xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

1722d7bd59551285b9b6398b2f0e801fc249803e

36

py

Python

build/lib/annotation_utils/old/util/checks/__init__.py

HienDT27/annotation_utils

1f4e95f4cfa08de5bbab20f90a6a75fba66a69b9

[ "MIT" ]

13

2020-01-28T04:45:22.000Z

2022-03-10T03:35:49.000Z

build/lib/annotation_utils/old/util/checks/__init__.py

HienDT27/annotation_utils

1f4e95f4cfa08de5bbab20f90a6a75fba66a69b9

[ "MIT" ]

4

2020-02-14T08:56:03.000Z

2021-05-21T10:38:30.000Z

build/lib/annotation_utils/old/util/checks/__init__.py

HienDT27/annotation_utils

1f4e95f4cfa08de5bbab20f90a6a75fba66a69b9

[ "MIT" ]

7

2020-04-10T07:56:25.000Z

2021-12-17T11:19:23.000Z

from .checks import check_shape_type

36

0.888889

6

36

5

1

0

0.083333

36

1

36

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

17483bae28c2421ff4d530529899de72f0aa2372

2,864

py

Python

signmob/collection/migrations/0002_auto_20190623_1521.py

okfde/signmob

7bc4c2eff988287c49ed2aea20b6d18f5461e3cc

[ "MIT" ]

2

2019-07-08T15:49:16.000Z

2019-07-11T20:38:59.000Z

signmob/collection/migrations/0002_auto_20190623_1521.py

okfde/signmob

7bc4c2eff988287c49ed2aea20b6d18f5461e3cc

[ "MIT" ]

2

2020-07-17T17:27:02.000Z

2021-05-10T00:16:53.000Z

signmob/collection/migrations/0002_auto_20190623_1521.py

okfde/signmob

7bc4c2eff988287c49ed2aea20b6d18f5461e3cc

[ "MIT" ]

null

# Generated by Django 2.2.2 on 2019-06-23 13:21 import django.contrib.gis.db.models.fields from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("schedule", "0011_event_calendar_not_null"), ("collection", "0001_initial"), ] operations = [ migrations.AddField( model_name="collectionevent", name="description", field=models.TextField(blank=True), ), migrations.AddField( model_name="collectionevent", name="event_occurence", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to="schedule.Occurrence", ), ), migrations.AddField( model_name="collectionevent", name="geo", field=django.contrib.gis.db.models.fields.PointField( blank=True, geography=True, null=True, srid=4326 ), ), migrations.AddField( model_name="collectionevent", name="name", field=models.CharField(blank=True, max_length=255), ), migrations.AddField( model_name="collectioneventmember", name="end", field=models.DateTimeField(blank=True, null=True), ), migrations.AddField( model_name="collectioneventmember", name="start", field=models.DateTimeField(blank=True, null=True), ), migrations.AddField( model_name="collectiongroup", name="calendar", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to="schedule.Calendar", ), ), migrations.AddField( model_name="collectiongroup", name="name", field=models.CharField(blank=True, max_length=255), ), migrations.AddField( model_name="collectionlocation", name="description", field=models.TextField(blank=True), ), migrations.AddField( model_name="collectionlocation", name="geo", field=django.contrib.gis.db.models.fields.PointField( blank=True, geography=True, null=True, srid=4326 ), ), migrations.AddField( model_name="collectionlocation", name="name", field=models.CharField(blank=True, max_length=255), ), migrations.AddField( model_name="collectionlocation", name="start", field=models.DateField(null=True), ), ]

31.472527

65

0.545042

251

2,864

6.119522

0.250996

0.140625

0.179688

0.210938

0.810547

0.61849

0

0.021288

0.343925

2,864

90

66

31.822222

0.796168

0.015712

0

0.809524

1

0

0.132765

0.024849

0

1

0

false

0

0.035714

0

0.071429

0

null

0

1

0

1

0

1

0

null

0

6

17625bb145d191c3d2942ec3c2f333df083dab7c

81

py

Python

clinical_study_questionnaire/api.py

somushiv/clinical_study_questionnaire

309054dd4a27d8814f477517cba80f26f464b648

[ "MIT" ]

null

clinical_study_questionnaire/api.py

somushiv/clinical_study_questionnaire

309054dd4a27d8814f477517cba80f26f464b648

[ "MIT" ]

null

clinical_study_questionnaire/api.py

somushiv/clinical_study_questionnaire

309054dd4a27d8814f477517cba80f26f464b648

[ "MIT" ]

null

import frappe @frappe.whitelist() def testapi(): return "Clinical Test Api"

13.5

30

0.716049

10

81

5.8

0.9

0

0.17284

81

5

31

16.2

0.865672

0

0.209877

0

1

0.25

true

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

6

176540deca60d672350520a6b7dba9466859f3d3

26,963

py

Python

spark_fhir_schemas/stu3/complex_types/messagedefinition.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

2

2020-10-31T23:25:01.000Z

2021-06-09T14:12:42.000Z

spark_fhir_schemas/stu3/complex_types/messagedefinition.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/stu3/complex_types/messagedefinition.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import ( StructType, StructField, StringType, ArrayType, BooleanType, DataType, ) # This file is auto-generated by generate_schema so do not edit manually # noinspection PyPep8Naming class MessageDefinitionSchema: """ Defines the characteristics of a message that can be shared between systems, including the type of event that initiates the message, the content to be transmitted and what response(s), if any, are permitted. """ # noinspection PyDefaultArgument @staticmethod def get_schema( max_nesting_depth: Optional[int] = 6, nesting_depth: int = 0, nesting_list: List[str] = [], max_recursion_limit: Optional[int] = 2, include_extension: Optional[bool] = False, extension_fields: Optional[List[str]] = [ "valueBoolean", "valueCode", "valueDate", "valueDateTime", "valueDecimal", "valueId", "valueInteger", "valuePositiveInt", "valueString", "valueTime", "valueUnsignedInt", "valueUri", "valueQuantity", ], extension_depth: int = 0, max_extension_depth: Optional[int] = 2, ) -> Union[StructType, DataType]: """ Defines the characteristics of a message that can be shared between systems, including the type of event that initiates the message, the content to be transmitted and what response(s), if any, are permitted. id: The logical id of the resource, as used in the URL for the resource. Once assigned, this value never changes. extension: May be used to represent additional information that is not part of the basic definition of the resource. In order to make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer is allowed to define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. meta: The metadata about the resource. This is content that is maintained by the infrastructure. Changes to the content may not always be associated with version changes to the resource. implicitRules: A reference to a set of rules that were followed when the resource was constructed, and which must be understood when processing the content. language: The base language in which the resource is written. text: A human-readable narrative that contains a summary of the resource, and may be used to represent the content of the resource to a human. The narrative need not encode all the structured data, but is required to contain sufficient detail to make it "clinically safe" for a human to just read the narrative. Resource definitions may define what content should be represented in the narrative to ensure clinical safety. contained: These resources do not have an independent existence apart from the resource that contains them - they cannot be identified independently, and nor can they have their own independent transaction scope. resourceType: This is a MessageDefinition resource url: An absolute URI that is used to identify this message definition when it is referenced in a specification, model, design or an instance. This SHALL be a URL, SHOULD be globally unique, and SHOULD be an address at which this message definition is (or will be) published. The URL SHOULD include the major version of the message definition. For more information see [Technical and Business Versions](resource.html#versions). identifier: A formal identifier that is used to identify this message definition when it is represented in other formats, or referenced in a specification, model, design or an instance. version: The identifier that is used to identify this version of the message definition when it is referenced in a specification, model, design or instance. This is an arbitrary value managed by the message definition author and is not expected to be globally unique. For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is not available. There is also no expectation that versions can be placed in a lexicographical sequence. name: A natural language name identifying the message definition. This name should be usable as an identifier for the module by machine processing applications such as code generation. title: A short, descriptive, user-friendly title for the message definition. status: The status of this message definition. Enables tracking the life-cycle of the content. experimental: A boolean value to indicate that this message definition is authored for testing purposes (or education/evaluation/marketing), and is not intended to be used for genuine usage. date: The date (and optionally time) when the message definition was published. The date must change if and when the business version changes and it must change if the status code changes. In addition, it should change when the substantive content of the message definition changes. publisher: The name of the individual or organization that published the message definition. contact: Contact details to assist a user in finding and communicating with the publisher. description: A free text natural language description of the message definition from a consumer's perspective. useContext: The content was developed with a focus and intent of supporting the contexts that are listed. These terms may be used to assist with indexing and searching for appropriate message definition instances. jurisdiction: A legal or geographic region in which the message definition is intended to be used. purpose: Explaination of why this message definition is needed and why it has been designed as it has. copyright: A copyright statement relating to the message definition and/or its contents. Copyright statements are generally legal restrictions on the use and publishing of the message definition. base: The MessageDefinition that is the basis for the contents of this resource. parent: Identifies a protocol or workflow that this MessageDefinition represents a step in. replaces: A MessageDefinition that is superseded by this definition. event: A coded identifier of a supported messaging event. category: The impact of the content of the message. focus: Identifies the resource (or resources) that are being addressed by the event. For example, the Encounter for an admit message or two Account records for a merge. responseRequired: Indicates whether a response is required for this message. allowedResponse: Indicates what types of messages may be sent as an application-level response to this message. """ from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema from spark_fhir_schemas.stu3.complex_types.meta import MetaSchema from spark_fhir_schemas.stu3.complex_types.narrative import NarrativeSchema from spark_fhir_schemas.stu3.simple_types.resourcelist import ResourceListSchema from spark_fhir_schemas.stu3.complex_types.identifier import IdentifierSchema from spark_fhir_schemas.stu3.complex_types.contactdetail import ( ContactDetailSchema, ) from spark_fhir_schemas.stu3.complex_types.usagecontext import ( UsageContextSchema, ) from spark_fhir_schemas.stu3.complex_types.codeableconcept import ( CodeableConceptSchema, ) from spark_fhir_schemas.stu3.complex_types.reference import ReferenceSchema from spark_fhir_schemas.stu3.complex_types.coding import CodingSchema from spark_fhir_schemas.stu3.complex_types.messagedefinition_focus import ( MessageDefinition_FocusSchema, ) from spark_fhir_schemas.stu3.complex_types.messagedefinition_allowedresponse import ( MessageDefinition_AllowedResponseSchema, ) if ( max_recursion_limit and nesting_list.count("MessageDefinition") >= max_recursion_limit ) or (max_nesting_depth and nesting_depth >= max_nesting_depth): return StructType([StructField("id", StringType(), True)]) # add my name to recursion list for later my_nesting_list: List[str] = nesting_list + ["MessageDefinition"] schema = StructType( [ # The logical id of the resource, as used in the URL for the resource. Once # assigned, this value never changes. StructField("id", StringType(), True), # May be used to represent additional information that is not part of the basic # definition of the resource. In order to make the use of extensions safe and # manageable, there is a strict set of governance applied to the definition and # use of extensions. Though any implementer is allowed to define an extension, # there is a set of requirements that SHALL be met as part of the definition of # the extension. StructField( "extension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # The metadata about the resource. This is content that is maintained by the # infrastructure. Changes to the content may not always be associated with # version changes to the resource. StructField( "meta", MetaSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # A reference to a set of rules that were followed when the resource was # constructed, and which must be understood when processing the content. StructField("implicitRules", StringType(), True), # The base language in which the resource is written. StructField("language", StringType(), True), # A human-readable narrative that contains a summary of the resource, and may be # used to represent the content of the resource to a human. The narrative need # not encode all the structured data, but is required to contain sufficient # detail to make it "clinically safe" for a human to just read the narrative. # Resource definitions may define what content should be represented in the # narrative to ensure clinical safety. StructField( "text", NarrativeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # These resources do not have an independent existence apart from the resource # that contains them - they cannot be identified independently, and nor can they # have their own independent transaction scope. StructField( "contained", ArrayType( ResourceListSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # This is a MessageDefinition resource StructField("resourceType", StringType(), True), # An absolute URI that is used to identify this message definition when it is # referenced in a specification, model, design or an instance. This SHALL be a # URL, SHOULD be globally unique, and SHOULD be an address at which this message # definition is (or will be) published. The URL SHOULD include the major version # of the message definition. For more information see [Technical and Business # Versions](resource.html#versions). StructField("url", StringType(), True), # A formal identifier that is used to identify this message definition when it # is represented in other formats, or referenced in a specification, model, # design or an instance. StructField( "identifier", IdentifierSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # The identifier that is used to identify this version of the message definition # when it is referenced in a specification, model, design or instance. This is # an arbitrary value managed by the message definition author and is not # expected to be globally unique. For example, it might be a timestamp (e.g. # yyyymmdd) if a managed version is not available. There is also no expectation # that versions can be placed in a lexicographical sequence. StructField("version", StringType(), True), # A natural language name identifying the message definition. This name should # be usable as an identifier for the module by machine processing applications # such as code generation. StructField("name", StringType(), True), # A short, descriptive, user-friendly title for the message definition. StructField("title", StringType(), True), # The status of this message definition. Enables tracking the life-cycle of the # content. StructField("status", StringType(), True), # A boolean value to indicate that this message definition is authored for # testing purposes (or education/evaluation/marketing), and is not intended to # be used for genuine usage. StructField("experimental", BooleanType(), True), # The date (and optionally time) when the message definition was published. The # date must change if and when the business version changes and it must change # if the status code changes. In addition, it should change when the substantive # content of the message definition changes. StructField("date", StringType(), True), # The name of the individual or organization that published the message # definition. StructField("publisher", StringType(), True), # Contact details to assist a user in finding and communicating with the # publisher. StructField( "contact", ArrayType( ContactDetailSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # A free text natural language description of the message definition from a # consumer's perspective. StructField("description", StringType(), True), # The content was developed with a focus and intent of supporting the contexts # that are listed. These terms may be used to assist with indexing and searching # for appropriate message definition instances. StructField( "useContext", ArrayType( UsageContextSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # A legal or geographic region in which the message definition is intended to be # used. StructField( "jurisdiction", ArrayType( CodeableConceptSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # Explaination of why this message definition is needed and why it has been # designed as it has. StructField("purpose", StringType(), True), # A copyright statement relating to the message definition and/or its contents. # Copyright statements are generally legal restrictions on the use and # publishing of the message definition. StructField("copyright", StringType(), True), # The MessageDefinition that is the basis for the contents of this resource. StructField( "base", ReferenceSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # Identifies a protocol or workflow that this MessageDefinition represents a # step in. StructField( "parent", ArrayType( ReferenceSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # A MessageDefinition that is superseded by this definition. StructField( "replaces", ArrayType( ReferenceSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # A coded identifier of a supported messaging event. StructField( "event", CodingSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # The impact of the content of the message. StructField("category", StringType(), True), # Identifies the resource (or resources) that are being addressed by the event. # For example, the Encounter for an admit message or two Account records for a # merge. StructField( "focus", ArrayType( MessageDefinition_FocusSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # Indicates whether a response is required for this message. StructField("responseRequired", BooleanType(), True), # Indicates what types of messages may be sent as an application-level response # to this message. StructField( "allowedResponse", ArrayType( MessageDefinition_AllowedResponseSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] return schema

51.851923

102

0.573156

2,684

26,963

5.60693

0.144188

0.048641

0.030899

0.044654

0.817662

0.808426

0.786896

0.763572

0.751479

0

0.002231

0.385046

26,963

519

103

51.95183

0.905374

0.399325

0

0.588235

0

0.028881

0

1

0.003268

false

0

0.045752

0

0.058824

0

null

0

1

0

null

0

6

bd7122736e7beb5cb96f82e02cf48ec3419fa5b3

122

py

Python

desc/local/__init__.py

LSSTDESC/desc-wfmon

fa73ee1a00e9503e6bd82d1f81d9806fd9623783

[ "BSD-3-Clause" ]

null

desc/local/__init__.py

LSSTDESC/desc-wfmon

fa73ee1a00e9503e6bd82d1f81d9806fd9623783

[ "BSD-3-Clause" ]

null

desc/local/__init__.py

LSSTDESC/desc-wfmon

fa73ee1a00e9503e6bd82d1f81d9806fd9623783

[ "BSD-3-Clause" ]

null

import importlib.metadata __version__ = importlib.metadata.version('desc-wfmon') from .local import install_dir, install

24.4

54

0.819672

15

122

6.333333

0.666667

0.357895

0.505263

0

0.090164

122

4

55

30.5

0.855856

0

0.081967

0

1

0

false

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

bdb05dd6d10338b636184233531f9dd118617696

35

py

Python

simulators/__init__.py

nisarkhanatwork/mctsnet

2ff9e8234bd4a944246aab803e3dd07082042f62

[ "Apache-2.0" ]

5

2021-03-02T09:11:58.000Z

2022-03-11T03:57:03.000Z

simulators/__init__.py

nisarkhanatwork/mctsnet

2ff9e8234bd4a944246aab803e3dd07082042f62

[ "Apache-2.0" ]

null

simulators/__init__.py

nisarkhanatwork/mctsnet

2ff9e8234bd4a944246aab803e3dd07082042f62

[ "Apache-2.0" ]

1

2021-02-19T20:22:46.000Z

from .rocksample import RockSample

17.5

34

0.857143

4

35

7.5

0.75

0

0.114286

35

1

35

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bdea135cafcb6901270c6857aad6aade3d3857ad

414

py

Python

mlqm/models/__init__.py

Nuclear-Physics-with-Machine-Learning/MLQM

69472921b130abb530b11840ab8c1b8c608b5089

[ "Apache-2.0" ]

8

2021-05-13T13:58:56.000Z

2022-02-28T22:11:06.000Z

mlqm/models/__init__.py

coreyjadams/AI-for-QM

69472921b130abb530b11840ab8c1b8c608b5089

[ "Apache-2.0" ]

1

2021-09-23T01:44:26.000Z

2021-09-23T17:51:43.000Z

mlqm/models/__init__.py

coreyjadams/AI-for-QM

69472921b130abb530b11840ab8c1b8c608b5089

[ "Apache-2.0" ]

1

2022-03-15T07:18:24.000Z

from .HarmonicOscillatorWavefunction import HarmonicOscillatorWavefunction from .PolynomialWavefunction import PolynomialWavefunction from .NeuralWavefunction import NeuralWavefunction from .DeepSetsWavefunction import DeepSetsWavefunction from .GaussianBoundaryCondition import GaussianBoundaryCondition from .ExponentialBoundaryCondition import ExponentialBoundaryCondition

51.75

74

0.835749

24

414

14.416667

0.333333

0

0.149758

414

7

75

59.142857

0.982955

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

da11ccf3a1c0725aff46c3a602d61cc3af53ea9a

38

py

Python

segmentation_research/backbones/__init__.py

cj-mclaughlin/segmentation_research

6d59ffccdb274430b2ef02258d120f65db9004d5

[ "MIT" ]

1

2021-07-19T04:46:46.000Z

segmentation_research/backbones/__init__.py

cj-mclaughlin/segmentation_research

6d59ffccdb274430b2ef02258d120f65db9004d5

[ "MIT" ]

null

segmentation_research/backbones/__init__.py

cj-mclaughlin/segmentation_research

6d59ffccdb274430b2ef02258d120f65db9004d5

[ "MIT" ]

null

from . import drn from . import resnet

19

20

0.763158

6

38

4.833333

0.666667

0.689655

0

0.184211

38

2

20

19

0.935484

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

da1d1560bbdf5a9ffa5baf84d7f53888d53d3c69

164

py

Python

2020/gcd.py

kyz/adventofcode

b3dd544624a8fc313ca1fad0d2f02f53bd79ce3d

[ "MIT" ]

null

2020/gcd.py

kyz/adventofcode

b3dd544624a8fc313ca1fad0d2f02f53bd79ce3d

[ "MIT" ]

null

2020/gcd.py

kyz/adventofcode

b3dd544624a8fc313ca1fad0d2f02f53bd79ce3d

[ "MIT" ]

null

# greatest common divisor def gcd(a, b): while b: a, b = b, a % b return a # lowest common multiple def lcm(a, b): return (a * b) // gcd(a, b)

16.4

31

0.536585

29

164

3.034483

0.413793

0.136364

0.113636

0.204545

0

0.323171

164

9

32

18.222222

0.792793

0.280488

0

1

0.333333

false

0

0.166667

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

da331d6ed56bea648f9b83dbf9063a03d473b295

510

py

Python

lib/hachoir/parser/program/__init__.py

0x20Man/Watcher3

4656b42bc5879a3741bb95f534b7c6612a25264d

[ "Apache-2.0" ]

320

2017-03-28T23:33:45.000Z

2022-02-17T08:45:01.000Z

lib/hachoir/parser/program/__init__.py

0x20Man/Watcher3

4656b42bc5879a3741bb95f534b7c6612a25264d

[ "Apache-2.0" ]

300

2017-03-28T19:22:54.000Z

2021-12-01T01:11:55.000Z

lib/hachoir/parser/program/__init__.py

0x20Man/Watcher3

4656b42bc5879a3741bb95f534b7c6612a25264d

[ "Apache-2.0" ]

90

2017-03-29T16:12:43.000Z

2022-03-01T06:23:48.000Z

from hachoir.parser.program.elf import ElfFile # noqa from hachoir.parser.program.exe import ExeFile # noqa from hachoir.parser.program.macho import MachoFile, MachoFatFile # noqa from hachoir.parser.program.python import PythonCompiledFile # noqa from hachoir.parser.program.java import JavaCompiledClassFile # noqa from hachoir.parser.program.prc import PRCFile # noqa from hachoir.parser.program.nds import NdsFile # noqa from hachoir.parser.program.java_serialized import JavaSerializedFile # noqa

56.666667

77

0.823529

66

510

6.348485

0.348485

0.210024

0.324582

0.458234

0.486874

0.152745

0

0.111765

510

8

78

63.75

0.924945

0.076471

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e50608b6153d6ba2a0d6a5a4eb0bb78742d77e72

39

py

Python

terrainbento/derived_models/model_100_basicSt/__init__.py

mcflugen/terrainbento

1b756477b8a8ab6a8f1275b1b30ec84855c840ea

[ "MIT" ]

null

terrainbento/derived_models/model_100_basicSt/__init__.py

mcflugen/terrainbento

1b756477b8a8ab6a8f1275b1b30ec84855c840ea

[ "MIT" ]

null

terrainbento/derived_models/model_100_basicSt/__init__.py

mcflugen/terrainbento

1b756477b8a8ab6a8f1275b1b30ec84855c840ea

[ "MIT" ]

null

from .model_100_basicSt import BasicSt

19.5

38

0.871795

6

39

5.333333

0.833333

0

0.085714

0.102564

39

1

39

0.828571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e5bc0993b907f649cbf9838455bf521acfec0b7f

50

py

Python

cs250/t1.py

icterguru/DrLutchClass

4ae75e047d00e36af7fd5019a7d751a44bc7daa8

[ "Apache-2.0" ]

null

cs250/t1.py

icterguru/DrLutchClass

4ae75e047d00e36af7fd5019a7d751a44bc7daa8

[ "Apache-2.0" ]

null

cs250/t1.py

icterguru/DrLutchClass

4ae75e047d00e36af7fd5019a7d751a44bc7daa8

[ "Apache-2.0" ]

1

2018-09-20T20:50:08.000Z

print "Hello, t1.py" print "Hello, Dr. Mokter"

8.333333

25

0.64

8

50

4

0.75

0.625

0

0.025

0.2

50

5

26

10

0.775

0

0.604167

0

null

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

00bfe2f174c7ed98aafc6235ea7f4e1da06b04f3

48

py

Python

instatag/__init__.py

Moduland/instatag

f5cf518b9f552f81db01ead3aa6406ead6e9753e

[ "MIT" ]

23

2017-06-21T16:24:29.000Z

2021-11-15T10:39:53.000Z

instatag/__init__.py

Moduland/instatag

f5cf518b9f552f81db01ead3aa6406ead6e9753e

[ "MIT" ]

2

2018-07-01T14:32:54.000Z

2018-07-31T05:17:08.000Z

instatag/__init__.py

Moduland/instatag

f5cf518b9f552f81db01ead3aa6406ead6e9753e

[ "MIT" ]

4

2017-07-07T17:21:18.000Z

2018-11-24T17:09:43.000Z

# -*- coding: utf-8 -*- from .instatag import *

16

23

0.583333

6

48

4.666667

1

0

0.025641

0.1875

48

3

24

16

0.692308

0.4375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

daec29e58e05da0b748263fb7679434af15c9eec

20

py

Python

python/testData/quickFixes/PyAddImportQuickFixTest/allVariantsSuggestedWhenExistingNonProjectImportFits/time.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/quickFixes/PyAddImportQuickFixTest/allVariantsSuggestedWhenExistingNonProjectImportFits/time.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/quickFixes/PyAddImportQuickFixTest/allVariantsSuggestedWhenExistingNonProjectImportFits/time.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def time(): pass

10

11

0.55

3

20

3.666667

1

0

0.3

20

2

12

10

0.785714

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

daf4c179757f25a6ba0fe65ff41b1c57f4e167c9

164

py

Python

daily/admin.py

yangfeiffei/Dsystem

8c4b677151d8a9777c265b0a8744c068d122e780

[ "MIT" ]

null

daily/admin.py

yangfeiffei/Dsystem

8c4b677151d8a9777c265b0a8744c068d122e780

[ "MIT" ]

null

daily/admin.py

yangfeiffei/Dsystem

8c4b677151d8a9777c265b0a8744c068d122e780

[ "MIT" ]

null

from django.contrib import admin from daily import models # Register your models here. admin.site.register(models.Daily) admin.site.register(models.Categories)

18.222222

38

0.804878

23

164

5.73913

0.521739

0.136364

0.257576

0.348485

0

0.115854

164

9

38

18.222222

0.910345

0.158537

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

97070228e3b9ba6a65b2a5888ebc2e8346de75cb

1,126

py

Python

basic_tutorials/strings.py

LearnPythonAndMakeGames/BasicPythonTutorialSeries

be129702680aaf1186fb62add13f94002d4baa63

[ "Apache-2.0" ]

7

2015-04-16T14:30:47.000Z

2021-08-18T15:37:12.000Z

basic_tutorials/strings.py

LearnPythonAndMakeGames/BasicPythonTutorialSeries

be129702680aaf1186fb62add13f94002d4baa63

[ "Apache-2.0" ]

null

basic_tutorials/strings.py

LearnPythonAndMakeGames/BasicPythonTutorialSeries

be129702680aaf1186fb62add13f94002d4baa63

[ "Apache-2.0" ]

2

2015-04-21T09:57:21.000Z

2020-01-07T08:41:41.000Z

attack_power = 100 # Print keyword only available python 2 and lower. print "Attack Power:", attack_power print "Attack Power: {} points".format(attack_power) print "Attack Power: {attack_power} points".format(attack_power=100) print "Attack Power: %s" % (attack_power) # python 1 and 2... won't work on 3 # Print as a built-in function print("Attack Power:", attack_power) print("Attack Power: {} points".format(attack_power)) # 0th 1st ... print("Attack Power: {0} points".format(attack_power, percent_to_hit, ...)) print("Attack Power: {attack_power} points".format(attack_power=100)) print "Attack Power".lower() # attack power print "Attack Power".upper() # ATTACK POWER print "Attack Power".capitalize() # Attack power print ":".join("Attack Power", "{}".format(attack_power)) # Attack Power : 100 print "Attack " + "Power" # Attack Power for character in "Attack Power": print character # A t t a c k P o w e r <--- each on its own line # A # t # t ap_string = "Attack Power" if "attack power" == ap_string.lower(): pass

33.117647

79

0.653641

159

1,126

4.528302

0.320755

0.504167

0.266667

0.183333

0.506944

0.416667

0.375

0

0.021493

0.21492

1,126

33

80

34.121212

0.792986

0.276199

0

0.350811

0

null

0.052632

0

null

0.736842

0

null

1

0

null

0

1

0

1

0

1

0

6

970e02d7316b564118ce5ed1d4b6fd689c804156

14,715

py

Python

pcdet/utils/memory_ensemble_utils.py

collector-m/ST3D

720e04aa3dc4bb95ac336171b240b6c3130144e5

[ "Apache-2.0" ]

184

2021-03-09T12:19:49.000Z

2022-03-31T09:19:05.000Z

pcdet/utils/memory_ensemble_utils.py

collector-m/ST3D

720e04aa3dc4bb95ac336171b240b6c3130144e5

[ "Apache-2.0" ]

36

2021-03-23T08:42:38.000Z

2022-03-31T09:14:41.000Z

pcdet/utils/memory_ensemble_utils.py

collector-m/ST3D

720e04aa3dc4bb95ac336171b240b6c3130144e5

[ "Apache-2.0" ]

22

2021-03-10T09:32:27.000Z

2022-03-28T05:01:45.000Z

import torch import numpy as np from scipy.optimize import linear_sum_assignment from pcdet.utils import common_utils from pcdet.ops.iou3d_nms import iou3d_nms_utils from pcdet.models.model_utils.model_nms_utils import class_agnostic_nms def consistency_ensemble(gt_infos_a, gt_infos_b, memory_ensemble_cfg): """ Args: gt_infos_a: gt_boxes: (N, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for previous pseudo boxes cls_scores: (N) iou_scores: (N) memory_counter: (N) gt_infos_b: gt_boxes: (M, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for current pseudo boxes cls_scores: (M) iou_scores: (M) memory_counter: (M) memory_ensemble_cfg: Returns: gt_infos: gt_boxes: (K, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for merged pseudo boxes cls_scores: (K) iou_scores: (K) memory_counter: (K) """ gt_box_a, _ = common_utils.check_numpy_to_torch(gt_infos_a['gt_boxes']) gt_box_b, _ = common_utils.check_numpy_to_torch(gt_infos_b['gt_boxes']) gt_box_a, gt_box_b = gt_box_a.cuda(), gt_box_b.cuda() new_gt_box = gt_infos_a['gt_boxes'] new_cls_scores = gt_infos_a['cls_scores'] new_iou_scores = gt_infos_a['iou_scores'] new_memory_counter = gt_infos_a['memory_counter'] # if gt_box_b or gt_box_a don't have any predictions if gt_box_b.shape[0] == 0: gt_infos_a['memory_counter'] += 1 return gt_infos_a elif gt_box_a.shape[0] == 0: return gt_infos_b # get ious iou_matrix = iou3d_nms_utils.boxes_iou3d_gpu(gt_box_a[:, :7], gt_box_b[:, :7]).cpu() ious, match_idx = torch.max(iou_matrix, dim=1) ious, match_idx = ious.numpy(), match_idx.numpy() gt_box_a, gt_box_b = gt_box_a.cpu().numpy(), gt_box_b.cpu().numpy() match_pairs_idx = np.concatenate(( np.array(list(range(gt_box_a.shape[0]))).reshape(-1, 1), match_idx.reshape(-1, 1)), axis=1) ######################################################### # filter matched pair boxes by IoU # if matching succeeded, use boxes with higher confidence ######################################################### iou_mask = (ious >= memory_ensemble_cfg.IOU_THRESH) matching_selected = match_pairs_idx[iou_mask] gt_box_selected_a = gt_box_a[matching_selected[:, 0]] gt_box_selected_b = gt_box_b[matching_selected[:, 1]] # assign boxes with higher confidence score_mask = gt_box_selected_a[:, 8] < gt_box_selected_b[:, 8] if memory_ensemble_cfg.get('WEIGHTED', None): weight = gt_box_selected_a[:, 8] / (gt_box_selected_a[:, 8] + gt_box_selected_b[:, 8]) min_scores = np.minimum(gt_box_selected_a[:, 8], gt_box_selected_b[:, 8]) max_scores = np.maximum(gt_box_selected_a[:, 8], gt_box_selected_b[:, 8]) weighted_score = weight * (max_scores - min_scores) + min_scores new_gt_box[matching_selected[:, 0], :7] = weight.reshape(-1, 1) * gt_box_selected_a[:, :7] + \ (1 - weight.reshape(-1, 1)) * gt_box_selected_b[:, :7] new_gt_box[matching_selected[:, 0], 8] = weighted_score else: new_gt_box[matching_selected[score_mask, 0], :] = gt_box_selected_b[score_mask, :] if gt_infos_a['cls_scores'] is not None: new_cls_scores[matching_selected[score_mask, 0]] = gt_infos_b['cls_scores'][ matching_selected[score_mask, 1]] if gt_infos_a['iou_scores'] is not None: new_iou_scores[matching_selected[score_mask, 0]] = gt_infos_b['iou_scores'][ matching_selected[score_mask, 1]] # for matching pairs, clear the ignore counter new_memory_counter[matching_selected[:, 0]] = 0 ####################################################### # If previous bboxes disappeared: ious <= 0.1 ####################################################### disappear_idx = (ious < memory_ensemble_cfg.IOU_THRESH).nonzero()[0] if memory_ensemble_cfg.get('MEMORY_VOTING', None) and memory_ensemble_cfg.MEMORY_VOTING.ENABLED: new_memory_counter[disappear_idx] += 1 # ignore gt_boxes that ignore_count == IGNORE_THRESH ignore_mask = new_memory_counter >= memory_ensemble_cfg.MEMORY_VOTING.IGNORE_THRESH new_gt_box[ignore_mask, 7] = -1 # remove gt_boxes that ignore_count >= RM_THRESH remain_mask = new_memory_counter < memory_ensemble_cfg.MEMORY_VOTING.RM_THRESH new_gt_box = new_gt_box[remain_mask] new_memory_counter = new_memory_counter[remain_mask] if gt_infos_a['cls_scores'] is not None: new_cls_scores = new_cls_scores[remain_mask] if gt_infos_a['iou_scores'] is not None: new_iou_scores = new_iou_scores[remain_mask] # Add new appear boxes ious_b2a, match_idx_b2a = torch.max(iou_matrix, dim=0) ious_b2a, match_idx_b2a = ious_b2a.numpy(), match_idx_b2a.numpy() newboxes_idx = (ious_b2a < memory_ensemble_cfg.IOU_THRESH).nonzero()[0] if newboxes_idx.shape[0] != 0: new_gt_box = np.concatenate((new_gt_box, gt_infos_b['gt_boxes'][newboxes_idx, :]), axis=0) if gt_infos_a['cls_scores'] is not None: new_cls_scores = np.concatenate((new_cls_scores, gt_infos_b['cls_scores'][newboxes_idx]), axis=0) if gt_infos_a['iou_scores'] is not None: new_iou_scores = np.concatenate((new_iou_scores, gt_infos_b['iou_scores'][newboxes_idx]), axis=0) new_memory_counter = np.concatenate((new_memory_counter, gt_infos_b['memory_counter'][newboxes_idx]), axis=0) new_gt_infos = { 'gt_boxes': new_gt_box, 'cls_scores': new_cls_scores if gt_infos_a['cls_scores'] is not None else None, 'iou_scores': new_iou_scores if gt_infos_a['iou_scores'] is not None else None, 'memory_counter': new_memory_counter } return new_gt_infos def nms_ensemble(gt_infos_a, gt_infos_b, memory_ensemble_cfg): """ Args: gt_infos_a: gt_boxes: (N, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for previous pseudo boxes cls_scores: (N) iou_scores: (N) memory_counter: (N) gt_infos_b: gt_boxes: (M, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for current pseudo boxes cls_scores: (M) iou_scores: (M) memory_counter: (M) memory_ensemble_cfg: Returns: gt_infos: gt_boxes: (K, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for merged pseudo boxes cls_scores: (K) iou_scores: (K) memory_counter: (K) """ gt_box_a, _ = common_utils.check_numpy_to_torch(gt_infos_a['gt_boxes']) gt_box_b, _ = common_utils.check_numpy_to_torch(gt_infos_b['gt_boxes']) if gt_box_b.shape[0] == 0: if memory_ensemble_cfg.get('MEMORY_VOTING', None) and memory_ensemble_cfg.MEMORY_VOTING.ENABLED: gt_infos_a['memory_counter'] += 1 return gt_infos_a elif gt_box_a.shape[0] == 0: return gt_infos_b gt_box_a, gt_box_b = gt_box_a.cuda(), gt_box_b.cuda() gt_boxes = torch.cat((gt_box_a, gt_box_b), dim=0) if gt_infos_a['cls_scores'] is not None: new_cls_scores = np.concatenate((gt_infos_a['cls_scores'], gt_infos_b['cls_scores']), axis=0) if gt_infos_a['iou_scores'] is not None: new_iou_scores = np.concatenate((gt_infos_a['iou_scores'], gt_infos_b['iou_scores']), axis=0) new_memory_counter = np.concatenate((gt_infos_a['memory_counter'], gt_infos_b['memory_counter']), axis=0) selected, selected_scores = class_agnostic_nms( box_scores=gt_boxes[:, -1], box_preds=gt_boxes[:, :7], nms_config=memory_ensemble_cfg.NMS_CONFIG ) gt_boxes = gt_boxes.cpu().numpy() if isinstance(selected, list): selected = np.array(selected) else: selected = selected.cpu().numpy() if memory_ensemble_cfg.get('MEMORY_VOTING', None) and memory_ensemble_cfg.MEMORY_VOTING.ENABLED: iou_matrix = iou3d_nms_utils.boxes_iou3d_gpu(gt_box_a[:, :7], gt_box_b[:, :7]) ious, _ = torch.max(iou_matrix, dim=1) ious = ious.cpu().numpy() gt_box_a_size = gt_box_a.shape[0] selected_a = selected[selected < gt_box_a_size] matched_mask = (ious[selected_a] > memory_ensemble_cfg.NMS_CONFIG.NMS_THRESH) match_idx = selected_a[matched_mask] new_memory_counter[match_idx] = 0 # for previous bboxes disappeared disappear_idx = (ious < memory_ensemble_cfg.NMS_CONFIG.NMS_THRESH).nonzero()[0] new_memory_counter[disappear_idx] += 1 # ignore gt_boxes that ignore_count == IGNORE_THRESH ignore_mask = new_memory_counter >= memory_ensemble_cfg.MEMORY_VOTING.IGNORE_THRESH gt_boxes[ignore_mask, 7] = -1 # remove gt_boxes that ignore_count >= RM_THRESH rm_idx = (new_memory_counter >= memory_ensemble_cfg.MEMORY_VOTING.RM_THRESH).nonzero()[0] selected = np.setdiff1d(selected, rm_idx) selected_gt_boxes = gt_boxes[selected] new_gt_infos = { 'gt_boxes': selected_gt_boxes, 'cls_scores': new_cls_scores[selected] if gt_infos_a['cls_scores'] is not None else None, 'iou_scores': new_iou_scores[selected] if gt_infos_a['iou_scores'] is not None else None, 'memory_counter': new_memory_counter[selected] } return new_gt_infos def bipartite_ensemble(gt_infos_a, gt_infos_b, memory_ensemble_cfg): """ Args: gt_infos_a: gt_boxes: (N, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for previous pseudo boxes cls_scores: (N) iou_scores: (N) memory_counter: (N) gt_infos_b: gt_boxes: (M, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for current pseudo boxes cls_scores: (M) iou_scores: (M) memory_counter: (M) memory_ensemble_cfg: Returns: gt_infos: gt_boxes: (K, 9) [x, y, z, dx, dy, dz, heading, label, scores] in LiDAR for merged pseudo boxes cls_scores: (K) iou_scores: (K) memory_counter: (K) """ gt_box_a, _ = common_utils.check_numpy_to_torch(gt_infos_a['gt_boxes']) gt_box_b, _ = common_utils.check_numpy_to_torch(gt_infos_b['gt_boxes']) gt_box_a, gt_box_b = gt_box_a.cuda(), gt_box_b.cuda() new_gt_box = gt_infos_a['gt_boxes'] new_cls_scores = gt_infos_a['cls_scores'] new_iou_scores = gt_infos_a['iou_scores'] new_memory_counter = gt_infos_a['memory_counter'] # if gt_box_b or gt_box_a don't have any predictions if gt_box_b.shape[0] == 0: gt_infos_a['memory_counter'] += 1 return gt_infos_a elif gt_box_a.shape[0] == 0: return gt_infos_b # bipartite matching iou_matrix = iou3d_nms_utils.boxes_iou3d_gpu(gt_box_a[:, :7], gt_box_b[:, :7]) iou_matrix = iou_matrix.cpu().numpy() a_idx, b_idx = linear_sum_assignment(-iou_matrix) gt_box_a, gt_box_b = gt_box_a.cpu().numpy(), gt_box_b.cpu().numpy() matching_paris_idx = np.concatenate((a_idx.reshape(-1, 1), b_idx.reshape(-1, 1)), axis=1) ious = iou_matrix[matching_paris_idx[:, 0], matching_paris_idx[:, 1]] # matched a boxes. matched_mask = ious > memory_ensemble_cfg.IOU_THRESH matching_selected = matching_paris_idx[matched_mask] gt_box_selected_a = gt_box_a[matching_selected[:, 0]] gt_box_selected_b = gt_box_b[matching_selected[:, 1]] # assign boxes with higher confidence score_mask = gt_box_selected_a[:, 8] < gt_box_selected_b[:, 8] new_gt_box[matching_selected[score_mask, 0], :] = gt_box_selected_b[score_mask, :] if gt_infos_a['cls_scores'] is not None: new_cls_scores[matching_selected[score_mask, 0]] = gt_infos_b['cls_scores'][ matching_selected[score_mask, 1]] if gt_infos_a['iou_scores'] is not None: new_iou_scores[matching_selected[score_mask, 0]] = gt_infos_b['iou_scores'][ matching_selected[score_mask, 1]] # for matched pairs, clear the ignore counter new_memory_counter[matching_selected[:, 0]] = 0 ############################################## # disppeared boxes for previous pseudo boxes ############################################## gt_box_a_idx = np.array(list(range(gt_box_a.shape[0]))) disappear_idx = np.setdiff1d(gt_box_a_idx, matching_selected[:, 0]) if memory_ensemble_cfg.get('MEMORY_VOTING', None) and memory_ensemble_cfg.MEMORY_VOTING.ENABLED: new_memory_counter[disappear_idx] += 1 # ignore gt_boxes that ignore_count == IGNORE_THRESH ignore_mask = new_memory_counter >= memory_ensemble_cfg.MEMORY_VOTING.IGNORE_THRESH new_gt_box[ignore_mask, 7] = -1 # remove gt_boxes that ignore_count >= RM_THRESH remain_mask = new_memory_counter < memory_ensemble_cfg.MEMORY_VOTING.RM_THRESH new_gt_box = new_gt_box[remain_mask] new_memory_counter = new_memory_counter[remain_mask] if gt_infos_a['cls_scores'] is not None: new_cls_scores = new_cls_scores[remain_mask] if gt_infos_a['iou_scores'] is not None: new_iou_scores = new_iou_scores[remain_mask] ############################################## # new appear boxes for current pseudo boxes ############################################## gt_box_b_idx = np.array(list(range(gt_box_b.shape[0]))) newboxes_idx = np.setdiff1d(gt_box_b_idx, matching_selected[:, 1]) if newboxes_idx.shape[0] != 0: new_gt_box = np.concatenate((new_gt_box, gt_infos_b['gt_boxes'][newboxes_idx, :]), axis=0) if gt_infos_a['cls_scores'] is not None: new_cls_scores = np.concatenate((new_cls_scores, gt_infos_b['cls_scores'][newboxes_idx]), axis=0) if gt_infos_a['iou_scores'] is not None: new_iou_scores = np.concatenate((new_iou_scores, gt_infos_b['iou_scores'][newboxes_idx]), axis=0) new_memory_counter = np.concatenate((new_memory_counter, gt_infos_b['memory_counter'][newboxes_idx]), axis=0) new_gt_infos = { 'gt_boxes': new_gt_box, 'cls_scores': new_cls_scores if gt_infos_a['cls_scores'] is not None else None, 'iou_scores': new_iou_scores if gt_infos_a['iou_scores'] is not None else None, 'memory_counter': new_memory_counter } return new_gt_infos

