xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

c718a453587f2523d16189c7363ca55c6bca7a18

2,931

py

Python

tests/fugue_sql/test_utils.py

gityow/fugue

e975625b33766d8b9dc64c6954871569b59367ec

[ "Apache-2.0" ]

null

tests/fugue_sql/test_utils.py

gityow/fugue

e975625b33766d8b9dc64c6954871569b59367ec

[ "Apache-2.0" ]

null

tests/fugue_sql/test_utils.py

gityow/fugue

e975625b33766d8b9dc64c6954871569b59367ec

[ "Apache-2.0" ]

null

from fugue_sql._utils import fill_sql_template def test_fill_sql_template(): data = {"a": 1, "b": "x"} assert ( "select * from tbl where a = 1 and b = 'x'" == fill_sql_template("select * from tbl where a = {{a}} and b = '{{b}}'", data) ) assert ("""select * from tbl where a = 1 and b = "x" """ == fill_sql_template("""select * from tbl where a = {{a}} and b = "{{b}}" """, data) ) assert ( """select * where b="%x" """ == fill_sql_template("""select * where b="%{{b}}" """, data)) assert ( """select * where b="x%" """ == fill_sql_template("""select * where b="{{b}}%" """, data)) assert ( """select * b like "{}%{}" """ == fill_sql_template("""select * b like "{}%{}" """, data)) assert ( """select * b like '%}' """ == fill_sql_template("""select * b like '%}' """, data)) assert ("""select * where b="%x" """ == fill_sql_template("""select * where b="%{{b}}" """, data) ) assert ("""select * where b="x%" """ == fill_sql_template("""select * where b="{{b}}%" """, data) ) assert "a=select " == fill_sql_template("a=select ", data) # try single quotes for finding json patterns assert "a=select * from b like '{%'" == fill_sql_template( "a=select * from b like '{%'", data ) assert "a=select * from b like '%}'" == fill_sql_template( "a=select * from b like '%}'", data ) # try double quotes for finding json patterns assert 'a=select * from b like "%}"' == fill_sql_template( 'a=select * from b like "%}"', data ) assert 'a=select * from b like "{%"' == fill_sql_template( 'a=select * from b like "{%"', data ) assert "1x1" == fill_sql_template("{{a}}{{b}}{{a}}", data) assert "" == fill_sql_template("", data) assert "%s" == fill_sql_template("%s", data) assert "%%s" == fill_sql_template("%%s", data) assert "1%%sx1" == fill_sql_template("{{a}}%%s{{b}}{{a}}", data) assert "1" == fill_sql_template("{{a}}", {"a": 1, "self": 2}) def test_fill_sql_template_array(): data = {"a": [0,1,2]} assert ( """select * from tbl where a in ('0','1','2')""" == fill_sql_template( """select * from tbl where a in ( {%- for i in a -%} {%- if loop.index0 < loop.length - 1 -%}'{{-i-}}', {%- else -%}'{{-i-}}' {%- endif -%} {%- endfor -%} )""", data) ) def upper(word): return word.upper() data = {"a": ['a','b','c']} assert ( """select * from tbl where a in ('A','B','C')""" == fill_sql_template( """select * from tbl where a in ( {%- for i in a -%} {%- if loop.index0 < loop.length - 1 -%}'{{-i|upper-}}', {%- else -%}'{{-i|upper-}}' {%- endif -%} {%- endfor -%} )""", data) )

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emma/interface/migrations/0002_auto__add_field_contract_date_signed.py

djangowebstudio/emma

afbdaa5c02b4164687356755fddba307eb682ef4

[ "BSD-3-Clause" ]

null

emma/interface/migrations/0002_auto__add_field_contract_date_signed.py

djangowebstudio/emma

afbdaa5c02b4164687356755fddba307eb682ef4

[ "BSD-3-Clause" ]

null

emma/interface/migrations/0002_auto__add_field_contract_date_signed.py

djangowebstudio/emma

afbdaa5c02b4164687356755fddba307eb682ef4

[ "BSD-3-Clause" ]

null

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Contract.date_signed' db.add_column('interface_contract', 'date_signed', self.gf('django.db.models.fields.DateField')(auto_now=True, default=datetime.date(2010, 6, 12), blank=True), keep_default=False) def backwards(self, orm): # Deleting field 'Contract.date_signed' db.delete_column('interface_contract', 'date_signed') models = { 'interface.album': { 'Meta': {'object_name': 'Album'}, 'album_identifier': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'album_name': ('django.db.models.fields.CharField', [], {'default': "'untitled album'", 'max_length': '255'}), 'album_pages': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'document': ('django.db.models.fields.files.FileField', [], {'max_length': '255', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['interface.Image']"}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.albumclass': { 'Meta': {'object_name': 'AlbumClass'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.author': { 'Meta': {'object_name': 'Author'}, 'author': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'notes': ('django.db.models.fields.TextField', [], {}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.category': { 'Meta': {'object_name': 'Category'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'image_cat': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, 'interface.contract': { 'Meta': {'object_name': 'Contract'}, 'contract': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'date_signed': ('django.db.models.fields.DateField', [], {'auto_now': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'user': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'username': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, 'interface.copyright': { 'Meta': {'object_name': 'Copyright'}, 'copyright': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'copyright_terms': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, 'interface.favorite': { 'Meta': {'object_name': 'Favorite'}, 'album_identifier': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'album_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'notes': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'tag': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, 'interface.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'image_group': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'image_pages': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'interface.image': { 'Meta': {'object_name': 'Image'}, 'date_created': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'date_entered': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'date_modified': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'group_status': ('django.db.models.fields.CharField', [], {'max_length': '8', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'image_category': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'image_name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'image_pages': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'image_path': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'image_real_name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'image_real_path': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.imagecount': { 'Meta': {'object_name': 'ImageCount'}, 'count': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, 'interface.keyword': { 'Meta': {'object_name': 'Keyword'}, 'cright': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'image_name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'image_path': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'keywords': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'notes': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'profile': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'source': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'subject': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.keywordcount': { 'Meta': {'object_name': 'KeywordCount'}, 'count': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'keyword': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'notes': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.mdall': { 'MDall': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'Meta': {'object_name': 'MDAll'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, 'interface.metadata': { 'MDall': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'Meta': {'object_name': 'Metadata'}, 'album': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'author': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'caption_writer': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'city': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'copyright': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'country': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'creator': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'creator_tool': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'credit': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'datetimeoriginal': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'document': ('django.db.models.fields.files.FileField', [], {'max_length': '255', 'blank': 'True'}), 'documentname': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'file_type': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'headline': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'instructions': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'keyword': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Keyword']"}), 'keywords': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'location': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'mime_type': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'orientation': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'profile': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'provincestate': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'softdate': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'source': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'subject': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.order': { 'Meta': {'object_name': 'Order'}, 'album_identifier': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'client': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'clientImage': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True'}), 'group_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['interface.Image']"}), 'image_LNID': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'notes': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'resolution': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'status': ('django.db.models.fields.SmallIntegerField', [], {'null': 'True'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'interface.query': { 'Meta': {'object_name': 'Query'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'mode': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'query': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'interface.user': { 'Meta': {'object_name': 'User'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'pagesize': ('django.db.models.fields.IntegerField', [], {'default': '8'}), 'search': ('django.db.models.fields.CharField', [], {'default': "'simple'", 'max_length': '255'}), 'setstr1': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'setstr2': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'setstr3': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'setstr4': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'setstr5': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'setting1': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'setting10': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'setting2': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'setting3': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'setting4': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'setting5': ('django.db.models.fields.NullBooleanField', [], {'null': 'True'}), 'setting6': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'setting7': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'setting8': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'setting9': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'ts': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.IntegerField', [], {'default': '0'}) } } complete_apps = ['interface']

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c748adc3a5216b0a946f8d2fecf39d7aa4d85ebb

374

py

Python

terrascript/data/fortios.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/data/fortios.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/data/fortios.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/data/fortios.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:16:41 UTC) # # For imports without namespace, e.g. # # >>> import terrascript.data.fortios # # instead of # # >>> import terrascript.data.fortinetdev.fortios # # This is only available for 'official' and 'partner' providers. from terrascript.data.fortinetdev.fortios import *

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py

Python

purple/helpers.py

a8ksh4/purple

11460cc44cda7edf6f8fce5a5c841d4b04a47741

[ "MIT" ]

3

2021-12-24T04:14:48.000Z

2022-01-19T16:48:14.000Z

purple/helpers.py

a8ksh4/purple

11460cc44cda7edf6f8fce5a5c841d4b04a47741

[ "MIT" ]

1

2022-01-25T05:32:21.000Z

purple/helpers.py

a8ksh4/purple

11460cc44cda7edf6f8fce5a5c841d4b04a47741

[ "MIT" ]

1

2022-01-19T00:41:04.000Z

def key(index): return 1 << index

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c771313e108f7daf29caacf358449193feac0774

5,972

py

Python

utils.py

Avashist1998/Viola-Jones_Algorithm

0de21a22485b0f60fa03397ce6592dd2c7e0e3a5

[ "MIT" ]

null

utils.py

Avashist1998/Viola-Jones_Algorithm

0de21a22485b0f60fa03397ce6592dd2c7e0e3a5

[ "MIT" ]

null

utils.py

Avashist1998/Viola-Jones_Algorithm

0de21a22485b0f60fa03397ce6592dd2c7e0e3a5

[ "MIT" ]

null

# The goal of the file to perform the feature extraction using the Haar_Features. # I hoping to define the function in this file and worry about the rest later. import cv2 import os import glob import numpy as np import matplotlib.pyplot as plt import pandas as pd import time # Setting up a assigning the label to the images # get the base path of the directory def intergal_image(image): [row,col] = image.shape i_image = image.copy() for i in range(0,row): for j in range(0,col): i_image[i,j] = sum(sum(image[0:i+1,0:j+1])) return i_image def feature_extraction(image): image_copy = image.copy() feature = [] [row, col] = image_copy.shape # Type 1 features (two vertical) for w in range(1,5): for h in range(1,9): for i in range(row-h+1): for j in range(col-2*w+1): output = -2*image_copy[i+h-1,j+w-1] + 2*image_copy[i,j+w-1] + image_copy[i+h-1,j+2*w-1] + image_copy[i+h-1,j] + image_copy[i,j+2*w-1] - image_copy[i,j] feature.append(output) print(len(feature)) #Type 2 features (two horizontal) for h in range(1,5): for w in range(1,9): for i in range(row-2*h+1): for j in range(col-w+1): output = 2*image_copy[i+h-1,j] + image_copy[i+2*h-1,j+w-1] + image_copy[i,j+w-1] - 2*image_copy[i+h-1,j+w-1] - image_copy[i+2*h-1,j] - image_copy[i,j] feature.append(output) print(len(feature)) # Type 3 feature (three Horizonatal) for h in range(1,3): for w in range(1,9): for i in range(row-4*h+1): for j in range(col-w+1): output = 2*image_copy[i+3*h-1,j+w-1] + 2*image_copy[i+h-1,j] - 2*image_copy[i+h-1,j+w-1] - 2*image_copy[i+3*h-1,j] - image_copy[i+4*h-1,j+w-1] + image_copy[i+4*h-1,j] - image_copy[i,j] + image_copy[i,j+w-1] feature.append(output) print(len(feature)) # Type 4 feature (two Vertical) for h in range(1,9): for w in range(1,3): for i in range(row-h+1): for j in range(col-4*w+1): output = 2*image_copy[i,j+w-1] + 2*image_copy[i+h-1,j+3*w-1] - 2*image_copy[i,j+3*w-1] - 2*image_copy[i+h-1,j+w-1] - image_copy[i,j]+ image_copy[i+h-1,j] - image_copy[i+h-1,j+4*w-1] + image_copy[i,j+4*w-1] feature.append(output) print(len(feature)) # Type 5 feature (four) for h in range(1,5): for w in range(1,5): for i in range(row-2*h+1): for j in range(col-2*w+1): output = image_copy[i,j]+ 4*image_copy[i+h-1,j+w-1] - 2*image_copy[i,j+w-1] - 2*image_copy[i+h-1,j] + image[i+2*h-1,j+2*w-1] - 2*image_copy[i+h-1,j+2*w-1] + image_copy[i,j+2*w-1] - 2*image_copy[i+2*h-1,j+w-1] + image_copy[i+2*h-1,j] feature.append(output) print(len(feature)) return feature #--------------------------------------------------------------------------------------------------------------------- base_path = os.getcwd() train_faces_files = glob.glob(base_path+ '/dataset/trainset/faces/*.png') train_faces_files.sort() train_non_faces_files = glob.glob(base_path+ '/dataset/trainset/non-faces/*.png') train_non_faces_files.sort() data = np.array([[]]) t0 = time.time() for names in train_faces_files: image = cv2.imread(names,cv2.IMREAD_GRAYSCALE) i_image = intergal_image(image) f = feature_extraction(i_image) data = np.append(data,f) num_image = len(train_faces_files) num_feature = int(len(data)/num_image) data = np.resize(data, (num_image,num_feature)) temp_data = np.array([[]]) for names in train_non_faces_files: image = cv2.imread(names,cv2.IMREAD_GRAYSCALE) i_image = intergal_image(image) f = feature_extraction(i_image) temp_data = np.append(temp_data,f) num_image = int(len(temp_data)/num_feature) temp_data = np.resize(temp_data, (num_image,num_feature)) label = [1]*len(train_faces_files) label_non_faces = [-1] * len(train_non_faces_files) label = np.append(label,label_non_faces) total_data = np.concatenate((data,temp_data),axis=0) final = np.insert(total_data, num_feature ,label,axis=1) pd.DataFrame((final).astype(int)).to_csv(base_path+ "/train_data.csv",header=None, index=None,float_format= '%10.5f') t1 = time.time() print((t1-t0)/60) #--------------------------------------------------------------------------------------------------------------------- # test file train_faces_files = glob.glob(base_path+ '/dataset/testset/faces/*.png') train_faces_files.sort() train_non_faces_files = glob.glob(base_path+ '/dataset/testset/non-faces/*.png') train_non_faces_files.sort() data = np.array([[]]) t0 = time.time() for names in train_faces_files: image = cv2.imread(names,cv2.IMREAD_GRAYSCALE) i_image = intergal_image(image) f = feature_extraction(i_image) data = np.append(data,f) num_image = len(train_faces_files) num_feature = int(len(data)/num_image) data = np.resize(data, (num_image,num_feature)) temp_data = np.array([[]]) for names in train_non_faces_files: image = cv2.imread(names,cv2.IMREAD_GRAYSCALE) i_image = intergal_image(image) f = feature_extraction(i_image) temp_data = np.append(temp_data,f) num_image = int(len(temp_data)/num_feature) temp_data = np.resize(temp_data, (num_image,num_feature)) label = [1]*len(train_faces_files) label_non_faces = [-1] * len(train_non_faces_files) label = np.append(label,label_non_faces) total_data = np.concatenate((data,temp_data),axis=0) final = np.insert(total_data, num_feature ,label,axis=1) #final.tofile(base_path + 'train_data.csv',sep=',',format='%10.5f') pd.DataFrame((final).astype(int)).to_csv(base_path+ "/test_data.csv",header=None, index=None,float_format= '%10.5f') t1 = time.time() print((t1-t0)/60) #---------------------------------------------------------------------------------------------------------------------

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c77cd30812a9e70f01088285d7cc9ee58cd2135c

160

py

Python

Part_3_advanced/m08_abstract_protocol/abstract_class/homework_1_start/new_movies/datetime_utils.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_3_advanced/m08_abstract_protocol/abstract_class/homework_1_start/new_movies/datetime_utils.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_3_advanced/m08_abstract_protocol/abstract_class/homework_1_start/new_movies/datetime_utils.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

from dateutil.relativedelta import relativedelta # type: ignore def full_years_between_dates(later, earlier): return relativedelta(later, earlier).years

26.666667

64

0.80625

19

160

6.631579

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0.125

160

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0

1

0

null

0

1

0

1

0

6

c782833324e4ef984f5e35fae938eed30c294c73

5,845

py

Python

basic_analysis_module/drawDataProcessUtils.py

PeterPaaan/XMe_DataAnalysis

0f95cec3bc99ab8895133aea78ddedbd649c1834

[ "Apache-2.0" ]

null

basic_analysis_module/drawDataProcessUtils.py

PeterPaaan/XMe_DataAnalysis

0f95cec3bc99ab8895133aea78ddedbd649c1834

[ "Apache-2.0" ]

null

basic_analysis_module/drawDataProcessUtils.py

PeterPaaan/XMe_DataAnalysis

0f95cec3bc99ab8895133aea78ddedbd649c1834

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # @Time : 2021/9/8 22:09 # @Author : Gang # @File : drawDataProcessUtils.py import numpy as np from basicAnalysisConst import * from myLog.MyLog import * class DrawDataProcessUtils: logger = MyLog("DrawDataProcessUtils", BASEDIR) @classmethod def calculate_draw_data(cls, data, key_para): SAMPLING_RATE = key_para["le_Sampling_Rate"] STRETCHING_RATE = key_para["le_Stretching_Rate"] PIEZO_RATE = key_para["le_Piezo_Rate"] # TODO # piezo的使用还没有加进来，问题的关键就是这个换算关系取决于压电，就很蛋疼 FACTOR = STRETCHING_RATE / SAMPLING_RATE log_G, start, zero, end, len_high, len_low, *_ = data ALL_TRACE_NUM = len(start) SELECT_TRACE_NUM = ALL_TRACE_NUM # datacut_temp=[[[(j-zero[i])*FACTOR,log_G[j]] for j in range(start[i],end[i])]for i in range(TRUE_NUM)] # 上的这种写法先放放 # 经过实验，可以知道的是上面这种写法被淘汰，另外，下面的双循环的方法可以被下面的矢量化的方法部分取代 # distance=np.array([[(j-zero[i])*FACTOR for j in range(start[i],end[i])] for i in range(ALL_TRACE_NUM)]) # conductance=np.array([[log_G[j] for j in range(start[i],end[i])] for i in range(ALL_TRACE_NUM)]) # length=np.array([(len_low[i]-len_high[i])*FACTOR for i in range(ALL_TRACE_NUM)]) distance = np.array([(np.arange(start[i], end[i]) - zero[i]) * FACTOR for i in range(ALL_TRACE_NUM)]) conductance = np.array([log_G[np.arange(start[i], end[i])] for i in range(ALL_TRACE_NUM)]) length = (len_low - len_high) * FACTOR distance_draw = distance.reshape(-1) conductance_draw = conductance.reshape(-1) return distance, conductance, length, distance_draw, conductance_draw, ALL_TRACE_NUM, SELECT_TRACE_NUM @classmethod def calculate_draw_data_with_select(cls, data, key_para): SAMPLING_RATE = key_para["le_Sampling_Rate"] STRETCHING_RATE = key_para["le_Stretching_Rate"] PIEZO_RATE = key_para["le_Piezo_Rate"] UPPER_LIMIT1 = key_para["le_Upper_Limit1"] UPPER_LIMIT2 = key_para["le_Upper_Limit2"] LOW_LIMIT1 = key_para["le_Low_Limit1"] LOW_LIMIT2 = key_para["le_Low_Limit2"] FACTOR = STRETCHING_RATE / SAMPLING_RATE log_G, start, zero, end, len_high, len_low, start1, end1, start2, end2 = data ALL_TRACE_NUM = len(start) # VALID_TRACE_INDEX=[] # for i in range(TRUE_NUM): # temp1=(end1[i]-start1[i])*FACTOR # temp2=(end2[i]-start2[i])*FACTOR # if temp1>UPPER_LIMIT1 or temp1>UPPER_LIMIT2 or temp1<LOW_LIMIT1 or temp2<LOW_LIMIT2: # continue # VALID_TRACE_INDEX.append(i) # 我认为下面的写法效率更高，可以等后面测试 temp1 = (end1 - start1) * FACTOR temp2 = (end2 - start2) * FACTOR VALID_TRACE_INDEX = \ np.where((temp1 >= LOW_LIMIT1) & (temp1 <= UPPER_LIMIT1) & (temp2 >= LOW_LIMIT2) & (temp2 <= UPPER_LIMIT2))[ 0] distance = np.array([(np.arange(start[i], end[i]) - zero[i]) * FACTOR for i in VALID_TRACE_INDEX]) conductance = np.array([log_G[np.arange(start[i], end[i])] for i in VALID_TRACE_INDEX]) length = np.array([(len_low[i] - len_high[i]) * FACTOR for i in VALID_TRACE_INDEX]) distance_draw = distance.reshape(-1) conductance_draw = conductance.reshape(-1) SELECT_TRACE_NUM = len(VALID_TRACE_INDEX) return distance, conductance, length, distance_draw, conductance_draw, ALL_TRACE_NUM, SELECT_TRACE_NUM @classmethod def calculate_draw_data_close(cls, data, key_para): SAMPLING_RATE = key_para["le_Sampling_Rate"] STRETCHING_RATE = key_para["le_Stretching_Rate"] PIEZO_RATE = key_para["le_Piezo_Rate"] # TODO # piezo的使用还没有加进来，问题的关键就是这个换算关系取决于压电，就很蛋疼 FACTOR = STRETCHING_RATE / SAMPLING_RATE log_G, start, zero, end, len_high, len_low, *_ = data ALL_TRACE_NUM = len(start) SELECT_TRACE_NUM = ALL_TRACE_NUM distance = np.array([(zero[i] - np.arange(start[i], end[i])) * FACTOR for i in range(ALL_TRACE_NUM)]) conductance = np.array([log_G[np.arange(start[i], end[i])] for i in range(ALL_TRACE_NUM)]) length = (len_high - len_low) * FACTOR distance_draw = distance.reshape(-1) conductance_draw = conductance.reshape(-1) return distance, conductance, length, distance_draw, conductance_draw, ALL_TRACE_NUM, SELECT_TRACE_NUM @classmethod def calculate_draw_data_close_with_select(cls, data, key_para): SAMPLING_RATE = key_para["le_Sampling_Rate"] STRETCHING_RATE = key_para["le_Stretching_Rate"] PIEZO_RATE = key_para["le_Piezo_Rate"] UPPER_LIMIT1 = key_para["le_Upper_Limit1"] UPPER_LIMIT2 = key_para["le_Upper_Limit2"] LOW_LIMIT1 = key_para["le_Low_Limit1"] LOW_LIMIT2 = key_para["le_Low_Limit2"] FACTOR = STRETCHING_RATE / SAMPLING_RATE log_G, start, zero, end, len_high, len_low, start1, end1, start2, end2 = data ALL_TRACE_NUM = len(start) temp1 = (start1 - end1) * FACTOR temp2 = (start2 - end2) * FACTOR VALID_TRACE_INDEX = np.where( (temp1 >= LOW_LIMIT1) & (temp1 <= UPPER_LIMIT1) & (temp2 >= LOW_LIMIT2) & (temp2 <= UPPER_LIMIT2) )[0] distance = np.array([(zero[i] - np.arange(start[i], end[i])) * FACTOR for i in VALID_TRACE_INDEX]) conductance = np.array([log_G[np.arange(start[i], end[i])] for i in VALID_TRACE_INDEX]) length = np.array([(len_high[i] - len_low[i]) * FACTOR for i in VALID_TRACE_INDEX]) distance_draw = distance.reshape(-1) conductance_draw = conductance.reshape(-1) SELECT_TRACE_NUM = len(VALID_TRACE_INDEX) return distance, conductance, length, distance_draw, conductance_draw, ALL_TRACE_NUM, SELECT_TRACE_NUM

44.280303

120

0.656116

804

5,845

4.470149

0.118159

0.055648

0.050083

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0.83222

0.825264

0.820812

0

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5,845

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121

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null

0

1

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null

0

6

c7d682ee843c20b1c3e5a2587f5980f332c048d9

4,524

py

Python

GAN/AC-BIGGAN-with-CIFAR10/utils/metrics.py

kiritowu/Deep-Learning

baaec55a3b32f9e02ca3d834f1408f6736bdc170

[ "MIT" ]

3

2021-12-16T02:26:10.000Z

2022-02-23T16:52:34.000Z

GAN/AC-BIGGAN-with-CIFAR10/utils/metrics.py

kiritowu/Deep-Learning

baaec55a3b32f9e02ca3d834f1408f6736bdc170

[ "MIT" ]

null

GAN/AC-BIGGAN-with-CIFAR10/utils/metrics.py

kiritowu/Deep-Learning

baaec55a3b32f9e02ca3d834f1408f6736bdc170

[ "MIT" ]

null

from typing import Optional import torch import torch.nn as nn from torch.utils import data from torchvision import transforms import PIL.Image as Image try: from torchmetrics.image import FID, IS except ModuleNotFoundError: raise ModuleNotFoundError( "torchmetrics is not found. Please install ignite by running `pip install torchmetrics[image]`" ) class FID10k(FID): def __init__(self, device=None, **kwargs) -> None: super().__init__(**kwargs) if not device: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.to(device) def interpolate229x229(self, batch): """ Resize images to 299 x 299 """ arr = [] for img in batch: pil_img = transforms.ToPILImage()(img) resized_img = pil_img.resize((299, 299), Image.BILINEAR) img_tensor = transforms.ToTensor()(resized_img) arr.append(img_tensor) return torch.stack(arr) def evaluate10k( self, generator: nn.Module, real_data: data.Dataset, latent_dim: int, n_classes: int, batch_size: int = 100, sample_size: int = 10_000, inv_preprocessing=None, )->float: n_batch = (sample_size + batch_size - 1) // batch_size data_loader = data.DataLoader(real_data, batch_size=batch_size) data_iter = iter(data_loader) with torch.no_grad(): for index in range(n_batch): latent_space = torch.normal( 0, 1, (batch_size, latent_dim), device=self._device, requires_grad=False) gen_labels = torch.randint( 0, n_classes, (batch_size,), device=self._device, requires_grad=False) real_img, _ = next(data_iter) fake_img = generator(latent_space, gen_labels) if inv_preprocessing: real_img = inv_preprocessing(real_img) fake_img = inv_preprocessing(fake_img) uint_real_img = (self.interpolate229x229(real_img)*255).type(torch.uint8) uint_fake_img = (self.interpolate229x229(fake_img)*255).type(torch.uint8) uint_real_img = uint_real_img.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) uint_fake_img = uint_fake_img.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) self.update(uint_real_img, real=True) self.update(uint_fake_img, real=False) return self.compute().cpu().item() class IS10k(IS): def __init__(self, device=None, **kwargs) -> None: super().__init__(**kwargs) if not device: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.to(device) def interpolate229x229(self, batch): """ Resize images to 299 x 299 """ arr = [] for img in batch: pil_img = transforms.ToPILImage()(img) resized_img = pil_img.resize((299, 299), Image.BILINEAR) img_tensor = transforms.ToTensor()(resized_img) arr.append(img_tensor) return torch.stack(arr) def evaluate10k( self, generator: nn.Module, latent_dim: int, n_classes: int, batch_size: int = 100, sample_size: int = 10_000, inv_preprocessing = None, )->float: n_batch = (sample_size + batch_size - 1) // batch_size with torch.no_grad(): for index in range(n_batch): latent_space = torch.normal( 0, 1, (batch_size, latent_dim), device=self._device, requires_grad=False) gen_labels = torch.randint( 0, n_classes, (batch_size,), device=self._device, requires_grad=False) fake_img = generator(latent_space, gen_labels) if inv_preprocessing: fake_img = inv_preprocessing(fake_img) uint_fake_img = (self.interpolate229x229(fake_img)*255).type(torch.uint8) uint_fake_img = uint_fake_img.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) self.update(uint_fake_img) return self.compute()[0].cpu().item()

36.483871

111

0.571618

521

4,524

4.712092

0.205374

0.045621

0.035845

0.034623

0.765377

0.753971

0.727902

0.719756

0

0.032695

0.330681

4,524

124

112

36.483871

0.778071

0.011715

0

0.723404

0

0.029754

0

1

0.06383

false

0

0.074468

0

0.202128

0

null

0

1

0

1

0

null

0

6

40078786194afce550cd4853bc8215e656238d87

17,333

py

Python

pyke/kepplot.py

ecalifornica/pyke

6a3fcc0513cf012044e4420cc4d17064e582d142

[ "MIT" ]

null

pyke/kepplot.py

ecalifornica/pyke

6a3fcc0513cf012044e4420cc4d17064e582d142

[ "MIT" ]

1

2017-07-25T19:23:05.000Z

pyke/kepplot.py

mirca/PyKE

6a3fcc0513cf012044e4420cc4d17064e582d142

[ "MIT" ]

null

from . import kepmsg, kepstat import math import numpy as np from matplotlib import pyplot as plt def location(shape): """shape the window, enforce absolute scaling, rotate the labels""" # position first axes inside the plotting window ax = plt.axes(shape) # force tick labels to be absolute rather than relative plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False)) plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False)) ax.yaxis.set_major_locator(plt.MaxNLocator(5)) # rotate y labels by 90 deg labels = ax.get_yticklabels() return ax def plot1d(x, y, cadence, lcolor, lwidth, fcolor, falpha, underfill): """plot a 1d distribution""" # pad first and last points in case a fill is required x = np.insert(x, [0], [x[0]]) x = np.append(x, [x[-1]]) y = np.insert(y, [0], [-1.0e10]) y = np.append(y, -1.0e10) # plot data so that data gaps are not spanned by a line ltime = np.array([], dtype='float64') ldata = np.array([], dtype='float32') for i in range(1, len(x)-1): if x[i] - x[i - 1] < 2.0 * cadence / 86400: ltime = np.append(ltime, x[i]) ldata = np.append(ldata, y[i]) else: plt.plot(ltime, ldata, color=lcolor, linestyle='-', linewidth=lwidth) ltime = np.array([], dtype='float64') ldata = np.array([], dtype='float32') plt.plot(ltime, ldata, color=lcolor, linestyle='-', linewidth=lwidth) # plot the fill color below data time series, with no data gaps if underfill: plt.fill(x, y, fc=fcolor, linewidth=0.0, alpha=falpha) def RangeOfPlot(x, y, pad, origin): """determine data limits""" xmin = x.min() xmax = x.max() ymin = y.min() ymax = y.max() xr = xmax - xmin yr = ymax - ymin plt.xlim(xmin - xr * pad, xmax + xr * pad) plt.ylim(ymin - yr * pad, ymax + yr * pad) if origin: if ymin - yr * pad <= 0.0: plt.ylim(1.0e-10, ymax + yr * pad) else: plt.ylim(ymin - yr * pad, ymax + yr * pad) def cleanx(time, logfile, verbose): """clean up x-axis of plot""" try: time0 = float(int(time[0] / 100) * 100.0) if time0 < 2.4e6: time0 += 2.4e6 timeout = time - time0 label = "BJD $-$ {}".format(time0) except: txt = ("ERROR -- KEPPLOT.CLEANX: cannot calculate plot scaling in " "x dimension") kepmsg.err(logfile, txt, verbose) return timeout, label def cleany(signal, cadence, logfile, verbose): """clean up y-axis of plot""" try: signal /= cadence nrm = math.ceil(math.log10(np.nanmax(signal))) - 1.0 signal = signal / 10 ** nrm if nrm == 0: label = 'Flux (e$^-$ s$^{-1}$)' else: label = "Flux ($10^%d$" % nrm + "e$^-$ s$^{-1}$)" except: txt = ("ERROR -- KEPPLOT.CLEANY: cannot calculate plot scaling in " "y dimension") kepmsg.err(logfile, txt, verbose) return signal, label def limits(x, y, logfile, verbose): """plot limits""" try: xmin = x.min() xmax = x.max() ymin = y.min() ymax = y.max() xr = xmax - xmin yr = ymax - ymin x = np.insert(x, [0], [x[0]]) x = np.append(x, [x[-1]]) y = np.insert(y, [0], [0.0]) y = np.append(y, 0.0) except: txt = 'ERROR -- KEPPLOT.LIMITS: cannot calculate plot limits' kepmsg.err(logfile, txt, verbose) return x, y, xmin, xmax, xr, ymin, ymax, yr def labels(xlab, ylab, labcol, fs): """plot labels""" plt.xlabel(xlab, fontsize=fs, color=labcol) plt.ylabel(ylab, fontsize=fs, color=labcol) def intScale1D(image, imscale): """intensity scale limits of 1d array""" nstat = 2; work2 = [] image = np.ma.array(image, mask=np.isnan(image)) work1 = np.array(np.sort(image), dtype=np.float32) for i in range(len(work1)): if 'nan' not in str(work1[i]).lower(): work2.append(work1[i]) work2 = np.array(work2, dtype=np.float32) if int(float(len(work2)) / 10 + 0.5) > nstat: nstat = int(float(len(work2)) / 10 + 0.5) zmin = np.median(work2[:nstat]) zmax = np.median(work2[-nstat:]) if imscale == 'logarithmic': if zmin < 0.0: zmin = 100.0 if np.any(image <= 0): image = np.log10(image + abs(image.min()) + 1) else: image = np.log10(image) zmin = math.log10(zmin) zmax = math.log10(zmax) if imscale == 'squareroot': if zmin < 0.0: zmin = 100.0 if np.any(image < 0): image = np.sqrt(image + abs(image.min())) else: image = np.sqrt(image) zmin = math.sqrt(zmin) zmax = math.sqrt(zmax) return image, zmin, zmax def intScale2D(image, imscale): """intensity scale limits of 2d array""" nstat = 2 work1 = np.array([], dtype=np.float32) (ysiz, xsiz) = np.shape(image) for i in range(ysiz): for j in range(xsiz): if np.isfinite(image[i, j]) and image[i, j] > 0.0: work1 = np.append(work1, image[i, j]) work2 = np.array(np.sort(work1)) if int(float(len(work2)) / 1000 + 0.5) > nstat: nstat = int(float(len(work2)) / 1000 + 0.5) zmin = np.median(work2[:nstat]) zmax = np.median(work2[-nstat:]) if imscale == 'logarithmic': image = np.log10(image) zmin = math.log10(zmin) zmax = math.log10(zmax) if imscale == 'squareroot': image = np.sqrt(image) zmin = math.sqrt(zmin) zmax = math.sqrt(zmax) return image, zmin, zmax def borders(maskimg, xdim, ydim, pixcoord1, pixcoord2, bit, lcolor, lstyle, lwidth): """plot mask borders in CCD coordinates""" for i in range(1, ydim): for j in range(1, xdim): if (kepstat.bitInBitmap(maskimg[i, j], bit) and not kepstat.bitInBitmap(maskimg[i - 1, j], bit)): x = np.array([pixcoord1[j - 1, i], pixcoord1[j, i]]) + 0.5 y = np.array([pixcoord2[j, i], pixcoord2[j , i]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if (not kepstat.bitInBitmap(maskimg[i, j], bit) and kepstat.bitInBitmap(maskimg[i - 1, j], bit)): x = np.array([pixcoord1[j - 1, i], pixcoord1[j, i]]) + 0.5 y = np.array([pixcoord2[j, i], pixcoord2[j, i]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if (kepstat.bitInBitmap(maskimg[i, j], bit) and not kepstat.bitInBitmap(maskimg[i, j - 1], bit)): x = np.array([pixcoord1[j, i], pixcoord1[j, i]]) - 0.5 y = np.array([pixcoord2[j, i - 1], pixcoord2[j, i]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if (not kepstat.bitInBitmap(maskimg[i, j], bit) and kepstat.bitInBitmap(maskimg[i, j - 1], bit)): x = np.array([pixcoord1[j, i], pixcoord1[j, i]]) - 0.5 y = np.array([pixcoord2[j, i - 1],pixcoord2[j, i]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) # corner cases for j in range(ydim): try: if (kepstat.bitInBitmap(maskimg[j, 0], bit) and not kepstat.bitInBitmap(maskimg[j - 1,0], bit)): x = np.array([pixcoord1[0, j], pixcoord1[1, j]]) - 0.5 y = np.array([pixcoord2[0, j], pixcoord2[0, j]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass try: if (not kepstat.bitInBitmap(maskimg[j + 1, 0], bit) and kepstat.bitInBitmap(maskimg[j,0],bit)): x = np.array([pixcoord1[0, j], pixcoord1[1, j]]) - 0.5 y = np.array([pixcoord2[0, j], pixcoord2[0, j]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass if kepstat.bitInBitmap(maskimg[j, 0], bit): x = np.array([pixcoord1[0, j], pixcoord1[0, j]]) - 0.5 try: y = np.array([pixcoord2[0, j], pixcoord2[0, j + 1]]) - 0.5 except: y = np.array([pixcoord2[0, j - 1], pixcoord2[0, j]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[j, xdim - 1], bit): x = np.array([pixcoord1[xdim - 1, j], pixcoord1[xdim - 1, j]]) + 0.5 try: y = (np.array([pixcoord2[xdim - 1, j], pixcoord2[xdim - 1, j + 1]]) - 0.5) except: y = (np.array([pixcoord2[xdim - 1, j - 1], pixcoord2[xdim - 1, j]]) + 0.5) plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) for i in range(xdim): try: if (kepstat.bitInBitmap(maskimg[0, i], bit) and not kepstat.bitInBitmap(maskimg[0, i - 1], bit)): x = np.array([pixcoord1[i, 0], pixcoord1[i, 0]]) - 0.5 y = np.array([pixcoord2[i, 0], pixcoord2[i, 1]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass try: if (not kepstat.bitInBitmap(maskimg[0, i + 1], bit) and kepstat.bitInBitmap(maskimg[0, i], bit)): x = np.array([pixcoord1[i, 0], pixcoord1[i, 0]]) + 0.5 y = np.array([pixcoord2[i, 0], pixcoord2[i, 1]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass if kepstat.bitInBitmap(maskimg[0, i], bit): try: x = np.array([pixcoord1[i, 0], pixcoord1[i + 1, 0]]) - 0.5 except: x = np.array([pixcoord1[i - 1, 0], pixcoord1[i, 0]]) + 0.5 y = np.array([pixcoord2[i, 0], pixcoord2[i, 0]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[ydim - 1, i], bit): try: x = (np.array([pixcoord1[i, ydim - 1], pixcoord1[i + 1, ydim - 1]]) - 0.5) except: x = (np.array([pixcoord1[i - 1, ydim - 1], pixcoord1[i, ydim - 1]]) - 0.5) y = np.array([pixcoord2[i, ydim - 1], pixcoord2[i, ydim - 1]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[ydim - 1, xdim - 1], bit): x = (np.array([pixcoord1[xdim - 2, ydim - 1], pixcoord1[xdim - 1, ydim - 1]]) + 0.5) y = (np.array([pixcoord2[xdim - 1, ydim - 1], pixcoord2[xdim - 1, ydim - 1]]) + 0.5) plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[0, xdim - 1], bit): x = np.array([pixcoord1[xdim - 1, 0], pixcoord1[xdim - 1, 0]]) + 0.5 y = np.array([pixcoord2[xdim - 1, 0], pixcoord2[xdim - 1, 1]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) return def PrfBorders(maskimg,xdim,ydim,pixcoord1,pixcoord2,bit,lcolor,lstyle,lwidth): """plot mask borders in CCD coordinates""" for i in range(1, ydim): for j in range(1, xdim): if (kepstat.bitInBitmap(maskimg[i, j], bit) and not kepstat.bitInBitmap(maskimg[i - 1, j], bit)): x = np.array([pixcoord1[j - 1, i], pixcoord1[j, i]]) + 0.5 y = np.array([pixcoord2[j, i], pixcoord2[j, i]]) - 0.5 plt.plot(x*50, y*50, color=lcolor, linestyle=lstyle, linewidth=lwidth) if (not kepstat.bitInBitmap(maskimg[i, j], bit) and kepstat.bitInBitmap(maskimg[i - 1, j], bit)): x = np.array([pixcoord1[j - 1, i], pixcoord1[j, i]]) + 0.5 y = np.array([pixcoord2[j , i], pixcoord2[j, i]]) - 0.5 plt.plot(x*50, y*50, color=lcolor, linestyle=lstyle, linewidth=lwidth) if (kepstat.bitInBitmap(maskimg[i, j], bit) and not kepstat.bitInBitmap(maskimg[i, j - 1], bit)): x = np.array([pixcoord1[j, i], pixcoord1[j, i]]) - 0.5 y = np.array([pixcoord2[j, i - 1], pixcoord2[j, i]]) + 0.5 plt.plot(x*50, y*50, color=lcolor, linestyle=lstyle, linewidth=lwidth) if (not kepstat.bitInBitmap(maskimg[i, j], bit) and kepstat.bitInBitmap(maskimg[i, j - 1], bit)): x = np.array([pixcoord1[j, i], pixcoord1[j, i]]) - 0.5 y = np.array([pixcoord2[j, i - 1], pixcoord2[j, i]]) + 0.5 plt.plot(x*50, y*50, color=lcolor, linestyle=lstyle, linewidth=lwidth) # corner cases for j in range(ydim): try: if (kepstat.bitInBitmap(maskimg[j, 0], bit) and not kepstat.bitInBitmap(maskimg[j - 1, 0], bit)): x = np.array([pixcoord1[0, j], pixcoord1[1, j]]) - 0.5 y = np.array([pixcoord2[0, j], pixcoord2[0, j]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass try: if (not kepstat.bitInBitmap(maskimg[j + 1, 0], bit) and kepstat.bitInBitmap(maskimg[j, 0], bit)): x = np.array([pixcoord1[0, j], pixcoord1[1, j]]) - 0.5 y = np.array([pixcoord2[0, j], pixcoord2[0, j]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass if kepstat.bitInBitmap(maskimg[j, 0], bit): x = np.array([pixcoord1[0,j],pixcoord1[0,j]]) - 0.5 try: y = np.array([pixcoord2[0, j], pixcoord2[0, j + 1]]) - 0.5 except: y = np.array([pixcoord2[0, j - 1], pixcoord2[0, j]]) + 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[j, xdim - 1], bit): x = np.array([pixcoord1[xdim - 1, j], pixcoord1[xdim - 1, j]]) + 0.5 try: y = (np.array([pixcoord2[xdim - 1, j], pixcoord2[xdim - 1, j + 1]]) - 0.5) except: y = (np.array([pixcoord2[xdim - 1, j - 1], pixcoord2[xdim - 1, j]]) + 0.5) plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) for i in range(xdim): try: if (kepstat.bitInBitmap(maskimg[0, i], bit) and not kepstat.bitInBitmap(maskimg[0, i - 1], bit)): x = np.array([pixcoord1[i, 0], pixcoord1[i, 0]]) - 0.5 y = np.array([pixcoord2[i, 0], pixcoord2[i, 1]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass try: if (not kepstat.bitInBitmap(maskimg[0, i + 1], bit) and kepstat.bitInBitmap(maskimg[0, i], bit)): x = np.array([pixcoord1[i, 0], pixcoord1[i, 0]]) + 0.5 y = np.array([pixcoord2[i, 0], pixcoord2[i, 1]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) except: pass if kepstat.bitInBitmap(maskimg[0, i], bit): try: x = np.array([pixcoord1[i, 0], pixcoord1[i + 1, 0]]) - 0.5 except: x = np.array([pixcoord1[i - 1, 0], pixcoord1[i, 0]]) + 0.5 y = np.array([pixcoord2[i,0],pixcoord2[i,0]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[ydim - 1, i], bit): try: x = (np.array([pixcoord1[i, ydim - 1], pixcoord1[i + 1, ydim-1]]) - 0.5) except: x = (np.array([pixcoord1[i - 1, ydim - 1], pixcoord1[i, ydim - 1]]) - 0.5) y = (np.array([pixcoord2[i, ydim - 1], pixcoord2[i, ydim - 1]]) + 0.5) plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[ydim - 1, xdim -1], bit): x = (np.array([pixcoord1[xdim - 2, ydim - 1], pixcoord1[xdim - 1, ydim - 1]]) + 0.5) y = (np.array([pixcoord2[xdim - 1, ydim - 1], pixcoord2[xdim - 1, ydim - 1]]) + 0.5) plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth) if kepstat.bitInBitmap(maskimg[0, xdim - 1], bit): x = np.array([pixcoord1[xdim - 1, 0], pixcoord1[xdim - 1, 0]]) + 0.5 y = np.array([pixcoord2[xdim - 1, 0], pixcoord2[xdim - 1, 1]]) - 0.5 plt.plot(x, y, color=lcolor, linestyle=lstyle, linewidth=lwidth)

