.apps.hqadmin.management.commands.deploy_in_progress import DEPLOY_IN_PROGRESS_FLAG
from corehq.apps.hqwebapp.doc_info import get_doc_info, get_object_info
from corehq.apps.hqwebapp.encoders import LazyEncoder
from corehq.apps.hqwebapp.forms import EmailAuthenticationForm, CloudCareAuthenticationForm
from corehq.apps.hqwebapp.utils import get_environment_friendly_name, update_session_language
from corehq.apps.locations.permissions import location_safe
from corehq.apps.locations.models import SQLLocation
from corehq.apps.users.util import format_username
from corehq.form_processor.backends.sql.dbaccessors import FormAccessorSQL, CaseAccessorSQL
from corehq.form_processor.exceptions import XFormNotFound, CaseNotFound
from corehq.util.context_processors import commcare_hq_names
from corehq.util.datadog.const import DATADOG_UNKNOWN
from corehq.util.datadog.metrics import JSERROR_COUNT
from corehq.util.datadog.utils import create_datadog_event, sanitize_url
from corehq.util.datadog.gauges import datadog_counter, datadog_gauge
from corehq.util.python_compatibility import soft_assert_type_text
from corehq.util.view_utils import reverse
import six
from six.moves import range
def is_deploy_in_progress():
cache = get_redis_default_cache()
return cache.get(DEPLOY_IN_PROGRESS_FLAG) is not None
def format_traceback_the_way_python_does(type, exc, tb):
"""
Returns a traceback that looks like the one python gives you in the shell, e.g.
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
NameError: name 'name' is not defined
"""
if six.PY3:
exc_message = six.text_type(exc)
else:
exc_message = exc.message
if isinstance(exc_message, bytes):
exc_message = exc_message.decode('utf-8')
return 'Traceback (most recent call last):\n{}{}: {}'.format(
''.join(traceback.format_tb(tb)),
type.__name__,
exc_message
)
def server_error(request, template_name='500.html'):
"""
500 error handler.
"""
urlname = resolve(request.path).url_name
submission_urls = [
'receiver_secure_post',
'receiver_secure_post_with_app_id',
'receiver_post_with_app_id'
]
if urlname in submission_urls + ['app_aware_restore']:
return HttpResponse(status=500)
domain = get_domain_from_url(request.path) or ''
# hat tip: http://www.arthurkoziel.com/2009/01/15/passing-mediaurl-djangos-500-error-view/
t = loader.get_template(template_name)
type, exc, tb = sys.exc_info()
traceback_text = format_traceback_the_way_python_does(type, exc, tb)
traceback_key = uuid.uuid4().hex
cache.cache.set(traceback_key, traceback_text, 60*60)
return HttpResponseServerError(t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
'domain': domain,
'500traceback': traceback_key,
},
request=request,
))
def not_found(request, template_name='404.html'):
"""
404 error handler.
"""
t = loader.get_template(template_name)
return HttpResponseNotFound(t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
},
request=request,
))
@require_GET
@location_safe
def redirect_to_default(req, domain=None):
if not req.user.is_authenticated:
if domain != None:
url = reverse('domain_login', args=[domain])
else:
url = reverse('login')
elif domain and _two_factor_needed(domain, req):
return TemplateResponse(
request=req,
template='two_factor/core/otp_required.html',
status=403,
)
else:
if domain:
domain = normalize_domain_name(domain)
domains = [Domain.get_by_name(domain)]
else:
domains = Domain.active_for_user(req.user)
if 0 == len(domains) and not req.user.is_superuser:
from corehq.apps.registration.views import track_domainless_new_user
track_domainless_new_user(req)
return redirect('registration_domain')
elif 1 == len(domains):
from corehq.apps.dashboard.views import dashboard_default
from corehq.apps.users.models import DomainMembershipError
if domains[0]:
domain = domains[0].name
couch_user = req.couch_user
try:
role = couch_user.get_role(domain)
except DomainMembershipError:
# commcare users without roles should always be denied access
if couch_user.is_commcare_user():
raise Http404()
else:
# web users without roles are redirected to the dashboard default
# view since some domains allow web users to request access if they
# don't have it
return dashboard_default(req, domain)
else:
if role and role.default_landing_page:
url = get_redirect_url(role.default_landing_page, domain)
elif couch_user.is_commcare_user():
url = reverse(get_cloudcare_urlname(domain), args=[domain])
else:
return dashboard_default(req, domain)
else:
raise Http404()
else:
url = settings.DOMAIN_SELECT_URL
return HttpResponseRedirect(url)
def _two_factor_needed(domain_name, request):
domain_name = normalize_domain_name(domain_name)
domain_obj = Domain.get_by_name(domain_name)
if domain_obj:
return (
domain_obj.two_factor_auth
and not request.couch_user.two_factor_disabled
and not request.user.is_verified()
)
def yui_crossdomain(req):
x_domain = """<?xml version="1.0"?>
<!DOCTYPE cross-domain-policy SYSTEM "http://www.macromedia.com/xml/dtds/cross-domain-policy.dtd">
<cross-domain-policy>
<allow-access-from domain="yui.yahooapis.com"/>
<allow-access-from domain="%s"/>
<site-control permitted-cross-domain-policies="master-only"/>
</cross-domain-policy>""" % get_site_domain()
return HttpResponse(x_domain, content_type="application/xml")
@login_required()
def password_change(req):
user_to_edit = User.objects.get(id=req.user.id)
if req.method == 'POST':
password_form = AdminPasswordChangeForm(user_to_edit, req.POST)
if password_form.is_valid():
password_form.save()
return HttpResponseRedirect('/')
else:
password_form = AdminPasswordChangeForm(user_to_edit)
template_name = "password_change.html"
return render(req, template_name, {"form": password_form})
def server_up(req):
"""
Health check view which can be hooked into server monitoring tools like 'pingdom'
Returns:
HttpResponse("success", status_code=200)
HttpResponse(error_message, status_code=500)
Hit serverup.txt to check all the default enabled services (always_check=True)
Hit serverup.txt?only={check_name} to only check a specific service
Hit serverup.txt?{check_name} to include a non-default check (currently only ``heartbeat``)
"""
only = req.GET.get('only', None)
if only and only in CHECKS:
checks_to_do = [only]
else:
checks_to_do = [
check
for check, check_info in CHECKS.items()
if check_info['always_check'] or req.GET.get(check, None) is not None
]
statuses = run_checks(checks_to_do)
failed_checks = [(check, status) for check, status in statuses if not status.success]
for check_name, status in statuses:
tags = [
'status:{}'.format('failed' if not status.success else 'ok'),
'check:{}'.format(check_name)
]
datadog_gauge('commcare.serverup.check', status.duration, tags=tags)
if failed_checks and not is_deploy_in_progress():
status_messages = [
html.linebreaks('<strong>{}</strong>: {}'.format(check, html.escape(status.msg)).strip())
for check, status in failed_checks
]
create_datadog_event(
'Serverup check failed', '\n'.join(status_messages),
alert_type='error', aggregation_key='serverup',
)
status_messages.insert(0, 'Failed Checks (%s):' % os.uname()[1])
return HttpResponse(''.join(status_messages), status=500)
else:
return HttpResponse("success")
def _no_permissions_message(request, template_name="403.html", message=None):
t = loader.get_template(template_name)
return t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
'message': message,
},
request=request,
)
def no_permissions(request, redirect_to=None, template_name="403.html", message=None):
"""
403 error handler.