42.900875

117

0.644648

2,190

14,715

3.918721

0.065297

0.055348

0.04288

0.023305

0.827779

0.801794

0.782452

0.743183

0.733629

0.715451

0

0.012487

0.221747

14,715

342

118

43.026316

0.736902

0.188787

0

0.585106

0

0.066797

0

1

0.015957

false

0

0.031915

0

0.095745

0

null

0

1

0

null

0

6

97159a6be287412247b5e236b366eec7bf0f7977

1,035

py

Python

utils/colorfy.py

obatsis/Distributed-NTUA

0bf39163b64aaefb2576be01337e0ec6e026ce6d

[ "MIT" ]

null

utils/colorfy.py

obatsis/Distributed-NTUA

0bf39163b64aaefb2576be01337e0ec6e026ce6d

[ "MIT" ]

null

utils/colorfy.py

obatsis/Distributed-NTUA

0bf39163b64aaefb2576be01337e0ec6e026ce6d

[ "MIT" ]

null

## Uppercase is BOLT # to use: from utils.beautyfy import * def red(string): return '\033[1;91m {}\033[00m'.format(string) def RED(string): return '\033[1;91m {}\033[00m'.format(string) def yellow(string): return '\033[93m {}\033[00m'.format(string) def YELLOW(string): return '\033[1;93m {}\033[00m'.format(string) def blue(string): return '\033[94m {}\033[00m'.format(string) def BLUE(string): return '\033[1;94m {}\033[00m'.format(string) def green(string): return '\033[92m {}\033[00m'.format(string) def GREEN(string): return '\033[1;92m {}\033[00m'.format(string) def cyan(string): return '\033[96m {}\033[00m'.format(string) def underline(string): return '\033[4m{}\033[00m'.format(string) def header(string): return '\033[95m{}\033[00m'.format(string) # HEADER = '\033[95m' # OKBLUE = '\033[94m' # OKCYAN = '\033[96m' # OKGREEN = '\033[92m' # WARNING = '\033[93m' # FAIL = '\033[91m' # ENDC = '\033[0m' # BOLD = '\033[1m' # UNDERLINE = '\033[4m'

23.522727

49

0.613527

151

1,035

4.205298

0.258278

0.207874

0.259843

0.311811

0.622047

0.555906

0.494488

0.140157

0

0.18267

0.174879

1,035

43

50

24.069767

0.56089

0.222222

0

0.090909

0

0.273072

0

1

0.5

false

0

0.5

1

0

null

1

0

null

0

1

0

1

0

6

973db94f1754a2486c012365556d28b87d81582a

144

py

Python

FieldPlayer.py

ymesika7/XGAnalytic

1a2aad12b8f1f8608835d269ade031c662398bcd

[ "Apache-2.0" ]

1

2020-05-06T16:43:00.000Z

FieldPlayer.py

ymesika7/XGAnalytic

1a2aad12b8f1f8608835d269ade031c662398bcd

[ "Apache-2.0" ]

null

FieldPlayer.py

ymesika7/XGAnalytic

1a2aad12b8f1f8608835d269ade031c662398bcd

[ "Apache-2.0" ]

null

from Player import Player class FieldPlayer(Player): def __init__(self, locations, color): Player.__init__(self, locations, color)

24

47

0.729167

17

144

5.705882

0.588235

0.164948

0.350515

0.453608

0

0.180556

144

6

47

24

0.822034

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

975293f5784e71425512ba0abb1df3b8b81b2ecd

30

py

Python

gpd_ws/devel/lib/python2.7/dist-packages/gpd_ros/srv/__init__.py

JisuHann/Point-Cloud--Grasp

083244632412709dbc29ac7841b6a837e4ed3cb6

[ "BSD-2-Clause" ]

null

gpd_ws/devel/lib/python2.7/dist-packages/gpd_ros/srv/__init__.py

JisuHann/Point-Cloud--Grasp

083244632412709dbc29ac7841b6a837e4ed3cb6

[ "BSD-2-Clause" ]

null

gpd_ws/devel/lib/python2.7/dist-packages/gpd_ros/srv/__init__.py

JisuHann/Point-Cloud--Grasp

083244632412709dbc29ac7841b6a837e4ed3cb6

[ "BSD-2-Clause" ]

1

2021-03-31T06:27:31.000Z

from ._detect_grasps import *

15

29

0.8

4

30

5.5

1

0

0.133333

30

1

30

0.846154

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9758780dfe8020c87cca2c415b4d82930e6c285c

7,054

py

Python

tests/logkeep/test_Application.py

c0yote/toolbag

5128af31f0069372ecb537dead4402a6aac33428

[ "MIT" ]

null

tests/logkeep/test_Application.py

c0yote/toolbag

5128af31f0069372ecb537dead4402a6aac33428

[ "MIT" ]

7

2018-03-18T22:50:24.000Z

2018-05-31T17:38:15.000Z

tests/logkeep/test_Application.py

c0yote/toolbag

5128af31f0069372ecb537dead4402a6aac33428

[ "MIT" ]

null

import os import unittest from unittest import skip from unittest.mock import MagicMock, mock_open, patch from toolbag.logkeep import _Application, main TEST_LOG_PATH = '/some/path/to/log.log' TEST_ERROR_RETURN = 'Some error occurred' TEST_LIMIT_VALUE_A = 10 TEST_LIMIT_VALUE_B = 11 TEST_LIMIT_VALUE_C = 0.5 TEST_LIMIT_GREATER = 12 TEST_LIMIT_GREATER_FLOAT = 0.6 TEST_ARGS = ['app', TEST_LOG_PATH] TEST_SIZE_ARGS = ['app', '--size_limit', f'{TEST_LIMIT_VALUE_A}', TEST_LOG_PATH] TEST_SIZE_FLOAT_ARGS = ['app', '--size_limit', f'{TEST_LIMIT_VALUE_C}', TEST_LOG_PATH] TEST_SIZE_ARGS = ['app', '--size_limit', f'{TEST_LIMIT_VALUE_A}', TEST_LOG_PATH] TEST_LINE_ARGS = ['app', '--line_limit', f'{TEST_LIMIT_VALUE_B}', TEST_LOG_PATH] TEST_SIZE_AND_LINE_ARGS = ['app', '--size_limit', f'{TEST_LIMIT_VALUE_A}', '--line_limit', f'{TEST_LIMIT_VALUE_B}', TEST_LOG_PATH] TEST_UNHANDLED_EXCEPTION_OBJ = Exception(TEST_ERROR_RETURN) class Application_TestCase(unittest.TestCase): @patch('sys.argv', TEST_SIZE_ARGS) @patch('toolbag.logkeep.Log') def test_size_threshold_storage(self, *stubs): app = _Application() self.assertEqual(app._size_limit_mb, TEST_LIMIT_VALUE_A) self.assertEqual(app._line_limit, None) @patch('sys.argv', TEST_SIZE_FLOAT_ARGS) @patch('toolbag.logkeep.Log') def test_size_threshold_storage_with_float(self, *stubs): app = _Application() self.assertEqual(app._size_limit_mb, TEST_LIMIT_VALUE_C) self.assertEqual(app._line_limit, None) @patch('sys.argv', TEST_LINE_ARGS) @patch('toolbag.logkeep.Log') def test_line_threshold_storage(self, *stubs): app = _Application() self.assertEqual(app._size_limit_mb, None) self.assertEqual(app._line_limit, TEST_LIMIT_VALUE_B) @patch('sys.argv', TEST_SIZE_AND_LINE_ARGS) @patch('toolbag.logkeep.Log') def test_size_and_line_threshold_storage(self, *stubs): app = _Application() self.assertEqual(app._size_limit_mb, TEST_LIMIT_VALUE_A) self.assertEqual(app._line_limit, TEST_LIMIT_VALUE_B) @patch('sys.argv', TEST_ARGS) @patch('toolbag.logkeep.Log') def test_no_threshold_storage(self, *stubs): app = _Application() self.assertEqual(app._size_limit_mb, None) self.assertEqual(app._line_limit, None) @patch('sys.argv', TEST_ARGS) @patch('toolbag.logkeep.Log') def test_work_log_creation(self, log_mock): app = _Application() log_mock.assert_called_with(TEST_LOG_PATH) @patch('sys.argv', TEST_ARGS) @patch('toolbag.logkeep.Log') def test_work_log_storage(self, log_mock): app = _Application() handle = log_mock() self.assertEqual(app._work_log, handle) @patch('sys.argv', TEST_ARGS) @patch('toolbag.logkeep.Log') def test_does_not_clone_log_on_no_thresholds(self, log_mock): app = _Application() app.run() log_mock.clone_to_a_backup.assert_not_called() @patch('sys.argv', TEST_SIZE_ARGS) @patch('toolbag.logkeep.Log') def test_clones_log_on_size_threshold(self, log_mock): handle = log_mock() handle.get_file_size_in_megabytes.return_value = TEST_LIMIT_GREATER app = _Application() app.run() handle.clone_to_a_backup.assert_called_with() @patch('sys.argv', TEST_SIZE_FLOAT_ARGS) @patch('toolbag.logkeep.Log') def test_clones_log_on_size_threshold_with_float(self, log_mock): handle = log_mock() handle.get_file_size_in_megabytes.return_value = TEST_LIMIT_GREATER_FLOAT app = _Application() app.run() handle.clone_to_a_backup.assert_called_with() @patch('sys.argv', TEST_LINE_ARGS) @patch('toolbag.logkeep.Log') def test_clones_log_on_line_count_threshold(self, log_mock): handle = log_mock() handle.get_line_count.return_value = TEST_LIMIT_GREATER app = _Application() app.run() handle.clone_to_a_backup.assert_called_with() @patch('sys.argv', TEST_SIZE_AND_LINE_ARGS) @patch('toolbag.logkeep.Log') def test_clones_log_on_size_and_line_count_threshold(self, log_mock): handle = log_mock() handle.get_line_count.return_value = TEST_LIMIT_GREATER app = _Application() app.run() handle.clone_to_a_backup.assert_called_with() @patch('sys.argv', TEST_ARGS) @patch('toolbag.logkeep.Log', side_effect=PermissionError(TEST_ERROR_RETURN)) def test_constructor_raises_runtimeerror_on_bad_permissions(self, *args): with self.assertRaises(RuntimeError) as e: _Application() @patch('sys.argv', TEST_ARGS) @patch('toolbag.logkeep.Log', side_effect=FileNotFoundError(TEST_ERROR_RETURN)) def test_constructor_raises_runtimeerror_on_file_not_found(self, *args): with self.assertRaises(RuntimeError) as e: _Application() @patch('sys.argv', TEST_SIZE_ARGS) @patch('toolbag.logkeep.Log') def test_run_raises_runtimeerror_on_bad_permissions(self, log_mock, *args): handle = log_mock() handle.clone_to_a_backup.side_effect = PermissionError(TEST_ERROR_RETURN) handle.get_file_size_in_megabytes.return_value = TEST_LIMIT_GREATER app = _Application() with self.assertRaises(RuntimeError) as e: app.run() @patch('toolbag.logkeep.Log') @patch('sys.argv', TEST_SIZE_ARGS) def test_run_raises_runtimeerror_on_file_not_found(self, log_mock, *args): handle = log_mock() handle.clone_to_a_backup.side_effect = FileNotFoundError(TEST_ERROR_RETURN) handle.get_file_size_in_megabytes.return_value = TEST_LIMIT_GREATER app = _Application() with self.assertRaises(RuntimeError) as e: app.run() class logkeep_main_TestCase(unittest.TestCase): @patch('toolbag.logkeep._Application', side_effect=RuntimeError(TEST_ERROR_RETURN)) @patch('sys.argv', TEST_ARGS) @patch('builtins.print') def test_main_handles_runtime_errors_from_application_constructor(self, print_mock, *args): main() print_mock.assert_called_with('Error: '+TEST_ERROR_RETURN) @patch('toolbag.logkeep._Application.run', side_effect=RuntimeError(TEST_ERROR_RETURN)) @patch('toolbag.logkeep.Log') @patch('sys.argv', TEST_ARGS) @patch('builtins.print') def test_main_handles_runtime_errors_from_application_run(self, print_mock, *args): main() print_mock.assert_called_with('Error: '+TEST_ERROR_RETURN) @patch('toolbag.logkeep._Application', side_effect=TEST_UNHANDLED_EXCEPTION_OBJ) @patch('sys.argv', TEST_ARGS) @patch('builtins.print') def test_main_handles_unhandled_exceptions_from_application_constructor(self, print_mock, *args): main() print_mock.assert_called_with('Unhandled Exception: '+repr(TEST_UNHANDLED_EXCEPTION_OBJ)) @patch('toolbag.logkeep._Application.run', side_effect=TEST_UNHANDLED_EXCEPTION_OBJ) @patch('toolbag.logkeep.Log') @patch('sys.argv', TEST_ARGS) @patch('builtins.print') def test_main_handles_unhandled_exceptions_from_application_run(self, print_mock, *args): main() print_mock.assert_called_with('Unhandled Exception: '+repr(TEST_UNHANDLED_EXCEPTION_OBJ))

37.322751

99

0.745818

987

7,054

4.892604

0.096251

0.06668

0.08656

0.066266

0.874301

0.851522

0.817768

0.771381

0.756264

0.727687

0

0.001645

0.138078

7,054

189

100

37.322751

0.792599

0

0.653846

0

0.139476

0.019986

0

0.160256

1

0.128205

false

0

0.032051

0

0.173077

0.076923

0

null

0

1

0

null

0

6

9773809a0e266aacd9703887ab60bfa9721eaf11

107

py

Python

bytevm/__main__.py

vrthra/PyVM

61fdf22533aceea2cc1f463f7cdbbc13eda6ff25

[ "MIT" ]

null

bytevm/__main__.py

vrthra/PyVM

61fdf22533aceea2cc1f463f7cdbbc13eda6ff25

[ "MIT" ]

null

bytevm/__main__.py

vrthra/PyVM

61fdf22533aceea2cc1f463f7cdbbc13eda6ff25

[ "MIT" ]

null

"""A main program for Bytevm.""" import sys from . import execfile execfile.ExecFile().cmdline(sys.argv)

15.285714

37

0.728972

15

107

5.2

0.733333

0.410256

0

0.130841

107

6

38

17.833333

0.83871

0.242991

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

c11ee98bc384d240f5f18436155cc3a0a653ecaa

29

py

Python

worker.py

asoucase/flask-celery-kubernetes-example

82d49762cdb41aa785b0aae2c64da6f3131b1cec

[ "MIT" ]

null

worker.py

asoucase/flask-celery-kubernetes-example

82d49762cdb41aa785b0aae2c64da6f3131b1cec

[ "MIT" ]

2

2020-09-09T17:55:49.000Z

2020-09-09T18:17:04.000Z

worker.py

asoucase/flask-celery-kubernetes-example

82d49762cdb41aa785b0aae2c64da6f3131b1cec

[ "MIT" ]

null

from flask_app.tasks import *

29

0.827586

5

29

4.6

1

0

0.103448

29

1

29

0.884615

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c14c8cc6020b8965ebb5d6b03ec0a1ee3ff8c814

177

py

Python

atividades/ex021.py

leonardoarthur/PythonAtividades

9bb2b6e1d7788d99276730de5f199cb0e9ab782f

[ "MIT" ]

1

2021-05-02T09:03:27.000Z

atividades/ex021.py

leonardoarthur/PythonAtividades

9bb2b6e1d7788d99276730de5f199cb0e9ab782f

[ "MIT" ]

null

atividades/ex021.py

leonardoarthur/PythonAtividades

9bb2b6e1d7788d99276730de5f199cb0e9ab782f

[ "MIT" ]

null

import pygame # colocando uma música dentro do programa WOW pygame.init() pygame.mixer.music.load('ex21.mp3') pygame.mixer.music.play() while(pygame.mixer.music.get_busy()):pass

29.5

45

0.785311

28

177

4.928571

0.714286

0.23913

0.347826

0

0.018293

0.073446

177

6

46

29.5

0.823171

0.242938

0

0.06015

0

1

0

true

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

1

0

6

c190be449ef362cd628079904d35e625a6f4eea4

34

py

Python

dash_labs/plugins/__init__.py

ruxi/dash-labs

991f8e479886672bb24dba9cf878dfd748777730

[ "MIT" ]

110

2021-04-16T14:41:54.000Z

2022-03-24T22:29:41.000Z

dash_labs/plugins/__init__.py

ruxi/dash-labs

991f8e479886672bb24dba9cf878dfd748777730

[ "MIT" ]

59

2021-04-16T10:42:34.000Z

2022-03-21T18:43:25.000Z

dash_labs/plugins/__init__.py

ruxi/dash-labs

991f8e479886672bb24dba9cf878dfd748777730

[ "MIT" ]

28

2021-04-16T16:26:32.000Z

2022-03-28T17:32:42.000Z

from .pages import page_container

17

33

0.852941

5

34

5.6

1

0

0.117647

34

1

34

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c192c200bee226f50944b34ee8d84b04aa94786d

287

py

Python

pax/nets.py

NTT123/pax

b80e1e4b6bfb763afd6b4fdefa31a051ca8a3335

[ "MIT" ]

11

2021-08-28T17:45:38.000Z

2022-01-26T17:50:03.000Z

pax/nets.py

NTT123/pax

b80e1e4b6bfb763afd6b4fdefa31a051ca8a3335

[ "MIT" ]

1

2021-09-13T17:29:33.000Z

2021-09-13T21:50:34.000Z

pax/nets.py

NTT123/pax

b80e1e4b6bfb763afd6b4fdefa31a051ca8a3335

[ "MIT" ]

null

"""Public nets.""" from pax._src.nets import ( ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, ResNet200, Transformer, ) __all__ = ( "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152", "ResNet200", "Transformer", )

13.045455

27

0.56446

22

287

7.136364

0.636364

0.203822

0.305732

0.420382

0.789809

0

0.147059

0.289199

287

21

28

13.666667

0.622549

0.041812

0

0.230483

0

1

0

false

0

0.055556

0

0.055556

0

1

0

null

1

0

1

0

null

0

6

c1aa1d8b11ef90a5a1266d8d879a35529e339d92

34

py

Python

historia/__init__.py

eranimo/historia

5e0b047d4bcdd534f48f8b9bf19d425b0b31a3fd

[ "MIT" ]

6

2016-04-26T18:39:36.000Z

2021-09-01T09:13:38.000Z

historia/__init__.py

eranimo/historia

5e0b047d4bcdd534f48f8b9bf19d425b0b31a3fd

[ "MIT" ]

null

historia/__init__.py

eranimo/historia

5e0b047d4bcdd534f48f8b9bf19d425b0b31a3fd

[ "MIT" ]

4

2016-04-10T23:47:23.000Z

2021-08-15T11:40:28.000Z

from historia.gen import Historia

17

33

0.852941

5

34

5.8

0.8

0

0.117647

34

1

34

0.966667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c1cab7b096f7e1c3801a82537132f2a33ac70b74

1,527

py

Python

tests/test_condition.py

vail130/norm

01a16d6c73c2c6fff92430ca2ca745b295de9a3a

[ "MIT" ]

null

tests/test_condition.py

vail130/norm

01a16d6c73c2c6fff92430ca2ca745b295de9a3a

[ "MIT" ]

1

2016-02-10T00:43:15.000Z

2016-02-10T01:14:37.000Z

tests/test_condition.py

vail130/norm

01a16d6c73c2c6fff92430ca2ca745b295de9a3a

[ "MIT" ]

1

2021-03-12T23:21:02.000Z

from __future__ import absolute_import, unicode_literals import unittest from mason import Table, Param, AND, OR class TheConditionClassToStringMethod(unittest.TestCase): def test_and_works_with_one_argument(self): table = Table('table') param = Param('param') self.assertEqual(str(AND(table.column == param)), 'table.column = %(param)s') def test_and_works_with_two_arguments(self): table = Table('table') param = Param('param') self.assertEqual(str(AND(table.column == param, table.column1 < param)), 'table.column = %(param)s AND table.column1 < %(param)s') def test_or_works_with_one_argument(self): table = Table('table') param = Param('param') self.assertEqual(str(OR(table.column == param)), 'table.column = %(param)s') def test_or_works_with_two_arguments(self): table = Table('table') param = Param('param') self.assertEqual(str(OR(table.column == param, table.column1 < param)), 'table.column = %(param)s OR table.column1 < %(param)s') def test_and_and_or_work_together(self): table = Table('table') param = Param('param') self.assertEqual(str(AND(table.column == param, OR(table.column1 < param, table.column2 > param))), 'table.column = %(param)s AND (table.column1 < %(param)s OR table.column2 > %(param)s)')

35.511628

113

0.595285

178

1,527

4.932584

0.179775

0.113895

0.182232

0.1082

0.759681

0.741458

0.712984

0.700456

0.657175

0

0.00722

0.274394

1,527

42

114

36.357143

0.785199

0

0.344828

0

0.034483

0.189915

0

0.172414

1

0.172414

false

0

0.103448

0

0.310345

0

null

0

1

0

1

0

null

0

6

de07561228172b9573039d2b575f6431ff22b7a4

38

py

Python

face_detectors/__init__.py

Saadmairaj/face-detectors

b705d84327343614dba0393b29e09207e3dd7f90

[ "Apache-2.0" ]

1

2021-12-25T13:05:10.000Z

face_detectors/__init__.py

Saadmairaj/face-detectors

b705d84327343614dba0393b29e09207e3dd7f90

[ "Apache-2.0" ]

null

face_detectors/__init__.py

Saadmairaj/face-detectors

b705d84327343614dba0393b29e09207e3dd7f90

[ "Apache-2.0" ]

null

from face_detectors.detectors import *

38

0.868421

5

38

6.4

0.8

0

0.078947

38

1

38

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

de0df530b25b89f6a0aab5f0e34a84e027e2c83d

5,389

py

Python

calculating_model_score/calculate_snips_slot.py

SmallStom/slot_learning

73753a1ecec61ee1cdb5bb6356f80f0ee552128a

[ "Apache-2.0" ]

418

2019-03-18T07:57:44.000Z

2022-03-30T13:42:16.000Z

calculating_model_score/calculate_snips_slot.py

SmallStom/slot_learning

73753a1ecec61ee1cdb5bb6356f80f0ee552128a

[ "Apache-2.0" ]

34

2019-03-18T08:20:36.000Z

2022-03-02T14:59:28.000Z

calculating_model_score/calculate_snips_slot.py

SmallStom/slot_learning

73753a1ecec61ee1cdb5bb6356f80f0ee552128a

[ "Apache-2.0" ]

100

2019-04-09T04:28:10.000Z

2022-03-10T08:05:05.000Z

import os from sklearn_metrics_function import show_metrics,delete_both_sides_is_O_word SNIPS_slot_label = ['[Padding]', '[##WordPiece]', '[CLS]', '[SEP]', 'B-album', 'B-artist', 'B-best_rating', 'B-city', 'B-condition_description', 'B-condition_temperature', 'B-country', 'B-cuisine', 'B-current_location', 'B-entity_name', 'B-facility', 'B-genre', 'B-geographic_poi', 'B-location_name', 'B-movie_name', 'B-movie_type', 'B-music_item', 'B-object_location_type', 'B-object_name', 'B-object_part_of_series_type', 'B-object_select', 'B-object_type', 'B-party_size_description', 'B-party_size_number', 'B-playlist', 'B-playlist_owner', 'B-poi', 'B-rating_unit', 'B-rating_value', 'B-restaurant_name', 'B-restaurant_type', 'B-served_dish', 'B-service', 'B-sort', 'B-spatial_relation', 'B-state', 'B-timeRange', 'B-track', 'B-year', 'I-album', 'I-artist', 'I-city', 'I-country', 'I-cuisine', 'I-current_location', 'I-entity_name', 'I-facility', 'I-genre', 'I-geographic_poi', 'I-location_name', 'I-movie_name', 'I-movie_type', 'I-music_item', 'I-object_location_type', 'I-object_name', 'I-object_part_of_series_type', 'I-object_select', 'I-object_type', 'I-party_size_description', 'I-playlist', 'I-playlist_owner', 'I-poi', 'I-restaurant_name', 'I-restaurant_type', 'I-served_dish', 'I-service', 'I-sort', 'I-spatial_relation', 'I-state', 'I-timeRange', 'I-track', 'O'] SNIPS_slot_effective_label = ['B-album', 'B-artist', 'B-best_rating', 'B-city', 'B-condition_description', 'B-condition_temperature', 'B-country', 'B-cuisine', 'B-current_location', 'B-entity_name', 'B-facility', 'B-genre', 'B-geographic_poi', 'B-location_name', 'B-movie_name', 'B-movie_type', 'B-music_item', 'B-object_location_type', 'B-object_name', 'B-object_part_of_series_type', 'B-object_select', 'B-object_type', 'B-party_size_description', 'B-party_size_number', 'B-playlist', 'B-playlist_owner', 'B-poi', 'B-rating_unit', 'B-rating_value', 'B-restaurant_name', 'B-restaurant_type', 'B-served_dish', 'B-service', 'B-sort', 'B-spatial_relation', 'B-state', 'B-timeRange', 'B-track', 'B-year', 'I-album', 'I-artist', 'I-city', 'I-country', 'I-cuisine', 'I-current_location', 'I-entity_name', 'I-facility', 'I-genre', 'I-geographic_poi', 'I-location_name', 'I-movie_name', 'I-movie_type', 'I-music_item', 'I-object_location_type', 'I-object_name', 'I-object_part_of_series_type', 'I-object_select', 'I-object_type', 'I-party_size_description', 'I-playlist', 'I-playlist_owner', 'I-poi', 'I-restaurant_name', 'I-restaurant_type', 'I-served_dish', 'I-service', 'I-sort', 'I-spatial_relation', 'I-state', 'I-timeRange', 'I-track', 'O'] SNIPS_slot_effective_label2 = ['B-album', 'B-artist', 'B-best_rating', 'B-city', 'B-condition_description', 'B-condition_temperature', 'B-country', 'B-cuisine', 'B-current_location', 'B-entity_name', 'B-facility', 'B-genre', 'B-geographic_poi', 'B-location_name', 'B-movie_name', 'B-movie_type', 'B-music_item', 'B-object_location_type', 'B-object_name', 'B-object_part_of_series_type', 'B-object_select', 'B-object_type', 'B-party_size_description', 'B-party_size_number', 'B-playlist', 'B-playlist_owner', 'B-poi', 'B-rating_unit', 'B-rating_value', 'B-restaurant_name', 'B-restaurant_type', 'B-served_dish', 'B-service', 'B-sort', 'B-spatial_relation', 'B-state', 'B-timeRange', 'B-track', 'B-year', 'I-album', 'I-artist', 'I-city', 'I-country', 'I-cuisine', 'I-current_location', 'I-entity_name', 'I-facility', 'I-genre', 'I-geographic_poi', 'I-location_name', 'I-movie_name', 'I-movie_type', 'I-music_item', 'I-object_location_type', 'I-object_name', 'I-object_part_of_series_type', 'I-object_select', 'I-object_type', 'I-party_size_description', 'I-playlist', 'I-playlist_owner', 'I-poi', 'I-restaurant_name', 'I-restaurant_type', 'I-served_dish', 'I-service', 'I-sort', 'I-spatial_relation', 'I-state', 'I-timeRange', 'I-track'] with open(os.path.join("SNIPS_slot", "seq.out")) as label_f: label_list = [label.replace("\n", "") for label in label_f.readlines()] label_list = [seq.split() for seq in label_list] #print(len(label_list), label_list) with open(os.path.join("SNIPS_slot", "label_test.txt")) as predict_f: predict_list = [predict_label.replace("\n", "") for predict_label in predict_f.readlines()] #print(len(predict_list), predict_list) predict_sentence_list = [] for word in predict_list: if "[CLS]" == word: a_sentence = [] a_sentence.append(word) if "[SEP]" == word: predict_sentence_list.append(a_sentence) #print(len(predict_sentence_list), predict_sentence_list) y_test_list = [] clean_y_predict_list = [] assert len(label_list)==len(predict_sentence_list) for y_test, y_predict in zip(label_list, predict_sentence_list): y_predict.remove('[CLS]') y_predict.remove('[SEP]') while '[Padding]' in y_predict: y_predict.remove('[Padding]') while '[##WordPiece]' in y_predict: y_predict.remove('[##WordPiece]') if len(y_predict)!=len(y_test): print(y_predict) print(y_test) print("~"*100) y_test_list.extend(y_test) clean_y_predict_list.extend(y_predict) assert len(y_test_list)==len(clean_y_predict_list) y_test_list, clean_y_predict_list = delete_both_sides_is_O_word(y_test_list, clean_y_predict_list) show_metrics(y_test=y_test_list, y_predict=clean_y_predict_list, labels=SNIPS_slot_effective_label)

107.78

1,277

0.705882

850

5,389

4.163529

0.12

0.038429

0.016954

0.030517

0.764058

0.751907

0.725911

0.703306

0.686352

0

0.000817

0.091112

5,389

49

1,278

109.979592

0.721723

0.023752

0

0.552977

0.11071

0

0.055556

1

0

false

0

0.055556

0

0.055556

0.083333

0

null

0

1

0

1

0

1

0

null

0

6

a9bc25d8e68c544d6e1c49ced89993801b475e53

18,667

py

Python

Models/plot_results_section2.py

Filoteea/dissertation_code

e538320c5c8a6801075c2380e6dedf78a9334a5e

[ "MIT" ]

null

Models/plot_results_section2.py

Filoteea/dissertation_code

e538320c5c8a6801075c2380e6dedf78a9334a5e

[ "MIT" ]

null

Models/plot_results_section2.py

Filoteea/dissertation_code

e538320c5c8a6801075c2380e6dedf78a9334a5e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on March 17 04:50:04 2022 @author: Filoteea Moldovan Adapted from: Edward Chung Script used for creating the plots in section 3.3. """ # Standard Library imports import matplotlib.dates as mdates import matplotlib.pyplot as plt import numpy as np import pandas as pd from numpy import genfromtxt # Semi-local imports import name_qch4_couple.plot_h2 # Local imports import chem_co # Set the date-time variable date = '2018-01' dates_tHour = pd.date_range( pd.to_datetime(date), pd.to_datetime(date) + pd.DateOffset(months=12), closed='left', freq='1H' ) # ============================================================================= # Plotting the MHD and WAO observations and their difference and # calculating the SD of this difference # ============================================================================= ''' Inputs: - H2 observations at WAO for 2018 - H2 observations at MHD for 2018 ''' # read observations obs_mhd, sigma_obs_H2 = chem_co.read_obs(dates_tHour, "MHD_10magl", 0) obs_wao, sigma_obs_H2 = chem_co.read_obs(dates_tHour, "WAO", 0) ''' # calculate SD dif = [] z = 0 count = 0 for i, j in zip(obs_mhd, obs_wao): if np.isnan(i) or np.isnan(j): count += 1 else: dif.append(i - j) z += 1 dev = np.std(dif) # Plot figs = {} axs = {} pobjs = {} zorder = { 'background': 1, 'final': 2 } fig_param = { 'mw': 10.5, 'mh': 7, 'mpw': 8.5, 'mph': 5.7, 'mgap': 0.05, 'mlmargin': 1.2, 'mbmargin': 1.5, 'ylblx': 0.05, 'ylbly': 1.5, # left, centre aligned 'fontsize': 15, 'fontsize2': 12, } plt.close('all') ylabel = u'$\chi$ H$_{2}$ (nmol mol$^{-1}$)' ylabel2 = 'Differences (nmol mol$^{-1}$)' ylim = [350., 600.] ylim2 = [-70., 600.] yticks = np.arange(300., 600., 50.) yticks2 = np.arange(-70., 600., 100.) var_long_name = 'mole_fraction_of_hydrogen' var_units = 'nmol mol-1' for i in range(12, 13): figs['main'] = plt.figure(figsize=(fig_param['mw'], fig_param['mh']), dpi=300) axs['main'] = {} pobjs['main'] = {} figs['main'].clf() # dev = np.std(np.array(bas_mhd[i]) - np.array(bas_wao[i])) name_qch4_couple.plot_h2.generic3( fig=figs['main'], axs=axs['main'], pobjs=pobjs['main'], new_axs={ 'date1': [ dict( rect=[ (1 * fig_param['mlmargin'] + fig_param['mgap']) / fig_param['mw'], (fig_param['mh'] - fig_param['mph'] + fig_param['mgap']) / fig_param['mh'], (fig_param['mpw'] - 2*fig_param['mgap']) / fig_param['mw'], (fig_param['mph'] - 2*fig_param['mgap']) / fig_param['mh'] ], label='date1', projection=None ), { "set_yticks": [[yticks], {}], "set_xlim": [[ pd.to_datetime(date), pd.to_datetime(date) + pd.DateOffset(months=12), ], {}], "set_ylim": [[ylim], {}], "tick_params": [[], dict( axis='both', which='major', direction='in', labelsize=fig_param['fontsize'], left=True, bottom=False, right=False, top=False, labelleft=True, labelbottom=False, labelright=False, labeltop=False, )], "xaxis.set_major_locator": [ [mdates.DayLocator(bymonthday=1)], {} ], "xaxis.set_major_formatter": [ [mdates.DateFormatter('%Y-%m-%d')], {} ], }, dict( patch_alpha=0.0 ) ] }, new_pobjs={ 'mhd': [ 'date1', 'plot', [dates_tHour, np.array(obs_mhd), 'o'], {'c': '#000000', 'ms': 1., 'mew': 1., 'zorder': zorder['final'], 'label': 'Observed MHD'} ], 'wao': [ 'date1', 'plot', [dates_tHour, np.array(obs_wao), 'o'], {'c': '#0012FF', 'ms': 1., 'mew': 1., 'zorder': zorder['final'], 'label': 'Observed WAO'} ], 'legend':[ 'date1', 'legend', [], dict( loc='upper left', numpoints=2, fontsize=fig_param['fontsize2'], ncol=3, markerscale=5.0/3.5, handletextpad=0.2, columnspacing=1.0, borderpad=0.2, borderaxespad=0.2 ) ] }, texts=[ { 'x': fig_param['mgap'] / fig_param['mw'], 'y': (fig_param['mh'] - 1/2*fig_param['mph']) / fig_param['mh'], 's': ylabel, 'ha': 'left', 'va': 'center', 'size': fig_param['fontsize'], 'rotation': 90 } ], legend_params=[ [], [], {} ] ) name_qch4_couple.plot_h2.generic3( fig=figs['main'], axs=axs['main'], pobjs=pobjs['main'], new_axs={ 'date1': [ dict( rect=[ (1 * fig_param['mlmargin'] + fig_param['mgap']) / fig_param['mw'], (fig_param['mh'] - fig_param['mph'] + fig_param['mgap']) / fig_param['mh'], (fig_param['mpw'] - 2*fig_param['mgap']) / fig_param['mw'], (fig_param['mph'] - 2*fig_param['mgap']) / fig_param['mh'] ], label='date1', projection=None ), { "set_yticks": [[yticks2], {}], "set_xlim": [[ pd.to_datetime(date), pd.to_datetime(date) + pd.DateOffset(months=12), ], {}], "set_ylim": [[ylim2], {}], "tick_params": [[], dict( axis='both', which='major', direction='in', labelsize=fig_param['fontsize'], left=False, bottom=True, right=True, top=False, labelleft=False, labelbottom=True, labelright=True, labeltop=False, )], "xaxis.set_major_locator": [ [mdates.DayLocator(bymonthday=1)], {} ], "xaxis.set_major_formatter": [ [mdates.DateFormatter('%Y-%m-%d')], {} ], }, dict( patch_alpha=0.0 ) ] }, new_pobjs={ 'residual': [ 'date1', 'plot', [dates_tHour, np.array(obs_mhd)-np.array(obs_wao), '--'], {'c': '#767676', 'ms': 1., 'mew': 0., 'zorder': zorder['final'], 'label': 'MHD-WAO - SD: {:.2f}'.format(dev)} ], 'legend':[ 'date1', 'legend', [], dict( loc='upper right', numpoints=2, fontsize=fig_param['fontsize2'], ncol=3, markerscale=5.0/3.5, handletextpad=0.2, columnspacing=1.0, borderpad=0.2, borderaxespad=0.2 ) ] }, texts=[ { 'x': 1, 'y': (fig_param['mh'] - 1/2*fig_param['mph']) / fig_param['mh'], 's': ylabel2, 'ha': 'right', 'va': 'center', 'size': fig_param['fontsize'], 'rotation': 270 } ], legend_params=[ [], [], {} ] ) for l in axs['main']['date1'].get_xticklabels(): l.set_ha("right") l.set_rotation(30) # figs['main'].savefig(f'outputs/obs_dif_sd.png') ''' # ============================================================================= # Plotting the MHD and WAO modelled 'baselines' and their difference and # calculating the SD of this difference # ============================================================================= ''' Inputs: - MHD modelled 'baseline' calculated in create_baseline.py - WAO modelled 'baseline' calculated in create_baseline.py S13 (Appendix 7.2.1.) was used for the final result ''' # import modelled 'baselines' bas_mhd = genfromtxt('outputs/models/baselines/lower_emm/2018/2018_mhd.csv', delimiter=',') bas_wao = genfromtxt('outputs/models/baselines/lower_emm/2018/2018_wao.csv', delimiter=',') bas_mhd = np.transpose(bas_mhd) bas_wao = np.transpose(bas_wao) # calculate SD dev = np.std(bas_mhd[12] - bas_wao[12]) # remove data point where observations are missing dif = [] z = 0 count = 0 for i in range(0, len(obs_mhd)): if np.isnan(obs_mhd[i]) or np.isnan(obs_wao[i]): dif.append(np.nan) else: dif.append(bas_mhd[12][i] - bas_wao[12][i]) mhd = bas_mhd[12] wao = bas_wao[12] for i in range(0, len(obs_mhd)): if np.isnan(obs_mhd[i]): mhd[i] = 0 if np.isnan(obs_wao[i]): wao[i] = 0 # Plot figs = {} axs = {} pobjs = {} zorder = { 'background': 1, 'final': 2 } fig_param = { 'mw': 10.5, 'mh': 7, 'mpw': 8.5, 'mph': 5.7, 'mgap': 0.05, 'mlmargin': 1.2, 'mbmargin': 1.5, 'ylblx': 0.05, 'ylbly': 1.5, # left, centre aligned 'fontsize': 15, 'fontsize2': 12, } plt.close('all') ylabel = u'$\chi$ H$_{2}$ (nmol mol$^{-1}$)' ylabel2 = 'Differences (nmol mol$^{-1}$)' ylim = [350., 600.] ylim2 = [-70., 600.] yticks = np.arange(300., 600., 50.) yticks2 = np.arange(-70., 600., 100.) var_long_name = 'mole_fraction_of_hydrogen' var_units = 'nmol mol-1' for i in range(12, 13): # allows to create the plots for all the scenarios in one run # in this case only plotting the modelled 'baseline' with the lowest SD figs['main'] = plt.figure(figsize=(fig_param['mw'], fig_param['mh']), dpi=300) axs['main'] = {} pobjs['main'] = {} figs['main'].clf() name_qch4_couple.plot_h2.generic3( fig=figs['main'], axs=axs['main'], pobjs=pobjs['main'], new_axs={ 'date1': [ dict( rect=[ (1 * fig_param['mlmargin'] + fig_param['mgap']) / fig_param['mw'], (fig_param['mh'] - fig_param['mph'] + fig_param['mgap']) / fig_param['mh'], (fig_param['mpw'] - 2*fig_param['mgap']) / fig_param['mw'], (fig_param['mph'] - 2*fig_param['mgap']) / fig_param['mh'] ], label='date1', projection=None ), { "set_yticks": [[yticks], {}], "set_xlim": [[ pd.to_datetime(date), pd.to_datetime(date) + pd.DateOffset(months=12), ], {}], "set_ylim": [[ylim], {}], "tick_params": [[], dict( axis='both', which='major', direction='in', labelsize=fig_param['fontsize'], left=True, bottom=False, right=False, top=False, labelleft=True, labelbottom=False, labelright=False, labeltop=False, )], "xaxis.set_major_locator": [ [mdates.DayLocator(bymonthday=1)], {} ], "xaxis.set_major_formatter": [ [mdates.DateFormatter('%Y-%m-%d')], {} ], }, dict( patch_alpha=0.0 ) ] }, new_pobjs={ 'mhd': [ 'date1', 'plot', [dates_tHour, np.array(mhd), 'o'], {'c': '#000000', 'ms': 1., 'mew': 1., 'zorder': zorder['final'], 'label': 'Mobelled MHD baseline'} ], 'wao': [ 'date1', 'plot', [dates_tHour, np.array(wao), 'o'], {'c': '#0012FF', 'ms': 1., 'mew': 1., 'zorder': zorder['final'], 'label': 'Modelled WAO baseline'} ], 'legend':[ 'date1', 'legend', [], dict( loc='upper left', numpoints=2, fontsize=fig_param['fontsize2'], ncol=3, markerscale=5.0/3.5, handletextpad=0.2, columnspacing=1.0, borderpad=0.2, borderaxespad=0.2 ) ] }, texts=[ { 'x': fig_param['mgap'] / fig_param['mw'], 'y': (fig_param['mh'] - 1/2*fig_param['mph']) / fig_param['mh'], 's': ylabel, 'ha': 'left', 'va': 'center', 'size': fig_param['fontsize'], 'rotation': 90 } ], legend_params=[ [], [], {} ] ) name_qch4_couple.plot_h2.generic3( fig=figs['main'], axs=axs['main'], pobjs=pobjs['main'], new_axs={ 'date1': [ dict( rect=[ (1 * fig_param['mlmargin'] + fig_param['mgap']) / fig_param['mw'], (fig_param['mh'] - fig_param['mph'] + fig_param['mgap']) / fig_param['mh'], (fig_param['mpw'] - 2*fig_param['mgap']) / fig_param['mw'], (fig_param['mph'] - 2*fig_param['mgap']) / fig_param['mh'] ], label='date1', projection=None ), { "set_yticks": [[yticks2], {}], "set_xlim": [[ pd.to_datetime(date), pd.to_datetime(date) + pd.DateOffset(months=12), ], {}], "set_ylim": [[ylim2], {}], "tick_params": [[], dict( axis='both', which='major', direction='in', labelsize=fig_param['fontsize'], left=False, bottom=True, right=True, top=False, labelleft=False, labelbottom=True, labelright=True, labeltop=False, )], "xaxis.set_major_locator": [ [mdates.DayLocator(bymonthday=1)], {} ], "xaxis.set_major_formatter": [ [mdates.DateFormatter('%Y-%m-%d')], {} ], }, dict( patch_alpha=0.0 ) ] }, new_pobjs={ 'residual': [ 'date1', 'plot', [dates_tHour, np.array(dif), '--'], {'c': '#767676', 'ms': 1., 'mew': 0., 'zorder': zorder['final'], 'label': 'MHD-WAO - SD: {:.2f}'.format(dev)} ], 'legend':[ 'date1', 'legend', [], dict( loc='upper right', numpoints=2, fontsize=fig_param['fontsize2'], ncol=3, markerscale=5.0/3.5, handletextpad=0.2, columnspacing=1.0, borderpad=0.2, borderaxespad=0.2 ) ] }, texts=[ { 'x': 1, 'y': (fig_param['mh'] - 1/2*fig_param['mph']) / fig_param['mh'], 's': ylabel2, 'ha': 'right', 'va': 'center', 'size': fig_param['fontsize'], 'rotation': 270 } ], legend_params=[ [], [], {} ] ) for l in axs['main']['date1'].get_xticklabels(): l.set_ha("right") l.set_rotation(30) # figs['main'].savefig(f'outputs/obs_dif_sd.png')