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0.735344

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40248b59c2a3a7e2ac873ee376548f2f0cf9670c

8,445

py

Python

tests/controllers/test_survey.py

reputage/SQSurvey

938a59806fa0877205a1a460ceb23ecdf2fa9201

[ "Apache-2.0" ]

null

tests/controllers/test_survey.py

reputage/SQSurvey

938a59806fa0877205a1a460ceb23ecdf2fa9201

[ "Apache-2.0" ]

null

tests/controllers/test_survey.py

reputage/SQSurvey

938a59806fa0877205a1a460ceb23ecdf2fa9201

[ "Apache-2.0" ]

null

import falcon import uuid try: import simplejson as json except ImportError: import json from didery.routing import * from didery.db.dbing import BaseSurveyDB, DB, DB_SURVEY_RESULTS_NAME def testSurveyPost(client): surveyResult = { "ip_address": "127.0.0.1" } response = client.simulate_post(SURVEY_BASE_PATH, body=json.dumps(surveyResult).encode()) resp_data = json.loads(response.content) resp_key = list(resp_data.keys())[0] assert len(resp_data) == 1 assert resp_data[resp_key]["survey_data"] == surveyResult def testSurveyGetAll(client): surveyResult = { "ip_address": "127.0.0.1" } client.simulate_post(SURVEY_BASE_PATH, body=json.dumps(surveyResult).encode()) response = json.loads(client.simulate_get(SURVEY_BASE_PATH).content) assert len(response["data"]) == 1 for survey in response["data"].values(): assert survey["survey_data"] == surveyResult def testSurveyGet(client): surveyResult = { "ip_address": "127.0.0.1" } response = client.simulate_post(SURVEY_BASE_PATH, body=json.dumps(surveyResult).encode()) id = list(json.loads(response.content).keys())[0] response = client.simulate_get("{}/{}".format(SURVEY_BASE_PATH, id)) assert json.loads(response.content)["survey_data"] == surveyResult def testSurveyGetAllInvalidQueryString(client): # Test that query params have values response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset&limit=10") exp_result = { "title": "Malformed Query String", "description": "url query string missing value(s)." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=10&limit") exp_result = { "title": "Malformed Query String", "description": "url query string missing value(s)." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result def testSurveyGetAllInvalidQueryValue(client): # Test that query params values are ints response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=a&limit=10") exp_result = { "title": "Malformed Query String", "description": "url query string value must be a number." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=10&limit=d") exp_result = { "title": "Malformed Query String", "description": "url query string value must be a number." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result def testSurveyGetAllNegativeQueryValue(client): # Test that query params values are ints response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=-1&limit=10") exp_result = { "title": "Malformed Query String", "description": "url query string value must be a positive number." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=0&limit=-10") exp_result = { "title": "Malformed Query String", "description": "url query string value must be a positive number." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result def testSurveyGetAllEmptyQueryValue(client): response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=10&limit=") exp_result = { "title": "Malformed Query String", "description": "url query string value must be a number." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=&limit=10") exp_result = { "title": "Malformed Query String", "description": "url query string value must be a number." } assert response.status == falcon.HTTP_400 assert json.loads(response.content) == exp_result def testValidGetAllWithQueryString(client): db = BaseSurveyDB(DB(DB_SURVEY_RESULTS_NAME)) exp_result = {"data": {}} for i in range(0, 11): history = { "id": "did:dad:NOf6ZghvGNbFc_wr3CC0tKZHz1qWAR4lD5aM-i0zSjw=", "changed": "2000-01-01T00:00:01+00:00", "signer": 1, "signers": [ "NOf6ZghvGNbFc_wr3CC0tKZHz1qWAR4lD5aM-i0zSjw=", "NOf6ZghvGNbFc_wr3CC0tKZHz1qWAR4lD5aM-i0zSjw=", "NOf6ZghvGNbFc_wr3CC0tKZHz1qWAR4lD5aM-i0zSjw=" ] } uid = str(uuid.uuid4()) db.save(uid, history) exp_result["data"][uid] = history response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=0&limit=11") result = json.loads(response.content) assert response.status == falcon.HTTP_200 assert result == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=0&limit=20") result = json.loads(response.content) assert response.status == falcon.HTTP_200 assert result == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=0&limit=0") result = json.loads(response.content) exp_result = {"data": {}} assert response.status == falcon.HTTP_200 assert result == exp_result response = client.simulate_get(SURVEY_BASE_PATH, query_string="offset=100&limit=10") assert response.status == falcon.HTTP_200 assert json.loads(response.content) == exp_result def testPostBodySize(client): surveyResult = { "Name": "xyz", "Email": "xyz@domain.com", "Response": { "Rank each of the five game concepts on ease of navigation.-SeedQuest": "1", "Rank each of the five game concepts on ease of navigation.-Cliffside": "1", "Rank each of the five game concepts on ease of navigation.-Laboratory": "1", "Rank each of the five game concepts on ease of navigation.-Mind Palace": "1", "Rank each of the five game concepts on ease of navigation.-Flatlands": "1", "Rank each of the five game concepts on how intuitive and enjoyable the gameplay is.-SeedQuest": "1", "Rank each of the five game concepts on how intuitive and enjoyable the gameplay is.-Laboratory": "1", "Rank each of the five game concepts on how intuitive and enjoyable the gameplay is.-Mind Palace": "1", "Rank each of the five game concepts on how intuitive and enjoyable the gameplay is.-Flatlands": "1", "Rank each of the five game concepts on how quickly you were able to learn the game path.-SeedQuest": "1", "Rank each of the five game concepts on how quickly you were able to learn the game path.-Cliffside": "1", "Rank each of the five game concepts on how quickly you were able to learn the game path.-Laboratory": "1", "Rank each of the five game concepts on how quickly you were able to learn the game path.-Mind Palace": "1", "Rank each of the five game concepts on how quickly you were able to learn the game path.-Flatlands": "1", "Rank each of the five game concepts on overall experience.-SeedQuest": "4th", "Rank each of the five game concepts on overall experience.-Cliffside": "3rd", "Rank each of the five game concepts on overall experience.-Laboratory": "4th", "Rank each of the five game concepts on overall experience.-Memory Palace": "5th", "Rank each of the five game concepts on overall experience.-Flatlands": "5th", "Do you have any other comments or suggestions about any of the game concepts-Game Navigation": "ewfsdcxcdsfewrfsdczxds", "Do you have any other comments or suggestions about any of the game concepts-Memorability": "1", "Do you have any other comments or suggestions about any of the game concepts-Art Style": "1" } } data = json.dumps(surveyResult) assert len(data) > 1000 response = client.simulate_post(SURVEY_BASE_PATH, body=json.dumps(surveyResult).encode()) assert response.status == falcon.HTTP_201

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0

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null

0

6

40424aca71843a5d8a65b235194039f86d210ad3

39

py

Python

sandbox/michelle.py

writecrow/crow_training

17324ce93608acf997c2880b587dd9483729b895

[ "MIT" ]

7

2018-02-27T15:24:10.000Z

2018-02-27T22:20:58.000Z

sandbox/michelle.py

writecrow/crow_training

17324ce93608acf997c2880b587dd9483729b895

[ "MIT" ]

11

2018-02-21T03:07:44.000Z

2018-02-27T22:33:29.000Z

sandbox/michelle.py

writecrow/crow_training

17324ce93608acf997c2880b587dd9483729b895

[ "MIT" ]

null

print("Hello Mark, This is Michelle.")

19.5

38

0.717949

6

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39

1

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null

0

1

0

1

0

6

40b2f20c94f194cf5bff0b09adc65a2debeb1e55

160

py

Python

paz/optimization/__init__.py

niqbal996/paz

f27205907367415d5b21f90e1a1d1d1ce598e889

[ "MIT" ]

300

2020-10-29T08:02:05.000Z

2022-03-30T21:47:32.000Z

paz/optimization/__init__.py

albertofernandezvillan/paz

9fbd50b993f37e1e807297a29c6044c09967c9cc

[ "MIT" ]

30

2020-10-29T12:40:32.000Z

2022-03-31T14:06:35.000Z

paz/optimization/__init__.py

albertofernandezvillan/paz

9fbd50b993f37e1e807297a29c6044c09967c9cc

[ "MIT" ]

62

2020-10-29T12:34:13.000Z

2022-03-29T05:21:45.000Z

from .losses import MultiBoxLoss from .losses import KeypointNetLoss from .losses import DiceLoss from .losses import FocalLoss from .losses import JaccardLoss

26.666667

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160

6.75

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0

1

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6

40d039b83d423a7126f84f03e449445afb22d6d6

3,249

py

Python

utility/feed_generation_utility.py

etfrenchvcu/DeepPrime2Sec

a61146c7c7d8a0f8821717b9f7aed8703cfdb2a1

[ "Apache-2.0" ]

18

2019-07-17T01:53:03.000Z

2021-12-10T13:08:51.000Z

utility/feed_generation_utility.py

etfrenchvcu/DeepPrime2Sec

a61146c7c7d8a0f8821717b9f7aed8703cfdb2a1

[ "Apache-2.0" ]

18

2019-10-13T02:45:50.000Z

2022-02-10T00:17:06.000Z

utility/feed_generation_utility.py

etfrenchvcu/DeepPrime2Sec

a61146c7c7d8a0f8821717b9f7aed8703cfdb2a1

[ "Apache-2.0" ]

9

2019-07-26T01:16:26.000Z

2020-11-14T01:57:24.000Z

import numpy as np from utility.file_utility import FileUtility def train_batch_generator_408(batch_size=64): ''' :param batch_size: :return: ''' start_idx = 0 train_lengths = [int(j) for j in FileUtility.load_list( 'datasets/train_length.txt')] X_train = np.load('datasets/X_train_408.npy') Y_train = np.array( np.load('datasets/train_mat_Y.npy')) while True: if not start_idx < len(train_lengths): start_idx = 0 X = X_train[start_idx:(min(start_idx + batch_size, len(train_lengths))), 0:train_lengths[min(start_idx + batch_size, len(train_lengths)) - 1]] Y = Y_train[start_idx:(min(start_idx + batch_size, len(train_lengths))), 0:train_lengths[min(start_idx + batch_size, len(train_lengths)) - 1], :] W = [] for idx in range(start_idx, (min(start_idx + batch_size, len(train_lengths)))): W.append([1 if l < train_lengths[idx] else 0 for l in range(0, train_lengths[min(start_idx + batch_size, len(train_lengths)) - 1])]) start_idx += batch_size yield X, Y, np.array(W) def validation_batch_generator_408(batch_size=100): ''' :param batch_size: :return: ''' test_lengths = [int(i) for i in FileUtility.load_list( 'datasets/test_length.txt')] X_test = np.load('datasets/X_test_408.npy') Y_test = np.array( np.load('datasets/test_mat_Y.npy')) start_idx = 0 while True: if not start_idx < len(test_lengths): start_idx = 0 X = X_test[start_idx:(min(start_idx + batch_size, len(test_lengths))), 0:test_lengths[min(start_idx + batch_size, len(test_lengths)) - 1]] Y = Y_test[start_idx:(min(start_idx + batch_size, len(test_lengths))), 0:test_lengths[min(start_idx + batch_size, len(test_lengths)) - 1], :] W = [] for idx in range(start_idx, (min(start_idx + batch_size, len(test_lengths)))): W.append([1 if l < test_lengths[idx] else 0 for l in range(0, test_lengths[min(start_idx + batch_size, len(test_lengths)) - 1])]) start_idx += batch_size yield X, Y, np.array(W) def validation_batches_fortest_408(batchsize=100): ''' :param batchsize: :return: ''' test_lengths = [int(i) for i in FileUtility.load_list( 'datasets/test_length.txt')] X_test = np.load('datasets/X_test_408.npy') Y_test = np.array( np.load('datasets/test_mat_Y.npy')) start_idx = 0 while start_idx < len(test_lengths): X = X_test[start_idx:(min(start_idx + batchsize, len(test_lengths))), 0:test_lengths[min(start_idx + batchsize, len(test_lengths)) - 1]] Y = Y_test[start_idx:(min(start_idx + batchsize, len(test_lengths))), 0:test_lengths[min(start_idx + batchsize, len(test_lengths)) - 1], :] W = [] for idx in range(start_idx, (min(start_idx + batchsize, len(test_lengths)))): W.append([1 if l < test_lengths[idx] else 0 for l in range(0, test_lengths[min(start_idx + batchsize, len(test_lengths)) - 1])]) start_idx += batchsize yield X, Y, np.array(W)

38.223529

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3,249

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null

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null

0

6

904bf72f33620fcf83e91de982459952e01f2ba1

53

py

Python

test/test_import.py

Rayman/toypkg

76f6862cdd4e923abd34d981c60cab292e7a4a29

[ "MIT" ]

null

test/test_import.py

Rayman/toypkg

76f6862cdd4e923abd34d981c60cab292e7a4a29

[ "MIT" ]

null

test/test_import.py

Rayman/toypkg

76f6862cdd4e923abd34d981c60cab292e7a4a29

[ "MIT" ]

null

def test_import(): import toypkg assert True

13.25

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53

3

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17.666667

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null

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0

1

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1

0

6

9092763ab41a807d06102c41f57c4af2ff30b015

8,792

py

Python

tests/functional/test_yyy_forking_and_reloading.py

arareko/pysoa

a90e428558500cf692f7f6e33fd358dd2779c328

[ "Apache-2.0" ]

null

tests/functional/test_yyy_forking_and_reloading.py

arareko/pysoa

a90e428558500cf692f7f6e33fd358dd2779c328

[ "Apache-2.0" ]

null

tests/functional/test_yyy_forking_and_reloading.py

arareko/pysoa

a90e428558500cf692f7f6e33fd358dd2779c328

[ "Apache-2.0" ]

1

2020-02-21T07:17:48.000Z

from __future__ import ( absolute_import, unicode_literals, ) import time import pytest from pysoa.common.constants import ERROR_CODE_ACTION_TIMEOUT from pysoa.common.transport.errors import MessageReceiveTimeout from tests.functional import ( get_container_logs, get_container_process_list, read_file_from_container, write_file_to_container, ) def test_double_import_trap_killed_intended_service(): with pytest.raises(AssertionError) as error_context: read_file_from_container('echo_service_double_import_trap', '/srv/echo_service-1.heartbeat') assert 'No container found for echo_service_double_import_trap_1' in error_context.value.args[0] assert 'ERROR: You have triggered a double-import trap' in get_container_logs('echo_service_double_import_trap') def test_heartbeat_file_watching_no_forking(pysoa_client): original_ts = float(read_file_from_container('meta_service', '/srv/meta_service-{{fid}}.heartbeat')) assert original_ts > 0 time.sleep(2.5) response = pysoa_client.call_action('meta', 'status') assert response.body['version'] == '2.1.7' new_ts = float(read_file_from_container('meta_service', '/srv/meta_service-{{fid}}.heartbeat')) assert new_ts > original_ts def test_heartbeat_file_forking_no_watching(pysoa_client): original_ts_1 = float(read_file_from_container('user_service', '/srv/user_service-1.heartbeat')) original_ts_2 = float(read_file_from_container('user_service', '/srv/user_service-2.heartbeat')) original_ts_3 = float(read_file_from_container('user_service', '/srv/user_service-3.heartbeat')) original_ts_4 = float(read_file_from_container('user_service', '/srv/user_service-4.heartbeat')) assert original_ts_1 > 0 assert original_ts_2 > 0 assert original_ts_3 > 0 assert original_ts_4 > 0 time.sleep(2.5) responses = pysoa_client.call_actions_parallel( 'user', [ {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, ], ) for response in responses: assert response.body['version'] == '1.0.17' new_ts_1 = float(read_file_from_container('user_service', '/srv/user_service-1.heartbeat')) new_ts_2 = float(read_file_from_container('user_service', '/srv/user_service-2.heartbeat')) new_ts_3 = float(read_file_from_container('user_service', '/srv/user_service-3.heartbeat')) new_ts_4 = float(read_file_from_container('user_service', '/srv/user_service-4.heartbeat')) assert new_ts_1 > original_ts_1 assert new_ts_2 > original_ts_2 assert new_ts_3 > original_ts_3 assert new_ts_4 > original_ts_4 def test_heartbeat_file_forking_and_watching(pysoa_client): original_ts_1 = float(read_file_from_container('echo_service', '/srv/echo_service-1.heartbeat')) original_ts_2 = float(read_file_from_container('echo_service', '/srv/echo_service-2.heartbeat')) original_ts_3 = float(read_file_from_container('echo_service', '/srv/echo_service-3.heartbeat')) assert original_ts_1 > 0 assert original_ts_2 > 0 assert original_ts_3 > 0 time.sleep(2.5) responses = pysoa_client.call_actions_parallel( 'echo', [ {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, ], ) for response in responses: assert response.body['version'] == '9.5.3' new_ts_1 = float(read_file_from_container('echo_service', '/srv/echo_service-1.heartbeat')) new_ts_2 = float(read_file_from_container('echo_service', '/srv/echo_service-2.heartbeat')) new_ts_3 = float(read_file_from_container('echo_service', '/srv/echo_service-3.heartbeat')) assert new_ts_1 > original_ts_1 assert new_ts_2 > original_ts_2 assert new_ts_3 > original_ts_3 def test_reload_no_forking(pysoa_client): print(get_container_process_list('meta_service')) assert read_file_from_container('meta_service', '/srv/meta/meta_service/version.py') == "__version__ = '2.1.7'" write_file_to_container('meta_service', '/srv/meta/meta_service/version.py', "__version__ = '7.1.2'") assert read_file_from_container('meta_service', '/srv/meta/meta_service/version.py') == "__version__ = '7.1.2'" time.sleep(10) print(get_container_process_list('meta_service')) response = pysoa_client.call_action('meta', 'status') assert response.body['version'] == '7.1.2' def test_reload_with_forking(pysoa_client): print(get_container_process_list('echo_service')) assert read_file_from_container('echo_service', '/srv/echo/echo_service/version.py') == "__version__ = '9.5.3'" write_file_to_container('echo_service', '/srv/echo/echo_service/version.py', "__version__ = '9.8.0'") assert read_file_from_container('echo_service', '/srv/echo/echo_service/version.py') == "__version__ = '9.8.0'" time.sleep(10) print(get_container_process_list('echo_service')) responses = pysoa_client.call_actions_parallel( 'echo', [ {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, ], ) for response in responses: assert response.body['version'] == '9.8.0' def test_no_reload_no_watcher(pysoa_client): print(get_container_process_list('user_service')) assert read_file_from_container('user_service', '/srv/user/user_service/version.py') == "__version__ = '1.0.17'" write_file_to_container('user_service', '/srv/user/user_service/version.py', "__version__ = '1.2.1'") assert read_file_from_container('user_service', '/srv/user/user_service/version.py') == "__version__ = '1.2.1'" time.sleep(10) print(get_container_process_list('user_service')) responses = pysoa_client.call_actions_parallel( 'user', [ {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, {'action': 'status'}, ], ) for response in responses: assert response.body['version'] == '1.0.17' def test_harakiri_graceful_restart(pysoa_client): original_ts_1 = float(read_file_from_container('echo_service', '/srv/echo_service-1.heartbeat')) original_ts_2 = float(read_file_from_container('echo_service', '/srv/echo_service-2.heartbeat')) original_ts_3 = float(read_file_from_container('echo_service', '/srv/echo_service-3.heartbeat')) assert original_ts_1 > 0 assert original_ts_2 > 0 assert original_ts_3 > 0 print(get_container_process_list('echo_service')) with pytest.raises(pysoa_client.CallActionError) as error_context: pysoa_client.call_action('echo', 'harakiri_loop_graceful', timeout=12) assert len(error_context.value.actions) == 1 assert len(error_context.value.actions[0].errors) == 1 assert error_context.value.actions[0].errors[0].code == ERROR_CODE_ACTION_TIMEOUT print(get_container_process_list('echo_service')) new_ts_1 = float(read_file_from_container('echo_service', '/srv/echo_service-1.heartbeat')) new_ts_2 = float(read_file_from_container('echo_service', '/srv/echo_service-2.heartbeat')) new_ts_3 = float(read_file_from_container('echo_service', '/srv/echo_service-3.heartbeat')) assert new_ts_1 > original_ts_1 assert new_ts_2 > original_ts_2 assert new_ts_3 > original_ts_3 def test_harakiri_forceful_restart(pysoa_client): original_ts_1 = float(read_file_from_container('echo_service', '/srv/echo_service-1.heartbeat')) original_ts_2 = float(read_file_from_container('echo_service', '/srv/echo_service-2.heartbeat')) original_ts_3 = float(read_file_from_container('echo_service', '/srv/echo_service-3.heartbeat')) assert original_ts_1 > 0 assert original_ts_2 > 0 assert original_ts_3 > 0 print(get_container_process_list('echo_service')) with pytest.raises(MessageReceiveTimeout): pysoa_client.call_action('echo', 'harakiri_loop_forceful', timeout=12) print(get_container_process_list('echo_service')) new_ts_1 = float(read_file_from_container('echo_service', '/srv/echo_service-1.heartbeat')) new_ts_2 = float(read_file_from_container('echo_service', '/srv/echo_service-2.heartbeat')) new_ts_3 = float(read_file_from_container('echo_service', '/srv/echo_service-3.heartbeat')) assert new_ts_1 > original_ts_1 assert new_ts_2 > original_ts_2 assert new_ts_3 > original_ts_3

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0

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0.144904

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0.064516

0

null

0

1

0

null

0

6

290fb371e6a41d6496f17e9f9f6ddf955c8ffbd0

137

py

Python

hcap/settings/general/seed.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

null

hcap/settings/general/seed.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

31

2020-04-11T13:38:17.000Z

2021-09-22T18:51:11.000Z

hcap/settings/general/seed.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

1

2020-04-08T17:04:39.000Z

from hcap.settings.env import env # Used by user seed commands SEED_DEFAULT_PASSWORD = env("SEED_DEFAULT_PASSWORD", default="Pass@123")

27.4

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0.79562

21

137

5

0.666667

0.209524

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0

0.02459

0.109489

137

4

73

34.25

0.836066

0.189781

0

0.266055

0.192661

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0

false

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0

1

0

null

1

0

1

0

null

0

1

0

6

2973e94847ea74f19a48097bd62bd29dec3524a4

91

py

Python

7_Modules/Package1/Package1_1/ModuleTest.py

Oscar-Oliveira/Python3

fa791225a6810b75890d24407b73c5e1b514acbe

[ "MIT" ]

null

7_Modules/Package1/Package1_1/ModuleTest.py

Oscar-Oliveira/Python3

fa791225a6810b75890d24407b73c5e1b514acbe

[ "MIT" ]

null

7_Modules/Package1/Package1_1/ModuleTest.py

Oscar-Oliveira/Python3

fa791225a6810b75890d24407b73c5e1b514acbe

[ "MIT" ]

null

""" Module """ def my_multiplier(value1, value2): return value1 * value2 * 1000

13

35

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10

91

5.5

0.8

0.436364

0

0.117647

0.252747

91

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1

0

1

0

null

0

1

0

1

0

6

462406933a0df9e604be174a72c14431607962b5

22,485

py

Python

django_project/src/imports/processing/tests.py

icrc-fdeniger/waterboard

10d95b15938b495f4c83c6e125cbb6a2ba41e506

[ "MIT" ]

1

2019-01-19T09:01:48.000Z

django_project/src/imports/processing/tests.py

icrc-fdeniger/waterboard

10d95b15938b495f4c83c6e125cbb6a2ba41e506

[ "MIT" ]

32

2017-12-15T14:35:17.000Z

2022-03-11T23:16:10.000Z

django_project/src/imports/processing/tests.py

icrc-fdeniger/waterboard

10d95b15938b495f4c83c6e125cbb6a2ba41e506

[ "MIT" ]

4

2019-02-13T07:52:05.000Z

2021-04-29T08:20:58.000Z

# -*- coding: utf-8 -*- import unittest from .errors import FileError, MultipleUuidError, NoRequiredColumnError, UnnamedColumnError from .functions import check_data, check_file_header, check_headers, for_insert, for_update, parse_data_file class TestCSVImport(unittest.TestCase): def test_checkFileHeader_emptyFile(self): data_raw = [] self.assertRaises(FileError, check_file_header, data_raw) def test_checkFileHeader_emptyFirstRow(self): data_raw = [[None, None, None], ['1', '2', '3']] self.assertRaises(FileError, check_file_header, data_raw) def test_checkFileHeader_columnsWithoutName(self): data_raw = [['a', None, 'b'], ['1', '2', '3']] self.assertRaises(UnnamedColumnError, check_file_header, data_raw) def test_getDataFile_checkNew(self): data_raw = [ ['feature_uuid', 'a', 'b'], ['uuid1', 'x', 'y'], ['', 'xy', 'yz'], ['', 'ab', 'bc'], ['', '', ''], [None, '', ''], ['uuid2', 'x', 'y'] ] expected_result = ( ['feature_uuid', 'a', 'b'], { 'uuid1': {'feature_uuid': 'uuid1', 'a': 'x', 'b': 'y'}, 'uuid2': {'feature_uuid': 'uuid2', 'a': 'x', 'b': 'y'}, 'new_feature_uuid_1': {'feature_uuid': 'new_feature_uuid_1', 'a': 'xy', 'b': 'yz'}, 'new_feature_uuid_2': {'feature_uuid': 'new_feature_uuid_2', 'a': 'ab', 'b': 'bc'}, 'new_feature_uuid_3': {'feature_uuid': 'new_feature_uuid_3', 'a': None, 'b': None}, 'new_feature_uuid_4': {'feature_uuid': 'new_feature_uuid_4', 'a': None, 'b': None} } ) self.assertEqual(parse_data_file(data_raw), expected_result) def test_getDataFile_multipleUuid(self): data_raw = [['feature_uuid', 'a', 'b'], ['uuid1', 'x', 'y'], ['uuid1', 'xy', 'yz'], ['uuid2', 'x', 'y']] self.assertRaises(MultipleUuidError, parse_data_file, data_raw) def test_getDataFile_ignoredAttributes(self): data_raw = [['feature_uuid', 'a', 'email', 'changeset'], ['uuid1', 'x', 'y', 1], ['uuid2', 'x', 'y', 2]] expected_result = (['feature_uuid', 'a'], { 'uuid1': {'feature_uuid': 'uuid1', 'a': 'x', 'changeset': 1}, 'uuid2': {'feature_uuid': 'uuid2', 'a': 'x', 'changeset': 2} }) self.assertEqual(parse_data_file(data_raw), expected_result) def test_check_headers_areSame(self): header_file = ['col1', 'col2', 'col3'] header_db = ['col1', 'col3', 'col2'] attributes = {'col1': {'required': False}, 'col2': {'required': False}, 'col3': {'required': True}} self.assertEqual(check_headers(header_file, header_db, attributes), []) def test_check_headers_moreInFile(self): header_file = ['col1', 'col2', 'col3', 'col4', 'col5'] header_db = ['col1', 'col3', 'col2'] attributes = {'col1': {'required': False}, 'col2': {'required': False}, 'col3': {'required': True}} self.assertEqual(check_headers(header_file, header_db, attributes), [ 'Column "col4" in uploaded file is not defined in database. Data will be inserted in database without ' 'values in column "col4".', 'Column "col5" in uploaded file is not defined in database. Data will be inserted in database without ' 'values in column "col5".']) def test_check_headers_lessInFile_notRequired(self): header_file = ['col1', 'col2', 'col3'] header_db = ['col1', 'col3', 'col2', 'col4'] attributes = {'col1': {'required': False}, 'col2': {'required': False}, 'col3': {'required': True}} self.assertEqual(check_headers(header_file, header_db, attributes), []) def test_check_headers_lessInFile_required_one(self): header_file = ['col1', 'col2'] header_db = ['col1', 'col3', 'col2', 'col4'] attributes = {'col1': {'required': False}, 'col2': {'required': False}, 'col3': {'required': True}} self.assertRaises(NoRequiredColumnError, check_headers, header_file, header_db, attributes) def test_check_headers_lessInFile_required_two(self): header_file = ['col1'] header_db = ['col1', 'col3', 'col2', 'col4'] attributes = {'col1': {'required': False}, 'col2': {'required': True}, 'col3': {'required': True}} self.assertRaises(NoRequiredColumnError, check_headers, header_file, header_db, attributes) def test_check_headers_lessInFile_required_three(self): header_file = ['col1'] header_db = ['col1', 'col3', 'col2', 'col4'] attributes = {'col1': {'required': False}, 'col2': {'required': True}, 'col3': {'required': True}, 'col4': {'required': True}} self.assertRaises(NoRequiredColumnError, check_headers, header_file, header_db, attributes) def test_for_update_sameRows(self): row_file = {'a': '123', 'b': 123, 'c': 'abc'} row_db = {'a': '123', 'b': 123, 'c': 'abc'} self.assertFalse(for_update(row_file, row_db)) def test_for_update_differentRows(self): row_file = {'a': '123', 'b': 123, 'c': 'ab'} row_db = {'a': '123', 'b': 123, 'c': 'abc'} self.assertTrue(for_update(row_file, row_db)) def test_for_update_moreRowsInFile(self): row_file = {'a': '123', 'b': 123, 'c': 'abc', 'd': 'abc'} row_db = {'a': '123', 'b': 123, 'c': 'abc'} self.assertFalse(for_update(row_file, row_db)) def test_for_update_moreRowsInDB(self): row_file = {'a': '123', 'b': 123, 'c': 'abc'} row_db = {'a': '123', 'b': 123, 'c': 'abc', 'd': 'abc'} self.assertFalse(for_update(row_file, row_db)) def test_for_insert_inDropdown(self): index_row = 3 row = {'a': 'abc'} attributes = {'a': {'type': 'DropDown', 'required': False, 'id': '1', 'options': ['abc', 'Eastern']}} self.assertEqual(for_insert(index_row, row, attributes), (True, '')) def test_for_insert_notInDropdown(self): index_row = 3 row = {'a': 'abc1'} attributes = {'a': {'type': 'DropDown', 'required': False, 'id': '1', 'options': ['abc', 'Eastern']}} self.assertEqual( for_insert(index_row, row, attributes), (False, 'Row 3: value in column "a" is not allowed (it should be one of the predefined values).') ) def test_for_insert_isInteger(self): index_row = 3 row = {'a': 1} attributes = {'a': {'type': 'Integer', 'required': False, 'id': '1'}} self.assertEqual(for_insert(index_row, row, attributes), (True, '')) def test_for_insert_isNotInteger(self): index_row = 3 row = {'a': 1.2} attributes = {'a': {'type': 'Integer', 'required': False, 'id': '1'}} self.assertEqual( for_insert(index_row, row, attributes), (False, 'Row 3: value in column "a" is not allowed (it should be a whole number).') ) def test_for_insert_isDecimal(self): index_row = 3 row = {'a': 1.2} attributes = {'a': {'type': 'Decimal', 'required': False, 'id': '1'}} self.assertEqual(for_insert(index_row, row, attributes), (True, '')) def test_for_insert_isNotDecimal(self): index_row = 3 row = {'a': '1.2'} attributes = {'a': {'type': 'Decimal', 'required': False, 'id': '1'}} self.assertEqual( for_insert(index_row, row, attributes), (False, 'Row 3: value in column "a" is not allowed (it should be a decimal number).') ) def test_for_insert_required_notEmpty(self): index_row = 3 row = {'a': 1.2} attributes = {'a': {'type': 'Decimal', 'required': True}} self.assertEqual(for_insert(index_row, row, attributes), (True, '')) def test_for_insert_required_empty(self): index_row = 3 row = {'a': None, 'b': 1} attributes = {'a': {'type': 'Decimal', 'required': True, 'id': '1'}} self.assertEqual(for_insert(index_row, row, attributes), (False, 'Row 3: value in column "a" is missing.')) def test_for_insert_multipleErrors(self): index_row = 3 row = {'a': None, 'b': 'abc', 'c': 'x'} attributes = {'a': {'type': 'Decimal', 'required': True}, 'b': {'type': 'Decimal', 'required': True}, 'c': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['abc', 'Eastern']}} expected_result = ( False, ( 'Row 3: value in column "a" is missing, value in column "b" is not allowed (it should ' 'be a decimal number), value in column "c" is not allowed (it should be one of the ' 'predefined values).' ) ) self.assertEqual(for_insert(index_row, row, attributes), expected_result) def test_check_data_empty_rows(self): data_file = { '453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}, 'new_feature_uuid_1': {'feature_uuid': 'new_feature_uuid_1', 'a': None, 'b': None}, 'new_feature_uuid_2': {'feature_uuid': 'new_feature_uuid_2', 'a': None, 'c': None} } data_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': False, 'id': '1'}, 'b': {'type': 'DropDown', 'required': False, 'id': '1', 'options': ['abc', 'Eastern']}, 'c': {'type': 'Decimal', 'required': False, 'id': '1'}} self.assertEqual( check_data(data_file, data_db, attributes), ( [ {'a': None, 'b': None, 'feature_uuid': 'new_feature_uuid_1'}, {'a': None, 'c': None, 'feature_uuid': 'new_feature_uuid_2'} ], [], [], [], { 'num_add': 2, 'num_discarded': 0, 'num_needs_correction': 0, 'num_unchanged': 2, 'num_update': 0 } ) ) def test_check_data_no_change(self): data_file = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} data_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['abc', 'Eastern']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} self.assertEqual( check_data(data_file, data_db, attributes), ([], [], [], [], { 'num_add': 0, 'num_discarded': 0, 'num_needs_correction': 0, 'num_unchanged': 2, 'num_update': 0 }) ) def test_check_data_oneForAdd(self): data_file = { '453abc': {'a': 123, 'b': 'abc', 'c': 1.23}, 'new_feature_uuid_1': {'a': 98, 'b': 'abc', 'c': 1.57} } data_db = {'453abc': {'a': 123, 'b': 'abc', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'abc', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['abc', 'Eastern']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} self.assertEqual( check_data(data_file, data_db, attributes), ( [{'a': 98, 'b': 'abc', 'c': 1.57}], [], [], [], { 'num_add': 1, 'num_discarded': 0, 'num_needs_correction': 0, 'num_unchanged': 1, 'num_update': 0 } ) ) def test_check_data_oneForUpdate(self): data_file = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.58}} data_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} self.assertEqual( check_data(data_file, data_db, attributes), ( [], [{'a': 98, 'b': 'cba', 'c': 1.58}], [], [], { 'num_add': 0, 'num_discarded': 0, 'num_needs_correction': 0, 'num_unchanged': 1, 'num_update': 1 } ) ) def test_check_data_oneDiscarded(self): data_file = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, 'AAA': {'a': 98, 'b': 'cba', 'c': 1.57}} data_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} self.assertEqual( check_data(data_file, data_db, attributes), ( [], [], [], ['Row 3 was discarded. (feature_uuid not in database or not blank)'], {'num_add': 0, 'num_discarded': 1, 'num_needs_correction': 0, 'num_unchanged': 1, 'num_update': 0} ) ) def test_check_data_oneUpdate_oneWith3Errors_oneDiscarded_oneForAdd(self): data_file = {'453abc': {'a': 1234, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 1.2, 'b': 'abc', 'c': None}, 'ABC': {'a': 98, 'b': 'xyz', 'c': 1.2}, 'new_feature_uuid_1': {'a': 98, 'b': 'cba', 'c': 1.2}} data_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} expected_result = ( [{'a': 98, 'b': 'cba', 'c': 1.2}], [{'a': 1234, 'b': 'xyz', 'c': 1.23}], [], [ 'Row 3: value in column "a" is not allowed (it should be a whole number), value in column "b" is not ' 'allowed (it should be one of the predefined values), value in column "c" is missing.', 'Row 4 was discarded. (feature_uuid not in database or not blank)' ], {'num_add': 1, 'num_discarded': 1, 'num_needs_correction': 1, 'num_unchanged': 0, 'num_update': 1} ) self.assertEqual(check_data(data_file, data_db, attributes), expected_result) def test_check_data_oneForAddWithError(self): data_file = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, 'new_feature_uuid_1': {'a': 1.2, 'b': 'cba', 'c': 2}} data_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} expected_result = ( [], [], [], ['Row 3: value in column "a" is not allowed (it should be a whole number).'], {'num_add': 0, 'num_discarded': 0, 'num_needs_correction': 1, 'num_unchanged': 1, 'num_update': 0} ) self.assertEqual(check_data(data_file, data_db, attributes), expected_result) def test_check_data_twoForAdd_noError(self): data_file = { '453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, 'new_feature_uuid_1': {'a': 1, 'b': 'cba', 'c': 2}, 'new_feature_uuid_2': {'a': 2, 'b': 'xyz', 'c': 2} } data_db = { '453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57} } attributes = { 'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'} } self.assertEqual( check_data(data_file, data_db, attributes), ( [{'a': 1, 'b': 'cba', 'c': 2}, {'a': 2, 'b': 'xyz', 'c': 2}], [], [], [], { 'num_add': 2, 'num_discarded': 0, 'num_needs_correction': 0, 'num_unchanged': 1, 'num_update': 0 } ) ) def test_check_data_twoForAdd_withError(self): data_from_file = { '453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, 'new_feature_uuid_1': {'a': 2, 'b': 'xyz', 'c': 2}, 'new_feature_uuid_2': {'a': 1, 'b': 'aaa', 'c': 2} } data_from_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} expected_result = ( [{'a': 2, 'b': 'xyz', 'c': 2}], [], [], [ 'Row 4: value in column "b" is not allowed (it should be one of the predefined values).'], { 'num_add': 1, 'num_discarded': 0, 'num_needs_correction': 1, 'num_unchanged': 1, 'num_update': 0 } ) self.assertEqual(check_data(data_from_file, data_from_db, attributes), expected_result) def test_check_data_three_discarded(self): data_from_file = { 'a': {'a': 123, 'b': 'xyz', 'c': 1.23}, 'b': {'a': 2, 'b': 'xyz', 'c': 2}, 'c': {'a': 1, 'b': 'aaa', 'c': 2}} data_from_db = {'453abc': {'a': 123, 'b': 'xyz', 'c': 1.23}, '653bnj': {'a': 98, 'b': 'cba', 'c': 1.57}} attributes = {'a': {'type': 'Integer', 'required': True, 'id': '1'}, 'b': {'type': 'DropDown', 'required': True, 'id': '1', 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True, 'id': '1'}} self.assertEqual( check_data(data_from_file, data_from_db, attributes), ( [], [], [], ['Rows 2, 3 and 4 were discarded. (feature_uuid not in database or not blank)'], {'num_add': 0, 'num_discarded': 3, 'num_needs_correction': 0, 'num_unchanged': 0, 'num_update': 0} ) ) def test_check_data_changeset_threeUpdate_oneAdd(self): data_from_file = { '453abc': {'a': 1, 'b': 'xyz', 'c': 2, 'changeset': '20'}, '653bnj': {'a': 1, 'b': 'xyz', 'c': 2, 'changeset': 20}, '556gbn': {'a': 1, 'b': 'xyz', 'c': 2}, 'new_feature_uuid_1': {'a': 1, 'b': 'cba', 'c': 2, 'changeset': '7'} } data_from_db = { '453abc': {'a': 123, 'b': 'xyz', 'c': 1.23, 'changeset_id': 20}, '653bnj': {'a': 123, 'b': 'xyz', 'c': 1.25, 'changeset_id': 20}, '556gbn': {'a': 123, 'b': 'xyz', 'c': 1.23, 'changeset_id': 20} } attributes = { 'a': {'type': 'Integer', 'required': True}, 'b': {'type': 'DropDown', 'required': True, 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True} } expected_result = ( [{'a': 1, 'b': 'cba', 'c': 2, 'changeset': '7'}], [{'a': 1, 'b': 'xyz', 'c': 2, 'changeset': '20'}, {'a': 1, 'b': 'xyz', 'c': 2, 'changeset': 20}, {'a': 1, 'b': 'xyz', 'c': 2}], [], [], { 'num_add': 1, 'num_discarded': 0, 'num_needs_correction': 0, 'num_unchanged': 0, 'num_update': 3 }) self.assertEqual(check_data(data_from_file, data_from_db, attributes), expected_result) def test_check_data_changeset_oneDiscarded_twoError(self): data_from_file = { '787nmj': {'a': 1, 'b': 'bbb', 'c': 2, 'changeset': '7'}, '789ght': {'a': 1, 'b': 'bbb', 'c': 2, 'changeset': 'a'}, '549uhj': {'a': 1, 'b': 'xyz', 'c': 2, 'changeset': 'a'} } data_from_db = { '787nmj': {'a': 123, 'b': 'xyz', 'c': 1.23, 'changeset_id': 20}, '789ght': {'a': 123, 'b': 'xyz', 'c': 1.23, 'changeset_id': 20}, '549uhj': {'a': 123, 'b': 'xyz', 'c': 2, 'changeset_id': 20} } attributes = { 'a': {'type': 'Integer', 'required': True}, 'b': {'type': 'DropDown', 'required': True, 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True} } expected_result = ([], [], [], [( 'Row 3: value in column "b" is not allowed (it should be one of the predefined values), value in ' 'column "changeset" is not allowed (it should be a whole number).'), 'Row 4: value in column "changeset" is not allowed (it should be a whole number).', 'Row 2 was discarded. (changeset is not the most recent one)'], { 'num_add': 0, 'num_discarded': 1, 'num_needs_correction': 2, 'num_unchanged': 0, 'num_update': 0 }) self.assertEqual(check_data(data_from_file, data_from_db, attributes), expected_result) def test_check_data_changeset_oneAdd_twoUnchanged_oneDiscarded(self): data_from_file = { '908hnj': {'a': 1, 'b': 'aaa', 'c': 2, 'changeset': '7'}, '897bnj': {'a': 1, 'b': 'aaa', 'c': 2, 'changeset': '20'} } data_from_db = { '908hnj': {'a': 1, 'b': 'aaa', 'c': 2, 'changeset_id': 20}, '897bnj': {'a': 1, 'b': 'aaa', 'c': 2, 'changeset_id': 20} } attributes = { 'a': {'type': 'Integer', 'required': True}, 'b': {'type': 'DropDown', 'required': True, 'options': ['cba', 'xyz']}, 'c': {'type': 'Decimal', 'required': True} } expected_result = ( [], [], [], ['Row 2 was discarded. (changeset is not the most recent one)'], { 'num_add': 0, 'num_discarded': 1, 'num_needs_correction': 0, 'num_unchanged': 1, 'num_update': 0 }) self.assertEqual(check_data(data_from_file, data_from_db, attributes), expected_result) if __name__ == '__main__': unittest.main()