"""
return HttpResponseForbidden(_no_permissions_message(request, template_name, message))
def no_permissions_exception(request, template_name="403.html", message=None):
return Http403(_no_permissions_message(request, template_name, message))
def csrf_failure(request, reason=None, template_name="csrf_failure.html"):
t = loader.get_template(template_name)
return HttpResponseForbidden(t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
},
request=request,
))
@sensitive_post_parameters('auth-password')
def _login(req, domain_name):
if req.user.is_authenticated and req.method == "GET":
redirect_to = req.GET.get('next', '')
if redirect_to:
return HttpResponseRedirect(redirect_to)
if not domain_name:
return HttpResponseRedirect(reverse('homepage'))
else:
return HttpResponseRedirect(reverse('domain_homepage', args=[domain_name]))
if req.method == 'POST' and domain_name and '@' not in req.POST.get('auth-username', '@'):
with mutable_querydict(req.POST):
req.POST['auth-username'] = format_username(req.POST['auth-username'], domain_name)
if 'auth-username' in req.POST:
couch_user = CouchUser.get_by_username(req.POST['auth-username'].lower())
if couch_user:
new_lang = couch_user.language
old_lang = req.session.get(LANGUAGE_SESSION_KEY)
update_session_language(req, old_lang, new_lang)
req.base_template = settings.BASE_TEMPLATE
context = {}
template_name = 'login_and_password/login.html'
custom_landing_page = settings.CUSTOM_LANDING_TEMPLATE
if custom_landing_page:
if isinstance(custom_landing_page, six.string_types):
soft_assert_type_text(custom_landing_page)
template_name = custom_landing_page
else:
template_name = custom_landing_page.get(req.get_host())
if template_name is None:
template_name = custom_landing_page.get('default', template_name)
elif domain_name:
domain_obj = Domain.get_by_name(domain_name)
req_params = req.GET if req.method == 'GET' else req.POST
context.update({
'domain': domain_name,
'hr_name': domain_obj.display_name(),
'next': req_params.get('next', '/a/%s/' % domain_name),
'allow_domain_requests': domain_obj.allow_domain_requests,
'current_page': {'page_name': _('Welcome back to %s!') % domain_obj.display_name()},
})
else:
commcare_hq_name = commcare_hq_names(req)['commcare_hq_names']["COMMCARE_HQ_NAME"]
context.update({
'current_page': {'page_name': _('Welcome back to %s!') % commcare_hq_name},
})
if settings.SERVER_ENVIRONMENT in settings.ICDS_ENVS:
auth_view = CloudCareLoginView
else:
auth_view = HQLoginView if not domain_name else CloudCareLoginView
demo_workflow_ab_v2 = ab_tests.SessionAbTest(ab_tests.DEMO_WORKFLOW_V2, req)
if settings.IS_SAAS_ENVIRONMENT:
context['demo_workflow_ab_v2'] = demo_workflow_ab_v2.context
response = auth_view.as_view(template_name=template_name, extra_context=context)(req)
if settings.IS_SAAS_ENVIRONMENT:
demo_workflow_ab_v2.update_response(response)
return response
@two_factor_exempt
@sensitive_post_parameters('auth-password')
def login(req):
# This is a wrapper around the _login view
if settings.SERVER_ENVIRONMENT in settings.ICDS_ENVS:
login_url = reverse('domain_login', kwargs={'domain': 'icds-cas'})
return HttpResponseRedirect(login_url)
req_params = req.GET if req.method == 'GET' else req.POST
domain = req_params.get('domain', None)
return _login(req, domain)
@location_safe
def domain_login(req, domain):
# This is a wrapper around the _login view which sets a different template
project = Domain.get_by_name(domain)
if not project:
raise Http404
# FYI, the domain context_processor will pick this up and apply the
# necessary domain contexts:
req.project = project
return _login(req, domain)
class HQLoginView(LoginView):
form_list = [
('auth', EmailAuthenticationForm),
('token', AuthenticationTokenForm),
('backup', BackupTokenForm),
]
extra_context = {}
def get_context_data(self, **kwargs):
context = super(HQLoginView, self).get_context_data(**kwargs)
context.update(self.extra_context)
context['implement_password_obfuscation'] = settings.OBFUSCATE_PASSWORD_FOR_NIC_COMPLIANCE
return context
class CloudCareLoginView(HQLoginView):
form_list = [
('auth', CloudCareAuthenticationForm),
('token', AuthenticationTokenForm),
('backup', BackupTokenForm),
]
@two_factor_exempt
def logout(req):
referer = req.META.get('HTTP_REFERER')
domain = get_domain_from_url(urlparse(referer).path) if referer else None
# we don't actually do anything with the response here:
django_logout(req, **{"template_name": settings.BASE_TEMPLATE})
if referer and domain:
domain_login_url = reverse('domain_login', kwargs={'domain': domain})
return HttpResponseRedirect('%s' % domain_login_url)
else:
return HttpResponseRedirect(reverse('login'))
@login_and_domain_required
@track_domain_request(calculated_prop='cp_n_downloads_custom_exports')
def retrieve_download(req, domain, download_id, template="hqwebapp/includes/file_download.html"):
next_url = req.GET.get('next', reverse('my_project_settings', args=[domain]))
return soil_views.retrieve_download(req, download_id, template,
extra_context={'domain': domain, 'next_url': next_url})
def dropbox_next_url(request, download_id):
return request.META.get('HTTP_REFERER', '/')
@login_required
@require_dropbox_session(next_url=dropbox_next_url)
def dropbox_upload(request, download_id):
download = DownloadBase.get(download_id)
if download is None:
logging.error("Download file request for expired/nonexistent file requested")
raise Http404
else:
filename = download.get_filename()
# Hack to get target filename from content disposition
match = re.search('filename="([^"]*)"', download.content_disposition)
dest = match.group(1) if match else 'download.txt'
try:
uploader = DropboxUploadHelper.create(
request.session.get(DROPBOX_ACCESS_TOKEN),
src=filename,
dest=dest,
download_id=download_id,
user=request.user,
)
except DropboxInvalidToken:
return HttpResponseRedirect(reverse(DropboxAuthInitiate.slug))
except DropboxUploadAlreadyInProgress:
uploader = DropboxUploadHelper.objects.get(download_id=download_id)
messages.warning(
request,
'The file is in the process of being synced to dropbox! It is {0:.2f}% '
'complete.'.format(uploader.progress * 100)
)
return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))
uploader.upload()
messages.success(
request,
_("Apps/{app}/{dest} is queued to sync to dropbox! You will receive an email when it"
" completes.".format(app=settings.DROPBOX_APP_NAME, dest=dest))
)
return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))
@require_superuser
def debug_notify(request):
try:
0 // 0
except ZeroDivisionError:
notify_exception(request,
"If you want to achieve a 500-style email-out but don't want the user to see a 500, use notify_exception(request[, message])")
return HttpResponse("Email should have been sent")
@require_POST
def jserror(request):
agent = request.META.get('HTTP_USER_AGENT', None)
os = browser_name = browser_version = bot = DATADOG_UNKNOWN
if agent:
parsed_agent = httpagentparser.detect(agent)
bot = parsed_agent.get('bot', False)
if 'os' in parsed_agent:
os = parsed_agent['os'].get('name', DATADOG_UNKNOWN)
if 'browser' in parsed_agent:
browser_version = parsed_agent['browser'].get('version', DATADOG_UNKNOWN)
browser_name = parsed_agent['browser'].get('name', DATADOG_UNKNOWN)
datadog_counter(JSERROR_COUNT, tags=[
'os:{}'.format(os),
'browser_version:{}'.format(browser_version),
'browser_name:{}'.format(browser_name),
'url:{}'.format(sanitize_url(request.POST.get('page', None))),
'file:{}'.format(request.POST.get('filename')),
'bot:{}'.format(bot),
])
return HttpResponse('')
@method_decorator([login_required], name='dispatch')
class BugReportView(View):
@property
def recipients(self):
"""
Returns:
list
"""
return settings.BUG_REPORT_RECIPIENTS
def post(self, req, *args, **kwargs):
report = dict([(key, req.POST.get(key, '')) for key in (
'subject',
'username',
'domain',
'url',
'message',
'app_id',
'cc',
'email',
'500traceback',
'sentry_id',
)])
try:
couch_user = req.couch_user
full_name = couch_user.full_name
if couch_user.is_commcare_user():
email = report['email']
else:
email = couch_user.get_email()
except Exception:
full_name = None
email = report['email']
report['full_name'] = full_name
report['email'] = email or report['username']
if report['domain']:
domain = report['domain']
elif len(couch_user.domains) == 1:
# This isn't a domain page, but the user has only one domain, so let's use that
domain = couch_user.domains[0]
else:
domain = "<no domain>"
message = (
"username: {username}\n"
"full name: {full_name}\n"
"domain: {domain}\n"
"url: {url}\n"
).format(**report)
domain_object = Domain.get_by_name(domain) if report['domain'] else None
debug_context = {
'datetime': datetime.utcnow(),
'self_started': '<unknown>',
'scale_backend': '<unknown>',
'has_handoff_info': '<unknown>',
'project_description': '<unknown>',
'sentry_error': '{}{}'.format(getattr(settings, 'SENTRY_QUERY_URL'), report['sentry_id'])
}
if domain_object:
current_project_description = domain_object.project_description if domain_object else None
new_project_description = req.POST.get('project_description')
if (domain_object and
req.couch_user.is_domain_admin(domain=domain) and
new_project_description and current_project_description != new_project_description):
domain_object.project_description = new_project_description
domain_object.save()
message += ((
"software plan: {software_plan}\n"
).format(
software_plan=Subscription.get_subscribed_plan_by_domain(domain),
))
debug_context.update({
'self_started': domain_object.internal.self_started,
'scale_backend': should_use_sql_backend(domain),
'has_handoff_info': bool(domain_object.internal.partner_contact),
'project_description': domain_object.project_description,
})
subject = '{subject} ({domain})'.format(subject=report['subject'], domain=domain)
cc = [el for el in report['cc'].strip().split(",") if el]
if full_name and not any([c in full_name for c in '<>"']):
reply_to = '"{full_name}" <{email}>'.format(**report)
else:
reply_to = report['email']
# if the person looks like a commcare user, fogbugz can't reply
# to their email, so just use the default
if settings.HQ_ACCOUNT_ROOT in reply_to:
reply_to = settings.SERVER_EMAIL
message += "Message:\n\n{message}\n".format(message=report['message'])
if req.POST.get('five-hundred-report'):
extra_message = ("This message was reported from a 500 error page! "
"Please fix this ASAP (as if you wouldn't anyway)...")
extra_debug_info = (
"datetime: {datetime}\n"
"Is self start: {self_started}\n"
"Is scale backend: {scale_backend}\n"
"Has Support Hand-off Info: {has_handoff_info}\n"
"Project description: {project_description}\n"
"Sentry Error: {sentry_error}\n"
).format(**debug_context)
traceback_info = cache.cache.get(report['500traceback']) or 'No traceback info available'
cache.cache.delete(report['500traceback'])
message = "\n\n".join([message, extra_debug_info, extra_message, traceback_info])
email = EmailMessage(
subject=subject,
body=message,
to=self.recipients,
headers={'Reply-To': reply_to},
cc=cc
)
uploaded_file = req.FILES.get('report_issue')
if uploaded_file:
filename = uploaded_file.name
content = uploaded_file.read()
email.attach(filename=filename, content=content)
# only fake the from email if it's an @dimagi.com account
is_icds_env = settings.SERVER_ENVIRONMENT in settings.ICDS_ENVS
if re.search(r'@dimagi\.com$', report['username']) and not is_icds_env:
email.from_email = report['username']
else:
email.from_email = settings.CCHQ_BUG_REPORT_EMAIL
email.send(fail_silently=False)
if req.POST.get('five-hundred-report'):
messages.success(
req,
"Your CommCare HQ Issue Report has been sent. We are working quickly to resolve this problem."
)
return HttpResponseRedirect(reverse('homepage'))
return HttpResponse()
def render_static(request, template, page_name):
"""
Takes an html file and renders it Commcare HQ's styling
"""
return render(request, "hqwebapp/blank.html",
{'tmpl': template, 'page_name': page_name})
def cda(request):
return render_static(request, "cda.html", _("Content Distribution Agreement"))
def apache_license(request):
return render_static(request, "apache_license.html", _("Apache License"))
def bsd_license(request):
return render_static(request, "bsd_license.html", _("BSD License"))
class BasePageView(TemplateView):
urlname = None # name of the view used in urls
page_title = None # what shows up in the <title>
template_name = 'hqwebapp/base_page.html'
@property
def page_name(self):
"""
This is what is visible to the user.
page_title is what shows up in <title> tags.
"""
return self.page_title
@property
def page_url(self):
raise NotImplementedError()
@property
def parent_pages(self):
"""
Specify parent pages as a list of
[{
'title': <name>,
'url: <url>,
}]
"""
return []
@property
def main_context(self):
"""
The shared context for rendering this page.