32.464348

104

0.371083

1,639

18,667

4.081757

0.164735

0.10284

0.032885

0.040359

0.837369

0.824215

0.798057

0.782362

0.770105

0

0.042715

0.472009

18,667

574

105

32.520906

0.636059

0.058338

0

0.502024

0

0.119973

0.025635

0

1

0

false

0

0.02834

0

0.02834

0

null

0

1

0

null

0

6

a9cd2ec94d16ed061fc349d32328e4b4dbfeb21f

73

py

Python

rpxdock/score/__init__.py

quecloud/rpxdock

41f7f98f5dacf24fc95897910263a0bec2209e59

[ "Apache-2.0" ]

null

rpxdock/score/__init__.py

quecloud/rpxdock

41f7f98f5dacf24fc95897910263a0bec2209e59

[ "Apache-2.0" ]

null

rpxdock/score/__init__.py

quecloud/rpxdock

41f7f98f5dacf24fc95897910263a0bec2209e59

[ "Apache-2.0" ]

1

2020-04-13T20:07:52.000Z

from .component import * from .scorefunc import * from .rpxhier import *

18.25

24

0.753425

9

73

6.111111

0.555556

0.363636

0

0.164384

73

3

25

24.333333

0.901639

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e7979ed258a22c023099e92c73c4cfe81788f021

22

py

Python

v3/as_drivers/as_GPS/__init__.py

Dilepa/micropython-async

3c8817d9ead33bcd8399d0935ffb24dd7bcd6e71

[ "MIT" ]

443

2017-01-01T20:54:46.000Z

2022-03-28T06:17:30.000Z

v3/as_drivers/as_GPS/__init__.py

Dilepa/micropython-async

3c8817d9ead33bcd8399d0935ffb24dd7bcd6e71

[ "MIT" ]

79

2017-01-28T17:53:32.000Z

2022-02-08T10:05:04.000Z

v3/as_drivers/as_GPS/__init__.py

Dilepa/micropython-async

3c8817d9ead33bcd8399d0935ffb24dd7bcd6e71

[ "MIT" ]

126

2017-02-17T13:06:01.000Z

2022-03-07T03:50:50.000Z

from .as_GPS import *

11

21

0.727273

4

22

3.75

1

0

0.181818

22

1

22

0.833333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e79bf5ecdef807e58dbecd8e5e7ff8a6617e25e0

65

py

Python

kmlfiles/__init__.py

aravindashokk/kmlfiles

d7eba48abb4d87019a0a0f3b6e1ba91720e4cc1e

[ "MIT" ]

null

kmlfiles/__init__.py

aravindashokk/kmlfiles

d7eba48abb4d87019a0a0f3b6e1ba91720e4cc1e

[ "MIT" ]

null

kmlfiles/__init__.py

aravindashokk/kmlfiles

d7eba48abb4d87019a0a0f3b6e1ba91720e4cc1e

[ "MIT" ]

null

# Inside of __init__.py from kmlfiles.read_kml import read_kml

21.666667

39

0.8

11

65

4.181818

0.818182

0.304348

0

0.153846

65

2

40

32.5

0.836364

0.323077

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e7de9ba7be1c3b30fe1b1f3403b7acb28cba343d

47

py

Python

zenml/models/__init__.py

bobbywlindsey/data-science

8c67abd75a1f70ce37a04aff074cc3416260a296

[ "MIT" ]

1

2018-07-17T08:23:29.000Z

zenml/models/__init__.py

bobbywlindsey/zenml

8c67abd75a1f70ce37a04aff074cc3416260a296

[ "MIT" ]

null

zenml/models/__init__.py

bobbywlindsey/zenml

8c67abd75a1f70ce37a04aff074cc3416260a296

[ "MIT" ]

null

from .pca import * from .random_forest import *

23.5

28

0.765957

7

47

5

0.714286

0

0.148936

47

2

28

23.5

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

99f0a7bc98a398f4ff4710b66e9db01bfa98711a

45

py

Python

general/setpasswords.py

pastorsj/MusicVines

0151305ae865b29ece92daf4fb3c5455451b067f

[ "MIT" ]

null

general/setpasswords.py

pastorsj/MusicVines

0151305ae865b29ece92daf4fb3c5455451b067f

[ "MIT" ]

null

general/setpasswords.py

pastorsj/MusicVines

0151305ae865b29ece92daf4fb3c5455451b067f

[ "MIT" ]

null

import os os.environ["neo4jpass"] = "SET ME"

15

34

0.688889

7

45

4.428571

0.857143

0

0.025641

0.133333

45

2

35

22.5

0.769231

0

0.333333

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

8245359e783e2257df881a84e90ecafb7d3a9608

106,415

py

Python

dannce/engine/generator.py

Darkweiss/dannce

dc64c73bebd4e3aeb5df6f4bc63e6b13e316877f

[ "MIT" ]

null

dannce/engine/generator.py

Darkweiss/dannce

dc64c73bebd4e3aeb5df6f4bc63e6b13e316877f

[ "MIT" ]

null

dannce/engine/generator.py

Darkweiss/dannce

dc64c73bebd4e3aeb5df6f4bc63e6b13e316877f

[ "MIT" ]

null

"""Generator module for dannce training. """ import os import numpy as np from tensorflow import keras from dannce.engine import processing as processing from dannce.engine import ops as ops from dannce.engine.video import LoadVideoFrame import imageio import warnings import time import scipy.ndimage.interpolation import tensorflow as tf # from tensorflow_graphics.geometry.transformation.axis_angle import rotate from multiprocessing.dummy import Pool as ThreadPool from typing import List, Dict, Tuple, Text class DataGenerator(keras.utils.Sequence): """Generate data for Keras. Attributes: batch_size (int): Batch size to generate camnames (List): List of camera names. clusterIDs (List): List of sampleIDs crop_height (Tuple): (first, last) pixels in image height crop_width (tuple): (first, last) pixels in image width currvideo (Dict): Contains open video objects currvideo_name (Dict): Contains open video object names dim_in (Tuple): Input dimension dim_out (Tuple): Output dimension extension (Text): Video extension indexes (np.ndarray): sample indices used for batch generation labels (Dict): Label dictionary list_IDs (List): List of sampleIDs mono (bool): If True, use grayscale image. n_channels_in (int): Number of input channels n_channels_out (int): Number of output channels out_scale (int): Scale of the output gaussians. samples_per_cluster (int): Samples per cluster shuffle (bool): If True, shuffle the samples. vidreaders (Dict): Dict containing video readers. predict_flag (bool): If True, use imageio for reading videos, rather than OpenCV """ def __init__( self, list_IDs: List, labels: Dict, clusterIDs: List, batch_size: int = 32, dim_in: Tuple = (32, 32, 32), n_channels_in: int = 1, n_channels_out: int = 1, out_scale: float = 5, shuffle: bool = True, camnames: List = [], crop_width: Tuple = (0, 1024), crop_height: Tuple = (20, 1300), samples_per_cluster: int = 0, vidreaders: Dict = None, chunks: int = 3500, mono: bool = False, mirror: bool = False, predict_flag: bool = False, ): """Initialize Generator. Args: list_IDs (List): List of sampleIDs labels (Dict): Label dictionary clusterIDs (List): List of sampleIDs batch_size (int, optional): Batch size to generate dim_in (Tuple, optional): Input dimension n_channels_in (int, optional): Number of input channels n_channels_out (int, optional): Number of output channels out_scale (float, optional): Scale of the output gaussians. shuffle (bool, optional): If True, shuffle the samples. camnames (List, optional): List of camera names. crop_width (Tuple, optional): (first, last) pixels in image width crop_height (Tuple, optional): (first, last) pixels in image height samples_per_cluster (int, optional): Samples per cluster vidreaders (Dict, optional): Dict containing video readers. chunks (int, optional): Size of chunks when using chunked mp4. mono (bool, optional): If True, use grayscale image. predict_flag (bool, optional): If True, use imageio for reading videos, rather than OpenCV """ self.dim_in = dim_in self.dim_out = dim_in self.batch_size = batch_size self.labels = labels self.vidreaders = vidreaders self.list_IDs = list_IDs self.n_channels_in = n_channels_in self.n_channels_out = n_channels_out self.shuffle = shuffle # sigma for the ground truth joint probability map Gaussians self.out_scale = out_scale self.camnames = camnames self.crop_width = crop_width self.crop_height = crop_height self.clusterIDs = clusterIDs self.samples_per_cluster = samples_per_cluster self._N_VIDEO_FRAMES = chunks self.mono = mono self.mirror = mirror self.predict_flag = predict_flag self.on_epoch_end() if self.vidreaders is not None: self.extension = ( "." + list(vidreaders[camnames[0][0]].keys())[0].rsplit(".")[-1] ) assert len(self.list_IDs) == len(self.clusterIDs) self.load_frame = LoadVideoFrame(self._N_VIDEO_FRAMES, self.vidreaders, self.camnames, self.predict_flag) def __len__(self) -> int: """Denote the number of batches per epoch. Returns: int: Batches per epoch """ return int(np.floor(len(self.list_IDs) / self.batch_size)) def on_epoch_end(self): """Update indexes after each epoch.""" self.indexes = np.arange(len(self.list_IDs)) if self.shuffle: np.random.shuffle(self.indexes) def random_rotate(self, X: np.ndarray, y_3d: np.ndarray, log: bool = False): """Rotate each sample by 0, 90, 180, or 270 degrees. log indicates whether to return the rotation pattern (for saving) as well. Args: X (np.ndarray): Input images y_3d (np.ndarray): Output 3d targets log (bool, optional): If True, log the rotations. Returns: Tuple[np.ndarray, np.ndarray]: Rotated X and y_3d. or Tuple[np.ndarray, np.ndarray, np.ndarray]: Rotated X, y_3d, and rot val """ rots = np.random.choice(np.arange(4), X.shape[0]) for i in range(X.shape[0]): if rots[i] == 0: pass elif rots[i] == 1: # Rotate180 X[i] = self.rot180(X[i]) y_3d[i] = self.rot180(y_3d[i]) elif rots[i] == 2: # Rotate90 X[i] = self.rot90(X[i]) y_3d[i] = self.rot90(y_3d[i]) elif rots[i] == 3: # Rotate -90/270 X[i] = self.rot90(X[i]) X[i] = self.rot180(X[i]) y_3d[i] = self.rot90(y_3d[i]) y_3d[i] = self.rot180(y_3d[i]) if log: return X, y_3d, rots else: return X, y_3d class DataGenerator_3Dconv(DataGenerator): """Update generator class to handle multiple experiments. Attributes: camera_params (Dict): Camera parameters dictionary. channel_combo (Text): Method for shuffling camera input order com3d (Dict): Dictionary of com3d data. COM_aug (bool): If True, augment the COM. crop_im (bool): If True, crop images. depth (bool): If True, appends voxel depth to sampled image features [DEPRECATED] dim_out_3d (Tuple): Dimensions of the 3D volume, in voxels distort (bool): If true, apply camera undistortion. expval (bool): If True, process an expected value network (AVG) gpu_id (Text): Identity of GPU to use. immode (Text): Toggles using 'video' or 'tif' files as image input [DEPRECATED] interp (Text): Interpolation method. labels_3d (Dict): Contains ground-truth 3D label coordinates. mode (Text): Toggles output label format to match MAX vs. AVG network requirements. multicam (bool): If True, formats data to work with multiple cameras as input. norm_im (bool): If True, normalize images. nvox (int): Number of voxels per box side rotation (bool): If True, use simple rotation augmentation. tifdirs (List): Directories of .tifs var_reg (bool): If True, adds a variance regularization term to the loss function. vmax (int): Maximum box dim (relative to the COM) vmin (int): Minimum box dim (relative to the COM) vsize (float): Side length of one voxel predict_flag (bool): If True, use imageio for reading videos, rather than OpenCV """ def __init__( self, list_IDs: List, labels: Dict, labels_3d: Dict, camera_params: Dict, clusterIDs: List, com3d: Dict, tifdirs: List, batch_size: int = 32, dim_in: Tuple = (32, 32, 32), n_channels_in: int = 1, n_channels_out: int = 1, out_scale: int = 5, shuffle: bool = True, camnames: List = [], crop_width: Tuple = (0, 1024), crop_height: Tuple = (20, 1300), vmin: int = -100, vmax: int = 100, nvox: int = 32, gpu_id: Text = "0", interp: Text = "linear", depth: bool = False, channel_combo=None, mode: Text = "3dprob", samples_per_cluster: int = 0, immode: Text = "tif", rotation: bool = False, vidreaders: Dict = None, distort: bool = True, expval: bool = False, multicam: bool = True, var_reg: bool = False, COM_aug: bool = None, crop_im: bool = True, norm_im: bool = True, chunks: int = 3500, mono: bool = False, mirror: bool = False, predict_flag: bool = False, ): """Initialize data generator. Args: list_IDs (List): List of sample Ids labels (Dict): Dictionary of labels labels_3d (Dict): Dictionary of 3d labels. camera_params (Dict): Camera parameters dictionary. clusterIDs (List): List of sample Ids com3d (Dict): Dictionary of com3d data. tifdirs (List): Directories of .tifs batch_size (int, optional): Batch size to generate dim_in (Tuple, optional): Input dimension n_channels_in (int, optional): Number of input channels n_channels_out (int, optional): Number of output channels out_scale (int, optional): Scale of the output gaussians. shuffle (bool, optional): If True, shuffle the samples. camnames (List, optional): List of camera names. crop_width (Tuple, optional): (first, last) pixels in image width crop_height (Tuple, optional): (first, last) pixels in image height vmin (int, optional): Minimum box dim (relative to the COM) vmax (int, optional): Maximum box dim (relative to the COM) nvox (int, optional): Number of voxels per box side gpu_id (Text, optional): Identity of GPU to use. interp (Text, optional): Interpolation method. depth (bool): If True, appends voxel depth to sampled image features [DEPRECATED] channel_combo (Text): Method for shuffling camera input order mode (Text): Toggles output label format to match MAX vs. AVG network requirements. samples_per_cluster (int, optional): Samples per cluster immode (Text): Toggles using 'video' or 'tif' files as image input [DEPRECATED] rotation (bool, optional): If True, use simple rotation augmentation. vidreaders (Dict, optional): Dict containing video readers. distort (bool, optional): If true, apply camera undistortion. expval (bool, optional): If True, process an expected value network (AVG) multicam (bool): If True, formats data to work with multiple cameras as input. var_reg (bool): If True, adds a variance regularization term to the loss function. COM_aug (bool, optional): If True, augment the COM. crop_im (bool, optional): If True, crop images. norm_im (bool, optional): If True, normalize images. chunks (int, optional): Size of chunks when using chunked mp4. mono (bool, optional): If True, use grayscale image. predict_flag (bool, optional): If True, use imageio for reading videos, rather than OpenCV """ DataGenerator.__init__( self, list_IDs, labels, clusterIDs, batch_size, dim_in, n_channels_in, n_channels_out, out_scale, shuffle, camnames, crop_width, crop_height, samples_per_cluster, vidreaders, chunks, mono, mirror, predict_flag, ) self.vmin = vmin self.vmax = vmax self.nvox = nvox self.vsize = (vmax - vmin) / nvox self.dim_out_3d = (nvox, nvox, nvox) self.labels_3d = labels_3d self.camera_params = camera_params self.interp = interp self.depth = depth self.channel_combo = channel_combo print(self.channel_combo) self.mode = mode self.immode = immode self.tifdirs = tifdirs self.com3d = com3d self.rotation = rotation self.distort = distort self.expval = expval self.multicam = multicam self.var_reg = var_reg self.COM_aug = COM_aug self.crop_im = crop_im # If saving npy as uint8 rather than training directly, dont normalize self.norm_im = norm_im self.gpu_id = gpu_id def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray]: """Generate one batch of data. Args: index (int): Frame index Returns: Tuple[np.ndarray, np.ndarray]: One batch of data X (np.ndarray): Input volume y (np.ndarray): Target """ # Generate indexes of the batch indexes = self.indexes[index * self.batch_size : (index + 1) * self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X, y = self.__data_generation(list_IDs_temp) return X, y def rot90(self, X: np.ndarray) -> np.ndarray: """Rotate X by 90 degrees CCW. Args: X (np.ndarray): Input volume. Returns: np.ndarray: Rotated volume """ X = np.transpose(X, [1, 0, 2, 3]) X = X[:, ::-1, :, :] return X def rot180(self, X): """Rotate X by 180 degrees. Args: X (np.ndarray): Input volume. Returns: np.ndarray: Rotated volume """ X = X[::-1, ::-1, :, :] return X def __data_generation(self, list_IDs_temp: List) -> Tuple: """Generate data containing batch_size samples. X : (n_samples, *dim, n_channels) Args: list_IDs_temp (List): List of experiment Ids Returns: Tuple: Batch_size training samples X: Input volumes y_3d: Targets rotangle: Rotation angle Raises: Exception: Invalid generator mode specified. """ # Initialization first_exp = int(self.list_IDs[0].split("_")[0]) X = np.zeros( ( self.batch_size * len(self.camnames[first_exp]), *self.dim_out_3d, self.n_channels_in + self.depth, ), dtype="float32", ) if self.mode == "3dprob": y_3d = np.zeros( (self.batch_size, self.n_channels_out, *self.dim_out_3d), dtype="float32", ) elif self.mode == "coordinates": y_3d = np.zeros((self.batch_size, 3, self.n_channels_out), dtype="float32") else: raise Exception("not a valid generator mode") if self.expval: sz = self.dim_out_3d[0] * self.dim_out_3d[1] * self.dim_out_3d[2] X_grid = np.zeros((self.batch_size, sz, 3), dtype="float32") # Generate data cnt = 0 for i, ID in enumerate(list_IDs_temp): sampleID = int(ID.split("_")[1]) experimentID = int(ID.split("_")[0]) # For 3D ground truth this_y_3d = self.labels_3d[ID] this_COM_3d = self.com3d[ID] if self.COM_aug is not None: this_COM_3d = this_COM_3d.copy().ravel() this_COM_3d = ( this_COM_3d + self.COM_aug * 2 * np.random.rand(len(this_COM_3d)) - self.COM_aug ) # Create and project the grid here, xgrid = np.arange( self.vmin + this_COM_3d[0] + self.vsize / 2, this_COM_3d[0] + self.vmax, self.vsize, ) ygrid = np.arange( self.vmin + this_COM_3d[1] + self.vsize / 2, this_COM_3d[1] + self.vmax, self.vsize, ) zgrid = np.arange( self.vmin + this_COM_3d[2] + self.vsize / 2, this_COM_3d[2] + self.vmax, self.vsize, ) (x_coord_3d, y_coord_3d, z_coord_3d) = np.meshgrid(xgrid, ygrid, zgrid) if self.mode == "3dprob": for j in range(self.n_channels_out): y_3d[i, j] = np.exp( -( (y_coord_3d - this_y_3d[1, j]) ** 2 + (x_coord_3d - this_y_3d[0, j]) ** 2 + (z_coord_3d - this_y_3d[2, j]) ** 2 ) / (2 * self.out_scale ** 2) ) # When the voxel grid is coarse, we will likely miss # the peak of the probability distribution, as it # will lie somewhere in the middle of a large voxel. # So here we renormalize to [~, 1] if self.mode == "coordinates": if this_y_3d.shape == y_3d[i].shape: y_3d[i] = this_y_3d else: msg = "Note: ignoring dimension mismatch in 3D labels" warnings.warn(msg) if self.expval: X_grid[i] = np.stack( ( x_coord_3d.ravel(), y_coord_3d.ravel(), z_coord_3d.ravel(), ), axis=1, ) for _ci, camname in enumerate(self.camnames[experimentID]): ts = time.time() # Need this copy so that this_y does not change this_y = np.round(self.labels[ID]["data"][camname]).copy() if np.all(np.isnan(this_y)): com_precrop = np.zeros_like(this_y[:, 0]) * np.nan else: # For projecting points, we should not use this offset com_precrop = np.nanmean(this_y, axis=1) # Store sample if not self.mirror or _ci == 0: # for pre-cropped tifs if self.immode == "tif": thisim = imageio.imread( os.path.join( self.tifdirs[experimentID], camname, "{}.tif".format(sampleID), ) ) # From raw video, need to crop elif self.immode == "vid": thisim = self.load_frame.load_vid_frame( self.labels[ID]["frames"][camname], camname, extension=self.extension, )[ self.crop_height[0] : self.crop_height[1], self.crop_width[0] : self.crop_width[1], ] # print("Decode frame took {} sec".format(time.time() - ts)) tss = time.time() # Load in the image file at the specified path elif self.immode == "arb_ims": thisim = imageio.imread( self.tifdirs[experimentID] + self.labels[ID]["frames"][camname][0] + ".jpg" ) if self.mirror: # Save copy of the first image loaded in, so that it can be flipped accordingly. self.raw_im = thisim.copy() if self.mirror and self.camera_params[experimentID][camname]["m"] == 1: thisim = self.raw_im.copy() thisim = thisim[-1::-1] elif self.mirror and self.camera_params[experimentID][camname]["m"] == 0: thisim = self.raw_im elif self.mirror: raise Exception("Invalid mirror parameter, m, must be 0 or 1") if self.immode == "vid" or self.immode == "arb_ims": this_y[0, :] = this_y[0, :] - self.crop_width[0] this_y[1, :] = this_y[1, :] - self.crop_height[0] com = np.nanmean(this_y, axis=1) if self.crop_im: if np.all(np.isnan(com)): thisim = np.zeros( ( self.dim_in[1], self.dim_in[0], self.n_channels_in, ) ) else: thisim = processing.cropcom( thisim, com, size=self.dim_in[0] ) # Project de novo or load in approximate (faster) # TODO(break up): This is hard to read, consider breaking up ts = time.time() proj_grid = ops.project_to2d( np.stack( ( x_coord_3d.ravel(), y_coord_3d.ravel(), z_coord_3d.ravel(), ), axis=1, ), self.camera_params[experimentID][camname]["K"], self.camera_params[experimentID][camname]["R"], self.camera_params[experimentID][camname]["t"], ) if self.depth: d = proj_grid[:, 2] # print("2D Proj took {} sec".format(time.time() - ts)) ts = time.time() if self.distort: """ Distort points using lens distortion parameters """ proj_grid = ops.distortPoints( proj_grid[:, :2], self.camera_params[experimentID][camname]["K"], np.squeeze( self.camera_params[experimentID][camname]["RDistort"] ), np.squeeze( self.camera_params[experimentID][camname]["TDistort"] ), ).T # print("Distort took {} sec".format(time.time() - ts)) # ts = time.time() if self.crop_im: proj_grid = proj_grid[:, :2] - com_precrop + self.dim_in[0] // 2 # Now all coordinates should map properly to the image # cropped around the COM else: # Then the only thing we need to correct for is # crops at the borders proj_grid = proj_grid[:, :2] proj_grid[:, 0] = proj_grid[:, 0] - self.crop_width[0] proj_grid[:, 1] = proj_grid[:, 1] - self.crop_height[0] (r, g, b) = ops.sample_grid(thisim, proj_grid, method=self.interp) # print("Sample grid took {} sec".format(time.time() - ts)) if ( ~np.any(np.isnan(com_precrop)) or (self.channel_combo == "avg") or not self.crop_im ): X[cnt, :, :, :, 0] = np.reshape( r, (self.nvox, self.nvox, self.nvox) ) X[cnt, :, :, :, 1] = np.reshape( g, (self.nvox, self.nvox, self.nvox) ) X[cnt, :, :, :, 2] = np.reshape( b, (self.nvox, self.nvox, self.nvox) ) if self.depth: X[cnt, :, :, :, 3] = np.reshape( d, (self.nvox, self.nvox, self.nvox) ) cnt = cnt + 1 # print("Projection grid took {} sec".format(time.time() - tss)) if self.multicam: X = np.reshape( X, ( self.batch_size, len(self.camnames[first_exp]), X.shape[1], X.shape[2], X.shape[3], X.shape[4], ), ) X = np.transpose(X, [0, 2, 3, 4, 5, 1]) if self.channel_combo == "avg": X = np.nanmean(X, axis=-1) # Randomly reorder the cameras fed into the first layer elif self.channel_combo == "random": X = X[:, :, :, :, :, np.random.permutation(X.shape[-1])] X = np.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4] * X.shape[5], ), order="F", ) else: X = np.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4] * X.shape[5], ), order="F", ) else: # Then leave the batch_size and num_cams combined y_3d = np.tile(y_3d, [len(self.camnames[experimentID]), 1, 1, 1, 1]) if self.mode == "3dprob": y_3d = np.transpose(y_3d, [0, 2, 3, 4, 1]) if self.rotation: if self.expval: # First make X_grid 3d X_grid = np.reshape( X_grid, (self.batch_size, self.nvox, self.nvox, self.nvox, 3), ) X, X_grid = self.random_rotate(X, X_grid) # Need to reshape back to raveled version X_grid = np.reshape(X_grid, (self.batch_size, -1, 3)) else: X, y_3d = self.random_rotate(X, y_3d) if self.mono and self.n_channels_in == 3: # Convert from RGB to mono using the skimage formula. Drop the duplicated frames. # Reshape so RGB can be processed easily. X = np.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], self.n_channels_in, -1, ), order="F", ) X = ( X[:, :, :, :, 0] * 0.2125 + X[:, :, :, :, 1] * 0.7154 + X[:, :, :, :, 2] * 0.0721 ) # Then we also need to return the 3d grid center coordinates, # for calculating a spatial expected value # Xgrid is typically symmetric for 90 and 180 degree rotations # (when vmax and vmin are symmetric) # around the z-axis, so no need to rotate X_grid. if self.expval: if self.var_reg: return ( [processing.preprocess_3d(X), X_grid], [y_3d, np.zeros((self.batch_size, 1))], ) if self.norm_im: # y_3d is in coordinates here. return [processing.preprocess_3d(X), X_grid], y_3d else: return [X, X_grid], y_3d else: if self.norm_im: return processing.preprocess_3d(X), y_3d else: return X, y_3d class DataGenerator_3Dconv_torch(DataGenerator): """Update generator class to resample from kmeans clusters after each epoch. Also handles data across multiple experiments Attributes: camera_params (Dict): Camera parameters dictionary. channel_combo (Text): Method for shuffling camera input order com3d (Dict): Dictionary of com3d data. COM_aug (bool): If True, augment the COM. crop_im (bool): If True, crop images. depth (bool): If True, appends voxel depth to sampled image features [DEPRECATED] device (torch.device): GPU device identifier dim_out_3d (Tuple): Dimensions of the 3D volume, in voxels distort (bool): If true, apply camera undistortion. expval (bool): If True, process an expected value network (AVG) gpu_id (Text): Identity of GPU to use. immode (Text): Toggles using 'video' or 'tif' files as image input [DEPRECATED] interp (Text): Interpolation method. labels_3d (Dict): Contains ground-truth 3D label coordinates. mode (Text): Toggles output label format to match MAX vs. AVG network requirements. multicam (bool): If True, formats data to work with multiple cameras as input. norm_im (bool): If True, normalize images. nvox (int): Number of voxels per box side rotation (bool): If True, use simple rotation augmentation. session (tf.compat.v1.InteractiveSession): tensorflow session. threadpool (Threadpool): threadpool object for parallelizing video loading tifdirs (List): Directories of .tifs var_reg (bool): If True, adds a variance regularization term to the loss function. vmax (int): Maximum box dim (relative to the COM) vmin (int): Minimum box dim (relative to the COM) vsize (float): Side length of one voxel predict_flag (bool): If True, use imageio for reading videos, rather than OpenCV """ def __init__( self, list_IDs, labels, labels_3d, camera_params, clusterIDs, com3d, tifdirs, batch_size=32, dim_in=(32, 32, 32), n_channels_in=1, n_channels_out=1, out_scale=5, shuffle=True, camnames=[], crop_width=(0, 1024), crop_height=(20, 1300), vmin=-100, vmax=100, nvox=32, gpu_id="0", interp="linear", depth=False, channel_combo=None, mode="3dprob", samples_per_cluster=0, immode="tif", rotation=False, vidreaders=None, distort=True, expval=False, multicam=True, var_reg=False, COM_aug=None, crop_im=True, norm_im=True, chunks=3500, mono=False, mirror=False, predict_flag=False, ): """Initialize data generator. Args: list_IDs (List): List of sample Ids labels (Dict): Dictionary of labels labels_3d (Dict): Dictionary of 3d labels. camera_params (Dict): Camera parameters dictionary. clusterIDs (List): List of sample Ids com3d (Dict): Dictionary of com3d data. tifdirs (List): Directories of .tifs batch_size (int, optional): Batch size to generate dim_in (Tuple, optional): Input dimension n_channels_in (int, optional): Number of input channels n_channels_out (int, optional): Number of output channels out_scale (int, optional): Scale of the output gaussians. shuffle (bool, optional): If True, shuffle the samples. camnames (List, optional): List of camera names. crop_width (Tuple, optional): (first, last) pixels in image width crop_height (Tuple, optional): (first, last) pixels in image height vmin (int, optional): Minimum box dim (relative to the COM) vmax (int, optional): Maximum box dim (relative to the COM) nvox (int, optional): Number of voxels per box side gpu_id (Text, optional): Identity of GPU to use. interp (Text, optional): Interpolation method. depth (bool): If True, appends voxel depth to sampled image features [DEPRECATED] channel_combo (Text): Method for shuffling camera input order mode (Text): Toggles output label format to match MAX vs. AVG network requirements. samples_per_cluster (int, optional): Samples per cluster immode (Text): Toggles using 'video' or 'tif' files as image input [DEPRECATED] rotation (bool, optional): If True, use simple rotation augmentation. vidreaders (Dict, optional): Dict containing video readers. distort (bool, optional): If true, apply camera undistortion. expval (bool, optional): If True, process an expected value network (AVG) multicam (bool): If True, formats data to work with multiple cameras as input. var_reg (bool): If True, adds a variance regularization term to the loss function. COM_aug (bool, optional): If True, augment the COM. crop_im (bool, optional): If True, crop images. norm_im (bool, optional): If True, normalize images. chunks (int, optional): Size of chunks when using chunked mp4. mono (bool, optional): If True, use grayscale image. predict_flag (bool, optional): If True, use imageio for reading videos, rather than OpenCV """ DataGenerator.__init__( self, list_IDs, labels, clusterIDs, batch_size, dim_in, n_channels_in, n_channels_out, out_scale, shuffle, camnames, crop_width, crop_height, samples_per_cluster, vidreaders, chunks, mono, mirror, predict_flag, ) self.vmin = vmin self.vmax = vmax self.nvox = nvox self.vsize = (vmax - vmin) / nvox self.dim_out_3d = (nvox, nvox, nvox) self.labels_3d = labels_3d self.camera_params = camera_params self.interp = interp self.depth = depth self.channel_combo = channel_combo print(self.channel_combo) self.gpu_id = gpu_id self.mode = mode self.immode = immode self.tifdirs = tifdirs self.com3d = com3d self.rotation = rotation self.distort = distort self.expval = expval self.multicam = multicam self.var_reg = var_reg self.COM_aug = COM_aug self.crop_im = crop_im # If saving npy as uint8 rather than training directly, dont normalize self.norm_im = norm_im # importing torch here allows other modes to run without pytorch installed self.torch = __import__("torch") self.device = self.torch.device("cuda:" + self.gpu_id) # self.device = self.torch.device('cpu') self.threadpool = ThreadPool(len(self.camnames[0])) ts = time.time() # Limit GPU memory usage by Tensorflow to leave memory for PyTorch config = tf.compat.v1.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.45 config.gpu_options.allow_growth = True self.session = tf.compat.v1.InteractiveSession(config=config, graph=tf.Graph()) for i, ID in enumerate(list_IDs): experimentID = int(ID.split("_")[0]) for camname in self.camnames[experimentID]: # M only needs to be computed once for each camera K = self.camera_params[experimentID][camname]["K"] R = self.camera_params[experimentID][camname]["R"] t = self.camera_params[experimentID][camname]["t"] M = self.torch.as_tensor( ops.camera_matrix(K, R, t), dtype=self.torch.float32 ) self.camera_params[experimentID][camname]["M"] = M print("Init took {} sec.".format(time.time() - ts)) def __getitem__(self, index: int): """Generate one batch of data. Args: index (int): Frame index Returns: Tuple[np.ndarray, np.ndarray]: One batch of data X (np.ndarray): Input volume y (np.ndarray): Target """ # Generate indexes of the batch indexes = self.indexes[index * self.batch_size : (index + 1) * self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X, y = self.__data_generation(list_IDs_temp) return X, y def rot90(self, X): """Rotate X by 90 degrees CCW. Args: X (np.ndarray): Volume Returns: X (np.ndarray): Rotated volume """ X = X.permute(1, 0, 2, 3) X = X.flip(1) return X def rot180(self, X): """Rotate X by 180 degrees. Args: X (np.ndarray): Volume Returns: X (np.ndarray): Rotated volume """ X = X.flip(0).flip(1) return X def project_grid(self, X_grid, camname, ID, experimentID): """Projects 3D voxel centers and sample images as projected 2D pixel coordinates Args: X_grid (np.ndarray): 3-D array containing center coordinates of each voxel. camname (Text): camera name ID (Text): string denoting a sample ID experimentID (int): identifier for a video recording session. Returns: np.ndarray: projected voxel centers, now in 2D pixels """ ts = time.time() # Need this copy so that this_y does not change this_y = self.torch.as_tensor( self.labels[ID]["data"][camname], dtype=self.torch.float32, device=self.device, ).round() if self.torch.all(self.torch.isnan(this_y)): com_precrop = self.torch.zeros_like(this_y[:, 0]) * self.torch.nan else: # For projecting points, we should not use this offset com_precrop = self.torch.mean(this_y, axis=1) this_y[0, :] = this_y[0, :] - self.crop_width[0] this_y[1, :] = this_y[1, :] - self.crop_height[0] com = self.torch.mean(this_y, axis=1) thisim = self.load_frame.load_vid_frame( self.labels[ID]["frames"][camname], camname, extension=self.extension, )[ self.crop_height[0]: self.crop_height[1], self.crop_width[0]: self.crop_width[1], ] return self.pj_grid_post(X_grid, camname, ID, experimentID, com, com_precrop, thisim) def pj_grid_mirror(self, X_grid, camname, ID, experimentID, thisim): this_y = self.torch.as_tensor( self.labels[ID]["data"][camname], dtype=self.torch.float32, device=self.device, ).round() if self.torch.all(self.torch.isnan(this_y)): com_precrop = self.torch.zeros_like(this_y[:, 0]) * self.torch.nan else: # For projecting points, we should not use this offset com_precrop = self.torch.mean(this_y, axis=1) this_y[0, :] = this_y[0, :] - self.crop_width[0] this_y[1, :] = this_y[1, :] - self.crop_height[0] com = self.torch.mean(this_y, axis=1) if not self.mirror: raise Exception("Trying to project onto mirrored images without mirror being set properly") if self.camera_params[experimentID][camname]["m"] == 1: passim = thisim[-1::-1].copy() elif self.camera_params[experimentID][camname]["m"] == 0: passim = thisim.copy() else: raise Exception("Invalid mirror parameter, m, must be 0 or 1") return self.pj_grid_post(X_grid, camname, ID, experimentID, com, com_precrop, passim) def pj_grid_post(self, X_grid, camname, ID, experimentID, com, com_precrop, thisim): # separate the porjection and sampling into its own function so that # when mirror == True, this can be called directly if self.crop_im: if self.torch.all(self.torch.isnan(com)): thisim = self.torch.zeros( (self.dim_in[1], self.dim_in[0], self.n_channels_in), dtype=self.torch.uint8, device=self.device, ) else: thisim = processing.cropcom(thisim, com, size=self.dim_in[0]) # print('Frame loading took {} sec.'.format(time.time() - ts)) ts = time.time() proj_grid = ops.project_to2d_torch( X_grid, self.camera_params[experimentID][camname]["M"], self.device ) # print('Project2d took {} sec.'.format(time.time() - ts)) ts = time.time() if self.distort: proj_grid = ops.distortPoints_torch( proj_grid[:, :2], self.camera_params[experimentID][camname]["K"], np.squeeze(self.camera_params[experimentID][camname]["RDistort"]), np.squeeze(self.camera_params[experimentID][camname]["TDistort"]), self.device, ) proj_grid = proj_grid.transpose(0, 1) # print('Distort took {} sec.'.format(time.time() - ts)) ts = time.time() if self.crop_im: proj_grid = proj_grid[:, :2] - com_precrop + self.dim_in[0] // 2 # Now all coordinates should map properly to the image cropped around the COM else: # Then the only thing we need to correct for is crops at the borders proj_grid = proj_grid[:, :2] proj_grid[:, 0] = proj_grid[:, 0] - self.crop_width[0] proj_grid[:, 1] = proj_grid[:, 1] - self.crop_height[0] rgb = ops.sample_grid_torch(thisim, proj_grid, self.device, method=self.interp) # print('Sample grid {} sec.'.format(time.time() - ts)) if ( ~self.torch.any(self.torch.isnan(com_precrop)) or (self.channel_combo == "avg") or not self.crop_im ): X = rgb.permute(0, 2, 3, 4, 1) return X # TODO(nesting): There is pretty deep locigal nesting in this function, # might be useful to break apart def __data_generation(self, list_IDs_temp): """Generate data containing batch_size samples. X : (n_samples, *dim, n_channels) Args: list_IDs_temp (List): List of experiment Ids Returns: Tuple: Batch_size training samples X: Input volumes y_3d: Targets rotangle: Rotation angle Raises: Exception: Invalid generator mode specified. """ # Initialization first_exp = int(self.list_IDs[0].split("_")[0]) X = self.torch.zeros( ( self.batch_size * len(self.camnames[first_exp]), *self.dim_out_3d, self.n_channels_in + self.depth, ), dtype=self.torch.uint8, device=self.device, ) if self.mode == "3dprob": y_3d = self.torch.zeros( (self.batch_size, self.n_channels_out, *self.dim_out_3d), dtype=self.torch.float32, device=self.device, ) elif self.mode == "coordinates": y_3d = self.torch.zeros( (self.batch_size, 3, self.n_channels_out), dtype=self.torch.float32, device=self.device, ) else: raise Exception("not a valid generator mode") sz = self.dim_out_3d[0] * self.dim_out_3d[1] * self.dim_out_3d[2] X_grid = self.torch.zeros( (self.batch_size, sz, 3), dtype=self.torch.float32, device=self.device, ) # Generate data for i, ID in enumerate(list_IDs_temp): sampleID = int(ID.split("_")[1]) experimentID = int(ID.split("_")[0]) # For 3D ground truth this_y_3d = self.torch.as_tensor( self.labels_3d[ID], dtype=self.torch.float32, device=self.device, ) this_COM_3d = self.torch.as_tensor( self.com3d[ID], dtype=self.torch.float32, device=self.device ) # Create and project the grid here, xgrid = self.torch.arange( self.vmin + this_COM_3d[0] + self.vsize / 2, this_COM_3d[0] + self.vmax, self.vsize, dtype=self.torch.float32, device=self.device, ) ygrid = self.torch.arange( self.vmin + this_COM_3d[1] + self.vsize / 2, this_COM_3d[1] + self.vmax, self.vsize, dtype=self.torch.float32, device=self.device, ) zgrid = self.torch.arange( self.vmin + this_COM_3d[2] + self.vsize / 2, this_COM_3d[2] + self.vmax, self.vsize, dtype=self.torch.float32, device=self.device, ) (x_coord_3d, y_coord_3d, z_coord_3d) = self.torch.meshgrid( xgrid, ygrid, zgrid ) if self.mode == "coordinates": if this_y_3d.shape == y_3d[i].shape: y_3d[i] = this_y_3d else: msg = "Note: ignoring dimension mismatch in 3D labels" warnings.warn(msg) X_grid[i] = self.torch.stack( ( x_coord_3d.transpose(0, 1).flatten(), y_coord_3d.transpose(0, 1).flatten(), z_coord_3d.transpose(0, 1).flatten(), ), axis=1, ) # Compute projected images in parallel using multithreading ts = time.time() num_cams = len(self.camnames[experimentID]) arglist = [] if self.mirror: # Here we only load the video once, and then parallelize the projection # and sampling after mirror flipping. For setups that collect views # in a single imgae with the use of mirrors loadim = self.load_frame.load_vid_frame( self.labels[ID]["frames"][self.camnames[experimentID][0]], self.camnames[experimentID][0], extension=self.extension, )[ self.crop_height[0]: self.crop_height[1], self.crop_width[0]: self.crop_width[1], ] for c in range(num_cams): arglist.append( [X_grid[i], self.camnames[experimentID][c], ID, experimentID, loadim] ) result = self.threadpool.starmap(self.pj_grid_mirror, arglist) else: for c in range(num_cams): arglist.append( [X_grid[i], self.camnames[experimentID][c], ID, experimentID] ) result = self.threadpool.starmap(self.project_grid, arglist) for c in range(num_cams): ic = c + i * len(self.camnames[experimentID]) X[ic, :, :, :, :] = result[c] # print('MP took {} sec.'.format(time.time()-ts)) if self.multicam: X = X.reshape( ( self.batch_size, len(self.camnames[first_exp]), X.shape[1], X.shape[2], X.shape[3], X.shape[4], ) ) X = X.permute((0, 2, 3, 4, 5, 1)) if self.channel_combo == "avg": X = self.torch.mean(X, axis=-1) # Randomly reorder the cameras fed into the first layer elif self.channel_combo == "random": X = X[:, :, :, :, :, self.torch.randperm(X.shape[-1])] X = X.transpose(4, 5).reshape( ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4] * X.shape[5], ) ) else: X = X.transpose(4, 5).reshape( ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4] * X.shape[5], ) ) else: # Then leave the batch_size and num_cams combined y_3d = y_3d.repeat(num_cams, 1, 1, 1, 1) # 3dprob is required for *training* MAX networks if self.mode == "3dprob": y_3d = y_3d.permute([0, 2, 3, 4, 1]) if self.rotation: if self.expval: # First make X_grid 3d X_grid = self.torch.reshape( X_grid, (self.batch_size, self.nvox, self.nvox, self.nvox, 3), ) X, X_grid = self.random_rotate(X, X_grid) # Need to reshape back to raveled version X_grid = self.torch.reshape(X_grid, (self.batch_size, -1, 3)) else: X, y_3d = self.random_rotate(X, y_3d) if self.mono and self.n_channels_in == 3: # Convert from RGB to mono using the skimage formula. Drop the duplicated frames. # Reshape so RGB can be processed easily. X = self.torch.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], len(self.camnames[first_exp]), -1, ), ) X = ( X[:, :, :, :, :, 0] * 0.2125 + X[:, :, :, :, :, 1] * 0.7154 + X[:, :, :, :, :, 2] * 0.0721 ) # Convert pytorch tensors back to numpy array ts = time.time() if self.torch.is_tensor(X): X = X.float().cpu().numpy() if self.torch.is_tensor(y_3d): y_3d = y_3d.cpu().numpy() # print('Numpy took {} sec'.format(time.time() - ts)) if self.expval: if self.torch.is_tensor(X_grid): X_grid = X_grid.cpu().numpy() if self.var_reg: return ( [processing.preprocess_3d(X), X_grid], [y_3d, self.torch.zeros((self.batch_size, 1))], ) if self.norm_im: # y_3d is in coordinates here. return [processing.preprocess_3d(X), X_grid], y_3d else: return [X, X_grid], y_3d else: if self.norm_im: return processing.preprocess_3d(X), y_3d else: return X, y_3d class DataGenerator_3Dconv_tf(DataGenerator): """Updated generator class to resample from kmeans clusters after each epoch. Uses tensorflow operations to accelerate generation of projection grid **Compatible with TF 2.0 and newer. Not compatible with 1.14 and previous versions. Also handles data across multiple experiments Attributes: camera_params (Dict): Camera parameters dictionary. channel_combo (Text): Method for shuffling camera input order com3d (Dict): Dictionary of com3d data. COM_aug (bool): If True, augment the COM. crop_im (bool): If True, crop images. depth (bool): If True, appends voxel depth to sampled image features [DEPRECATED] device (Text): GPU device identifier dim_out_3d (Tuple): Dimensions of the 3D volume, in voxels distort (bool): If true, apply camera undistortion. expval (bool): If True, process an expected value network (AVG) gpu_id (Text): Identity of GPU to use. immode (Text): Toggles using 'video' or 'tif' files as image input [DEPRECATED] interp (Text): Interpolation method. labels_3d (Dict): Contains ground-truth 3D label coordinates. mode (Text): Toggles output label format to match MAX vs. AVG network requirements. multicam (bool): If True, formats data to work with multiple cameras as input. norm_im (bool): If True, normalize images. nvox (int): Number of voxels per box side rotation (bool): If True, use simple rotation augmentation. session (tf.compat.v1.InteractiveSession): tensorflow session. threadpool (Threadpool): threadpool object for parallelizing video loading tifdirs (List): Directories of .tifs var_reg (bool): If True, adds a variance regularization term to the loss function. vmax (int): Maximum box dim (relative to the COM) vmin (int): Minimum box dim (relative to the COM) vsize (float): Side length of one voxel predict_flag (bool): If True, use imageio for reading videos, rather than OpenCV """ def __init__( self, list_IDs, labels, labels_3d, camera_params, clusterIDs, com3d, tifdirs, batch_size=32, dim_in=(32, 32, 32), n_channels_in=1, n_channels_out=1, out_scale=5, shuffle=True, camnames=[], crop_width=(0, 1024), crop_height=(20, 1300), vmin=-100, vmax=100, nvox=32, gpu_id="0", interp="linear", depth=False, channel_combo=None, mode="3dprob", samples_per_cluster=0, immode="tif", rotation=False, vidreaders=None, distort=True, expval=False, multicam=True, var_reg=False, COM_aug=None, crop_im=True, norm_im=True, chunks=3500, mono=False, mirror=False, predict_flag=False, ): """Initialize data generator. Args: list_IDs (List): List of sample Ids labels (Dict): Dictionary of labels labels_3d (Dict): Dictionary of 3d labels. camera_params (Dict): Camera parameters dictionary. clusterIDs (List): List of sample Ids com3d (Dict): Dictionary of com3d data. tifdirs (List): Directories of .tifs batch_size (int, optional): Batch size to generate dim_in (Tuple, optional): Input dimension n_channels_in (int, optional): Number of input channels n_channels_out (int, optional): Number of output channels out_scale (int, optional): Scale of the output gaussians. shuffle (bool, optional): If True, shuffle the samples. camnames (List, optional): List of camera names. crop_width (Tuple, optional): (first, last) pixels in image width crop_height (Tuple, optional): (first, last) pixels in image height vmin (int, optional): Minimum box dim (relative to the COM) vmax (int, optional): Maximum box dim (relative to the COM) nvox (int, optional): Number of voxels per box side gpu_id (Text, optional): Identity of GPU to use. interp (Text, optional): Interpolation method. depth (bool): If True, appends voxel depth to sampled image features [DEPRECATED] channel_combo (Text): Method for shuffling camera input order mode (Text): Toggles output label format to match MAX vs. AVG network requirements. samples_per_cluster (int, optional): Samples per cluster immode (Text): Toggles using 'video' or 'tif' files as image input [DEPRECATED] rotation (bool, optional): If True, use simple rotation augmentation. vidreaders (Dict, optional): Dict containing video readers. distort (bool, optional): If true, apply camera undistortion. expval (bool, optional): If True, process an expected value network (AVG) multicam (bool): If True, formats data to work with multiple cameras as input. var_reg (bool): If True, adds a variance regularization term to the loss function. COM_aug (bool, optional): If True, augment the COM. crop_im (bool, optional): If True, crop images. norm_im (bool, optional): If True, normalize images. chunks (int, optional): Size of chunks when using chunked mp4. mono (bool, optional): If True, use grayscale image. predict_flag (bool, optional): If True, use imageio for reading videos, rather than OpenCV """ DataGenerator.__init__( self, list_IDs, labels, clusterIDs, batch_size, dim_in, n_channels_in, n_channels_out, out_scale, shuffle, camnames, crop_width, crop_height, samples_per_cluster, vidreaders, chunks, mono, mirror, predict_flag, ) self.vmin = vmin self.vmax = vmax self.nvox = nvox self.vsize = (vmax - vmin) / nvox self.dim_out_3d = (nvox, nvox, nvox) self.labels_3d = labels_3d self.camera_params = camera_params self.interp = interp self.depth = depth self.channel_combo = channel_combo print(self.channel_combo) self.gpu_id = gpu_id self.mode = mode self.immode = immode self.tifdirs = tifdirs self.com3d = com3d self.rotation = rotation self.distort = distort self.expval = expval self.multicam = multicam self.var_reg = var_reg self.COM_aug = COM_aug self.crop_im = crop_im # If saving npy as uint8 rather than training directly, dont normalize self.norm_im = norm_im self.config = tf.compat.v1.ConfigProto() self.config.gpu_options.per_process_gpu_memory_fraction = 0.8 self.config.gpu_options.allow_growth = True self.session = tf.compat.v1.InteractiveSession(config=self.config) self.device = "/GPU:" + self.gpu_id self.threadpool = ThreadPool(len(self.camnames[0])) with tf.device(self.device): ts = time.time() for i, ID in enumerate(list_IDs): experimentID = int(ID.split("_")[0]) for camname in self.camnames[experimentID]: # M only needs to be computed once for each camera K = self.camera_params[experimentID][camname]["K"] R = self.camera_params[experimentID][camname]["R"] t = self.camera_params[experimentID][camname]["t"] self.camera_params[experimentID][camname]["M"] = np.array( ops.camera_matrix(K, R, t), dtype="float32" ) print("Init took {} sec.".format(time.time() - ts)) def __getitem__(self, index): """Generate one batch of data. Args: index (int): Frame index Returns: Tuple[np.ndarray, np.ndarray]: One batch of data X (np.ndarray): Input volume y (np.ndarray): Target """ # Generate indexes of the batch indexes = self.indexes[index * self.batch_size : (index + 1) * self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X, y = self.__data_generation(list_IDs_temp) return X, y @tf.function def rot90(self, X): """Rotate X by 90 degrees CCW. Args: X (np.ndarray): Volume Returns: X (np.ndarray): Rotated volume """ X = tf.transpose(X, [1, 0, 2, 3]) X = X[:, ::-1, :, :] return X @tf.function def rot180(self, X): """Rotate X by 180 degrees. Args: X (np.ndarray): Volume Returns: X (np.ndarray): Rotated volume """ X = X[::-1, ::-1, :, :] return X def project_grid(self, X_grid, camname, ID, experimentID, device): """Projects 3D voxel centers and sample images as projected 2D pixel coordinates Args: X_grid (np.ndarray): 3-D array containing center coordinates of each voxel. camname (Text): camera name ID (Text): string denoting a sample ID experimentID (int): identifier for a video recording session. Returns: np.ndarray: projected voxel centers, now in 2D pixels """ ts = time.time() with tf.device(device): # Need this copy so that this_y does not change this_y = np.round(self.labels[ID]["data"][camname]).copy() if np.all(np.isnan(this_y)): com_precrop = np.zeros_like(this_y[:, 0]) * np.nan else: # For projecting points, we should not use this offset com_precrop = np.nanmean(this_y, axis=1) if self.immode == "vid": ts = time.time() thisim = self.load_frame.load_vid_frame( self.labels[ID]["frames"][camname], camname, extension=self.extension, )[ self.crop_height[0] : self.crop_height[1], self.crop_width[0] : self.crop_width[1], ] # print("Frame loading took {} sec.".format(time.time()-ts)) this_y[0, :] = this_y[0, :] - self.crop_width[0] this_y[1, :] = this_y[1, :] - self.crop_height[0] com = np.nanmean(this_y, axis=1) if self.crop_im: # Cropping takes negligible time if np.all(np.isnan(com)): thisim = np.zeros( (self.dim_in[1], self.dim_in[0], self.n_channels_in), dtype="uint8", ) else: thisim = processing.cropcom(thisim, com, size=self.dim_in[0]) # Project de novo ts = time.time() X_grid = tf.convert_to_tensor(X_grid) pts1 = tf.ones((X_grid.shape[0], 1), dtype="float32") projPts = tf.concat((X_grid, pts1), 1) M = tf.convert_to_tensor( self.camera_params[experimentID][camname]["M"], dtype="float32" ) proj_grid = ops.project_to2d_tf(projPts, M) # print("2D Project took {} sec.".format(time.time() - ts)) if self.distort: ts = time.time() proj_grid = ops.distortPoints_tf( proj_grid, tf.constant( self.camera_params[experimentID][camname]["K"], dtype="float32", ), tf.squeeze( tf.constant( self.camera_params[experimentID][camname]["RDistort"], dtype="float32", ) ), tf.squeeze( tf.constant( self.camera_params[experimentID][camname]["TDistort"], dtype="float32", ) ), ) proj_grid = tf.transpose(proj_grid, (1, 0)) # print("tf Distort took {} sec.".format(time.time() - ts)) if self.crop_im: proj_grid = proj_grid - com_precrop + self.dim_in[0] // 2 # Now all coordinates should map properly to the image # cropped around the COM else: # Then the only thing we need to correct for is crops at the borders proj_grid = proj_grid - tf.cast( tf.stack([self.crop_width[0], self.crop_height[0]]), "float32", ) ts = time.time() rgb = ops.sample_grid_tf(thisim, proj_grid, device, method=self.interp) # print("Sample grid tf took {} sec".format(time.time() - ts)) X = tf.reshape(rgb, (self.nvox, self.nvox, self.nvox, 3)) return X # TODO(nesting): There is pretty deep locigal nesting in this function, # might be useful to break apart def __data_generation(self, list_IDs_temp): """Generate data containing batch_size samples. X : (n_samples, *dim, n_channels) Args: list_IDs_temp (List): List of experiment Ids Returns: Tuple: Batch_size training samples X: Input volumes y_3d: Targets rotangle: Rotation angle Raises: Exception: Invalid generator mode specified. """ # Initialization ts = time.time() first_exp = int(self.list_IDs[0].split("_")[0]) with tf.device(self.device): if self.mode == "3dprob": y_3d = tf.zeros( (self.batch_size, self.n_channels_out, *self.dim_out_3d), dtype="float32", ) elif self.mode == "coordinates": y_3d = tf.zeros( (self.batch_size, 3, self.n_channels_out), dtype="float32" ) else: raise Exception("not a valid generator mode") # sz = self.dim_out_3d[0] * self.dim_out_3d[1] * self.dim_out_3d[2] # X_grid = tf.zeros((self.batch_size, sz, 3), dtype = 'float32') # Generate data for i, ID in enumerate(list_IDs_temp): sampleID = int(ID.split("_")[1]) experimentID = int(ID.split("_")[0]) # For 3D ground truth this_y_3d = self.labels_3d[ID] this_COM_3d = self.com3d[ID] with tf.device(self.device): xgrid = tf.range( self.vmin + this_COM_3d[0] + self.vsize / 2, this_COM_3d[0] + self.vmax, self.vsize, dtype="float32", ) ygrid = tf.range( self.vmin + this_COM_3d[1] + self.vsize / 2, this_COM_3d[1] + self.vmax, self.vsize, dtype="float32", ) zgrid = tf.range( self.vmin + this_COM_3d[2] + self.vsize / 2, this_COM_3d[2] + self.vmax, self.vsize, dtype="float32", ) (x_coord_3d, y_coord_3d, z_coord_3d) = tf.meshgrid(xgrid, ygrid, zgrid) if self.mode == "coordinates": if this_y_3d.shape == y_3d.shape: if i == 0: y_3d = tf.expand_dims(y_3d, 0) else: y_3d = tf.stack(y_3d, tf.expand_dims(this_y_3d, 0), axis=0) else: msg = "Note: ignoring dimension mismatch in 3D labels" warnings.warn(msg) xg = tf.stack( ( tf.keras.backend.flatten(x_coord_3d), tf.keras.backend.flatten(y_coord_3d), tf.keras.backend.flatten(z_coord_3d), ), axis=1, ) if i == 0: X_grid = tf.expand_dims(xg, 0) else: X_grid = tf.concat([X_grid, tf.expand_dims(xg, 0)], axis=0) # print('Initialization took {} sec.'.format(time.time() - ts)) if tf.executing_eagerly(): # Compute projection grids using multithreading num_cams = int(len(self.camnames[experimentID])) arglist = [] for c in range(num_cams): arglist.append( [ xg, self.camnames[experimentID][c], ID, experimentID, self.device, ] ) result = self.threadpool.starmap(self.project_grid, arglist) for c in range(num_cams): if i == 0 and c == 0: X = tf.expand_dims(result[c], 0) else: X = tf.concat([X, tf.expand_dims(result[c], 0)], axis=0) else: for c in range(num_cams): if c == 0: X = tf.expand_dims( self.project_grid( xg, self.camnames[experimentID][c], ID, experimentID, self.device, ), 0, ) else: X = tf.concat( ( X, tf.expand_dims( self.project_grid( xg, self.camnames[experimentID][c], ID, experimentID, self.device, ), 0, ), ), axis=0, ) ts = time.time() with tf.device(self.device): if self.multicam: X = tf.reshape( X, ( self.batch_size, len(self.camnames[first_exp]), X.shape[1], X.shape[2], X.shape[3], X.shape[4], ), ) X = tf.transpose(X, [0, 2, 3, 4, 5, 1]) if self.channel_combo == "avg": X = tf.mean(X, axis=-1) # Randomly reorder the cameras fed into the first layer elif self.channel_combo == "random": X = tf.transpose(X, [5, 0, 1, 2, 3, 4]) X = tf.random.shuffle(X) X = tf.transpose(X, [1, 2, 3, 4, 0, 5]) X = tf.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4] * X.shape[5], ), ) else: X = tf.transpose(X, [0, 1, 2, 3, 5, 4]) X = tf.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4] * X.shape[5], ), ) else: # Then leave the batch_size and num_cams combined y_3d = tf.tile(y_3d, [len(self.camnames[experimentID]), 1, 1, 1, 1]) if self.interp == "linear": # fix rotation issue for linear interpolation sample_grid method X = tf.squeeze(X) X = self.rot90(X[:, ::-1, :, :]) X = self.rot180(X) X = tf.expand_dims(X, 0) if self.mode == "3dprob": y_3d = tf.transpose(y_3d, [0, 2, 3, 4, 1]) X = tf.cast(X, "float32") if self.rotation: if self.expval: # First make X_grid 3d X_grid = tf.reshape( X_grid, (self.batch_size, self.nvox, self.nvox, self.nvox, 3), ) X, X_grid = self.random_rotate(X, X_grid) # Need to reshape back to raveled version X_grid = tf.reshape(X_grid, (self.batch_size, -1, 3)) else: X, y_3d = self.random_rotate(X, y_3d) # Then we also need to return the 3d grid center coordinates, # for calculating a spatial expected value # Xgrid is typically symmetric for 90 and 180 degree rotations # (when vmax and vmin are symmetric) # around the z-axis, so no need to rotate X_grid. # ts = time.time() # Eager execution enabled in TF 2, tested in TF 2.0, 2.1, and 2.2 if tf.executing_eagerly(): X = X.numpy() y_3d = y_3d.numpy() X_grid = X_grid.numpy() else: # For compatibility with TF 1.14 # Eager execution disabled on 1.14; enabling eager causes model to fail # Works on 1.14, but very slow. Graph grows in loop... X = X.eval(session=self.session) y_3d = y_3d.eval(session=self.session) X_grid = X_grid.eval(session=self.session) # print('Eval took {} sec.'.format(time.time()-ts)) # print('Wrap-up took {} sec.'.format(time.time()-ts)) if self.mono and self.n_channels_in == 3: # Convert from RGB to mono using the skimage formula. Drop the duplicated frames. # Reshape so RGB can be processed easily. X = np.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], self.n_channels_in, -1, ), order="F", ) X = ( X[:, :, :, :, 0] * 0.2125 + X[:, :, :, :, 1] * 0.7154 + X[:, :, :, :, 2] * 0.0721 ) if self.expval: if self.var_reg: return ( [processing.preprocess_3d(X), X_grid], [y_3d, np.zeros((self.batch_size, 1))], ) if self.norm_im: # y_3d is in coordinates here. return [processing.preprocess_3d(X), X_grid], y_3d else: return [X, X_grid], y_3d else: if self.norm_im: return processing.preprocess_3d(X), y_3d else: return X, y_3d def random_continuous_rotation(X, y_3d, max_delta=5): """Rotates X and y_3d a random amount around z-axis. Args: X (np.ndarray): input image volume y_3d (np.ndarray): 3d target (for MAX network) or voxel center grid (for AVG network) max_delta (int, optional): maximum range for rotation angle. Returns: np.ndarray: rotated image volumes np.ndarray: rotated grid coordimates """ rotangle = np.random.rand() * (2 * max_delta) - max_delta X = tf.reshape(X, [X.shape[0], X.shape[1], X.shape[2], -1]).numpy() y_3d = tf.reshape( y_3d, [y_3d.shape[0], y_3d.shape[1], y_3d.shape[2], -1] ).numpy() for i in range(X.shape[0]): X[i] = tf.keras.preprocessing.image.apply_affine_transform( X[i], theta=rotangle, row_axis=0, col_axis=1, channel_axis=2, fill_mode="nearest", cval=0.0, order=1, ) y_3d[i] = tf.keras.preprocessing.image.apply_affine_transform( y_3d[i], theta=rotangle, row_axis=0, col_axis=1, channel_axis=2, fill_mode="nearest", cval=0.0, order=1, ) X = tf.reshape(X, [X.shape[0], X.shape[1], X.shape[2], X.shape[2], -1]).numpy() y_3d = tf.reshape( y_3d, [y_3d.shape[0], y_3d.shape[1], y_3d.shape[2], y_3d.shape[2], -1], ).numpy() return X, y_3d # TODO(inherit): Several methods are repeated, consider inheriting from parent class DataGenerator_3Dconv_frommem(keras.utils.Sequence): """Generate 3d conv data from memory. Attributes: augment_brightness (bool): If True, applies brightness augmentation augment_continuous_rotation (bool): If True, applies rotation augmentation in increments smaller than 90 degrees augment_hue (bool): If True, applies hue augmentation batch_size (int): Batch size bright_val (float): Brightness augmentation range (-bright_val, bright_val), as fraction of raw image brightness chan_num (int): Number of input channels data (np.ndarray): Image volumes expval (bool): If True, crafts input for an AVG network hue_val (float): Hue augmentation range (-hue_val, hue_val), as fraction of raw image hue range indexes (np.ndarray): Sample indices used for batch generation labels (Dict): Label dictionary list_IDs (List): List of sampleIDs nvox (int): Number of voxels in each grid dimension random (bool): If True, shuffles camera order for each batch rotation (bool): If True, applies rotation augmentation in 90 degree increments rotation_val (float): Range of angles used for continuous rotation augmentation shuffle (bool): If True, shuffle the samples before each epoch var_reg (bool): If True, returns input used for variance regularization xgrid (np.ndarray): For the AVG network, this contains the 3D grid coordinates n_rand_views (int): Number of reviews to sample randomly from the full set replace (bool): If True, samples n_rand_views with replacement """ def __init__( self, list_IDs, data, labels, batch_size, rotation=True, random=True, chan_num=3, shuffle=True, expval=False, xgrid=None, var_reg=False, nvox=64, augment_brightness=True, augment_hue=True, augment_continuous_rotation=True, bright_val=0.05, hue_val=0.05, rotation_val=5, replace=True, n_rand_views=None, heatmap_reg=False, heatmap_reg_coeff=0.01, ): """Initialize data generator. Args: list_IDs (List): List of sampleIDs data data (np.ndarray): Image volumes labels (Dict): Label dictionar batch_size (int): batch size rotation (bool, optional): If True, applies rotation augmentation in 90 degree increments random (bool, optional): If True, shuffles camera order for each batch chan_num (int, optional): Number of input channels shuffle (bool, optional): If True, shuffle the samples before each epoch expval (bool, optional): If True, crafts input for an AVG network xgrid (None, optional): For the AVG network, this contains the 3D grid coordinates var_reg (bool, optional): If True, returns input used for variance regularization nvox (int, optional): Number of voxels in each grid dimension augment_brightness (bool, optional): If True, applies brightness augmentation augment_hue (bool, optional): If True, applies hue augmentation augment_continuous_rotation (bool, optional): If True, applies rotation augmentation in increments smaller than 90 degree bright_val (float, optional): brightness augmentation range (-bright_val, bright_val), as fraction of raw image brightness hue_val (float, optional): Hue augmentation range (-hue_val, hue_val), as fraction of raw image hue range rotation_val (float, optional): Range of angles used for continuous rotation augmentation n_rand_views (int, optional): Number of reviews to sample randomly from the full set replace (bool, optional): If True, samples n_rand_views with replacement """ self.list_IDs = list_IDs self.data = data self.labels = labels self.rotation = rotation self.batch_size = batch_size self.random = random self.chan_num = chan_num self.shuffle = shuffle self.expval = expval self.augment_hue = augment_hue self.augment_continuous_rotation = augment_continuous_rotation self.augment_brightness = augment_brightness self.var_reg = var_reg self.xgrid = xgrid self.nvox = nvox self.bright_val = bright_val self.hue_val = hue_val self.rotation_val = rotation_val self.n_rand_views = n_rand_views self.replace = replace self.heatmap_reg = heatmap_reg self.heatmap_reg_coeff = heatmap_reg_coeff self.on_epoch_end() def __len__(self): """Denote the number of batches per epoch. Returns: int: Batches per epoch """ return int(np.floor(len(self.list_IDs) / self.batch_size)) def __getitem__(self, index): """Generate one batch of data. Args: index (int): Frame index Returns: Tuple[np.ndarray, np.ndarray]: One batch of data X (np.ndarray): Input volume y (np.ndarray): Target """ # Generate indexes of the batch indexes = self.indexes[index * self.batch_size : (index + 1) * self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X, y = self.__data_generation(list_IDs_temp) return X, y def on_epoch_end(self): """Update indexes after each epoch.""" self.indexes = np.arange(len(self.list_IDs)) if self.shuffle: np.random.shuffle(self.indexes) def rot90(self, X): """Rotate X by 90 degrees CCW. Args: X (np.ndarray): Image volume or grid Returns: X (np.ndarray): Rotated image volume or grid """ X = np.transpose(X, [1, 0, 2, 3]) X = X[:, ::-1, :, :] return X def rot180(self, X): """Rotate X by 180 degrees. Args: X (np.ndarray): Image volume or grid Returns: X (np.ndarray): Rotated image volume or grid """ X = X[::-1, ::-1, :, :] return X def random_rotate(self, X, y_3d): """Rotate each sample by 0, 90, 180, or 270 degrees. Args: X (np.ndarray): Image volumes y_3d (np.ndarray): 3D grid coordinates (AVG) or training target volumes (MAX) Returns: X (np.ndarray): Rotated image volumes y_3d (np.ndarray): Rotated 3D grid coordinates (AVG) or training target volumes (MAX) """ rots = np.random.choice(np.arange(4), X.shape[0]) for i in range(X.shape[0]): if rots[i] == 0: pass elif rots[i] == 1: # Rotate180 X[i] = self.rot180(X[i]) y_3d[i] = self.rot180(y_3d[i]) elif rots[i] == 2: # Rotate90 X[i] = self.rot90(X[i]) y_3d[i] = self.rot90(y_3d[i]) elif rots[i] == 3: # Rotate -90/270 X[i] = self.rot90(X[i]) X[i] = self.rot180(X[i]) y_3d[i] = self.rot90(y_3d[i]) y_3d[i] = self.rot180(y_3d[i]) return X, y_3d def visualize(self, original, augmented): """Plots example image after augmentation Args: original (np.ndarray): image before augmentation augmented (np.ndarray): image after augmentation. """ import matplotlib.pyplot as plt fig = plt.figure() plt.subplot(1, 2, 1) plt.title("Original image") plt.imshow(original) plt.subplot(1, 2, 2) plt.title("Augmented image") plt.imshow(augmented) plt.show() input("Press Enter to continue...") def do_augmentation(self, X, X_grid, y_3d): """Applies augmentation Args: X (np.ndarray): image volumes X_grid (np.ndarray): 3D grid coordinates y_3d (np.ndarray): training targets Returns: X (np.ndarray): Augemented image volumes X_grid (np.ndarray): 3D grid coordinates y_3d (np.ndarray): Training targets """ if self.rotation: if self.expval: # First make X_grid 3d X_grid = np.reshape( X_grid, (self.batch_size, self.nvox, self.nvox, self.nvox, 3), ) X, X_grid = self.random_rotate(X.copy(), X_grid.copy()) # Need to reshape back to raveled version X_grid = np.reshape(X_grid, (self.batch_size, -1, 3)) else: X, y_3d = self.random_rotate(X.copy(), y_3d.copy()) if self.augment_continuous_rotation: if self.expval: # First make X_grid 3d X_grid = np.reshape( X_grid, (self.batch_size, self.nvox, self.nvox, self.nvox, 3), ) X, X_grid = random_continuous_rotation( X.copy(), X_grid.copy(), self.rotation_val ) # Need to reshape back to raveled version X_grid = np.reshape(X_grid, (self.batch_size, -1, 3)) else: X, y_3d = random_continuous_rotation( X.copy(), y_3d.copy(), self.rotation_val ) if self.augment_hue and self.chan_num == 3: for n_cam in range(int(X.shape[-1] / self.chan_num)): channel_ids = np.arange( n_cam * self.chan_num, n_cam * self.chan_num + self.chan_num, ) X[..., channel_ids] = tf.image.random_hue( X[..., channel_ids], self.hue_val ) elif self.augment_hue: warnings.warn( "Trying to augment hue with an image that is not RGB. Skipping." ) if self.augment_brightness: for n_cam in range(int(X.shape[-1] / self.chan_num)): channel_ids = np.arange( n_cam * self.chan_num, n_cam * self.chan_num + self.chan_num, ) X[..., channel_ids] = tf.image.random_brightness( X[..., channel_ids], self.bright_val ) return X, X_grid, y_3d def do_random(self, X): """Randomly re-order camera views Args: X (np.ndarray): image volumes Returns: X (np.ndarray): Shuffled image volumes """ if self.random: X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], X.shape[3], self.chan_num, -1), order='F') X = X[:, :, :, :, :, np.random.permutation(X.shape[-1])] X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4]*X.shape[5]), order='F') if self.n_rand_views is not None: # Select a set of cameras randomly with replacement. X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], X.shape[3], self.chan_num, -1), order='F') if self.replace: X = X[..., np.random.randint(X.shape[-1], size=(self.n_rand_views,))] else: if not self.random: raise Exception("For replace=False for n_rand_views, random must be turned on") X = X[:, :, :, :, :, :self.n_rand_views] X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], X.shape[3], X.shape[4]*X.shape[5]), order='F') return X def get_max_gt_ind(self, X_grid, y_3d): """Uses the gt label position to find the index of the voxel corresponding to it. Used for heatmap regularization. """ diff = np.sum((X_grid[:, :, :, np.newaxis] - y_3d[:, np.newaxis, :, :])**2, axis=2) inds = np.argmin(diff, axis=1) grid_d = int(np.round(X_grid.shape[1]**(1/3))) inds = np.unravel_index(inds, (grid_d, grid_d, grid_d)) return np.stack(inds, axis=1) def __data_generation(self, list_IDs_temp): """Generate data containing batch_size samples. X : (n_samples, *dim, n_channels) Args: list_IDs_temp (List): List of experiment Ids Returns: Tuple: Batch_size training samples X: Input volumes y_3d: Targets Raises: Exception: For replace=False for n_rand_views, random must be turned on. """ # Initialization X = np.zeros((self.batch_size, *self.data.shape[1:])) y_3d = np.zeros((self.batch_size, *self.labels.shape[1:])) # Only used when if self.expval: X_grid = np.zeros((self.batch_size, *self.xgrid.shape[1:])) else: X_grid = None for i, ID in enumerate(list_IDs_temp): X[i] = self.data[ID].copy() y_3d[i] = self.labels[ID] if self.expval: X_grid[i] = self.xgrid[ID] X, X_grid, y_3d = self.do_augmentation(X, X_grid, y_3d) # Randomly re-order, if desired X = self.do_random(X) if self.expval: if self.heatmap_reg: return [X, X_grid, self.get_max_gt_ind(X_grid, y_3d)], [y_3d, self.heatmap_reg_coeff*np.ones((self.batch_size, y_3d.shape[-1]), dtype='float32')] return [X, X_grid], y_3d else: return X, y_3d class DataGenerator_3Dconv_npy(DataGenerator_3Dconv_frommem): """Generates 3d conv data from npy files. Attributes: augment_brightness (bool): If True, applies brightness augmentation augment_continuous_rotation (bool): If True, applies rotation augmentation in increments smaller than 90 degrees augment_hue (bool): If True, applies hue augmentation batch_size (int): Batch size bright_val (float): Brightness augmentation range (-bright_val, bright_val), as fraction of raw image brightness chan_num (int): Number of input channels labels_3d (Dict): training targets expval (bool): If True, crafts input for an AVG network hue_val (float): Hue augmentation range (-hue_val, hue_val), as fraction of raw image hue range indexes (np.ndarray): Sample indices used for batch generation list_IDs (List): List of sampleIDs nvox (int): Number of voxels in each grid dimension random (bool): If True, shuffles camera order for each batch rotation (bool): If True, applies rotation augmentation in 90 degree increments rotation_val (float): Range of angles used for continuous rotation augmentation shuffle (bool): If True, shuffle the samples before each epoch var_reg (bool): If True, returns input used for variance regularization n_rand_views (int): Number of reviews to sample randomly from the full set replace (bool): If True, samples n_rand_views with replacement imdir (Text): Name of image volume npy subfolder griddir (Text): Name of grid volumw npy subfolder mono (bool): If True, return monochrome image volumes sigma (float): For MAX network, size of target Gaussian (mm) cam1 (bool): If True, prepares input for training a single camea network prefeat (bool): If True, prepares input for a network performing volume feature extraction before fusion npydir (Dict): path to each npy volume folder for each recording (i.e. experiment) """ def __init__(self, list_IDs, labels_3d, npydir, batch_size, rotation=True, random=False, chan_num=3, shuffle=True, expval=False, var_reg=False, imdir='image_volumes', griddir='grid_volumes', nvox=64, n_rand_views=None, mono=False, cam1=False, replace=True, prefeat=False, sigma=10, augment_brightness=True, augment_hue=True, augment_continuous_rotation=True, bright_val=0.05, hue_val=0.05, rotation_val=5, heatmap_reg=False, heatmap_reg_coeff=0.01, ): """Generates 3d conv data from npy files. Args: list_IDs (List): List of sampleIDs labels_3d (Dict): training targets npydir (Dict): path to each npy volume folder for each recording (i.e. experiment) batch_size (int): Batch size rotation (bool, optional): If True, applies rotation augmentation in 90 degree increments random (bool, optional): If True, shuffles camera order for each batch chan_num (int, optional): Number of input channels shuffle (bool, optional): If True, shuffle the samples before each epoch expval (bool, optional): If True, crafts input for an AVG network var_reg (bool, optional): If True, returns input used for variance regularization imdir (Text, optional): Name of image volume npy subfolder griddir (Text, optional): Name of grid volumw npy subfolder nvox (int, optional): Number of voxels in each grid dimension n_rand_views (int, optional): Number of reviews to sample randomly from the full set mono (bool, optional): If True, return monochrome image volumes cam1 (bool, optional): If True, prepares input for training a single camea network replace (bool, optional): If True, samples n_rand_views with replacement prefeat (bool, optional): If True, prepares input for a network performing volume feature extraction before fusion sigma (float, optional): For MAX network, size of target Gaussian (mm) augment_brightness (bool, optional): If True, applies brightness augmentation augment_hue (bool, optional): If True, applies hue augmentation augment_continuous_rotation (bool, optional): If True, applies rotation augmentation in increments smaller than 90 degrees bright_val (float, optional): Brightness augmentation range (-bright_val, bright_val), as fraction of raw image brightness hue_val (float, optional): Hue augmentation range (-hue_val, hue_val), as fraction of raw image hue range rotation_val (float, optional): Range of angles used for continuous rotation augmentation """ self.list_IDs = list_IDs self.labels_3d = labels_3d self.npydir = npydir self.rotation = rotation self.batch_size = batch_size self.random = random self.chan_num = chan_num self.shuffle = shuffle self.expval = expval self.var_reg = var_reg self.griddir = griddir self.imdir = imdir self.nvox = nvox self.n_rand_views = n_rand_views self.mono = mono self.cam1 = cam1 self.replace = replace self.prefeat = prefeat self.sigma = sigma self.augment_hue = augment_hue self.augment_continuous_rotation = augment_continuous_rotation self.augment_brightness = augment_brightness self.bright_val = bright_val self.hue_val = hue_val self.rotation_val = rotation_val self.heatmap_reg = heatmap_reg self.heatmap_reg_coeff = heatmap_reg_coeff self.on_epoch_end() def __len__(self): """Denote the number of batches per epoch. Returns: int: Batches per epoch """ return int(np.floor(len(self.list_IDs) / self.batch_size)) def __getitem__(self, index): """Generate one batch of data. Args: index (int): Frame index Returns: Tuple[np.ndarray, np.ndarray]: One batch of data X (np.ndarray): Input volume y (np.ndarray): Target """ # Generate indexes of the batch indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X, y = self.__data_generation(list_IDs_temp) return X, y def on_epoch_end(self): """Update indexes after each epoch.""" self.indexes = np.arange(len(self.list_IDs)) if self.shuffle == True: print("SHUFFLING DATA INDICES") np.random.shuffle(self.indexes) def rot90(self, X): # Rotate 90 X = np.transpose(X, [1, 0, 2, 3]) X = X[:, ::-1, :, :] return X def rot180(self,X): #Rotate 180 X = X[::-1, ::-1, :, :] return X def random_rotate(self, X, y_3d): """ Rotate each sample by 0, 90, 180, or 270 degrees """ rots = np.random.choice(np.arange(4), X.shape[0]) for i in range(X.shape[0]): if rots[i]==0: pass elif rots[i]==1: #Rotate180 X[i] = self.rot180(X[i]) y_3d[i] = self.rot180(y_3d[i]) elif rots[i]==2: #Rotate90 X[i] = self.rot90(X[i]) y_3d[i] = self.rot90(y_3d[i]) elif rots[i]==3: #Rotate -90/270 X[i] = self.rot90(X[i]) X[i] = self.rot180(X[i]) y_3d[i] = self.rot90(y_3d[i]) y_3d[i] = self.rot180(y_3d[i]) else: raise Exception("Failed to rotate properly") return X, y_3d def __data_generation(self, list_IDs_temp): """Generate data containing batch_size samples. X : (n_samples, *dim, n_channels) Args: list_IDs_temp (List): List of experiment Ids Returns: Tuple: Batch_size training samples X: Input volumes y_3d or y_3d_max: Targets Raises: Exception: For replace=False for n_rand_views, random must be turned on. """ # Initialization X = [] y_3d = [] X_grid = [] for i, ID in enumerate(list_IDs_temp): # Need to look up the experiment ID to get the correct directory IDkey = ID.split("_") eID = int(IDkey[0]) sID = IDkey[1] X.append(np.load(os.path.join(self.npydir[eID], self.imdir, '0_' + sID + '.npy')).astype('float32')) y_3d.append(self.labels_3d[ID]) X_grid.append(np.load(os.path.join(self.npydir[eID], self.griddir, '0_' + sID + '.npy'))) X = np.stack(X) y_3d = np.stack(y_3d) X_grid = np.stack(X_grid) if not self.expval: y_3d_max = np.zeros((self.batch_size, self.nvox, self.nvox, self.nvox, y_3d.shape[-1])) if not self.expval: X_grid = np.reshape(X_grid, (-1, self.nvox, self.nvox, self.nvox, 3)) for gridi in range(X_grid.shape[0]): x_coord_3d = X_grid[gridi, :, :, :, 0] y_coord_3d = X_grid[gridi, :, :, :, 1] z_coord_3d = X_grid[gridi, :, :, :, 2] for j in range(y_3d_max.shape[-1]): y_3d_max[gridi, :, :, :, j] = \ np.exp(-((y_coord_3d-y_3d[gridi, 1, j])**2 + (x_coord_3d-y_3d[gridi, 0, j])**2 + (z_coord_3d-y_3d[gridi, 2, j])**2)/(2*self.sigma**2)) if self.mono and self.chan_num == 3: # Convert from RGB to mono using the skimage formula. Drop the duplicated frames. # Reshape so RGB can be processed easily. X = np.reshape( X, ( X.shape[0], X.shape[1], X.shape[2], X.shape[3], self.chan_num, -1, ), order="F", ) X = ( X[:, :, :, :, 0] * 0.2125 + X[:, :, :, :, 1] * 0.7154 + X[:, :, :, :, 2] * 0.0721 ) ncam = int(X.shape[-1]//self.chan_num) X, X_grid, y_3d = self.do_augmentation(X, X_grid, y_3d) # Randomly re-order, if desired X = self.do_random(X) if self.cam1: # collapse the cameras to the batch dimensions. X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], X.shape[3], self.chan_num, -1), order='F') X = np.transpose(X, [0, 5, 1, 2, 3, 4]) X = np.reshape(X, (-1, X.shape[2], X.shape[3], X.shape[4], X.shape[5])) if self.expval: y_3d = np.tile(y_3d, [ncam, 1, 1]) X_grid = np.tile(X_grid, [ncam, 1, 1]) else: y_3d = np.tile(y_3d, [ncam, 1, 1, 1, 1]) X = processing.preprocess_3d(X) XX = [] if self.prefeat: for ix in range(ncam): XX.append(X[..., ix*self.chan_num:(ix+1)*self.chan_num]) X = XX if self.expval: if not self.prefeat: X = [X] X = X + [X_grid] if self.expval: if self.heatmap_reg: return [X, X_grid, self.get_max_gt_ind(X_grid, y_3d)], [y_3d, self.heatmap_reg_coeff*np.ones((self.batch_size, y_3d.shape[-1]), dtype='float32')] return X, y_3d else: return X, y_3d_max