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119

0.506427

2,718

22,485

3.996689

0.066593

0.055233

0.017491

0.040044

0.874804

0.842953

0.80429

0.771518

0.739575

0.709012

0

0.048375

0.277385

22,485

474

120

47.436709

0.620199

0.000934

0

0.394737

0

0.015789

0.267563

0

0.1

1

0.1

false

0

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0

0.113158

0

null

0

1

0

null

0

6

463afbd78683c87845fac6bedfa6cb0506f041fe

6,781

py

Python

firewall/pluginManager.py

uzairAK/serverom-panel

3dcde05ad618e6bef280db7d3180f926fe2ab1db

[ "MIT" ]

null

firewall/pluginManager.py

uzairAK/serverom-panel

3dcde05ad618e6bef280db7d3180f926fe2ab1db

[ "MIT" ]

null

firewall/pluginManager.py

uzairAK/serverom-panel

3dcde05ad618e6bef280db7d3180f926fe2ab1db

[ "MIT" ]

null

from .signals import * from plogical.pluginManagerGlobal import pluginManagerGlobal class pluginManager: @staticmethod def preFirewallHome(request): return pluginManagerGlobal.globalPlug(request, preFirewallHome) @staticmethod def postFirewallHome(request, response): return pluginManagerGlobal.globalPlug(request, postFirewallHome, response) @staticmethod def preAddRule(request): return pluginManagerGlobal.globalPlug(request, preAddRule) @staticmethod def postAddRule(request, response): return pluginManagerGlobal.globalPlug(request, postAddRule, response) @staticmethod def preDeleteRule(request): return pluginManagerGlobal.globalPlug(request, preDeleteRule) @staticmethod def postDeleteRule(request, response): return pluginManagerGlobal.globalPlug(request, postDeleteRule, response) @staticmethod def preReloadFirewall(request): return pluginManagerGlobal.globalPlug(request, preReloadFirewall) @staticmethod def postReloadFirewall(request, response): return pluginManagerGlobal.globalPlug(request, postReloadFirewall, response) @staticmethod def preStartFirewall(request): return pluginManagerGlobal.globalPlug(request, preStartFirewall) @staticmethod def postStartFirewall(request, response): return pluginManagerGlobal.globalPlug(request, postStartFirewall, response) @staticmethod def preStopFirewall(request): return pluginManagerGlobal.globalPlug(request, preStopFirewall) @staticmethod def postStopFirewall(request, response): return pluginManagerGlobal.globalPlug(request, postStopFirewall, response) @staticmethod def preFirewallStatus(request): return pluginManagerGlobal.globalPlug(request, preFirewallStatus) @staticmethod def postFirewallStatus(request, response): return pluginManagerGlobal.globalPlug(request, postFirewallStatus, response) @staticmethod def preSecureSSH(request): return pluginManagerGlobal.globalPlug(request, preSecureSSH) @staticmethod def postSecureSSH(request, response): return pluginManagerGlobal.globalPlug(request, postSecureSSH, response) @staticmethod def preSaveSSHConfigs(request): return pluginManagerGlobal.globalPlug(request, preSaveSSHConfigs) @staticmethod def postSaveSSHConfigs(request, response): return pluginManagerGlobal.globalPlug(request, postSaveSSHConfigs, response) @staticmethod def preDeleteSSHKey(request): return pluginManagerGlobal.globalPlug(request, preDeleteSSHKey) @staticmethod def postDeleteSSHKey(request, response): return pluginManagerGlobal.globalPlug(request, postDeleteSSHKey, response) @staticmethod def preAddSSHKey(request): return pluginManagerGlobal.globalPlug(request, preAddSSHKey) @staticmethod def postAddSSHKey(request, response): return pluginManagerGlobal.globalPlug(request, postAddSSHKey, response) @staticmethod def preLoadModSecurityHome(request): return pluginManagerGlobal.globalPlug(request, preLoadModSecurityHome) @staticmethod def postLoadModSecurityHome(request, response): return pluginManagerGlobal.globalPlug(request, postLoadModSecurityHome, response) @staticmethod def preSaveModSecConfigurations(request): return pluginManagerGlobal.globalPlug(request, preSaveModSecConfigurations) @staticmethod def postSaveModSecConfigurations(request, response): return pluginManagerGlobal.globalPlug(request, postSaveModSecConfigurations, response) @staticmethod def preModSecRules(request): return pluginManagerGlobal.globalPlug(request, preModSecRules) @staticmethod def postModSecRules(request, response): return pluginManagerGlobal.globalPlug(request, postModSecRules, response) @staticmethod def preSaveModSecRules(request): return pluginManagerGlobal.globalPlug(request, preSaveModSecRules) @staticmethod def postSaveModSecRules(request, response): return pluginManagerGlobal.globalPlug(request, postSaveModSecRules, response) @staticmethod def preModSecRulesPacks(request): return pluginManagerGlobal.globalPlug(request, preModSecRulesPacks) @staticmethod def postModSecRulesPacks(request, response): return pluginManagerGlobal.globalPlug(request, postModSecRulesPacks, response) @staticmethod def preGetOWASPAndComodoStatus(request): return pluginManagerGlobal.globalPlug(request, preGetOWASPAndComodoStatus) @staticmethod def postGetOWASPAndComodoStatus(request, response): return pluginManagerGlobal.globalPlug(request, postGetOWASPAndComodoStatus, response) @staticmethod def preInstallModSecRulesPack(request): return pluginManagerGlobal.globalPlug(request, preInstallModSecRulesPack) @staticmethod def postInstallModSecRulesPack(request, response): return pluginManagerGlobal.globalPlug(request, postInstallModSecRulesPack, response) @staticmethod def preGetRulesFiles(request): return pluginManagerGlobal.globalPlug(request, preGetRulesFiles) @staticmethod def postGetRulesFiles(request, response): return pluginManagerGlobal.globalPlug(request, postGetRulesFiles, response) @staticmethod def preEnableDisableRuleFile(request): return pluginManagerGlobal.globalPlug(request, preEnableDisableRuleFile) @staticmethod def postEnableDisableRuleFile(request, response): return pluginManagerGlobal.globalPlug(request, postEnableDisableRuleFile, response) @staticmethod def preCSF(request): return pluginManagerGlobal.globalPlug(request, preCSF) @staticmethod def postCSF(request, response): return pluginManagerGlobal.globalPlug(request, postCSF, response) @staticmethod def preChangeStatus(request): return pluginManagerGlobal.globalPlug(request, preChangeStatus) @staticmethod def postChangeStatus(request, response): return pluginManagerGlobal.globalPlug(request, postChangeStatus, response) @staticmethod def preModifyPorts(request): return pluginManagerGlobal.globalPlug(request, preModifyPorts) @staticmethod def postModifyPorts(request, response): return pluginManagerGlobal.globalPlug(request, postModifyPorts, response) @staticmethod def preModifyIPs(request): return pluginManagerGlobal.globalPlug(request, preModifyIPs) @staticmethod def postModifyIPs(request, response): return pluginManagerGlobal.globalPlug(request, postModifyIPs, response)

34.596939

94

0.764342

490

6,781

10.577551

0.12449

0.138916

0.324137

0.388964

0.490835

0.26394

0

0.171361

6,781

196

95

34.596939

0.922406

0

0.326531

0

1

0.326531

false

0

0.013605

0.326531

0.673469

0

1

null

0

1

0

null

0

1

0

1

0

6

4659ef8ad5a56ccb89b28121d451b5638378d319

12,439

py

Python

python/test/mapreduce/mapper_pipeline_test.py

rolepoint/appengine-mapreduce

8710047353b8cb37938ec170c3019dfb099e5697

[ "Apache-2.0" ]

null

python/test/mapreduce/mapper_pipeline_test.py

rolepoint/appengine-mapreduce

8710047353b8cb37938ec170c3019dfb099e5697

[ "Apache-2.0" ]

1

2015-01-30T02:50:09.000Z

2015-01-30T02:52:00.000Z

python/test/mapreduce/mapper_pipeline_test.py

rolepoint/appengine-mapreduce

8710047353b8cb37938ec170c3019dfb099e5697

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright 2011 Google Inc. All Rights Reserved. # pylint: disable=g-bad-name import datetime import unittest from mapreduce.lib import pipeline from google.appengine.api import files from google.appengine.ext import db from mapreduce import context from mapreduce import errors from mapreduce import input_readers from mapreduce import mapper_pipeline from mapreduce import model from mapreduce import output_writers from mapreduce import test_support from testlib import testutil class TestEntity(db.Model): """Test entity class.""" data = db.StringProperty() dt = db.DateTimeProperty(default=datetime.datetime(2000, 1, 1)) class TestOutputEntity(db.Model): """TestOutput entity class.""" data = db.StringProperty() class RetryCount(db.Model): """Use to keep track of slice/shard retries.""" retries = db.IntegerProperty() def test_fail_map(_): """Always fail job immediately.""" raise errors.FailJobError() def test_slice_retry_map(entity): """Raise exception for 11 times when data is 100.""" if entity.data == "100": retry_count = RetryCount.get_by_key_name(entity.data) if not retry_count: retry_count = RetryCount(key_name=entity.data, retries=0) if retry_count.retries < 11: retry_count.retries += 1 retry_count.put() raise Exception() TestOutputEntity(key_name=entity.data, data=entity.data).put() def test_shard_retry_map(entity): """Raise exception 12 times when data is 100.""" if entity.data == "100": retry_count = RetryCount.get_by_key_name(entity.data) if not retry_count: retry_count = RetryCount(key_name=entity.data, retries=0) if retry_count.retries < 12: retry_count.retries += 1 retry_count.put() raise Exception() TestOutputEntity(key_name=entity.data, data=entity.data).put() def test_shard_retry_too_many_map(entity): """Raise shard retry exception 45 times when data is 100.""" if entity.data == "100": retry_count = RetryCount.get_by_key_name(entity.data) if not retry_count: retry_count = RetryCount(key_name=entity.data, retries=0) if retry_count.retries < 45: retry_count.retries += 1 retry_count.put() raise Exception() TestOutputEntity(key_name=entity.data, data=entity.data).put() def test_map(entity): """Test map handler.""" yield (entity.data, "") def test_empty_handler(entity): """Test handler that does nothing.""" pass class CleanupPipelineTest(testutil.HandlerTestBase): """Tests for the CleanupPipeline class.""" def setUp(self): testutil.HandlerTestBase.setUp(self) pipeline.Pipeline._send_mail = self._send_mail self.emails = [] def _send_mail(self, sender, subject, body, html=None): """Callback function for sending mail.""" self.emails.append((sender, subject, body, html)) def testCleanup_Flat(self): """Tests cleaning up a flat list of files.""" # Prepare test data entity_count = 200 for i in range(entity_count): TestEntity(data=str(i)).put() TestEntity(data=str(i)).put() # Run map p = mapper_pipeline.MapperPipeline( "test", handler_spec=__name__ + ".test_map", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", output_writer_spec= output_writers.__name__ + ".KeyValueBlobstoreOutputWriter", params={ "input_reader": { "entity_kind": __name__ + ".TestEntity", }, }, ) p.start() test_support.execute_until_empty(self.taskqueue) finished_map = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) # Can open files file_list = finished_map.outputs.default.value self.assertTrue(len(file_list) > 0) for name in file_list: files.open(name, "r").read(0) # Cleanup cleanup = mapper_pipeline._CleanupPipeline(file_list) cleanup.start() test_support.execute_until_empty(self.taskqueue) # Cannot open files for name in file_list: self.assertRaises(files.Error, files.open, name, "r") def testCleanup_ListOfLists(self): """Tests cleaning up a list of file lists.""" # Prepare test data entity_count = 200 for i in range(entity_count): TestEntity(data=str(i)).put() TestEntity(data=str(i)).put() # Run map p = mapper_pipeline.MapperPipeline( "test", handler_spec=__name__ + ".test_map", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", output_writer_spec= output_writers.__name__ + ".KeyValueBlobstoreOutputWriter", params={ "input_reader": { "entity_kind": __name__ + ".TestEntity", }, }, ) p.start() test_support.execute_until_empty(self.taskqueue) finished_map = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) # Can open files file_list = finished_map.outputs.default.value self.assertTrue(len(file_list) > 0) for name in file_list: files.open(name, "r").read(0) grouped_list = [file_list] # Cleanup cleanup = mapper_pipeline._CleanupPipeline(grouped_list) cleanup.start() test_support.execute_until_empty(self.taskqueue) # Cannot open files for name in file_list: self.assertRaises(files.Error, files.open, name, "r") class MapperPipelineTest(testutil.HandlerTestBase): """Tests for MapperPipeline.""" def setUp(self): testutil.HandlerTestBase.setUp(self) pipeline.Pipeline._send_mail = self._send_mail self.emails = [] def _send_mail(self, sender, subject, body, html=None): """Callback function for sending mail.""" self.emails.append((sender, subject, body, html)) def testEmptyMapper(self): """Test empty mapper over empty dataset.""" p = mapper_pipeline.MapperPipeline( "empty_map", handler_spec=__name__ + ".test_empty_handler", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", params={ "input_reader": { "entity_kind": __name__ + ".TestEntity", # Test datetime can be json serialized. "filters": [("dt", "=", datetime.datetime(2000, 1, 1))], }, }, ) p.start() test_support.execute_until_empty(self.taskqueue) self.assertEquals(1, len(self.emails)) self.assertTrue(self.emails[0][1].startswith( "Pipeline successful:")) p = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) # Verify outputs. # Counter output counters = p.outputs.counters.value self.assertTrue(counters) self.assertTrue(context.COUNTER_MAPPER_WALLTIME_MS in counters) # Default output. self.assertEqual([], p.outputs.default.value) # Job id output. self.assertTrue(p.outputs.job_id.filled) state = model.MapreduceState.get_by_job_id(p.outputs.job_id.value) self.assertEqual(model.MapreduceState.RESULT_SUCCESS, state.result_status) # Result status output. self.assertEqual(model.MapreduceState.RESULT_SUCCESS, p.outputs.result_status.value) def testFailedMap(self): for i in range(1): TestEntity(data=str(i)).put() pipeline.pipeline._DEFAULT_MAX_ATTEMPTS = 1 p = mapper_pipeline.MapperPipeline( "test", handler_spec=__name__ + ".test_fail_map", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", params={ "input_reader": { "entity_kind": __name__ + "." + TestEntity.__name__, }, }, shards=5) p.start() test_support.execute_until_empty(self.taskqueue) p = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) self.assertTrue(p.was_aborted) self.assertTrue(p.outputs.job_id.filled) state = model.MapreduceState.get_by_job_id(p.outputs.job_id.value) self.assertEqual(model.MapreduceState.RESULT_FAILED, state.result_status) self.assertFalse(p.outputs.result_status.filled) self.assertFalse(p.outputs.default.filled) self.assertEquals(1, len(self.emails)) self.assertTrue(self.emails[0][1].startswith( "Pipeline aborted:")) def testProcessEntities(self): """Test empty mapper over non-empty dataset.""" for _ in range(100): TestEntity().put() p = mapper_pipeline.MapperPipeline( "empty_map", handler_spec=__name__ + ".test_empty_handler", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", params={ "input_reader": { "entity_kind": __name__ + ".TestEntity", }, }, ) p.start() test_support.execute_until_empty(self.taskqueue) self.assertEquals(1, len(self.emails)) self.assertTrue(self.emails[0][1].startswith( "Pipeline successful:")) p = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) self.assertTrue(p.outputs.job_id.filled) counters = p.outputs.counters.value self.assertTrue(counters) self.assertTrue(context.COUNTER_MAPPER_WALLTIME_MS in counters) self.assertEquals(100, counters[context.COUNTER_MAPPER_CALLS]) self.assertEqual(model.MapreduceState.RESULT_SUCCESS, p.outputs.result_status.value) self.assertEqual([], p.outputs.default.value) def testSliceRetry(self): entity_count = 200 db.delete(TestOutputEntity.all()) db.delete(RetryCount.all()) for i in range(entity_count): TestEntity(data=str(i)).put() p = mapper_pipeline.MapperPipeline( "test", handler_spec=__name__ + ".test_slice_retry_map", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", params={ "input_reader": { "entity_kind": __name__ + "." + TestEntity.__name__, }, }, shards=5) p.start() test_support.execute_until_empty(self.taskqueue) self.assertEquals(1, len(self.emails)) self.assertTrue(self.emails[0][1].startswith( "Pipeline successful:")) p = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) outputs = [] for output in TestOutputEntity.all(): outputs.append(int(output.data)) outputs.sort() expected_outputs = [i for i in range(entity_count)] expected_outputs.sort() self.assertEquals(expected_outputs, outputs) def testShardRetry(self): entity_count = 200 db.delete(TestOutputEntity.all()) db.delete(RetryCount.all()) for i in range(entity_count): TestEntity(data=str(i)).put() p = mapper_pipeline.MapperPipeline( "test", handler_spec=__name__ + ".test_shard_retry_map", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", params={ "input_reader": { "entity_kind": __name__ + "." + TestEntity.__name__, }, }, shards=5) p.start() test_support.execute_until_empty(self.taskqueue) self.assertEquals(1, len(self.emails)) self.assertTrue(self.emails[0][1].startswith( "Pipeline successful:")) p = mapper_pipeline.MapperPipeline.from_id(p.pipeline_id) outputs = [] for output in TestOutputEntity.all(): outputs.append(int(output.data)) outputs.sort() expected_outputs = [i for i in range(entity_count)] expected_outputs.sort() self.assertEquals(expected_outputs, outputs) def testShardRetryTooMany(self): entity_count = 200 db.delete(TestOutputEntity.all()) db.delete(RetryCount.all()) for i in range(entity_count): TestEntity(data=str(i)).put() p = mapper_pipeline.MapperPipeline( "test", handler_spec=__name__ + ".test_shard_retry_too_many_map", input_reader_spec=input_readers.__name__ + ".DatastoreInputReader", params={ "input_reader": { "entity_kind": __name__ + "." + TestEntity.__name__, }, }, shards=5) p.max_attempts = 1 p.start() test_support.execute_until_empty(self.taskqueue) state = model.MapreduceState.all().get() self.assertEqual(model.MapreduceState.RESULT_FAILED, state.result_status) self.assertEquals(1, len(self.emails)) self.assertTrue(self.emails[0][1].startswith( "Pipeline aborted:")) if __name__ == "__main__": unittest.main()

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0.741297

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12,439

411

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30.265207

0.800696

0.076855

0

0.723549

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0.069338

0.026364

0

0.122867

1

0.061433

false

0.003413

0.044369

0

0.136519

0

null

0

1

0

1

0

null

0

6

469dca0b079020ec732b7d643edce4955e33299f

106

py

Python

cased/plugins/__init__.py

cased/cased-python

e3c529e3fe816331277812bf4e3db537eb5a54fc

[ "MIT" ]

null

cased/plugins/__init__.py

cased/cased-python

e3c529e3fe816331277812bf4e3db537eb5a54fc

[ "MIT" ]

null

cased/plugins/__init__.py

cased/cased-python

e3c529e3fe816331277812bf4e3db537eb5a54fc

[ "MIT" ]

null

from cased.plugins.casedplugin import DataPlugin from cased.plugins.casedplugin import CasedDefaultPlugin

35.333333

56

0.886792

12

106

7.833333

0.583333

0.191489

0.340426

0.574468

0.702128

0

0.075472

106

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true

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null

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1

0

1

0

null

0

1

0

1

0

6

46a22e4eb342d4b6aa4a45089fe8603feb3934f5

81

py

Python

n_in_a_row/game_state/__init__.py

olokshyn/n_in_a_row

d47c16b58c755d640ced4e74854fa37653304e5d

[ "MIT" ]

null

n_in_a_row/game_state/__init__.py

olokshyn/n_in_a_row

d47c16b58c755d640ced4e74854fa37653304e5d

[ "MIT" ]

null

n_in_a_row/game_state/__init__.py

olokshyn/n_in_a_row

d47c16b58c755d640ced4e74854fa37653304e5d

[ "MIT" ]

null

from .game_state import GameState from .game_tree_builder import GameTreeBuilder

27

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0.098765

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true

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null

0

1

0

1

0

1

0

6

d3bf2c2f3f27590dfcb369b48d62ab8029da71a0

43,433

py

Python

gnsstools/galileo/e6b_strings.py

GNSSX/GNSS-DSP-tools

a06f83b2f25e3ffebc7502f363028ddfb1d5c8f7

[ "MIT" ]

2

2020-02-28T18:13:51.000Z

2020-07-04T20:19:39.000Z

gnsstools/galileo/e6b_strings.py

GNSSNWO/GNSS-DSP-tools

a06f83b2f25e3ffebc7502f363028ddfb1d5c8f7

[ "MIT" ]

null

gnsstools/galileo/e6b_strings.py

GNSSNWO/GNSS-DSP-tools

a06f83b2f25e3ffebc7502f363028ddfb1d5c8f7

[ "MIT" ]

null

# Code strings the E6-B code # OCR from European patent EP1825626B1.pdf (may have a few errors) # The codes for the following PRNs have been compared with recorded signals and should be error-free: # 1 2 3 4 5 7 8 9 11 12 13 14 15 18 19 21 24 25 26 27 30 31 33 36 e6b_strings = { 1: "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", 2: "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", 3: "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", 4: "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", 5: "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", 6: "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", 7: "8EQSl2WAo9UQa6NYV1xVSacFTg37EUve0J7S+GZYAd6PfXThdNg5MYeFDtJp4QVckGDDRB37kco8rYSlwR/tEmcUyMubuTTQPySgNQ0yh6ZNGG+lyUVjTHHw16T/UCS73S156Ot13n8xmFWF0Fbu8eup5MEAQ1d2p9NGXG2uIbHYmg/fAmSlq5+cekQY9rWjF+2CjmPWqlZsURlCID5i66TfvLgY7WIs8DMC4bOoXAsPBfHLePmpDHfvhWW/cD3PXUXI2VZ2X5lMj7MkPOCJ5o5wXzEL+em4GwROMHxyM8wdTjI39q9bGmmavBH8TB23qZi5yxlFT1q57X0//l7lWmGErPodOkgbt0qy5w4ogJ+S+hXLmeoKx8u6E4W72j2FblwsOqN57AiOrUhteC/SjFQz7BHwHMmWovFb87WzZ1KhiPrze/3BcKVEEdXQs21SBk2I/0x5wfDiglzWvZxoEcCxNvnvpydZHpjn1nOtK4G49wMyNWifnreKTahdPCa1Lws9gicEihkbPCgFMRBytVbouMPdlh5yz7YjXcasotFkQawhUcN9mqsbE7225Oguj91H/ZnoughRMcTMXYAca5BggDJvZaXF/ND8IoYq9qrLeRUq7s4N3z1DLg0AJTkBTKjHx4L15AKxLDT0QOx0OfjOFoL6bCtInA+4ZQ5/sgr1k0FlUinEUKopsksnn5uLOLYvz8Fm4AEl1q7lPDzfTOQSBtgp4Auu+nm421HOzk4stmoeMKHDmiw5WYuGdak1u6pOkp3yfKo9QQuum3TawUNbPobJwn/VESqbnZ5/emWxTwiWLzQBPeyQakh/wjPbi9JN6GFqjqrqb0vRiq+zwA", 8: 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Python

integrations/airflow/tests/test_marquez_dag.py

mobuchowski/marquez

a1964623e13e95ee98b93517f11cdf116a1d1184

[ "Apache-2.0" ]

null

integrations/airflow/tests/test_marquez_dag.py

mobuchowski/marquez

a1964623e13e95ee98b93517f11cdf116a1d1184

[ "Apache-2.0" ]

null

integrations/airflow/tests/test_marquez_dag.py

mobuchowski/marquez

a1964623e13e95ee98b93517f11cdf116a1d1184

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import mock import logging from airflow.models import (TaskInstance, DagRun) from airflow.operators.dummy_operator import DummyOperator from airflow.utils.decorators import apply_defaults from airflow.utils.db import provide_session from airflow.utils.dates import days_ago from airflow.utils import timezone from airflow.utils.state import State from marquez_client.models import JobType, DatasetType from marquez_airflow.dag import _EXTRACTORS as _DAG_EXTRACTORS from marquez_airflow import DAG from marquez_airflow.extractors import ( BaseExtractor, StepMetadata, Source, Dataset ) from marquez_airflow.models import ( DbTableName, DbTableSchema, DbColumn ) from marquez_airflow.utils import get_location, get_job_name from uuid import UUID log = logging.getLogger(__name__) NO_INPUTS = [] NO_OUTPUTS = [] DEFAULT_DATE = timezone.datetime(2016, 1, 1) DAG_ID = 'test_dag' DAG_RUN_ID = 'test_run_id_for_task_completed_and_failed' DAG_RUN_ARGS = {'external_trigger': False} # TODO: check with a different namespace and owner DAG_NAMESPACE = 'default' DAG_OWNER = 'anonymous' DAG_DESCRIPTION = \ 'A simple DAG to test the marquez.DAG metadata extraction flow.' DAG_DEFAULT_ARGS = { 'owner': DAG_OWNER, 'depends_on_past': False, 'start_date': days_ago(1), 'email_on_failure': False, 'email_on_retry': False, 'email': ['owner@test.com'] } TASK_ID_COMPLETED = 'test_task_completed' TASK_ID_FAILED = 'test_task_failed' @pytest.fixture @provide_session def clear_db_airflow_dags(session=None): session.query(DagRun).delete() session.query(TaskInstance).delete() @provide_session def test_new_run_id(clear_db_airflow_dags, session=None): dag = DAG( DAG_ID, schedule_interval='@daily', default_args=DAG_DEFAULT_ARGS, description=DAG_DESCRIPTION ) run_id = dag.new_run_id() assert UUID(run_id).version == 4 # tests a simple workflow with default extraction mechanism @mock.patch('marquez_airflow.DAG.new_run_id') @mock.patch('marquez_airflow.marquez.Marquez.get_or_create_marquez_client') @provide_session def test_marquez_dag(mock_get_or_create_marquez_client, mock_uuid, clear_db_airflow_dags, session=None): dag = DAG( DAG_ID, schedule_interval='@daily', default_args=DAG_DEFAULT_ARGS, description=DAG_DESCRIPTION ) # (1) Mock the marquez client method calls mock_marquez_client = mock.Mock() mock_get_or_create_marquez_client.return_value = mock_marquez_client run_id_completed = "my-test_marquez_dag-uuid-completed" run_id_failed = "my-test_marquez_dag-uuid-failed" mock_uuid.side_effect = [run_id_completed, run_id_failed] # (2) Add task that will be marked as completed task_will_complete = DummyOperator( task_id=TASK_ID_COMPLETED, dag=dag ) completed_task_location = get_location(task_will_complete.dag.fileloc) # (3) Add task that will be marked as failed task_will_fail = DummyOperator( task_id=TASK_ID_FAILED, dag=dag ) failed_task_location = get_location(task_will_complete.dag.fileloc) # (4) Create DAG run and mark as running dagrun = dag.create_dagrun( run_id=DAG_RUN_ID, execution_date=DEFAULT_DATE, state=State.RUNNING) # Assert namespace meta call mock_marquez_client.create_namespace.assert_called_once_with(DAG_NAMESPACE, DAG_OWNER) # Assert source and dataset meta calls mock_marquez_client.create_source.assert_not_called() mock_marquez_client.create_dataset.assert_not_called() # Assert job meta calls create_job_calls = [ mock.call( job_name=f"{DAG_ID}.{TASK_ID_COMPLETED}", job_type=JobType.BATCH, location=completed_task_location, input_dataset=None, output_dataset=None, context=mock.ANY, description=DAG_DESCRIPTION, namespace_name=DAG_NAMESPACE, run_id=None ), mock.call( job_name=f"{DAG_ID}.{TASK_ID_FAILED}", job_type=JobType.BATCH, location=failed_task_location, input_dataset=None, output_dataset=None, context=mock.ANY, description=DAG_DESCRIPTION, namespace_name=DAG_NAMESPACE, run_id=None ) ] log.info( f"{ [name for name, args, kwargs in mock_marquez_client.mock_calls]}") mock_marquez_client.create_job.assert_has_calls(create_job_calls) # Assert job run meta calls create_job_run_calls = [ mock.call( job_name=f"{DAG_ID}.{TASK_ID_COMPLETED}", run_id=mock.ANY, run_args=DAG_RUN_ARGS, nominal_start_time=mock.ANY, nominal_end_time=mock.ANY, namespace_name=DAG_NAMESPACE ), mock.call( job_name=f"{DAG_ID}.{TASK_ID_FAILED}", run_id=mock.ANY, run_args=DAG_RUN_ARGS, nominal_start_time=mock.ANY, nominal_end_time=mock.ANY, namespace_name=DAG_NAMESPACE ) ] mock_marquez_client.create_job_run.assert_has_calls(create_job_run_calls) # (5) Start task that will be marked as completed task_will_complete.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE) # (6) Start task that will be marked as failed ti1 = TaskInstance(task=task_will_fail, execution_date=DEFAULT_DATE) ti1.state = State.FAILED session.add(ti1) session.commit() dag.handle_callback(dagrun, success=True, session=session) # Assert start run meta calls start_job_run_calls = [ mock.call(run_id_completed, mock.ANY), mock.call(run_id_failed, mock.ANY) ] mock_marquez_client.mark_job_run_as_started.assert_has_calls( start_job_run_calls ) mock_marquez_client.mark_job_run_as_completed.assert_called_once_with( run_id=run_id_completed, at=mock.ANY ) # When a task run completes, the task outputs are also updated in order # to link a job version (=task version) to a dataset version. # Using a DummyOperator, no outputs exists, so assert that the create # dataset call is not invoked. mock_marquez_client.create_dataset.assert_not_called() dag.handle_callback(dagrun, success=False, session=session) mock_marquez_client.mark_job_run_as_failed.assert_called_once_with( run_id=run_id_failed, at=mock.ANY ) # Assert an attempt to version the outputs of a task is not made when # a task fails mock_marquez_client.create_dataset.assert_not_called() class TestFixtureDummyOperator(DummyOperator): @apply_defaults def __init__(self, *args, **kwargs): super(TestFixtureDummyOperator, self).__init__(*args, **kwargs) class TestFixtureDummyExtractor(BaseExtractor): operator_class = TestFixtureDummyOperator source = Source( type="DummySource", name="dummy_source_name", connection_url="http://dummy/source/url") def __init__(self, operator): super().__init__(operator) def extract(self) -> [StepMetadata]: inputs = [ Dataset.from_table(self.source, "extract_input1") ] outputs = [ Dataset.from_table(self.source, "extract_output1") ] return [StepMetadata( name=get_job_name(task=self.operator), inputs=inputs, outputs=outputs, context={ "extract": "extract" } )] def extract_on_complete(self, task_instance) -> [StepMetadata]: return [] class TestFixtureDummyExtractorOnComplete(BaseExtractor): operator_class = TestFixtureDummyOperator source = Source( type="DummySource", name="dummy_source_name", connection_url="http://dummy/source/url") def __init__(self, operator): super().__init__(operator) def extract(self) -> [StepMetadata]: return [] def extract_on_complete(self, task_instance) -> [StepMetadata]: inputs = [ Dataset.from_table_schema(self.source, DbTableSchema( schema_name='schema', table_name=DbTableName('extract_on_complete_input1'), columns=[DbColumn( name='field1', type='text', description='', ordinal_position=1 ), DbColumn( name='field2', type='text', description='', ordinal_position=2 )] )) ] outputs = [ Dataset.from_table(self.source, "extract_on_complete_output1") ] return [StepMetadata( name=get_job_name(task=self.operator), inputs=inputs, outputs=outputs, context={ "extract_on_complete": "extract_on_complete" } )] # test the lifecycle including with extractors @mock.patch('marquez_airflow.DAG.new_run_id') @mock.patch('marquez_airflow.marquez.Marquez.get_or_create_marquez_client') @provide_session def test_marquez_dag_with_extractor(mock_get_or_create_marquez_client, mock_uuid, clear_db_airflow_dags, session=None): # --- test setup dag_id = 'test_marquez_dag_with_extractor' dag = DAG( dag_id, schedule_interval='@daily', default_args=DAG_DEFAULT_ARGS, description=DAG_DESCRIPTION ) run_id = "my-test-uuid" mock_uuid.side_effect = [run_id] # Mock the marquez client method calls mock_marquez_client = mock.Mock() mock_get_or_create_marquez_client.return_value = mock_marquez_client # Add task that will be marked as completed task_will_complete = TestFixtureDummyOperator( task_id=TASK_ID_COMPLETED, dag=dag ) completed_task_location = get_location(task_will_complete.dag.fileloc) # Add the dummy extractor to the list for the task above _DAG_EXTRACTORS[task_will_complete.__class__] = TestFixtureDummyExtractor # --- pretend run the DAG # Create DAG run and mark as running dagrun = dag.create_dagrun( run_id='test_marquez_dag_with_extractor_run_id', execution_date=DEFAULT_DATE, state=State.RUNNING) # --- Asserts that the job starting triggers metadata updates # Namespace created mock_marquez_client.create_namespace.assert_called_once_with(DAG_NAMESPACE, DAG_OWNER) # Datasets are updated mock_marquez_client.create_source.assert_called_with( 'dummy_source_name', 'DummySource', 'http://dummy/source/url' ) mock_marquez_client.create_dataset.assert_has_calls([ mock.call( dataset_name='extract_input1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_input1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id=None ), mock.call( dataset_name='extract_output1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_output1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id=None ) ]) # job is updated mock_marquez_client.create_job.assert_called_once_with( job_name=f"{dag_id}.{TASK_ID_COMPLETED}", job_type=JobType.BATCH, location=completed_task_location, input_dataset=[{'namespace': 'default', 'name': 'extract_input1'}], output_dataset=[{'namespace': 'default', 'name': 'extract_output1'}], context=mock.ANY, description=DAG_DESCRIPTION, namespace_name=DAG_NAMESPACE, run_id=None ) assert mock_marquez_client.create_job.mock_calls[0].\ kwargs['context'].get('extract') == 'extract' # run is created mock_marquez_client.create_job_run.assert_called_once_with( job_name=f"{dag_id}.{TASK_ID_COMPLETED}", run_id=run_id, run_args=DAG_RUN_ARGS, nominal_start_time=mock.ANY, nominal_end_time=mock.ANY, namespace_name=DAG_NAMESPACE ) log.info("Marquez client calls when starting:") for call in mock_marquez_client.mock_calls: log.info(call) assert [name for name, args, kwargs in mock_marquez_client.mock_calls] == [ 'create_namespace', 'create_source', 'create_dataset', 'create_source', 'create_dataset', 'create_job', 'create_job_run' ] mock_marquez_client.reset_mock() # --- Pretend complete the task task_will_complete.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE) dag.handle_callback(dagrun, success=True, session=session) # run is started mock_marquez_client.mark_job_run_as_started.assert_called_once_with( run_id, mock.ANY ) # --- Assert that the right marquez calls are done # job is updated before completion mock_marquez_client.create_job.assert_has_calls([ mock.call( namespace_name=DAG_NAMESPACE, job_name=f"{dag_id}.{TASK_ID_COMPLETED}", job_type=JobType.BATCH, location=completed_task_location, input_dataset=[ {'namespace': 'default', 'name': 'extract_input1'} ], output_dataset=[ {'namespace': 'default', 'name': 'extract_output1'} ], context=mock.ANY, description=DAG_DESCRIPTION, run_id=run_id ) ]) assert mock_marquez_client.create_job.mock_calls[0].\ kwargs['context'].get('extract') == 'extract' mock_marquez_client.mark_job_run_as_completed.assert_called_once_with( run_id=run_id, at=mock.ANY ) # When a task run completes, the task outputs are also updated in order # to link a job version (=task version) to a dataset version. mock_marquez_client.create_dataset.assert_has_calls([ mock.call( dataset_name='extract_input1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_input1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id=None ), mock.call( dataset_name='extract_output1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_output1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id=run_id ) ]) log.info("Marquez client calls when completing:") for call in mock_marquez_client.mock_calls: log.info(call) assert [name for name, args, kwargs in mock_marquez_client.mock_calls] == [ 'create_namespace', 'create_source', 'create_dataset', 'create_source', 'create_dataset', 'create_job', 'mark_job_run_as_started', 'mark_job_run_as_completed' ] @mock.patch('marquez_airflow.DAG.new_run_id') @mock.patch('marquez_airflow.marquez.Marquez.get_or_create_marquez_client') @provide_session def test_marquez_dag_with_extract_on_complete( mock_get_or_create_marquez_client, mock_uuid, clear_db_airflow_dags, session=None): # --- test setup dag_id = 'test_marquez_dag_with_extractor' dag = DAG( dag_id, schedule_interval='@daily', default_args=DAG_DEFAULT_ARGS, description=DAG_DESCRIPTION ) run_id = "my-test-uuid" mock_uuid.side_effect = [run_id] # Mock the marquez client method calls mock_marquez_client = mock.Mock() mock_get_or_create_marquez_client.return_value = mock_marquez_client # Add task that will be marked as completed task_will_complete = TestFixtureDummyOperator( task_id=TASK_ID_COMPLETED, dag=dag ) completed_task_location = get_location(task_will_complete.dag.fileloc) # Add the dummy extractor to the list for the task above _DAG_EXTRACTORS[task_will_complete.__class__] = \ TestFixtureDummyExtractorOnComplete # Create DAG run and mark as running dagrun = dag.create_dagrun( run_id='test_marquez_dag_with_extractor_run_id', execution_date=DEFAULT_DATE, state=State.RUNNING) # Namespace created mock_marquez_client.create_namespace.assert_called_once_with(DAG_NAMESPACE, DAG_OWNER) log.info("Marquez client calls when starting:") for call in mock_marquez_client.mock_calls: log.info(call) assert [name for name, args, kwargs in mock_marquez_client.mock_calls] == [ 'create_namespace' ] mock_marquez_client.reset_mock() # --- Pretend complete the task task_will_complete.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE) dag.handle_callback(dagrun, success=True, session=session) # Datasets are updated mock_marquez_client.create_source.assert_called_with( 'dummy_source_name', 'DummySource', 'http://dummy/source/url' ) # Datasets get called twice, once to reenact the _begin_run_flow # and then again at _end_run_flow w/ the run id appended for # the output dataset mock_marquez_client.create_dataset.assert_has_calls([ mock.call( dataset_name='schema.extract_on_complete_input1', dataset_type=DatasetType.DB_TABLE, physical_name='schema.extract_on_complete_input1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=mock.ANY, run_id=None ), mock.call( dataset_name='extract_on_complete_output1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_on_complete_output1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id=None ), mock.call( dataset_name='schema.extract_on_complete_input1', dataset_type=DatasetType.DB_TABLE, physical_name='schema.extract_on_complete_input1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=mock.ANY, run_id=None ), mock.call( dataset_name='extract_on_complete_output1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_on_complete_output1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id='my-test-uuid' ) ]) # job is updated mock_marquez_client.create_job.assert_has_calls([ mock.call( job_name=f"{dag_id}.{TASK_ID_COMPLETED}", job_type=JobType.BATCH, location=completed_task_location, input_dataset=[{'namespace': 'default', 'name': 'schema.extract_on_complete_input1'}], output_dataset=[{'namespace': 'default', 'name': 'extract_on_complete_output1'}], context=mock.ANY, description=DAG_DESCRIPTION, namespace_name=DAG_NAMESPACE, run_id=None ), mock.call( job_name=f"{dag_id}.{TASK_ID_COMPLETED}", job_type=JobType.BATCH, location=completed_task_location, input_dataset=[{'namespace': 'default', 'name': 'schema.extract_on_complete_input1'}], output_dataset=[{'namespace': 'default', 'name': 'extract_on_complete_output1'}], context=mock.ANY, description=DAG_DESCRIPTION, namespace_name=DAG_NAMESPACE, run_id='my-test-uuid' ) ]) assert mock_marquez_client.create_job.mock_calls[0].\ kwargs['context'].get('extract_on_complete') == 'extract_on_complete' # run is created mock_marquez_client.create_job_run.assert_called_once_with( job_name=f"{dag_id}.{TASK_ID_COMPLETED}", run_id=run_id, run_args=DAG_RUN_ARGS, nominal_start_time=mock.ANY, nominal_end_time=mock.ANY, namespace_name=DAG_NAMESPACE ) # run is started mock_marquez_client.mark_job_run_as_started.assert_called_once_with( run_id, mock.ANY ) # --- Assert that the right marquez calls are done # job is updated before completion mock_marquez_client.create_job.assert_has_calls([ mock.call( namespace_name=DAG_NAMESPACE, job_name=f"{dag_id}.{TASK_ID_COMPLETED}", job_type=JobType.BATCH, location=completed_task_location, input_dataset=[ {'namespace': 'default', 'name': 'schema.extract_on_complete_input1'} ], output_dataset=[ {'namespace': 'default', 'name': 'extract_on_complete_output1'} ], context=mock.ANY, description=DAG_DESCRIPTION, run_id=run_id ) ]) assert mock_marquez_client.create_job.mock_calls[0].\ kwargs['context'].get('extract_on_complete') == 'extract_on_complete' mock_marquez_client.mark_job_run_as_completed.assert_called_once_with( run_id=run_id, at=mock.ANY ) # When a task run completes, the task outputs are also updated in order # to link a job version (=task version) to a dataset version. mock_marquez_client.create_dataset.assert_has_calls([ mock.call( dataset_name='schema.extract_on_complete_input1', dataset_type=DatasetType.DB_TABLE, physical_name='schema.extract_on_complete_input1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=mock.ANY, run_id=None ), mock.call( dataset_name='extract_on_complete_output1', dataset_type=DatasetType.DB_TABLE, physical_name='extract_on_complete_output1', source_name='dummy_source_name', namespace_name=DAG_NAMESPACE, fields=[], run_id=run_id ) ]) log.info("Marquez client calls when completing:") for call in mock_marquez_client.mock_calls: log.info(call) assert [name for name, args, kwargs in mock_marquez_client.mock_calls] == [ 'create_namespace', 'create_source', 'create_dataset', 'create_source', 'create_dataset', 'create_job', 'create_job_run', 'create_source', 'create_dataset', 'create_source', 'create_dataset', 'create_job', 'mark_job_run_as_started', 'mark_job_run_as_completed' ]