"""
return {
'current_page': {
'page_name': self.page_name,
'title': self.page_title,
'url': self.page_url,
'parents': self.parent_pages,
},
}
@property
def page_context(self):
"""
The Context for the settings page
"""
return {}
def get_context_data(self, **kwargs):
context = super(BasePageView, self).get_context_data(**kwargs)
context.update(self.main_context)
context.update(self.page_context)
return context
def render_to_response(self, context, **response_kwargs):
"""
Returns a response with a template rendered with the given context.
"""
return render(self.request, self.template_name, context)
class BaseSectionPageView(BasePageView):
section_name = ""
template_name = "hqwebapp/base_section.html"
@property
def section_url(self):
raise NotImplementedError
@property
def main_context(self):
context = super(BaseSectionPageView, self).main_context
context.update({
'section': {
'page_name': self.section_name,
'url': self.section_url,
}
})
return context
class PaginatedItemException(Exception):
pass
class CRUDPaginatedViewMixin(object):
"""
Mix this in with a TemplateView view object.
For usage tips, see the docs for UI Helpers > Paginated CRUD View.
"""
DEFAULT_LIMIT = 10
limit_text = ugettext_noop("items per page")
empty_notification = ugettext_noop("You have no items.")
loading_message = ugettext_noop("Loading...")
deleted_items_header = ugettext_noop("Deleted Items:")
new_items_header = ugettext_noop("New Items:")
def _safe_escape(self, expression, default):
try:
return expression()
except ValueError:
return default
@property
def parameters(self):
"""
Specify GET or POST from a request object.
"""
raise NotImplementedError("you need to implement get_param_source")
@property
@memoized
def page(self):
return self._safe_escape(
lambda: int(self.parameters.get('page', 1)),
1
)
@property
@memoized
def limit(self):
return self._safe_escape(
lambda: int(self.parameters.get('limit', self.DEFAULT_LIMIT)),
self.DEFAULT_LIMIT
)
@property
def total(self):
raise NotImplementedError("You must implement total.")
@property
def sort_by(self):
return self.parameters.GET.get('sortBy', 'abc')
@property
def skip(self):
return (self.page - 1) * self.limit
@property
def action(self):
action = self.parameters.get('action')
if action not in self.allowed_actions:
raise Http404()
return action
@property
def column_names(self):
raise NotImplementedError("you must return a list of column names")
@property
def pagination_context(self):
create_form = self.get_create_form()
return {
'pagination': {
'page': self.page,
'limit': self.limit,
'total': self.total,
'limit_options': list(range(self.DEFAULT_LIMIT, 51, self.DEFAULT_LIMIT)),
'column_names': self.column_names,
'num_columns': len(self.column_names),
'text': {
'limit': self.limit_text,
'empty': self.empty_notification,
'loading': self.loading_message,
'deleted_items': self.deleted_items_header,
'new_items': self.new_items_header,
},
'create_item_form': self.get_create_form_response(create_form) if create_form else None,
}
}
@property
def allowed_actions(self):
return [
'create',
'update',
'delete',
'paginate',
'refresh',
]
@property
def paginate_crud_response(self):
"""
Return this in the post method of your view class.
"""
response = getattr(self, '%s_response' % self.action)
return HttpResponse(json.dumps(response, cls=LazyEncoder))
@property
def create_response(self):
create_form = self.get_create_form()
new_item = None
if create_form.is_valid():
new_item = self.get_create_item_data(create_form)
create_form = self.get_create_form(is_blank=True)
return {
'newItem': new_item,
'form': self.get_create_form_response(create_form)
}
@property
def update_response(self):
update_form = self.get_update_form()
updated_item = None
if update_form.is_valid():
updated_item = self.get_updated_item_data(update_form)
return {
'updatedItem': updated_item,
'form': self.get_update_form_response(update_form),
}
@property
def refresh_response(self):
try:
self.refresh_item(self.item_id)
except PaginatedItemException as e:
return {
'error': _("<strong>Problem Refreshing List:</strong> %s") % e,
}
return {
'success': True,
'currentPage': self.page,
'total': self.total,
'paginatedList': list(self.paginated_list),
}
@property
def delete_response(self):
try:
response = self.get_deleted_item_data(self.item_id)
return {
'deletedItem': response
}
except PaginatedItemException as e:
return {
'error': _("<strong>Problem Deleting:</strong> %s") % e,
}
@property
def item_id(self):
try:
return self.parameters['itemId']
except KeyError:
raise PaginatedItemException(_("The item's ID was not passed to the server."))
@property
def paginate_response(self):
return {
'success': True,
'currentPage': self.page,
'total': self.total,
'paginatedList': list(self.paginated_list),
}
@property
def paginated_list(self):
"""
This should return a list (or generator object) of data formatted as follows:
[
{
'itemData': {
'id': <id of item>,
<json dict of item data for the knockout model to use>
},
'template': <knockout template id>
}
]
"""
raise NotImplementedError("Return a list of data for the request response.")
def get_create_form(self, is_blank=False):
"""
This should be a crispy form that creates an item.
It's not required if you just want a paginated view.
"""
pass
def get_create_form_response(self, create_form):
return render_to_string(
'hqwebapp/includes/create_item_form.html', {
'form': create_form
}
)
def get_update_form(self, initial_data=None):
raise NotImplementedError("You must return a form object that will update an Item")
def get_update_form_response(self, update_form):
return render_to_string(
'hqwebapp/partials/update_item_form.html', {
'form': update_form
}
)
def refresh_item(self, item_id):
"""
Process the item that triggered a list refresh here.
"""
raise NotImplementedError("You must implement refresh_item")
def get_create_item_data(self, create_form):
"""
This should return a dict of data for the created item.
{
'itemData': {
'id': <id of item>,
<json dict of item data for the knockout model to use>
},
'template': <knockout template id>
}
"""
raise NotImplementedError("You must implement get_new_item_data")
def get_updated_item_data(self, update_form):
"""
This should return a dict of data for the updated item.
{
'itemData': {
'id': <id of item>,
<json dict of item data for the knockout model to use>
},
'template': <knockout template id>
}
"""
raise NotImplementedError("You must implement get_updated_item_data")
def get_deleted_item_data(self, item_id):
"""
This should return a dict of data for the deleted item.
{
'itemData': {
'id': <id of item>,
<json dict of item data for the knockout model to use>
},
'template': <knockout template id>
}
"""
raise NotImplementedError("You must implement get_deleted_item_data")
@login_required
def quick_find(request):
query = request.GET.get('q')
redirect = request.GET.get('redirect') != 'false'
if not query:
return HttpResponseBadRequest('GET param "q" must be provided')
def deal_with_doc(doc, domain, doc_info_fn):
if request.couch_user.is_superuser or (domain and request.couch_user.is_member_of(domain)):
doc_info = doc_info_fn(doc)
else:
raise Http404()
if redirect and doc_info.link:
messages.info(request, _("We've redirected you to the %s matching your query") % doc_info.type_display)
return HttpResponseRedirect(doc_info.link)
elif redirect and request.couch_user.is_superuser:
return HttpResponseRedirect('{}?id={}'.format(reverse('raw_doc'), doc.get('_id')))
else:
return json_response(doc_info)
couch_dbs = [None] + settings.COUCH_SETTINGS_HELPER.extra_db_names
for db_name in couch_dbs:
try:
doc = get_db(db_name).get(query)
except ResourceNotFound:
pass
else:
domain = doc.get('domain') or doc.get('domains', [None])[0]
doc_info_fn = functools.partial(get_doc_info, domain_hint=domain)
return deal_with_doc(doc, domain, doc_info_fn)
for accessor in (FormAccessorSQL.get_form, CaseAccessorSQL.get_case):
try:
doc = accessor(query)
except (XFormNotFound, CaseNotFound):
pass
else:
domain = doc.domain
return deal_with_doc(doc, domain, get_object_info)
for django_model in (SQLLocation,):
try:
if hasattr(django_model, 'by_id') and callable(django_model.by_id):
doc = django_model.by_id(query)
else:
doc = django_model.objects.get(pk=query)
except django_model.DoesNotExist:
continue
else:
if doc is None:
continue
domain = doc.domain
return deal_with_doc(doc, domain, get_object_info)
raise Http404()
def osdd(request, template='osdd.xml'):
response = render(request, template, {
'url_base': get_url_base(),
'env': get_environment_friendly_name()
})
response['Content-Type'] = 'application/xml'
return response
class MaintenanceAlertsView(BasePageView):
urlname = 'alerts'
page_title = ugettext_noop("Maintenance Alerts")
template_name = 'hqwebapp/maintenance_alerts.html'
@method_decorator(require_superuser)
def dispatch(self, request, *args, **kwargs):
return super(MaintenanceAlertsView, self).dispatch(request, *args, **kwargs)
@property
def page_context(self):
from corehq.apps.hqwebapp.models import MaintenanceAlert
return {
'alerts': [{
'created': six.text_type(alert.created),
'active': alert.active,
'html': alert.html,
'id': alert.id,
'domains': ", ".join(alert.domains) if alert.domains else "All domains",
} for alert in MaintenanceAlert.objects.order_by('-active', '-created')[:5]]
}
@property
def page_url(self):
return reverse(self.urlname)
@require_POST
@require_superuser
def create_alert(request):
from corehq.apps.hqwebapp.models import MaintenanceAlert
alert_text = request.POST.get('alert_text')
domains = request.POST.get('domains').split() or None
MaintenanceAlert(active=False, text=alert_text, domains=domains).save()
return HttpResponseRedirect(reverse('alerts'))
@require_POST
@require_superuser
def activate_alert(request):
from corehq.apps.hqwebapp.models import MaintenanceAlert
ma = MaintenanceAlert.objects.get(id=request.POST.get('alert_id'))
ma.active = True
ma.save()
return HttpResponseRedirect(reverse('alerts'))
@require_POST
@require_superuser
def deactivate_alert(request):
from corehq.apps.hqwebapp.models import MaintenanceAlert
ma = MaintenanceAlert.objects.get(id=request.POST.get('alert_id'))
ma.active = False
ma.save()