38.403104

134

0.5085

12,386

106,415

4.232682

0.05918

0.009785

0.012017

0.017854

0.835578

0.808511

0.775855

0.750505

0.728551

0.713348

0

0.023121

0.395621

106,415

2,770

135

38.416968

0.792024

0.32424

0

0.677608

0

0.017943

0

0.001444

0.000586

1

0.025791

false

0.003517

0.008793

0

0.067995

0.003517

0

null

0

1

0

null

0

6

4169d41903e31adaea5325ef7eede568b5fbfeab

134

py

Python

pro_tes/middlewares/middleware.py

soumyadipDe/proTES

986b69c2408244a64fde811fbadbf9d8bd3c01e0

[ "Apache-2.0" ]

null

pro_tes/middlewares/middleware.py

soumyadipDe/proTES

986b69c2408244a64fde811fbadbf9d8bd3c01e0

[ "Apache-2.0" ]

null

pro_tes/middlewares/middleware.py

soumyadipDe/proTES

986b69c2408244a64fde811fbadbf9d8bd3c01e0

[ "Apache-2.0" ]

null

class AbstractMiddleware: def send_data(self): pass #def process_middleware_response(self): # pass

14.888889

43

0.604478

13

134

6

0.769231

0.205128

0

0.328358

134

9

44

14.888889

0.866667

0.343284

0

1

0.333333

false

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

416d65587382d6565390e49345f343837e3714d5

44

py

Python

tools/__init__.py

alex18212010045/pytorch-priv

0c007d693ef20ed0168b8b766e58835af5e8eebf

[ "MIT" ]

60

2017-12-29T03:31:48.000Z

2021-10-03T09:13:08.000Z

tools/__init__.py

alex18212010045/pytorch-priv

0c007d693ef20ed0168b8b766e58835af5e8eebf

[ "MIT" ]

1

2018-01-24T02:19:47.000Z

2018-01-24T06:21:06.000Z

tools/__init__.py

alex18212010045/pytorch-priv

0c007d693ef20ed0168b8b766e58835af5e8eebf

[ "MIT" ]

28

2017-12-29T06:15:10.000Z

2021-06-01T11:01:47.000Z

"""Useful tools """ from .painter import *

8.8

22

0.636364

5

44

5.6

1

0

0.181818

44

4

23

11

0.777778

0.272727

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

419c82edd2ae1e1a0ebe9a5948269e51aadb9e9a

34,485

py

Python

slise/slise.py

vishalbelsare/pyslise

3d81fe62a58755fa71755a0c02e56b9aa3e15e96

[ "MIT" ]

3

2021-05-06T11:31:29.000Z

2022-03-18T19:20:33.000Z

slise/slise.py

vishalbelsare/pyslise

3d81fe62a58755fa71755a0c02e56b9aa3e15e96

[ "MIT" ]

null

slise/slise.py

vishalbelsare/pyslise

3d81fe62a58755fa71755a0c02e56b9aa3e15e96

[ "MIT" ]