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0.269062

23,987

728

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0

0.001374

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0.070175

0

null

0

1

0

1

0

null

0

6

310461f740761fe3fece24641e0162474574c110

131

py

Python

tests/auth_example/auth_example/views.py

KitchenStories/django-rest-swagger

4262cb5156285adcdf661d5204d45eefd269aaca

[ "BSD-2-Clause" ]

1

2021-02-17T13:11:41.000Z

tests/auth_example/auth_example/views.py

KitchenStories/django-rest-swagger

4262cb5156285adcdf661d5204d45eefd269aaca

[ "BSD-2-Clause" ]

9

2020-06-05T17:07:13.000Z

2022-01-13T00:36:30.000Z

tests/auth_example/auth_example/views.py

KitchenStories/django-rest-swagger

4262cb5156285adcdf661d5204d45eefd269aaca

[ "BSD-2-Clause" ]

1

2021-02-18T11:05:55.000Z

from django.http import HttpResponse def permission_denied_handler(request): return HttpResponse("you have no permissions!")

21.833333

51

0.801527

16

131

6.4375

0.9375

0

0.129771

131

5

52

26.2

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0.333333

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0.333333

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null

0

1

0

null

0

1

0

1

0

6

31266ef63be16cc1934d60293d6b49c4f1d7b820

196

py

Python

src/clients/twitter/__init__.py

juanitodread/gorrion

1f2c16b7402c237dfb4b47f0fa0afeb3bff7bd19

[ "Apache-2.0" ]

1

2020-09-18T17:53:03.000Z

src/clients/twitter/__init__.py

juanitodread/gorrion

1f2c16b7402c237dfb4b47f0fa0afeb3bff7bd19

[ "Apache-2.0" ]

6

2020-10-27T03:31:41.000Z

2021-09-16T18:58:44.000Z

src/clients/twitter/__init__.py

juanitodread/gorrion

1f2c16b7402c237dfb4b47f0fa0afeb3bff7bd19

[ "Apache-2.0" ]

null

# flake8: noqa from src.clients.twitter.client import ( Twitter, TwitterLocal, ) from src.clients.twitter.models import PublishedTweet from src.clients.twitter.config import TwitterConfig

24.5

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54

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null

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1

0

1

0

null

0

1

0

1

0

6

313519530f3b7fd1e6f86640611dcd6119ce7085

37

py

Python

discord/webhook/async_.py

Harukomaze/disnake

541f5c9623a02be894cd1015dbb344070700cb87

[ "MIT" ]

null

discord/webhook/async_.py

Harukomaze/disnake

541f5c9623a02be894cd1015dbb344070700cb87

[ "MIT" ]

null

discord/webhook/async_.py

Harukomaze/disnake

541f5c9623a02be894cd1015dbb344070700cb87

[ "MIT" ]

null

from disnake.webhook.async_ import *

18.5

36

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37

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37

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37

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0

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1

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1

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0

null

0

1

0

1

0

1

0

6

316b29aee0c5c22612f6454966a5b61d32abe8f1

427

py

Python

whatnot/utils.py

willmeyers/unofficial-whatnot-api

b7b0792b77824a38b61f45c0e01f346673566a78

[ "MIT" ]

null

whatnot/utils.py

willmeyers/unofficial-whatnot-api

b7b0792b77824a38b61f45c0e01f346673566a78

[ "MIT" ]

null

whatnot/utils.py

willmeyers/unofficial-whatnot-api

b7b0792b77824a38b61f45c0e01f346673566a78

[ "MIT" ]

1

2021-06-09T22:44:03.000Z

import requets BASE_URL = 'https://api.whatnot.com/graphql/' def query(query, variables): resp = requets.post(f'{BASE_URL}', params=variables, data={ 'query': query } ) return resp.json() def send_mutation(query, variables): resp = requets.post(f'{BASE_URL}', params=variables, data={ 'query': query } ) return resp.json()

15.814815

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427

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null

0

1

0

1

0

null

0

6

3170941d662209873a4cdb4b02e25a9b3b97f34c

104,484

py

Python

anuga/abstract_2d_finite_volumes/tests/test_quantity.py

samcom12/anuga_core

f4378114dbf02d666fe6423de45798add5c42806

[ "Python-2.0", "OLDAP-2.7" ]

null

anuga/abstract_2d_finite_volumes/tests/test_quantity.py

samcom12/anuga_core

f4378114dbf02d666fe6423de45798add5c42806

[ "Python-2.0", "OLDAP-2.7" ]

null

anuga/abstract_2d_finite_volumes/tests/test_quantity.py

samcom12/anuga_core

f4378114dbf02d666fe6423de45798add5c42806

[ "Python-2.0", "OLDAP-2.7" ]