return HttpResponseRedirect(reverse('alerts'))
def redirect_to_dimagi(endpoint):
def _redirect(request, lang_code=None):
if settings.SERVER_ENVIRONMENT in [
'production',
'india',
'staging',
'changeme',
'localdev',
]:
return HttpResponsePermanentRedirect(
"https://www.dimagi.com/{}{}".format(
endpoint,
"?lang={}".format(lang_code) if lang_code else "",
)
)
return redirect_to_default(request)
return _redirect
def temporary_google_verify(request):
# will remove once google search console verify process completes
# BMB 4/20/18
return render(request, "google9633af922b8b0064.html")
| 34.475143 | 138 | 0.638117 | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
import functools
import json
import logging
import os
import re
import sys
import traceback
import uuid
from datetime import datetime
from django.utils import html
from six.moves.urllib.parse import urlparse
from django.conf import settings
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from django.contrib.auth.forms import AdminPasswordChangeForm
from django.contrib.auth.models import User
from django.contrib.auth.views import logout as django_logout
from django.core import cache
from django.core.mail.message import EmailMessage
from django.http import HttpResponseRedirect, HttpResponse, Http404, \
HttpResponseServerError, HttpResponseNotFound, HttpResponseBadRequest, \
HttpResponseForbidden, HttpResponsePermanentRedirect
from django.shortcuts import redirect, render
from django.template import loader
from django.template.loader import render_to_string
from django.template.response import TemplateResponse
from django.urls import resolve
from django.utils.decorators import method_decorator
from django.utils.translation import ugettext as _, ugettext_noop, LANGUAGE_SESSION_KEY
from django.views.decorators.debug import sensitive_post_parameters
from django.views.decorators.http import require_GET, require_POST
from django.views.generic import TemplateView
from django.views.generic.base import View
from djangular.views.mixins import JSONResponseMixin
import httpagentparser
from couchdbkit import ResourceNotFound
from two_factor.views import LoginView
from two_factor.forms import AuthenticationTokenForm, BackupTokenForm
from corehq.apps.analytics import ab_tests
from corehq.apps.hqadmin.service_checks import CHECKS, run_checks
from corehq.apps.users.landing_pages import get_redirect_url, get_cloudcare_urlname
from corehq.apps.users.models import CouchUser
from corehq.form_processor.utils.general import should_use_sql_backend
from dimagi.utils.couch.cache.cache_core import get_redis_default_cache
from dimagi.utils.couch.database import get_db
from memoized import memoized
from dimagi.utils.django.request import mutable_querydict
from dimagi.utils.logging import notify_exception
from dimagi.utils.parsing import string_to_datetime
from dimagi.utils.web import get_url_base, json_response, get_site_domain
from no_exceptions.exceptions import Http403
from soil import DownloadBase
from soil import views as soil_views
from corehq.apps.accounting.models import Subscription
from corehq.apps.domain.decorators import require_superuser, login_and_domain_required, two_factor_exempt, \
track_domain_request
from corehq.apps.domain.models import Domain
from corehq.apps.domain.utils import normalize_domain_name, get_domain_from_url
from corehq.apps.dropbox.decorators import require_dropbox_session
from corehq.apps.dropbox.exceptions import DropboxUploadAlreadyInProgress, DropboxInvalidToken
from corehq.apps.dropbox.models import DropboxUploadHelper
from corehq.apps.dropbox.views import DROPBOX_ACCESS_TOKEN, DropboxAuthInitiate
from corehq.apps.hqadmin.management.commands.deploy_in_progress import DEPLOY_IN_PROGRESS_FLAG
from corehq.apps.hqwebapp.doc_info import get_doc_info, get_object_info
from corehq.apps.hqwebapp.encoders import LazyEncoder
from corehq.apps.hqwebapp.forms import EmailAuthenticationForm, CloudCareAuthenticationForm
from corehq.apps.hqwebapp.utils import get_environment_friendly_name, update_session_language
from corehq.apps.locations.permissions import location_safe
from corehq.apps.locations.models import SQLLocation
from corehq.apps.users.util import format_username
from corehq.form_processor.backends.sql.dbaccessors import FormAccessorSQL, CaseAccessorSQL
from corehq.form_processor.exceptions import XFormNotFound, CaseNotFound
from corehq.util.context_processors import commcare_hq_names
from corehq.util.datadog.const import DATADOG_UNKNOWN
from corehq.util.datadog.metrics import JSERROR_COUNT
from corehq.util.datadog.utils import create_datadog_event, sanitize_url
from corehq.util.datadog.gauges import datadog_counter, datadog_gauge
from corehq.util.python_compatibility import soft_assert_type_text
from corehq.util.view_utils import reverse
import six
from six.moves import range
def is_deploy_in_progress():
cache = get_redis_default_cache()
return cache.get(DEPLOY_IN_PROGRESS_FLAG) is not None
def format_traceback_the_way_python_does(type, exc, tb):
if six.PY3:
exc_message = six.text_type(exc)
else:
exc_message = exc.message
if isinstance(exc_message, bytes):
exc_message = exc_message.decode('utf-8')
return 'Traceback (most recent call last):\n{}{}: {}'.format(
''.join(traceback.format_tb(tb)),
type.__name__,
exc_message
)
def server_error(request, template_name='500.html'):
urlname = resolve(request.path).url_name
submission_urls = [
'receiver_secure_post',
'receiver_secure_post_with_app_id',
'receiver_post_with_app_id'
]
if urlname in submission_urls + ['app_aware_restore']:
return HttpResponse(status=500)
domain = get_domain_from_url(request.path) or ''
t = loader.get_template(template_name)
type, exc, tb = sys.exc_info()
traceback_text = format_traceback_the_way_python_does(type, exc, tb)
traceback_key = uuid.uuid4().hex
cache.cache.set(traceback_key, traceback_text, 60*60)
return HttpResponseServerError(t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
'domain': domain,
'500traceback': traceback_key,
},
request=request,
))
def not_found(request, template_name='404.html'):
t = loader.get_template(template_name)
return HttpResponseNotFound(t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
},
request=request,
))
@require_GET
@location_safe
def redirect_to_default(req, domain=None):
if not req.user.is_authenticated:
if domain != None:
url = reverse('domain_login', args=[domain])
else:
url = reverse('login')
elif domain and _two_factor_needed(domain, req):
return TemplateResponse(
request=req,
template='two_factor/core/otp_required.html',
status=403,
)
else:
if domain:
domain = normalize_domain_name(domain)
domains = [Domain.get_by_name(domain)]
else:
domains = Domain.active_for_user(req.user)
if 0 == len(domains) and not req.user.is_superuser:
from corehq.apps.registration.views import track_domainless_new_user
track_domainless_new_user(req)
return redirect('registration_domain')
elif 1 == len(domains):
from corehq.apps.dashboard.views import dashboard_default
from corehq.apps.users.models import DomainMembershipError
if domains[0]:
domain = domains[0].name
couch_user = req.couch_user
try:
role = couch_user.get_role(domain)
except DomainMembershipError:
if couch_user.is_commcare_user():
raise Http404()
else:
return dashboard_default(req, domain)
else:
if role and role.default_landing_page:
url = get_redirect_url(role.default_landing_page, domain)
elif couch_user.is_commcare_user():
url = reverse(get_cloudcare_urlname(domain), args=[domain])
else:
return dashboard_default(req, domain)
else:
raise Http404()
else:
url = settings.DOMAIN_SELECT_URL
return HttpResponseRedirect(url)
def _two_factor_needed(domain_name, request):
domain_name = normalize_domain_name(domain_name)
domain_obj = Domain.get_by_name(domain_name)
if domain_obj:
return (
domain_obj.two_factor_auth
and not request.couch_user.two_factor_disabled
and not request.user.is_verified()
)
def yui_crossdomain(req):
x_domain = """<?xml version="1.0"?>
<!DOCTYPE cross-domain-policy SYSTEM "http://www.macromedia.com/xml/dtds/cross-domain-policy.dtd">
<cross-domain-policy>
<allow-access-from domain="yui.yahooapis.com"/>
<allow-access-from domain="%s"/>
<site-control permitted-cross-domain-policies="master-only"/>
</cross-domain-policy>""" % get_site_domain()
return HttpResponse(x_domain, content_type="application/xml")
@login_required()
def password_change(req):
user_to_edit = User.objects.get(id=req.user.id)
if req.method == 'POST':
password_form = AdminPasswordChangeForm(user_to_edit, req.POST)
if password_form.is_valid():
password_form.save()
return HttpResponseRedirect('/')
else:
password_form = AdminPasswordChangeForm(user_to_edit)
template_name = "password_change.html"
return render(req, template_name, {"form": password_form})
def server_up(req):
only = req.GET.get('only', None)
if only and only in CHECKS:
checks_to_do = [only]
else:
checks_to_do = [
check
for check, check_info in CHECKS.items()
if check_info['always_check'] or req.GET.get(check, None) is not None
]
statuses = run_checks(checks_to_do)
failed_checks = [(check, status) for check, status in statuses if not status.success]
for check_name, status in statuses:
tags = [
'status:{}'.format('failed' if not status.success else 'ok'),
'check:{}'.format(check_name)
]
datadog_gauge('commcare.serverup.check', status.duration, tags=tags)
if failed_checks and not is_deploy_in_progress():
status_messages = [
html.linebreaks('<strong>{}</strong>: {}'.format(check, html.escape(status.msg)).strip())
for check, status in failed_checks
]
create_datadog_event(
'Serverup check failed', '\n'.join(status_messages),
alert_type='error', aggregation_key='serverup',
)
status_messages.insert(0, 'Failed Checks (%s):' % os.uname()[1])
return HttpResponse(''.join(status_messages), status=500)
else:
return HttpResponse("success")
def _no_permissions_message(request, template_name="403.html", message=None):
t = loader.get_template(template_name)
return t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
'message': message,
},
request=request,
)
def no_permissions(request, redirect_to=None, template_name="403.html", message=None):
return HttpResponseForbidden(_no_permissions_message(request, template_name, message))