1

2021-08-20T13:46:31.000Z

""" This script contains the main slise functions, and classes """ from __future__ import annotations from typing import Union, Tuple, Callable, List from warnings import warn from matplotlib.pyplot import Figure import numpy as np from scipy.special import expit as sigmoid from slise.data import ( DataScaling, add_constant_columns, add_intercept_column, remove_constant_columns, normalise_robust, scale_same, unscale_model, ) from slise.optimisation import graduated_optimisation, loss_sharp from slise.initialisation import initialise_candidates from slise.utils import SliseWarning, mat_mul_inter, limited_logit from slise.plot import ( print_slise, plot_2d, fill_column_names, fill_prediction_str, plot_dist, plot_image, plot_dist_single, ) def regression( X: np.ndarray, Y: np.ndarray, epsilon: float, lambda1: float = 0, lambda2: float = 0, intercept: bool = True, normalise: bool = False, initialisation: Callable[ np.ndarray, np.ndarray, float, ..., Tuple[np.ndarray, float] ] = initialise_candidates, beta_max: float = 20, max_approx: float = 1.15, max_iterations: int = 300, debug: bool = False, ) -> SliseRegression: """Use SLISE for robust regression In robust regression we fit regression models that can handle data that contains outliers. SLISE accomplishes this by fitting a model such that the largest possible subset of the data items have an error less than a given value. All items with an error larger than that are considered potential outliers and do not affect the resulting model. It is highly recommended that you normalise the data, either before using SLISE or by setting normalise = TRUE. This is a wrapper that is equivalent to `SliseRegression(epsilon, **kwargs).fit(X, Y)` Args: X (np.ndarray): the data matrix Y (np.ndarray): the response vector epsilon (float): the error tolerance lambda1 (float, optional): the L1 regularistaion strength. Defaults to 0. lambda2 (float, optional): the L2 regularisation strength. Defaults to 0. intercept (bool, optional): add an intercept term. Defaults to True. normalise (bool, optional): should X aclasses not be scaled). Defaults to False. initialisation (Callable[ np.ndarray, np.ndarray, ..., Tuple[np.ndarray, float] ], optional): function that takes X, Y and gives an initial values for alpha and beta. Defaults to initialise_candidates. beta_max (float, optional): the stopping sigmoid steepness. Defaults to 20. max_approx (float, optional): approximation ratio when selecting the next beta. Defaults to 1.15. max_iterations (int, optional): maximum number of OWL-QN iterations. Defaults to 300. debug (bool, optional): print debug statements each graduated optimisation step. Defaults to False. Returns: SliseRegression: object containing the regression result """ return SliseRegression( epsilon, lambda1, lambda2, intercept, normalise, initialisation, beta_max, max_approx, max_iterations, debug, ).fit(X, Y) def explain( X: np.ndarray, Y: np.ndarray, epsilon: float, x: Union[np.ndarray, int], y: Union[float, None] = None, lambda1: float = 0, lambda2: float = 0, logit: bool = False, normalise: bool = False, initialisation: Callable[ np.ndarray, np.ndarray, float, ..., Tuple[np.ndarray, float] ] = initialise_candidates, beta_max: float = 20, max_approx: float = 1.15, max_iterations: int = 300, debug: bool = False, ) -> SliseExplainer: """Use SLISE for explaining outcomes from black box models. SLISE can also be used to provide local model-agnostic explanations for outcomes from black box models. To do this we replace the ground truth response vector with the predictions from the complex model. Furthermore, we force the model to fit a selected item (making the explanation local). This gives us a local approximation of the complex model with a simpler linear model. In contrast to other methods SLISE creates explanations using real data (not some discretised and randomly sampled data) so we can be sure that all inputs are valid (i.e. in the correct data manifold, and follows the constraints used to generate the data, e.g., the laws of physics). It is highly recommended that you normalise the data, either before using SLISE or by setting normalise = TRUE. This is a wrapper that is equivalent to `SliseExplainer(X, Y, epsilon, **kwargs).explain(x, y)` Args: X (np.ndarray): the data matrix Y (np.ndarray): the vector of predictions epsilon (float): the error tolerance x (Union[np.ndarray, int]): the data item to explain, or an index to get the item from self.X y (Union[float, None], optional): the outcome to explain. If x is an index then this should be None (y is taken from self.Y). Defaults to None. lambda1 (float, optional): the L1 regularistaion strength. Defaults to 0. lambda2 (float, optional): the L2 regularistaion strength. Defaults to 0. logit (bool, optional): do a logit transformation on the Y vector, this is recommended opnly if Y consists of probabilities. Defaults to False. normalise (bool, optional): should X and Y be normalised (note that epsilon will not be scaled). Defaults to False. initialisation (Callable[ np.ndarray, np.ndarray, float, ..., Tuple[np.ndarray, float] ], optional): function that takes (X, Y, epslion) and gives an initial values for alpha and beta. Defaults to initialise_candidates. beta_max (float, optional): the final sigmoid steepness. Defaults to 20. max_approx (float, optional): approximation ratio when selecting the next beta. Defaults to 1.15. max_iterations (int, optional): maximum number of OWL-QN iterations. Defaults to 300. debug (bool, optional): print debug statements each graduated optimisation step. Defaults to False. Returns: SliseExplainer: object containing the explanation """ return SliseExplainer( X, Y, epsilon, lambda1, lambda2, logit, normalise, initialisation, beta_max, max_approx, max_iterations, debug, ).explain(x, y) class SliseRegression: """ Class for holding the result from using SLISE for regression. Can also be used sklearn-style to do regression. """ def __init__( self, epsilon: float, lambda1: float = 0, lambda2: float = 0, intercept: bool = True, normalise: bool = False, initialisation: Callable[ np.ndarray, np.ndarray, float, ..., Tuple[np.ndarray, float] ] = initialise_candidates, beta_max: float = 20, max_approx: float = 1.15, max_iterations: int = 300, debug: bool = False, ): """Use SLISE for robust regression. In robust regression we fit regression models that can handle data that contains outliers. SLISE accomplishes this by fitting a model such that the largest possible subset of the data items have an error less than a given value. All items with an error larger than that are considered potential outliers and do not affect the resulting model. This constructor prepares the parameters, call `fit` to fit a robust regression to a dataset. It is highly recommended that you normalise the data, either before using SLISE or by setting normalise = TRUE. Args: epsilon (float): the error tolerance lambda1 (float, optional): the L1 regularistaion strength. Defaults to 0. lambda2 (float, optional): the L2 regularisation strength. Defaults to 0. intercept (bool, optional): add an intercept term. Defaults to True. normalise (bool, optional): should X and Y be normalised (note that epsilon will not be scaled). Defaults to False. initialisation (Callable[ np.ndarray, np.ndarray, ..., Tuple[np.ndarray, float] ], optional): function that takes (X, Y, epslion) and gives an initial values for alpha and beta. Defaults to initialise_candidates. beta_max (float, optional): the stopping sigmoid steepness. Defaults to 20. max_approx (float, optional): approximation ratio when selecting the next beta. Defaults to 1.15. max_iterations (int, optional): maximum number of OWL-QN iterations. Defaults to 300. debug (bool, optional): print debug statements each graduated optimisation step. Defaults to False. """ self.epsilon_orig = epsilon self.lambda1 = lambda1 self.lambda2 = lambda2 self.intercept = intercept self.normalise = normalise self.initialisation = initialisation self.beta_max = beta_max self.max_approx = max_approx self.max_iterations = max_iterations self.debug = debug self.alpha = None self.coefficients = None self.epsilon = epsilon self.X = None self.Y = None self.scale = None def fit(self, X: np.ndarray, Y: np.ndarray) -> SliseRegression: """Robustly fit a linear regression to a dataset Args: X (np.ndarray): the data matrix Y (np.ndarray): the response vector Returns: SliseRegression: self, containing the regression result """ if len(X.shape) == 1: X = np.reshape(X, X.shape + (1,)) else: X = X.copy() Y = Y.copy() self.X = X self.Y = Y # Preprocessing if self.normalise: X, x_cols = remove_constant_columns(X) if self.X.shape[1] == X.shape[1]: x_cols = None X, x_center, x_scale = normalise_robust(X) Y, y_center, y_scale = normalise_robust(Y) self.scale = DataScaling(x_center, x_scale, y_center, y_scale, x_cols) if self.intercept: X = add_intercept_column(X) # Initialisation alpha, beta = self.initialisation(X, Y, self.epsilon_orig) # Optimisation alpha = graduated_optimisation( alpha, X, Y, epsilon=self.epsilon_orig, lambda1=self.lambda1, lambda2=self.lambda2, beta=beta, beta_max=self.beta_max, max_approx=self.max_approx, max_iterations=self.max_iterations, debug=self.debug, ) self.alpha = alpha if self.normalise: alpha2 = self.scale.unscale_model(alpha) if not self.intercept: if np.abs(alpha2[0]) > 1e-8: warn( "Intercept introduced due to scaling, consider setting intercept=True (or normalise=False)", SliseWarning, ) self.intercept = True self.alpha = np.concatenate(([0], alpha)) else: alpha2 = alpha2[1:] self.coefficients = alpha2 self.epsilon = self.epsilon_orig * y_scale else: self.coefficients = alpha return self def get_params(self, normalised: bool = False) -> np.ndarray: """Get the coefficients of the linear model Args: normalised (bool, optional): if the data is normalised within SLISE, return a linear model ftting the normalised data. Defaults to False. Returns: np.ndarray: the coefficients of the linear model """ return self.alpha if normalised else self.coefficients @property def normalised(self): if self.normalise: return add_constant_columns(self.alpha, self.scale.columns, self.intercept) else: return None def predict(self, X: Union[np.ndarray, None] = None) -> np.ndarray: """Use the fitted model to predict new responses Args: X (Union[np.ndarray, None], optional): data matrix to predict, or None for using the fitted dataset. Defaults to None. Returns: np.ndarray: the predicted response """ if X is None: return mat_mul_inter(self.X, self.coefficients) else: return mat_mul_inter(X, self.coefficients) def score( self, X: Union[np.ndarray, None] = None, Y: Union[np.ndarray, None] = None ) -> float: """Calculate the loss. Lower is better and it should usually be negative (unless the regularisation is very (/too?) strong). Args: X (Union[np.ndarray, None], optional): data matrix, or None for using the fitted dataset. Defaults to None. Y (Union[np.ndarray, None], optional): response vector, or None for using the fitted dataset. Defaults to None. Returns: float: the loss """ if X is None or Y is None: X = self.X Y = self.Y if self.normalise: X = self.scale.scale_x(X) Y = self.scale.scale_y(Y) return loss_sharp( self.alpha, X, Y, self.epsilon_orig, self.lambda1, self.lambda2 ) loss = score def subset( self, X: Union[np.ndarray, None] = None, Y: Union[np.ndarray, None] = None ) -> np.ndarray: """Get the subset (of non-outliers) used for the robust regression model Args: X (Union[np.ndarray, None], optional): data matrix, or None for using the fitted dataset. Defaults to None. Y (Union[np.ndarray, None], optional): response vector, or None for using the fitted dataset. Defaults to None. Returns: np.ndarray: the selected subset as a boolean mask """ if X is None or Y is None: X = self.X Y = self.Y Y2 = mat_mul_inter(X, self.coefficients) return (Y2 - Y) ** 2 < self.epsilon ** 2 def print( self, variables: Union[List[str], None] = None, decimals: int = 3, num_var: int = 10, ): """Print the current robust regression result Args: variables ( Union[List[str], None], optional): names of the variables/columns in X. Defaults to None. num_var (int, optional): exclude zero weights if there are too many variables. Defaults to 10. decimals (int, optional): the precision to use for printing. Defaults to 3. """ print_slise( self.coefficients, self.intercept, self.subset(), self.score(), self.epsilon, variables, "SLISE Regression", decimals, num_var, alpha=self.normalised, ) def plot_2d( self, title: str = "SLISE Regression", label_x: str = "x", label_y: str = "y", decimals: int = 3, fig: Union[Figure, None] = None, ) -> SliseRegression: """Plot the regression in a 2D scatter plot with a line for the regression model Args: title (str, optional): plot title. Defaults to "SLISE Regression". label_x (str, optional): x-axis label. Defaults to "x". label_y (str, optional): y-axis label. Defaults to "y". decimals (int, optional): number of decimals when writing numbers. Defaults to 3. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. Raises: SliseException: if the data has too many dimensions """ plot_2d( self.X, self.Y, self.coefficients, self.epsilon, None, None, False, title, label_x, label_y, decimals, fig, ) def plot_dist( self, title: str = "SLISE Regression", variables: list = None, decimals: int = 3, fig: Union[Figure, None] = None, ) -> SliseExplainer: """Plot the regression with density distributions for the dataset and a barplot for the model. Args: title (str, optional): title of the plot. Defaults to "SLISE Explanation". variables (list, optional): names for the variables. Defaults to None. decimals (int, optional): the number of decimals to write. Defaults to 3. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. """ plot_dist( self.X, self.Y, self.coefficients, self.subset(), self.normalised, None, None, None, None, title, variables, decimals, fig, ) def plot_subset( self, title: str = "Response Distribution", decimals: int = 0, fig: Union[Figure, None] = None, ): """Plot a density distributions for response and the response of the subset Args: title (str, optional): title of the plot. Defaults to "Response Distribution". decimals (int, optional): number of decimals when writing the subset size. Defaults to 0. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. """ plot_dist_single(self.Y, self.subset(), None, title, decimals, fig) class SliseExplainer: """ Class for holding the result from using SLISE as an explainer. Can also be used sklearn-style to create explanations. """ def __init__( self, X: np.ndarray, Y: np.ndarray, epsilon: float, lambda1: float = 0, lambda2: float = 0, logit: bool = False, normalise: bool = False, initialisation: Callable[ np.ndarray, np.ndarray, float, ..., Tuple[np.ndarray, float] ] = initialise_candidates, beta_max: float = 20, max_approx: float = 1.15, max_iterations: int = 300, debug: bool = False, ): """Use SLISE for explaining outcomes from black box models. SLISE can also be used to provide local model-agnostic explanations for outcomes from black box models. To do this we replace the ground truth response vector with the predictions from the complex model. Furthermore, we force the model to fit a selected item (making the explanation local). This gives us a local approximation of the complex model with a simpler linear model. In contrast to other methods SLISE creates explanations using real data (not some discretised and randomly sampled data) so we can be sure that all inputs are valid (i.e. in the correct data manifold, and follows the constraints used to generate the data, e.g., the laws of physics). This prepares the dataset used for the explanations, call `explain` on this object to explain outcomes. It is highly recommended that you normalise the data, either before using SLISE or by setting normalise = TRUE. Args: X (np.ndarray): the data matrix Y (np.ndarray): the vector of predictions epsilon (float): the error tolerance lambda1 (float, optional): the L1 regularistaion strength. Defaults to 0. lambda2 (float, optional): the L2 regularistaion strength. Defaults to 0. logit (bool, optional): do a logit transformation on the Y vector, this is recommended opnly if Y consists of probabilities. Defaults to False. normalise (bool, optional): should X and Y be normalised (note that epsilon will not be scaled). Defaults to False. initialisation (Callable[ np.ndarray, np.ndarray, float, ..., Tuple[np.ndarray, float] ], optional): function that takes (X, Y, epslion) and gives an initial values for alpha and beta. Defaults to initialise_candidates. beta_max (float, optional): the final sigmoid steepness. Defaults to 20. max_approx (float, optional): approximation ratio when selecting the next beta. Defaults to 1.15. max_iterations (int, optional): maximum number of OWL-QN iterations. Defaults to 300. debug (bool, optional): print debug statements each graduated optimisation step. Defaults to False. """ self.epsilon_orig = epsilon self.lambda1 = lambda1 self.lambda2 = lambda2 self.logit = logit self.normalise = normalise self.initialisation = initialisation self.beta_max = beta_max self.max_approx = max_approx self.max_iterations = max_iterations self.debug = debug if len(X.shape) == 1: X = np.reshape(X, X.shape + (1,)) else: X = X.copy() Y = Y.copy() self.X = X self.Y = Y self.x = None self.y = None self.alpha = None self.coefficients = None # Preprocess data if logit: Y = limited_logit(Y) if self.normalise: X2, x_cols = remove_constant_columns(X) if X.shape[1] == X2.shape[1]: x_cols = None X, x_center, x_scale = normalise_robust(X2) Y, y_center, y_scale = normalise_robust(Y) self.scale = DataScaling(x_center, x_scale, y_center, y_scale, x_cols) self.epsilon = epsilon * y_scale else: self.scale = None self.epsilon = epsilon self.X2 = X self.Y2 = Y def explain( self, x: Union[np.ndarray, int], y: Union[float, None] = None ) -> SliseExplainer: """Explain an outcome from a black box model Args: x (Union[np.ndarray, int]): the data item to explain, or an index to get the item from self.X y (Union[float, None], optional): the outcome to explain. If x is an index then this should be None (y is taken from self.Y). Defaults to None. Returns: SliseExplainer: self, with values set to the explanation """ if y is None: self.y = self.Y[x] self.x = self.X[x, :] y = self.Y2[x] x = self.X2[x, :] else: x = np.atleast_1d(x) self.x = x self.y = y if self.logit: y = limited_logit(y) if self.normalise: x = self.scale.scale_x(x) y = self.scale.scale_y(y) X = self.X2 - x[None, :] Y = self.Y2 - y alpha, beta = self.initialisation(X, Y, self.epsilon_orig) alpha = graduated_optimisation( alpha, X, Y, epsilon=self.epsilon_orig, lambda1=self.lambda1, lambda2=self.lambda2, beta=beta, beta_max=self.beta_max, max_approx=self.max_approx, max_iterations=self.max_iterations, debug=self.debug, ) alpha = np.concatenate( (y - np.sum(alpha * x, dtype=x.dtype, keepdims=True), alpha) ) self.alpha = alpha if self.normalise: alpha2 = self.scale.unscale_model(alpha) alpha2[0] = self.y - np.sum(self.x * alpha2[1:]) self.coefficients = alpha2 else: self.coefficients = alpha return self def get_params(self, normalised: bool = False) -> np.ndarray: """Get the explanation as the coefficients of a linear model (approximating the black box model) Args: normalised (bool, optional): if the data is normalised within SLISE, return a linear model fitting the normalised data. Defaults to False. Returns: np.ndarray: the coefficients of the linear model (the first scalar in the vector is the intercept) """ return self.alpha if normalised else self.coefficients @property def normalised(self): if self.normalise: return add_constant_columns(self.alpha, self.scale.columns, True) else: return None def predict(self, X: Union[np.ndarray, None] = None) -> np.ndarray: """Use the approximating linear model to predict new outcomes Args: X (Union[np.ndarray, None], optional): data matrix to predict, or None for using the fitted dataset. Defaults to None. Returns: np.ndarray: prediction vector """ if X is None: Y = mat_mul_inter(self.X, self.coefficients) else: Y = mat_mul_inter(X, self.coefficients) if self.scaler.logit: Y = sigmoid(Y) return Y def score( self, X: Union[np.ndarray, None] = None, Y: Union[np.ndarray, None] = None ) -> float: """Calculate the loss. Lower is better and it should usually be negative (unless the regularisation is very (/too?) strong). Args: X (Union[np.ndarray, None], optional): data matrix, or None for using the fitted dataset. Defaults to None. Y (Union[np.ndarray, None], optional): response vector, or None for using the fitted dataset. Defaults to None. Returns: float: the loss """ x = self.x y = self.y if self.logit: y = limited_logit(y) if self.normalise: x = self.scale.scale_x(x) y = self.scale.scale_y(y) if X is None or Y is None: X = self.X2 Y = self.Y2 else: if self.logit: Y = limited_logit(Y) if self.normalise: X = self.scale.scale_x(X) Y = self.scale.scale_y(Y) X = X - x[None, :] Y = Y - y return loss_sharp( self.alpha[1:], X, Y, self.epsilon_orig, self.lambda1, self.lambda2, ) loss = score def subset( self, X: Union[np.ndarray, None] = None, Y: Union[np.ndarray, None] = None ) -> np.ndarray: """Get the subset / neighbourhood used for the approximation (explanation) Args: X (Union[np.ndarray, None], optional): data matrix, or None for using the fitted dataset. Defaults to None. Y (Union[np.ndarray, None], optional): response vector, or None for using the fitted dataset. Defaults to None. Returns: np.ndarray: the subset as a boolean mask """ if X is None or Y is None: X = self.X Y = self.Y if self.logit: Y = limited_logit(Y) res = mat_mul_inter(X, self.coefficients) - Y return res ** 2 < self.epsilon ** 2 def get_impact( self, normalised: bool = False, x: Union[None, np.ndarray] = None ) -> np.ndarray: """Get the "impact" of different variables on the outcome. The impact is the (normalised) model times the (normalised) item. Args: normalised (bool, optional): return the normalised impact (if normalisation is used). Defaults to False. x (Union[None, np.ndarray], optional): the item to calculate the impact for (uses the explained item if None). Defaults to None. Returns: np.ndarray: the impact vector """ if x is None: x = self.x if normalised and self.normalise: return add_constant_columns( add_intercept_column(self.scale.scale_x(x)) * self.alpha, self.scale.columns, True, ) else: return add_intercept_column(x) * self.coefficients def print( self, variables: Union[List[str], None] = None, classes: Union[List[str], None] = None, num_var: int = 10, decimals: int = 3, ): """Print the current explanation Args: variables (Union[List[str], None], optional): the names of the (columns/) variables. Defaults to None. classes (Union[List[str], None], optional): the names of the classes, if explaining a classifier. Defaults to None. num_var (int, optional): exclude zero weights if there are too many variables. Defaults to 10. decimals (int, optional): the precision to use for printing. Defaults to 3. """ print_slise( self.coefficients, True, self.subset(), self.score(), self.epsilon, variables, "SLISE Explanation", decimals, num_var, unscaled=self.x, unscaled_y=self.y, impact=self.get_impact(False), scaled=None if self.scale is None else self.scale.scale_x(self.x, False), alpha=self.normalised, scaled_impact=None if self.scale is None else self.get_impact(True), classes=classes, unscaled_preds=self.Y, logit=self.logit, ) def plot_2d( self, title: str = "SLISE Explanation", label_x: str = "x", label_y: str = "y", decimals: int = 3, fig: Union[Figure, None] = None, ) -> SliseRegression: """Plot the explanation in a 2D scatter plot (where the explained item is marked) with a line for the approximating model. Args: title (str, optional): plot title. Defaults to "SLISE Explanation". label_x (str, optional): x-axis label. Defaults to "x". label_y (str, optional): y-axis label. Defaults to "y". decimals (int, optional): number of decimals when writing numbers. Defaults to 3. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. Raises: SliseException: if the data has too many dimensions """ plot_2d( self.X, self.Y, self.coefficients, self.epsilon, self.x, self.y, self.logit, title, label_x, label_y, decimals, fig, ) def plot_image( self, width: int, height: int, saturated: bool = True, title: str = "SLISE Explanation", classes: Union[List, str, None] = None, decimals: int = 3, fig: Union[Figure, None] = None, ) -> SliseExplainer: """Plot the current explanation for a black and white image (e.g. MNIST) Args: width (int): the width of the image height (int): the height of the image saturated (bool, optional): should the explanation be more saturated. Defaults to True. title (str, optional): title of the plot. Defaults to "SLISE Explanation". classes (Union[List, str, None], optional): list of class names (first the negative, then the positive), or a single (positive) class name. Defaults to None. decimals (int, optional): the number of decimals to write. Defaults to 3. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. """ plot_image( self.x, self.y, self.Y, self.coefficients, width, height, saturated, title, classes, decimals, fig, ) def plot_dist( self, title: str = "SLISE Explanation", variables: list = None, decimals: int = 3, fig: Union[Figure, None] = None, ) -> SliseExplainer: """Plot the current explanation with density distributions for the dataset and a barplot for the model. The barbplot contains both the approximating linear model (where the weights can be loosely interpreted as the importance of the different variables and their sign) and the "impact" which is the (scaled) model time the (scaled) item values (which demonstrates how the explained item interacts with the approximating linear model, since a negative weight times a negative value actually supports a positive prediction). Args: title (str, optional): title of the plot. Defaults to "SLISE Explanation". variables (list, optional): names for the variables. Defaults to None. decimals (int, optional): the number of decimals to write. Defaults to 3. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. """ plot_dist( self.X, self.Y, self.coefficients, self.subset(), self.normalised, self.x, self.y, self.get_impact(False), self.get_impact(True) if self.normalise else None, title, variables, decimals, fig, ) def plot_subset( self, title: str = "Prediction Distribution", decimals: int = 0, fig: Union[Figure, None] = None, ): """Plot a density distributions for predictions and the predictions of the subset Args: title (str, optional): title of the plot. Defaults to "Prediction Distribution". decimals (int, optional): number of decimals when writing the subset size. Defaults to 0. fig (Union[Figure, None], optional): Pyplot figure to plot on, if None then a new plot is created and shown. Defaults to None. """ plot_dist_single(self.Y, self.subset(), self.y, title, decimals, fig)

39.05436

231

0.599739

4,321

34,485

4.732701

0.090488

0.043032

0.0178

0.017604

0.7978

0.77956

0.759658

0.739364

0.718289

0.694425

0

0.008692

0.319385

34,485

882

232

39.098639

0.862596

0.475424

0

0.715356

0

0.015543

0

1

0.048689

false

0

0.020599

0

0.11236

0.009363

0

null

0

1

0

1

0

null

0

6

41a12adb994472b8b3633239b79a64ae8e3353b3

260

py

Python

odoo-13.0/addons/mrp_subcontracting/models/__init__.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/addons/mrp_subcontracting/models/__init__.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/addons/mrp_subcontracting/models/__init__.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

# -*- coding: utf-8 -*- from . import mrp_bom from . import product from . import res_company from . import res_partner from . import stock_move from . import stock_move_line from . import stock_picking from . import stock_rule from . import stock_warehouse

20

29

0.765385

39

260

4.871795

0.435897

0.473684

0.394737

0.2

0

0.004608

0.165385

260

12

30

21.666667

0.870968

0.080769

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

41a6e57d6529007ecf97110bebec75a150b57f27

31

py

Python

src/odin/odin/mongo/__init__.py

wenshuoliu/odin

7998ee7541b3de44dd149899168983e964f2b8f7

[ "Apache-2.0" ]

4

2020-12-15T15:57:14.000Z

2020-12-16T21:52:23.000Z

src/odin/odin/mongo/__init__.py

wenshuoliu/odin

7998ee7541b3de44dd149899168983e964f2b8f7

[ "Apache-2.0" ]

2

2021-03-15T02:49:56.000Z

2021-03-27T12:42:38.000Z

src/odin/odin/mongo/__init__.py

wenshuoliu/odin

7998ee7541b3de44dd149899168983e964f2b8f7

[ "Apache-2.0" ]

5

2020-12-15T19:09:00.000Z

2021-04-21T20:40:38.000Z

from odin.mongo.store import *

15.5

30

0.774194

5

31

4.8

1

0

0.129032

31

1

31

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

68c1dde563d81a9fe4f69720cb4675614c6d22f7

2,312

py

Python

epytope/Data/pssms/smmpmbec/mat/A_30_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smmpmbec/mat/A_30_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smmpmbec/mat/A_30_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

A_30_01_9 = {0: {'A': -0.273, 'C': 0.188, 'E': 0.612, 'D': 0.566, 'G': 0.063, 'F': 0.287, 'I': -0.054, 'H': -0.485, 'K': -0.921, 'M': -0.389, 'L': -0.073, 'N': 0.183, 'Q': 0.237, 'P': 0.557, 'S': -0.23, 'R': -0.832, 'T': 0.104, 'W': 0.296, 'V': 0.041, 'Y': 0.121}, 1: {'A': -0.287, 'C': 0.263, 'E': 0.858, 'D': 0.713, 'G': -0.157, 'F': -0.334, 'I': -0.231, 'H': 0.256, 'K': 0.006, 'M': -0.198, 'L': 0.008, 'N': 0.265, 'Q': -0.209, 'P': 0.781, 'S': -0.534, 'R': 0.493, 'T': -0.734, 'W': -0.087, 'V': -0.558, 'Y': -0.317}, 2: {'A': 0.278, 'C': 0.26, 'E': 0.49, 'D': 0.656, 'G': 0.316, 'F': -0.316, 'I': 0.086, 'H': -0.599, 'K': -0.83, 'M': -0.113, 'L': -0.087, 'N': -0.009, 'Q': 0.084, 'P': 0.337, 'S': 0.158, 'R': -1.159, 'T': 0.231, 'W': 0.155, 'V': 0.185, 'Y': -0.123}, 3: {'A': -0.118, 'C': -0.122, 'E': 0.192, 'D': 0.179, 'G': 0.187, 'F': -0.064, 'I': -0.021, 'H': 0.011, 'K': -0.009, 'M': -0.209, 'L': 0.045, 'N': -0.034, 'Q': 0.144, 'P': -0.052, 'S': 0.011, 'R': -0.13, 'T': 0.068, 'W': 0.05, 'V': -0.028, 'Y': -0.099}, 4: {'A': -0.156, 'C': 0.141, 'E': 0.15, 'D': 0.267, 'G': 0.056, 'F': -0.109, 'I': 0.012, 'H': -0.129, 'K': 0.045, 'M': -0.065, 'L': 0.062, 'N': 0.028, 'Q': -0.061, 'P': 0.103, 'S': -0.091, 'R': -0.167, 'T': -0.117, 'W': 0.189, 'V': -0.095, 'Y': -0.063}, 5: {'A': 0.093, 'C': 0.118, 'E': 0.193, 'D': 0.252, 'G': 0.013, 'F': -0.098, 'I': 0.036, 'H': -0.028, 'K': 0.083, 'M': -0.022, 'L': 0.082, 'N': -0.032, 'Q': -0.001, 'P': 0.081, 'S': -0.119, 'R': -0.153, 'T': -0.086, 'W': -0.186, 'V': 0.011, 'Y': -0.237}, 6: {'A': 0.017, 'C': -0.073, 'E': 0.38, 'D': 0.302, 'G': 0.087, 'F': 0.022, 'I': -0.222, 'H': -0.083, 'K': 0.168, 'M': -0.217, 'L': -0.104, 'N': 0.003, 'Q': -0.011, 'P': -0.353, 'S': 0.045, 'R': -0.065, 'T': 0.171, 'W': 0.039, 'V': -0.037, 'Y': -0.068}, 7: {'A': 0.073, 'C': 0.043, 'E': 0.039, 'D': 0.209, 'G': 0.07, 'F': -0.407, 'I': -0.051, 'H': -0.048, 'K': 0.2, 'M': 0.162, 'L': -0.056, 'N': 0.023, 'Q': 0.128, 'P': -0.263, 'S': -0.047, 'R': 0.117, 'T': 0.053, 'W': 0.034, 'V': -0.013, 'Y': -0.265}, 8: {'A': -0.746, 'C': 0.283, 'E': 0.384, 'D': 0.349, 'G': -0.216, 'F': 0.251, 'I': -0.299, 'H': 0.249, 'K': -1.203, 'M': -0.051, 'L': -0.189, 'N': 0.392, 'Q': 0.561, 'P': 0.122, 'S': 0.043, 'R': -0.09, 'T': 0.044, 'W': 0.599, 'V': -0.408, 'Y': -0.076}, -1: {'con': 4.23841}}

2,312

0.396194

557

2,312

1.639138

0.314183

0.019715

0

0.375967

0.161332

2,312

1

2,312

0.094894

0

0.079118

0

1

0

false

0

1

null

0

1

0

1

0

null

0

6

ec18ace0f077ad4df50e84619d1d9c0b72f79892

546

py

Python

tests/converters/test_darwin_case.py

gieseladev/lettercase

2b4b97d5b96fcb5cd12f2eec93e0c64c78b84f6f

[ "MIT" ]

null

tests/converters/test_darwin_case.py

gieseladev/lettercase

2b4b97d5b96fcb5cd12f2eec93e0c64c78b84f6f

[ "MIT" ]

null

tests/converters/test_darwin_case.py

gieseladev/lettercase

2b4b97d5b96fcb5cd12f2eec93e0c64c78b84f6f

[ "MIT" ]

null

from lettercase import snake_to_darwin_case, to_darwin_case # only need to test this conversion because all others are implemented using this def test_snake_to_darwin_case(): assert snake_to_darwin_case("snake_case") == "Snake_Case" assert snake_to_darwin_case("this") == "This" def test_to_darwin_case(): assert to_darwin_case("dom_dom_dom") == "Dom_Dom_Dom" assert to_darwin_case("DOM_DOM_DOM") == "Dom_Dom_Dom" assert to_darwin_case("domDomDom") == "Dom_Dom_Dom" assert to_darwin_case("DomDomDom") == "Dom_Dom_Dom"

36.4

81

0.760073

86

546

4.360465

0.255814

0.224

0.32

0.192

0.528

0.384

0

0.137363

546

14

82

39

0.796178

0.144689

0

0.24086

0

0.666667

1

0.222222

true

0

0.111111

0

0.333333

0

null

1

0

null

0

1

0

1

0

6

ec530dc2f3a6679fd1b48ce7562c1065931c5d6c

802

py

Python

torchvision/transforms/functional_tensor.py

liyichao/vision

53b062ca58932bbf387b96f2dd3397c4495b735b

[ "BSD-3-Clause" ]

1

2020-01-31T01:06:21.000Z

torchvision/transforms/functional_tensor.py

liyichao/vision

53b062ca58932bbf387b96f2dd3397c4495b735b

[ "BSD-3-Clause" ]

null

torchvision/transforms/functional_tensor.py

liyichao/vision

53b062ca58932bbf387b96f2dd3397c4495b735b

[ "BSD-3-Clause" ]

null

import torch import torchvision.transforms.functional as F def vflip(img_tensor): """Vertically flip the given the Image Tensor. Args: img_tensor (Tensor): Image Tensor to be flipped in the form [C, H, W]. Returns: Tensor: Vertically flipped image Tensor. """ if not F._is_tensor_image(img_tensor): raise TypeError('tensor is not a torch image.') return img_tensor.flip(-2) def hflip(img_tensor): """Horizontally flip the given the Image Tensor. Args: img_tensor (Tensor): Image Tensor to be flipped in the form [C, H, W]. Returns: Tensor: Horizontally flipped image Tensor. """ if not F._is_tensor_image(img_tensor): raise TypeError('tensor is not a torch image.') return img_tensor.flip(-1)

23.588235

78

0.662095

114

802

4.535088

0.324561

0.139265

0.046422

0.058027

0.746615

0

0.003317

0.24813

802

33

79

24.30303

0.854063

0.457606

0

0.4

0

0.146597

0

1

0.2

false

0

0.2

0

0.6

0

null

0

1

0

null

0

1

0

6

ec561830f130c4cca8ebe01d7490803e883af192

24

py

Python

pyzorder/__init__.py

smatsumoto78/pyzorder

1940f08d680536eb8d2ec0680d27e510bc5073d9

[ "MIT" ]

9

2019-11-13T02:57:34.000Z

2021-11-21T18:50:45.000Z

pyzorder/__init__.py

smatsumoto78/pyzorder

1940f08d680536eb8d2ec0680d27e510bc5073d9

[ "MIT" ]

null

pyzorder/__init__.py

smatsumoto78/pyzorder

1940f08d680536eb8d2ec0680d27e510bc5073d9

[ "MIT" ]

null

from .pyzorder import *

12

23

0.75

3

24

6

1

0

0.166667

24

1

24

0.9

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6b56f3af129c411d43802d2867a25b36b3ed8a74

359

py

Python

private/templates/default/maintenance.py

smeissner/eden

9c4c78f0808e53c52d3caa4fa68162cddc174547

[ "MIT" ]

1

2021-01-21T18:24:25.000Z

private/templates/default/maintenance.py

smeissner/eden

9c4c78f0808e53c52d3caa4fa68162cddc174547

[ "MIT" ]

null

private/templates/default/maintenance.py

smeissner/eden

9c4c78f0808e53c52d3caa4fa68162cddc174547

[ "MIT" ]

null

# -*- coding: utf-8 -*- #from gluon import * #from s3 import * # ============================================================================= class Daily(): """ Daily Maintenance Tasks """ def __call__(self): # @ToDo: cleanup scheduler logs return # END =========================================================================

21.117647

79

0.32312

23

359

4.869565

0.869565

0

0.006757

0.175487

359

16

80

22.4375

0.371622

0.746518

0

0.0625

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6b68784411c3c2b4c9f99313f667d398acf58fd7

32

py

Python

Gammal/square.py

Magdyedwar1996/python-level-one-codes

066086672f43488bc8b32c620b5e2f94cedfe3da

[ "MIT" ]

1

2021-11-16T14:14:38.000Z

Gammal/square.py

Magdyedwar1996/python-level-one-codes

066086672f43488bc8b32c620b5e2f94cedfe3da

[ "MIT" ]

null

Gammal/square.py

Magdyedwar1996/python-level-one-codes

066086672f43488bc8b32c620b5e2f94cedfe3da

[ "MIT" ]

null

def square (x): return x * x

16

0.5625

6

32

3

0.666667

0

0.3125

32

2

16

0.818182

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

6bad227606379f1098485785aa791277b61916a5

281

py

Python

trade_remedies_caseworker/core/templatetags/organisation_initialism.py

uktrade/trade-remedies-caseworker

fece9fde3cb241d96cbc1aaf7188d976f8621600

[ "MIT" ]

1

2020-08-27T09:53:00.000Z

trade_remedies_caseworker/core/templatetags/organisation_initialism.py

uktrade/trade-remedies-caseworker

fece9fde3cb241d96cbc1aaf7188d976f8621600

[ "MIT" ]

7

2020-10-14T16:23:42.000Z

2021-09-24T14:18:47.000Z

trade_remedies_caseworker/core/templatetags/organisation_initialism.py

uktrade/trade-remedies-caseworker

fece9fde3cb241d96cbc1aaf7188d976f8621600

[ "MIT" ]

null

from core.templatetags import register from django.conf import settings """ Template tag to display organisation initialism Usage: {% organisation_initialism %} """ @register.simple_tag def organisation_initialism(): return settings.ORGANISATION_INITIALISM

20.071429

48

0.758007

29

281

7.206897

0.62069

0.421053

0

0.174377

281

13

49

21.615385

0.900862

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

1

0

null

0

1

0

1

0

6

d4176aa24d19ee65fc8184057bd45ac0f1dbd31b

39

py

Python

guet/commands/set/__init__.py

AbhishekMashetty/pairprogrammingmasetty

0528d4999b472ec6d94058193275a505eaf2c762

[ "Apache-2.0" ]

13

2018-12-21T22:47:28.000Z

2021-12-17T14:27:35.000Z

guet/commands/set/__init__.py

chiptopher/guet

1099ee623311ba1d052237612efc9b06b7ff68bb

[ "Apache-2.0" ]

63

2018-08-30T11:19:12.000Z

2021-05-13T12:11:08.000Z

guet/commands/set/__init__.py

chiptopher/guet

1099ee623311ba1d052237612efc9b06b7ff68bb

[ "Apache-2.0" ]

7

2019-05-21T13:52:37.000Z

2022-01-30T22:57:21.000Z

from ._set import SetCommittersCommand

19.5

38

0.871795

4

39

8.25

1

0

0.102564

39

1

39

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d44471b1cb96cc46a2d1bbc5bd0ca3507cd80afb

100

py

Python

headliner/model/__init__.py

datitran/headliner

b59de8ad7920f22eab3a74e54585c7de388659ce

[ "MIT" ]

1

2019-10-16T17:04:20.000Z

headliner/model/__init__.py

lucko515/headliner

ac2cef164a7fbad19b93501177cf25993cf6c588

[ "MIT" ]

null

headliner/model/__init__.py

lucko515/headliner

ac2cef164a7fbad19b93501177cf25993cf6c588

[ "MIT" ]

null

from .summarizer_attention import SummarizerAttention from .summarizer_basic import SummarizerBasic

33.333333

53

0.9

10

100

8.8

0.7

0.318182

0

0.08

100

2

54

50

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d45ba668311d2c254dc9c59ef2705ce0a6bb6d08

9,770

py

Python

scratch/test_avoiders_of_3_4_4_pairs.py

PermutaTriangle/PermStruct

6a494aa9c3ae3c63d27ecbcc4b478f0501eb3e48

[ "BSD-3-Clause" ]

1

2015-09-14T17:23:33.000Z

scratch/test_avoiders_of_3_4_4_pairs.py

PermutaTriangle/PermStruct

6a494aa9c3ae3c63d27ecbcc4b478f0501eb3e48

[ "BSD-3-Clause" ]

null

scratch/test_avoiders_of_3_4_4_pairs.py

PermutaTriangle/PermStruct

6a494aa9c3ae3c63d27ecbcc4b478f0501eb3e48

[ "BSD-3-Clause" ]

null

import permstruct import permstruct.dag from permstruct.lib import Permutations import time def enume(perm_prop, N): for n in range(N+1): print sum([1 for perm in Permutations(n) if perm_prop(perm)]) print 'Done counting!' time.sleep(5) # Since we usually don't want overlays: overlays = False #------------------------------------------------# # Avoiding one classical pattern of length 3 and two of length 4 #-- Symmetry-class 1 --# # Info # SUCCESS! # Details: A116721 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([1, 3, 2, 4]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X_mon1(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 2 --# # Info # FAILURE # Details: A116735 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([1, 3, 4, 2]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 3 --# # Info # FAILURE # Details: A116728 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([2, 1, 3, 4]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 4 --# # Info # FAILURE # Details: A116731 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([2, 1, 4, 3]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 5 --# # Info # FAILURE # Details: A116729 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([2, 3, 1, 4]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 6 --# # Info # FAILURE # Details: A116711 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([2, 3, 4, 1]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 7 --# # Info -> Symmetry-class 1 # FAILURE # Details: A116721 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([2, 4, 1, 3]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 8 --# # Info # FAILURE # Details: A116727 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 2, 4, 3]) and p.avoids([3, 4, 1, 2]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 9 --# # Info -> Symmetry-class 4 # FAILURE # Details: A116731 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 2, 4]) and p.avoids([1, 3, 4, 2]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 10 --# # Info -> Symmetry-class 4 # FAILURE # Details: A116731 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 2, 4]) and p.avoids([2, 1, 4, 3]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 11 --# # Info # FAILURE # Details: A116733 # # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 2, 4]) and p.avoids([2, 3, 4, 1]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 12 --# # Info # FAILURE # Details: A027927 # Number of plane regions after drawing (general position) convex n-gon # and all diagonals. # G.f.: x^2*(1-3*x+5*x^2-3*x^3+x^4)/(1-x)^5 # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 2, 4]) and p.avoids([2, 4, 1, 3]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 4 # max_rules = 100 # ignored = 1 #-- Symmetry-class 13 --# # Lemma # Av(321, 132, 3412) is needed as a unit # Info # SUCCESS! # Details: 1, 1, 2, 4, 8, 10, 12, 14, 16 # NOT ON OEIS # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 2]) and p.avoids([3, 4, 1, 2]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X_mon1(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 3 # max_rules = 100 # ignored = 1 # Info # SUCCESS! # Details: Seems to be A046092 after length 3 (non-inclusive) # 4 times triangular numbers: 2*n*(n+1) # G.f.: 4*x/(1-x)^3 # E.g.f.: exp(x)*(2*x^2+4*x) # # BUT: There is no mention of permutations or patterns for this # sequence! # # The exact cover was VERY slow on this problem. We should modify it so it # covers the short permutations first. perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 2, 4]) and p.avoids([3, 4, 1, 2]) # enume(perm_prop, 8) perm_bound = 7 # inp_dag = permstruct.dag.N_P_X2_mon2(perm_prop, perm_bound) inp_dag = permstruct.dag.N_P_taylored_for_av_321_1324_3412(perm_bound) max_rule_size = (5, 5) max_non_empty = 5 max_rules = 8 ignored = 1 #-- Symmetry-class 14 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([1, 4, 2, 3]) #-- Symmetry-class 15 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([2, 1, 4, 3]) #-- Symmetry-class 16 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([2, 3, 1, 4]) #-- Symmetry-class 17 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([2, 3, 4, 1]) #-- Symmetry-class 18 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([2, 4, 1, 3]) #-- Symmetry-class 19 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([3, 1, 2, 4]) #-- Symmetry-class 20 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([3, 1, 4, 2]) #-- Symmetry-class 21 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([3, 4, 1, 2]) #-- Symmetry-class 22 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([1, 3, 4, 2]) and p.avoids([4, 1, 2, 3]) #-- Symmetry-class 23 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 1, 4, 3]) and p.avoids([2, 3, 4, 1]) #-- Symmetry-class 24 --# # Info # SUCCESS! # Details: A000325 # These seem to be the Grassmannian permutations # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 1, 4, 3]) and p.avoids([2, 4, 1, 3]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X_mon1(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 3 # max_rules = 100 # ignored = 1 #-- Symmetry-class 25 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 1, 4, 3]) and p.avoids([3, 4, 1, 2]) #-- Symmetry-class 26 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 3, 4, 1]) and p.avoids([2, 4, 1, 3]) #-- Symmetry-class 27 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 3, 4, 1]) and p.avoids([3, 4, 1, 2]) #-- Symmetry-class 28 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 3, 4, 1]) and p.avoids([4, 1, 2, 3]) #-- Symmetry-class 29 --# # Info # SUCCESS! # Details: A034943 # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 4, 1, 3]) and p.avoids([3, 1, 4, 2]) # enume(perm_prop, 8) # perm_bound = 8 # inp_dag = permstruct.dag.N_P_X_mon1(perm_prop, perm_bound) # max_rule_size = (3, 3) # max_non_empty = 3 # max_rules = 100 # ignored = 1 #-- Symmetry-class 30 --# # perm_prop = lambda p: p.avoids([3, 2, 1]) and p.avoids([2, 4, 1, 3]) and p.avoids([3, 4, 1, 2]) #------------------------------------------------# if not overlays: permstruct.exhaustive(perm_prop, perm_bound, inp_dag, max_rule_size, max_non_empty, max_rules, ignore_first = ignored) else: permstruct.exhaustive_with_overlays(perm_prop, perm_bound, inp_dag, max_rule_size, max_non_empty, max_rules, overlay_dag, max_overlay_cnt, max_overlay_size, min_rule_size=(1,1))