null

#!/usr/bin/env python from __future__ import print_function from __future__ import division from builtins import zip from builtins import map from builtins import str from builtins import range from past.utils import old_div import unittest from math import sqrt, pi import tempfile from anuga.abstract_2d_finite_volumes.quantity import * from anuga.file_conversion.asc2dem import asc2dem from anuga.config import epsilon from anuga.fit_interpolate.fit import fit_to_mesh #from anuga.pyvolution.least_squares import fit_to_mesh from anuga.abstract_2d_finite_volumes.generic_domain \ import Generic_Domain from anuga.geospatial_data.geospatial_data import Geospatial_data from anuga.coordinate_transforms.geo_reference import Geo_reference from anuga.geometry.polygon import * import numpy as num import pprint #Aux for fit_interpolate.fit example def linear_function(point): point = num.array(point) return point[:, 0]+3*point[:, 1] #return point[:,1] def axes2points(x, y): """Generate all combinations of grid point coordinates from x and y axes Args: * x: x coordinates (array) * y: y coordinates (array) Returns: * P: Nx2 array consisting of coordinates for all grid points defined by x and y axes. The x coordinate will vary the fastest to match the way 2D numpy arrays are laid out by default ('C' order). That way, the x and y coordinates will match a corresponding 2D array A when flattened (A.flat[:] or A.reshape(-1)) Note: Example x = [1, 2, 3] y = [10, 20] P = [[1, 10], [2, 10], [3, 10], [1, 20], [2, 20], [3, 20]] """ import numpy # Reverse y coordinates to have them start at bottom of array y = numpy.flipud(y) # Repeat x coordinates for each y (fastest varying) X = numpy.kron(numpy.ones(len(y)), x) # Repeat y coordinates for each x (slowest varying) Y = numpy.kron(y, numpy.ones(len(x))) # Check N = len(X) assert len(Y) == N # Create Nx2 array of x and y coordinates X = numpy.reshape(X, (N, 1)) Y = numpy.reshape(Y, (N, 1)) P = numpy.concatenate((X, Y), axis=1) # Return return P class Test_Quantity(unittest.TestCase): def setUp(self): a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe elements = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] self.mesh1 = Generic_Domain(points[:3], [elements[0]]) self.mesh1.check_integrity() #print self.mesh1.__class__ #print isinstance(self.mesh1, Domain) self.mesh4 = Generic_Domain(points, elements) self.mesh4.check_integrity() # UTM round Onslow a = [240000, 7620000] b = [240000, 7680000] c = [300000, 7620000] points = [a, b, c] elements = [[0, 2, 1]] self.mesh_onslow = Generic_Domain(points, elements) self.mesh_onslow.check_integrity() def tearDown(self): pass #print " Tearing down" def test_creation(self): quantity = Quantity(self.mesh1, [[1, 2, 3]]) assert num.allclose(quantity.vertex_values, [[1., 2., 3.]]) try: quantity = Quantity() except: pass else: raise Exception('Should have raised empty quantity exception') # FIXME(Ole): Temporarily disabled 18 Jan 2009 #try: # quantity = Quantity([1,2,3]) #except AssertionError: # pass #except: # raise Exception('Should have raised "mising mesh object" error') def test_creation_zeros(self): quantity = Quantity(self.mesh1) assert num.allclose(quantity.vertex_values, [[0., 0., 0.]]) quantity = Quantity(self.mesh4) assert num.allclose(quantity.vertex_values, [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]) def test_set_boundary_values(self): quantity = Quantity(self.mesh1) quantity.set_boundary_values() assert num.allclose(quantity.boundary_values, [0.0, 0.0, 0.0]) def test_set_boundary_values_with_function(self): quantity = Quantity(self.mesh1) #assert num.allclose(quantity.vertex_values, [[0.,0.,0.]]) def simple(x, y): return x+3*y quantity.set_boundary_values(simple) assert num.allclose(quantity.boundary_values, [1.0, 4.0, 3.0]) def test_set_boundary_values_with_constant(self): quantity = Quantity(self.mesh1) #assert num.allclose(quantity.vertex_values, [[0.,0.,0.]]) quantity.set_boundary_values(10.0) assert num.allclose(quantity.boundary_values, [10.0, 10.0, 10.0]) def test_set_boundary_values_with_array(self): quantity = Quantity(self.mesh1) #assert num.allclose(quantity.vertex_values, [[0.,0.,0.]]) quantity.set_boundary_values([10.0, 4.0, 5.0]) assert num.allclose(quantity.boundary_values, [10.0, 4.0, 5.0]) def test_set_boundary_values_with_wrong_sized_array(self): quantity = Quantity(self.mesh1) #assert num.allclose(quantity.vertex_values, [[0.,0.,0.]]) try: quantity.set_boundary_values([10.0, 4.0, 5.0, 8.0]) except: pass else: msg = 'Should have caught this' raise Exception(msg) def test_set_boundary_values_from_edges(self): quantity = Quantity(self.mesh4) def simple(x, y): return x+3*y quantity.set_values(simple) assert num.allclose(quantity.boundary_values, [ 0., 0., 0., 0., 0., 0.]) quantity.set_boundary_values_from_edges() assert num.allclose(quantity.boundary_values, [ 1., 3., 3., 6., 10., 9.]) def test_interpolation(self): quantity = Quantity(self.mesh1, [[1, 2, 3]]) assert num.allclose(quantity.centroid_values, [2.0]) # Centroid assert num.allclose(quantity.edge_values, [[2.5, 2.0, 1.5]]) def test_interpolation2(self): quantity = Quantity(self.mesh4, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroid quantity.extrapolate_second_order() #print quantity.vertex_values assert num.allclose(quantity.vertex_values, [[3.5, -1.0, 3.5], [3.+2./3, 6.+2./3, 4.+2./3], [4.6, 3.4, 1.], [-5.0, 1.0, 4.0]]) #print quantity.edge_values assert num.allclose(quantity.edge_values, [[1.25, 3.5, 1.25], [5. + 2/3.0, 4.0 + 1.0/6, 5.0 + 1.0/6], [2.2, 2.8, 4.0], [2.5, -0.5, -2.0]]) def test_save_to_array(self): quantity = Quantity(self.mesh4, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroid cellsize = 1.0 x, y, z = quantity.save_to_array(cellsize=cellsize, smooth=False) #x,y,z = quantity.save_to_array(smooth=False) from pprint import pprint #pprint(x) #pprint(y) #pprint(z) x_ex = [0., 1., 2., 3., 4.] y_ex = [0., 1., 2., 3., 4.] z_ex = [[2.00000000e+00, 2.50000000e+00, 0.00000000e+00, 4.50000000e+00, 9.00000000e+00], [1.50000000e+00, 5.00000000e+00, 0.00000000e+00, 4.50000000e+00, -9.99900000e+03], [3.00000000e+00, 3.00000000e+00, 3.00000000e+00, -9.99900000e+03, -9.99900000e+03], [-1.50000000e+00, -1.50000000e+00, -9.99900000e+03, -9.99900000e+03, -9.99900000e+03], [-6.00000000e+00, -9.99900000e+03, -9.99900000e+03, -9.99900000e+03, -9.99900000e+03]] assert num.allclose(x_ex, x) assert num.allclose(y_ex, y) assert num.allclose(z_ex, z) Plot = False if Plot: import pylab import numpy #a = numpy.where(a == -9999, numpy.nan, a) #a = numpy.where(a > 10.0, numpy.nan, a) #z = z[::-1,:] print(z) print(z.shape) print(x) print(y) nrows = z.shape[0] ncols = z.shape[1] ratio = float(nrows)/float(ncols) print(ratio) #y = numpy.arange(nrows)*cellsize #x = numpy.arange(ncols)*cellsize #Setup fig size to correpond to array size fig = pylab.figure(figsize=(10, 10*ratio)) levels = numpy.arange(-7, 10, 0.1) CF = pylab.contourf(x, y, z, levels=levels) CB = pylab.colorbar(CF, shrink=0.8, extend='both') #CC = pylab.contour(x,y,a, levels=levels) pylab.show() x, y, z = quantity.save_to_array(cellsize=cellsize, smooth=True) x_ex = [0., 1., 2., 3., 4.] y_ex = [0., 1., 2., 3., 4.] z_ex = [[2.00000000e+00, 2.33333333e+00, 2.66666667e+00, 5.83333333e+00, 9.00000000e+00], [2.50000000e+00, 2.83333333e+00, 2.66666667e+00, 5.83333333e+00, -9.99900000e+03], [3.00000000e+00, 2.83333333e+00, 2.66666667e+00, -9.99900000e+03, -9.99900000e+03], [-1.50000000e+00, -1.66666667e+00, -9.99900000e+03, -9.99900000e+03, -9.99900000e+03], [-6.00000000e+00, -9.99900000e+03, -9.99900000e+03, -9.99900000e+03, -9.99900000e+03]] #pprint(z) assert num.allclose(x_ex, x) assert num.allclose(y_ex, y) assert num.allclose(z_ex, z) if Plot: import pylab import numpy #a = numpy.where(a == -9999, numpy.nan, a) #a = numpy.where(a > 10.0, numpy.nan, a) #a = a[::-1,:] nrows = z.shape[0] ncols = z.shape[1] ratio = float(nrows)/float(ncols) print(ratio) #Setup fig size to correpond to array size fig = pylab.figure(figsize=(10, 10*ratio)) levels = numpy.arange(-7, 10, 0.1) CF = pylab.contourf(x, y, z, levels=levels) CB = pylab.colorbar(CF, shrink=0.8, extend='both') #CC = pylab.contour(x,y,a, levels=[0.0,1.0,2.0,3.0]) pylab.show() def test_get_extrema_1(self): quantity = Quantity(self.mesh4, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroids v = quantity.get_maximum_value() assert v == 5 v = quantity.get_minimum_value() assert v == 0 i = quantity.get_maximum_index() assert i == 1 i = quantity.get_minimum_index() assert i == 3 x, y = quantity.get_maximum_location() xref, yref = 4.0/3, 4.0/3 assert x == xref assert y == yref v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 5) x, y = quantity.get_minimum_location() v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 0) def test_get_maximum_2(self): a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe vertices = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] domain = Generic_Domain(points, vertices) quantity = Quantity(domain) quantity.set_values(lambda x, y: x+2*y) # 2 4 4 6 v = quantity.get_maximum_value() assert v == 6 v = quantity.get_minimum_value() assert v == 2 i = quantity.get_maximum_index() assert i == 3 i = quantity.get_minimum_index() assert i == 0 x, y = quantity.get_maximum_location() xref, yref = 2.0/3, 8.0/3 assert x == xref assert y == yref v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 6) x, y = quantity.get_minimum_location() v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 2) #Multiple locations for maximum - #Test that the algorithm picks the first occurrence v = quantity.get_maximum_value(indices=[0, 1, 2]) assert num.allclose(v, 4) i = quantity.get_maximum_index(indices=[0, 1, 2]) assert i == 1 x, y = quantity.get_maximum_location(indices=[0, 1, 2]) xref, yref = 4.0/3, 4.0/3 assert x == xref assert y == yref v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 4) # More test of indices...... v = quantity.get_maximum_value(indices=[2, 3]) assert num.allclose(v, 6) i = quantity.get_maximum_index(indices=[2, 3]) assert i == 3 x, y = quantity.get_maximum_location(indices=[2, 3]) xref, yref = 2.0/3, 8.0/3 assert x == xref assert y == yref v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 6) def test_boundary_allocation(self): quantity = Quantity(self.mesh4, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert quantity.boundary_values.shape[0] == len(self.mesh4.boundary) def test_set_values(self): quantity = Quantity(self.mesh4) # get referece to data arrays centroid_values = quantity.centroid_values vertex_values = quantity.vertex_values quantity.set_values([[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]], location='vertices') assert num.allclose(quantity.vertex_values, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert id(vertex_values) == id(quantity.vertex_values) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroid assert num.allclose(quantity.edge_values, [[2.5, 2.0, 1.5], [5., 5., 5.], [4.5, 4.5, 0.], [3.0, -1.5, -1.5]]) # Test default quantity.set_values([[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert num.allclose(quantity.vertex_values, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroid assert num.allclose(quantity.edge_values, [[2.5, 2.0, 1.5], [5., 5., 5.], [4.5, 4.5, 0.], [3.0, -1.5, -1.5]]) # Test centroids quantity.set_values([1, 2, 3, 4], location='centroids') assert num.allclose(quantity.centroid_values, [ 1., 2., 3., 4.]) # Centroid # Test exceptions try: quantity.set_values([[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]], location='bas kamel tuba') except: pass try: quantity.set_values([[1, 2, 3], [0, 0, 9]]) except ValueError: pass except: raise Exception('should have raised ValueeError') def test_set_values_const(self): quantity = Quantity(self.mesh4) quantity.set_values(1.0, location='vertices') assert num.allclose(quantity.vertex_values, [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]) assert num.allclose(quantity.centroid_values, [1, 1, 1, 1]) # Centroid assert num.allclose(quantity.edge_values, [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]) quantity.set_values(2.0, location='centroids') assert num.allclose(quantity.centroid_values, [2, 2, 2, 2]) def test_set_values_func(self): quantity = Quantity(self.mesh4) def f(x, y): return x+y quantity.set_values(f, location='vertices') #print "quantity.vertex_values",quantity.vertex_values assert num.allclose(quantity.vertex_values, [[2, 0, 2], [2, 2, 4], [4, 2, 4], [4, 2, 4]]) assert num.allclose(quantity.centroid_values, [4.0/3, 8.0/3, 10.0/3, 10.0/3]) assert num.allclose(quantity.edge_values, [[1, 2, 1], [3, 3, 2], [3, 4, 3], [3, 4, 3]]) quantity.set_values(f, location='centroids') assert num.allclose(quantity.centroid_values, [4.0/3, 8.0/3, 10.0/3, 10.0/3]) def test_integral(self): quantity = Quantity(self.mesh4) # Try constants first const = 5 quantity.set_values(const, location='vertices') #print 'Q', quantity.get_integral() assert num.allclose(quantity.get_integral(), self.mesh4.get_area() * const) # Try with a linear function def f(x, y): return x+y quantity.set_values(f, location='vertices') ref_integral = (4.0/3 + 8.0/3 + 10.0/3 + 10.0/3) * 2 assert num.allclose(quantity.get_integral(), ref_integral) def test_integral_with_region(self): quantity = Quantity(self.mesh4) # Try constants first const = 5 quantity.set_values(const, location='vertices') #print 'Q', quantity.get_integral() assert num.allclose(quantity.get_integral(), self.mesh4.get_area() * const) # Try with a linear function def f(x, y): return x+y quantity.set_values(f, location='vertices') from anuga import Region reg1 = Region(self.mesh4, indices=[2]) ref_integral = (10.0/3) * 2 assert num.allclose(quantity.get_integral(region=reg1), ref_integral) reg2 = Region(self.mesh4, indices=[2, 3]) ref_integral = (10.0/3 + 10.0/3) * 2 assert num.allclose(quantity.get_integral(region=reg2), ref_integral) id = [2, 3] ref_integral = (10.0/3 + 10.0/3) * 2 assert num.allclose(quantity.get_integral(indices=id), ref_integral) def test_set_vertex_values(self): quantity = Quantity(self.mesh4) quantity.set_vertex_values([0, 1, 2, 3, 4, 5]) assert num.allclose(quantity.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) assert num.allclose(quantity.centroid_values, [1., 7./3, 11./3, 8./3]) # Centroid assert num.allclose(quantity.edge_values, [[1., 1.5, 0.5], [3., 2.5, 1.5], [3.5, 4.5, 3.], [2.5, 3.5, 2]]) def test_set_vertex_values_subset(self): quantity = Quantity(self.mesh4) quantity.set_vertex_values([0, 1, 2, 3, 4, 5]) quantity.set_vertex_values([0, 20, 30, 50], indices=[0, 2, 3, 5]) assert num.allclose(quantity.vertex_values, [[1, 0, 20], [1, 20, 4], [4, 20, 50], [30, 1, 4]]) def test_set_vertex_values_using_general_interface(self): quantity = Quantity(self.mesh4) quantity.set_values([0, 1, 2, 3, 4, 5]) assert num.allclose(quantity.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) #Centroid assert num.allclose(quantity.centroid_values, [1., 7./3, 11./3, 8./3]) assert num.allclose(quantity.edge_values, [[1., 1.5, 0.5], [3., 2.5, 1.5], [3.5, 4.5, 3.], [2.5, 3.5, 2]]) def test_set_vertex_values_using_general_interface_with_subset(self): """test_set_vertex_values_using_general_interface_with_subset(self): Test that indices and polygon works (for constants values) """ quantity = Quantity(self.mesh4) quantity.set_values([0, 2, 3, 5], indices=[0, 2, 3, 5]) assert num.allclose(quantity.vertex_values, [[0, 0, 2], [0, 2, 0], [0, 2, 5], [3, 0, 0]]) # Constant quantity.set_values(0.0) quantity.set_values(3.14, indices=[0, 2], location='vertices') # Indices refer to triangle numbers assert num.allclose(quantity.vertex_values, [[3.14, 3.14, 3.14], [0, 0, 0], [3.14, 3.14, 3.14], [0, 0, 0]]) # Now try with polygon (pick points where y>2) polygon = [[0, 2.1], [4, 2.1], [4, 7], [0, 7]] quantity.set_values(0.0) quantity.set_values(3.14, polygon=polygon) assert num.allclose(quantity.vertex_values, [[0, 0, 0], [0, 0, 0], [0, 0, 0], [3.14, 3.14, 3.14]]) # Another polygon (pick triangle 1 and 2 (rightmost triangles) # using centroids polygon = [[2.1, 0.0], [3.5, 0.1], [2, 2.2], [0.2, 2]] quantity.set_values(0.0) quantity.set_values(3.14, location='centroids', polygon=polygon) assert num.allclose(quantity.vertex_values, [[0, 0, 0], [3.14, 3.14, 3.14], [3.14, 3.14, 3.14], [0, 0, 0]]) # Same polygon now use vertices (default) polygon = [[2.1, 0.0], [3.5, 0.1], [2, 2.2], [0.2, 2]] quantity.set_values(0.0) #print 'Here 2' quantity.set_values(3.14, polygon=polygon) assert num.allclose(quantity.vertex_values, [[0, 0, 0], [3.14, 3.14, 3.14], [3.14, 3.14, 3.14], [0, 0, 0]]) # Test input checking try: quantity.set_values(3.14, polygon=polygon, indices=[0, 2]) except: pass else: msg = 'Should have caught this' raise Exception(msg) def test_set_vertex_values_using_general_interface_subset_and_geo(self): """test_set_vertex_values_using_general_interface_with_subset(self): Test that indices and polygon works using georeferencing """ quantity = Quantity(self.mesh4) G = Geo_reference(56, 10, 100) quantity.domain.set_georeference(G) # Constant quantity.set_values(0.0) quantity.set_values(3.14, indices=[0, 2], location='vertices') # Indices refer to triangle numbers here - not vertices (why?) assert num.allclose(quantity.vertex_values, [[3.14, 3.14, 3.14], [0, 0, 0], [3.14, 3.14, 3.14], [0, 0, 0]]) # Now try with polygon (pick points where y>2) polygon = num.array([[0, 2.1], [4, 2.1], [4, 7], [0, 7]]) polygon += [G.xllcorner, G.yllcorner] quantity.set_values(0.0) quantity.set_values(3.14, polygon=polygon, location='centroids') assert num.allclose(quantity.vertex_values, [[0, 0, 0], [0, 0, 0], [0, 0, 0], [3.14, 3.14, 3.14]]) # Another polygon (pick triangle 1 and 2 (rightmost triangles) polygon = num.array([[2.1, 0.0], [3.5, 0.1], [2, 2.2], [0.2, 2]]) polygon += [G.xllcorner, G.yllcorner] quantity.set_values(0.0) quantity.set_values(3.14, polygon=polygon) msg = ('quantity.vertex_values=\n%s\nshould be close to\n' '[[0,0,0],\n' ' [3.14,3.14,3.14],\n' ' [3.14,3.14,3.14],\n' ' [0,0,0]]' % str(quantity.vertex_values)) assert num.allclose(quantity.vertex_values, [[0, 0, 0], [3.14, 3.14, 3.14], [3.14, 3.14, 3.14], [0, 0, 0]]), msg def test_set_values_using_fit(self): quantity = Quantity(self.mesh4) #Get (enough) datapoints data_points = [[0.66666667, 0.66666667], [1.33333333, 1.33333333], [2.66666667, 0.66666667], [0.66666667, 2.66666667], [0.0, 1.0], [0.0, 3.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [1.0, 3.0], [2.0, 1.0], [3.0, 0.0], [3.0, 1.0]] z = linear_function(data_points) #Use built-in fit_interpolate.fit quantity.set_values(Geospatial_data(data_points, z), alpha=0) #quantity.set_values(points = data_points, values = z, alpha = 0) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print quantity.vertex_values, answer assert num.allclose(quantity.vertex_values.flat, answer) #Now try by setting the same values directly vertex_attributes = fit_to_mesh(data_points, quantity.domain.get_nodes(), quantity.domain.get_triangles(), point_attributes=z, alpha=0, verbose=False) #print vertex_attributes quantity.set_values(vertex_attributes) assert num.allclose(quantity.vertex_values.flat, answer) def test_test_set_values_using_fit_w_geo(self): #Mesh vertex_coordinates = [[0.76, 0.76], [0.76, 5.76], [5.76, 0.76]] triangles = [[0, 2, 1]] mesh_georef = Geo_reference(56, -0.76, -0.76) mesh1 = Generic_Domain(vertex_coordinates, triangles, geo_reference=mesh_georef) mesh1.check_integrity() #Quantity quantity = Quantity(mesh1) #Data data_points = [[201.0, 401.0], [201.0, 403.0], [203.0, 401.0]] z = [2, 4, 4] data_georef = Geo_reference(56, -200, -400) #Reference ref = fit_to_mesh(data_points, vertex_coordinates, triangles, point_attributes=z, data_origin=data_georef.get_origin(), mesh_origin=mesh_georef.get_origin(), alpha=0) assert num.allclose(ref, [0, 5, 5]) #Test set_values quantity.set_values(Geospatial_data( data_points, z, data_georef), alpha=0) #quantity.set_values(points = data_points, # values = z, # data_georef = data_georef, # alpha = 0) #quantity.set_values(points = data_points, # values = z, # data_georef = data_georef, # alpha = 0) assert num.allclose(quantity.vertex_values.flat, ref) #Test set_values using geospatial data object quantity.vertex_values[:] = 0.0 geo = Geospatial_data(data_points, z, data_georef) quantity.set_values(geospatial_data=geo, alpha=0) assert num.allclose(quantity.vertex_values.flat, ref) def test_set_values_from_file1(self): quantity = Quantity(self.mesh4) #Get (enough) datapoints data_points = [[0.66666667, 0.66666667], [1.33333333, 1.33333333], [2.66666667, 0.66666667], [0.66666667, 2.66666667], [0.0, 1.0], [0.0, 3.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [1.0, 3.0], [2.0, 1.0], [3.0, 0.0], [3.0, 1.0]] data_geo_spatial = Geospatial_data(data_points, geo_reference=Geo_reference(56, 0, 0)) data_points_absolute = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(data_points_absolute) att = 'spam_and_eggs' #Create .txt file ptsfile = tempfile.mktemp(".txt") file = open(ptsfile, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(data_points_absolute, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) file.write(row + "\n") file.close() #Check that values can be set from file quantity.set_values(filename=ptsfile, attribute_name=att, alpha=0) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print quantity.vertex_values.flat #print answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=ptsfile, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(ptsfile) def Xtest_set_values_from_file_using_polygon(self): """test_set_values_from_file_using_polygon(self): Test that polygon restriction works for general points data """ quantity = Quantity(self.mesh4) #Get (enough) datapoints data_points = [[0.66666667, 0.66666667], [1.33333333, 1.33333333], [2.66666667, 0.66666667], [0.66666667, 2.66666667], [0.0, 1.0], [0.0, 3.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [1.0, 3.0], [2.0, 1.0], [3.0, 0.0], [3.0, 1.0]] data_geo_spatial = Geospatial_data(data_points, geo_reference=Geo_reference(56, 0, 0)) data_points_absolute = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(data_points_absolute) att = 'spam_and_eggs' #Create .txt file ptsfile = tempfile.mktemp(".txt") file = open(ptsfile, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(data_points_absolute, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) file.write(row + "\n") file.close() # Create restricting polygon (containing node #4 (2,2) and # centroid of triangle #1 (bce) polygon = [[1.0, 1.0], [4.0, 1.0], [4.0, 4.0], [1.0, 4.0]] #print self.mesh4.nodes #print inside_polygon(self.mesh4.nodes, polygon) assert num.allclose(inside_polygon(self.mesh4.nodes, polygon), 4) #print quantity.domain.get_vertex_coordinates() #print quantity.domain.get_nodes() # Check that values can be set from file quantity.set_values(filename=ptsfile, polygon=polygon, location='unique vertices', alpha=0) # Get indices for vertex coordinates in polygon indices = inside_polygon(quantity.domain.get_vertex_coordinates(), polygon) points = num.take(quantity.domain.get_vertex_coordinates(), indices) answer = linear_function(points) #print quantity.vertex_values.flat #print answer # Check vertices in polygon have been set assert num.allclose(num.take(quantity.vertex_values.flat, indices), answer) # Check vertices outside polygon are zero indices = outside_polygon(quantity.domain.get_vertex_coordinates(), polygon) assert num.allclose(num.take(quantity.vertex_values.flat, indices), 0.0) #Cleanup import os os.remove(ptsfile) def test_cache_test_set_values_from_file(self): # FIXME (Ole): What is this about? # I don't think it checks anything new quantity = Quantity(self.mesh4) #Get (enough) datapoints data_points = [[0.66666667, 0.66666667], [1.33333333, 1.33333333], [2.66666667, 0.66666667], [0.66666667, 2.66666667], [0.0, 1.0], [0.0, 3.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [1.0, 3.0], [2.0, 1.0], [3.0, 0.0], [3.0, 1.0]] georef = Geo_reference(56, 0, 0) data_geo_spatial = Geospatial_data(data_points, geo_reference=georef) data_points_absolute = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(data_points_absolute) att = 'spam_and_eggs' # Create .txt file ptsfile = tempfile.mktemp(".txt") file = open(ptsfile, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(data_points_absolute, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) file.write(row + "\n") file.close() # Check that values can be set from file quantity.set_values(filename=ptsfile, attribute_name=att, alpha=0, use_cache=True, verbose=False) answer = linear_function(quantity.domain.get_vertex_coordinates()) assert num.allclose(quantity.vertex_values.flat, answer) # Check that values can be set from file using default attribute quantity.set_values(filename=ptsfile, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) # Check cache quantity.set_values(filename=ptsfile, attribute_name=att, alpha=0, use_cache=True, verbose=False) #Cleanup import os os.remove(ptsfile) def test_set_values_from_lat_long(self): quantity = Quantity(self.mesh_onslow) #Get (enough) datapoints data_points = [[-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6]] data_geo_spatial = Geospatial_data(data_points, points_are_lats_longs=True) points_UTM = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(points_UTM) att = 'elevation' #Create .txt file txt_file = tempfile.mktemp(".txt") file = open(txt_file, "w") file.write(" lat,long," + att + " \n") for data_point, attribute in zip(data_points, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) #print "row", row file.write(row + "\n") file.close() #Check that values can be set from file quantity.set_values(filename=txt_file, attribute_name=att, alpha=0) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print "quantity.vertex_values.flat", quantity.vertex_values.flat #print "answer",answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=txt_file, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(txt_file) def test_set_values_from_lat_long_2(self): quantity = Quantity(self.mesh_onslow) #Get (enough) datapoints data_points = [[-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6]] data_geo_spatial = Geospatial_data(data_points, points_are_lats_longs=True) points_UTM = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(points_UTM) att = 'elevation' #Create .txt file txt_file = tempfile.mktemp(".txt") file = open(txt_file, "w") file.write(" lat,long," + att + " \n") for data_point, attribute in zip(data_points, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) #print "row", row file.write(row + "\n") file.close() #Check that values can be set from file quantity.set_values(filename=txt_file, attribute_name=att, alpha=0) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print "quantity.vertex_values.flat", quantity.vertex_values.flat #print "answer",answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=txt_file, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(txt_file) def test_set_values_from_UTM_pts(self): quantity = Quantity(self.mesh_onslow) #Get (enough) datapoints data_points = [[-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6]] data_geo_spatial = Geospatial_data(data_points, points_are_lats_longs=True) points_UTM = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(points_UTM) att = 'elevation' #Create .txt file txt_file = tempfile.mktemp(".txt") file = open(txt_file, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(points_UTM, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) #print "row", row file.write(row + "\n") file.close() pts_file = tempfile.mktemp(".pts") convert = Geospatial_data(txt_file) convert.export_points_file(pts_file) #Check that values can be set from file quantity.set_values_from_file(pts_file, att, 0, 'vertices', None) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print "quantity.vertex_values.flat", quantity.vertex_values.flat #print "answer",answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file quantity.set_values(filename=pts_file, attribute_name=att, alpha=0) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print "quantity.vertex_values.flat", quantity.vertex_values.flat #print "answer",answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=txt_file, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(txt_file) os.remove(pts_file) def test_set_values_from_UTM_pts_verbose(self): quantity = Quantity(self.mesh_onslow) #Get (enough) datapoints data_points = [[-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], [-21.5, 114.5], [-21.4, 114.6], [-21.45, 114.65], [-21.35, 114.65], [-21.45, 114.55], [-21.45, 114.6], ] data_geo_spatial = Geospatial_data(data_points, points_are_lats_longs=True) points_UTM = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(points_UTM) att = 'elevation' #Create .txt file txt_file = tempfile.mktemp(".txt") file = open(txt_file, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(points_UTM, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) #print "row", row file.write(row + "\n") file.close() pts_file = tempfile.mktemp(".pts") convert = Geospatial_data(txt_file) convert.export_points_file(pts_file) #Check that values can be set from file quantity.set_values_from_file(pts_file, att, 0, 'vertices', None, verbose=False, max_read_lines=2) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print "quantity.vertex_values.flat", quantity.vertex_values.flat #print "answer",answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file quantity.set_values(filename=pts_file, attribute_name=att, alpha=0) answer = linear_function(quantity.domain.get_vertex_coordinates()) #print "quantity.vertex_values.flat", quantity.vertex_values.flat #print "answer",answer assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=txt_file, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(txt_file) os.remove(pts_file) def test_set_values_from_file_with_georef1(self): #Mesh in zone 56 (absolute coords) x0 = 314036.58727982 y0 = 6224951.2960092 a = [x0+0.0, y0+0.0] b = [x0+0.0, y0+2.0] c = [x0+2.0, y0+0.0] d = [x0+0.0, y0+4.0] e = [x0+2.0, y0+2.0] f = [x0+4.0, y0+0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe elements = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] #absolute going in .. mesh4 = Generic_Domain(points, elements, geo_reference=Geo_reference(56, 0, 0)) mesh4.check_integrity() quantity = Quantity(mesh4) #Get (enough) datapoints (relative to georef) data_points_rel = [[0.66666667, 0.66666667], [1.33333333, 1.33333333], [2.66666667, 0.66666667], [0.66666667, 2.66666667], [0.0, 1.0], [0.0, 3.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [1.0, 3.0], [2.0, 1.0], [3.0, 0.0], [3.0, 1.0]] data_geo_spatial = Geospatial_data(data_points_rel, geo_reference=Geo_reference(56, x0, y0)) data_points_absolute = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(data_points_absolute) att = 'spam_and_eggs' #Create .txt file ptsfile = tempfile.mktemp(".txt") file = open(ptsfile, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(data_points_absolute, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) file.write(row + "\n") file.close() #file = open(ptsfile, 'r') #lines = file.readlines() #file.close() #Check that values can be set from file quantity.set_values(filename=ptsfile, attribute_name=att, alpha=0) answer = linear_function(quantity.domain.get_vertex_coordinates()) assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=ptsfile, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(ptsfile) def test_set_values_from_file_with_georef2(self): #Mesh in zone 56 (relative coords) x0 = 314036.58727982 y0 = 6224951.2960092 #x0 = 0.0 #y0 = 0.0 a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe elements = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] mesh4 = Generic_Domain(points, elements, geo_reference=Geo_reference(56, x0, y0)) mesh4.check_integrity() quantity = Quantity(mesh4) #Get (enough) datapoints data_points = [[x0+0.66666667, y0+0.66666667], [x0+1.33333333, y0+1.33333333], [x0+2.66666667, y0+0.66666667], [x0+0.66666667, y0+2.66666667], [x0+0.0, y0+1.0], [x0+0.0, y0+3.0], [x0+1.0, y0+0.0], [x0+1.0, y0+1.0], [x0+1.0, y0+2.0], [x0+1.0, y0+3.0], [x0+2.0, y0+1.0], [x0+3.0, y0+0.0], [x0+3.0, y0+1.0]] data_geo_spatial = Geospatial_data(data_points, geo_reference=Geo_reference(56, 0, 0)) data_points_absolute = data_geo_spatial.get_data_points(absolute=True) attributes = linear_function(data_points_absolute) att = 'spam_and_eggs' #Create .txt file ptsfile = tempfile.mktemp(".txt") file = open(ptsfile, "w") file.write(" x,y," + att + " \n") for data_point, attribute in zip(data_points_absolute, attributes): row = str(data_point[0]) + ',' + str(data_point[1]) \ + ',' + str(attribute) file.write(row + "\n") file.close() #Check that values can be set from file quantity.set_values(filename=ptsfile, attribute_name=att, alpha=0) answer = linear_function(quantity.domain. get_vertex_coordinates(absolute=True)) assert num.allclose(quantity.vertex_values.flat, answer) #Check that values can be set from file using default attribute quantity.set_values(filename=ptsfile, alpha=0) assert num.allclose(quantity.vertex_values.flat, answer) #Cleanup import os os.remove(ptsfile) def test_set_values_from_utm_grid_file(self): x0 = 0.0 y0 = 0.0 a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe elements = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] mesh4 = Generic_Domain(points, elements) # geo_reference = Geo_reference(56, x0, y0)) mesh4.check_integrity() quantity = Quantity(mesh4) """ Format of asc file ncols 11 nrows 12 xllcorner 240000 yllcorner 7620000 cellsize 6000 NODATA_value -9999 """ ncols = 11 # Nx nrows = 12 # Ny xllcorner = x0 yllcorner = y0 cellsize = 1.0 NODATA_value = -9999 #xllcorner = 0 #yllcorner = 100 #cellsize = 10 #NODATA_value = -9999 #Create .asc file #txt_file = tempfile.mktemp(".asc") txt_file = 'test_asc.asc' datafile = open(txt_file, "w") datafile.write('ncols '+str(ncols)+"\n") datafile.write('nrows '+str(nrows)+"\n") datafile.write('xllcorner '+str(xllcorner)+"\n") datafile.write('yllcorner '+str(yllcorner)+"\n") datafile.write('cellsize '+str(cellsize)+"\n") datafile.write('NODATA_value '+str(NODATA_value)+"\n") x = num.linspace(xllcorner, xllcorner+(ncols-1)*cellsize, ncols) y = num.linspace(yllcorner, yllcorner+(nrows-1)*cellsize, nrows) points = axes2points(x, y) #print points #print x.shape, x #print y.shape, y datavalues = linear_function(points) #print datavalues datavalues = datavalues.reshape(nrows, ncols) #print datavalues #print datavalues.shape for row in datavalues: #print row datafile.write(" ".join(str(elem) for elem in row) + "\n") datafile.close() #print quantity.vertex_values #print quantity.centroid_values quantity.set_values(filename=txt_file, location='vertices', indices=None, verbose=False) # check order of vertices answer = [[6., 0., 2.], [6., 2., 8.], [8., 2., 4.], [12., 6., 8.]] #print quantity.vertex_values assert num.allclose(quantity.vertex_values, answer) #print quantity.vertex_values #print quantity.centroid_values quantity.set_values(0.0) #print quantity.vertex_values #print quantity.centroid_values quantity.set_values(filename=txt_file, location='centroids', indices=None, verbose=False) #print quantity.vertex_values #print quantity.centroid_values answer = [2.66666667, 5.33333333, 4.66666667, 8.66666667] assert num.allclose(quantity.centroid_values, answer) # check dem file # use the same reference solution used above for testing # convert test_asc.asc file to .dem file txt_file_prj = 'test_asc.prj' fid = open(txt_file_prj, 'w') fid.write("""Projection UTM Zone 56 Datum WGS84 Zunits NO Units METERS Spheroid WGS84 Xshift 0.0000000000 Yshift 10000000.0000000000 Parameters """) fid.close() txt_file_dem = 'test_asc.dem' asc2dem(name_in=txt_file, name_out='test_asc', use_cache=False, verbose=False) quantity.set_values(0.0) quantity.set_values(filename=txt_file_dem, location='vertices', indices=None, verbose=False) # check order of vertices answer = [[6., 0., 2.], [6., 2., 8.], [8., 2., 4.], [12., 6., 8.]] #print quantity.vertex_values #print quantity.vertex_values, 'vertex values' assert num.allclose(quantity.vertex_values, answer) #print quantity.vertex_values #print quantity.centroid_values quantity.set_values(0.0) #print quantity.vertex_values #print quantity.centroid_values quantity.set_values(filename=txt_file_dem, location='centroids', indices=None, verbose=False) #print quantity.vertex_values #print quantity.centroid_values , 'centroid values' answer = [2.66666667, 5.33333333, 4.66666667, 8.66666667] assert num.allclose(quantity.centroid_values, answer) #Cleanup #import os try: os.remove(txt_file) os.remove(txt_file_prj) os.remove(txt_file_dem) except: pass def test_set_values_from_quantity(self): quantity1 = Quantity(self.mesh4) quantity1.set_vertex_values([0, 1, 2, 3, 4, 5]) assert num.allclose(quantity1.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) quantity2 = Quantity(self.mesh4) quantity2.set_values(quantity=quantity1) assert num.allclose(quantity2.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) quantity2.set_values(quantity=2*quantity1) assert num.allclose(quantity2.vertex_values, [[2, 0, 4], [2, 4, 8], [8, 4, 10], [6, 2, 8]]) quantity2.set_values(quantity=2*quantity1 + 3) assert num.allclose(quantity2.vertex_values, [[5, 3, 7], [5, 7, 11], [11, 7, 13], [9, 5, 11]]) #Check detection of quantity as first orgument quantity2.set_values(2*quantity1 + 3) assert num.allclose(quantity2.vertex_values, [[5, 3, 7], [5, 7, 11], [11, 7, 13], [9, 5, 11]]) def Xtest_set_values_from_quantity_using_polygon(self): """test_set_values_from_quantity_using_polygon(self): Check that polygon can be used to restrict set_values when using another quantity as argument. """ # Create restricting polygon (containing node #4 (2,2) and # centroid of triangle #1 (bce) polygon = [[1.0, 1.0], [4.0, 1.0], [4.0, 4.0], [1.0, 4.0]] assert num.allclose(inside_polygon(self.mesh4.nodes, polygon), 4) quantity1 = Quantity(self.mesh4) quantity1.set_vertex_values([0, 1, 2, 3, 4, 5]) assert num.allclose(quantity1.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) quantity2 = Quantity(self.mesh4) quantity2.set_values(quantity=quantity1, polygon=polygon) msg = 'Only node #4(e) at (2,2) should have values applied ' assert num.allclose(quantity2.vertex_values, [[0, 0, 0], [0, 0, 4], [4, 0, 0], [0, 0, 4]]), msg #bac, bce, ecf, dbe def test_overloading(self): quantity1 = Quantity(self.mesh4) quantity1.set_vertex_values([0, 1, 2, 3, 4, 5]) assert num.allclose(quantity1.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) quantity2 = Quantity(self.mesh4) quantity2.set_values([[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]], location='vertices') quantity3 = Quantity(self.mesh4) quantity3.set_values([[2, 2, 2], [7, 8, 9], [7, 6, 3], [3, 8, -8]], location='vertices') # Negation Q = -quantity1 assert num.allclose(Q.vertex_values, -quantity1.vertex_values) assert num.allclose(Q.centroid_values, -quantity1.centroid_values) assert num.allclose(Q.edge_values, -quantity1.edge_values) # Addition Q = quantity1 + 7 assert num.allclose(Q.vertex_values, quantity1.vertex_values + 7) assert num.allclose(Q.centroid_values, quantity1.centroid_values + 7) assert num.allclose(Q.edge_values, quantity1.edge_values + 7) Q = 7 + quantity1 assert num.allclose(Q.vertex_values, quantity1.vertex_values + 7) assert num.allclose(Q.centroid_values, quantity1.centroid_values + 7) assert num.allclose(Q.edge_values, quantity1.edge_values + 7) Q = quantity1 + quantity2 assert num.allclose(Q.vertex_values, quantity1.vertex_values + quantity2.vertex_values) assert num.allclose(Q.centroid_values, quantity1.centroid_values + quantity2.centroid_values) assert num.allclose(Q.edge_values, quantity1.edge_values + quantity2.edge_values) Q = quantity1 + quantity2 - 3 assert num.allclose(Q.vertex_values, quantity1.vertex_values + quantity2.vertex_values - 3) Q = quantity1 - quantity2 assert num.allclose(Q.vertex_values, quantity1.vertex_values - quantity2.vertex_values) #Scaling Q = quantity1*3 assert num.allclose(Q.vertex_values, quantity1.vertex_values*3) assert num.allclose(Q.centroid_values, quantity1.centroid_values*3) assert num.allclose(Q.edge_values, quantity1.edge_values*3) Q = 3*quantity1 assert num.allclose(Q.vertex_values, quantity1.vertex_values*3) #Multiplication Q = quantity1 * quantity2 #print Q.vertex_values #print Q.centroid_values #print quantity1.centroid_values #print quantity2.centroid_values assert num.allclose(Q.vertex_values, quantity1.vertex_values * quantity2.vertex_values) #Linear combinations Q = 4*quantity1 + 2 assert num.allclose(Q.vertex_values, 4*quantity1.vertex_values + 2) Q = quantity1*quantity2 + 2 assert num.allclose(Q.vertex_values, quantity1.vertex_values * quantity2.vertex_values + 2) Q = quantity1*quantity2 + quantity3 assert num.allclose(Q.vertex_values, quantity1.vertex_values * quantity2.vertex_values + quantity3.vertex_values) Q = quantity1*quantity2 + 3*quantity3 assert num.allclose(Q.vertex_values, quantity1.vertex_values * quantity2.vertex_values + 3*quantity3.vertex_values) Q = quantity1*quantity2 + 3*quantity3 + 5.0 assert num.allclose(Q.vertex_values, quantity1.vertex_values * quantity2.vertex_values + 3*quantity3.vertex_values + 5) Q = quantity1*quantity2 - quantity3 assert num.allclose(Q.vertex_values, quantity1.vertex_values * quantity2.vertex_values - quantity3.vertex_values) Q = 1.5*quantity1*quantity2 - 3*quantity3 + 5.0 assert num.allclose(Q.vertex_values, 1.5*quantity1.vertex_values * quantity2.vertex_values - 3*quantity3.vertex_values + 5) #Try combining quantities and arrays and scalars Q = 1.5*quantity1*quantity2.vertex_values -\ 3*quantity3.vertex_values + 5.0 assert num.allclose(Q.vertex_values, 1.5*quantity1.vertex_values * quantity2.vertex_values - 3*quantity3.vertex_values + 5) #Powers Q = quantity1**2 assert num.allclose(Q.vertex_values, quantity1.vertex_values**2) Q = quantity1**2 + quantity2**2 assert num.allclose(Q.vertex_values, quantity1.vertex_values**2 + quantity2.vertex_values**2) Q = (quantity1**2 + quantity2**2)**0.5 assert num.allclose(Q.vertex_values, (quantity1.vertex_values**2 + quantity2.vertex_values**2)**0.5) def test_compute_gradient(self): quantity = Quantity(self.mesh4) #Set up for a gradient of (2,0) at mid triangle quantity.set_values([2.0, 4.0, 6.0, 2.0], location='centroids') #Gradients quantity.compute_gradients() a = quantity.x_gradient b = quantity.y_gradient #print self.mesh4.centroid_coordinates #print a, b #The central triangle (1) #(using standard gradient based on neigbours controid values) assert num.allclose(a[1], 2.0) assert num.allclose(b[1], 0.0) #Left triangle (0) using two point gradient #q0 = q1 + a*(x0-x1) + b*(y0-y1) <=> #2 = 4 + a*(-2/3) + b*(-2/3) assert num.allclose(a[0] + b[0], 3) #From orthogonality (a*(y0-y1) + b*(x0-x1) == 0) assert num.allclose(a[0] - b[0], 0) #Right triangle (2) using two point gradient #q2 = q1 + a*(x2-x1) + b*(y2-y1) <=> #6 = 4 + a*(4/3) + b*(-2/3) assert num.allclose(2*a[2] - b[2], 3) #From orthogonality (a*(y1-y2) + b*(x2-x1) == 0) assert num.allclose(a[2] + 2*b[2], 0) #Top triangle (3) using two point gradient #q3 = q1 + a*(x3-x1) + b*(y3-y1) <=> #2 = 4 + a*(-2/3) + b*(4/3) assert num.allclose(a[3] - 2*b[3], 3) #From orthogonality (a*(y1-y3) + b*(x3-x1) == 0) assert num.allclose(2*a[3] + b[3], 0) #print a, b quantity.extrapolate_second_order() #Apply q(x,y) = qc + a*(x-xc) + b*(y-yc) assert num.allclose(quantity.vertex_values[0, :], [3., 0., 3.]) assert num.allclose(quantity.vertex_values[1, :], [ 4./3, 16./3, 16./3]) #a = 1.2, b=-0.6 #q(4,0) = 6 + a*(4 - 8/3) + b*(-2/3) assert num.allclose(quantity.vertex_values[2, 2], 8) def test_get_gradients(self): quantity = Quantity(self.mesh4) #Set up for a gradient of (2,0) at mid triangle quantity.set_values([2.0, 4.0, 6.0, 2.0], location='centroids') #Gradients quantity.compute_gradients() a, b = quantity.get_gradients() #print self.mesh4.centroid_coordinates #print a, b #The central triangle (1) #(using standard gradient based on neigbours controid values) assert num.allclose(a[1], 2.0) assert num.allclose(b[1], 0.0) #Left triangle (0) using two point gradient #q0 = q1 + a*(x0-x1) + b*(y0-y1) <=> #2 = 4 + a*(-2/3) + b*(-2/3) assert num.allclose(a[0] + b[0], 3) #From orthogonality (a*(y0-y1) + b*(x0-x1) == 0) assert num.allclose(a[0] - b[0], 0) #Right triangle (2) using two point gradient #q2 = q1 + a*(x2-x1) + b*(y2-y1) <=> #6 = 4 + a*(4/3) + b*(-2/3) assert num.allclose(2*a[2] - b[2], 3) #From orthogonality (a*(y1-y2) + b*(x2-x1) == 0) assert num.allclose(a[2] + 2*b[2], 0) #Top triangle (3) using two point gradient #q3 = q1 + a*(x3-x1) + b*(y3-y1) <=> #2 = 4 + a*(-2/3) + b*(4/3) assert num.allclose(a[3] - 2*b[3], 3) #From orthogonality (a*(y1-y3) + b*(x3-x1) == 0) assert num.allclose(2*a[3] + b[3], 0) def test_second_order_extrapolation2(self): quantity = Quantity(self.mesh4) #Set up for a gradient of (3,1), f(x) = 3x+y quantity.set_values([2.0+2.0/3, 4.0+4.0/3, 8.0+2.0/3, 2.0+8.0/3], location='centroids') #Gradients quantity.compute_gradients() a = quantity.x_gradient b = quantity.y_gradient #print a, b assert num.allclose(a[1], 3.0) assert num.allclose(b[1], 1.0) #Work out the others quantity.extrapolate_second_order() #print quantity.vertex_values assert num.allclose(quantity.vertex_values[1, 0], 2.0) assert num.allclose(quantity.vertex_values[1, 1], 6.0) assert num.allclose(quantity.vertex_values[1, 2], 8.0) def test_backup_saxpy_centroid_values(self): quantity = Quantity(self.mesh4) #Set up for a gradient of (3,1), f(x) = 3x+y c_values = num.array([2.0+2.0/3, 4.0+4.0/3, 8.0+2.0/3, 2.0+8.0/3]) d_values = num.array([1.0, 2.0, 3.0, 4.0]) quantity.set_values(c_values, location='centroids') #Backup quantity.backup_centroid_values() #print quantity.vertex_values assert num.allclose(quantity.centroid_values, quantity.centroid_backup_values) quantity.set_values(d_values, location='centroids') quantity.saxpy_centroid_values(2.0, 3.0) assert num.allclose(quantity.centroid_values, 2.0*d_values + 3.0*c_values) def test_first_order_extrapolator(self): quantity = Quantity(self.mesh4) #Test centroids quantity.set_values([1., 2., 3., 4.], location='centroids') assert num.allclose(quantity.centroid_values, [1, 2, 3, 4]) # Centroid #Extrapolate quantity.extrapolate_first_order() #Check that gradient is zero a, b = quantity.get_gradients() assert num.allclose(a, [0, 0, 0, 0]) assert num.allclose(b, [0, 0, 0, 0]) #Check vertices but not edge values assert num.allclose(quantity.vertex_values, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]]) def test_second_order_extrapolator(self): quantity = Quantity(self.mesh4) #Set up for a gradient of (3,0) at mid triangle quantity.set_values([2.0, 4.0, 8.0, 2.0], location='centroids') quantity.extrapolate_second_order() quantity.limit() #Assert that central triangle is limited by neighbours assert quantity.vertex_values[1, 0] >= quantity.vertex_values[0, 0] assert quantity.vertex_values[1, 0] >= quantity.vertex_values[3, 1] assert quantity.vertex_values[1, 1] <= quantity.vertex_values[2, 1] assert quantity.vertex_values[1, 1] >= quantity.vertex_values[0, 2] assert quantity.vertex_values[1, 2] <= quantity.vertex_values[2, 0] assert quantity.vertex_values[1, 2] >= quantity.vertex_values[3, 1] #Assert that quantities are conserved for k in range(quantity.centroid_values.shape[0]): assert num.allclose(quantity.centroid_values[k], old_div(num.sum(quantity.vertex_values[k, :]),3)) def test_limit_vertices_by_all_neighbours(self): quantity = Quantity(self.mesh4) #Create a deliberate overshoot (e.g. from gradient computation) quantity.set_values([[3, 0, 3], [2, 2, 6], [5, 3, 8], [8, 3, 5]]) #Limit quantity.limit_vertices_by_all_neighbours() #Assert that central triangle is limited by neighbours assert quantity.vertex_values[1, 0] >= quantity.vertex_values[0, 0] assert quantity.vertex_values[1, 0] <= quantity.vertex_values[3, 1] assert quantity.vertex_values[1, 1] <= quantity.vertex_values[2, 1] assert quantity.vertex_values[1, 1] >= quantity.vertex_values[0, 2] assert quantity.vertex_values[1, 2] <= quantity.vertex_values[2, 0] assert quantity.vertex_values[1, 2] <= quantity.vertex_values[3, 1] #Assert that quantities are conserved for k in range(quantity.centroid_values.shape[0]): assert num.allclose(quantity.centroid_values[k], old_div(num.sum(quantity.vertex_values[k, :]),3)) def test_limit_edges_by_all_neighbours(self): quantity = Quantity(self.mesh4) #Create a deliberate overshoot (e.g. from gradient computation) quantity.set_values([[3, 0, 3], [2, 2, 6], [5, 3, 8], [8, 3, 5]]) #Limit quantity.limit_edges_by_all_neighbours() #Assert that central triangle is limited by neighbours assert quantity.edge_values[1, 0] <= quantity.centroid_values[2] assert quantity.edge_values[1, 0] >= quantity.centroid_values[0] assert quantity.edge_values[1, 1] <= quantity.centroid_values[2] assert quantity.edge_values[1, 1] >= quantity.centroid_values[0] assert quantity.edge_values[1, 2] <= quantity.centroid_values[2] assert quantity.edge_values[1, 2] >= quantity.centroid_values[0] #Assert that quantities are conserved for k in range(quantity.centroid_values.shape[0]): assert num.allclose(quantity.centroid_values[k], old_div(num.sum(quantity.vertex_values[k, :]),3)) def test_limit_edges_by_neighbour(self): quantity = Quantity(self.mesh4) #Create a deliberate overshoot (e.g. from gradient computation) quantity.set_values([[3, 0, 3], [2, 2, 6], [5, 3, 8], [8, 3, 5]]) #Limit quantity.limit_edges_by_neighbour() #Assert that central triangle is limited by neighbours assert quantity.edge_values[1, 0] <= quantity.centroid_values[3] assert quantity.edge_values[1, 0] >= quantity.centroid_values[1] assert quantity.edge_values[1, 1] <= quantity.centroid_values[2] assert quantity.edge_values[1, 1] >= quantity.centroid_values[1] assert quantity.edge_values[1, 2] <= quantity.centroid_values[1] assert quantity.edge_values[1, 2] >= quantity.centroid_values[0] #Assert that quantities are conserved for k in range(quantity.centroid_values.shape[0]): assert num.allclose(quantity.centroid_values[k], old_div(num.sum(quantity.vertex_values[k, :]),3)) def test_limiter2(self): """Taken from test_shallow_water """ quantity = Quantity(self.mesh4) quantity.domain.beta_w = 0.9 #Test centroids quantity.set_values([2., 4., 8., 2.], location='centroids') assert num.allclose(quantity.centroid_values, [2, 4, 8, 2]) # Centroid #Extrapolate quantity.extrapolate_second_order() assert num.allclose(quantity.vertex_values[1, :], [0.0, 6, 6]) #Limit quantity.limit() # limited value for beta_w = 0.9 assert num.allclose(quantity.vertex_values[1, :], [2.2, 4.9, 4.9]) # limited values for beta_w = 0.5 #assert allclose(quantity.vertex_values[1,:], [3.0, 4.5, 4.5]) #Assert that quantities are conserved for k in range(quantity.centroid_values.shape[0]): assert num.allclose(quantity.centroid_values[k], old_div(num.sum(quantity.vertex_values[k, :]),3)) def test_distribute_first_order(self): quantity = Quantity(self.mesh4) #Test centroids quantity.set_values([1., 2., 3., 4.], location='centroids') assert num.allclose(quantity.centroid_values, [1, 2, 3, 4]) # Centroid #Extrapolate from centroid to vertices and edges quantity.extrapolate_first_order() #Interpolate #quantity.interpolate_from_vertices_to_edges() assert num.allclose(quantity.vertex_values, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]]) assert num.allclose(quantity.edge_values, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]]) def test_interpolate_from_vertices_to_edges(self): quantity = Quantity(self.mesh4) quantity.vertex_values = num.array( [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]], float) quantity.interpolate_from_vertices_to_edges() assert num.allclose(quantity.edge_values, [[1., 1.5, 0.5], [3., 2.5, 1.5], [3.5, 4.5, 3.], [2.5, 3.5, 2]]) def test_interpolate_from_edges_to_vertices(self): quantity = Quantity(self.mesh4) quantity.edge_values = num.array([[1., 1.5, 0.5], [3., 2.5, 1.5], [3.5, 4.5, 3.], [2.5, 3.5, 2]], float) quantity.interpolate_from_edges_to_vertices() assert num.allclose(quantity.vertex_values, [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]]) def test_distribute_second_order(self): quantity = Quantity(self.mesh4) #Test centroids quantity.set_values([2., 4., 8., 2.], location='centroids') assert num.allclose(quantity.centroid_values, [2, 4, 8, 2]) # Centroid #Extrapolate quantity.extrapolate_second_order() assert num.allclose(quantity.vertex_values[1, :], [0.0, 6, 6]) def test_update_explicit(self): quantity = Quantity(self.mesh4) #Test centroids quantity.set_values([1., 2., 3., 4.], location='centroids') assert num.allclose(quantity.centroid_values, [1, 2, 3, 4]) # Centroid #Set explicit_update quantity.explicit_update = num.array([1., 1., 1., 1.]) #Update with given timestep quantity.update(0.1) x = num.array([1, 2, 3, 4]) + num.array([.1, .1, .1, .1]) assert num.allclose(quantity.centroid_values, x) def test_update_semi_implicit(self): quantity = Quantity(self.mesh4) #Test centroids quantity.set_values([1., 2., 3., 4.], location='centroids') assert num.allclose(quantity.centroid_values, [1, 2, 3, 4]) # Centroid #Set semi implicit update quantity.semi_implicit_update = num.array([1., 1., 1., 1.]) #Update with given timestep timestep = 0.1 quantity.update(timestep) sem = old_div(num.array([1., 1., 1., 1.]),num.array([1, 2, 3, 4])) denom = num.ones(4, float)-timestep*sem x = old_div(num.array([1, 2, 3, 4]),denom) assert num.allclose(quantity.centroid_values, x) def test_both_updates(self): quantity = Quantity(self.mesh4) #Test centroids quantity.set_values([1., 2., 3., 4.], location='centroids') assert num.allclose(quantity.centroid_values, [1, 2, 3, 4]) # Centroid #Set explicit_update quantity.explicit_update = num.array([4., 3., 2., 1.]) #Set semi implicit update quantity.semi_implicit_update = num.array([1., 1., 1., 1.]) #Update with given timestep timestep = 0.1 quantity.update(0.1) sem = old_div(num.array([1., 1., 1., 1.]),num.array([1, 2, 3, 4])) denom = num.ones(4, float)-timestep*sem x = num.array([1., 2., 3., 4.]) x += timestep*num.array([4.0, 3.0, 2.0, 1.0]) x /= denom assert num.allclose(quantity.centroid_values, x) def set_array_values_by_index(self): from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 1) #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[1, 1, 1], [2, 2, 2]]) value = [7] indices = [1] quantity.set_array_values_by_index(value, location='centroids', indices=indices) #print "quantity.centroid_values",quantity.centroid_values assert num.allclose(quantity.centroid_values, [1, 7]) quantity.set_array_values([15, 20, 25], indices=indices) assert num.allclose(quantity.centroid_values, [1, 20]) quantity.set_array_values([15, 20, 25], indices=indices) assert num.allclose(quantity.centroid_values, [1, 20]) def test_setting_some_vertex_values(self): """ set values based on triangle lists. """ from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 3) #print "vertices",vertices #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]) # Check that constants work value = 7 indices = [1] quantity.set_values(value, location='centroids', indices=indices) #print "quantity.centroid_values",quantity.centroid_values assert num.allclose(quantity.centroid_values, [1, 7, 3, 4, 5, 6]) value = [7] indices = [1] quantity.set_values(value, location='centroids', indices=indices) #print "quantity.centroid_values",quantity.centroid_values assert num.allclose(quantity.centroid_values, [1, 7, 3, 4, 5, 6]) value = [[15, 20, 25]] quantity.set_values(value, indices=indices) #print "1 quantity.vertex_values",quantity.vertex_values assert num.allclose(quantity.vertex_values[1], value[0]) #print "quantity",quantity.vertex_values values = [10, 100, 50] quantity.set_values(values, indices=[0, 1, 5], location='centroids') #print "2 quantity.vertex_values",quantity.vertex_values assert num.allclose(quantity.vertex_values[0], [10, 10, 10]) assert num.allclose(quantity.vertex_values[5], [50, 50, 50]) #quantity.interpolate() #print "quantity.centroid_values",quantity.centroid_values assert num.allclose(quantity.centroid_values, [10, 100, 3, 4, 5, 50]) quantity = Quantity(domain, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]) values = [10, 100, 50] #this will be per unique vertex, indexing the vertices #print "quantity.vertex_values",quantity.vertex_values quantity.set_values(values, indices=[0, 1, 5]) #print "quantity.vertex_values",quantity.vertex_values assert num.allclose(quantity.vertex_values[0], [1, 50, 10]) assert num.allclose(quantity.vertex_values[5], [6, 6, 6]) assert num.allclose(quantity.vertex_values[1], [100, 10, 50]) quantity = Quantity(domain, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]) values = [[31, 30, 29], [400, 400, 400], [1000, 999, 998]] quantity.set_values(values, indices=[3, 3, 5]) quantity.interpolate() assert num.allclose(quantity.centroid_values, [1, 2, 3, 400, 5, 999]) values = [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]] quantity.set_values(values) # testing the standard set values by vertex # indexed by vertex_id in general_mesh.coordinates values = [0, 1, 2, 3, 4, 5, 6, 7] quantity.set_values(values) #print "1 quantity.vertex_values",quantity.vertex_values assert num.allclose(quantity.vertex_values, [[4., 5., 0.], [1., 0., 5.], [5., 6., 1.], [2., 1., 6.], [6., 7., 2.], [3., 2., 7.]]) def test_setting_unique_vertex_values(self): """ set values based on unique_vertex lists. """ from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 3) #print "vertices",vertices #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5]]) value = 7 indices = [1, 5] quantity.set_values(value, location='unique vertices', indices=indices) #print "quantity.centroid_values",quantity.centroid_values assert num.allclose(quantity.vertex_values[0], [0, 7, 0]) assert num.allclose(quantity.vertex_values[1], [7, 1, 7]) assert num.allclose(quantity.vertex_values[2], [7, 2, 7]) def test_get_values(self): """ get values based on triangle lists. """ from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 3) #print "points",points #print "vertices",vertices #print "boundary",boundary #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5]]) #print "quantity.get_values(location = 'unique vertices')", \ # quantity.get_values(location = 'unique vertices') #print "quantity.get_values(location = 'unique vertices')", \ # quantity.get_values(indices=[0,1,2,3,4,5,6,7], \ # location = 'unique vertices') answer = [0.5, 2, 4, 5, 0, 1, 3, 4.5] assert num.allclose(answer, quantity.get_values(location='unique vertices')) indices = [0, 5, 3] answer = [0.5, 1, 5] assert num.allclose(answer, quantity.get_values(indices=indices, location='unique vertices')) #print "quantity.centroid_values",quantity.centroid_values #print "quantity.get_values(location = 'centroids') ",\ # quantity.get_values(location = 'centroids') def test_get_values_2(self): """Different mesh (working with domain object) - also check centroids. """ a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe vertices = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] domain = Generic_Domain(points, vertices) quantity = Quantity(domain) quantity.set_values(lambda x, y: x+2*y) # 2 4 4 6 assert num.allclose(quantity.get_values(location='centroids'), [2, 4, 4, 6]) assert num.allclose(quantity.get_values(location='centroids', indices=[1, 3]), [4, 6]) assert num.allclose(quantity.get_values(location='vertices'), [[4, 0, 2], [4, 2, 6], [6, 2, 4], [8, 4, 6]]) assert num.allclose(quantity.get_values(location='vertices', indices=[1, 3]), [[4, 2, 6], [8, 4, 6]]) assert num.allclose(quantity.get_values(location='edges'), [[1, 3, 2], [4, 5, 3], [3, 5, 4], [5, 7, 6]]) assert num.allclose(quantity.get_values(location='edges', indices=[1, 3]), [[4, 5, 3], [5, 7, 6]]) # Check averaging over vertices #a: 0 #b: (4+4+4)/3 #c: (2+2+2)/3 #d: 8 #e: (6+6+6)/3 #f: 4 assert num.allclose(quantity.get_values(location='unique vertices'), [0, 4, 2, 8, 6, 4]) def test_get_vertex_values(self): """ get values based on triangle lists. """ from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 3) #print "points",points #print "vertices",vertices #print "boundary",boundary #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5]]) #====================================================== # Default: get_vertex_values just returns the individual # vertex values within each triangle #====================================================== Q, V = quantity.get_vertex_values(xy=False) answer = [0., 0., 0., 1., 1., 1., 2., 2., 2., 3., 3., 3., 4., 4., 4., 5., 5., 5.] v_answer = num.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11], [12, 13, 14], [15, 16, 17]]) assert num.allclose(answer, Q) assert num.allclose(v_answer, V) #====================================================== # Set output to be smooth, so get one unique value at # each node. V now provides id to unique node id #====================================================== domain.smooth = True Q, V = quantity.get_vertex_values(xy=False) answer = num.array([0.5, 2, 4, 5, 0, 1, 3, 4.5]) v_answer = num.array([[4, 5, 0], [1, 0, 5], [5, 6, 1], [2, 1, 6], [6, 7, 2], [3, 2, 7]]) assert num.allclose(answer, Q) assert num.allclose(v_answer, V) #====================================================== # Set output to be smooth, and if using discontinuous # algorithms, get one unique value at # each node, based on centroid values. # V now provides id to unique node id #====================================================== domain.smooth = True domain.using_discontinuous_elevation = True quantity.centroid_values[:] = num.array([100, 101, 102, 103, 104, 105]) Q, V = quantity.get_vertex_values(xy=False) answer = answer + 100.0 v_answer = num.array([[4, 5, 0], [1, 0, 5], [5, 6, 1], [2, 1, 6], [6, 7, 2], [3, 2, 7]]) assert num.allclose(answer, Q) assert num.allclose(v_answer, V) def test_get_interpolated_values(self): from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 3) domain = Generic_Domain(points, vertices, boundary) #Constant values quantity = Quantity(domain, [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5]]) # Get interpolated values at centroids interpolation_points = domain.get_centroid_coordinates() answer = quantity.get_values(location='centroids') #print quantity.get_values(points=interpolation_points) assert num.allclose(answer, quantity.get_values( interpolation_points=interpolation_points)) #Arbitrary values quantity = Quantity(domain, [[0, 1, 2], [3, 1, 7], [2, 1, 2], [3, 3, 7], [1, 4, -9], [2, 5, 0]]) # Get interpolated values at centroids interpolation_points = domain.get_centroid_coordinates() answer = quantity.get_values(location='centroids') #print answer #print quantity.get_values(interpolation_points=interpolation_points) assert num.allclose(answer, quantity.get_values(interpolation_points=interpolation_points, verbose=False)) #FIXME TODO #indices = [0,5,3] #answer = [0.5,1,5] #assert allclose(answer, # quantity.get_values(indices=indices, \ # location = 'unique vertices')) def test_get_interpolated_values_2(self): a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe vertices = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] domain = Generic_Domain(points, vertices) quantity = Quantity(domain) quantity.set_values(lambda x, y: x+2*y) # 2 4 4 6 #First pick one point x, y = 2.0/3, 8.0/3 v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 6) # Then another to test that algorithm won't blindly # reuse interpolation matrix x, y = 4.0/3, 4.0/3 v = quantity.get_values(interpolation_points=[[x, y]]) assert num.allclose(v, 4) def test_get_interpolated_values_with_georef(self): zone = 56 xllcorner = 308500 yllcorner = 6189000 a = [0.0, 0.0] b = [0.0, 2.0] c = [2.0, 0.0] d = [0.0, 4.0] e = [2.0, 2.0] f = [4.0, 0.0] points = [a, b, c, d, e, f] #bac, bce, ecf, dbe vertices = [[1, 0, 2], [1, 2, 4], [4, 2, 5], [3, 1, 4]] domain = Generic_Domain(points, vertices, geo_reference=Geo_reference(zone, xllcorner, yllcorner)) quantity = Quantity(domain) quantity.set_values(lambda x, y: x+2*y) # 2 4 4 6 #First pick one point (and turn it into absolute coordinates) x, y = 2.0/3, 8.0/3 v = quantity.get_values(interpolation_points=[ [x+xllcorner, y+yllcorner]]) assert num.allclose(v, 6) # Then another to test that algorithm won't blindly # reuse interpolation matrix x, y = 4.0/3, 4.0/3 v = quantity.get_values(interpolation_points=[ [x+xllcorner, y+yllcorner]]) assert num.allclose(v, 4) # Try two points pts = [[2.0/3 + xllcorner, 8.0/3 + yllcorner], [4.0/3 + xllcorner, 4.0/3 + yllcorner]] v = quantity.get_values(interpolation_points=pts) assert num.allclose(v, [6, 4]) # Test it using the geospatial data format with absolute input points and default georef pts = Geospatial_data(data_points=pts) v = quantity.get_values(interpolation_points=pts) assert num.allclose(v, [6, 4]) # Test it using the geospatial data format with relative input points pts = Geospatial_data(data_points=[[2.0/3, 8.0/3], [4.0/3, 4.0/3]], geo_reference=Geo_reference(zone, xllcorner, yllcorner)) v = quantity.get_values(interpolation_points=pts) assert num.allclose(v, [6, 4]) def test_getting_some_vertex_values(self): """ get values based on triangle lists. """ from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular #Create basic mesh points, vertices, boundary = rectangular(1, 3) #print "points",points #print "vertices",vertices #print "boundary",boundary #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]) value = [7] indices = [1] quantity.set_values(value, location='centroids', indices=indices) #print "quantity.centroid_values",quantity.centroid_values #print "quantity.get_values(location = 'centroids') ",\ # quantity.get_values(location = 'centroids') assert num.allclose(quantity.centroid_values, quantity.get_values(location='centroids')) value = [[15, 20, 25]] quantity.set_values(value, indices=indices) #print "1 quantity.vertex_values",quantity.vertex_values assert num.allclose(quantity.vertex_values, quantity.get_values()) assert num.allclose(quantity.edge_values, quantity.get_values(location='edges')) # get a subset of elements subset = quantity.get_values(location='centroids', indices=[0, 5]) answer = [quantity.centroid_values[0], quantity.centroid_values[5]] assert num.allclose(subset, answer) subset = quantity.get_values(location='edges', indices=[0, 5]) answer = [quantity.edge_values[0], quantity.edge_values[5]] #print "subset",subset #print "answer",answer assert num.allclose(subset, answer) subset = quantity.get_values(indices=[1, 5]) answer = [quantity.vertex_values[1], quantity.vertex_values[5]] #print "subset",subset #print "answer",answer assert num.allclose(subset, answer) def test_smooth_vertex_values(self): """ get values based on triangle lists. """ from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular # from anuga.shallow_water.shallow_water_domain import Domain #Create basic mesh points, vertices, boundary = rectangular(2, 2) #print "points",points #print "vertices",vertices #print "boundary",boundary #Create shallow water domain domain = Generic_Domain(points, vertices, boundary) #print "domain.number_of_elements ",domain.number_of_elements quantity = Quantity(domain, [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]]) #print "quantity.get_values(location = 'unique vertices')", \ # quantity.get_values(location = 'unique vertices') #print "quantity.get_values(location = 'unique vertices')", \ # quantity.get_values(indices=[0,1,2,3,4,5,6,7], \ # location = 'unique vertices') #print quantity.get_values(location = 'unique vertices') #print quantity.domain.number_of_triangles_per_node #print quantity.vertex_values #answer = [0.5, 2, 3, 3, 3.5, 4, 4, 5, 6.5] #assert allclose(answer, # quantity.get_values(location = 'unique vertices')) quantity.smooth_vertex_values() #print quantity.vertex_values answer_vertex_values = [[3, 3.5, 0.5], [2, 0.5, 3.5], [3.5, 4, 2], [3, 2, 4], [4, 5, 3], [3.5, 3, 5], [5, 6.5, 3.5], [4, 3.5, 6.5]] assert num.allclose(answer_vertex_values, quantity.vertex_values) # Just another (slightly larger) test of get_values assert num.allclose(quantity.get_values(location='centroids'), quantity.centroid_values) assert num.allclose(quantity.get_values(location='vertices'), quantity.vertex_values) assert num.allclose(quantity.get_values(location='edges'), quantity.edge_values) def test_maximum(self): quantity = Quantity(self.mesh4) # get referece to data arrays centroid_values = quantity.centroid_values vertex_values = quantity.vertex_values edge_values = quantity.edge_values quantity.set_values([[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]], location='vertices') assert num.allclose(quantity.vertex_values, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert id(vertex_values) == id(quantity.vertex_values) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroid assert num.allclose(quantity.edge_values, [[2.5, 2.0, 1.5], [5., 5., 5.], [4.5, 4.5, 0.], [3.0, -1.5, -1.5]]) other_quantity = Quantity(self.mesh4) other_quantity.set_values([[0, 0, 0], [1, 1, 6], [10, 10, 10], [0, 0, 4]], location='vertices') #=============================== quantity.maximum(other_quantity) #=============================== exact_vertex_values = num.array([[1., 2., 3.], [5., 5., 6.], [10., 10., 10.], [0., 3., 4.]]) exact_centroid_values = num.array([2., 5., 10., 1.33333333]) exact_edge_values = num.array([[2.5, 2., 1.5], [5., 5., 5., ], [10., 10., 10.], [3., 2., 0.]]) assert num.allclose(quantity.vertex_values, exact_vertex_values) assert num.allclose(quantity.centroid_values, exact_centroid_values) # Centroid assert num.allclose(quantity.edge_values, exact_edge_values) def test_minimum(self): quantity = Quantity(self.mesh4) # get referece to data arrays centroid_values = quantity.centroid_values vertex_values = quantity.vertex_values edge_values = quantity.edge_values quantity.set_values([[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]], location='vertices') assert num.allclose(quantity.vertex_values, [[1, 2, 3], [5, 5, 5], [0, 0, 9], [-6, 3, 3]]) assert id(vertex_values) == id(quantity.vertex_values) assert num.allclose(quantity.centroid_values, [ 2., 5., 3., 0.]) # Centroid assert num.allclose(quantity.edge_values, [[2.5, 2.0, 1.5], [5., 5., 5.], [4.5, 4.5, 0.], [3.0, -1.5, -1.5]]) other_quantity = Quantity(self.mesh4) other_quantity.set_values([[0, 0, 0], [1, 1, 6], [10, 10, 10], [0, 0, 4]], location='vertices') #=============================== quantity.minimum(other_quantity) #=============================== exact_vertex_values = num.array([[0., 0., 0.], [1., 1., 5.], [0., 0., 9.], [-6., 0., 3.]]) exact_centroid_values = num.array([0., 2.66666667, 3., 0.]) exact_edge_values = num.array([[0., 0., 0.], [3.5, 3.5, 1., ], [4.5, 4.5, 0.], [2., -1.5, -1.5]]) assert num.allclose(quantity.vertex_values, exact_vertex_values) assert num.allclose(quantity.centroid_values, exact_centroid_values) # Centroid assert num.allclose(quantity.edge_values, exact_edge_values) #------------------------------------------------------------- if __name__ == "__main__": # _set_values_from_asc') suite = unittest.makeSuite(Test_Quantity, 'test_') runner = unittest.TextTestRunner(verbosity=1) runner.run(suite)