def no_permissions_exception(request, template_name="403.html", message=None):
return Http403(_no_permissions_message(request, template_name, message))
def csrf_failure(request, reason=None, template_name="csrf_failure.html"):
t = loader.get_template(template_name)
return HttpResponseForbidden(t.render(
context={
'MEDIA_URL': settings.MEDIA_URL,
'STATIC_URL': settings.STATIC_URL,
},
request=request,
))
@sensitive_post_parameters('auth-password')
def _login(req, domain_name):
if req.user.is_authenticated and req.method == "GET":
redirect_to = req.GET.get('next', '')
if redirect_to:
return HttpResponseRedirect(redirect_to)
if not domain_name:
return HttpResponseRedirect(reverse('homepage'))
else:
return HttpResponseRedirect(reverse('domain_homepage', args=[domain_name]))
if req.method == 'POST' and domain_name and '@' not in req.POST.get('auth-username', '@'):
with mutable_querydict(req.POST):
req.POST['auth-username'] = format_username(req.POST['auth-username'], domain_name)
if 'auth-username' in req.POST:
couch_user = CouchUser.get_by_username(req.POST['auth-username'].lower())
if couch_user:
new_lang = couch_user.language
old_lang = req.session.get(LANGUAGE_SESSION_KEY)
update_session_language(req, old_lang, new_lang)
req.base_template = settings.BASE_TEMPLATE
context = {}
template_name = 'login_and_password/login.html'
custom_landing_page = settings.CUSTOM_LANDING_TEMPLATE
if custom_landing_page:
if isinstance(custom_landing_page, six.string_types):
soft_assert_type_text(custom_landing_page)
template_name = custom_landing_page
else:
template_name = custom_landing_page.get(req.get_host())
if template_name is None:
template_name = custom_landing_page.get('default', template_name)
elif domain_name:
domain_obj = Domain.get_by_name(domain_name)
req_params = req.GET if req.method == 'GET' else req.POST
context.update({
'domain': domain_name,
'hr_name': domain_obj.display_name(),
'next': req_params.get('next', '/a/%s/' % domain_name),
'allow_domain_requests': domain_obj.allow_domain_requests,
'current_page': {'page_name': _('Welcome back to %s!') % domain_obj.display_name()},
})
else:
commcare_hq_name = commcare_hq_names(req)['commcare_hq_names']["COMMCARE_HQ_NAME"]
context.update({
'current_page': {'page_name': _('Welcome back to %s!') % commcare_hq_name},
})
if settings.SERVER_ENVIRONMENT in settings.ICDS_ENVS:
auth_view = CloudCareLoginView
else:
auth_view = HQLoginView if not domain_name else CloudCareLoginView
demo_workflow_ab_v2 = ab_tests.SessionAbTest(ab_tests.DEMO_WORKFLOW_V2, req)
if settings.IS_SAAS_ENVIRONMENT:
context['demo_workflow_ab_v2'] = demo_workflow_ab_v2.context
response = auth_view.as_view(template_name=template_name, extra_context=context)(req)
if settings.IS_SAAS_ENVIRONMENT:
demo_workflow_ab_v2.update_response(response)
return response
@two_factor_exempt
@sensitive_post_parameters('auth-password')
def login(req):
# This is a wrapper around the _login view
if settings.SERVER_ENVIRONMENT in settings.ICDS_ENVS:
login_url = reverse('domain_login', kwargs={'domain': 'icds-cas'})
return HttpResponseRedirect(login_url)
req_params = req.GET if req.method == 'GET' else req.POST
domain = req_params.get('domain', None)
return _login(req, domain)
@location_safe
def domain_login(req, domain):
# This is a wrapper around the _login view which sets a different template
project = Domain.get_by_name(domain)
if not project:
raise Http404
# FYI, the domain context_processor will pick this up and apply the
# necessary domain contexts:
req.project = project
return _login(req, domain)
class HQLoginView(LoginView):
form_list = [
('auth', EmailAuthenticationForm),
('token', AuthenticationTokenForm),
('backup', BackupTokenForm),
]
extra_context = {}
def get_context_data(self, **kwargs):
context = super(HQLoginView, self).get_context_data(**kwargs)
context.update(self.extra_context)
context['implement_password_obfuscation'] = settings.OBFUSCATE_PASSWORD_FOR_NIC_COMPLIANCE
return context
class CloudCareLoginView(HQLoginView):
form_list = [
('auth', CloudCareAuthenticationForm),
('token', AuthenticationTokenForm),
('backup', BackupTokenForm),
]
@two_factor_exempt
def logout(req):
referer = req.META.get('HTTP_REFERER')
domain = get_domain_from_url(urlparse(referer).path) if referer else None
# we don't actually do anything with the response here:
django_logout(req, **{"template_name": settings.BASE_TEMPLATE})
if referer and domain:
domain_login_url = reverse('domain_login', kwargs={'domain': domain})
return HttpResponseRedirect('%s' % domain_login_url)
else:
return HttpResponseRedirect(reverse('login'))
@login_and_domain_required
@track_domain_request(calculated_prop='cp_n_downloads_custom_exports')
def retrieve_download(req, domain, download_id, template="hqwebapp/includes/file_download.html"):
next_url = req.GET.get('next', reverse('my_project_settings', args=[domain]))
return soil_views.retrieve_download(req, download_id, template,
extra_context={'domain': domain, 'next_url': next_url})
def dropbox_next_url(request, download_id):
return request.META.get('HTTP_REFERER', '/')
@login_required
@require_dropbox_session(next_url=dropbox_next_url)
def dropbox_upload(request, download_id):
download = DownloadBase.get(download_id)
if download is None:
logging.error("Download file request for expired/nonexistent file requested")
raise Http404
else:
filename = download.get_filename()
match = re.search('filename="([^"]*)"', download.content_disposition)
dest = match.group(1) if match else 'download.txt'
try:
uploader = DropboxUploadHelper.create(
request.session.get(DROPBOX_ACCESS_TOKEN),
src=filename,
dest=dest,
download_id=download_id,
user=request.user,
)
except DropboxInvalidToken:
return HttpResponseRedirect(reverse(DropboxAuthInitiate.slug))
except DropboxUploadAlreadyInProgress:
uploader = DropboxUploadHelper.objects.get(download_id=download_id)
messages.warning(
request,
'The file is in the process of being synced to dropbox! It is {0:.2f}% '
'complete.'.format(uploader.progress * 100)
)
return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))
uploader.upload()
messages.success(
request,
_("Apps/{app}/{dest} is queued to sync to dropbox! You will receive an email when it"
" completes.".format(app=settings.DROPBOX_APP_NAME, dest=dest))
)
return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))
@require_superuser
def debug_notify(request):
try:
0 // 0
except ZeroDivisionError:
notify_exception(request,
"If you want to achieve a 500-style email-out but don't want the user to see a 500, use notify_exception(request[, message])")
return HttpResponse("Email should have been sent")
@require_POST
def jserror(request):
agent = request.META.get('HTTP_USER_AGENT', None)
os = browser_name = browser_version = bot = DATADOG_UNKNOWN
if agent:
parsed_agent = httpagentparser.detect(agent)
bot = parsed_agent.get('bot', False)
if 'os' in parsed_agent:
os = parsed_agent['os'].get('name', DATADOG_UNKNOWN)
if 'browser' in parsed_agent:
browser_version = parsed_agent['browser'].get('version', DATADOG_UNKNOWN)
browser_name = parsed_agent['browser'].get('name', DATADOG_UNKNOWN)
datadog_counter(JSERROR_COUNT, tags=[
'os:{}'.format(os),
'browser_version:{}'.format(browser_version),
'browser_name:{}'.format(browser_name),
'url:{}'.format(sanitize_url(request.POST.get('page', None))),
'file:{}'.format(request.POST.get('filename')),
'bot:{}'.format(bot),
])
return HttpResponse('')
@method_decorator([login_required], name='dispatch')
class BugReportView(View):
@property
def recipients(self):
return settings.BUG_REPORT_RECIPIENTS
def post(self, req, *args, **kwargs):
report = dict([(key, req.POST.get(key, '')) for key in (
'subject',
'username',
'domain',
'url',
'message',
'app_id',
'cc',
'email',
'500traceback',
'sentry_id',
)])
try:
couch_user = req.couch_user
full_name = couch_user.full_name
if couch_user.is_commcare_user():
email = report['email']
else:
email = couch_user.get_email()
except Exception:
full_name = None
email = report['email']
report['full_name'] = full_name
report['email'] = email or report['username']
if report['domain']:
domain = report['domain']
elif len(couch_user.domains) == 1:
# This isn't a domain page, but the user has only one domain, so let's use that
domain = couch_user.domains[0]
else:
domain = "<no domain>"
message = (
"username: {username}\n"
"full name: {full_name}\n"
"domain: {domain}\n"
"url: {url}\n"
).format(**report)
domain_object = Domain.get_by_name(domain) if report['domain'] else None
debug_context = {
'datetime': datetime.utcnow(),
'self_started': '<unknown>',
'scale_backend': '<unknown>',
'has_handoff_info': '<unknown>',
'project_description': '<unknown>',
'sentry_error': '{}{}'.format(getattr(settings, 'SENTRY_QUERY_URL'), report['sentry_id'])
}
if domain_object:
current_project_description = domain_object.project_description if domain_object else None
new_project_description = req.POST.get('project_description')
if (domain_object and
req.couch_user.is_domain_admin(domain=domain) and
new_project_description and current_project_description != new_project_description):
domain_object.project_description = new_project_description
domain_object.save()
message += ((
"software plan: {software_plan}\n"
).format(
software_plan=Subscription.get_subscribed_plan_by_domain(domain),
))
debug_context.update({
'self_started': domain_object.internal.self_started,
'scale_backend': should_use_sql_backend(domain),
'has_handoff_info': bool(domain_object.internal.partner_contact),
'project_description': domain_object.project_description,
})
subject = '{subject} ({domain})'.format(subject=report['subject'], domain=domain)
cc = [el for el in report['cc'].strip().split(",") if el]
if full_name and not any([c in full_name for c in '<>"']):
reply_to = '"{full_name}" <{email}>'.format(**report)
else:
reply_to = report['email']
# if the person looks like a commcare user, fogbugz can't reply
if settings.HQ_ACCOUNT_ROOT in reply_to:
reply_to = settings.SERVER_EMAIL
message += "Message:\n\n{message}\n".format(message=report['message'])
if req.POST.get('five-hundred-report'):
extra_message = ("This message was reported from a 500 error page! "
"Please fix this ASAP (as if you wouldn't anyway)...")