26.476965

97

0.569294

1,658

9,770

3.188179

0.103136

0.123156

0.117291

0.070753

0.747446

0.743663

0.73969

0.737798

0.73723

0.709232

0

0.098012

0.253327

9,770

368

98

26.548913

0.626594

0.796622

0

0.277778

0

0.008197

0

null

0

0.111111

null

0.055556

0

null

0

1

0

null

0

1

0

6

d48569072c38e01c75061ea0dc332d6a7bfadeed

1,591

py

Python

web/transiq/restapi/migrations/0015_auto_20180904_1114.py

manibhushan05/transiq

763fafb271ce07d13ac8ce575f2fee653cf39343

[ "Apache-2.0" ]

null

web/transiq/restapi/migrations/0015_auto_20180904_1114.py

manibhushan05/transiq

763fafb271ce07d13ac8ce575f2fee653cf39343

[ "Apache-2.0" ]

14

2020-06-05T23:06:45.000Z

2022-03-12T00:00:18.000Z

web/transiq/restapi/migrations/0015_auto_20180904_1114.py

manibhushan05/transiq

763fafb271ce07d13ac8ce575f2fee653cf39343

[ "Apache-2.0" ]

null

# Generated by Django 2.0.5 on 2018-09-04 11:14 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('restapi', '0014_auto_20180829_1539'), ] operations = [ migrations.AlterField( model_name='bookingstatuses', name='status', field=models.CharField(choices=[('confirmed', 'Confirmed'), ('loaded', 'Loaded'), ('lr_generated', 'Lr Generated'), ('advance_paid', 'Advance_Paid'), ('reconciled', 'Reconciled'), ('unloaded', 'Unloaded'), ('pod_uploaded', 'PoD Uploaded'), ('pod_verified', 'PoD Verified'), ('invoice_raised', 'Invoice Raised'), ('invoice_confirmed', 'Invoice Confirmed'), ('balance_paid', 'Balance Paid'), ('party_invoice_sent', 'Party Invoice Sent'), ('inward_followup', 'Inward Followup'), ('complete', 'Complete')], default='confirmed', max_length=35, null=True), ), migrations.AlterField( model_name='historicalbookingstatuses', name='status', field=models.CharField(choices=[('confirmed', 'Confirmed'), ('loaded', 'Loaded'), ('lr_generated', 'Lr Generated'), ('advance_paid', 'Advance_Paid'), ('reconciled', 'Reconciled'), ('unloaded', 'Unloaded'), ('pod_uploaded', 'PoD Uploaded'), ('pod_verified', 'PoD Verified'), ('invoice_raised', 'Invoice Raised'), ('invoice_confirmed', 'Invoice Confirmed'), ('balance_paid', 'Balance Paid'), ('party_invoice_sent', 'Party Invoice Sent'), ('inward_followup', 'Inward Followup'), ('complete', 'Complete')], default='confirmed', max_length=35, null=True), ), ]

66.291667

562

0.654305

161

1,591

6.285714

0.36646

0.043478

0.055336

0.057312

0.754941

0

0.026022

0.15462

1,591

23

563

69.173913

0.726394

0.028284

0

0.470588

1

0

0.492228

0.031088

0

1

0

false

0

0.058824

0

0.235294

0

null

0

1

0

1

0

null

0

6

d4856d7166a2177349ba9a288e5aa944e4d1836d

76

py

Python

fundamentals-of-programming/labs/lab_5-11/ui/ui.py

vampy/university

9496cb63594dcf1cc2cec8650b8eee603f85fdab

[ "MIT" ]

6

2015-06-22T19:43:13.000Z

2019-07-15T18:08:41.000Z

fundamentals-of-programming/labs/lab_5-11/ui/ui.py

vampy/university

9496cb63594dcf1cc2cec8650b8eee603f85fdab

[ "MIT" ]

null

fundamentals-of-programming/labs/lab_5-11/ui/ui.py

vampy/university

9496cb63594dcf1cc2cec8650b8eee603f85fdab

[ "MIT" ]

1

2015-09-26T09:01:54.000Z

#!/usr/bin/python from console import Console class UI(Console): pass

10.857143

27

0.710526

11

76

4.909091

0.818182

0

0.184211

76

6

28

12.666667

0.870968

0.210526

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

2e4f0d9fbb1bf594c984bebaa8594d86eb355249

344

py

Python

venv/lib/python3.9/site-packages/blurhash/__init__.py

briansodenkin/People-Counting-in-Real-Time

b40b4483ecdcf0cc3c67e4c45f6ab512c12fd485

[ "MIT" ]

66

2019-05-09T22:12:42.000Z

2022-02-26T03:17:52.000Z

venv/lib/python3.9/site-packages/blurhash/__init__.py

briansodenkin/People-Counting-in-Real-Time

b40b4483ecdcf0cc3c67e4c45f6ab512c12fd485

[ "MIT" ]

4

2019-10-04T17:19:31.000Z

2021-06-17T07:41:18.000Z

venv/lib/python3.9/site-packages/blurhash/__init__.py

briansodenkin/People-Counting-in-Real-Time

b40b4483ecdcf0cc3c67e4c45f6ab512c12fd485

[ "MIT" ]

2

2020-02-22T22:31:25.000Z

2020-08-09T01:42:34.000Z

from .blurhash import blurhash_encode as encode from .blurhash import blurhash_decode as decode from .blurhash import blurhash_components as components from .blurhash import srgb_to_linear as srgb_to_linear from .blurhash import linear_to_srgb as linear_to_srgb __all__ = ['encode', 'decode', 'components', 'srgb_to_linear', 'linear_to_srgb']

43

80

0.825581

51

344

5.196078

0.215686

0.226415

0.339623

0.29434

0

0.110465

344

7

81

49.142857

0.866013

0

0.145349

0

1

0

false

0

0.833333

0

0.833333

0

null

1

0

1

0

null

0

1

0

1

0

6

2e58aa2a93671bec8a696a19540cfe35282a03d3

107

py

Python

ostn02python/__init__.py

IanHopkinson/ostn02python

54e6aa52308859f0fcc306090612489e2a4e754e

[ "BSD-2-Clause-FreeBSD" ]

null

ostn02python/__init__.py

IanHopkinson/ostn02python

54e6aa52308859f0fcc306090612489e2a4e754e

[ "BSD-2-Clause-FreeBSD" ]

null

ostn02python/__init__.py

IanHopkinson/ostn02python

54e6aa52308859f0fcc306090612489e2a4e754e

[ "BSD-2-Clause-FreeBSD" ]

null

import ostn02python.OSGB #import parse_grid, grid_to_ll import ostn02python.OSTN02 #import OSGB36_to_ETRS89

53.5

55

0.878505

16

107

5.5625

0.625

0.404494

0

0.10101

0.074766

107

2

56

53.5

0.79798

0.485981

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2e6230b5750b90bdfc4932d555e515044ece2bec

54

py

Python

src/tests/utils/__init__.py

samrika25/TRAVIS_HEROKU_GIT

bcae6d0422d9a0369810944a91dd03db7df0d058

[ "MIT" ]

null

src/tests/utils/__init__.py

samrika25/TRAVIS_HEROKU_GIT

bcae6d0422d9a0369810944a91dd03db7df0d058

[ "MIT" ]

4

2021-03-30T12:35:36.000Z

2021-06-10T18:11:24.000Z

src/tests/utils/__init__.py

samrika25/TRAVIS_HEROKU_GIT

bcae6d0422d9a0369810944a91dd03db7df0d058

[ "MIT" ]

2

2021-02-07T16:16:36.000Z

2021-07-13T05:26:51.000Z

from .test_page import * from .test_paginator import *

27

29

0.796296

8

54

5.125

0.625

0.390244

0

0.12963

54

2

29

27

0.87234

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2e6b79a6c0f245527d124391ebd380d41aa3bc32

5,208

py

Python

src/genie/libs/parser/ios/tests/test_show_rpf.py

kacann/genieparser

76e19003199c393c59a33546726de3ff5486da80

[ "Apache-2.0" ]

null

src/genie/libs/parser/ios/tests/test_show_rpf.py

kacann/genieparser

76e19003199c393c59a33546726de3ff5486da80

[ "Apache-2.0" ]

null

src/genie/libs/parser/ios/tests/test_show_rpf.py

kacann/genieparser

76e19003199c393c59a33546726de3ff5486da80

[ "Apache-2.0" ]

1

2021-07-07T18:07:56.000Z

# Python import unittest from unittest.mock import Mock # ATS from ats.topology import Device # Metaparset from genie.metaparser.util.exceptions import SchemaEmptyParserError, \ SchemaMissingKeyError # Parser from genie.libs.parser.ios.show_rpf import ShowIpRpf, ShowIpv6Rpf from genie.libs.parser.iosxe.tests.test_show_rpf import test_show_ipv6_rpf as test_show_ipv6_rpf_iosxe # ============================================= # Unit test for 'show ip rpf <x.x.x.x>' # Unit test for 'show ip rpf vrf xxx <x.x.x.x>' # ============================================== class test_show_ip_rpf(unittest.TestCase): device = Device(name='aDevice') empty_output = {'execute.return_value': ''} golden_parsed_output = { "vrf": { "default": { "source_address": "192.168.16.226", "source_host": "?", "mofrr": "Enabled", "path": { "192.168.145.2 Ethernet1/4": { "interface_name": "Ethernet1/4", "neighbor_host": "?", "neighbor_address": "192.168.145.2", "table_type": "unicast", "table_feature": "ospf", "table_feature_instance": "200", "distance_preferred_lookup": True, "lookup_topology": "ipv4 multicast base", "originated_topology": "ipv4 unicast base" } } } } } golden_output = {'execute.return_value': '''\ Router# show ip rpf 192.168.16.226 RPF information for ? (192.168.16.226) MoFRR Enabled RPF interface: Ethernet1/4 RPF neighbor: ? (192.168.145.2) RPF route/mask: 255.255.255.225 RPF type: unicast (ospf 200) Doing distance-preferred lookups across tables RPF topology: ipv4 multicast base, originated from ipv4 unicast base '''} golden_parsed_output2 = { "vrf": { "VRF1": { "source_address": "192.168.16.226", "source_host": "?", "mofrr": "Enabled", "path": { "192.168.145.2 Ethernet1/4": { "interface_name": "Ethernet1/4", "neighbor_host": "?", "neighbor_address": "192.168.145.2", "table_type": "unicast", "table_feature": "ospf", "table_feature_instance": "200", "distance_preferred_lookup": True, "lookup_topology": "ipv4 multicast base", "originated_topology": "ipv4 unicast base" } } } } } golden_output2 = {'execute.return_value': '''\ Router# show ip rpf 192.168.16.226 RPF information for ? (192.168.16.226) MoFRR Enabled RPF interface: Ethernet1/4 RPF neighbor: ? (192.168.145.2) RPF route/mask: 255.255.255.225 RPF type: unicast (ospf 200) Doing distance-preferred lookups across tables RPF topology: ipv4 multicast base, originated from ipv4 unicast base '''} def test_empty(self): self.device1 = Mock(**self.empty_output) obj = ShowIpRpf(device=self.device1) with self.assertRaises(SchemaEmptyParserError): parsed_output = obj.parse(mroute='172.16.10.13') def test_golden_vrf_default(self): self.device = Mock(**self.golden_output) obj = ShowIpRpf(device=self.device) parsed_output = obj.parse(mroute='192.168.16.226') self.assertEqual(parsed_output,self.golden_parsed_output) def test_golden_vrf_non_default(self): self.device = Mock(**self.golden_output2) obj = ShowIpRpf(device=self.device) parsed_output = obj.parse(mroute='192.168.16.226', vrf='VRF1') self.assertEqual(parsed_output,self.golden_parsed_output2) # ============================================= # Unit test for 'show ipv6 rpf <x.x.x.x>' # Unit test for 'show ipv6 rpf vrf xxx <x.x.x.x>' # ============================================== class test_show_ipv6_rpf(test_show_ipv6_rpf_iosxe): def test_empty(self): self.device1 = Mock(**self.empty_output) obj = ShowIpv6Rpf(device=self.device1) with self.assertRaises(SchemaEmptyParserError): parsed_output = obj.parse(mroute='2001:99:99::99') def test_golden_vrf_default(self): self.device = Mock(**self.golden_output) obj = ShowIpv6Rpf(device=self.device) parsed_output = obj.parse(mroute='2001:99:99::99') self.assertEqual(parsed_output,self.golden_parsed_output) def test_golden_vrf_non_default(self): self.device = Mock(**self.golden_output2) obj = ShowIpv6Rpf(device=self.device) parsed_output = obj.parse(mroute='2001:99:99::99', vrf='VRF1') self.assertEqual(parsed_output,self.golden_parsed_output2) if __name__ == '__main__': unittest.main()

37.73913

102

0.549923

563

5,208

4.902309

0.186501

0.030435

0.008696

0.031884

0.82971

0.807971

0.792754

0.773188

0

0.073481

0.304916

5,208

138

103

37.73913

0.68895

0.074117

0

0.672897

0

0.33264

0.019543

0

0.056075

1

0.056075

false

0

0.056075

0

0.186916

0

null

0

1

0

null

0

6

cf3fefcf7f84bd417e5e91e117988f8ac35ef44b

148

py

Python

slack/signature/__init__.py

timgates42/python-slack-sdk

6339fbe81031c9aec3f95927ac03706fd31f3544

[ "MIT" ]

2,486

2016-11-03T14:31:43.000Z

2020-10-26T23:07:44.000Z

slack/signature/__init__.py

timgates42/python-slack-sdk

6339fbe81031c9aec3f95927ac03706fd31f3544

[ "MIT" ]

721

2016-11-03T21:26:56.000Z

2020-10-26T12:41:29.000Z

slack/signature/__init__.py

timgates42/python-slack-sdk

6339fbe81031c9aec3f95927ac03706fd31f3544

[ "MIT" ]

627

2016-11-02T19:04:19.000Z

2020-10-25T19:21:13.000Z

from slack_sdk.signature import SignatureVerifier # noqa from slack import deprecation deprecation.show_message(__name__, "slack_sdk.signature")

24.666667

57

0.837838

18

148

6.5

0.611111

0.153846

0.290598

0

0.101351

148

5

58

29.6

0.879699

0.027027

0

0.133803

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

cf8781433225c17f0a98614e2d463f07c4c5572b

31

py

Python

python/xpctl/sql/__init__.py

domyounglee/baseline

2261abfb7e770cc6f3d63a7f6e0015238d0e11f8

[ "Apache-2.0" ]

2

2018-07-06T02:01:12.000Z

2018-07-06T02:01:14.000Z

python/xpctl/sql/__init__.py

domyounglee/baseline

2261abfb7e770cc6f3d63a7f6e0015238d0e11f8

[ "Apache-2.0" ]

null

python/xpctl/sql/__init__.py

domyounglee/baseline

2261abfb7e770cc6f3d63a7f6e0015238d0e11f8

[ "Apache-2.0" ]

3

2019-05-27T04:52:21.000Z

2022-02-15T00:22:53.000Z

from xpctl.sql.backend import *

31

0.806452

5

31

5

1

0

0.096774

31

1

31

0.892857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d85cd4a52d05288d5a8d76a68a46980a536890b0

20

py

Python

src/sage/libs/gap/__init__.py

switzel/sage

7eb8510dacf61b691664cd8f1d2e75e5d473e5a0

[ "BSL-1.0" ]

5

2015-01-04T07:15:06.000Z

2022-03-04T15:15:18.000Z

src/sage/libs/gap/__init__.py

switzel/sage

7eb8510dacf61b691664cd8f1d2e75e5d473e5a0

[ "BSL-1.0" ]

null

src/sage/libs/gap/__init__.py

switzel/sage

7eb8510dacf61b691664cd8f1d2e75e5d473e5a0

[ "BSL-1.0" ]

10

2016-09-28T13:12:40.000Z

2022-02-12T09:28:34.000Z

# libgap import all

6.666667

10

0.75

3

20

5

1

0

0.2

20

2

11

10

0.9375

0.3

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d860a77e7577ee66c71516cdb948c35a25864114

118

py

Python

coderdojochi/views/__init__.py

rgroves/weallcode-website

ead60d3272dbbfe610b2d500978d1de44aef6386

[ "MIT" ]

15

2019-05-04T00:24:00.000Z

2021-08-21T16:34:05.000Z

coderdojochi/views/__init__.py

rgroves/weallcode-website

ead60d3272dbbfe610b2d500978d1de44aef6386

[ "MIT" ]

73

2019-04-24T15:53:42.000Z

2021-08-06T20:41:41.000Z

coderdojochi/views/__init__.py

rgroves/weallcode-website

ead60d3272dbbfe610b2d500978d1de44aef6386

[ "MIT" ]

20

2019-04-26T20:13:08.000Z

2021-06-21T14:53:21.000Z

from .calendar import * from .meetings import * from .profile import * from .sessions import * from .welcome import *

19.666667

23

0.745763

15

118

5.866667

0.466667

0.454545

0

0.169492

118

5

24

23.6

0.897959

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d8b3dd2bfb52f33b3899944f33e77d42e0b3fb8a

46

py

Python

ark_nlp/processor/graph/__init__.py

Zrealshadow/ark-nlp

159045d17747524bd4e9af7f65f1d0283e8098e6

[ "Apache-2.0" ]

258

2021-09-04T14:01:13.000Z

2022-03-31T16:34:52.000Z

ark_nlp/processor/graph/__init__.py

yubuyuabc/ark-nlp

165d35cfacd7476791c0aeba19bf43f4f8079553

[ "Apache-2.0" ]

17

2022-01-13T04:46:02.000Z

2022-03-31T16:34:07.000Z

ark_nlp/processor/graph/__init__.py

yubuyuabc/ark-nlp

165d35cfacd7476791c0aeba19bf43f4f8079553

[ "Apache-2.0" ]

36

2021-11-17T06:18:45.000Z

2022-03-30T11:32:26.000Z

from .text_level_gcn import TextLevelGCNGraph

23

45

0.891304

6

46

6.5

1

0

0.086957

46

1

46

0.928571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2b2a973894d85bc3f40360cf68258089f09a0861

51

py

Python

kits19_3d_segmentation/configs/__init__.py

motokimura/kits19_3d_segmentation

871a24eaad388b8da427e0ab3c95f951629e36d6

[ "MIT" ]

6

2021-03-08T11:46:36.000Z

2022-03-25T03:20:02.000Z

kits19_3d_segmentation/configs/__init__.py

motokimura/kits19_3d_segmentation

871a24eaad388b8da427e0ab3c95f951629e36d6

[ "MIT" ]

1

2021-03-09T02:06:14.000Z

2021-03-09T14:38:05.000Z

kits19_3d_segmentation/configs/__init__.py

motokimura/kits19_3d_segmentation

871a24eaad388b8da427e0ab3c95f951629e36d6

[ "MIT" ]

1

2022-02-26T14:30:50.000Z

from .load_config import load_config # noqa: F401

25.5

50

0.784314

8

51

4.75

0.75

0.526316

0

0.069767

0.156863

51

1

51

0.813953

0.196078

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2b37d6fe8355424f2bada5985baa7926f67f737c

45

py

Python

quake/job/__init__.py

It4innovations/quake

a57f37e5c871e0c7c00b84aef638b925ef96690a

[ "MIT" ]

1

2021-03-26T14:23:44.000Z

quake/job/__init__.py

It4innovations/quake

a57f37e5c871e0c7c00b84aef638b925ef96690a

[ "MIT" ]

null

quake/job/__init__.py

It4innovations/quake

a57f37e5c871e0c7c00b84aef638b925ef96690a

[ "MIT" ]

null

from .config import JobConfiguration # noqa

22.5

44

0.8

5

45

7.2

1

0

0.155556

45

1

45

0.947368

0.088889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2b5549957ee4375ba450c3ab021c8842e9378d7d

44

py

Python

playground.py

Bibhuprasad740/xoxo

afe6a25e288df76399b66cd100eb8c2bb6107906

[ "MIT" ]

null

playground.py

Bibhuprasad740/xoxo

afe6a25e288df76399b66cd100eb8c2bb6107906

[ "MIT" ]

null

playground.py

Bibhuprasad740/xoxo

afe6a25e288df76399b66cd100eb8c2bb6107906

[ "MIT" ]

null

#comment print("(New Branch)Playground.py")

14.666667

34

0.75

6

44

5.5

1

0

0.068182

44

2

35

22

0.804878

0.159091

0

0.694444

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

2b69d9a8f76fb488395fcac78303d16b7134d262

1,801

py

Python

tests/kyu_6_tests/test_rotate_array.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

tests/kyu_6_tests/test_rotate_array.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

tests/kyu_6_tests/test_rotate_array.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

import unittest from katas.kyu_6.rotate_array import rotate class RotateTestCase(unittest.TestCase): def setUp(self): self.data = [1, 2, 3, 4, 5] def test_equals(self): self.assertEqual(rotate(self.data, 1), [5, 1, 2, 3, 4]) def test_equals_2(self): self.assertEqual(rotate(self.data, 2), [4, 5, 1, 2, 3]) def test_equals_3(self): self.assertEqual(rotate(self.data, 3), [3, 4, 5, 1, 2]) def test_equals_4(self): self.assertEqual(rotate(self.data, 4), [2, 3, 4, 5, 1]) def test_equals_5(self): self.assertEqual(rotate(self.data, 5), [1, 2, 3, 4, 5]) def test_equals_6(self): self.assertEqual(rotate(self.data, 0), [1, 2, 3, 4, 5]) def test_equals_7(self): self.assertEqual(rotate(self.data, -1), [2, 3, 4, 5, 1]) def test_equals_8(self): self.assertEqual(rotate(self.data, -2), [3, 4, 5, 1, 2]) def test_equals_9(self): self.assertEqual(rotate(self.data, -3), [4, 5, 1, 2, 3]) def test_equals_10(self): self.assertEqual(rotate(self.data, -4), [5, 1, 2, 3, 4]) def test_equals_11(self): self.assertEqual(rotate(self.data, -5), [1, 2, 3, 4, 5]) def test_equals_12(self): self.assertEqual(rotate(self.data, 7), [4, 5, 1, 2, 3]) def test_equals_13(self): self.assertEqual(rotate(self.data, 11), [5, 1, 2, 3, 4]) def test_equals_14(self): self.assertEqual(rotate(self.data, 12478), [3, 4, 5, 1, 2]) def test_equals_15(self): self.assertEqual(rotate(['a', 'b', 'c'], 1), ['c', 'a', 'b']) def test_equals_16(self): self.assertEqual(rotate([1.0, 2.0, 3.0], 1), [3.0, 1.0, 2.0]) def test_equals_17(self): self.assertEqual(rotate([True, True, False], 1), [False, True, True])

30.016667

77

0.590228

298

1,801

3.449664

0.137584

0.140078

0.214981

0.413424

0.729572

0.601167

0.328794

0.101167

0

0.096057

0.22543

1,801

59

78

30.525424

0.64086

0

0.003331

0

0.435897

1

0.461538

false

0

0.051282

0

0.538462

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

991926332f5ad31ce752d2442b7a6965f5651ce1

12,340

py

Python

scripts/schema.py

htmlcssphpjs/graphql

01fa314de7fdb2d9f90b54d73a0fc79b44c05fdb

[ "MIT" ]

null

scripts/schema.py

htmlcssphpjs/graphql

01fa314de7fdb2d9f90b54d73a0fc79b44c05fdb

[ "MIT" ]

null

scripts/schema.py

htmlcssphpjs/graphql

01fa314de7fdb2d9f90b54d73a0fc79b44c05fdb

[ "MIT" ]

null

import strawberry, hashlib from typing import Optional, List from scripts.send import emailt from models import db_session from models.users import User from models.ideas import Idea db_session.global_init('database.db') def hash_password(password): h = hashlib.md5(password.encode()) return h.hexdigest() @strawberry.type class UsersType: id: str name: str pitch: str picture: str skills: Optional[str] roadmap: Optional[str] email: str hashed_password: str @strawberry.type class IdeasType: id: str title: str pitch: str descriptions: str media: Optional[str] jobs: Optional[str] team: Optional[str] roadmap: Optional[str] @strawberry.type class Result: error: str id: str @strawberry.type class Mutation: Create = [] @strawberry.mutation def create_user(self, id: str, name: str, pitch: str, picture: str, skills: str, roadmap: str, email: str, hashed_password: str) -> List[Result]: try: session = db_session.create_session() Create = [] user = User( id=id, name=name, pitch=pitch, picture=picture, skills=skills, roadmap=roadmap, email=email, hashed_password=hash_password(hashed_password) ) session.add(user) session.commit() result = Result( error='None', id=id ) Create.append(result) try: data = { "name": name, "mail": email, "username": id, "url": 'url' } emailt(data, 'create', email) except Exception as e: print(e) return Create except Exception as e: print(e) Create = [] result = Result( error='ID is busy', id='None' ) Create.append(result) return Create @strawberry.mutation def create_idea(self, id: str, title: str, pitch: str, descriptions: str, media: Optional[str], jobs: Optional[str], team: Optional[str], roadmap: Optional[str]) -> List[Result]: try: session = db_session.create_session() Create = [] idea = Idea( id=id, title=title, pitch=pitch, descriptions=descriptions, media=media, jobs=jobs, team=team, roadmap=roadmap ) session.add(idea) session.commit() result = Result( error='None', id=id ) Create.append(result) return Create except Exception as e: print(e) Create = [] result = Result( error='ID is busy', id='None' ) Create.append(result) return Create @strawberry.mutation def delete_user(self, id: str) -> List[Result]: try: session = db_session.create_session() user_all = session.query(User).all() Delete = [] email = '' name = '' for user in user_all: if user.id == id: email = user.email name = user.name find = session.query(User).filter(User.id == id) find.delete() session.commit() result = Result( error='None', id=id ) Delete.append(result) try: data = { "name": name, "mail": email, "username": id, "url": 'url' } emailt(data, 'delete', email) except Exception as e: print(e) return Delete except Exception as e: print(e) Delete = [] result = Result( error='error', id='None' ) Delete.append(result) return Delete @strawberry.mutation def delete_idea(self, id: str) -> List[Result]: try: session = db_session.create_session() Delete = [] find = session.query(Idea).filter(Idea.id == id) find.delete() session.commit() result = Result( error='None', id=id ) Delete.append(result) return Delete except Exception as e: print(e) Delete = [] result = Result( error='error', id='None' ) Delete.append(result) return Delete @strawberry.mutation def update_user(self, id: str, name: str, pitch: str, picture: str, skills: str, roadmap: str, email: str, hashed_password: str) -> List[Result]: try: session = db_session.create_session() Create = [] user_all = session.query(User).all() for user in user_all: if user.id == id: if id: user.id = id if name: user.name = name if pitch: user.pitch = pitch if picture: user.picture = picture if skills: user.skills = skills if roadmap: user.roadmap = roadmap if email: user.email = email if hashed_password: user.hashed_password = hash_password(hashed_password) session.commit() result = Result( error='None', id=id ) Create.append(result) return Create except Exception as e: print(e) Create = [] result = Result( error='ID is busy', id='None' ) Create.append(result) return Create @strawberry.mutation def update_idea(self, id: str, title: str, pitch: str, descriptions: str, media: Optional[str], jobs: Optional[str], team: Optional[str], roadmap: Optional[str]) -> List[Result]: try: session = db_session.create_session() Create = [] idea_all = session.query(Idea).all() for idea in idea_all: if idea.id == id: idea.id = id idea.title = title idea.pitch = pitch idea.descriptions = descriptions idea.media = media idea.jobs = jobs idea.team = team idea.roadmap = roadmap session.commit() result = Result( error='None', id=id ) Create.append(result) return Create except Exception as e: print(e) Create = [] result = Result( error='ID is busy', id='None' ) Create.append(result) return Create @strawberry.type class Query: @strawberry.field def find_user(self, id: Optional[str] = None) -> List[UsersType]: session = db_session.create_session() user_all = session.query(User).all() users_list = [] for user in user_all: if (id): if str(user.id) == str(id): user_as_dict = User( id=user.id, name=user.name, pitch=user.pitch, picture=user.picture, skills=user.skills, roadmap=user.roadmap, email=user.email, hashed_password=user.hashed_password ) users_list.append(user_as_dict) break else: break print(users_list) return users_list @strawberry.field def get_users(self, id: Optional[str] = None) -> List[UsersType]: session = db_session.create_session() user_all = session.query(User).all() users_list = [] for user in user_all: if (id): if str(user.id) == str(id): user_as_dict = User( id=user.id, name=user.name, pitch=user.pitch, picture=user.picture, skills=user.skills, roadmap=user.roadmap, email=user.email, hashed_password=user.hashed_password ) users_list.append(user_as_dict) break else: user_as_dict = User( id=user.id, name=user.name, pitch=user.pitch, picture=user.picture, skills=user.skills, roadmap=user.roadmap, email=user.email, hashed_password=user.hashed_password ) users_list.append(user_as_dict) print(users_list) return users_list @strawberry.field def find_idea(self, id: Optional[str] = None) -> List[IdeasType]: session = db_session.create_session() idea_all = session.query(Idea).all() ideas_list = [] for idea in idea_all: if (id): if str(idea.id) == str(id): idea_as_dict = Idea( id=idea.id, title=idea.title, pitch=idea.pitch, descriptions=idea.descriptions, media=idea.media, jobs=idea.jobs, team=idea.team, roadmap=idea.roadmap ) ideas_list.append(idea_as_dict) break else: break print(ideas_list) return ideas_list @strawberry.field def get_ideas(self, id: Optional[str] = None) -> List[IdeasType]: session = db_session.create_session() idea_all = session.query(Idea).all() ideas_list = [] for idea in idea_all: if (id): if str(idea.id) == str(id): idea_as_dict = Idea( id=idea.id, title=idea.title, pitch=idea.pitch, descriptions=idea.descriptions, media=idea.media, jobs=idea.jobs, team=idea.team, roadmap=idea.roadmap ) ideas_list.append(idea_as_dict) break else: idea_as_dict = Idea( id=idea.id, title=idea.title, pitch=idea.pitch, descriptions=idea.descriptions, media=idea.media, jobs=idea.jobs, team=idea.team, roadmap=idea.roadmap ) ideas_list.append(idea_as_dict) print(ideas_list) return ideas_list schema = strawberry.Schema(query=Query, mutation=Mutation)

30.696517

183

0.43436

1,103

12,340

4.763373

0.074343

0.037686

0.038828

0.041873

0.791968

0.771603

0.74153

0.724971

0.713932

0.679292

0

0.000157

0.482415

12,340

401

184

30.773067

0.822452

0

0.717877

0

0.01382

0

1

0.030726

false

0.030726

0.01676

0

0.162011

0.03352

0

null

0

1

0

1

0

null

0

6

99268425708c71db9ebe919461453bfbdc911b30

380

py

Python

utils/errors.py

roygbip/yuyuko

e8027cc638fc92f3a2d671e347215390f3ab0f20

[ "Apache-2.0" ]

null

utils/errors.py

roygbip/yuyuko

e8027cc638fc92f3a2d671e347215390f3ab0f20

[ "Apache-2.0" ]

null

utils/errors.py

roygbip/yuyuko

e8027cc638fc92f3a2d671e347215390f3ab0f20

[ "Apache-2.0" ]

null

class Error(object): def __init__(self, msg: str = "") -> None: super().__init__() self._msg = msg def __str__(self) -> str: return self._msg def __repr__(self) -> str: return self._msg def __eq__(self, o: object) -> bool: return self._msg == o.__repr__ def __nonzero__(self) -> bool: return self._msg != ""

22.352941

46

0.555263

46

380

3.869565

0.347826

0.235955

0.292135

0.191011

0.258427

0

0.305263

380

16

47

23.75

0.674242

0

0.166667

0

1

0.416667

false

0

0.333333

0.833333

0

null

1

0

null

0

1

0

1

0

6

99362e95d46b10b893dd77df75240db4d18fa926

211

py

Python

paramnet/__init__.py

spcornelius/paramnet

95050664bb6a2c464670eddf8678b3fb795e6bab

[ "MIT" ]

null

paramnet/__init__.py

spcornelius/paramnet

95050664bb6a2c464670eddf8678b3fb795e6bab

[ "MIT" ]

null

paramnet/__init__.py

spcornelius/paramnet

95050664bb6a2c464670eddf8678b3fb795e6bab

[ "MIT" ]

null

import paramnet.exceptions from paramnet.exceptions import * import paramnet.base from paramnet.base import * import paramnet.meta from paramnet.meta import * import paramnet.view from paramnet.view import *

17.583333

33

0.815166

28

211

6.142857

0.25

0.325581

0.348837

0

0.127962

211

11

34

19.181818

0.934783

0

1

0

true

0

1

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

9968809f62daa4f65250ea60ca197d2d559bd317

164

py

Python

raven/projects/Scripts/Na23Calc.py

arfc/2019-12-bigdata-npps

ebf03664c1d96541956d317f3a305323cf76c23d

[ "CC-BY-4.0" ]

null

raven/projects/Scripts/Na23Calc.py

arfc/2019-12-bigdata-npps

ebf03664c1d96541956d317f3a305323cf76c23d

[ "CC-BY-4.0" ]

2

2019-10-26T14:32:13.000Z

2019-12-17T17:48:05.000Z

raven/projects/Scripts/Na23Calc.py

arfc/2019-12-bigdata-npps

ebf03664c1d96541956d317f3a305323cf76c23d

[ "CC-BY-4.0" ]

3

2019-10-25T18:50:31.000Z

2020-06-23T04:17:28.000Z

import MassFractionCalc def evaluate(self): return MassFractionCalc.return_value('Na23',self.salt_type,self.fuel_type,self.U235F4_mole_frac,self.UF4_mole_frac)

41

119

0.841463

24

164

5.458333

0.625

0.122137

0

0.045455

0.060976

164

3

120

54.666667

0.805195

0

0.02439

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

998bc235a4ab4742e0bad9a149a26aa2275f5e52

80

py

Python

expkit/experiment/__init__.py

jonathangingras/expkit

1943543ac6b23e80c59b56b4e998f0b0aaa7d6c8

[ "WTFPL" ]

null

expkit/experiment/__init__.py

jonathangingras/expkit

1943543ac6b23e80c59b56b4e998f0b0aaa7d6c8

[ "WTFPL" ]

null

expkit/experiment/__init__.py

jonathangingras/expkit

1943543ac6b23e80c59b56b4e998f0b0aaa7d6c8

[ "WTFPL" ]

null

from .dataset import * from .experiment_setup import * from .shortcuts import *

20

31

0.775

10

80

6.1

0.6

0.327869

0

0.15

80

3

32

26.666667

0.897059

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

5135bfa31c0e171f09e65266c565ad84cc42805e

9,865

py

Python

concordat/interface_test.py

bmoore813/concordat

9a6d92211423583b57d9f771bc3795f8957741c2

[ "Apache-2.0" ]

null

concordat/interface_test.py

bmoore813/concordat

9a6d92211423583b57d9f771bc3795f8957741c2

[ "Apache-2.0" ]

null

concordat/interface_test.py

bmoore813/concordat

9a6d92211423583b57d9f771bc3795f8957741c2

[ "Apache-2.0" ]

null

from typing import Dict, Tuple import pytest from beartype.roar import ( BeartypeCallHintPepReturnException, BeartypeCallHintPepParamException, ) from concordat.interface import InterfaceMeta, abstract_method class IValid(metaclass=InterfaceMeta): @abstract_method def run(self, path: str, id: int) -> None: pass @abstract_method def read(self, path: str) -> None: pass class Valid(IValid): def run(self, path: str, id: int) -> None: print(f"{path} and {id}") def read(self, path: str) -> None: print(f"path is {path}") # # TestBuildErrors: def test_build_missing_method() -> None: """Test to see if a method is missing on the implementation class """ with pytest.raises(NotImplementedError): class MissingMethod(IValid): def read(self, path: str) -> None: print(f"path is {path}") def test_build_misspelled_method() -> None: """Test to make sure that the methods are spelled the same on the implementation class """ with pytest.raises(NotImplementedError): class MisspelledMethod(IValid): def runs(self, path: str, id: int) -> None: print(f"{path} and {id}") def read(self, path: str) -> None: print(f"path is {path}") def test_build_wrong_arg_names() -> None: """Test to make sure that the parameternames are exactly the same """ with pytest.raises(TypeError): class BadNames(IValid): def run(self, poop: str, identification: int) -> None: print(f"{poop} and {identification}") def read(self, path: str) -> None: print(f"path is {path}") def test_build_wrong_type_hints() -> None: """Test to make sure that the type hints match exactly with what we get from the interface """ with pytest.raises(TypeError): class BadTypeHints(IValid): def run(self, path: Dict, id: str) -> Tuple: return f"{path} and {id}" # type:ignore def read(self, path: int) -> str: return f"path is {path}" # # TestRuntimeErrors: def test_run_wrong_arg_types() -> None: with pytest.raises(BeartypeCallHintPepParamException): v = Valid() v.run("test/path", "not an int") # type:ignore def test_no_errors() -> None: v = Valid() v.run("test/path", 1) v.read("hello") def test_bad_return_type() -> None: with pytest.raises(BeartypeCallHintPepReturnException): class IZeus(metaclass=InterfaceMeta): @abstract_method def run(self, path: str, id: int) -> None: pass @abstract_method def read(self, path: str) -> int: pass class Zeus(IZeus): def run(self, path: str, id: int) -> None: print(f"{path} and {id}") def read(self, path: str) -> int: print(f"path is {path}") return "p" z = Zeus() z.run("test/path", 1) z.read("hello") def test_custom_return_type() -> None: class CustomType: def __init__(self) -> None: prop = "test property" class IZeus(metaclass=InterfaceMeta): @abstract_method def run(self) -> CustomType: pass class Zeus(IZeus): def run(self) -> CustomType: return CustomType() z = Zeus() z.run() with pytest.raises(BeartypeCallHintPepReturnException): class Zeus(IZeus): def run(self) -> CustomType: return 1 z = Zeus() z.run() # # TODO: Error # def test_multiple_return_types() -> None: # class CustomType: # def __init__(self) -> None: # prop = "test property" # class IZeus(metaclass=InterfaceMeta): # @abstract_method # def run(self, has_value: List, name: str) -> Tuple[List, str]: # pass # class Zeus(IZeus): # def run(self, has_value: List, name: str) -> Tuple[List, str]: # return has_value, name # z = Zeus() # a = [1, 2] # z.run(has_value=a, name="Im a name") # TestInheritanceErrors: def test_inheritance_empty() -> None: class InheritEmpty(Valid): pass def test_inheritance_empty_2() -> None: class InheritEmpty(Valid): pass class InheritEmpty2(InheritEmpty): pass def test_inheritance_enhancement() -> None: class Enhancement(Valid): def new_func(self, path: str, id: int, extra: str) -> None: print(f"{path} and {id} and {extra}") enhance = Enhancement() enhance.run("test", 69) enhance.read("considerthisread") enhance.new_func("path", 900, "extrasauceplz") with pytest.raises(BeartypeCallHintPepParamException): enhance.run("test", "not an int") # type:ignore with pytest.raises(TypeError): enhance.read("read", "extra arg") # type:ignore def test_inheritance_override_method() -> None: with pytest.raises(TypeError): class BadOverride(Valid): def run(self, sheesh: str, id: int) -> None: print(f"{sheesh} and {id}") class IStatic(metaclass=InterfaceMeta): @abstract_method def poop(path: str, id: int) -> None: ... @abstract_method def pee(path: str) -> None: ... class Static(IStatic): @staticmethod def poop(path: str, id: int) -> None: print(f"{path} and {id}") @staticmethod def pee(path: str) -> None: print(f"path is {path}") # class TestBuildErrors: def test_static_build_missing_method() -> None: """Test to see if a method is missing on the implementation class """ with pytest.raises(NotImplementedError): class MissingMethod(IStatic): @staticmethod def poop(path: str, id: int) -> None: print(f"path is {path}") def test_static_build_misspelled_method() -> None: """Test to make sure that the methods are spelled the same on the implementation class """ with pytest.raises(NotImplementedError): class MisspelledMethod(IStatic): def poop(path: str, id: int) -> None: print(f"{path} and {id}") def piz(path: str) -> None: print(f"path is {path}") def test_static_build_wrong_arg_names() -> None: """Test to make sure that the paramternames are exactly the same """ with pytest.raises(TypeError): class BadNames(IStatic): def poop(self, poop: str, identification: int) -> None: print(f"{poop} and {identification}") def pee(self, path: str) -> None: print(f"path is {path}") def test_static_build_wrong_type_hints() -> None: """Test to make sure that the type hints match exactly with what we get from the interface """ with pytest.raises(TypeError): class BadTypeHints(IStatic): def poop(path: Dict, id: str) -> Tuple: return f"{path} and {id}" def pee(path: int) -> str: return f"path is {path}" # TestRuntimeErrors: def test_static_run_wrong_arg_types() -> None: with pytest.raises(BeartypeCallHintPepParamException): v = Static() v.poop("test/path", "not an int") # type:ignore def test_static_no_errors() -> None: v = Static() v.poop("test/path", 1) v.pee("hello") def test_bad_return_type() -> None: with pytest.raises(BeartypeCallHintPepReturnException): class IZeus(metaclass=InterfaceMeta): @abstract_method def run(self, path: str, id: int) -> None: pass @abstract_method def read(self, path: str) -> int: pass class Zeus(IZeus): @staticmethod def run(path: str, id: int) -> None: print(f"{path} and {id}") @staticmethod def read(path: str) -> int: print(f"path is {path}") return "p" z = Zeus() z.run("test/path", 1) z.read("hello") # TestInheritanceErrors: def test_static_inheritance_empty() -> None: class InheritEmpty(Static): pass def test_static_inheritance_empty_2() -> None: class InheritEmpty(Static): pass class InheritEmpty2(InheritEmpty): pass def test_static_inheritance_enhancement() -> None: class Enhancement(Static): def new_func(self, path: str, id: int, extra: str) -> None: print(f"{path} and {id} and {extra}") enhance = Enhancement() enhance.poop("test", 69) enhance.pee("considerthisread") enhance.new_func("path", 900, "extrasauceplz") with pytest.raises(BeartypeCallHintPepParamException): enhance.poop("test", "not an int") # type:ignore with pytest.raises(TypeError): enhance.pee("read", "extra arg") # type:ignore def test_static_inheritance_override_method() -> None: with pytest.raises(TypeError): class BadOverride(Static): def poop(self, sheesh: str, id: int) -> None: print(f"{sheesh} and {id}") def test_no_abc_impplementation() -> None: class MyClass(metaclass=InterfaceMeta): def __init__(self, path: str) -> None: print(path) def run(self, check: bool) -> int: return 1 m = MyClass(path="1") m.run(True) with pytest.raises(BeartypeCallHintPepReturnException): class BadClass(metaclass=InterfaceMeta): def __init__(self, path: int) -> None: print(path) def run(self, check: bool) -> int: return "hellp" b = BadClass(1) b.run(False)