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0.740003

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0.347173

104,484

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0.682759

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false

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0.005441

0

null

0

1

0

null

0

6

3189197c2934f8cd2261e7516806da385083c85d

339

py

Python

modules/tests/test_weather.py

anamayagarodia/JARVIS-on-Messenger

d7198db0afe99cf3c0f7aacd5d5a16641deba809

[ "MIT" ]

6

2017-05-17T23:46:16.000Z

2017-05-18T19:50:15.000Z

modules/tests/test_weather.py

anamayagarodia/JARVIS-on-Messenger

d7198db0afe99cf3c0f7aacd5d5a16641deba809

[ "MIT" ]

null

modules/tests/test_weather.py

anamayagarodia/JARVIS-on-Messenger

d7198db0afe99cf3c0f7aacd5d5a16641deba809

[ "MIT" ]

2

2018-08-06T06:03:58.000Z

2020-01-08T07:57:37.000Z

import modules def test_weather(): assert('weather' == modules.process_query('tell me the weather in London')[0]) assert('weather' == modules.process_query('weather Delhi')[0]) assert('weather' == modules.process_query('What\'s the weather in Texas?')[0]) assert('weather' != modules.process_query('something random')[0])

42.375

82

0.699115

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339

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339

7

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1

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null

0

1

0

1

0

6

31945cb30edaf65a600a39ecfec83118ba06e341

1,857

py

Python

tests/test_hash.py

MicrohexHQ/liboqs

6340a0ba71f66ad494ede00d38f358b2a4546164

[ "MIT" ]

null

tests/test_hash.py

MicrohexHQ/liboqs

6340a0ba71f66ad494ede00d38f358b2a4546164

[ "MIT" ]

null

tests/test_hash.py

MicrohexHQ/liboqs

6340a0ba71f66ad494ede00d38f358b2a4546164

[ "MIT" ]

1

2020-10-12T13:30:00.000Z

import hashlib import helpers import pytest import sys @helpers.filtered_test @pytest.mark.skipif(sys.platform.startswith("win"), reason="Not supported on Windows") def test_aes(): helpers.run_subprocess( [helpers.path_to_executable('test_aes')], ) @helpers.filtered_test @pytest.mark.skipif(sys.platform.startswith("win"), reason="Not supported on Windows") def test_sha3(): helpers.run_subprocess( [helpers.path_to_executable('test_sha3')], ) @helpers.filtered_test @pytest.mark.parametrize('msg', ['', 'a', 'abc', '1234567890123456789012345678901678901567890']) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Not supported on Windows") def test_sha256(msg): output = helpers.run_subprocess( [helpers.path_to_executable('test_hash'), 'sha256'], input = msg.encode(), ) assert(output.rstrip() == hashlib.sha256(msg.encode()).hexdigest()) @helpers.filtered_test @pytest.mark.parametrize('msg', ['', 'a', 'abc', '1234567890123456789012345678901678901567890']) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Not supported on Windows") def test_sha384(msg): output = helpers.run_subprocess( [helpers.path_to_executable('test_hash'), 'sha384'], input = msg.encode(), ) assert(output.rstrip() == hashlib.sha384(msg.encode()).hexdigest()) @helpers.filtered_test @pytest.mark.parametrize('msg', ['', 'a', 'abc', '1234567890123456789012345678901678901567890']) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Not supported on Windows") def test_sha512(msg): output = helpers.run_subprocess( [helpers.path_to_executable('test_hash'), 'sha512'], input = msg.encode(), ) assert(output.rstrip() == hashlib.sha512(msg.encode()).hexdigest()) if __name__ == "__main__": import sys pytest.main(sys.argv)

34.388889

96

0.706516

215

1,857

5.925581

0.204651

0.062794

0.074568

0.098116

0.867347

0.77551

0.701727

0

0.097893

0.130856

1,857

53

97

35.037736

0.69145

0

0.5

0

0.191272

0.069504

0

0.065217

1

0.108696

false

0

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0

0.217391

0

null

0

1

0

null

0

6

31c40436b83c2b1b5cf6a031de834bad6a5aeb45

30

py

Python

hello.py

AleksandrTheFirst/pythonTests

0c1d7e10ccf34f633d5aea15f039fa6e434c494f

[ "Apache-2.0" ]

null

hello.py

AleksandrTheFirst/pythonTests

0c1d7e10ccf34f633d5aea15f039fa6e434c494f

[ "Apache-2.0" ]

null

hello.py

AleksandrTheFirst/pythonTests

0c1d7e10ccf34f633d5aea15f039fa6e434c494f

[ "Apache-2.0" ]

null

print("Hello from Aleksandr")

15

29

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30

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1

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0.1

30

1

30

0.851852

0

0.666667

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true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

9ec4e8a15d0480accb64acdf16f67e7a2701ef39

28,269

py

Python

AutoEn.py

nileshchilka1/AutoEnsembler

4df8fe160bbfae16e9a9a401ef8a7c8a20ffc414

[ "MIT" ]

3

2020-10-24T06:45:58.000Z

2021-11-05T10:42:56.000Z

AutoEn.py

nileshchilka1/AutoEnsembler

4df8fe160bbfae16e9a9a401ef8a7c8a20ffc414

[ "MIT" ]

null

AutoEn.py

nileshchilka1/AutoEnsembler

4df8fe160bbfae16e9a9a401ef8a7c8a20ffc414

[ "MIT" ]

null

import numpy as np from sklearn.model_selection import train_test_split,GridSearchCV,RandomizedSearchCV from sklearn.metrics import accuracy_score,confusion_matrix from sklearn.metrics import r2_score import warnings def findCombinationsUtil(arr, index, num, reducedNum,size,unique_classes): global combinations if (reducedNum < 0): return; if (reducedNum == 0): comb = [] for i in range(index): comb.append(arr[i]/10) if len(comb) == size: comb = zero_padding(comb,unique_classes) combinations.append(comb) return; prev = 1 if(index == 0) else arr[index - 1]; for k in range(prev, num + 1): arr[index] = k; findCombinationsUtil(arr, index + 1, num, reducedNum - k,size,unique_classes); def findCombinations(n,size,unique_classes): arr = [0] * n; findCombinationsUtil(arr, 0, n, n,size,unique_classes) def zero_padding(lst,size): l=len(lst) for i in range(size-l): lst.append(0) return lst combinations = [] def find_all_combinations(unique_classes): global combinations n = 10 for i in range(2,unique_classes+1): findCombinations(n,i,unique_classes); combinations += combinations zeros = [0] * unique_classes zeros[0] = 1 combinations.append(zeros) from itertools import permutations all_combinations = [] for comb in combinations: perm = permutations(comb) for i in list(perm): if i not in all_combinations: all_combinations.append(i) return all_combinations class AutoEnClassifier: def __init__(self,LR=True,SVC=False,RF=True,AB=False,KNN=False,random_state=0,GridSearch=False,optimize=None,scoring='accuracy'): self.__LR = LR self.__SVC = SVC self.__RF = RF self.__AB = AB self.__KNN = KNN self.__random_state = random_state self.__GridSearch = GridSearch self.__optimize = optimize if not GridSearch: warnings.warn('model will use RandomizedSearch') self.__scoring = scoring def fit(self,X_train,y_train,validation_split=0.2,validation_data=False): self.__storing_model_names = [] self.__X_train = X_train self.__y_train = y_train if validation_data: self.__X_test = validation_data[0] self.__y_test = validation_data[1] else: self.__X_train,self.__X_test,self.__y_train,self.__y_test = train_test_split(X_train,y_train,test_size=validation_split,random_state=self.__random_state) if self.__LR: AutoEnClassifier.LR_model_fit(self,param_grid=None) self.__storing_model_names.append('LR_score') if self.__SVC: AutoEnClassifier.SVC_model_fit(self,param_grid=None) self.__storing_model_names.append('SVC_score') if self.__RF: AutoEnClassifier.RF_model_fit(self,param_grid=None) self.__storing_model_names.append('RF_score') if self.__AB: AutoEnClassifier.AB_model_fit(self,param_grid=None) self.__storing_model_names.append('AB_score') if self.__KNN: AutoEnClassifier.KNN_model_fit(self,list_neighbors=None) self.__storing_model_names.append('KNN_score') AutoEnClassifier.find_best(self) def LR_model_fit(self,param_grid=None): from sklearn.linear_model import LogisticRegression LR_model = LogisticRegression() if param_grid == None: parameters = {'C':[0.1,0.5,1,5,10], 'solver':['newton-cg', 'lbfgs', 'sag', 'saga'], } if self.__GridSearch: self.__LR_model = GridSearchCV(estimator=LR_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__LR_model = RandomizedSearchCV(estimator=LR_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__LR_model = GridSearchCV(estimator=LR_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__LR_model = RandomizedSearchCV(estimator=LR_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__LR_model.fit(self.__X_train,self.__y_train) print(f'LR_score : {accuracy_score(self.__LR_model.predict(self.__X_test),self.__y_test)}') def SVC_model_fit(self,param_grid=None): from sklearn.svm import SVC SVC_model = SVC(probability=True) if param_grid == None: parameters = [{'kernel': ['rbf','poly'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}] if self.__GridSearch: self.__SVC_model = GridSearchCV(estimator=SVC_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__SVC_model = RandomizedSearchCV(estimator=SVC_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__SVC_model = GridSearchCV(estimator=SVC_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__SVC_model = RandomizedSearchCV(estimator=SVC_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__SVC_model.fit(self.__X_train,self.__y_train) print(f'SVC_score : {accuracy_score(self.__SVC_model.predict(self.__X_test),self.__y_test)}') def RF_model_fit(self,param_grid=None): from sklearn.ensemble import RandomForestClassifier RF_model = RandomForestClassifier() if param_grid == None: parameters = {'n_estimators' :[10,50,100,500], 'max_depth' : [4,8,10,12,16], 'min_samples_leaf' : [0.1, 0.2, 0.3, 0.4, 0.5] } if self.__GridSearch: self.__RF_model = GridSearchCV(estimator=RF_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__RF_model = RandomizedSearchCV(estimator=RF_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__RF_model = GridSearchCV(estimator=RF_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__RF_model = RandomizedSearchCV(estimator=RF_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__RF_model.fit(self.__X_train,self.__y_train) print(f'RF_score : {accuracy_score(self.__RF_model.predict(self.__X_test),self.__y_test)}') def AB_model_fit(self,param_grid=None): from sklearn.ensemble import AdaBoostClassifier AB_model = AdaBoostClassifier() if param_grid == None: parameters = {'n_estimators' :[10,50,100,500], 'learning_rate' : [0.01,0.5,0.1,0.15,0.2], } if self.__GridSearch: self.__AB_model = GridSearchCV(estimator=AB_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__AB_model = RandomizedSearchCV(estimator=AB_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__AB_model = GridSearchCV(estimator=AB_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__AB_model = RandomizedSearchCV(estimator=AB_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__AB_model.fit(self.__X_train,self.__y_train) print(f'AB_score : {accuracy_score(self.__AB_model.predict(self.__X_test),self.__y_test)}') def KNN_model_fit(self,list_neighbors=None): from sklearn.neighbors import KNeighborsClassifier if list_neighbors == None: list_neighbors = [3,5,7,9,11,13,15] n_neighbor_score_model = [None,0,None] for neighbor in list_neighbors: self.__KNN_model = KNeighborsClassifier(n_neighbors=neighbor) self.__KNN_model = self.__KNN_model.fit(self.__X_train,self.__y_train) model_score = self.__KNN_model.score(self.__X_test,self.__y_test) if model_score > n_neighbor_score_model[1]: n_neighbor_score_model[0] = neighbor n_neighbor_score_model[1] = model_score n_neighbor_score_model[2] = self.__KNN_model self.__KNN_model = n_neighbor_score_model[2] y_predict = self.__KNN_model.predict_proba(self.__X_test) y_predict = np.argmax(y_predict,axis=1) print(f'KNN_score with {n_neighbor_score_model[0]} neighbors: {accuracy_score(self.__y_test,y_predict)}') def find_best(self): global combinations combinations = [] Total_models = self.__LR + self.__SVC + self.__RF + self.__KNN + self.__AB optimize_count = None combinations = find_all_combinations(Total_models) combinations = np.array(combinations) all_proba = [] count = 1 self.__best_score = [0] + [None] * Total_models if self.__LR: LR_model_y_predict_proba = self.__LR_model.predict_proba(self.__X_test) all_proba.append(LR_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__SVC: SVC_model_y_predict_proba = self.__SVC_model.predict_proba(self.__X_test) all_proba.append(SVC_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__RF: RF_model_y_predict_proba = self.__RF_model.predict_proba(self.__X_test) all_proba.append(RF_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__AB: AB_model_y_predict_proba = self.__AB_model.predict_proba(self.__X_test) all_proba.append(AB_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__KNN: KNN_model_y_predict_proba = self.__KNN_model.predict_proba(self.__X_test) all_proba.append(KNN_model_y_predict_proba) if self.__best_score[count] == None: count += 1 all_proba = np.array(all_proba) all_proba = np.sum(np.multiply(combinations.T ,np.array([all_proba]).T ).T,axis=1) for proba,comb in zip(all_proba,combinations): y_predict = np.argmax(proba,axis=1) latest_score = accuracy_score(self.__y_test,y_predict) if latest_score > self.__best_score[0]: self.__best_score[0] = latest_score for i in range(0,len(comb)): self.__best_score[i+1] = comb[i] if self.__optimize == 'FP': optimize_count = confusion_matrix(self.__y_test,y_predict)[1][0] elif self.__optimize == 'FN': optimize_count = confusion_matrix(self.__y_test,y_predict)[0][1] elif latest_score == self.__best_score[0] and self.__optimize == 'FP': FP_count = confusion_matrix(self.__y_test,y_predict)[1][0] if FP_count < optimize_count: print(f'optimized FP from {optimize_count} to {FP_count}') optimize_count = FP_count self.__best_score[0] = latest_score for i in range(0,len(comb)): self.__best_score[i+1] = comb[i] elif latest_score == self.__best_score[0] and self.__optimize == 'FN': FN_count = confusion_matrix(self.__y_test,y_predict)[0][1] if FN_count < optimize_count: print(f'optimized FN from {optimize_count} to {FN_count}') optimize_count = FN_count self.__best_score[0] = latest_score for i in range(0,len(comb)): self.__best_score[i+1] = comb[i] print(f'AutoEn_score : {self.__best_score[0]}') for i in range(len(self.__storing_model_names)): print(f'weight for {self.__storing_model_names[i]} : {self.__best_score[i+1]}') def predict(self,X_test): all_proba = [] count = 1 try: if self.__LR: LR_model_y_predict_proba = self.__LR_model.predict_proba(X_test) LR_model_y_predict_proba = np.multiply(LR_model_y_predict_proba,self.__best_score[count]) all_proba.append(LR_model_y_predict_proba) count+=1 if self.__SVC: SVC_model_y_predict_proba = self.__SVC_model.predict_proba(X_test) SVC_model_y_predict_proba = np.multiply(SVC_model_y_predict_proba,self.__best_score[count]) all_proba.append(SVC_model_y_predict_proba) count+=1 if self.__RF: RF_model_y_predict_proba = self.__RF_model.predict_proba(X_test) RF_model_y_predict_proba = np.multiply(RF_model_y_predict_proba,self.__best_score[count]) all_proba.append(RF_model_y_predict_proba) count+=1 if self.__AB: AB_model_y_predict_proba = self.__AB_model.predict_proba(X_test) AB_model_y_predict_proba = np.multiply(AB_model_y_predict_proba,self.__best_score[count]) all_proba.append(AB_model_y_predict_proba) count+=1 if self.__KNN: KNN_model_y_predict_proba = self.__KNN_model.predict_proba(X_test) KNN_model_y_predict_proba = np.multiply(KNN_model_y_predict_proba,self.__best_score[count]) all_proba.append(KNN_model_y_predict_proba) count+=1 y_predict = np.sum(all_proba,axis=0) except AttributeError: print('model not fitted yet') return None except: print('something went wrong') return None y_predict = np.argmax(y_predict,axis=1) return y_predict class AutoEnRegressor: def __init__(self,LA=True,SVR=False,RF=True,AB=False,KNN=False,random_state=0,GridSearch=False,scoring='r2'): self.__LA = LA self.__SVR = SVR self.__RF = RF self.__AB = AB self.__KNN = KNN self.__random_state = random_state self.__GridSearch = GridSearch if not GridSearch: warnings.warn('model will use RandomizedSearch') self.__scoring = scoring def fit(self,X_train,y_train,validation_split=0.2,validation_data=False): self.__storing_model_names = [] self.__X_train = X_train self.__y_train = y_train if validation_data: self.__X_test = validation_data[0] self.__y_test = validation_data[1] else: self.__X_train,self.__X_test,self.__y_train,self.__y_test = train_test_split(X_train,y_train,test_size=validation_split,random_state=self.__random_state) if self.__LA: AutoEnRegressor.LA_model_fit(self,param_grid=None) self.__storing_model_names.append('LA_score') if self.__SVR: AutoEnRegressor.SVR_model_fit(self,param_grid=None) self.__storing_model_names.append('SVR_score') if self.__RF: AutoEnRegressor.RF_model_fit(self,param_grid=None) self.__storing_model_names.append('RF_score') if self.__AB: AutoEnRegressor.AB_model_fit(self,param_grid=None) self.__storing_model_names.append('AB_score') if self.__KNN: AutoEnRegressor.KNN_model_fit(self,list_neighbors=None) self.__storing_model_names.append('KNN_score') AutoEnRegressor.find_best(self) def LA_model_fit(self,param_grid=None): from sklearn.linear_model import Lasso LA_model = Lasso() if param_grid == None: parameters = {'alpha':[0.01,0.5,1,2,5] } if self.__GridSearch: self.__LA_model = GridSearchCV(estimator=LA_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__LA_model = RandomizedSearchCV(estimator=LA_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__LA_model = GridSearchCV(estimator=LA_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__LA_model = RandomizedSearchCV(estimator=LA_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__LA_model.fit(self.__X_train,self.__y_train) print(f'LA_score : {r2_score(self.__y_test,self.__LA_model.predict(self.__X_test))}') def SVR_model_fit(self,param_grid=None): from sklearn.svm import SVR SVR_model = SVR() if param_grid == None: parameters = [{'kernel': ['rbf','poly'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}] if self.__GridSearch: self.__SVR_model = GridSearchCV(estimator=SVR_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__SVR_model = RandomizedSearchCV(estimator=SVR_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__SVR_model = GridSearchCV(estimator=SVR_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__SVR_model = RandomizedSearchCV(estimator=SVR_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__SVR_model.fit(self.__X_train,self.__y_train) print(f'SVR_score : {r2_score(self.__y_test,self.__SVR_model.predict(self.__X_test))}') def RF_model_fit(self,param_grid=None): from sklearn.ensemble import RandomForestRegressor RF_model = RandomForestRegressor() if param_grid == None: parameters = {'n_estimators' :[10,50,100,500], 'max_depth' : [4,8,10,12,16], 'min_samples_leaf' : [0.1, 0.2, 0.3, 0.4, 0.5] } if self.__GridSearch: self.__RF_model = GridSearchCV(estimator=RF_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__RF_model = RandomizedSearchCV(estimator=RF_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__RF_model = GridSearchCV(estimator=RF_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__RF_model = RandomizedSearchCV(estimator=RF_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__RF_model.fit(self.__X_train,self.__y_train) print(f'RF_score : {r2_score(self.__y_test,self.__RF_model.predict(self.__X_test))}') def AB_model_fit(self,param_grid=None): from sklearn.ensemble import AdaBoostRegressor AB_model = AdaBoostRegressor() if param_grid == None: parameters = {'n_estimators' :[10,50,100,500], 'learning_rate' : [0.01,0.5,0.1,0.15,0.2], } if self.__GridSearch: self.__AB_model = GridSearchCV(estimator=AB_model, param_grid=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__AB_model = RandomizedSearchCV(estimator=AB_model, param_distributions=parameters, cv=5,scoring=self.__scoring,n_jobs=-1) else: if self.__GridSearch: self.__AB_model = GridSearchCV(estimator=AB_model, param_grid=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) else: self.__AB_model = RandomizedSearchCV(estimator=AB_model, param_distributions=param_grid, cv=5,scoring=self.__scoring,n_jobs=-1) self.__AB_model.fit(self.__X_train,self.__y_train) print(f'AB_score : {r2_score(self.__y_test,self.__AB_model.predict(self.__X_test))}') def KNN_model_fit(self,list_neighbors=None): from sklearn.neighbors import KNeighborsRegressor if list_neighbors == None: list_neighbors = [3,5,7,9,11,13,15] n_neighbor_score_model = [None,0,None] for neighbor in list_neighbors: self.__KNN_model = KNeighborsRegressor(n_neighbors=neighbor) self.__KNN_model = self.__KNN_model.fit(self.__X_train,self.__y_train) model_score = self.__KNN_model.score(self.__X_test,self.__y_test) if model_score > n_neighbor_score_model[1]: n_neighbor_score_model[0] = neighbor n_neighbor_score_model[1] = model_score n_neighbor_score_model[2] = self.__KNN_model self.__KNN_model = n_neighbor_score_model[2] y_predict = self.__KNN_model.predict(self.__X_test) print(f'KNN_score with {n_neighbor_score_model[0]} neighbors: {r2_score(self.__y_test,y_predict)}') def find_best(self): global combinations combinations = [] Total_models = self.__LA + self.__SVR + self.__RF + self.__KNN + self.__AB combinations = np.array(find_all_combinations(Total_models)) all_proba = [] count = 1 self.__best_score = [0] + [None] * Total_models if self.__LA: LA_model_y_predict_proba = self.__LA_model.predict(self.__X_test) all_proba.append(LA_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__SVR: SVR_model_y_predict_proba = self.__SVR_model.predict(self.__X_test) all_proba.append(SVR_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__RF: RF_model_y_predict_proba = self.__RF_model.predict(self.__X_test) all_proba.append(RF_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__AB: AB_model_y_predict_proba = self.__AB_model.predict(self.__X_test) all_proba.append(AB_model_y_predict_proba) if self.__best_score[count] == None: count += 1 if self.__KNN: KNN_model_y_predict_proba = self.__KNN_model.predict(self.__X_test) all_proba.append(KNN_model_y_predict_proba) if self.__best_score[count] == None: count += 1 all_proba = np.array(all_proba) all_proba = np.sum(np.multiply(combinations.T ,np.array([all_proba]).T ).T,axis=1) for y_predict,comb in zip(all_proba,combinations): latest_score = r2_score(self.__y_test,y_predict) if latest_score > self.__best_score[0]: self.__best_score[0] = latest_score for i in range(0,len(comb)): self.__best_score[i+1] = comb[i] print(f'AutoEn_score : {self.__best_score[0]}') for i in range(len(self.__storing_model_names)): print(f'weight for {self.__storing_model_names[i]} : {self.__best_score[i+1]}') def predict(self,X_test): all_proba = [] count = 1 try: if self.__LA: LA_model_y_predict = self.__LA_model.predict(X_test) LA_model_y_predict = np.multiply(LA_model_y_predict,self.__best_score[count]) all_proba.append(LA_model_y_predict) count+=1 if self.__SVR: SVR_model_y_predict = self.__SVR_model.predict(X_test) SVR_model_y_predict = np.multiply(SVR_model_y_predict,self.__best_score[count]) all_proba.append(SVR_model_y_predict) count+=1 if self.__RF: RF_model_y_predict = self.__RF_model.predict(X_test) RF_model_y_predict = np.multiply(RF_model_y_predict,self.__best_score[count]) all_proba.append(RF_model_y_predict) count+=1 if self.__AB: AB_model_y_predict = self.__AB_model.predict(X_test) AB_model_y_predict = np.multiply(AB_model_y_predict,self.__best_score[count]) all_proba.append(AB_model_y_predict) count+=1 if self.__KNN: KNN_model_y_predict = self.__KNN_model.predict(X_test) KNN_model_y_predict = np.multiply(KNN_model_y_predict,self.__best_score[count]) all_proba.append(KNN_model_y_predict) count+=1 y_predict = np.sum(all_proba,axis=0) except AttributeError: print('model not fitted yet') return None except: print('something went wrong') return None return y_predict

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9ef4cd147d43d2ad1ef9d58b3b2a7ce4421f14c7

19,189

py

Python

api/vm/snapshot/views.py

klebed/esdc-ce

2c9e4591f344247d345a83880ba86777bb794460

[ "Apache-2.0" ]

97

2016-11-15T14:44:23.000Z

2022-03-13T18:09:15.000Z

api/vm/snapshot/views.py

klebed/esdc-ce

2c9e4591f344247d345a83880ba86777bb794460

[ "Apache-2.0" ]

334

2016-11-17T19:56:57.000Z

2022-03-18T10:45:53.000Z

api/vm/snapshot/views.py

klebed/esdc-ce

2c9e4591f344247d345a83880ba86777bb794460

[ "Apache-2.0" ]

33

2017-01-02T16:04:13.000Z

2022-02-07T19:20:24.000Z

from vms.models import SnapshotDefine from api.decorators import api_view, request_data, setting_required from api.permissions import IsAdminOrReadOnly from api.utils.db import get_object from api.vm.utils import get_vm, get_vms # noinspection PyProtectedMember from api.image.base.views import image_snapshot from api.vm.snapshot.utils import get_disk_id, filter_disk_id, output_extended_snap_count from api.vm.snapshot.vm_define_snapshot import SnapshotDefineView from api.vm.snapshot.vm_snapshot import VmSnapshot from api.vm.snapshot.vm_snapshot_list import VmSnapshotList __all__ = ('vm_define_snapshot_list_all', 'vm_define_snapshot_list', 'vm_define_snapshot', 'vm_snapshot_list', 'vm_snapshot', 'image_snapshot') #: vm_status: GET: @api_view(('GET',)) @request_data(permissions=(IsAdminOrReadOnly,)) # get_vms() = IsVmOwner @setting_required('VMS_VM_SNAPSHOT_ENABLED') def vm_define_snapshot_list_all(request, data=None): """ List (:http:get:`GET </vm/define/snapshot>`) all snapshot definitions for all VMs. .. http:get:: /vm/define/snapshot :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-no| :arg data.full: Return list of objects with all snapshot definition details (default: false) :type data.full: boolean :arg data.extended: Include total number of snapshots for each snapshot definition (default: false) :type data.extended: boolean :arg data.order_by: :ref:`Available fields for sorting <order_by>`: ``name``, ``disk_id``, ``hostname``, \ ``created`` (default: ``hostname,-created``) :type data.order_by: string :status 200: SUCCESS :status 403: Forbidden """ extra = output_extended_snap_count(request, data) # TODO: check indexes snap_define = SnapshotDefine.objects.select_related('vm', 'periodic_task', 'periodic_task__crontab')\ .filter(vm__in=get_vms(request))\ .order_by(*SnapshotDefineView.get_order_by(data)) if extra: snap_define = snap_define.extra(extra) return SnapshotDefineView(request, data=data).get(None, snap_define, many=True, extended=bool(extra)) #: vm_status: GET: @api_view(('GET',)) @request_data(permissions=(IsAdminOrReadOnly,)) # get_vm() = IsVmOwner @setting_required('VMS_VM_SNAPSHOT_ENABLED') def vm_define_snapshot_list(request, hostname_or_uuid, data=None): """ List (:http:get:`GET </vm/(hostname_or_uuid)/define/snapshot>`) all VM snapshot definitions. .. http:get:: /vm/(hostname_or_uuid)/define/snapshot :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg data.full: Return list of objects with all snapshot definition details (default: false) :type data.full: boolean :arg data.disk_id: Filter by disk number/ID :type data.disk_id: integer :arg data.extended: Include total number of snapshots for each snapshot definition (default: false) :type data.extended: boolean :arg data.order_by: :ref:`Available fields for sorting <order_by>`: ``name``, ``disk_id``, ``created`` \ (default: ``-created``) :type data.order_by: string :status 200: SUCCESS :status 403: Forbidden :status 404: VM not found :status 412: Invalid disk_id """ vm = get_vm(request, hostname_or_uuid, exists_ok=True, noexists_fail=True, sr=('node', 'owner')) query_filter = {'vm': vm} query_filter = filter_disk_id(vm, query_filter, data) extra = output_extended_snap_count(request, data) # TODO: check indexes snap_define = SnapshotDefine.objects.select_related('vm', 'periodic_task', 'periodic_task__crontab')\ .filter(**query_filter).order_by(*SnapshotDefineView.get_order_by(data)) if extra: snap_define = snap_define.extra(extra) return SnapshotDefineView(request, data=data).get(vm, snap_define, many=True, extended=bool(extra)) #: vm_status: GET: #: vm_status: POST: running, stopped, stopping #: vm_status: PUT: running, stopped, stopping #: vm_status:DELETE: running, stopped, stopping @api_view(('GET', 'POST', 'PUT', 'DELETE')) @request_data(permissions=(IsAdminOrReadOnly,)) # get_vm() = IsVmOwner @setting_required('VMS_VM_SNAPSHOT_ENABLED') def vm_define_snapshot(request, hostname_or_uuid, snapdef, data=None): """ Show (:http:get:`GET </vm/(hostname_or_uuid)/define/snapshot/(snapdef)>`), create (:http:post:`POST </vm/(hostname_or_uuid)/define/snapshot/(snapdef)>`), remove (:http:delete:`DELETE </vm/(hostname_or_uuid)/define/snapshot/(snapdef)>`) or update (:http:put:`PUT </vm/(hostname_or_uuid)/define/snapshot/(snapdef)>`) a VM snapshot definition and schedule. .. http:get:: /vm/(hostname_or_uuid)/define/snapshot/(snapdef) :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapdef: **required** - Snapshot definition name :type snapdef: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :arg data.extended: Include total number of snapshots (default: false) :type data.extended: boolean :status 200: SUCCESS :status 403: Forbidden :status 404: VM not found / Snapshot definition not found :status 412: Invalid disk_id .. http:post:: /vm/(hostname_or_uuid)/define/snapshot/(snapdef) :DC-bound?: * |dc-yes| :Permissions: * |Admin| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapdef: **required** - Snapshot definition name (predefined: hourly, daily, weekly, monthly) :type snapdef: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :arg data.schedule: **required** - Schedule in UTC CRON format (e.g. 30 4 * * 6) :type data.schedule: string :arg data.retention: **required** - Maximum number of snapshots to keep :type data.retention: integer :arg data.active: Enable or disable snapshot schedule (default: true) :type data.active: boolean :arg data.desc: Snapshot definition description :type data.desc: string :arg data.fsfreeze: Whether to send filesystem freeze command to QEMU agent socket before \ creating snapshot (requires QEMU Guest Agent) (default: false) :type data.fsfreeze: boolean :status 200: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: VM not found :status 406: Snapshot definition already exists :status 412: Invalid disk_id :status 423: Node is not operational / VM is not operational .. http:put:: /vm/(hostname_or_uuid)/define/snapshot/(snapdef) :DC-bound?: * |dc-yes| :Permissions: * |Admin| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapdef: **required** - Snapshot definition name :type snapdef: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :arg data.schedule: Schedule in UTC CRON format (e.g. 30 4 * * 6) :type data.schedule: string :arg data.retention: Maximum number of snapshots to keep :type data.retention: integer :arg data.active: Enable or disable snapshot schedule :type data.active: boolean :arg data.desc: Snapshot definition description :type data.desc: string :status 200: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: VM not found / Snapshot definition not found :status 412: Invalid disk_id :status 423: Node is not operational / VM is not operational .. http:delete:: /vm/(hostname_or_uuid)/define/snapshot/(snapdef) :DC-bound?: * |dc-yes| :Permissions: * |Admin| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapdef: **required** - Snapshot definition name :type snapdef: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :status 200: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: VM not found / Snapshot definition not found :status 412: Invalid disk_id :status 423: Node is not operational / VM is not operational """ vm = get_vm(request, hostname_or_uuid, exists_ok=True, noexists_fail=True) disk_id, real_disk_id, zfs_filesystem = get_disk_id(request, vm, data) extra = output_extended_snap_count(request, data) define = get_object(request, SnapshotDefine, {'name': snapdef, 'vm': vm, 'disk_id': real_disk_id}, sr=('vm', 'periodic_task', 'periodic_task__crontab'), extra={'select': extra}) return SnapshotDefineView(request, data=data).response(vm, define, extended=bool(extra)) #: vm_status: GET: #: vm_status: PUT: running, stopped, stopping #: vm_status:DELETE: running, stopped, stopping @api_view(('GET', 'PUT', 'DELETE')) @request_data() # get_vm() = IsVmOwner @setting_required('VMS_VM_SNAPSHOT_ENABLED') def vm_snapshot_list(request, hostname_or_uuid, data=None): """ List (:http:get:`GET </vm/(hostname_or_uuid)/snapshot>`) all VM snapshots or synchronize (:http:put:`PUT </vm/(hostname_or_uuid)/snapshot>`) snapshots of VM's disk on compute node with snapshots saved in database. Delete (:http:delete:`DELETE </vm/(hostname_or_uuid)/snapshot>`) VM snapshots specified by the list (data.snapnames). .. http:get:: /vm/(hostname_or_uuid)/snapshot :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg data.full: Return list of objects with all snapshot details (default: false) :type data.full: boolean :arg data.disk_id: Filter by disk number/ID :type data.disk_id: integer :arg data.type: Filter by snapshot type (1 - Automatic, 2 - Manual) :type data.type: integer :arg data.define: Filter by snapshot definition name :type data.define: string :arg data.order_by: :ref:`Available fields for sorting <order_by>`: ``name``, ``disk_id``, \ ``size``, ``created`` (default: ``-created``) :type data.order_by: string :status 200: SUCCESS :status 403: Forbidden :status 404: VM not found :status 412: Invalid disk_id / Invalid snapshot type .. http:put:: /vm/(hostname_or_uuid)/snapshot :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-yes| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :status 200: SUCCESS :status 201: PENDING :status 400: FAILURE :status 403: Forbidden :status 404: VM not found :status 412: Invalid disk_id :status 423: Node is not operational / VM is not operational :status 428: VM is not installed .. http:delete:: /vm/(hostname_or_uuid)/snapshot :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-yes| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg data.snapnames: **required** - List of snapshot names to be deleted :type data.snapnames: array :status 200: SUCCESS :status 201: PENDING :status 400: FAILURE :status 403: Forbidden :status 404: VM not found :status 412: Invalid snapnames / Invalid disk_id :status 417: VM snapshot status is not OK :status 423: Node is not operational / VM is not operational :status 428: VM is not installed """ return VmSnapshotList(request, hostname_or_uuid, data).response() #: vm_status: GET: #: vm_status: POST: running, stopped, stopping #: vm_status: PUT: stopped #: vm_status:DELETE: running, stopped, stopping @api_view(('GET', 'POST', 'PUT', 'DELETE')) @request_data() # get_vm() = IsVmOwner @setting_required('VMS_VM_SNAPSHOT_ENABLED') def vm_snapshot(request, hostname_or_uuid, snapname, data=None): """ Show (:http:get:`GET </vm/(hostname_or_uuid)/snapshot/(snapname)>`), create (:http:post:`POST </vm/(hostname_or_uuid)/snapshot/(snapname)>`), destroy (:http:delete:`DELETE </vm/(hostname_or_uuid)/snapshot/(snapname)>`) or rollback (:http:put:`PUT </vm/(hostname_or_uuid)/snapshot/(snapname)>`) a snapshot of VM's disk. .. http:get:: /vm/(hostname_or_uuid)/snapshot/(snapname) :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-no| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapname: **required** - Snapshot name :type snapname: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :status 200: SUCCESS :status 403: Forbidden :status 404: VM not found / Snapshot not found :status 412: Invalid disk_id .. http:post:: /vm/(hostname_or_uuid)/snapshot/(snapname) :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-yes| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapname: **required** - Snapshot name :type snapname: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :arg data.note: Snapshot comment :type data.note: string :arg data.fsfreeze: Whether to send filesystem freeze command to QEMU agent socket before \ creating snapshot (requires QEMU Guest Agent) (default: false) :type data.fsfreeze: boolean :status 200: SUCCESS :status 201: PENDING :status 400: FAILURE :status 403: Forbidden :status 404: VM not found :status 406: Snapshot already exists :status 412: Invalid disk_id :status 423: Node is not operational / VM is not operational :status 417: VM snapshot limit reached / VM snapshot size limit reached / DC snapshot size limit reached :status 428: VM is not installed .. http:put:: /vm/(hostname_or_uuid)/snapshot/(snapname) .. warning:: A snapshot rollback will restore disk data from the snapshot; \ All data created after the snapshot will be lost (including all newer snapshots)! .. warning:: When restoring a snapshot into another server's disk all existing snapshots \ on the target server will be lost! :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-yes| - Rollback snapshot :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapname: **required** - Snapshot name :type snapname: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :arg data.force: Force recursive rollback (default: true) :type data.force: boolean :arg data.target_hostname_or_uuid: Target server hostname or uuid \ (default: source and destination server are the same) :type data.target_hostname_or_uuid: string :arg data.target_disk_id: Target disk number/ID; Makes sense (and required) only when \ ``target_hostname_or_uuid`` is specified (default: not used, because the snapshot is restored on the same disk ID) :type data.target_disk_id: integer :status 200: SUCCESS :status 201: PENDING :status 400: FAILURE :status 403: Forbidden :status 404: VM not found / Snapshot not found :status 409: VM has pending tasks :status 412: Invalid disk_id :status 417: VM snapshot status is not OK / VM has more recent snapshots (force=false) / \ Target VM has snapshots (force=false and target_hostname_or_uuid is set) :status 423: Node is not operational / VM is not operational / VM is not stopped / VM is locked or has slave VMs :status 428: VM is not installed/ VM brand mismatch / Disk size mismatch .. http:put:: /vm/(hostname_or_uuid)/snapshot/(snapname) :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-no| - Update snapshot note :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapname: **required** - Snapshot name :type snapname: string :arg data.note: **required** - Snapshot comment :type data.note: string :status 200: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: VM not found / Snapshot not found .. http:delete:: /vm/(hostname_or_uuid)/snapshot/(snapname) :DC-bound?: * |dc-yes| :Permissions: * |VmOwner| :Asynchronous?: * |async-yes| :arg hostname_or_uuid: **required** - Server hostname or uuid :type hostname_or_uuid: string :arg snapname: **required** - Snapshot name :type snapname: string :arg data.disk_id: **required** - Disk number/ID (default: 1) :type data.disk_id: integer :status 200: SUCCESS :status 201: PENDING :status 400: FAILURE :status 403: Forbidden :status 404: VM not found / Snapshot not found :status 412: Invalid disk_id :status 417: VM snapshot status is not OK :status 423: Node is not operational / VM is not operational """ return VmSnapshot(request, hostname_or_uuid, snapname, data).response()