extra_debug_info = (
"datetime: {datetime}\n"
"Is self start: {self_started}\n"
"Is scale backend: {scale_backend}\n"
"Has Support Hand-off Info: {has_handoff_info}\n"
"Project description: {project_description}\n"
"Sentry Error: {sentry_error}\n"
).format(**debug_context)
traceback_info = cache.cache.get(report['500traceback']) or 'No traceback info available'
cache.cache.delete(report['500traceback'])
message = "\n\n".join([message, extra_debug_info, extra_message, traceback_info])
email = EmailMessage(
subject=subject,
body=message,
to=self.recipients,
headers={'Reply-To': reply_to},
cc=cc
)
uploaded_file = req.FILES.get('report_issue')
if uploaded_file:
filename = uploaded_file.name
content = uploaded_file.read()
email.attach(filename=filename, content=content)
# only fake the from email if it's an @dimagi.com account
is_icds_env = settings.SERVER_ENVIRONMENT in settings.ICDS_ENVS
if re.search(r'@dimagi\.com$', report['username']) and not is_icds_env:
email.from_email = report['username']
else:
email.from_email = settings.CCHQ_BUG_REPORT_EMAIL
email.send(fail_silently=False)
if req.POST.get('five-hundred-report'):
messages.success(
req,
"Your CommCare HQ Issue Report has been sent. We are working quickly to resolve this problem."
)
return HttpResponseRedirect(reverse('homepage'))
return HttpResponse()
def render_static(request, template, page_name):
return render(request, "hqwebapp/blank.html",
{'tmpl': template, 'page_name': page_name})
def cda(request):
return render_static(request, "cda.html", _("Content Distribution Agreement"))
def apache_license(request):
return render_static(request, "apache_license.html", _("Apache License"))
def bsd_license(request):
return render_static(request, "bsd_license.html", _("BSD License"))
class BasePageView(TemplateView):
urlname = None
page_title = None
template_name = 'hqwebapp/base_page.html'
@property
def page_name(self):
return self.page_title
@property
def page_url(self):
raise NotImplementedError()
@property
def parent_pages(self):
return []
@property
def main_context(self):
return {
'current_page': {
'page_name': self.page_name,
'title': self.page_title,
'url': self.page_url,
'parents': self.parent_pages,
},
}
@property
def page_context(self):
return {}
def get_context_data(self, **kwargs):
context = super(BasePageView, self).get_context_data(**kwargs)
context.update(self.main_context)
context.update(self.page_context)
return context
def render_to_response(self, context, **response_kwargs):
return render(self.request, self.template_name, context)
class BaseSectionPageView(BasePageView):
section_name = ""
template_name = "hqwebapp/base_section.html"
@property
def section_url(self):
raise NotImplementedError
@property
def main_context(self):
context = super(BaseSectionPageView, self).main_context
context.update({
'section': {
'page_name': self.section_name,
'url': self.section_url,
}
})
return context
class PaginatedItemException(Exception):
pass
class CRUDPaginatedViewMixin(object):
DEFAULT_LIMIT = 10
limit_text = ugettext_noop("items per page")
empty_notification = ugettext_noop("You have no items.")
loading_message = ugettext_noop("Loading...")
deleted_items_header = ugettext_noop("Deleted Items:")
new_items_header = ugettext_noop("New Items:")
def _safe_escape(self, expression, default):
try:
return expression()
except ValueError:
return default
@property
def parameters(self):
raise NotImplementedError("you need to implement get_param_source")
@property
@memoized
def page(self):
return self._safe_escape(
lambda: int(self.parameters.get('page', 1)),
1
)
@property
@memoized
def limit(self):
return self._safe_escape(
lambda: int(self.parameters.get('limit', self.DEFAULT_LIMIT)),
self.DEFAULT_LIMIT
)
@property
def total(self):
raise NotImplementedError("You must implement total.")
@property
def sort_by(self):
return self.parameters.GET.get('sortBy', 'abc')
@property
def skip(self):
return (self.page - 1) * self.limit
@property
def action(self):
action = self.parameters.get('action')
if action not in self.allowed_actions:
raise Http404()
return action
@property
def column_names(self):
raise NotImplementedError("you must return a list of column names")
@property
def pagination_context(self):
create_form = self.get_create_form()
return {
'pagination': {
'page': self.page,
'limit': self.limit,
'total': self.total,
'limit_options': list(range(self.DEFAULT_LIMIT, 51, self.DEFAULT_LIMIT)),
'column_names': self.column_names,
'num_columns': len(self.column_names),
'text': {
'limit': self.limit_text,
'empty': self.empty_notification,
'loading': self.loading_message,
'deleted_items': self.deleted_items_header,
'new_items': self.new_items_header,
},
'create_item_form': self.get_create_form_response(create_form) if create_form else None,
}
}
@property
def allowed_actions(self):
return [
'create',
'update',
'delete',
'paginate',
'refresh',
]
@property
def paginate_crud_response(self):
response = getattr(self, '%s_response' % self.action)
return HttpResponse(json.dumps(response, cls=LazyEncoder))
@property
def create_response(self):
create_form = self.get_create_form()
new_item = None
if create_form.is_valid():
new_item = self.get_create_item_data(create_form)
create_form = self.get_create_form(is_blank=True)
return {
'newItem': new_item,
'form': self.get_create_form_response(create_form)
}
@property
def update_response(self):
update_form = self.get_update_form()
updated_item = None
if update_form.is_valid():
updated_item = self.get_updated_item_data(update_form)
return {
'updatedItem': updated_item,
'form': self.get_update_form_response(update_form),
}
@property
def refresh_response(self):
try:
self.refresh_item(self.item_id)
except PaginatedItemException as e:
return {
'error': _("<strong>Problem Refreshing List:</strong> %s") % e,
}
return {
'success': True,
'currentPage': self.page,
'total': self.total,
'paginatedList': list(self.paginated_list),
}
@property
def delete_response(self):
try:
response = self.get_deleted_item_data(self.item_id)
return {
'deletedItem': response
}
except PaginatedItemException as e:
return {
'error': _("<strong>Problem Deleting:</strong> %s") % e,
}
@property
def item_id(self):
try:
return self.parameters['itemId']
except KeyError:
raise PaginatedItemException(_("The item's ID was not passed to the server."))
@property
def paginate_response(self):
return {
'success': True,
'currentPage': self.page,
'total': self.total,
'paginatedList': list(self.paginated_list),
}
@property
def paginated_list(self):
raise NotImplementedError("Return a list of data for the request response.")
def get_create_form(self, is_blank=False):
pass
def get_create_form_response(self, create_form):
return render_to_string(
'hqwebapp/includes/create_item_form.html', {
'form': create_form
}
)
def get_update_form(self, initial_data=None):
raise NotImplementedError("You must return a form object that will update an Item")
def get_update_form_response(self, update_form):
return render_to_string(
'hqwebapp/partials/update_item_form.html', {
'form': update_form
}
)
def refresh_item(self, item_id):
raise NotImplementedError("You must implement refresh_item")
def get_create_item_data(self, create_form):
raise NotImplementedError("You must implement get_new_item_data")
def get_updated_item_data(self, update_form):
raise NotImplementedError("You must implement get_updated_item_data")
def get_deleted_item_data(self, item_id):
raise NotImplementedError("You must implement get_deleted_item_data")
@login_required
def quick_find(request):
query = request.GET.get('q')
redirect = request.GET.get('redirect') != 'false'
if not query:
return HttpResponseBadRequest('GET param "q" must be provided')
def deal_with_doc(doc, domain, doc_info_fn):
if request.couch_user.is_superuser or (domain and request.couch_user.is_member_of(domain)):
doc_info = doc_info_fn(doc)
else:
raise Http404()
if redirect and doc_info.link:
messages.info(request, _("We've redirected you to the %s matching your query") % doc_info.type_display)
return HttpResponseRedirect(doc_info.link)
elif redirect and request.couch_user.is_superuser:
return HttpResponseRedirect('{}?id={}'.format(reverse('raw_doc'), doc.get('_id')))
else:
return json_response(doc_info)
couch_dbs = [None] + settings.COUCH_SETTINGS_HELPER.extra_db_names
for db_name in couch_dbs:
try:
doc = get_db(db_name).get(query)
except ResourceNotFound:
pass
else:
domain = doc.get('domain') or doc.get('domains', [None])[0]
doc_info_fn = functools.partial(get_doc_info, domain_hint=domain)
return deal_with_doc(doc, domain, doc_info_fn)
for accessor in (FormAccessorSQL.get_form, CaseAccessorSQL.get_case):
try:
doc = accessor(query)
except (XFormNotFound, CaseNotFound):
pass
else:
domain = doc.domain
return deal_with_doc(doc, domain, get_object_info)
for django_model in (SQLLocation,):
try:
if hasattr(django_model, 'by_id') and callable(django_model.by_id):
doc = django_model.by_id(query)
else:
doc = django_model.objects.get(pk=query)
except django_model.DoesNotExist:
continue
else:
if doc is None:
continue
domain = doc.domain
return deal_with_doc(doc, domain, get_object_info)
raise Http404()
def osdd(request, template='osdd.xml'):
response = render(request, template, {
'url_base': get_url_base(),
'env': get_environment_friendly_name()
})
response['Content-Type'] = 'application/xml'
return response
class MaintenanceAlertsView(BasePageView):
urlname = 'alerts'
page_title = ugettext_noop("Maintenance Alerts")
template_name = 'hqwebapp/maintenance_alerts.html'
@method_decorator(require_superuser)
def dispatch(self, request, *args, **kwargs):
return super(MaintenanceAlertsView, self).dispatch(request, *args, **kwargs)
@property
def page_context(self):
from corehq.apps.hqwebapp.models import MaintenanceAlert
return {
'alerts': [{
'created': six.text_type(alert.created),
'active': alert.active,
'html': alert.html,
'id': alert.id,
'domains': ", ".join(alert.domains) if alert.domains else "All domains",
} for alert in MaintenanceAlert.objects.order_by('-active', '-created')[:5]]
}
@property
def page_url(self):
return reverse(self.urlname)
@require_POST
@require_superuser
def create_alert(request):
from corehq.apps.hqwebapp.models import MaintenanceAlert
alert_text = request.POST.get('alert_text')
domains = request.POST.get('domains').split() or None
MaintenanceAlert(active=False, text=alert_text, domains=domains).save()
return HttpResponseRedirect(reverse('alerts'))
@require_POST
@require_superuser
def activate_alert(request):
from corehq.apps.hqwebapp.models import MaintenanceAlert
ma = MaintenanceAlert.objects.get(id=request.POST.get('alert_id'))
ma.active = True
ma.save()
return HttpResponseRedirect(reverse('alerts'))
@require_POST
@require_superuser
def deactivate_alert(request):
from corehq.apps.hqwebapp.models import MaintenanceAlert
ma = MaintenanceAlert.objects.get(id=request.POST.get('alert_id'))
ma.active = False
ma.save()
return HttpResponseRedirect(reverse('alerts'))
def redirect_to_dimagi(endpoint):
def _redirect(request, lang_code=None):
if settings.SERVER_ENVIRONMENT in [
'production',
'india',
'staging',
'changeme',
'localdev',
]:
return HttpResponsePermanentRedirect(
"https://www.dimagi.com/{}{}".format(
endpoint,
"?lang={}".format(lang_code) if lang_code else "",
)
)
return redirect_to_default(request)
return _redirect
def temporary_google_verify(request):
return render(request, "google9633af922b8b0064.html")
| true | true |
f7295da7d222acd8a82f33191d3b5261cefa3096 | 14,546 | py | Python | examples/pytorch/gcmc/model.py | Woooosz/dgl | 729ff2ef385f302af562c8305b1006d067d2067f | [
"Apache-2.0"
] | null | null | null | examples/pytorch/gcmc/model.py | Woooosz/dgl | 729ff2ef385f302af562c8305b1006d067d2067f | [
"Apache-2.0"
] | null | null | null | examples/pytorch/gcmc/model.py | Woooosz/dgl | 729ff2ef385f302af562c8305b1006d067d2067f | [
"Apache-2.0"
] | null | null | null | """NN modules"""
import torch as th
import torch.nn as nn
from torch.nn import init
import dgl.function as fn
import dgl.nn.pytorch as dglnn
from utils import get_activation
class GCMCGraphConv(nn.Module):
"""Graph convolution module used in the GCMC model.