25.623377

72

0.5852

1,151

9,865

4.911381

0.119896

0.033434

0.035379

0.039625

0.845038

0.770564

0.733062

0.698921

0.679993

0.668318

0

0.003449

0.294678

9,865

384

73

25.690104

0.808997

0.13259

0

0.622222

0

0.079986

0

0.002604

0

1

0.293333

false

0.057778

0.017778

0.035556

0.497778

0.106667

0

null

0

1

0

1

0

null

0

1

0

1

0

6

5148dfda652988ef6c275e31f7b6d2fe90bf6ec6

15,747

py

Python

sdk/python/pulumi_gcp/dataproc/job.py

23doors/pulumi-gcp

ded01b199f95b164884266ea3e6f8206c8231270

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

1

2019-12-20T22:08:20.000Z

sdk/python/pulumi_gcp/dataproc/job.py

pellizzetti/pulumi-gcp

fad74dd55a0cf7723f73046bb0e6fcbfd948ba84

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

null

sdk/python/pulumi_gcp/dataproc/job.py

pellizzetti/pulumi-gcp

fad74dd55a0cf7723f73046bb0e6fcbfd948ba84

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from .. import utilities, tables class Job(pulumi.CustomResource): driver_controls_files_uri: pulumi.Output[str] """ If present, the location of miscellaneous control files which may be used as part of job setup and handling. If not present, control files may be placed in the same location as driver_output_uri. """ driver_output_resource_uri: pulumi.Output[str] """ A URI pointing to the location of the stdout of the job's driver program. """ force_delete: pulumi.Output[bool] """ By default, you can only delete inactive jobs within Dataproc. Setting this to true, and calling destroy, will ensure that the job is first cancelled before issuing the delete. """ hadoop_config: pulumi.Output[dict] hive_config: pulumi.Output[dict] labels: pulumi.Output[dict] """ The list of labels (key/value pairs) to add to the job. """ pig_config: pulumi.Output[dict] placement: pulumi.Output[dict] project: pulumi.Output[str] """ The project in which the `cluster` can be found and jobs subsequently run against. If it is not provided, the provider project is used. """ pyspark_config: pulumi.Output[dict] reference: pulumi.Output[dict] region: pulumi.Output[str] """ The Cloud Dataproc region. This essentially determines which clusters are available for this job to be submitted to. If not specified, defaults to `global`. """ scheduling: pulumi.Output[dict] spark_config: pulumi.Output[dict] sparksql_config: pulumi.Output[dict] status: pulumi.Output[dict] def __init__(__self__, resource_name, opts=None, force_delete=None, hadoop_config=None, hive_config=None, labels=None, pig_config=None, placement=None, project=None, pyspark_config=None, reference=None, region=None, scheduling=None, spark_config=None, sparksql_config=None, __props__=None, __name__=None, __opts__=None): """ Manages a job resource within a Dataproc cluster within GCE. For more information see [the official dataproc documentation](https://cloud.google.com/dataproc/). !> **Note:** This resource does not support 'update' and changing any attributes will cause the resource to be recreated. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] force_delete: By default, you can only delete inactive jobs within Dataproc. Setting this to true, and calling destroy, will ensure that the job is first cancelled before issuing the delete. :param pulumi.Input[dict] labels: The list of labels (key/value pairs) to add to the job. :param pulumi.Input[str] project: The project in which the `cluster` can be found and jobs subsequently run against. If it is not provided, the provider project is used. :param pulumi.Input[str] region: The Cloud Dataproc region. This essentially determines which clusters are available for this job to be submitted to. If not specified, defaults to `global`. The **hadoop_config** object supports the following: * `archiveUris` (`pulumi.Input[list]`) * `args` (`pulumi.Input[list]`) * `fileUris` (`pulumi.Input[list]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `mainClass` (`pulumi.Input[str]`) * `mainJarFileUri` (`pulumi.Input[str]`) * `properties` (`pulumi.Input[dict]`) The **hive_config** object supports the following: * `continueOnFailure` (`pulumi.Input[bool]`) * `jarFileUris` (`pulumi.Input[list]`) * `properties` (`pulumi.Input[dict]`) * `queryFileUri` (`pulumi.Input[str]`) * `queryLists` (`pulumi.Input[list]`) * `scriptVariables` (`pulumi.Input[dict]`) The **pig_config** object supports the following: * `continueOnFailure` (`pulumi.Input[bool]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `properties` (`pulumi.Input[dict]`) * `queryFileUri` (`pulumi.Input[str]`) * `queryLists` (`pulumi.Input[list]`) * `scriptVariables` (`pulumi.Input[dict]`) The **placement** object supports the following: * `clusterName` (`pulumi.Input[str]`) * `clusterUuid` (`pulumi.Input[str]`) The **pyspark_config** object supports the following: * `archiveUris` (`pulumi.Input[list]`) * `args` (`pulumi.Input[list]`) * `fileUris` (`pulumi.Input[list]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `mainPythonFileUri` (`pulumi.Input[str]`) * `properties` (`pulumi.Input[dict]`) * `pythonFileUris` (`pulumi.Input[list]`) The **reference** object supports the following: * `job_id` (`pulumi.Input[str]`) The **scheduling** object supports the following: * `maxFailuresPerHour` (`pulumi.Input[float]`) The **spark_config** object supports the following: * `archiveUris` (`pulumi.Input[list]`) * `args` (`pulumi.Input[list]`) * `fileUris` (`pulumi.Input[list]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `mainClass` (`pulumi.Input[str]`) * `mainJarFileUri` (`pulumi.Input[str]`) * `properties` (`pulumi.Input[dict]`) The **sparksql_config** object supports the following: * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `properties` (`pulumi.Input[dict]`) * `queryFileUri` (`pulumi.Input[str]`) * `queryLists` (`pulumi.Input[list]`) * `scriptVariables` (`pulumi.Input[dict]`) > This content is derived from https://github.com/terraform-providers/terraform-provider-google/blob/master/website/docs/r/dataproc_job.html.markdown. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['force_delete'] = force_delete __props__['hadoop_config'] = hadoop_config __props__['hive_config'] = hive_config __props__['labels'] = labels __props__['pig_config'] = pig_config if placement is None: raise TypeError("Missing required property 'placement'") __props__['placement'] = placement __props__['project'] = project __props__['pyspark_config'] = pyspark_config __props__['reference'] = reference __props__['region'] = region __props__['scheduling'] = scheduling __props__['spark_config'] = spark_config __props__['sparksql_config'] = sparksql_config __props__['driver_controls_files_uri'] = None __props__['driver_output_resource_uri'] = None __props__['status'] = None super(Job, __self__).__init__( 'gcp:dataproc/job:Job', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, driver_controls_files_uri=None, driver_output_resource_uri=None, force_delete=None, hadoop_config=None, hive_config=None, labels=None, pig_config=None, placement=None, project=None, pyspark_config=None, reference=None, region=None, scheduling=None, spark_config=None, sparksql_config=None, status=None): """ Get an existing Job resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] driver_controls_files_uri: If present, the location of miscellaneous control files which may be used as part of job setup and handling. If not present, control files may be placed in the same location as driver_output_uri. :param pulumi.Input[str] driver_output_resource_uri: A URI pointing to the location of the stdout of the job's driver program. :param pulumi.Input[bool] force_delete: By default, you can only delete inactive jobs within Dataproc. Setting this to true, and calling destroy, will ensure that the job is first cancelled before issuing the delete. :param pulumi.Input[dict] labels: The list of labels (key/value pairs) to add to the job. :param pulumi.Input[str] project: The project in which the `cluster` can be found and jobs subsequently run against. If it is not provided, the provider project is used. :param pulumi.Input[str] region: The Cloud Dataproc region. This essentially determines which clusters are available for this job to be submitted to. If not specified, defaults to `global`. The **hadoop_config** object supports the following: * `archiveUris` (`pulumi.Input[list]`) * `args` (`pulumi.Input[list]`) * `fileUris` (`pulumi.Input[list]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `mainClass` (`pulumi.Input[str]`) * `mainJarFileUri` (`pulumi.Input[str]`) * `properties` (`pulumi.Input[dict]`) The **hive_config** object supports the following: * `continueOnFailure` (`pulumi.Input[bool]`) * `jarFileUris` (`pulumi.Input[list]`) * `properties` (`pulumi.Input[dict]`) * `queryFileUri` (`pulumi.Input[str]`) * `queryLists` (`pulumi.Input[list]`) * `scriptVariables` (`pulumi.Input[dict]`) The **pig_config** object supports the following: * `continueOnFailure` (`pulumi.Input[bool]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `properties` (`pulumi.Input[dict]`) * `queryFileUri` (`pulumi.Input[str]`) * `queryLists` (`pulumi.Input[list]`) * `scriptVariables` (`pulumi.Input[dict]`) The **placement** object supports the following: * `clusterName` (`pulumi.Input[str]`) * `clusterUuid` (`pulumi.Input[str]`) The **pyspark_config** object supports the following: * `archiveUris` (`pulumi.Input[list]`) * `args` (`pulumi.Input[list]`) * `fileUris` (`pulumi.Input[list]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `mainPythonFileUri` (`pulumi.Input[str]`) * `properties` (`pulumi.Input[dict]`) * `pythonFileUris` (`pulumi.Input[list]`) The **reference** object supports the following: * `job_id` (`pulumi.Input[str]`) The **scheduling** object supports the following: * `maxFailuresPerHour` (`pulumi.Input[float]`) The **spark_config** object supports the following: * `archiveUris` (`pulumi.Input[list]`) * `args` (`pulumi.Input[list]`) * `fileUris` (`pulumi.Input[list]`) * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `mainClass` (`pulumi.Input[str]`) * `mainJarFileUri` (`pulumi.Input[str]`) * `properties` (`pulumi.Input[dict]`) The **sparksql_config** object supports the following: * `jarFileUris` (`pulumi.Input[list]`) * `loggingConfig` (`pulumi.Input[dict]`) * `driverLogLevels` (`pulumi.Input[dict]`) * `properties` (`pulumi.Input[dict]`) * `queryFileUri` (`pulumi.Input[str]`) * `queryLists` (`pulumi.Input[list]`) * `scriptVariables` (`pulumi.Input[dict]`) The **status** object supports the following: * `details` (`pulumi.Input[str]`) * `state` (`pulumi.Input[str]`) * `stateStartTime` (`pulumi.Input[str]`) * `substate` (`pulumi.Input[str]`) > This content is derived from https://github.com/terraform-providers/terraform-provider-google/blob/master/website/docs/r/dataproc_job.html.markdown. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["driver_controls_files_uri"] = driver_controls_files_uri __props__["driver_output_resource_uri"] = driver_output_resource_uri __props__["force_delete"] = force_delete __props__["hadoop_config"] = hadoop_config __props__["hive_config"] = hive_config __props__["labels"] = labels __props__["pig_config"] = pig_config __props__["placement"] = placement __props__["project"] = project __props__["pyspark_config"] = pyspark_config __props__["reference"] = reference __props__["region"] = region __props__["scheduling"] = scheduling __props__["spark_config"] = spark_config __props__["sparksql_config"] = sparksql_config __props__["status"] = status return Job(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

45.643478

345

0.612561

1,684

15,747

5.52791

0.142518

0.139435

0.064454

0.053067

0.773338

0.736384

0.731658

0.724353

0

0.000087

0.26894

15,747

344

346

45.776163

0.808548

0.537817

0

0.022727

1

0

0.1505

0.022184

0

1

0.045455

false

0.011364

0.068182

0.022727

0.340909

0

null

0

1

0

1

0

null

0

6

5aa635f5e35eb1eb055c4acf7c32ba24fa54ced4

45

py

Python

pgreaper/io/__init__.py

vincentlaucsb/sqlify

cc84be6efef7b904aacc463d5b0211b3e52a8f25

[ "MIT" ]

8

2017-05-01T10:11:40.000Z

2017-07-26T08:52:43.000Z

pgreaper/io/__init__.py

vincentlaucsb/pgreaper

cc84be6efef7b904aacc463d5b0211b3e52a8f25

[ "MIT" ]

4

2017-05-01T13:11:05.000Z

2017-08-06T06:18:34.000Z

pgreaper/io/__init__.py

vincentlaucsb/sqlify

cc84be6efef7b904aacc463d5b0211b3e52a8f25

[ "MIT" ]

null

from .json_reader import JSONStreamingDecoder

45

0.911111

5

45

8

1

0

0.066667

45

1

45

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5ab91551496f52e230550f46bbeeead29f193031

5,703

py

Python

DAY_3/SOLUTION_2.py

Malod219/AdventOfCode2019

df4c45a6917580075622dd063e007860c5425328

[ "Unlicense" ]

null

DAY_3/SOLUTION_2.py

Malod219/AdventOfCode2019

df4c45a6917580075622dd063e007860c5425328

[ "Unlicense" ]

null

DAY_3/SOLUTION_2.py

Malod219/AdventOfCode2019

df4c45a6917580075622dd063e007860c5425328

[ "Unlicense" ]

null

with open("input1.txt","r") as f: data = f.readlines() data[0] = data[0].split(',') data[1] = data[1].split(',') # Might need to change w if too big w = 22000 h = w # 2000x2000 Wirespace wireSpace = [[0 for x in range(w)] for y in range(h)] centerX = w//2 centerY = h//2 currentPosX = centerX currentPosY = centerY # Plot all wire commands in the first line for command in data[0]: #print(command) opcode, parameter = command[0], int(command[1:]) try: if(opcode == "R"): for i in range(parameter): wireSpace[currentPosY][currentPosX+i] = 1 currentPosX += parameter elif(opcode == "L"): for i in range(parameter): wireSpace[currentPosY][currentPosX-i] = 1 currentPosX -= parameter elif(opcode == "U"): for i in range(parameter): wireSpace[currentPosY-i][currentPosX] = 1 currentPosY -= parameter elif(opcode == "D"): for i in range(parameter): wireSpace[currentPosY+i][currentPosX] = 1 currentPosY += parameter except: print("Failed.\nOpcode: {}\nParameter: {}\nCurrent XY Coordinate: {}, {}".format(opcode, parameter, currentPosX, currentPosY)) break; currentPosX = centerX currentPosY = centerY collisionPoints = [] for command in data[1]: #print(command) opcode, parameter = command[0], int(command[1:]) try: if(opcode == "R"): for i in range(parameter): if(wireSpace[currentPosY][currentPosX+i] == 1): if(currentPosX != centerX & currentPosY != centerY): wireSpace[currentPosY][currentPosX+i]=2 collisionPoints.append([currentPosX+i,currentPosY,99999,99999]) currentPosX += parameter elif(opcode == "L"): for i in range(parameter): if(wireSpace[currentPosY][currentPosX-i] == 1): if(currentPosX != centerX & currentPosY != centerY): wireSpace[currentPosY][currentPosX-i]=2 collisionPoints.append([currentPosX-i,currentPosY,99999,99999]) currentPosX -= parameter elif(opcode == "U"): for i in range(parameter): if(wireSpace[currentPosY-i][currentPosX] == 1): if(currentPosX != centerX & currentPosY != centerY): wireSpace[currentPosY-i][currentPosX]=2 collisionPoints.append([currentPosX,currentPosY-i,99999,99999]) currentPosY -= parameter elif(opcode == "D"): for i in range(parameter): if(wireSpace[currentPosY+i][currentPosX] == 1): if(currentPosX != centerX & currentPosY != centerY): wireSpace[currentPosY+i][currentPosX]=2 collisionPoints.append([currentPosX,currentPosY+i,99999,99999]) currentPosY += parameter except: print("Failed.\nOpcode: {}\nParameter: {}\nCurrent XY Coordinate: {}, {}".format(opcode, parameter, currentPosX, currentPosY)) break; def getStepsCountToIntersect(data,pos): currentPosX = centerX currentPosY = centerY steps = 0 # Plot all wire commands in the first line for command in data: #print(command) opcode, parameter = command[0], int(command[1:]) try: if(opcode == "R"): for i in range(parameter): if wireSpace[currentPosY][currentPosX+i] == 2: for j in range(len(collisionPoints)): if( currentPosX+i == collisionPoints[j][0] and currentPosY == collisionPoints[j][1] ): collisionPoints[j][pos] = min(collisionPoints[j][pos], steps+i) currentPosX += parameter elif(opcode == "L"): for i in range(parameter): if wireSpace[currentPosY][currentPosX-i] == 2: for j in range(len(collisionPoints)): if( currentPosX-i == collisionPoints[j][0] and currentPosY == collisionPoints[j][1] ): collisionPoints[j][pos] = min(collisionPoints[j][pos], steps+i) currentPosX -= parameter elif(opcode == "U"): for i in range(parameter): if wireSpace[currentPosY-i][currentPosX] == 2: for j in range(len(collisionPoints)): if( currentPosX == collisionPoints[j][0] and currentPosY-i == collisionPoints[j][1] ): collisionPoints[j][pos] = min(collisionPoints[j][pos], steps+i) currentPosY -= parameter elif(opcode == "D"): for i in range(parameter): if wireSpace[currentPosY+i][currentPosX] == 2: for j in range(len(collisionPoints)): if( currentPosX == collisionPoints[j][0] and currentPosY+i == collisionPoints[j][1] ): collisionPoints[j][pos] = min(collisionPoints[j][pos], steps+i) currentPosY += parameter steps+=parameter except: print("Failed.\nOpcode: {}\nParameter: {}\nCurrent XY Coordinate: {}, {}".format(opcode, parameter, currentPosX, currentPosY)) break; getStepsCountToIntersect(data[0],2) getStepsCountToIntersect(data[1],3) minSum = 99999 for point in collisionPoints: minSum = min(minSum, point[2]+point[3]) print(minSum) print("Succesfully ended")

42.879699

138

0.550587

561

5,703

5.597148

0.137255

0.040127

0.02293

0.042038

0.83758

0

0.027344

0.32667

5,703

132

139

43.204545

0.790365

0.031036

0

0.594828

0

0.04295

0

1

0.008621

false

0

0.008621

0.043103

0

null

0

1

0

null

0

6

51aafd5e472cb867a4504109b9913174123657d8

213

py

Python

hibiscus_connect/hibiscus_connect/doctype/hibiscus_connect_bank_account/hibiscus_connect_bank_account.py

itsdave-de/hibiscus_connect

b535657336a4c37f558ef76cd7662984d833e4dc

[ "MIT" ]

null

hibiscus_connect/hibiscus_connect/doctype/hibiscus_connect_bank_account/hibiscus_connect_bank_account.py

itsdave-de/hibiscus_connect

b535657336a4c37f558ef76cd7662984d833e4dc

[ "MIT" ]

null

hibiscus_connect/hibiscus_connect/doctype/hibiscus_connect_bank_account/hibiscus_connect_bank_account.py

itsdave-de/hibiscus_connect

b535657336a4c37f558ef76cd7662984d833e4dc

[ "MIT" ]

null

# Copyright (c) 2021, itsdave GmbH and contributors # For license information, please see license.txt # import frappe from frappe.model.document import Document class HibiscusConnectBankAccount(Document): pass

23.666667

51

0.807512

26

213

6.615385

0.807692

0

0.021622

0.131455

213

8

52

26.625

0.908108

0.521127

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

51cc4e9bbadf64adc659dec8ed89c875e19578e7

23,457

py

Python

flash/tabular/regression/data.py

ar90n/lightning-flash

61e1a2d3b72f8fbbffe6ace14fb5b5bb35c5f131

[ "Apache-2.0" ]

null

flash/tabular/regression/data.py

ar90n/lightning-flash

61e1a2d3b72f8fbbffe6ace14fb5b5bb35c5f131

[ "Apache-2.0" ]

null

flash/tabular/regression/data.py

ar90n/lightning-flash

61e1a2d3b72f8fbbffe6ace14fb5b5bb35c5f131

[ "Apache-2.0" ]

null

# Copyright The PyTorch Lightning team. # # 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 typing import Any, Dict, List, Optional, Type, Union from flash.core.data.io.input import Input from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE, InputTransform from flash.core.utilities.imports import _PANDAS_AVAILABLE, _TABULAR_TESTING from flash.core.utilities.stages import RunningStage from flash.tabular.data import TabularData from flash.tabular.regression.input import ( TabularRegressionCSVInput, TabularRegressionDataFrameInput, TabularRegressionDictInput, TabularRegressionListInput, ) if _PANDAS_AVAILABLE: from pandas.core.frame import DataFrame else: DataFrame = object # Skip doctests if requirements aren't available if not _TABULAR_TESTING: __doctest_skip__ = ["TabularRegressionData", "TabularRegressionData.*"] class TabularRegressionData(TabularData): """The ``TabularRegressionData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of classmethods for loading data for tabular regression.""" @classmethod def from_data_frame( cls, categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_field: Optional[str] = None, parameters: Optional[Dict[str, Any]] = None, train_data_frame: Optional[DataFrame] = None, val_data_frame: Optional[DataFrame] = None, test_data_frame: Optional[DataFrame] = None, predict_data_frame: Optional[DataFrame] = None, input_cls: Type[Input] = TabularRegressionDataFrameInput, transform: INPUT_TRANSFORM_TYPE = InputTransform, transform_kwargs: Optional[Dict] = None, **data_module_kwargs: Any, ) -> "TabularRegressionData": """Creates a :class:`~flash.tabular.regression.data.TabularRegressionData` object from the given data frames. .. note:: The ``categorical_fields``, ``numerical_fields``, and ``target_field`` do not need to be provided if ``parameters`` are passed instead. These can be obtained from the :attr:`~flash.tabular.data.TabularData.parameters` attribute of the :class:`~flash.tabular.data.TabularData` object that contains your training data. The targets will be extracted from the ``target_field`` in the data frames. To learn how to customize the transforms applied for each stage, read our :ref:`customizing transforms guide <customizing_transforms>`. Args: categorical_fields: The fields (column names) in the data frames containing categorical data. numerical_fields: The fields (column names) in the data frames containing numerical data. target_field: The field (column name) in the data frames containing the targets. parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_field`` are not provided (e.g. when loading data for inference or validation). train_data_frame: The DataFrame to use when training. val_data_frame: The DataFrame to use when validating. test_data_frame: The DataFrame to use when testing. predict_data_frame: The DataFrame to use when predicting. input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data. transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use. transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms. data_module_kwargs: Additional keyword arguments to provide to the :class:`~flash.core.data.data_module.DataModule` constructor. Returns: The constructed :class:`~flash.tabular.regression.data.TabularRegressionData`. Examples ________ .. testsetup:: >>> from pandas import DataFrame >>> train_data = DataFrame.from_dict({ ... "age": [2, 4, 1], ... "animal": ["cat", "dog", "cat"], ... "weight": [6, 10, 5], ... }) >>> predict_data = DataFrame.from_dict({ ... "animal": ["dog", "dog", "cat"], ... "weight": [7, 12, 5], ... }) We have a DataFrame ``train_data`` with the following contents: .. doctest:: >>> train_data.head(3) age animal weight 0 2 cat 6 1 4 dog 10 2 1 cat 5 and a DataFrame ``predict_data`` with the following contents: .. doctest:: >>> predict_data.head(3) animal weight 0 dog 7 1 dog 12 2 cat 5 .. doctest:: >>> from flash import Trainer >>> from flash.tabular import TabularRegressor, TabularRegressionData >>> datamodule = TabularRegressionData.from_data_frame( ... "animal", ... "weight", ... "age", ... train_data_frame=train_data, ... predict_data_frame=predict_data, ... batch_size=4, ... ) >>> model = TabularRegressor.from_data(datamodule, backbone="tabnet") >>> trainer = Trainer(fast_dev_run=True) >>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Training... >>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Predicting... .. testcleanup:: >>> del train_data >>> del predict_data """ ds_kw = dict( categorical_fields=categorical_fields, numerical_fields=numerical_fields, target_field=target_field, parameters=parameters, ) train_input = input_cls(RunningStage.TRAINING, train_data_frame, **ds_kw) ds_kw["parameters"] = train_input.parameters if train_input else parameters return cls( train_input, input_cls(RunningStage.VALIDATING, val_data_frame, **ds_kw), input_cls(RunningStage.TESTING, test_data_frame, **ds_kw), input_cls(RunningStage.PREDICTING, predict_data_frame, **ds_kw), transform=transform, transform_kwargs=transform_kwargs, **data_module_kwargs, ) @classmethod def from_csv( cls, categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_field: Optional[str] = None, parameters: Optional[Dict[str, Any]] = None, train_file: Optional[str] = None, val_file: Optional[str] = None, test_file: Optional[str] = None, predict_file: Optional[str] = None, input_cls: Type[Input] = TabularRegressionCSVInput, transform: INPUT_TRANSFORM_TYPE = InputTransform, transform_kwargs: Optional[Dict] = None, **data_module_kwargs: Any, ) -> "TabularRegressionData": """Creates a :class:`~flash.tabular.regression.data.TabularRegressionData` object from the given CSV files. .. note:: The ``categorical_fields``, ``numerical_fields``, and ``target_field`` do not need to be provided if ``parameters`` are passed instead. These can be obtained from the :attr:`~flash.tabular.data.TabularData.parameters` attribute of the :class:`~flash.tabular.data.TabularData` object that contains your training data. The targets will be extracted from the ``target_field`` in the CSV files. To learn how to customize the transforms applied for each stage, read our :ref:`customizing transforms guide <customizing_transforms>`. Args: categorical_fields: The fields (column names) in the CSV files containing categorical data. numerical_fields: The fields (column names) in the CSV files containing numerical data. target_field: The field (column name) in the CSV files containing the targets. parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_field`` are not provided (e.g. when loading data for inference or validation). train_file: The path to the CSV file to use when training. val_file: The path to the CSV file to use when validating. test_file: The path to the CSV file to use when testing. predict_file: The path to the CSV file to use when predicting. input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data. transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use. transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms. data_module_kwargs: Additional keyword arguments to provide to the :class:`~flash.core.data.data_module.DataModule` constructor. Returns: The constructed :class:`~flash.tabular.regression.data.TabularRegressionData`. Examples ________ .. testsetup:: >>> from pandas import DataFrame >>> DataFrame.from_dict({ ... "age": [2, 4, 1], ... "animal": ["cat", "dog", "cat"], ... "weight": [6, 10, 5], ... }).to_csv("train_data.csv") >>> DataFrame.from_dict({ ... "animal": ["dog", "dog", "cat"], ... "weight": [7, 12, 5], ... }).to_csv("predict_data.csv") We have a ``train_data.csv`` with the following contents: .. code-block:: age,animal,weight 2,cat,6 4,dog,10 1,cat,5 and a ``predict_data.csv`` with the following contents: .. code-block:: animal,weight dog,7 dog,12 cat,5 .. doctest:: >>> from flash import Trainer >>> from flash.tabular import TabularRegressor, TabularRegressionData >>> datamodule = TabularRegressionData.from_csv( ... "animal", ... "weight", ... "age", ... train_file="train_data.csv", ... predict_file="predict_data.csv", ... batch_size=4, ... ) >>> model = TabularRegressor.from_data(datamodule, backbone="tabnet") >>> trainer = Trainer(fast_dev_run=True) >>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Training... >>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Predicting... .. testcleanup:: >>> import os >>> os.remove("train_data.csv") >>> os.remove("predict_data.csv") """ ds_kw = dict( categorical_fields=categorical_fields, numerical_fields=numerical_fields, target_field=target_field, parameters=parameters, ) train_input = input_cls(RunningStage.TRAINING, train_file, **ds_kw) ds_kw["parameters"] = train_input.parameters if train_input else parameters return cls( train_input, input_cls(RunningStage.VALIDATING, val_file, **ds_kw), input_cls(RunningStage.TESTING, test_file, **ds_kw), input_cls(RunningStage.PREDICTING, predict_file, **ds_kw), transform=transform, transform_kwargs=transform_kwargs, **data_module_kwargs, ) @classmethod def from_dicts( cls, categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_field: Optional[str] = None, parameters: Optional[Dict[str, Any]] = None, train_dict: Optional[Dict[str, List[Any]]] = None, val_dict: Optional[Dict[str, List[Any]]] = None, test_dict: Optional[Dict[str, List[Any]]] = None, predict_dict: Optional[Dict[str, List[Any]]] = None, input_cls: Type[Input] = TabularRegressionDictInput, transform: INPUT_TRANSFORM_TYPE = InputTransform, transform_kwargs: Optional[Dict] = None, **data_module_kwargs: Any, ) -> "TabularRegressionData": """Creates a :class:`~flash.tabular.regression.data.TabularRegressionData` object from the given dictionary. .. note:: The ``categorical_fields``, ``numerical_fields``, and ``target_field`` do not need to be provided if ``parameters`` are passed instead. These can be obtained from the :attr:`~flash.tabular.data.TabularData.parameters` attribute of the :class:`~flash.tabular.data.TabularData` object that contains your training data. The targets will be extracted from the ``target_field`` in the data frames. To learn how to customize the transforms applied for each stage, read our :ref:`customizing transforms guide <customizing_transforms>`. Args: categorical_fields: The fields (column names) in the dictionary containing categorical data. numerical_fields: The fields (column names) in the dictionary containing numerical data. target_field: The field (column name) in the dictionary containing the targets. parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_field`` are not provided (e.g. when loading data for inference or validation). train_dict: The dictionary to use when training. val_dict: The dictionary to use when validating. test_dict: The dictionary to use when testing. predict_dict: The dictionary to use when predicting. input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data. transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use. transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms. data_module_kwargs: Additional keyword arguments to provide to the :class:`~flash.core.data.data_module.DataModule` constructor. Returns: The constructed :class:`~flash.tabular.regression.data.TabularRegressionData`. Examples ________ .. testsetup:: >>> train_data = { ... "age": [2, 4, 1], ... "animal": ["cat", "dog", "cat"], ... "weight": [6, 10, 5], ... } >>> predict_data = { ... "animal": ["dog", "dog", "cat"], ... "weight": [7, 12, 5], ... } We have a dictionary ``train_data`` with the following contents: .. code-block:: { "age": [2, 4, 1], "animal": ["cat", "dog", "cat"], "weight": [6, 10, 5] } and a dictionary ``predict_data`` with the following contents: .. code-block:: { "animal": ["dog", "dog", "cat"], "weight": [7, 12, 5] } .. doctest:: >>> from flash import Trainer >>> from flash.tabular import TabularRegressor, TabularRegressionData >>> datamodule = TabularRegressionData.from_dicts( ... "animal", ... "weight", ... "age", ... train_dict=train_data, ... predict_dict=predict_data, ... batch_size=4, ... ) >>> model = TabularRegressor.from_data(datamodule, backbone="tabnet") >>> trainer = Trainer(fast_dev_run=True) >>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Training... >>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Predicting... .. testcleanup:: >>> del train_data >>> del predict_data """ ds_kw = dict( categorical_fields=categorical_fields, numerical_fields=numerical_fields, target_field=target_field, parameters=parameters, ) train_input = input_cls(RunningStage.TRAINING, train_dict, **ds_kw) ds_kw["parameters"] = train_input.parameters if train_input else parameters return cls( train_input, input_cls(RunningStage.VALIDATING, val_dict, **ds_kw), input_cls(RunningStage.TESTING, test_dict, **ds_kw), input_cls(RunningStage.PREDICTING, predict_dict, **ds_kw), transform=transform, transform_kwargs=transform_kwargs, **data_module_kwargs, ) @classmethod def from_lists( cls, categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_field: Optional[str] = None, parameters: Optional[Dict[str, Any]] = None, train_list: List[Union[tuple, dict]] = None, val_list: List[Union[tuple, dict]] = None, test_list: List[Union[tuple, dict]] = None, predict_list: List[Union[tuple, dict]] = None, input_cls: Type[Input] = TabularRegressionListInput, transform: INPUT_TRANSFORM_TYPE = InputTransform, transform_kwargs: Optional[Dict] = None, **data_module_kwargs: Any, ) -> "TabularRegressionData": """Creates a :class:`~flash.tabular.regression.data.TabularRegressionData` object from the given data (in the form of list of a tuple or a dictionary). .. note:: The ``categorical_fields``, ``numerical_fields``, and ``target_field`` do not need to be provided if ``parameters`` are passed instead. These can be obtained from the :attr:`~flash.tabular.data.TabularData.parameters` attribute of the :class:`~flash.tabular.data.TabularData` object that contains your training data. The targets will be extracted from the ``target_field`` in the data frames. To learn how to customize the transforms applied for each stage, read our :ref:`customizing transforms guide <customizing_transforms>`. Args: categorical_fields: The fields (column names) in the dictionary containing categorical data. numerical_fields: The fields (column names) in the dictionary containing numerical data. target_field: The field (column name) in the dictionary containing the targets. parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_field`` are not provided (e.g. when loading data for inference or validation). train_list: The data to use when training. val_list: The data to use when validating. test_list: The data to use when testing. predict_list: The data to use when predicting. input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data. transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use. transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms. data_module_kwargs: Additional keyword arguments to provide to the :class:`~flash.core.data.data_module.DataModule` constructor. Returns: The constructed :class:`~flash.tabular.regression.data.TabularRegressionData`. Examples ________ .. testsetup:: >>> train_data = [ ... {"age": 2, "animal": "cat", "weight": 6}, ... {"age": 4, "animal": "dog", "weight": 10}, ... {"age": 1, "animal": "cat", "weight": 5}, ... ] >>> predict_data = [ ... {"animal": "dog", "weight": 7}, ... {"animal": "dog", "weight": 12}, ... {"animal": "cat", "weight": 5}, ... ] We have a list of dictionaries ``train_data`` with the following contents: .. code-block:: [ {"age": 2, animal": "cat", "weight": 6}, {"age": 4, animal": "dog", "weight": 10}, {"age": 1, animal": "cat", "weight": 5}, ] and a list of dictionaries ``predict_data`` with the following contents: .. code-block:: [ {"animal": "dog", "weight": 7}, {"animal": "dog", "weight": 12}, {"animal": "cat", "weight": 5}, ] .. doctest:: >>> from flash import Trainer >>> from flash.tabular import TabularRegressor, TabularRegressionData >>> datamodule = TabularRegressionData.from_lists( ... "animal", ... "weight", ... "age", ... train_list=train_data, ... predict_list=predict_data, ... batch_size=4, ... ) >>> model = TabularRegressor.from_data(datamodule, backbone="tabnet") >>> trainer = Trainer(fast_dev_run=True) >>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Training... >>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE Predicting... .. testcleanup:: >>> del train_data >>> del predict_data """ ds_kw = dict( categorical_fields=categorical_fields, numerical_fields=numerical_fields, target_field=target_field, parameters=parameters, ) train_input = input_cls(RunningStage.TRAINING, train_list, **ds_kw) ds_kw["parameters"] = train_input.parameters if train_input else parameters return cls( train_input, input_cls(RunningStage.VALIDATING, val_list, **ds_kw), input_cls(RunningStage.TESTING, test_list, **ds_kw), input_cls(RunningStage.PREDICTING, predict_list, **ds_kw), transform=transform, transform_kwargs=transform_kwargs, **data_module_kwargs, )

42.417722

119

0.600503

2,503

23,457

5.470635

0.093488

0.022493

0.024538

0.017089

0.849923

0.821076

0.789454

0.745636

0.738699

0

0.006282

0.294283

23,457

552

120

42.494565

0.820889

0.611843

0

0.556291

0

0.024993

0.019042

0

1

0.02649

false

0

0.05298

0

0.112583

0

null

0

1

0

null

0

6

51ceabe7e875765b3a8f237ceaf50bbc1f1f1ad2

688

py

Python

nlpsandbox/__init__.py

Sage-Bionetworks/nlp-sandbox-client

e51720b35ca3413ccee71b9cdc223ce3578fe0fd

[ "Apache-2.0" ]

3

2021-06-15T16:36:10.000Z

2021-11-15T01:44:46.000Z

nlpsandbox/__init__.py

nlpsandbox/nlpsandbox-client

8cba4f65ff2c06cbef7dc50f45b0aec9b8ee0476

[ "Apache-2.0" ]

165

2020-11-23T00:36:40.000Z

2022-03-24T00:53:59.000Z

nlpsandbox/__init__.py

data2health/nlp-sandbox-evaluation

e51720b35ca3413ccee71b9cdc223ce3578fe0fd

[ "Apache-2.0" ]

3

2020-12-11T00:04:13.000Z

2022-01-03T16:59:10.000Z

# flake8: noqa """ NLP Sandbox API NLP Sandbox REST API # noqa: E501 The version of the OpenAPI document: 1.2.0 Contact: team@nlpsandbox.io Generated by: https://openapi-generator.tech """ __version__ = "1.0.0" # import ApiClient from nlpsandbox.api_client import ApiClient # import Configuration from nlpsandbox.configuration import Configuration # import exceptions from nlpsandbox.exceptions import OpenApiException from nlpsandbox.exceptions import ApiAttributeError from nlpsandbox.exceptions import ApiTypeError from nlpsandbox.exceptions import ApiValueError from nlpsandbox.exceptions import ApiKeyError from nlpsandbox.exceptions import ApiException

23.724138

51

0.799419

81

688

6.728395

0.444444

0.205505

0.26422

0.330275

0

0.017007

0.145349

688

28

52

24.571429

0.909864

0.34593

0

1

0

0.011933

0

1

0

false

0

0.888889

0

0.888889

0

null

1

0

1

0

null

0

1

0

1

0

6

cf8e45d00c9c528530305179ae196bdf53de28e5

97

py

Python

lib/models/__init__.py

Cakin-Kwong/Pose2Mesh_RELEASE

699abe9496231f8c2fdc31a2985156ec03d8a51b

[ "MIT" ]

null

lib/models/__init__.py

Cakin-Kwong/Pose2Mesh_RELEASE

699abe9496231f8c2fdc31a2985156ec03d8a51b

[ "MIT" ]

null

lib/models/__init__.py

Cakin-Kwong/Pose2Mesh_RELEASE

699abe9496231f8c2fdc31a2985156ec03d8a51b

[ "MIT" ]

null

import models.pose2mesh_net import models.posenet import models.meshnet import models.project_net

24.25

27

0.886598

14

97

6

0.5

0.571429

0

0.011111

0.072165

97

4

28

24.25

0.922222

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

cf9fd6079dfaf7ba9c5db0b91f92160a1e4fe5b4

227

py

Python

boundlexx/utils/backends.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

1

2021-04-23T11:49:50.000Z

boundlexx/utils/backends.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

1

2021-04-17T18:17:12.000Z

boundlexx/utils/backends.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

null

from django_prometheus.cache.backends.redis import ( RedisCache as RedisPrometheusCache, ) from redis_lock.django_cache import RedisCache as RedisLockCache class RedisCache(RedisLockCache, RedisPrometheusCache): pass

25.222222

64

0.828194

24

227

7.708333

0.583333

0.172973

0.194595

0

0.123348

227

8

65

28.375

0.929648

0

1

0

true

0.166667

0.333333

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

6

cfa504d07e90320a033eb24c3bb9a4bd980cc35b

183

py

Python

model/__init__.py

uniaim-event-team/watch-link

0ad2665fd88cba2fdb7e7c8f533bb5a8e6d91b31

[ "MIT" ]

2

2020-05-05T14:53:00.000Z

2020-05-05T14:53:13.000Z

model/__init__.py

uniaim-event-team/watch-link

0ad2665fd88cba2fdb7e7c8f533bb5a8e6d91b31

[ "MIT" ]

1

2021-03-01T02:00:11.000Z

model/__init__.py

uniaim-event-team/watch-link

0ad2665fd88cba2fdb7e7c8f533bb5a8e6d91b31

[ "MIT" ]

null

from .base import Session, BaseObject, metadata # noqa from .freee import * # noqa from .google_chat import * # noqa from .slack import * # noqa from .watch_link import * # noqa

30.5

55

0.710383

25

183

5.12

0.52

0.25

0.328125

0

0.202186

183

5

56

36.6

0.876712

0.131148

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

5c8766bc58b05ada454e1e940bc4f52fdc686f97

43

py

Python

analytics/pyinstaller_hooks/hook-scipy.py

jonyrock-back/hastic-server

3d74de8b5d2e10ab393af36d20069afff3f4a205

[ "Apache-2.0" ]

null

analytics/pyinstaller_hooks/hook-scipy.py

jonyrock-back/hastic-server

3d74de8b5d2e10ab393af36d20069afff3f4a205

[ "Apache-2.0" ]

null

analytics/pyinstaller_hooks/hook-scipy.py

jonyrock-back/hastic-server

3d74de8b5d2e10ab393af36d20069afff3f4a205

[ "Apache-2.0" ]

null

hiddenimports=['scipy._lib.messagestream']

21.5

42

0.813953

4

43

8.5

1

0

0.023256

43

1

43

0.809524

0

0.55814

0

1

0

false

0

1

0

1

0

1

0

null

0

1

0

1

null

0

1

0

1

0

6

5cb6fee0be438e1ce17f8239f5a4d94ecef84b65

142

py

Python

src/util.py

kathanm/digit-recognization-neural-net

744cb253c1767a99b19fee6fb6acbc28f5e07822

[ "MIT" ]

null

src/util.py

kathanm/digit-recognization-neural-net

744cb253c1767a99b19fee6fb6acbc28f5e07822

[ "MIT" ]

null

src/util.py

kathanm/digit-recognization-neural-net

744cb253c1767a99b19fee6fb6acbc28f5e07822

[ "MIT" ]

null

import numpy as np def sigmoid(n): return 1.0 / (1.0 + np.exp(-n)) def sigmoid_derivative(n): return sigmoid(n) * (1 - sigmoid(n))

15.777778

40

0.612676

25

142

3.44

0.48

0.27907

0

0.045045

0.21831

142

8

41

17.75

0.72973

0

1

0.4

false

0

0.2

0.4

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

7a787fb857cd36efcadab672c3679f62ec06159b

200

py

Python

locale/pot/api/plotting/_autosummary/pyvista-themes-ParaViewTheme-silhouette-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

4

2020-08-07T08:19:19.000Z

2020-12-04T09:51:11.000Z

locale/pot/api/plotting/_autosummary/pyvista-themes-DarkTheme-silhouette-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

19

2020-08-06T00:24:30.000Z

2022-03-30T19:22:24.000Z

locale/pot/api/plotting/_autosummary/pyvista-themes-ParaViewTheme-silhouette-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

1

2021-03-09T07:50:40.000Z

# Set parameters of the silhouette. # import pyvista pyvista.global_theme.silhouette.color = 'grey' pyvista.global_theme.silhouette.line_width = 2.0 pyvista.global_theme.silhouette.feature_angle = 20

28.571429

50

0.82

28

200

5.678571

0.642857

0.245283

0.339623

0.528302

0

0.021978

0.09

200

6

51

33.333333

0.851648

0.165

0

0.02439

0

1

0

true

0

0.25

0

0.25

0

1

0

null

1

0

1

0

null

0

1

0

6

7a8cd4de03a07a5b3b5c0c83aff09fe39cb96553

337

py

Python

pfhedge/stochastic/__init__.py

YieldLabs/pfhedge

a5ba9d054a8418cb8b27bb67d81a8fc8fb83ef57

[ "MIT" ]

null

pfhedge/stochastic/__init__.py

YieldLabs/pfhedge

a5ba9d054a8418cb8b27bb67d81a8fc8fb83ef57

[ "MIT" ]

null

pfhedge/stochastic/__init__.py

YieldLabs/pfhedge

a5ba9d054a8418cb8b27bb67d81a8fc8fb83ef57

[ "MIT" ]

null

from .brownian import generate_brownian from .brownian import generate_geometric_brownian from .cir import generate_cir from .heston import generate_heston from .local_volatility import generate_local_volatility_process from .random import randn_antithetic from .random import randn_sobol_boxmuller from .vasicek import generate_vasicek

37.444444

63

0.881306

45

337

6.311111

0.355556

0.295775

0.126761

0.183099

0

0.094955

337

8

64

42.125

0.931148

0

1

0

1

0

true

0

1

0

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

1

0

6

7aa18cf193499b1264aee8a5b6f43066f089d867

28

py

Python

datas_utils/log/__init__.py

iatlab/datas-utils

b8eef303de5a5d5a57182c0627b721dde0b6b300

[ "MIT" ]

328

2019-05-27T03:09:02.000Z

2022-03-31T05:12:04.000Z

diva_io/utils/__init__.py

AnjaliPC/Object_Detection_Tracking

f86caaec97669a6da56f1b402cca4e179a85d2f0

[ "MIT" ]

43

2019-06-05T14:04:09.000Z

2022-01-25T03:16:39.000Z

diva_io/utils/__init__.py

AnjaliPC/Object_Detection_Tracking

f86caaec97669a6da56f1b402cca4e179a85d2f0

[ "MIT" ]