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null

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1

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null

0

6

730daf2050dff29afc293bab7dc3c14330e6afd3

193

py

Python

toolchain/riscv/MSYS/python/Lib/test/test_ctypes.py

zhiqiang-hu/bl_iot_sdk

154ee677a8cc6a73e6a42a5ff12a8edc71e6d15d

[ "Apache-2.0" ]

207

2018-10-01T08:53:01.000Z

2022-03-14T12:15:54.000Z

toolchain/riscv/MSYS/python/Lib/test/test_ctypes.py

zhiqiang-hu/bl_iot_sdk

154ee677a8cc6a73e6a42a5ff12a8edc71e6d15d

[ "Apache-2.0" ]

8

2019-06-29T14:18:51.000Z

2022-02-19T07:30:27.000Z

toolchain/riscv/MSYS/python/Lib/test/test_ctypes.py

zhiqiang-hu/bl_iot_sdk

154ee677a8cc6a73e6a42a5ff12a8edc71e6d15d

[ "Apache-2.0" ]

76

2020-03-16T01:47:46.000Z

2022-03-21T16:37:07.000Z

import unittest from test.support import import_module ctypes_test = import_module('ctypes.test') load_tests = ctypes_test.load_tests if __name__ == "__main__": unittest.main()

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null

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0

6

73273f3f91426eb2164d8b8a7c4607e98f96daf2

34,146

py

Python

stylegan2/NN_getDist_testCode_forStylegan2.py

chenqiguo/GAN_replication

18e71914164f0d735354afb0134ce00570080ecd

[ "OLDAP-2.3" ]

2

2021-11-11T00:18:28.000Z

2021-12-28T01:10:25.000Z

stylegan2/NN_getDist_testCode_forStylegan2.py

chenqiguo/GAN_replication

18e71914164f0d735354afb0134ce00570080ecd

[ "OLDAP-2.3" ]

null

stylegan2/NN_getDist_testCode_forStylegan2.py

chenqiguo/GAN_replication

18e71914164f0d735354afb0134ce00570080ecd

[ "OLDAP-2.3" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Sep 23 16:09:15 2020 @author: guo.1648 """ # referenced from NN_query_testCode_forStylegan2.py, # and NN_getDist_testCode_forBiggan.py. # this code is for stylegan2 sample sheet. # this code also do 1 NN matching for the generated images and the original images, # but the purpose is to compute the corresponding matching distance for each result, # and then use these matching distances and human perception for each mathing pair, # to find out the NN distance threshold which is the largest matching distance satisfying # 100% perceptual replication. # Note: we will combine this stylegan2 result and the biggan result to find out the threshold! import cv2 import os import numpy as np #from sklearn.neighbors import KNeighborsClassifier from sklearn.neighbors import NearestNeighbors #from skimage import img_as_ubyte #import torchvision.transforms as transforms """ #### for FLOWER_128: 8189 images dataset (the original FLOWER dataset) src_sampleSheetImg = '/scratch/stylegan2/results/results_FLOWER_128/00000-stylegan2-FLOWER_128-1gpu-config-f/fakes002526.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_128/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes002526/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes002526/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes002526/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes002526/NNmatchDist.txt' """ """ # for rebuttal: #### for FLOWER_128: 8189 images dataset (the original FLOWER dataset) src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes001925/fakes001925.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_128/jpg/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes001925/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes001925/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes001925/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128/fakes001925/NNmatchDist.txt' """ """ #### for FLOWER_128_sub1000: 1000 images dataset (resume) src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_128_sub1000_resume/00000-stylegan2-FLOWER_128_sub1000-1gpu-config-f/fakes003248.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_128_sub1000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000_resume/fakes003248/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000_resume/fakes003248/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000_resume/fakes003248/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000_resume/fakes003248/NNmatchDist.txt' """ """ # for rebuttal: #### for FLOWER_128_sub1000: 1000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_128_sub1000/00000-stylegan2-FLOWER_128_sub1000-1gpu-config-f/fakes001684.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_128_sub1000/jpg/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000/fakes001684/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000/fakes001684/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000/fakes001684/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub1000/fakes001684/NNmatchDist.txt' """ """ #### for FLOWER_128_sub4000: 4000 images dataset (resume) src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_128_sub4000_resume/00000-stylegan2-FLOWER_128_sub4000-1gpu-config-f/fakes003248.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_128_sub4000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000_resume/fakes003248/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000_resume/fakes003248/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000_resume/fakes003248/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000_resume/fakes003248/NNmatchDist.txt' """ """ # for rebuttal: #### for FLOWER_128_sub4000: 4000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_128_sub4000/00000-stylegan2-FLOWER_128_sub4000-1gpu-config-f/fakes001925.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_128_sub4000/jpg/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000/fakes001925/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000/fakes001925/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000/fakes001925/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_128_sub4000/fakes001925/NNmatchDist.txt' """ """ #### for CelebA_128_sub200: 200 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CelebA_128_sub200/00000-stylegan2-CelebA_128_sub200-1gpu-config-f/fakes007700.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CelebA_128_sub200/jpg/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub200/fakes007700/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub200/fakes007700/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub200/fakes007700/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub200/fakes007700/NNmatchDist.txt' """ """ #### for CelebA_128_sub600: 600 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CelebA_128_sub600/00000-stylegan2-CelebA_128_sub600-1gpu-config-f/fakes005414.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CelebA_128_sub600/jpg/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub600/fakes005414/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub600/fakes005414/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub600/fakes005414/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub600/fakes005414/NNmatchDist.txt' """ """ #### for CelebA_128_sub1000: 1000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CelebA_128_sub1000/00000-stylegan2-CelebA_128_sub1000-1gpu-config-f/fakes004933.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CelebA_128_sub1000/jpg/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub1000/fakes004933/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub1000/fakes004933/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub1000/fakes004933/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub1000/fakes004933/NNmatchDist.txt' """ """ #### for CelebA_128_sub4000: 4000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CelebA_128_sub4000/00000-stylegan2-CelebA_128_sub4000-1gpu-config-f/fakes003369.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CelebA_128_sub4000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub4000/fakes003369/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub4000/fakes003369/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub4000/fakes003369/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub4000/fakes003369/NNmatchDist.txt' """ """ #### for CelebA_128_sub8000: 8000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CelebA_128_sub8000/00000-stylegan2-CelebA_128_sub8000-1gpu-config-f/fakes001684.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CelebA_128_sub8000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub8000/fakes001684/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub8000/fakes001684/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub8000/fakes001684/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CelebA_128_sub8000/fakes001684/NNmatchDist.txt' """ """ #### for MNIST_128_sub10000: 10000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_MNIST_128_sub10000/00002-stylegan2-MNIST_128_sub10000-1gpu-config-f/fakes005173.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/MNIST_128_sub10000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000/fakes005173/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000/fakes005173/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000/fakes005173/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000/fakes005173/NNmatchDist.txt' """ """ #### for MNIST_128_sub10000: 10000 images dataset, bi: src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_MNIST_128_sub10000/00002-stylegan2-MNIST_128_sub10000-1gpu-config-f/fakes005173.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/MNIST_128_sub10000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000_3ch/fakes005173/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000_3ch/fakes005173/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000_3ch/fakes005173/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub10000_3ch/fakes005173/NNmatchDist.txt' """ """ #### for MNIST_128_sub30000: 30000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_MNIST_128_sub30000/00000-stylegan2-MNIST_128_sub30000-1gpu-config-f/fakes005053.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/MNIST_128_sub30000/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000/fakes005053/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000/fakes005053/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000/fakes005053/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000/fakes005053/NNmatchDist.txt' """ """ #### for MNIST_128_sub30000: 30000 images dataset, bi: src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_MNIST_128_sub30000/00000-stylegan2-MNIST_128_sub30000-1gpu-config-f/fakes005053.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/MNIST_128_sub30000_bi/' # these images are generated from tfrecords using code mycode_loadImgFromTFrecords.py dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000_bi/fakes005053/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000_bi/fakes005053/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000_bi/fakes005053/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_sub30000_bi/fakes005053/NNmatchDist.txt' """ """ #### for MNIST_128_train: 60000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_MNIST_128_train/00000-stylegan2-MNIST_128_train-1gpu-config-f/fakes003609.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/data/MNIST/resized/train/train_60000/' # these images are just the whole MNIST resized training set dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_train/fakes003609/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_train/fakes003609/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_train/fakes003609/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/MNIST_128_train/fakes003609/NNmatchDist.txt' """ """ #### for LSUN_128_sub10000: 10000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_LSUN_128_sub10000/00000-stylegan2-LSUN_128_sub10000-1gpu-config-f/fakes004812.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/LSUN_128_sub10000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub10000/fakes004812/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub10000/fakes004812/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub10000/fakes004812/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub10000/fakes004812/NNmatchDist.txt' """ """ #### for LSUN_128_sub30000: 30000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_LSUN_128_sub30000/00020-stylegan2-LSUN_128_sub30000-1gpu-config-f/fakes004692.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/LSUN_128_sub30000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub30000/fakes004692/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub30000/fakes004692/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub30000/fakes004692/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub30000/fakes004692/NNmatchDist.txt' """ """ #### for LSUN_128_sub60000: 60000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_LSUN_128_sub60000/00000-stylegan2-LSUN_128_sub60000-1gpu-config-f/fakes006497.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/LSUN_128_sub60000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub60000/fakes006497/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub60000/fakes006497/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub60000/fakes006497/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub60000/fakes006497/NNmatchDist.txt' """ """ #### for LSUN_128_sub1000_resume: 1000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_LSUN_128_sub1000_resume/00000-stylegan2-LSUN_128_sub1000-1gpu-config-f/fakes002165.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/LSUN_128_sub1000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub1000_resume/fakes002165/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub1000_resume/fakes002165/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub1000_resume/fakes002165/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub1000_resume/fakes002165/NNmatchDist.txt' """ """ #### for LSUN_128_sub5000_resume: 5000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_LSUN_128_sub5000_resume/00000-stylegan2-LSUN_128_sub5000-1gpu-config-f/fakes000000.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/LSUN_128_sub5000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub5000_resume/fakes000000/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub5000_resume/fakes000000/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub5000_resume/fakes000000/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub5000_resume/fakes000000/NNmatchDist.txt' """ """ #### for LSUN_128_sub200: 200 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_LSUN_128_sub200/00000-stylegan2-LSUN_128_sub200-1gpu-config-f/fakes006497.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/LSUN_128_sub200/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub200/fakes006497/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub200/fakes006497/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub200/fakes006497/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/LSUN_128_sub200/fakes006497/NNmatchDist.txt' """ """ # parameters: im_size = 128 # note: the sample sheet is of 32x32: num_row = 32 num_col = 32 """ ### for rebuttal: CIFAR10: """ #### for CIFAR10_32_sub1000: 1000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CIFAR10_32_sub1000/00000-stylegan2-CIFAR10_32_sub1000-1gpu-config-f/fakes002813.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CIFAR10_32_sub1000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub1000/fakes002813/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub1000/fakes002813/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub1000/fakes002813/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub1000/fakes002813/NNmatchDist.txt' """ """ #### for CIFAR10_32_sub4000: 4000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CIFAR10_32_sub4000/00000-stylegan2-CIFAR10_32_sub4000-1gpu-config-f/fakes003014.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CIFAR10_32_sub4000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub4000/fakes003014/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub4000/fakes003014/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub4000/fakes003014/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub4000/fakes003014/NNmatchDist.txt' """ """ #### for CIFAR10_32_sub8000: 8000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CIFAR10_32_sub8000/00000-stylegan2-CIFAR10_32_sub8000-1gpu-config-f/fakes003014.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CIFAR10_32_sub8000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub8000/fakes003014/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub8000/fakes003014/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub8000/fakes003014/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub8000/fakes003014/NNmatchDist.txt' """ """ #### for CIFAR10_32_sub10000: 10000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_CIFAR10_32_sub10000/00000-stylegan2-CIFAR10_32_sub10000-1gpu-config-f/fakes002009.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/CIFAR10_32_sub10000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub10000/fakes002009/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub10000/fakes002009/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub10000/fakes002009/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/CIFAR10_32_sub10000/fakes002009/NNmatchDist.txt' """ """ # parameters: for CIFAR10: im_size = 32 # note: the sample sheet is of 32x32: num_row = 32 num_col = 32 """ ### for rebuttal: image size 256x256: """ #### for FLOWER_256_sub1000: 1000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_256_sub1000/00002-stylegan2-FLOWER_256_sub1000-1gpu-config-f/fakes004435.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_256_sub1000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub1000/fakes004435/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub1000/fakes004435/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub1000/fakes004435/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub1000/fakes004435/NNmatchDist.txt' """ """ #### for FLOWER_256_sub4000: 4000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_256_sub4000/00002-stylegan2-FLOWER_256_sub4000-1gpu-config-f/fakes006128.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_256_sub4000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub4000/fakes006128/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub4000/fakes006128/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub4000/fakes006128/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub4000/fakes006128/NNmatchDist.txt' """ """ #### for FLOWER_256_sub6000: 6000 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_256_sub6000/00002-stylegan2-FLOWER_256_sub6000-1gpu-config-f/fakes006290.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_256_sub6000/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub6000/fakes006290/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub6000/fakes006290/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub6000/fakes006290/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256_sub6000/fakes006290/NNmatchDist.txt' """ #""" #### for FLOWER_256: 8189 images dataset src_sampleSheetImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/results/results_FLOWER_256/00002-stylegan2-FLOWER_256-1gpu-config-f/fakes006209.png' srcRootDir_originDataImg = '/eecf/cbcsl/data100b/Chenqi/stylegan2/datasets_images/FLOWER_256/jpg/' dstRootDir_viewSampleSheetImgs = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256/fakes006209/view_sampleSheetImgs/' dstRootDir_NNmatchResult = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256/fakes006209/NNmatchResult/' dstImgName_NNmatchSheet = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256/fakes006209/NNmatchResultSheet.png' dstTxtName_matchDist = '/eecf/cbcsl/data100b/Chenqi/stylegan2/imgs/NN_query/FLOWER_256/fakes006209/NNmatchDist.txt' #""" #""" # parameters: only for 256x256: im_size = 256 # note: the sample sheet is of 32x32: num_row = 16 num_col = 30 #""" # Newly added: only used for MNIST dataset: # binarize the images! #biFlag = True # for MNIST dataset biFlag = False # for other (RGB or grayscale) dataset def dealWith_sampleSheet(): sampleSheet_img = cv2.imread(src_sampleSheetImg) (sheet_img_height, sheet_img_width, ch) = sampleSheet_img.shape single_img_height = sheet_img_height//num_row # 128 single_img_width = sheet_img_width//num_col # 128 # a list to store each image in the sampleSheet_img sample_img_list = [] # split the sampleSheet img into batch_size (here 16) images: for i in range(num_row): for j in range(num_col): start_row_pos = i*single_img_height end_row_pos = (i+1)*single_img_height start_col_pos = j*single_img_width end_col_pos = (j+1)*single_img_width single_sample_img = sampleSheet_img[start_row_pos:end_row_pos,start_col_pos:end_col_pos,:] # Newly added: if biFlag: single_sample_img_gray = single_sample_img[:,:,0] _,single_sample_img = cv2.threshold(single_sample_img_gray,127,255,cv2.THRESH_BINARY) sample_img_list.append(single_sample_img) return sample_img_list def image_to_feature_vector(image): # Note: the image is already resized to a fixed size. # flatten the image into a list of raw pixel intensities: return image.flatten() def generateTrainSet(len_featVec, dim): all_origin_img_vecs = [] # this is our feature space all_origin_img_names = [] for (dirpath, dirnames, filenames) in os.walk(srcRootDir_originDataImg): for filename in filenames: if ".jpg" in filename or ".png" in filename: print("------------------deal with---------------------") print(filename) origin_img = cv2.imread(srcRootDir_originDataImg+filename) if biFlag: origin_img = origin_img[:,:,0] """ # NO need to do this here: already 128x128 ! origin_img_centCrop = my_center_crop(origin_img, min(origin_img.shape[0],origin_img.shape[1])) # resize using linear interpolation: origin_img_centCrop_resize = cv2.resize(origin_img_centCrop, dim) """ # also convert it to feature vector: origin_img_centCrop_resize_vec = image_to_feature_vector(origin_img) assert(len(origin_img_centCrop_resize_vec)==len_featVec) all_origin_img_vecs.append(origin_img_centCrop_resize_vec) all_origin_img_names.append(filename) return (np.array(all_origin_img_vecs), all_origin_img_names) def combine_matchingResult(match_img_list): # combine the match_img together into a corresponding sheet (single_img_height, single_img_width, ch) = match_img_list[0].shape match_img_sheet = np.zeros((single_img_height*num_row,single_img_width*num_col,ch),dtype=np.uint8) for i in range(num_row): for j in range(num_col): start_row_pos = i*single_img_height end_row_pos = (i+1)*single_img_height start_col_pos = j*single_img_width end_col_pos = (j+1)*single_img_width match_img_idx = i*num_col + j match_img_sheet[start_row_pos:end_row_pos,start_col_pos:end_col_pos,:] = match_img_list[match_img_idx] # save this sheet cv2.imwrite(dstImgName_NNmatchSheet, match_img_sheet) return def query_NN_wrapper(sample_img_list): # this is a wrapper func! # first, get the training set from original images: len_featVec = len(image_to_feature_vector(sample_img_list[0])) dim = (sample_img_list[0].shape[1],sample_img_list[0].shape[0]) trainSet_feats, all_origin_img_names = generateTrainSet(len_featVec, dim) neigh = NearestNeighbors(n_neighbors=1) # radius=0.4 neigh.fit(trainSet_feats) # then, query: match_img_list = [] match_distance_strs = '' for i in range(len(sample_img_list)): single_sample_img = sample_img_list[i] # get the query vector: single_sample_img_vec = image_to_feature_vector(single_sample_img) # NN to search: match_distance, match_idx = neigh.kneighbors([single_sample_img_vec], 1, return_distance=True) match_distance = match_distance[0][0] match_idx = match_idx[0][0] match_imgName = all_origin_img_names[match_idx] if biFlag: match_img = trainSet_feats[match_idx,:].reshape((dim[1],dim[0],1)) else: match_img = trainSet_feats[match_idx,:].reshape((dim[1],dim[0],3)) match_img_list.append(match_img) # save the matching result: im_h = cv2.hconcat([single_sample_img, match_img]) cv2.imwrite(dstRootDir_NNmatchResult+str(i+1)+'_'+match_imgName, im_h) # newly added: also save the corresponding match_distance into txt file: match_distance_strs += str(i+1)+'_'+match_imgName + ': match_distance = ' + str(match_distance) + '\n' # also combine the match_img together into a corresponding sheet! combine_matchingResult(match_img_list) # newly added: also save the corresponding match_distance into txt file: f = open(dstTxtName_matchDist, 'w') f.write(match_distance_strs) f.close() return if __name__ == '__main__': # first, deal with the sample sheet: sample_img_list = dealWith_sampleSheet() #""" # for debug: save the generated sample images to visualize: for i in range(len(sample_img_list)): single_sample_img = sample_img_list[i] cv2.imwrite(dstRootDir_viewSampleSheetImgs+str(i+1)+'.png', single_sample_img) #""" # finally, query each single_sample_img into original dataset (FLOWER_128_xxx here); # also, save the matching results: query_NN_wrapper(sample_img_list)

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7332bebccb309a9f8e8be9bb2faac99f22225132

115

py

Python

syft/frameworks/torch/fl/__init__.py

Rishav1/PySyft

f620ee12727b52b19a317f263789830b57ee2539

[ "Apache-2.0" ]

2

2019-05-29T13:09:02.000Z

2019-06-14T17:40:51.000Z

syft/frameworks/torch/federated/__init__.py

mukira/PySyft

94595008e8326d3111406ae143099b311fc3f2e6

[ "Apache-2.0" ]

3

2019-05-24T01:16:56.000Z

2019-09-18T13:02:30.000Z

syft/frameworks/torch/federated/__init__.py

mukira/PySyft

94595008e8326d3111406ae143099b311fc3f2e6

[ "Apache-2.0" ]

1

2022-03-12T08:04:34.000Z

from .dataset import BaseDataset from .dataset import FederatedDataset from .dataloader import FederatedDataLoader

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7dfbcacae2138ffdbf116e2370cff42e7ca663b0

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py

Python

PyML/utils/__init__.py

ArjixWasTaken/PyML

1a72a8e95e32520826c8c8cb564cb675911cbe5e

[ "MIT" ]

10

2021-11-19T21:53:00.000Z

2022-03-05T14:53:50.000Z

PyML/utils/__init__.py

ArjixWasTaken/PyML

1a72a8e95e32520826c8c8cb564cb675911cbe5e

[ "MIT" ]

null

PyML/utils/__init__.py

ArjixWasTaken/PyML

1a72a8e95e32520826c8c8cb564cb675911cbe5e

[ "MIT" ]

1

2021-11-21T10:20:42.000Z

from PyML.utils.table import Table

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b401ff42dd8ae44f656bddedeb74021da37e07ff

33

py

Python

src/qrcode/pyqart/qr/ec/__init__.py

lapinozz/ArtCoder

a1bf769dc58d157395c1d139b54baba64b012297

[ "MIT" ]

525

2016-08-01T00:37:00.000Z

2022-03-24T14:25:04.000Z

src/qrcode/pyqart/qr/ec/__init__.py

lapinozz/ArtCoder

a1bf769dc58d157395c1d139b54baba64b012297

[ "MIT" ]

5

2016-08-08T07:12:05.000Z

2022-03-28T04:15:53.000Z

src/qrcode/pyqart/qr/ec/__init__.py

lapinozz/ArtCoder

a1bf769dc58d157395c1d139b54baba64b012297

[ "MIT" ]

69

2016-08-01T01:09:47.000Z

2022-03-24T14:25:04.000Z

from .rsencoder import RSEncoder

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6

b43767663d85c442559c7d6fb1163b132a95c9b1

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py

Python

src/ecmwf_models/era5/interface.py

wpreimes/ecmwf_models

574a22779753e835c9d79f63345ac505286da8fb

[ "MIT" ]

null

src/ecmwf_models/era5/interface.py

wpreimes/ecmwf_models

574a22779753e835c9d79f63345ac505286da8fb

[ "MIT" ]

null

src/ecmwf_models/era5/interface.py

wpreimes/ecmwf_models

574a22779753e835c9d79f63345ac505286da8fb

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ This module contains ERA5/ERA5-Land specific child classes of the netcdf and grib base classes, that are used for reading all ecmwf products. """ from ecmwf_models.interface import ERANcImg, ERANcDs, ERAGrbImg, ERAGrbDs from typing import Tuple, Optional from typing_extensions import Literal from pygeogrids.grids import CellGrid # ERA5 products supported by the reader. _supported_products = ['era5', 'era5-land'] def _assert_product(product: str) -> str: if product not in _supported_products: raise ValueError(f"Got product {product} but expected one of " f"{_supported_products}") return product class ERA5NcImg(ERANcImg): def __init__(self, filename: str, parameter: Optional[Tuple[str, ...]] = ("swvl1", "swvl2"), product: Literal['era5', 'era5-land'] = 'era5', subgrid: Optional[CellGrid] = None, mask_seapoints: Optional[bool] = False, array_1D: Optional[bool] = False, ): """ Reader for a single ERA5 netcdf image file. Parameters ---------- filename: str Path to the image file to read. parameter: list or str, optional (default: ['swvl1', 'swvl2']) Name of parameters to read from the image file. product: str, optional (default: 'era5') What era5 product, either era5 or era5-land. subgrid: pygeogrids.CellGrid, optional (default: None) Read only data for points of this grid and not global values. mask_seapoints : bool, optional (default: False) Read the land-sea mask to mask points over water and set them to nan. This option needs the 'lsm' parameter to be in the file! array_1D: bool, optional (default: False) Read data as list, instead of 2D array, used for reshuffling. """ super(ERA5NcImg, self).__init__( filename=filename, product=_assert_product(product), parameter=parameter, subgrid=subgrid, mask_seapoints=mask_seapoints, array_1D=array_1D, ) class ERA5NcDs(ERANcDs): """ Reader for a stack of ERA5 netcdf image files. Parameters ---------- root_path: str Path to the image files to read. parameter: list or str, optional (default: ('swvl1', 'swvl2')) Name of parameters to read from the image file. product: str, optional (default: 'era5') What era5 product, either era5 or era5-land. h_steps : list, optional (default: (0,6,12,18)) List of full hours to read images for. subgrid: pygeogrids.CellGrid, optional (default: None) Read only data for points of this grid and not global values. mask_seapoints : bool, optional (default: False) Read the land-sea mask to mask points over water and set them to nan. This option needs the 'lsm' parameter to be in the file! array_1D: bool, optional (default: False) Read data as list, instead of 2D array, used for reshuffling. """ def __init__( self, root_path: str, parameter: Tuple[str, ...] = ("swvl1", "swvl2"), product: Literal['era5', 'era5-land'] = 'era5', h_steps: Tuple[int, ...] = (0, 6, 12, 18), subgrid: Optional[CellGrid] = None, mask_seapoints: Optional[bool] = False, array_1D: Optional[bool] = False, ): super(ERA5NcDs, self).__init__( root_path=root_path, product=_assert_product(product), parameter=parameter, subgrid=subgrid, h_steps=h_steps, array_1D=array_1D, mask_seapoints=mask_seapoints, ) class ERA5GrbImg(ERAGrbImg): def __init__( self, filename: str, parameter: Optional[Tuple[str, ...]] = ("swvl1", "swvl2"), subgrid: Optional[CellGrid] = None, mask_seapoints: Optional[bool] = False, array_1D=False, ): """ Reader for a single ERA5 grib image file. Parameters ---------- filename: str Path to the image file to read. parameter: list or str, optional (default: ['swvl1', 'swvl2']) Name of parameters to read from the image file. subgrid: pygeogrids.CellGrid, optional (default: None) Read only data for points of this grid and not global values. mask_seapoints : bool, optional (default: False) Read the land-sea mask to mask points over water and set them to nan. This option needs the 'lsm' parameter to be in the file! array_1D: bool, optional (default: False) Read data as list, instead of 2D array, used for reshuffling. """ super(ERA5GrbImg, self).__init__( filename=filename, product="era5", parameter=parameter, subgrid=subgrid, mask_seapoints=mask_seapoints, array_1D=array_1D, ) class ERA5GrbDs(ERAGrbDs): def __init__( self, root_path: str, parameter: Tuple[str, ...] = ("swvl1", "swvl2"), h_steps: Tuple[int, ...] = (0, 6, 12, 18), product: Literal['era5', 'era5-land'] = "era5", subgrid: Optional[CellGrid] = None, mask_seapoints: Optional[bool] = False, array_1D: Optional[bool] = False, ): """ Reader for a stack of ERA5 grib image file. Parameters ---------- root_path: str Path to the image files to read. parameter: list or str, optional (default: ['swvl1', 'swvl2']) Name of parameters to read from the image file. h_steps : list, optional (default: [0,6,12,18]) List of full hours to read images for. product: str, optional (default: 'era5') What era5 product, either era5 or era5-land. subgrid: pygeogrids.CellGrid, optional (default: None) Read only data for points of this grid and not global values. mask_seapoints : bool, optional (default: False) Read the land-sea mask to mask points over water and set them to nan. This option needs the 'lsm' parameter to be in the file! array_1D: bool, optional (default: False) Read data as list, instead of 2D array, used for reshuffling. """ super(ERA5GrbDs, self).__init__( root_path=root_path, product=_assert_product(product), parameter=parameter, subgrid=subgrid, h_steps=h_steps, mask_seapoints=mask_seapoints, array_1D=array_1D, )

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null

0

1

0

null

0

6

b47bf6ed3cb1d7d33d6817359f648466e679806a

8,252

py

Python

encode_quantification/preprocess.py

LRGASP/lrgasp-challenge-2-evaluation

a658b76b9f1356bf3786b75522b28464cdde8a2b

[ "MIT" ]

1

2021-07-13T18:35:00.000Z

encode_quantification/preprocess.py

LRGASP/lrgasp-challenge-2-evaluation

a658b76b9f1356bf3786b75522b28464cdde8a2b

[ "MIT" ]

null

encode_quantification/preprocess.py

LRGASP/lrgasp-challenge-2-evaluation

a658b76b9f1356bf3786b75522b28464cdde8a2b

[ "MIT" ]

null

import base64 import io import pandas as pd import numpy as np import time import zipfile import pickle from app import cache from library.k_values.main import get_kvalues_dict from preprocess_util import * @cache.memoize() def load_data(contents): return pd.read_csv(contents[0], sep='\t',header=None,skiprows=1, comment='#') # content_type, content_string = contents.split(',') # decoded = base64.b64decode(content_string) # df = pd.read_csv(io.StringIO(decoded.decode('utf-8')), sep='\t',header=None) # return df @cache.memoize() def load_zipped_data(contents): if 'zip' in contents[0]: list_of_df = [] method_names = [] with zipfile.ZipFile(contents[0]) as myzip: list_of_files = myzip.namelist() for path in list_of_files: with myzip.open(path) as myfile: list_of_df.append(pd.read_csv(myfile, sep='\t',skiprows=1,header=None)) method_names.append(path.split('.')[0]) return list_of_df,method_names @cache.memoize() def load_annotation(contents,is_long_read=True,K_value_selection='Condition_number'): path = 'encode_quantification/library/k_value_dicts/' if contents[0] == 'human': with open('{}/lrgasp_gencode_v38_sirvs.pkl'.format(path),'rb') as f: return pickle.load(f) elif contents[0] == 'mouse': with open('{}/lrgasp_gencode_vM27_sirvs.pkl'.format(path),'rb') as f: return pickle.load(f) elif contents[0] == 'ensembl_human': with open('{}/Homo_sapiens.GRCh38.104.chr.pkl'.format(path),'rb') as f: return pickle.load(f) else: with open(contents[0],'r') as f: return get_kvalues_dict(io.StringIO(f.read()),is_long_read,K_value_selection) # content_type, content_string = contents.split(',') # decoded = base64.b64decode(content_string) # return io.StringIO(decoded.decode('utf-8')) @cache.memoize() def preprocess_single_sample(list_of_contents,replicate_column,is_long_read=True,K_value_selection='Condition_number'): estimated_df = load_data(list_of_contents[0]).set_index(0)[[replicate_column]] estimated_df.index.name = 'isoform' estimated_df.columns = ['estimated_abund'] kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict = load_annotation(list_of_contents[1],is_long_read,K_value_selection) if (list_of_contents[2] is not None): true_expression_df = load_data(list_of_contents[2]).set_index(0)[[replicate_column]] true_expression_df.index.name = 'isoform' true_expression_df.columns = ['true_abund'] df = estimated_df.join(true_expression_df,on='isoform',how='inner').reset_index() else: raise Exception('No ground truth data is given') anno_df = pd.DataFrame({'K_value':pd.Series(kvalues_dict),'num_exons':pd.Series(num_exon_dict),'isoform_length':pd.Series(isoform_length_dict),'gene':pd.Series(isoform_gene_dict)}) anno_df.index.name = 'isoform' anno_df = anno_df.reset_index() df = preprocess_single_sample_util(df, kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict) return df,anno_df @cache.memoize() def preprocess_multi_sample_diff_condition(list_of_contents,ground_truth_given,is_long_read=True,K_value_selection='Condition_number'): estimated_df = load_data(list_of_contents[0]).set_index(0) kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict = load_annotation(list_of_contents[1],is_long_read,K_value_selection) if (ground_truth_given): true_expression_df = load_data(list_of_contents[2]).set_index(0) intersected_index = true_expression_df.index.intersection(estimated_df.index) estimated_df = estimated_df.loc[intersected_index,:].reset_index() true_expression_df = true_expression_df.loc[intersected_index,:].reset_index() else: estimated_df = estimated_df.reset_index() true_expression_df = None anno_df = pd.DataFrame({'K_value':pd.Series(kvalues_dict),'num_exons':pd.Series(num_exon_dict),'isoform_length':pd.Series(isoform_length_dict),'gene':pd.Series(isoform_gene_dict)}) anno_df.index.name = 'isoform' anno_df = anno_df.reset_index() df = preprocess_multi_sample_diff_condition_util(estimated_df,true_expression_df, kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict) return df,anno_df @cache.memoize() def preprocess_single_sample_multi_method(list_of_contents,replicate_column,is_long_read=True,K_value_selection='Condition_number'): estimated_dfs,method_names = load_zipped_data(list_of_contents[0]) kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict = load_annotation(list_of_contents[1],is_long_read,K_value_selection) if (list_of_contents[2] is not None): true_expression_df = load_data(list_of_contents[2]).set_index(0)[[replicate_column]] true_expression_df.index.name = 'isoform' true_expression_df.columns = ['true_abund'] else: raise Exception('No ground truth data is given') dfs = [] for estimated_df in estimated_dfs: estimated_df = estimated_df.set_index(0)[[replicate_column]] estimated_df.index.name = 'isoform' estimated_df.columns = ['estimated_abund'] df = estimated_df.join(true_expression_df,on='isoform',how='inner').reset_index() dfs.append(preprocess_single_sample_util(df, kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict)) anno_df = pd.DataFrame({'K_value':pd.Series(kvalues_dict),'num_exons':pd.Series(num_exon_dict),'isoform_length':pd.Series(isoform_length_dict),'gene':pd.Series(isoform_gene_dict)}) anno_df.index.name = 'isoform' anno_df = anno_df.reset_index() return dfs,anno_df,method_names @cache.memoize() def preprocess_multi_sample_multi_method(list_of_contents,ground_truth_given,is_long_read=True,K_value_selection='Condition_number'): estimated_dfs,method_names = load_zipped_data(list_of_contents[0]) kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict = load_annotation(list_of_contents[1],is_long_read,K_value_selection) if (ground_truth_given): true_expression_df = load_data(list_of_contents[2]).set_index(0) else: true_expression_df = None dfs = [] for estimated_df in estimated_dfs: estimated_df = estimated_df.set_index(0) if (ground_truth_given): intersected_index = true_expression_df.index.intersection(estimated_df.index) estimated_df = estimated_df.loc[intersected_index,:].reset_index() temp_true_expression_df = true_expression_df.loc[intersected_index,:].reset_index() dfs.append(preprocess_multi_sample_diff_condition_util(estimated_df,temp_true_expression_df, kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict)) else: estimated_df = estimated_df.reset_index() dfs.append(preprocess_multi_sample_diff_condition_util(estimated_df,None, kvalues_dict,num_exon_dict,isoform_length_dict,isoform_gene_dict)) anno_df = pd.DataFrame({'K_value':pd.Series(kvalues_dict),'num_exons':pd.Series(num_exon_dict),'isoform_length':pd.Series(isoform_length_dict),'gene':pd.Series(isoform_gene_dict)}) anno_df.index.name = 'isoform' anno_df = anno_df.reset_index() return dfs,anno_df,method_names # @cache.memoize() # def calculate_statistics_multi_sample_same_condition(list_of_contents): # df = preprocess_files_multi_sample_same_condition(list_of_contents) # estimated_df = load_data(list_of_contents[0]) # if (list_of_contents[2] is not None): # true_expression_df = load_data(list_of_contents[2]) # return get_multi_sample_same_conditon_metrics(estimated_df,true_expression_df,df) # return [] # @cache.memoize() # def preprocess_files_multi_sample_same_condition(list_of_contents): # estimated_df = load_data(list_of_contents[0]) # annotation = load_annotation(list_of_contents[1]) # if (list_of_contents[2] is not None): # true_expression_df = load_data(list_of_contents[2]) # df = preprocess_multi_sample_df_same_condition(estimated_df,true_expression_df,annotation) # return df

54.649007

184

0.744304

1,189

8,252

4.761144

0.120269

0.034976

0.069246

0.041335

0.827062

0.806395

0.764176

0.746511

0.739622

0.717011

0

0.008503

0.144935

8,252

150

185

55.013333

0.793793

0.138027

0

0.596639

0

0.082381

0.01989

0

1

0.058824

false

0

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0.226891

0

null

0

1

0

null

0

6

81e7a4ba47357cbe64d3ab51e78f3af1c28debe0

1,378

py

Python

sys_simulator/a2c/parallel.py

lbaiao/sys-simulator-2

94f00d43309fe7b56dac5099bd4024695ba317b6

[ "MIT" ]