Parameters
----------
in_feats : int
Input feature size.
out_feats : int
Output feature size.
weight : bool, optional
If True, apply a linear layer. Otherwise, aggregating the messages
without a weight matrix or with an shared weight provided by caller.
device: str, optional
Which device to put data in. Useful in mix_cpu_gpu training and
multi-gpu training
"""
def __init__(self,
in_feats,
out_feats,
weight=True,
device=None,
dropout_rate=0.0):
super(GCMCGraphConv, self).__init__()
self._in_feats = in_feats
self._out_feats = out_feats
self.device = device
self.dropout = nn.Dropout(dropout_rate)
if weight:
self.weight = nn.Parameter(th.Tensor(in_feats, out_feats))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self):
"""Reinitialize learnable parameters."""
if self.weight is not None:
init.xavier_uniform_(self.weight)
def forward(self, graph, feat, weight=None):
"""Compute graph convolution.
Normalizer constant :math:`c_{ij}` is stored as two node data "ci"
and "cj".
Parameters
----------
graph : DGLGraph
The graph.
feat : torch.Tensor
The input feature
weight : torch.Tensor, optional
Optional external weight tensor.
dropout : torch.nn.Dropout, optional
Optional external dropout layer.
Returns
-------
torch.Tensor
The output feature
"""
with graph.local_scope():
if isinstance(feat, tuple):
feat, _ = feat # dst feature not used
cj = graph.srcdata['cj']
ci = graph.dstdata['ci']
if self.device is not None:
cj = cj.to(self.device)
ci = ci.to(self.device)
if weight is not None:
if self.weight is not None:
raise DGLError('External weight is provided while at the same time the'
' module has defined its own weight parameter. Please'
' create the module with flag weight=False.')
else:
weight = self.weight
if weight is not None:
feat = dot_or_identity(feat, weight, self.device)
feat = feat * self.dropout(cj)
graph.srcdata['h'] = feat
graph.update_all(fn.copy_src(src='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.dstdata['h']
rst = rst * ci
return rst
class GCMCLayer(nn.Module):
r"""GCMC layer
.. math::
z_j^{(l+1)} = \sigma_{agg}\left[\mathrm{agg}\left(
\sum_{j\in\mathcal{N}_1}\frac{1}{c_{ij}}W_1h_j, \ldots,
\sum_{j\in\mathcal{N}_R}\frac{1}{c_{ij}}W_Rh_j
\right)\right]
After that, apply an extra output projection:
.. math::
h_j^{(l+1)} = \sigma_{out}W_oz_j^{(l+1)}
The equation is applied to both user nodes and movie nodes and the parameters
are not shared unless ``share_user_item_param`` is true.
Parameters
----------
rating_vals : list of int or float
Possible rating values.
user_in_units : int
Size of user input feature
movie_in_units : int
Size of movie input feature
msg_units : int
Size of message :math:`W_rh_j`
out_units : int
Size of of final output user and movie features
dropout_rate : float, optional
Dropout rate (Default: 0.0)
agg : str, optional
Function to aggregate messages of different ratings.
Could be any of the supported cross type reducers:
"sum", "max", "min", "mean", "stack".
(Default: "stack")
agg_act : callable, str, optional
Activation function :math:`sigma_{agg}`. (Default: None)
out_act : callable, str, optional
Activation function :math:`sigma_{agg}`. (Default: None)
share_user_item_param : bool, optional
If true, user node and movie node share the same set of parameters.
Require ``user_in_units`` and ``move_in_units`` to be the same.
(Default: False)
device: str, optional
Which device to put data in. Useful in mix_cpu_gpu training and
multi-gpu training
"""
def __init__(self,
rating_vals,
user_in_units,
movie_in_units,
msg_units,
out_units,
dropout_rate=0.0,
agg='stack', # or 'sum'
agg_act=None,
out_act=None,
share_user_item_param=False,
device=None):
super(GCMCLayer, self).__init__()
self.rating_vals = rating_vals
self.agg = agg
self.share_user_item_param = share_user_item_param
self.ufc = nn.Linear(msg_units, out_units)
if share_user_item_param:
self.ifc = self.ufc
else:
self.ifc = nn.Linear(msg_units, out_units)
if agg == 'stack':
# divide the original msg unit size by number of ratings to keep
# the dimensionality
assert msg_units % len(rating_vals) == 0
msg_units = msg_units // len(rating_vals)
self.dropout = nn.Dropout(dropout_rate)
self.W_r = nn.ParameterDict()
subConv = {}
for rating in rating_vals:
# PyTorch parameter name can't contain "."
rating = str(rating).replace('.', '_')
rev_rating = 'rev-%s' % rating
if share_user_item_param and user_in_units == movie_in_units:
self.W_r[rating] = nn.Parameter(th.randn(user_in_units, msg_units))
self.W_r['rev-%s' % rating] = self.W_r[rating]
subConv[rating] = GCMCGraphConv(user_in_units,
msg_units,
weight=False,
device=device,
dropout_rate=dropout_rate)
subConv[rev_rating] = GCMCGraphConv(user_in_units,
msg_units,
weight=False,
device=device,
dropout_rate=dropout_rate)
else:
self.W_r = None
subConv[rating] = GCMCGraphConv(user_in_units,
msg_units,
weight=True,
device=device,
dropout_rate=dropout_rate)
subConv[rev_rating] = GCMCGraphConv(movie_in_units,
msg_units,
weight=True,
device=device,
dropout_rate=dropout_rate)
self.conv = dglnn.HeteroGraphConv(subConv, aggregate=agg)
self.agg_act = get_activation(agg_act)
self.out_act = get_activation(out_act)
self.device = device
self.reset_parameters()
def partial_to(self, device):
"""Put parameters into device except W_r
Parameters
----------
device : torch device
Which device the parameters are put in.
"""
assert device == self.device
if device is not None:
self.ufc.cuda(device)
if self.share_user_item_param is False:
self.ifc.cuda(device)
self.dropout.cuda(device)
def reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, graph, ufeat=None, ifeat=None):
"""Forward function
Parameters
----------
graph : DGLHeteroGraph
User-movie rating graph. It should contain two node types: "user"
and "movie" and many edge types each for one rating value.
ufeat : torch.Tensor, optional
User features. If None, using an identity matrix.
ifeat : torch.Tensor, optional
Movie features. If None, using an identity matrix.
Returns
-------
new_ufeat : torch.Tensor
New user features
new_ifeat : torch.Tensor
New movie features
"""
in_feats = {'user' : ufeat, 'movie' : ifeat}
mod_args = {}
for i, rating in enumerate(self.rating_vals):
rating = str(rating).replace('.', '_')
rev_rating = 'rev-%s' % rating
mod_args[rating] = (self.W_r[rating] if self.W_r is not None else None,)
mod_args[rev_rating] = (self.W_r[rev_rating] if self.W_r is not None else None,)
out_feats = self.conv(graph, in_feats, mod_args=mod_args)
ufeat = out_feats['user']
ifeat = out_feats['movie']
ufeat = ufeat.view(ufeat.shape[0], -1)
ifeat = ifeat.view(ifeat.shape[0], -1)
# fc and non-linear
ufeat = self.agg_act(ufeat)
ifeat = self.agg_act(ifeat)
ufeat = self.dropout(ufeat)
ifeat = self.dropout(ifeat)
ufeat = self.ufc(ufeat)
ifeat = self.ifc(ifeat)
return self.out_act(ufeat), self.out_act(ifeat)
class BiDecoder(nn.Module):
r"""Bi-linear decoder.