107

2019-05-27T06:26:38.000Z

2022-03-25T03:32:58.000Z

from .log import get_logger

14

27

0.821429

5

28

4.4

1

0

0.142857

28

1

28

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7aa7df2db682a4e1d60b6d85214f1b4644e43919

4,765

py

Python

tests/test-criticisms/test_evaluate.py

NunoEdgarGFlowHub/edward

298fb539261c71e34d5e7aa5a37ed8a029df0820

[ "Apache-2.0" ]

1

2021-01-11T03:33:36.000Z

tests/test-criticisms/test_evaluate.py

NunoEdgarGFlowHub/edward

298fb539261c71e34d5e7aa5a37ed8a029df0820

[ "Apache-2.0" ]

null

tests/test-criticisms/test_evaluate.py

NunoEdgarGFlowHub/edward

298fb539261c71e34d5e7aa5a37ed8a029df0820

[ "Apache-2.0" ]

1

2021-06-13T06:58:00.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import edward as ed import numpy as np import tensorflow as tf from edward.models import Bernoulli, Categorical, Multinomial, Normal class test_evaluate_class(tf.test.TestCase): def test_metrics(self): with self.test_session(): x = Normal(loc=0.0, scale=1.0) x_data = tf.constant(0.0) ed.evaluate('mean_squared_error', {x: x_data}, n_samples=1) ed.evaluate(['mean_squared_error'], {x: x_data}, n_samples=1) ed.evaluate(['mean_squared_error', 'mean_absolute_error'], {x: x_data}, n_samples=1) self.assertRaises(TypeError, ed.evaluate, x, {x: x_data}, n_samples=1) self.assertRaises(NotImplementedError, ed.evaluate, 'hello world', {x: x_data}, n_samples=1) def test_metrics_classification(self): with self.test_session(): x = Bernoulli(probs=0.51) x_data = tf.constant(1) self.assertAllClose( 1.0, ed.evaluate('binary_accuracy', {x: x_data}, n_samples=1)) x = Bernoulli(probs=0.51, sample_shape=5) x_data = tf.constant([1, 1, 1, 0, 0]) self.assertAllClose( 0.6, ed.evaluate('binary_accuracy', {x: x_data}, n_samples=1)) x = Bernoulli(probs=tf.constant([0.51, 0.49, 0.49])) x_data = tf.constant([1, 0, 1]) self.assertAllClose( 2.0 / 3, ed.evaluate('binary_accuracy', {x: x_data}, n_samples=1)) x = Categorical(probs=tf.constant([0.48, 0.51, 0.01])) x_data = tf.constant(1) self.assertAllClose( 1.0, ed.evaluate('sparse_categorical_accuracy', {x: x_data}, n_samples=1)) x = Categorical(probs=tf.constant([0.48, 0.51, 0.01]), sample_shape=5) x_data = tf.constant([1, 1, 1, 0, 2]) self.assertAllClose( 0.6, ed.evaluate('sparse_categorical_accuracy', {x: x_data}, n_samples=1)) x = Categorical( probs=tf.constant([[0.48, 0.51, 0.01], [0.51, 0.48, 0.01]])) x_data = tf.constant([1, 2]) self.assertAllClose( 0.5, ed.evaluate('sparse_categorical_accuracy', {x: x_data}, n_samples=1)) x = Multinomial(total_count=1.0, probs=tf.constant([0.48, 0.51, 0.01])) x_data = tf.constant([0, 1, 0], dtype=x.dtype.as_numpy_dtype) self.assertAllClose( 1.0, ed.evaluate('categorical_accuracy', {x: x_data}, n_samples=1)) x = Multinomial(total_count=1.0, probs=tf.constant([0.48, 0.51, 0.01]), sample_shape=5) x_data = tf.constant( [[0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1]], dtype=x.dtype.as_numpy_dtype) self.assertAllClose( 0.6, ed.evaluate('categorical_accuracy', {x: x_data}, n_samples=1)) def test_data(self): with self.test_session(): x_ph = tf.placeholder(tf.float32, []) x = Normal(loc=x_ph, scale=1.0) y = 2.0 * Normal(loc=0.0, scale=1.0) x_data = tf.constant(0.0) x_ph_data = np.array(0.0) y_data = tf.constant(20.0) ed.evaluate('mean_squared_error', {x: x_data, x_ph: x_ph_data}, n_samples=1) ed.evaluate('mean_squared_error', {y: y_data}, n_samples=1) self.assertRaises(TypeError, ed.evaluate, 'mean_squared_error', {'y': y_data}, n_samples=1) def test_n_samples(self): with self.test_session(): x = Normal(loc=0.0, scale=1.0) x_data = tf.constant(0.0) ed.evaluate('mean_squared_error', {x: x_data}, n_samples=1) ed.evaluate('mean_squared_error', {x: x_data}, n_samples=5) self.assertRaises(TypeError, ed.evaluate, 'mean_squared_error', {x: x_data}, n_samples='1') def test_output_key(self): with self.test_session(): x_ph = tf.placeholder(tf.float32, []) x = Normal(loc=x_ph, scale=1.0) y = 2.0 * x x_data = tf.constant(0.0) x_ph_data = np.array(0.0) y_data = tf.constant(20.0) ed.evaluate('mean_squared_error', {x: x_data, x_ph: x_ph_data}, n_samples=1) ed.evaluate('mean_squared_error', {y: y_data, x_ph: x_ph_data}, n_samples=1) ed.evaluate('mean_squared_error', {x: x_data, y: y_data, x_ph: x_ph_data}, n_samples=1, output_key=x) self.assertRaises(KeyError, ed.evaluate, 'mean_squared_error', {x: x_data, y: y_data, x_ph: x_ph_data}, n_samples=1) self.assertRaises(TypeError, ed.evaluate, 'mean_squared_error', {x: x_data, y: y_data, x_ph: x_ph_data}, n_samples=1, output_key='x') if __name__ == '__main__': tf.test.main()

39.708333

80

0.60063

731

4,765

3.668947

0.103967

0.061521

0.107383

0.111484

0.828486

0.815063

0.784862

0.773676

0.717375

0.699478

0

0.054266

0.249738

4,765

119

81

40.042017

0.695944

0

0.514019

0

0.096327

0.016999

0

0.130841

1

0.046729

false

0

0.065421

0

0.121495

0.009346

0

null

0

1

0

null

0

6

8f8734139aebfe314bc9d38b456ad61641621c21

95

py

Python

utils.py

tommyip/aoc2021

51a8b51d2910a344d94cd03388c676201e815503

[ "MIT" ]

null

utils.py

tommyip/aoc2021

51a8b51d2910a344d94cd03388c676201e815503

[ "MIT" ]

null

utils.py

tommyip/aoc2021

51a8b51d2910a344d94cd03388c676201e815503

[ "MIT" ]

null

import sys def read_input(f): return [f(line.strip()) for line in sys.stdin.readlines()]

15.833333

62

0.684211

16

95

4

0.8125

0

0.168421

95

5

63

19

0.810127

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

8f95a26ced0bbfe18968f9e6efc70ee35ba34c4c

23

py

Python

SecureWitnessApp/views/comments/__init__.py

karttrak/3240-team10

d07ca17717004aab6f2e8c97f4bb9ac21453ce60

[ "MIT" ]

null

SecureWitnessApp/views/comments/__init__.py

karttrak/3240-team10

d07ca17717004aab6f2e8c97f4bb9ac21453ce60

[ "MIT" ]

null

SecureWitnessApp/views/comments/__init__.py

karttrak/3240-team10

d07ca17717004aab6f2e8c97f4bb9ac21453ce60

[ "MIT" ]

null

from .comments import *

23

0.782609

3

23

6

1

0

0.130435

23

1

23

0.9

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8fd95b7994efd0ad9567499232cd6e8ffcc5d519

164

py

Python

src/backend/src/run.py

DhrubojyotiBis1/web

68ad34c1536f37d4f123bd1336a6ce763a68729a

[ "MIT" ]

null

src/backend/src/run.py

DhrubojyotiBis1/web

68ad34c1536f37d4f123bd1336a6ce763a68729a

[ "MIT" ]

null

src/backend/src/run.py

DhrubojyotiBis1/web

68ad34c1536f37d4f123bd1336a6ce763a68729a

[ "MIT" ]

5

2020-09-16T12:03:54.000Z

2020-09-27T12:45:05.000Z

from advolet_app import app, db from advolet_app.models import User if __name__ == "__main__": #development db.create_all() #Production app.run()

18.222222

35

0.695122

22

164

4.681818

0.681818

0.213592

0.271845

0

0.213415

164

9

36

18.222222

0.79845

0.128049

0

0.056338

0

1

0

true

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

8ffba6afd9bf5e9848c891a855943ede73568c3b

563

py

Python

detic/__init__.py

axinc-ai/Detic

8beeece2a99384e1e7b66b070028b2a4fe765220

[ "Apache-2.0" ]

956

2022-01-10T00:17:51.000Z

2022-03-31T17:01:38.000Z

detic/__init__.py

axinc-ai/Detic

8beeece2a99384e1e7b66b070028b2a4fe765220

[ "Apache-2.0" ]

43

2022-01-10T02:24:03.000Z

2022-03-31T01:58:53.000Z

detic/__init__.py

axinc-ai/Detic

8beeece2a99384e1e7b66b070028b2a4fe765220

[ "Apache-2.0" ]

62

2022-01-10T02:19:00.000Z

2022-03-31T18:43:38.000Z

# Copyright (c) Facebook, Inc. and its affiliates. from .modeling.meta_arch import custom_rcnn from .modeling.roi_heads import detic_roi_heads from .modeling.roi_heads import res5_roi_heads from .modeling.backbone import swintransformer from .modeling.backbone import timm from .data.datasets import lvis_v1 from .data.datasets import imagenet from .data.datasets import cc from .data.datasets import objects365 from .data.datasets import oid from .data.datasets import coco_zeroshot try: from .modeling.meta_arch import d2_deformable_detr except: pass

29.631579

54

0.820604

83

563

5.409639

0.445783

0.160356

0.213808

0.293987

0.231626

0

0.012146

0.122558

563

19

55

29.631579

0.896761

0.085258

0

1

0

true

0.066667

0.8

0

0.8

0

null

0

1

0

null

0

1

0

1

0

6

64ef215d00bf9c5de596314eafc522a336a8371a

14,691

py

Python

pandapipes/test/pipeflow_internals/test_pipeflow_analytic_comparison.py

SteffenMeinecke/pandapipes

2d0631c053735e4116a145bae9975379135b9c36

[ "BSD-3-Clause" ]

null

pandapipes/test/pipeflow_internals/test_pipeflow_analytic_comparison.py

SteffenMeinecke/pandapipes

2d0631c053735e4116a145bae9975379135b9c36

[ "BSD-3-Clause" ]

null

pandapipes/test/pipeflow_internals/test_pipeflow_analytic_comparison.py

SteffenMeinecke/pandapipes

2d0631c053735e4116a145bae9975379135b9c36

[ "BSD-3-Clause" ]

null

# Copyright (c) 2020-2022 by Fraunhofer Institute for Energy Economics # and Energy System Technology (IEE), Kassel, and University of Kassel. All rights reserved. # Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. import os import numpy as np import pandapipes import pandas as pd import pytest from pandapipes.component_models import Pipe, Junction from pandapipes.idx_node import PINIT, TINIT from pandapipes.pipeflow_setup import get_lookup from pandapipes.test.pipeflow_internals import internals_data_path from pandapipes.properties.fluids import _add_fluid_to_net def test_gas_internal_nodes(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 209.1e-3 pandapipes.create_junction(net, pn_bar=51, tfluid_k=285.15) pandapipes.create_junction(net, pn_bar=51, tfluid_k=285.15) pandapipes.create_pipe_from_parameters(net, 0, 1, 12.0, d, k_mm=.5, sections=12) pandapipes.create_ext_grid(net, 0, p_bar=51 - 1.01325, t_k=285.15, type="pt") pandapipes.create_sink(net, 1, mdot_kg_per_s=0.82752 * 45000 / 3600) _add_fluid_to_net(net, pandapipes.create_constant_fluid( name="natural_gas", fluid_type="gas", viscosity=11.93e-6, heat_capacity=2185, compressibility=1, der_compressibility=0, density=0.82752 )) pandapipes.pipeflow(net, stop_condition="tol", iter=70, friction_model="nikuradse", transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) pipe_results = Pipe.get_internal_results(net, [0]) data = pd.read_csv(os.path.join(internals_data_path, "gas_sections_an.csv"), sep=';', header=0, keep_default_na=False) p_an = data["p1"] / 1e5 v_an = data["v"] v_an = v_an.drop([0]) pipe_p_data_idx = np.where(pipe_results["PINIT"][:, 0] == 0) pipe_v_data_idx = np.where(pipe_results["VINIT_MEAN"][:, 0] == 0) pipe_p_data = pipe_results["PINIT"][pipe_p_data_idx, 1] pipe_v_data = pipe_results["VINIT_MEAN"][pipe_v_data_idx, 1] node_pit = net["_pit"]["node"] junction_idx_lookup = get_lookup(net, "node", "index")[Junction.table_name()] from_junction_nodes = junction_idx_lookup[net["pipe"]["from_junction"].values] to_junction_nodes = junction_idx_lookup[net["pipe"]["to_junction"].values] p_pandapipes = np.zeros(len(pipe_p_data[0]) + 2) p_pandapipes[0] = node_pit[from_junction_nodes[0], PINIT] p_pandapipes[1:-1] = pipe_p_data[:] p_pandapipes[-1] = node_pit[to_junction_nodes[0], PINIT] p_pandapipes = p_pandapipes + 1.01325 v_pandapipes = pipe_v_data[0, :] p_diff = np.abs(1 - p_pandapipes / p_an) v_diff = np.abs(v_an - v_pandapipes) assert np.all(p_diff < 0.01) assert np.all(v_diff < 0.4) def test_temperature_internal_nodes_single_pipe(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_pipe_from_parameters(net, 0, 1, 6, d, k_mm=.1, sections=6, alpha_w_per_m2k=5) pandapipes.create_ext_grid(net, 0, p_bar=5, t_k=330, type="pt") pandapipes.create_sink(net, 1, mdot_kg_per_s=1) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.pipeflow(net, stop_condition="tol", iter=3, friction_model="nikuradse", mode="all", transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) pipe_results = Pipe.get_internal_results(net, [0]) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_one_pipe_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] pipe_temp_data_idx = np.where(pipe_results["TINIT"][:, 0] == 0) pipe_temp_data = pipe_results["TINIT"][pipe_temp_data_idx, 1] node_pit = net["_pit"]["node"] junction_idx_lookup = get_lookup(net, "node", "index")[Junction.table_name()] from_junction_nodes = junction_idx_lookup[net["pipe"]["from_junction"].values] to_junction_nodes = junction_idx_lookup[net["pipe"]["to_junction"].values] temp_pandapipes = np.zeros(len(pipe_temp_data[0]) + 2) temp_pandapipes[0] = node_pit[from_junction_nodes[0], TINIT] temp_pandapipes[1:-1] = pipe_temp_data[:] temp_pandapipes[-1] = node_pit[to_junction_nodes[0], TINIT] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) def test_temperature_internal_nodes_tee_2ab_1zu(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 j0 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j1 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j2 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j3 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_ext_grid(net, j0, p_bar=5, t_k=350, type="pt") pandapipes.create_sink(net, j2, mdot_kg_per_s=1) pandapipes.create_sink(net, j3, mdot_kg_per_s=1) pandapipes.create_pipe_from_parameters(net, j0, j1, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j1, j2, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j1, j3, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.pipeflow(net, stop_condition="tol", iter=70, friction_model="nikuradse", mode='all', transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_tee_2ab_1zu_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] temp_pandapipes = net.res_junction["t_k"] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) def test_temperature_internal_nodes_tee_2zu_1ab(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 j0 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j1 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j2 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j3 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_pipe_from_parameters(net, j0, j2, 2.5, d, k_mm=.1, sections=3, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j1, j2, 2.5, d, k_mm=.1, sections=3, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j2, j3, 2.5, d, k_mm=.1, sections=3, alpha_w_per_m2k=5) pandapipes.create_ext_grid(net, j0, p_bar=5, t_k=350, type="pt") pandapipes.create_ext_grid(net, j1, p_bar=5, t_k=350, type="pt") pandapipes.create_sink(net, j3, mdot_kg_per_s=1) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.pipeflow(net, stop_condition="tol", iter=3, friction_model="nikuradse", mode='all', transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_tee_2zu_1ab_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] temp_pandapipes = net.res_junction["t_k"] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) def test_temperature_internal_nodes_tee_2zu_1ab_direction_changed(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 j0 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j1 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j2 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j3 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_ext_grid(net, j0, p_bar=5, t_k=350, type="pt") pandapipes.create_ext_grid(net, j1, p_bar=5, t_k=350, type="pt") pandapipes.create_sink(net, j3, mdot_kg_per_s=1) pandapipes.create_pipe_from_parameters(net, j0, j2, 2.5, d, k_mm=.1, sections=5, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j2, j1, 2.5, d, k_mm=.1, sections=5, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j2, j3, 2.5, d, k_mm=.1, sections=5, alpha_w_per_m2k=5) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.pipeflow(net, stop_condition="tol", iter=70, friction_model="nikuradse", mode='all', transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_tee_2zu_1ab_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] temp_pandapipes = net.res_junction["t_k"] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) def test_temperature_internal_nodes_2zu_2ab(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 j0 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j1 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j2 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j3 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j4 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_ext_grid(net, j0, p_bar=5, t_k=350, type="pt") pandapipes.create_ext_grid(net, j1, p_bar=5, t_k=300, type="pt") pandapipes.create_sink(net, j3, mdot_kg_per_s=1) pandapipes.create_sink(net, j4, mdot_kg_per_s=1) pandapipes.create_pipe_from_parameters(net, j0, j2, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j1, j2, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j2, j3, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j2, j4, 2.5, d, k_mm=.1, alpha_w_per_m2k=5) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.pipeflow(net, stop_condition="tol", iter=70, friction_model="nikuradse", mode='all', transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_2zu_2ab_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] temp_pandapipes = net.res_junction["t_k"] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) def test_temperature_internal_nodes_masche_1load(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 j0 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j1 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j2 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j3 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_pipe_from_parameters(net, j0, j1, 2.5, d, k_mm=.1, sections=6, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j1, j2, 2.5, d, k_mm=.1, sections=6, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j1, j3, 2.5, d, k_mm=.1, sections=6, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j3, j2, 2.5, d, k_mm=.1, sections=6, alpha_w_per_m2k=5) pandapipes.create_ext_grid(net, j0, p_bar=5, t_k=350, type="pt") pandapipes.create_sink(net, j2, mdot_kg_per_s=1) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.pipeflow(net, stop_condition="tol", iter=70, friction_model="nikuradse", mode='all', transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_masche_1load_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] temp_pandapipes = net.res_junction["t_k"] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) def test_temperature_internal_nodes_masche_1load_changed_direction(): """ :return: :rtype: """ net = pandapipes.create_empty_network("net", add_stdtypes=False) d = 75e-3 j0 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j2 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) j3 = pandapipes.create_junction(net, pn_bar=5, tfluid_k=283) pandapipes.create_pipe_from_parameters(net, j0, j2, 2.5, d, k_mm=.1, sections=5, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j0, j3, 2.5, d, k_mm=.1, sections=5, alpha_w_per_m2k=5) pandapipes.create_pipe_from_parameters(net, j3, j2, 2.5, d, k_mm=.1, sections=5, alpha_w_per_m2k=5) pandapipes.create_fluid_from_lib(net, "water", overwrite=True) pandapipes.create_ext_grid(net, j0, p_bar=5, t_k=350, type="pt") pandapipes.create_sink(net, j3, mdot_kg_per_s=1) pandapipes.pipeflow(net, stop_condition="tol", iter=70, friction_model="nikuradse", mode='all', transient=False, nonlinear_method="automatic", tol_p=1e-4, tol_v=1e-4) data = pd.read_csv(os.path.join(internals_data_path, "Temperature_masche_1load_direction_an.csv"), sep=';', header=0, keep_default_na=False) temp_an = data["T"] temp_pandapipes = net.res_junction["t_k"] temp_diff = np.abs(1 - temp_pandapipes / temp_an) assert np.all(temp_diff < 0.01) if __name__ == "__main__": pytest.main([r'pandapipes/test/pipflow_internals/test_pipeflow_analytic_comparison.py'])

41.617564

99

0.664829

2,245

14,691

4.03118

0.091759

0.153812

0.074254

0.083536

0.857017

0.846409

0.834586

0.834254

0.811823

0.807403

0

0.051985

0.207814

14,691

352

100

41.735795

0.72564

0.026819

0

0.640693

0

0.054479

0.020164

0

0.038961

1

0.034632

false

0

0.04329

0

0.077922

0

null

0

1

0

null

0

6

8f3dd8485e1aee739dbaf7114a2a8017e49aba20

47

py

Python

Continuous_Algo/envs/gym-nqubit/gym_nqubit/envs/__init__.py

NemoHimma/ZeroRL

c37cb3105981b7d9331749941df3e3f449976fde

[ "MIT" ]

null

Continuous_Algo/envs/gym-nqubit/gym_nqubit/envs/__init__.py

NemoHimma/ZeroRL

c37cb3105981b7d9331749941df3e3f449976fde

[ "MIT" ]

null

Continuous_Algo/envs/gym-nqubit/gym_nqubit/envs/__init__.py

NemoHimma/ZeroRL

c37cb3105981b7d9331749941df3e3f449976fde

[ "MIT" ]

null

from gym_nqubit.envs.NqubitEnv import NqubitEnv

47

0.893617

7

47

5.857143

0.857143

0

0.06383

47

1

47

0.931818

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8f7fafbcfb79deb68667418aab9c334a8940bcd9

6,320

py

Python

snakecat/__init__.py

amironov73/snakecat

d23adc763060648358b3bcb458c04fdc537fe703

[ "MIT" ]

2

2021-12-23T06:57:40.000Z

2021-12-23T06:58:07.000Z

snakecat/__init__.py

amironov73/snakecat

d23adc763060648358b3bcb458c04fdc537fe703

[ "MIT" ]

null

snakecat/__init__.py

amironov73/snakecat

d23adc763060648358b3bcb458c04fdc537fe703

[ "MIT" ]

null

# coding: utf-8 """ Модуль содержит основную функциональность по работе с сервером ИРБИС64, в т. ч. для манипуляций с записями. """ __version__ = '0.1.100' __author__ = 'Alexey Mironov' __email__ = 'amironov73@gmail.com' __title__ = 'snakecat' __summary__ = 'ctypes wrapper for irbis64_client.dll' __uri__ = 'http://arsmagna.ru' __license__ = 'MIT License' __copyright__ = 'Copyright 2021 Alexey Mironov' from snakecat.constants import NO_ERROR, ERR_USER, ERR_BUSY, \ ERR_UNKNOWN, ERR_BUFSIZE, TERM_NOT_EXISTS, TERM_LAST_IN_LIST, \ TERM_FIRST_IN_LIST, ERR_DBEWLOCK, ERR_RECLOCKED, VERSION_ERROR, \ READ_WRONG_MFN, REC_DELETE, REC_PHYS_DELETE, ERROR_CLIENT_FMT, \ SERVER_EXECUTE_ERROR, ANSWER_LENGTH_ERROR, WRONG_PROTOCOL, \ CLIENT_NOT_IN_LIST, CLIENT_NOT_IN_USE, CLIENT_IDENTIFIER_WRONG, \ CLIENT_LIST_OVERLOAD, CLIENT_ALREADY_EXISTS, CLIENT_NOT_ALLOWED, \ WRONG_PASSWORD, FILE_NOT_EXISTS, SERVER_OVERLOAD, PROCESS_ERROR, \ GLOBAL_ERROR, SYSPATH, DATAPATH, DBNPATH2, DBNPATH3, DBNPATH10, \ FULLTEXTPATH, INTERNALRESOURCEPATH, IRBIS_READER, \ IRBIS_ADMINISTRATOR, IRBIS_CATALOG, IRBIS_COMPLECT, \ IRBIS_BOOKLAND, IRBIS_BOOKPROVD, MAX_POSTINGS_IN_PACKET, \ ANSI, UTF from snakecat.dllwrapper import IC_reg, IC_unreg, \ IC_set_client_time_live, IC_set_show_waiting, IC_set_webserver, \ IC_set_webcgi, IC_set_blocksocket, IC_isbusy, IC_update_ini, \ IC_getresourse, IC_clearresourse, IC_getresoursegroup, \ IC_getbinaryresourse, IC_putresourse, IC_read, IC_readformat, \ IC_update, IC_updategroup, IC_runlock, IC_ifupdate, IC_maxmfn, \ IC_fieldn, IC_field, IC_fldadd, IC_fldrep, IC_nfields, IC_nocc, \ IC_fldtag, IC_fldempty, IC_changemfn, IC_recdel, IC_recundel, \ IC_recunlock, IC_getmfn, IC_recdummy, IC_isactualized, IC_islocked, \ IC_isdeleted, IC_nexttrm, IC_nexttrmgroup, IC_prevtrm, \ IC_prevtrmgroup, IC_posting, IC_postinggroup, IC_postingformat, \ IC_search, IC_searchscan, IC_sformat, IC_record_sformat, \ IC_sformatgroup, IC_print, IC_stat, IC_gbl, IC_adm_restartserver, \ IC_adm_getdeletedlist, IC_adm_getalldeletedlists, IC_adm_dbempty, \ IC_adm_newdb, IC_adm_dbunlock, IC_adm_dbunlockmfn, \ IC_adm_dbstartcreatedictionry, IC_adm_dbstartreorgmaster, \ IC_adm_getclientlist, IC_adm_getclientslist, IC_adm_getprocesslist, \ IC_adm_setclientslist, IC_adm_dbdelete, IC_adm_dbstartreorgdictionry, \ IC_nooperation, IC_reset_delim, IC_delim_reset from snakecat.comfort import connect, disconnect, read_record, \ get_max_mfn, hide_window, error_to_string, from_ansi, \ from_utf, search, search_format, format_record, fm, \ print_form, get_deleted_records, to_ansi, to_utf, from_irbis, \ to_irbis, read_terms, trim_prefix, read_file, clear_cache, \ write_file, unlock_record, actualize_record, actualize_database, \ create_record, add_field, write_record, replace_field, \ remove_field, empty_record, delete_record, undelete_record, \ mark_record_unlocked, record_locked, record_deleted, \ record_actualized, use_web_gateway __all__ = ['NO_ERROR', 'ERR_USER', 'ERR_BUSY', 'ERR_UNKNOWN', 'ERR_BUFSIZE', 'TERM_NOT_EXISTS', 'TERM_LAST_IN_LIST', 'TERM_FIRST_IN_LIST', 'ERR_DBEWLOCK', 'ERR_RECLOCKED', 'VERSION_ERROR', 'READ_WRONG_MFN', 'REC_DELETE', 'REC_PHYS_DELETE', 'ERROR_CLIENT_FMT', 'SERVER_EXECUTE_ERROR', 'ANSWER_LENGTH_ERROR', 'WRONG_PROTOCOL', 'CLIENT_NOT_IN_LIST', 'CLIENT_NOT_IN_USE', 'CLIENT_IDENTIFIER_WRONG', 'CLIENT_LIST_OVERLOAD', 'CLIENT_ALREADY_EXISTS', 'CLIENT_NOT_ALLOWED', 'WRONG_PASSWORD', 'FILE_NOT_EXISTS', 'SERVER_OVERLOAD', 'PROCESS_ERROR', 'GLOBAL_ERROR', 'SYSPATH', 'DATAPATH', 'DBNPATH2', 'DBNPATH3', 'DBNPATH10', 'FULLTEXTPATH', 'INTERNALRESOURCEPATH', 'IRBIS_READER', 'IRBIS_ADMINISTRATOR', 'IRBIS_CATALOG', 'IRBIS_COMPLECT', 'IRBIS_BOOKLAND', 'IRBIS_BOOKPROVD', 'MAX_POSTINGS_IN_PACKET', 'ANSI', 'UTF', 'IC_reg', 'IC_unreg', 'IC_set_client_time_live', 'IC_set_show_waiting', 'IC_set_webserver', 'IC_set_webcgi', 'IC_set_blocksocket', 'IC_isbusy', 'IC_update_ini', 'IC_getresourse', 'IC_clearresourse', 'IC_getresoursegroup', 'IC_getbinaryresourse', 'IC_putresourse', 'IC_read', 'IC_readformat', 'IC_update', 'IC_updategroup', 'IC_runlock', 'IC_ifupdate', 'IC_maxmfn', 'IC_fieldn', 'IC_field', 'IC_fldadd', 'IC_fldrep', 'IC_nfields', 'IC_nocc', 'IC_fldtag', 'IC_fldempty', 'IC_changemfn', 'IC_recdel', 'IC_recundel', 'IC_recunlock', 'IC_getmfn', 'IC_recdummy', 'IC_isactualized', 'IC_islocked', 'IC_isdeleted', 'IC_nexttrm', 'IC_nexttrmgroup', 'IC_prevtrm', 'IC_prevtrmgroup', 'IC_posting', 'IC_postinggroup', 'IC_postingformat', 'IC_search', 'IC_searchscan', 'IC_sformat', 'IC_record_sformat', 'IC_sformatgroup', 'IC_print', 'IC_stat', 'IC_gbl', 'IC_adm_restartserver', 'IC_adm_getdeletedlist', 'IC_adm_getalldeletedlists', 'IC_adm_dbempty', 'IC_adm_newdb', 'IC_adm_dbunlock', 'IC_adm_dbunlockmfn', 'IC_adm_dbstartcreatedictionry', 'IC_adm_dbstartreorgmaster', 'IC_adm_getclientlist', 'IC_adm_getclientslist', 'IC_adm_getprocesslist', 'IC_adm_setclientslist', 'IC_adm_dbdelete', 'IC_adm_dbstartreorgdictionry', 'IC_nooperation', 'IC_delim_reset', 'IC_reset_delim', 'connect', 'disconnect', 'read_record', 'get_max_mfn', 'hide_window', 'error_to_string', 'from_ansi', 'from_utf', 'search', 'search_format', 'format_record', 'fm', 'print_form', 'get_deleted_records', 'to_ansi', 'to_utf', 'from_irbis', 'to_irbis', 'read_terms', 'trim_prefix', 'read_file', 'clear_cache', 'write_file', 'unlock_record', 'actualize_record', 'actualize_database', 'create_record', 'add_field', 'write_record', 'replace_field', 'remove_field', 'empty_record', 'delete_record', 'undelete_record', 'mark_record_unlocked', 'record_locked', 'record_actualized', 'record_deleted', 'use_web_gateway']

54.482759

75

0.709652

764

6,320

5.311518

0.281414

0.036964

0.010843

0.0069

0.883687

0

0.004623

0.178481

6,320

115

76

54.956522

0.776965

0.019304

0

0.353796

0.045234

0

1

0

false

0.019802

0.029703

0

0.029703

0.039604

0

null

0

1

0

null

0

6

56ad567bca8735420283245c23060916dd1bd113

2,383

py

Python

layers/prepost_layer.py

kimmo1019/Geformer

776c841c3a364d6b27dd93d66526ab6f03fde56d

[ "MIT" ]

null

layers/prepost_layer.py

kimmo1019/Geformer

776c841c3a364d6b27dd93d66526ab6f03fde56d

[ "MIT" ]

null

layers/prepost_layer.py

kimmo1019/Geformer

776c841c3a364d6b27dd93d66526ab6f03fde56d

[ "MIT" ]

null

import tensorflow as tf class PrePostProcessingFnnWrapper(tf.keras.layers.Layer): """Wrapper class for Fnn that applies layer pre-processing and post-processing.""" def __init__(self, layer, params): super(PrePostProcessingFnnWrapper, self).__init__() self.layer = layer self.params = params self.postprocess_dropout = params["layer_postprocess_dropout"] def build(self, input_shape): # Create normalization layer self.layer_norm = tf.keras.layers.LayerNormalization( epsilon=1e-6, dtype="float32") super(PrePostProcessingFnnWrapper, self).build(input_shape) def get_config(self): return { "params": self.params, } def call(self, x, *args, **kwargs): """Calls wrapped layer with same parameters.""" # Preprocessing: apply layer normalization training = kwargs["training"] y = self.layer_norm(x) # Get layer output y = self.layer(y, *args, **kwargs) # Postprocessing: apply dropout and residual connection if training: y = tf.nn.dropout(y, rate=self.postprocess_dropout) return x + y class PrePostProcessingAttWrapper(tf.keras.layers.Layer): """Wrapper class for Attention that applies layer pre-processing and post-processing.""" def __init__(self, layer, params): super(PrePostProcessingAttWrapper, self).__init__() self.layer = layer self.params = params self.postprocess_dropout = params["layer_postprocess_dropout"] def build(self, input_shape): # Create normalization layer self.layer_norm = tf.keras.layers.LayerNormalization( epsilon=1e-6, dtype="float32") super(PrePostProcessingAttWrapper, self).build(input_shape) def get_config(self): return { "params": self.params, } def call(self, x, *args, **kwargs): """Calls wrapped layer with same parameters.""" # Preprocessing: apply layer normalization training = kwargs["training"] y = self.layer_norm(x) # Get layer output y, w = self.layer(y, *args, **kwargs) # Postprocessing: apply dropout and residual connection if training: y = tf.nn.dropout(y, rate=self.postprocess_dropout) return x + y, w

34.536232

92

0.636173

260

2,383

5.7

0.242308

0.060729

0.035088

0.024292

0.860999

0.816464

0

0.004556

0.263114

2,383

69

93

34.536232

0.839408

0.219052

0

0.697674

0

0.050164

0.027263

0

1

0.186047

false

0

0.023256

0.046512

0.348837

0

null

0

1

0

null

0

6

710f71af94d501bce8bf87272db1134c046767c1

241

py

Python

djavError/models/__init__.py

dasmith2/djavError

6fc1bfcf8b1443be817a9bd8ec2d59e7682521dd

[ "MIT" ]

null

djavError/models/__init__.py

dasmith2/djavError

6fc1bfcf8b1443be817a9bd8ec2d59e7682521dd

[ "MIT" ]

null

djavError/models/__init__.py

dasmith2/djavError

6fc1bfcf8b1443be817a9bd8ec2d59e7682521dd

[ "MIT" ]

null

# flake8: noqa from djavError.models.error import Error from djavError.models.long_request import LongRequest from djavError.models.notification import Notification from djavError.models.too_many_queries_request import TooManyQueriesRequest

40.166667

75

0.879668

30

241

6.933333

0.5

0.25

0.365385

0

0.004505

0.078838

241

5

76

48.2

0.932432

0.049793

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

713a6598eca6da1253a19280057a68e80965c90f

17,570

py

Python

pybind/slxos/v16r_1_00b/rbridge_id/fabric/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/rbridge_id/fabric/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/rbridge_id/fabric/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ import ecmp import route import port_channel import login_policy class fabric(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-rbridge - based on the path /rbridge-id/fabric. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: This function is used to configure fabric parameters such as ECMP load balancing parameters and multicast priority. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__ecmp','__route','__port_channel','__login_policy',) _yang_name = 'fabric' _rest_name = 'fabric' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__port_channel = YANGDynClass(base=YANGListType("po_id",port_channel.port_channel, yang_name="port-channel", rest_name="port-channel", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='po-id', extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}), is_container='list', yang_name="port-channel", rest_name="port-channel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='list', is_config=True) self.__route = YANGDynClass(base=route.route, is_container='container', presence=False, yang_name="route", rest_name="route", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure routing related parameters'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) self.__login_policy = YANGDynClass(base=login_policy.login_policy, is_container='container', presence=False, yang_name="login-policy", rest_name="login-policy", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-add-mode': None, u'cli-full-command': None, u'callpoint': u'switch_login_policy', u'info': u'Configure switch login parameters in a fabric.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) self.__ecmp = YANGDynClass(base=ecmp.ecmp, is_container='container', presence=False, yang_name="ecmp", rest_name="ecmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ECMP parameters', u'callpoint': u'Ecmp_loadbalance', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'rbridge-id', u'fabric'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'rbridge-id', u'fabric'] def _get_ecmp(self): """ Getter method for ecmp, mapped from YANG variable /rbridge_id/fabric/ecmp (container) YANG Description: This function allows to configure ECMP related parameters. """ return self.__ecmp def _set_ecmp(self, v, load=False): """ Setter method for ecmp, mapped from YANG variable /rbridge_id/fabric/ecmp (container) If this variable is read-only (config: false) in the source YANG file, then _set_ecmp is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ecmp() directly. YANG Description: This function allows to configure ECMP related parameters. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ecmp.ecmp, is_container='container', presence=False, yang_name="ecmp", rest_name="ecmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ECMP parameters', u'callpoint': u'Ecmp_loadbalance', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """ecmp must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ecmp.ecmp, is_container='container', presence=False, yang_name="ecmp", rest_name="ecmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ECMP parameters', u'callpoint': u'Ecmp_loadbalance', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True)""", }) self.__ecmp = t if hasattr(self, '_set'): self._set() def _unset_ecmp(self): self.__ecmp = YANGDynClass(base=ecmp.ecmp, is_container='container', presence=False, yang_name="ecmp", rest_name="ecmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ECMP parameters', u'callpoint': u'Ecmp_loadbalance', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) def _get_route(self): """ Getter method for route, mapped from YANG variable /rbridge_id/fabric/route (container) YANG Description: Function to configure routing related information such as multicast priority. """ return self.__route def _set_route(self, v, load=False): """ Setter method for route, mapped from YANG variable /rbridge_id/fabric/route (container) If this variable is read-only (config: false) in the source YANG file, then _set_route is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_route() directly. YANG Description: Function to configure routing related information such as multicast priority. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=route.route, is_container='container', presence=False, yang_name="route", rest_name="route", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure routing related parameters'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """route must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=route.route, is_container='container', presence=False, yang_name="route", rest_name="route", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure routing related parameters'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True)""", }) self.__route = t if hasattr(self, '_set'): self._set() def _unset_route(self): self.__route = YANGDynClass(base=route.route, is_container='container', presence=False, yang_name="route", rest_name="route", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure routing related parameters'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) def _get_port_channel(self): """ Getter method for port_channel, mapped from YANG variable /rbridge_id/fabric/port_channel (list) """ return self.__port_channel def _set_port_channel(self, v, load=False): """ Setter method for port_channel, mapped from YANG variable /rbridge_id/fabric/port_channel (list) If this variable is read-only (config: false) in the source YANG file, then _set_port_channel is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_port_channel() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGListType("po_id",port_channel.port_channel, yang_name="port-channel", rest_name="port-channel", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='po-id', extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}), is_container='list', yang_name="port-channel", rest_name="port-channel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='list', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """port_channel must be of a type compatible with list""", 'defined-type': "list", 'generated-type': """YANGDynClass(base=YANGListType("po_id",port_channel.port_channel, yang_name="port-channel", rest_name="port-channel", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='po-id', extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}), is_container='list', yang_name="port-channel", rest_name="port-channel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='list', is_config=True)""", }) self.__port_channel = t if hasattr(self, '_set'): self._set() def _unset_port_channel(self): self.__port_channel = YANGDynClass(base=YANGListType("po_id",port_channel.port_channel, yang_name="port-channel", rest_name="port-channel", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='po-id', extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}), is_container='list', yang_name="port-channel", rest_name="port-channel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vlag load-balancing', u'cli-suppress-mode': None, u'cli-incomplete-no': None, u'cli-incomplete-command': None, u'callpoint': u'node_vlag_cp'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='list', is_config=True) def _get_login_policy(self): """ Getter method for login_policy, mapped from YANG variable /rbridge_id/fabric/login_policy (container) YANG Description: This function control the switch login configurations - Allow FLOGI/FDISC duplicate port WWN to login into switch. """ return self.__login_policy def _set_login_policy(self, v, load=False): """ Setter method for login_policy, mapped from YANG variable /rbridge_id/fabric/login_policy (container) If this variable is read-only (config: false) in the source YANG file, then _set_login_policy is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_login_policy() directly. YANG Description: This function control the switch login configurations - Allow FLOGI/FDISC duplicate port WWN to login into switch. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=login_policy.login_policy, is_container='container', presence=False, yang_name="login-policy", rest_name="login-policy", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-add-mode': None, u'cli-full-command': None, u'callpoint': u'switch_login_policy', u'info': u'Configure switch login parameters in a fabric.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """login_policy must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=login_policy.login_policy, is_container='container', presence=False, yang_name="login-policy", rest_name="login-policy", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-add-mode': None, u'cli-full-command': None, u'callpoint': u'switch_login_policy', u'info': u'Configure switch login parameters in a fabric.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True)""", }) self.__login_policy = t if hasattr(self, '_set'): self._set() def _unset_login_policy(self): self.__login_policy = YANGDynClass(base=login_policy.login_policy, is_container='container', presence=False, yang_name="login-policy", rest_name="login-policy", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-add-mode': None, u'cli-full-command': None, u'callpoint': u'switch_login_policy', u'info': u'Configure switch login parameters in a fabric.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='container', is_config=True) ecmp = __builtin__.property(_get_ecmp, _set_ecmp) route = __builtin__.property(_get_route, _set_route) port_channel = __builtin__.property(_get_port_channel, _set_port_channel) login_policy = __builtin__.property(_get_login_policy, _set_login_policy) _pyangbind_elements = {'ecmp': ecmp, 'route': route, 'port_channel': port_channel, 'login_policy': login_policy, }

69.173228

927

0.736995

2,433

17,570

5.10522

0.084258

0.043475

0.045085

0.028983

0.816037

0.794058

0.785364

0.776749

0.771919

0.761613

0

0.000392

0.12988

17,570

253

928

69.44664

0.812075

0.158224

0

0.427632

0

0.026316

0.397863

0.159281

0

1

0.098684

false

0

0.078947

0

0.302632

0

null

0

1

0

null

0

6

8567ca0f8e58934e7652673eb4f35baca337e389

46

py

Python

xfconf/xfconf.py

cr33dog/pyxfce

ce3fa5e8c556e14a8127d67192484fe2f59b5595

[ "BSD-3-Clause" ]

4

2017-08-23T06:32:19.000Z

2019-11-05T09:59:24.000Z

xfconf/xfconf.py

cr33dog/pyxfce

ce3fa5e8c556e14a8127d67192484fe2f59b5595

[ "BSD-3-Clause" ]

null

xfconf/xfconf.py

cr33dog/pyxfce

ce3fa5e8c556e14a8127d67192484fe2f59b5595

[ "BSD-3-Clause" ]

2

2017-09-03T17:32:12.000Z

2021-02-27T20:12:34.000Z

#!/usr/bin/env python from _xfconf import *

9.2

21

0.695652

7

46

4.428571

1

0

0.173913

46

4

22

11.5

0.815789

0.434783

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

85b568f36daeca0f9617378081e97b0fd8e6f9c3

40

py

Python

pipelines/__init__.py

awagot/CNN-POD

ebee234831ff58609563a925b7a47e0f4c30a16e

[ "CC0-1.0" ]

2

2021-04-08T10:30:58.000Z

2021-08-18T11:23:05.000Z

pipelines/__init__.py

awagot/CNN-POD

ebee234831ff58609563a925b7a47e0f4c30a16e

[ "CC0-1.0" ]

1

2021-04-07T21:28:59.000Z

pipelines/__init__.py

awagot/CNN-POD

ebee234831ff58609563a925b7a47e0f4c30a16e

[ "CC0-1.0" ]

2

2021-04-09T09:41:32.000Z

2021-04-16T13:09:43.000Z

from pipelines.default_pipeline import *

40

0.875

5

40

6.8

1

0

0.075

40

1

40

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6