Given a bipartite graph G, for each edge (i, j) ~ G, compute the likelihood
of it being class r by:
.. math::
p(M_{ij}=r) = \text{softmax}(u_i^TQ_rv_j)
The trainable parameter :math:`Q_r` is further decomposed to a linear
combination of basis weight matrices :math:`P_s`:
.. math::
Q_r = \sum_{s=1}^{b} a_{rs}P_s
Parameters
----------
in_units : int
Size of input user and movie features
num_classes : int
Number of classes.
num_basis : int, optional
Number of basis. (Default: 2)
dropout_rate : float, optional
Dropout raite (Default: 0.0)
"""
def __init__(self,
in_units,
num_classes,
num_basis=2,
dropout_rate=0.0):
super(BiDecoder, self).__init__()
self._num_basis = num_basis
self.dropout = nn.Dropout(dropout_rate)
self.Ps = nn.ParameterList()
for i in range(num_basis):
self.Ps.append(nn.Parameter(th.randn(in_units, in_units)))
self.combine_basis = nn.Linear(self._num_basis, num_classes, bias=False)
self.reset_parameters()
def reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, graph, ufeat, ifeat):
"""Forward function.
Parameters
----------
graph : DGLHeteroGraph
"Flattened" user-movie graph with only one edge type.
ufeat : th.Tensor
User embeddings. Shape: (|V_u|, D)
ifeat : th.Tensor
Movie embeddings. Shape: (|V_m|, D)
Returns
-------
th.Tensor
Predicting scores for each user-movie edge.
"""
with graph.local_scope():
ufeat = self.dropout(ufeat)
ifeat = self.dropout(ifeat)
graph.nodes['movie'].data['h'] = ifeat
basis_out = []
for i in range(self._num_basis):
graph.nodes['user'].data['h'] = ufeat @ self.Ps[i]
graph.apply_edges(fn.u_dot_v('h', 'h', 'sr'))
basis_out.append(graph.edata['sr'])
out = th.cat(basis_out, dim=1)
out = self.combine_basis(out)
return out
class DenseBiDecoder(BiDecoder):
r"""Dense bi-linear decoder.
Dense implementation of the bi-linear decoder used in GCMC. Suitable when
the graph can be efficiently represented by a pair of arrays (one for source
nodes; one for destination nodes).
Parameters
----------
in_units : int
Size of input user and movie features
num_classes : int
Number of classes.
num_basis : int, optional
Number of basis. (Default: 2)
dropout_rate : float, optional
Dropout raite (Default: 0.0)
"""
def __init__(self,
in_units,
num_classes,
num_basis=2,
dropout_rate=0.0):
super(DenseBiDecoder, self).__init__(in_units,
num_classes,
num_basis,
dropout_rate)
def forward(self, ufeat, ifeat):
"""Forward function.
Compute logits for each pair ``(ufeat[i], ifeat[i])``.
Parameters
----------
ufeat : th.Tensor
User embeddings. Shape: (B, D)
ifeat : th.Tensor
Movie embeddings. Shape: (B, D)
Returns
-------
th.Tensor
Predicting scores for each user-movie edge. Shape: (B, num_classes)
"""
ufeat = self.dropout(ufeat)
ifeat = self.dropout(ifeat)
basis_out = []
for i in range(self._num_basis):
ufeat_i = ufeat @ self.Ps[i]
out = th.einsum('ab,ab->a', ufeat_i, ifeat)
basis_out.append(out.unsqueeze(1))
out = th.cat(basis_out, dim=1)
out = self.combine_basis(out)
return out
def dot_or_identity(A, B, device=None):
# if A is None, treat as identity matrix
if A is None:
return B
elif len(A.shape) == 1:
if device is None:
return B[A]
else:
return B[A].to(device)
else:
return A @ B
| 35.050602 | 92 | 0.532586 | import torch as th
import torch.nn as nn
from torch.nn import init
import dgl.function as fn
import dgl.nn.pytorch as dglnn
from utils import get_activation
class GCMCGraphConv(nn.Module):
def __init__(self,
in_feats,
out_feats,
weight=True,
device=None,
dropout_rate=0.0):
super(GCMCGraphConv, self).__init__()
self._in_feats = in_feats
self._out_feats = out_feats
self.device = device
self.dropout = nn.Dropout(dropout_rate)
if weight:
self.weight = nn.Parameter(th.Tensor(in_feats, out_feats))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self):
if self.weight is not None:
init.xavier_uniform_(self.weight)
def forward(self, graph, feat, weight=None):
with graph.local_scope():
if isinstance(feat, tuple):
feat, _ = feat
cj = graph.srcdata['cj']
ci = graph.dstdata['ci']
if self.device is not None:
cj = cj.to(self.device)
ci = ci.to(self.device)
if weight is not None:
if self.weight is not None:
raise DGLError('External weight is provided while at the same time the'
' module has defined its own weight parameter. Please'
' create the module with flag weight=False.')
else:
weight = self.weight
if weight is not None:
feat = dot_or_identity(feat, weight, self.device)
feat = feat * self.dropout(cj)
graph.srcdata['h'] = feat
graph.update_all(fn.copy_src(src='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.dstdata['h']
rst = rst * ci
return rst
class GCMCLayer(nn.Module):
def __init__(self,
rating_vals,
user_in_units,
movie_in_units,
msg_units,
out_units,
dropout_rate=0.0,
agg='stack',
agg_act=None,
out_act=None,
share_user_item_param=False,
device=None):
super(GCMCLayer, self).__init__()
self.rating_vals = rating_vals
self.agg = agg
self.share_user_item_param = share_user_item_param
self.ufc = nn.Linear(msg_units, out_units)
if share_user_item_param:
self.ifc = self.ufc
else:
self.ifc = nn.Linear(msg_units, out_units)
if agg == 'stack':
assert msg_units % len(rating_vals) == 0
msg_units = msg_units // len(rating_vals)
self.dropout = nn.Dropout(dropout_rate)
self.W_r = nn.ParameterDict()
subConv = {}
for rating in rating_vals:
rating = str(rating).replace('.', '_')
rev_rating = 'rev-%s' % rating
if share_user_item_param and user_in_units == movie_in_units:
self.W_r[rating] = nn.Parameter(th.randn(user_in_units, msg_units))
self.W_r['rev-%s' % rating] = self.W_r[rating]
subConv[rating] = GCMCGraphConv(user_in_units,
msg_units,
weight=False,
device=device,
dropout_rate=dropout_rate)
subConv[rev_rating] = GCMCGraphConv(user_in_units,
msg_units,
weight=False,
device=device,
dropout_rate=dropout_rate)
else:
self.W_r = None
subConv[rating] = GCMCGraphConv(user_in_units,
msg_units,
weight=True,
device=device,
dropout_rate=dropout_rate)
subConv[rev_rating] = GCMCGraphConv(movie_in_units,
msg_units,
weight=True,
device=device,
dropout_rate=dropout_rate)
self.conv = dglnn.HeteroGraphConv(subConv, aggregate=agg)
self.agg_act = get_activation(agg_act)
self.out_act = get_activation(out_act)
self.device = device
self.reset_parameters()
def partial_to(self, device):
assert device == self.device
if device is not None:
self.ufc.cuda(device)
if self.share_user_item_param is False:
self.ifc.cuda(device)
self.dropout.cuda(device)
def reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, graph, ufeat=None, ifeat=None):
in_feats = {'user' : ufeat, 'movie' : ifeat}
mod_args = {}
for i, rating in enumerate(self.rating_vals):
rating = str(rating).replace('.', '_')
rev_rating = 'rev-%s' % rating
mod_args[rating] = (self.W_r[rating] if self.W_r is not None else None,)
mod_args[rev_rating] = (self.W_r[rev_rating] if self.W_r is not None else None,)
out_feats = self.conv(graph, in_feats, mod_args=mod_args)
ufeat = out_feats['user']
ifeat = out_feats['movie']
ufeat = ufeat.view(ufeat.shape[0], -1)
ifeat = ifeat.view(ifeat.shape[0], -1)
# fc and non-linear
ufeat = self.agg_act(ufeat)
ifeat = self.agg_act(ifeat)
ufeat = self.dropout(ufeat)
ifeat = self.dropout(ifeat)
ufeat = self.ufc(ufeat)
ifeat = self.ifc(ifeat)
return self.out_act(ufeat), self.out_act(ifeat)
class BiDecoder(nn.Module):
def __init__(self,
in_units,
num_classes,
num_basis=2,
dropout_rate=0.0):
super(BiDecoder, self).__init__()
self._num_basis = num_basis
self.dropout = nn.Dropout(dropout_rate)
self.Ps = nn.ParameterList()
for i in range(num_basis):
self.Ps.append(nn.Parameter(th.randn(in_units, in_units)))
self.combine_basis = nn.Linear(self._num_basis, num_classes, bias=False)
self.reset_parameters()
def reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, graph, ufeat, ifeat):
with graph.local_scope():
ufeat = self.dropout(ufeat)
ifeat = self.dropout(ifeat)
graph.nodes['movie'].data['h'] = ifeat
basis_out = []
for i in range(self._num_basis):
graph.nodes['user'].data['h'] = ufeat @ self.Ps[i]
graph.apply_edges(fn.u_dot_v('h', 'h', 'sr'))
basis_out.append(graph.edata['sr'])
out = th.cat(basis_out, dim=1)
out = self.combine_basis(out)
return out
class DenseBiDecoder(BiDecoder):
def __init__(self,
in_units,
num_classes,
num_basis=2,
dropout_rate=0.0):
super(DenseBiDecoder, self).__init__(in_units,
num_classes,
num_basis,
dropout_rate)
def forward(self, ufeat, ifeat):
ufeat = self.dropout(ufeat)
ifeat = self.dropout(ifeat)
basis_out = []
for i in range(self._num_basis):
ufeat_i = ufeat @ self.Ps[i]
out = th.einsum('ab,ab->a', ufeat_i, ifeat)
basis_out.append(out.unsqueeze(1))
out = th.cat(basis_out, dim=1)
out = self.combine_basis(out)
return out
def dot_or_identity(A, B, device=None):
# if A is None, treat as identity matrix
if A is None:
return B
elif len(A.shape) == 1:
if device is None:
return B[A]
else:
return B[A].to(device)
else:
return A @ B
| true | true |
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