1

2020-06-14T13:50:28.000Z

sys_simulator/a2c/parallel.py

lbaiao/sys-simulator-2

94f00d43309fe7b56dac5099bd4024695ba317b6

[ "MIT" ]

null

sys_simulator/a2c/parallel.py

lbaiao/sys-simulator-2

94f00d43309fe7b56dac5099bd4024695ba317b6

[ "MIT" ]

null

import numpy as np def step(args): env = args[0] agents = args[1] a, b, c, d = env.step(agents) c = c * np.ones(len(agents)) return a, b, c, d, env, agents def unpack_multi_agent(x, n_envs, n_agents): total = n_envs * n_agents states, rewards, dones, _, envs, agents = zip(*x) states = np.array(states).reshape(total, -1) rewards = np.array(rewards).reshape(total, -1) dones = np.array(dones).reshape(total, -1) return states, rewards, dones, envs, agents def unpack_multi_agent_test(x, n_envs, n_agents): total = n_envs * n_agents states, rewards, dones, _, envs, agents = zip(*x) states = np.array(states).reshape(total, -1) rewards = np.array(rewards) dones = np.array(dones).reshape(total, -1) return states, rewards, dones, envs, agents def env_step(pool, envs, agents): n_envs = len(envs) n_agents = len(agents[0]) aux = pool.map(step, zip(envs, agents)) next_obs, reward, done, envs, agents = \ unpack_multi_agent(aux, n_envs, n_agents) return next_obs, reward, done, envs, agents def env_step_test(pool, envs, agents): n_envs = len(envs) n_agents = len(agents[0]) aux = pool.map(step, zip(envs, agents)) next_obs, reward, done, envs, agents = \ unpack_multi_agent_test(aux, n_envs, n_agents) return next_obs, reward, done, envs, agents

29.956522

54

0.650218

215

1,378

4.004651

0.181395

0.139373

0.102207

0.083624

0.880372

0.796748

0

0.00831

0.214078

1,378

45

55

30.622222

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0

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0

1

0.142857

false

0

0.028571

0

0.314286

0

null

0

1

0

null

0

6

81f516752dfba634ef794934bbf7813fcc7ca825

2,632

py

Python

src/test/cli/test_agent.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

5

2016-01-01T15:50:21.000Z

2018-11-27T17:38:15.000Z

src/test/cli/test_agent.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

27

2015-12-17T07:49:56.000Z

2018-07-13T15:06:33.000Z

src/test/cli/test_agent.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

7

2015-12-01T22:04:24.000Z

2021-11-28T13:21:35.000Z

import unittest from mock import patch, MagicMock from flotilla.cli.agent import start_agent ENVIRONMENT = 'test' SERVICE = 'test-app' REGION = 'us-east-1' ELB = 'elb-1234' class TestAgent(unittest.TestCase): @patch('flotilla.cli.agent.get_queue') @patch('flotilla.cli.agent.get_instance_id') @patch('flotilla.cli.agent.DynamoDbTables') @patch('flotilla.cli.agent.Manager') @patch('flotilla.cli.agent.RepeatingFunc') @patch('boto.ec2.elb.connect_to_region') @patch('boto.dynamodb2.connect_to_region') @patch('boto.kms.connect_to_region') @patch('boto3.resource') def test_start_agent_no_elb(self, resource, kms, dynamo, elb, repeat, manager, tables, get_instance_id, get_queue): get_queue.return_value = None get_instance_id.return_value = 'i-123456' start_agent(ENVIRONMENT, SERVICE, REGION, None, 0.1, 0.1) dynamo.assert_called_with(REGION) kms.assert_called_with(REGION) elb.assert_not_called() self.assertEquals(2, repeat.call_count) @patch('flotilla.cli.agent.get_instance_id') @patch('flotilla.cli.agent.DynamoDbTables') @patch('flotilla.cli.agent.FlotillaAgentDynamo') @patch('flotilla.cli.agent.Manager') @patch('flotilla.cli.agent.RepeatingFunc') @patch('boto.ec2.elb.connect_to_region') @patch('boto.dynamodb2.connect_to_region') @patch('boto.kms.connect_to_region') @patch('boto3.resource') def test_start_agent_elb(self, resource, kms, dynamo, elb, repeat, manager, agent_db, tables, get_instance_id): get_instance_id.return_value = 'i-123456' start_agent(ENVIRONMENT, SERVICE, REGION, ELB, 0.1, 0.1) elb.assert_called_with(REGION) @patch('flotilla.cli.agent.get_queue') @patch('flotilla.cli.agent.get_instance_id') @patch('flotilla.cli.agent.DynamoDbTables') @patch('flotilla.cli.agent.FlotillaAgentDynamo') @patch('flotilla.cli.agent.Manager') @patch('flotilla.cli.agent.RepeatingFunc') @patch('boto.ec2.elb.connect_to_region') @patch('boto.dynamodb2.connect_to_region') @patch('boto.kms.connect_to_region') @patch('boto3.resource') def test_start_agent_messaging(self, resource, kms, dynamo, elb, repeat, manager, agent_db, tables, get_instance_id, get_queue): get_instance_id.return_value = 'i-123456' get_queue.return_value = MagicMock() start_agent(ENVIRONMENT, SERVICE, REGION, ELB, 0.1, 0.1) self.assertEquals(3, repeat.call_count)

37.070423

79

0.677052

333

2,632

5.132132

0.186186

0.109421

0.159157

0.196606

0.773552

0.734933

0.709772

0

0.021657

0.193009

2,632

70

80

37.6

0.782957

0

0.578947

0

0.329027

0.292933

0

0.105263

1

0.052632

false

0

0.052632

0

0.122807

0

null

0

1

0

1

0

null

0

6

c34fa9d1872e5eb2eab91e38808c0ca72bc226d4

36,228

py

Python

src/seam/boundary/distance_calculation.py

MahiroGoto/seam

bd690dcef3305b9200aae287db085a38693bcafd

[ "MIT" ]

null

src/seam/boundary/distance_calculation.py

MahiroGoto/seam

bd690dcef3305b9200aae287db085a38693bcafd

[ "MIT" ]

null

src/seam/boundary/distance_calculation.py

MahiroGoto/seam

bd690dcef3305b9200aae287db085a38693bcafd

[ "MIT" ]

null

import math from typing import Dict, Union import math import numpy as np from compas.geometry import Vector, Point, Rotation, Plane from compas.datastructures import Mesh from seam.utils import utils, primitive, parameters from seam.Branch import discrete_curve, boundary_control import igl import logging ## logging settings ## logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) file_handler = logging.StreamHandler() formatter = logging.Formatter('%(levelname)s : %(message)s') file_handler.setFormatter(formatter) logger.addHandler(file_handler) ######################## ###################################### ## calculating distance differences ## class Differences: def __init__(self, MESH, seam_vertex_keys_list): self.mesh = MESH self.svkl = seam_vertex_keys_list self.seam_num = len(self.svkl) # self.width_01 = width_01 ## settings ## self.distances_list = self.get_distances_list_from_every_boundary() self.short_way, self.long_way, self.ave_way, self.way_list = self.get_short_long_ave_way_length_on_every_vertex() self.gap_ratio = self.get_gap_ratio() # ## results ## # self.realm_dict = self.get_seam_realm_dict_from_vertex_geodesic() ####################################################### ## functions ## def compute_geodesic_to_every_vertex(self, mesh, vertices_start): v, f = mesh.to_vertices_and_faces() v = np.array(v) f = np.array(f) vertices_target = np.arange(len(v)) # all vertices are targets vstart = np.array(vertices_start) distances = igl.exact_geodesic(v, f, vstart, vertices_target) return distances def compute_geodesic_from_start_to_target_vkeys(self, mesh, start_v_keys_list, target_v_keys_list): v, f = mesh.to_vertices_and_faces() v = np.array(v) f = np.array(f) vertices_start = np.array(start_v_keys_list) vertices_target = np.array(target_v_keys_list) distances = igl.exact_geodesic(v, f, vertices_start, vertices_target) return distances ## get values ## def get_distances_list_from_every_boundary(self): distances_list = [] for seam_vkeys in self.svkl: distances = list(compute_geodesic_to_every_vertex(self.mesh, seam_vkeys)) distances_list.append(distances) return distances_list ## calculating ## def get_short_long_ave_way_length_on_every_vertex(self): # distances_list = get_distances_list_from_every_seams(mesh, seam_vertex_keys_list) vkeys = self.mesh.vertices() way_list = [] for vkey in vkeys: vdis_list = [distances[vkey] for distances in self.distances_list] vdis_list.sort() d00 = vdis_list[0] d01 = vdis_list[1] way = d00 + d01 way_list.append(way) temp_list = [way for way in way_list] temp_list.sort() short_way = temp_list[0] long_way = temp_list[-1] ave_way = sum(temp_list) / len(temp_list) return short_way, long_way, ave_way, way_list def get_gap_ratio(self): gap_ratio = (self.long_way - self.short_way) / self.long_way return gap_ratio # ####################################################### # def get_seam_realm_dict_from_vertex_geodesic(self): # realm_dict = {} # for i in range(3): # realm_dict["piece_0" + str(i)] = [] # vkeys = self.mesh.vertices() # for vkey in vkeys: # dist_list = [distances[vkey] for distances in self.distances_list] # closest = min(dist_list) # seam_number = dist_list.index(closest) # realm_dict["piece_0" + str(seam_number)].append(vkey) # return realm_dict # # def equal_differences(self, time): # # if len(self.distances_list) <= 2: # difs = [(d01 * time - d00 * (1 - time)) for d00, d01 in zip(self.distances_list[0], self.distances_list[1])] # return difs ## for connection detail ## def calculate_custom_differences_with_two_boundaries(self, base_boundary_num, time, minValue=0.5, frequency=2.5): difs = [] distances_00 = self.distances_list[0] distances_01 = self.distances_list[1] for D_00, D_01, way in zip(distances_00, distances_01, self.way_list): x = abs(self.long_way - way) / (self.long_way - self.short_way) width = self.long_way * minValue freq = frequency alpha = freq * math.pi * x a = minValue y = ((1 - a) / 2) * math.cos(alpha) + ((a + 1) / 2) if y > 1: y = 1 elif y < a: y = a if base_boundary_num == 0: dif = (D_01) * time - (D_00 * y) * (1 - time) else: dif = (D_00) * time - (D_01 * y) * (1 - time) # value = width * math.cos(alpha) + width / 2 # if value > width: # value = width # elif value < 0: # value = 0 # addv = value * (0.5 - abs(0.5 - time)) # ## get the difference value ## # if base_boundary_num == 0: # dif = ((D_01 + addv) * time - (D_00) * (1 - time)) # else: # dif = ((D_00 + addv) * time - (D_01) * (1 - time)) difs.append(dif) return difs # for d00, d01 in zip(self.distances_list[0], self.distances_list[1]): # way = d00 + d01 # x = abs(self.long_way - way) # y = abs(self.ave_way - way) # check_value = abs(self.long_way - self.ave_way) # value = width * math.cos((2.7 * math.pi / (self.long_way - self.short_way)) * x) + width / 2 # # value = value * (1 - 1 / (y + 1)) # if value >= width: # value = width # elif value < 0: value = 0 # else: # value = value # addv = value * (0.5 - abs(0.5 - time)) # dif00 = ((d01 + addv) * time - d00 * (1 - time)) # difs00.append(dif00) # return difs00 # def custom_differences_01_second(self, time, width=70): # """ # diftype == 1.2 # """ # # width = self.width_01 # difs01 = [] # time_ = time # for d00, d01 in zip(self.distances_list[0], self.distances_list[1]): # way = d00 + d01 # x = abs(self.long_way - way) # y = abs(self.ave_way - way) # check_value = abs(self.long_way - self.ave_way) # value = 10 * width * math.cos((2 * math.pi / (self.long_way - self.short_way)) * x) - 1.5 * width # # value = value * (1 - 1/(y+1)) # if value <= 0: # value = 0 # elif value > width: # value = width # else: # value = value # addv = value * (0.5 - abs(0.5 - time)) # dif01 = (d01 - addv) * time - (d00) * (1 - time) # difs01.append(dif01) # return difs01 # # ## for connection detail 2.0 ## # def custom_differences_03_first(self, time, width=210, flat_ratio=0.2): # """ # diftype == 3.1 # """ # difs00 = [] # for d00, d01 in zip(self.distances_list[0], self.distances_list[1]): # way = d00 + d01 # check_value = abs(self.ave_way - way) # l_value = abs(self.long_way - way) # s_value = abs(self.short_way - way) # wid = width # if self.ave_way - way >= 0: # ## define the short side ## # value = wid * ((1/(s_value+1))**0.4) - wid * ((1/abs(self.short_way-self.ave_way))**0.4) * (1 + flat_ratio) # else: # ## define the long side ## # value = wid * ((1/(l_value+1))**0.2) - wid * ((1/abs(self.long_way-self.ave_way))**0.2) * (1 + flat_ratio) # if value < 0: value = 0 # addv = value * (0.5 - abs(0.5-time)) # # dif = (d01 + addv/2)*time - (d00)*(1-time) # difs00.append(dif) # return difs00 # # def custom_differences_03_second(self, time, width=210, flat_ratio=0.2): # """ # diftype == 3.2 # """ # difs01 = [] # for d00, d01 in zip(self.distances_list[0], self.distances_list[1]): # way = d00 + d01 # check_value = abs(self.ave_way - way) # l_value = abs(self.long_way - way) # s_value = abs(self.short_way - way) # wid = width # if self.ave_way - way >= 0: # ## define the short side ## # value = wid * ((1/(s_value+1))**0.3) - wid * ((1/abs(self.short_way -self.ave_way))**0.3) * (1 + flat_ratio) # else: # ## define the long side ## # value = wid * ((1/(l_value+1))**0.15) - wid * ((1/abs(self.long_way - self.ave_way))**0.15) * (1 + flat_ratio) # if value < 0: value = 0 # addv = value * (0.5 - abs(0.5-time)) # # dif = (d01 - addv/2)*time - (d00)*(1-time) # difs01.append(dif) # return difs01 # # ## for distance gap ## # def custom_differences_02_first(self, time): # """ # diftype == 2.1 # """ # width = (self.short_way * self.gap_ratio) # ## get the differences from start seam and from last seam ## # difs00 = [] # for d00, d01 in zip(self.distances_list[0], self.distances_list[1]): # way = d00 + d01 # x = abs(self.ave_way - way) # check_value = abs(self.ave_way - self.long_way) # wid = width # if (self.ave_way - way) >= 0: # ## make the value smoother ## # value = wid * ((x / check_value) ** 1.5) * (1 - 1 / (x + 1)) # else: # value = 0 # addv = value * (0.5 - abs(0.5 - time)) # dif00 = ((d01 - addv) * time - d00 * (1 - time)) # difs00.append(dif00) # return difs00 # # def custom_differences_02_second(self, time): # """ # diftype == 2.2 # """ # width = (self.short_way * self.gap_ratio) # difs01 = [] # time_ = time # for d00, d01 in zip(self.distances_list[0], self.distances_list[1]): # way = d00 + d01 # x = abs(self.ave_way - way) # check_value = abs(self.ave_way - self.long_way) # wid = width # if (self.ave_way - way) >= 0: # ## make the value smoother ## # value = wid * ((x / check_value) ** 1.5) * (1 - 1 / (x + 1)) # else: # value = 0 # addv = value * (0.5 - abs(0.5 - time_)) # dif01 = ((d01 + addv) * time_ - d00 * (1 - time_)) # difs01.append(dif01) # return difs01 ###################################### ## calculating distance attribution ## class Distance_Attributes_two: def __init__(self, MESH, boundary_vertex_keys_list): self.mesh = MESH self.bvkl = boundary_vertex_keys_list self.boundary_num = len(self.bvkl) ## settings ## self.distances_list = self.get_distances_list_from_every_boundary() self.short_way, self.long_way, self.ave_way, self.way_list \ = self.get_short_long_ave_way_length_on_every_vertex() self.gap_ratio = self.get_gap_ratio() def get_distances_list_from_every_boundary(self): distances_list = [] for boundary_vkeys in self.bvkl: distances = list(compute_geodesic_to_every_vertex(self.mesh, boundary_vkeys)) distances_list.append(distances) return distances_list ## calculating ## def get_short_long_ave_way_length_on_every_vertex(self): # distances_list = get_distances_list_from_every_seams(mesh, seam_vertex_keys_list) vkeys = self.mesh.vertices() way_list = [] for vkey in vkeys: vdis_list = [distances[vkey] for distances in self.distances_list] vdis_list.sort() d00 = vdis_list[0] d01 = vdis_list[1] way = d00 + d01 way_list.append(way) temp_list = [way for way in way_list] temp_list.sort() short_way = temp_list[0] long_way = temp_list[-1] ave_way = sum(temp_list) / len(temp_list) return short_way, long_way, ave_way, way_list def get_gap_ratio(self): gap_ratio = (self.long_way - self.short_way) / self.long_way return gap_ratio def get_attributes_from_one_base_boundary(self, base_boundary_num, time, minValue=0.5, frequency=1.0, longWayExtention=False): attrs = [] difs = [] distances_00 = self.distances_list[0] distances_01 = self.distances_list[1] for D_00, D_01, way in zip(distances_00, distances_01, self.way_list): x = (way / self.long_way) compare = self.short_way / self.long_way freq = frequency ## from 1 to 3 ## alpha = freq * math.pi * (x - compare) / (1 - compare) a = minValue ## fomura for the distortion ## if not longWayExtention: ## short way extention ## y = (((1 - a) / 2) + ((a + 1) / 4)) * math.sin(alpha) + ((a + 1) / 2) + ((a + 1) / 4) if y > 1: y = 1 else: ## long way extention ## y = (((1 - a) / 2) + ((a + 1) / 4)) * math.cos(alpha) + ((a + 1) / 2) + ((a + 1) / 4) if y > 1: y = 1 ## distribute the boundary area depending on the distance calculation with distortion fomura ## if base_boundary_num == 0: d_00 = D_00 * (1-time) * y d_01 = D_01 * (time) ## set base distance ## base_distance = d_00 target_distance = d_01 elif base_boundary_num == 1: d_00 = D_00 * (time) d_01 = D_01 * (1 - time) * y ## set base distance ## base_distance = d_01 target_distance = d_00 else: print("error with setting the base_boundary_num") break ## select the short value ## short_distance = min([d_00, d_01]) if short_distance == d_00: attr = 0 else: attr = 1 attrs.append(attr) ## calculate differences ## dif = base_distance - target_distance difs.append(dif) return attrs, difs class Distance_Attributes_three: def __init__(self, MESH, boundary_vertex_keys_list): self.mesh = MESH self.bvkl = boundary_vertex_keys_list self.boundary_num = len(self.bvkl) ## settings ## self.distances_list = self.get_distances_list_from_every_boundary() self.short_way, self.long_way, self.ave_way, self.way_list \ = self.get_short_long_ave_way_length_on_every_vertex() self.gap_ratio = self.get_gap_ratio() def get_distances_list_from_every_boundary(self): distances_list = [] for boundary_vkeys in self.bvkl: distances = list(compute_geodesic_to_every_vertex(self.mesh, boundary_vkeys)) distances_list.append(distances) return distances_list ## calculating ## def get_short_long_ave_way_length_on_every_vertex(self): # distances_list = get_distances_list_from_every_seams(mesh, seam_vertex_keys_list) vkeys = self.mesh.vertices() way_list = [] for vkey in vkeys: vdis_list = [distances[vkey] for distances in self.distances_list] vdis_list.sort() d00 = vdis_list[0] d01 = vdis_list[1] way = d00 + d01 way_list.append(way) temp_list = [way for way in way_list] temp_list.sort() short_way = temp_list[0] long_way = temp_list[-1] ave_way = sum(temp_list) / len(temp_list) return short_way, long_way, ave_way, way_list def get_gap_ratio(self): gap_ratio = (self.long_way - self.short_way) / self.long_way return gap_ratio def get_attributes_from_one_base_boundary(self, base_boundary_num, time, minValue=0.75, frequency=2.0, longWayExtention=False): attrs = [] difs = [] distances_00 = self.distances_list[0] distances_01 = self.distances_list[1] distances_02 = self.distances_list[2] for D_00, D_01, D_02, way in zip(distances_00, distances_01, distances_02, self.way_list): # x = way / self.long_way # compare = self.short_way / self.long_way x = (self.long_way - way) / (self.long_way - self.short_way) ## x is changing from 0 with long_way to 1 with short_way ## freq = frequency alpha = freq * math.pi * x a = minValue ## fomura for the distortion ## if longWayExtention: ## short way extention ## y = ((1 - a) / 2) * math.sin(alpha) + ((a + 1) / 2) if y > 1: y = 1 elif y < a: y = a else: ## long way extention ## y = ((1 - a) / 2) * math.cos(alpha) + ((a + 1) / 2) if y > 1: y = 1 elif y < a: y = a ## distribute the boundary area depending on the distance calculation with distortion fomura ## if base_boundary_num == 0: d_00 = D_00 * (1-time) * y d_01 = D_01 * (time) d_02 = D_02 * (time) ## set base distance ## base_dist = d_00 target_dists = [d_01, d_02] elif base_boundary_num == 1: d_00 = D_00 * (time) d_01 = D_01 * (1-time) * y d_02 = D_02 * (time) ## set base distance ## base_dist = d_01 target_dists = [d_00, d_02] elif base_boundary_num == 2: d_00 = D_00 * (time) d_01 = D_01 * (time) d_02 = D_02 * (1-time) * y ## set base distance ## base_dist = d_02 target_dists = [d_00, d_01] else: print("error with setting the base_boundary_num") break ## select short value ## short_distance = min([d_00, d_01, d_02]) if short_distance == d_00: attr = 0 elif short_distance == d_01: attr = 1 else: attr = 2 attrs.append(attr) ## calculate differences ## dif = base_dist - min(target_dists) difs.append(dif) return attrs, difs class Distance_Attributes_four: def __init__(self, MESH, boundary_vertex_keys_list): self.mesh = MESH self.bvkl = boundary_vertex_keys_list self.boundary_num = len(self.bvkl) ## settings ## self.distances_list = self.get_distances_list_from_every_boundary() self.short_way, self.long_way, self.ave_way, self.way_list \ = self.get_short_long_ave_way_length_on_every_vertex() self.gap_ratio = self.get_gap_ratio() def get_distances_list_from_every_boundary(self): distances_list = [] for boundary_vkeys in self.bvkl: distances = list(compute_geodesic_to_every_vertex(self.mesh, boundary_vkeys)) distances_list.append(distances) return distances_list ## calculating ## def get_short_long_ave_way_length_on_every_vertex(self): vkeys = self.mesh.vertices() way_list = [] for vkey in vkeys: vdis_list = [distances[vkey] for distances in self.distances_list] vdis_list.sort() d00 = vdis_list[0] d01 = vdis_list[1] way = d00 + d01 way_list.append(way) temp_list = [way for way in way_list] temp_list.sort() short_way = temp_list[0] long_way = temp_list[-1] ave_way = sum(temp_list) / len(temp_list) return short_way, long_way, ave_way, way_list def get_gap_ratio(self): gap_ratio = (self.long_way - self.short_way) / self.long_way return gap_ratio def get_attributs_from_one_base_boundary(self, base_boundary_num, time, minValue=0.75, frequency=2.0, longWayExtention=False): attrs = [] difs = [] distances_00 = self.distances_list[0] distances_01 = self.distances_list[1] distances_02 = self.distances_list[2] distances_03 = self.distances_list[3] for D_00, D_01, D_02, D_03, way in zip(distances_00, distances_01, distances_02, distances_03, self.way_list): x = (self.long_way - way) / (self.long_way - self.short_way) ## x is changing from 0 at long_way to 1 at short_way ## freq = frequency alpha = freq * math.pi * x a = minValue ## fomura for the distortion ## if longWayExtention: ## short way extention ## y = ((1 - a) / 2) * math.sin(alpha) + ((a + 1) / 2) if y > 1: y = 1 elif y < a: y = a else: ## long way extention ## y = ((1 - a) / 2) * math.cos(alpha) + ((a + 1) / 2) if y > 1: y = 1 elif y < a: y = a ## distribute the boundary area depending on the distance calculation with distortion fomura ## if base_boundary_num == 0: d_00 = D_00 * (1-time) * y d_01 = D_01 * (time) d_02 = D_02 * (time) d_03 = D_03 * (time) ## set base distance ## base_dist = d_00 target_dists = [d_01, d_02, d_03] elif base_boundary_num == 1: d_00 = D_00 * (time) d_01 = D_01 * (1-time) * y d_02 = D_02 * (time) d_03 = D_03 * (time) ## set base distance ## base_dist = d_01 target_dists = [d_00, d_02, d_03] elif base_boundary_num == 2: d_00 = D_00 * (time) d_01 = D_01 * (time) d_02 = D_02 * (1-time) * y d_03 = D_03 * (time) ## set base distance ## base_dist = d_02 target_dists = [d_00, d_01, d_03] elif base_boundary_num == 3: d_00 = D_00 * (time) d_01 = D_01 * (time) d_02 = D_02 * (time) d_03 = D_03 * (1-time) * y ## set base distance ## base_dist = d_03 target_dists = [d_00, d_01, d_02] else: print("error with setting the base_boundary_num") break ## select short value ## short_distance = min([d_00, d_01, d_02, d_03]) if short_distance == d_00: attr = 0 elif short_distance == d_01: attr = 1 elif short_distance == d_02: attr = 2 else: attr = 3 attrs.append(attr) ## calculate differences ## dif = base_dist - min(target_dists) difs.append(dif) return attrs, difs ################################################################################## ## fandamental geodesics ## ################################################################################## def compute_geodesic_to_every_vertex(mesh, vertices_start): """ compute distances from sevelral vertices to all vertices of the mesh vertices are described with keys """ v, f = mesh.to_vertices_and_faces() v = np.array(v) f = np.array(f) vertices_target = np.arange(len(v)) # all vertices are targets vstart = np.array(vertices_start) distances = igl.exact_geodesic(v, f, vstart, vertices_target) return distances def compute_geodesic_from_start_to_target_vkeys(mesh, start_v_keys_list, target_v_keys_list): """ compute distances from one edges to another edge and get longest way and shortest way """ v, f = mesh.to_vertices_and_faces() v = np.array(v) f = np.array(f) vertices_start = np.array(start_v_keys_list) vertices_target = np.array(target_v_keys_list) distances = igl.exact_geodesic(v, f, vertices_start, vertices_target) return distances def get_distances_list_from_every_boundary(mesh, boundary_vertex_keys_list): distances_list = [] for seam_vkeys in boundary_vertex_keys_list: distances = list(compute_geodesic_to_every_vertex(mesh, seam_vkeys)) distances_list.append(distances) return distances_list ## get way informations ## def calculate_way_length_on_every_vertex(mesh, seam_vertex_keys_list): distances_list = get_distances_list_from_every_boundary(mesh, seam_vertex_keys_list) vkeys = mesh.vertices() way_list = [] for vkey in vkeys: vdis_list = [distances[vkey] for distances in distances_list] vdis_list.sort() d00 = vdis_list[0] d01 = vdis_list[1] way = d00 + d01 way_list.append(way) temp_list = [way for way in way_list] temp_list.sort() short_way = temp_list[0] long_way = temp_list[-1] ave_way = sum(temp_list) / len(temp_list) return short_way, long_way, ave_way, way_list def get_gap_ratio(mesh, boundary_vertex_keys_list): short_way, long_way, ave_way, Ds_list = calculate_way_length_on_every_vertex(mesh, boundary_vertex_keys_list) gap_ratio = (long_way - short_way) / long_way return gap_ratio # ################################################################################## # ## combination of several types of geodesics in a mesh ## # ################################################################################## # def vertex_distribution_of_geodesic_realm(mesh, distances_list): # # distances_list = get_distances_list_from_every_seams(mesh, seam_vertex_keys_list) # realm_dict = {} # for i in range(len(distances_list)): # realm_dict["seam_0" + str(i)] = [] # vkeys = mesh.vertices() # for vkey in vkeys: # dist_list = [distances[vkey] for distances in distances_list] # closest = min(dist_list) # seam_number = dist_list.index(closest) # realm_dict["seam_0" + str(seam_number)].append(vkey) # return realm_dict # # def custom_differences_00(mesh, distances_list, time): # ## pure distance differences no additional distance ## # global result # # distances_list = get_distances_list_from_every_seams(mesh, seam_vertex_keys_list) # vkeys = mesh.vertices() # if len(distances_list) <= 2: # difs = [abs(d01 * time - d00 * (1 - time)) for d00, d01 in zip(distances_list[0], distances_list[1])] # result = difs # elif len(distances_list) >= 3: # difs_list = [] # for i in range(len(distances_list)): # first_distances = distances_list[i] # difs = [] # for vkey in vkeys: # left_distances_list = [] # for j, distances in enumerate(distances_list): # if j != i: # left_distances_list.append(distances) # dist_list = [distances[vkey] for distances in distances_list] # first_seam_dist = first_distances[vkey] # target_seam_dist = min(dist_list) # # d00 = first_seam_dist # d01 = target_seam_dist # dif = abs(d01*time - d00*(1-time)) # difs.append(dif) # difs_list.append(difs) # result = difs_list # return result # # def custom_differences_01(mesh, seam_vertex_keys_list, time, width=50): # """ # this transformed distances is for the connection detail between two seams # """ # ## custom distance differences with changing offset ## # distances_list = get_distances_list_from_every_boundary(mesh, seam_vertex_keys_list) # short_way, long_way, ave_way, Ds_list = calculate_way_length_on_every_vertex(mesh, seam_vertex_keys_list) # ## get the differences from start seam and from last seam ## # difs00 = [] # for d00, d01 in zip(distances_list[0], distances_list[1]): # way = d00 + d01 # x = abs(long_way - way) # y = abs(ave_way - way) # check_value = abs(long_way - ave_way) # value = width * math.cos((2 * math.pi / (long_way - short_way)) * x) # value = value * (1 - 1 / (y + 1)) # if value <= 0: # value = 0 # else: # value = value # addv = value * (0.5 - abs(0.5-time)) # dif = abs((d01 + addv) * time - d00 * (1 - time)) # difs00.append(dif) # difs01 = [] # for d00, d01 in zip(distances_list[0], distances_list[1]): # way = d00 + d01 # x = abs(long_way - way) # y = abs(ave_way - way) # check_value = abs(long_way - ave_way) # wid = width # value = wid * math.cos((2 * math.pi / (long_way - short_way)) * x) # # value = value * (1 - 1/(y+1)) # if value >= 0: # value = 0 # else: # value = value # addv = value * (0.5 - abs(0.5 - time)) # dif01 = abs((d01 + addv) * time - (d00) * (1 - time)) # difs01.append(dif01) # difs_list = [difs00, difs01] # return difs_list # # def custom_differences_02(mesh, seam_vertex_keys_list, time, gap_ratio): # """ # # this transformed distances is for the adapting way gap solution # # """ # ## custom distance differences with changing offset ## # difs_list = [] # distances_list = get_distances_list_from_every_boundary(mesh, seam_vertex_keys_list) # short_way, long_way, ave_way, Ds_list = calculate_way_length_on_every_vertex(mesh, seam_vertex_keys_list) # width = (short_way * gap_ratio) # ## get the differences from start seam and from last seam ## # difs00 = [] # for d00, d01 in zip(distances_list[0], distances_list[1]): # way = d00 + d01 # x = abs(ave_way - way) # check_value = abs(ave_way - long_way) # wid = width # if (ave_way - way) >= 0: # ## make the value smoother ## # value = wid * ((x / check_value) ** 1.5) * (1 - 1 / (x + 1)) # else: # value = 0 # addv = value * (0.5 - abs(0.5 - time)) # dif00 = abs((d01 - addv) * time - d00 * (1 - time)) # difs00.append(dif00) # difs01 = [] # for d00, d01 in zip(distances_list[0], distances_list[1]): # way = d00 + d01 # x = abs(ave_way - way) # check_value = abs(ave_way - long_way) # wid = width # if (ave_way - way) >= 0: # ## make the value smoother ## # value = wid * ((x / check_value) ** 1.5) * (1 - 1 / (x + 1)) # else: # value = 0 # addv = value * (0.5 - abs(0.5 - time)) # dif01 = abs((d01 + addv) * time - d00 * (1 - time)) # difs01.append(dif01) # difs_list = [difs00, difs01] # return difs_list # """ """ ############################################################################# ## for offsetting path points ## ############################################################################# def get_closest_points_from_pts_cloud(fromPt, toPtsCloud, num_getPts): distances = [] for toPt in toPtsCloud: dis = fromPt.distance_to_point(toPt) distances.append(dis) list = [dis for dis in distances] list.sort() closest_pts = [] closest_distances = list[:num_getPts] for distance in closest_distances: count = 0 for toPt in toPtsCloud: d = fromPt.distance_to_point(toPt) if d == distance: closest_pts.append(toPt) count += 1 if count == num_getPts: break if num_getPts == 1: closest_pts = closest_pts[0] closest_distances = closest_distances[0] return closest_pts, closest_distances def get_distance_to_pts_cloud(fromPt, toPtsCloud): closest_pts, closest_distances = get_closest_points_from_pts_cloud(fromPt, toPtsCloud, 3) closest_distance = closest_distances[0] for i in range(1): ## first two points ## measure = closest_distances[0] + closest_distances[1] x = (closest_distances[1] / measure) * closest_pts[0].x + (closest_distances[0] / measure) * closest_pts[1].x y = (closest_distances[1] / measure) * closest_pts[0].y + (closest_distances[0] / measure) * closest_pts[1].y z = (closest_distances[1] / measure) * closest_pts[0].z + (closest_distances[0] / measure) * closest_pts[1].z point_00 = Point(x, y, z) distance_00 = fromPt.distance_to_point(point_00) if distance_00 == 0: cl_distance = 0 cl_point = point_00 continue ## third point ## measure = closest_distances[0] + closest_distances[2] x = (closest_distances[0] / measure) * closest_pts[2].x + (closest_distances[2] / measure) * closest_pts[0].x y = (closest_distances[0] / measure) * closest_pts[2].y + (closest_distances[2] / measure) * closest_pts[0].y z = (closest_distances[0] / measure) * closest_pts[2].z + (closest_distances[2] / measure) * closest_pts[0].z point_01 = Point(x, y, z) distance_01 = fromPt.distance_to_point(point_01) if distance_01 == 0: cl_distance = 0 cl_point = point_01 continue ## cl point 00 ## measure = distance_00 + distance_01 x = (distance_01 / measure) * point_01.x + (distance_00 / measure) * point_00.x y = (distance_01 / measure) * point_01.y + (distance_00 / measure) * point_00.y z = (distance_01 / measure) * point_01.z + (distance_00 / measure) * point_00.z clpt00 = Point(x, y, z) cld00 = fromPt.distance_to_point(clpt00) if cld00 == 0: cl_distance = 0 cl_point = clpt00 continue ## cl point_01 ## measure = closest_distances[0] + cld00 x = (closest_distances[0] / measure) * clpt00.x + (cld00 / measure) * closest_pts[0].x y = (closest_distances[0] / measure) * clpt00.y + (cld00 / measure) * closest_pts[0].y z = (closest_distances[0] / measure) * clpt00.z + (cld00 / measure) * closest_pts[0].z cl_point = Point(x, y, z) cl_distance = fromPt.distance_to_point(cl_point) if cl_distance == 0: cl_distance = 0 cl_point = cl_point continue cllist = [closest_distance, distance_00, distance_01, cld00, cl_distance] clptlist = [closest_pts[0], point_00, point_01, clpt00, cl_point] cl_distance = min(cllist) cl_point = clptlist[cllist.index(cl_distance)] return cl_distance, cl_point

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c376a4a46e7c2662c729919f42beec49eb6a9bf1

196

py

Python

examples/chalicelib/blueprints/__init__.py

cuenca-mx/agave

d4719bdbab8e200c98d206475df6adb275e9fdcc

[ "MIT" ]

3

2020-12-11T16:48:44.000Z

2021-03-29T00:05:57.000Z

examples/chalicelib/blueprints/__init__.py

cuenca-mx/agave

d4719bdbab8e200c98d206475df6adb275e9fdcc

[ "MIT" ]

115

2020-08-26T13:26:07.000Z

2022-03-31T23:58:22.000Z

examples/chalicelib/blueprints/__init__.py

cuenca-mx/agave

d4719bdbab8e200c98d206475df6adb275e9fdcc

[ "MIT" ]

null

__all__ = ['AuthedRestApiBlueprint'] from agave.blueprints import RestApiBlueprint from .authed import AuthedBlueprint class AuthedRestApiBlueprint(AuthedBlueprint, RestApiBlueprint): ...

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5ef55d5d210a859399452deba1e7091825667b83

132

py

Python

Section09_Decorator/Practice/Square.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

1

2020-10-20T07:41:51.000Z

Section09_Decorator/Practice/Square.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

Section09_Decorator/Practice/Square.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

class Square: def __init__(self, side): self.side = side def __str__(self): return 'A square with side %s' % self.side

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5efd591c57ebdb81900fd2d8adf59ec48e35b757

22

py

Python

vogue/commands/__init__.py

mayabrandi/vogue

463e6417a9168eadb0d11dea2d0f97919494bcd3

[ "MIT" ]

111

2015-01-15T11:53:20.000Z

2022-03-26T19:55:24.000Z

vogue/commands/__init__.py

mayabrandi/vogue

463e6417a9168eadb0d11dea2d0f97919494bcd3

[ "MIT" ]

2,995

2015-01-15T16:14:20.000Z

2022-03-31T13:36:32.000Z

arnold/commands/__init__.py

Clinical-Genomics/arnold

8b0dfe5a97736b60ffc3498b4f54c91f31bfe410

[ "MIT" ]

55

2015-05-31T19:09:49.000Z

2021-11-01T10:50:31.000Z

from .base import cli

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6

6f1baf6baf8fc209962c569140f0029a776b9e23

14,220

py

Python

cogs/admin.py

TheRealKeto/Phantom

fa38bb4aa81b33583a345d10a77572acdbafd389

[ "MIT" ]

3

2021-05-11T12:46:09.000Z

2022-03-19T18:27:32.000Z

cogs/admin.py

AushaTeam/Phantom

fa38bb4aa81b33583a345d10a77572acdbafd389

[ "MIT" ]

1

2021-06-02T05:13:10.000Z

2021-06-03T23:39:02.000Z

cogs/admin.py

AushaTeam/Phantom

fa38bb4aa81b33583a345d10a77572acdbafd389

[ "MIT" ]

3

2021-07-13T18:45:39.000Z

2022-02-01T05:00:07.000Z

from discord.ext import commands import aiofiles import aiohttp import aiosqlite import asyncio import discord import glob import math import time import platform class Admin(commands.Cog): def __init__(self, bot): self.bot = bot def get_modules(self): if platform.system() == 'Windows': modules = glob.glob('cogs\*.py') else: modules = glob.glob('cogs/*.py') return sorted([module.replace('/', '.').replace('\\', '.')[:-3].split('.')[-1] for module in modules]) @commands.group(invoke_without_command=True) @commands.is_owner() async def module(self, ctx): if ctx.prefix == f'<@!{self.bot.user.id}> ': prefix = f'{ctx.prefix}`' else: prefix = f'`{ctx.prefix}' embed = discord.Embed(title='Module Commands') embed.add_field(name='Edit', value=f'{prefix}module edit <module>`', inline=False) embed.add_field(name='List', value=f'{prefix}module list`', inline=False) embed.add_field(name='Load', value=f'{prefix}module load <module>`', inline=False) embed.add_field(name='Reload', value=f'{prefix}module reload <all/module>`', inline=False) embed.add_field(name='Unload', value=f'{prefix}module unload <module>`', inline=False) embed.add_field(name='Note:', value='Use commas to separate multiple modules.', inline=False) embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) @module.command() @commands.is_owner() @commands.guild_only() async def edit(self, ctx, *modules): local_modules = self.get_modules() modules = [module.lower() for module in modules] if len(modules) > 1: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', description='You can only edit one module at a time!') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) return if modules[0] not in local_modules: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', description=f'Module `{modules[0]}` does not exist!') embed.add_field(name='Available modules:', value=f"`{'`, `'.join(local_modules)}`") embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) return embed = discord.Embed(title='Edit Module', description=f'Send a link to the raw code you wish to update the `{modules[0]}` module to.') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) message = await ctx.send(embed=embed) try: answer = await self.bot.wait_for('message', check=lambda message: message.author == ctx.author, timeout=60) except asyncio.exceptions.TimeoutError: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', value='No response given in 1 minute, cancelling.') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await message.edit(embed=embed) return await answer.delete() async with aiofiles.open(f'cogs/{modules[0]}.py', 'r') as f: old_module = await f.read() try: async with aiohttp.ClientSession() as session: async with session.get(answer.content) as response: new_module = await response.text().replace(' ', ' ') # fuck space indents, shifts FTW except aiohttp.client_exceptions.InvalidURL: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', value='Response is not a valid URL.') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await message.edit(embed=embed) return if old_module == new_module: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', value=f'URL content is the same as current module `{modules[0]}` content.') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await message.edit(embed=embed) return async with aiofiles.open(f'cogs/{modules[0]}.py', 'w') as f: await f.write(new_module) try: self.bot.reload_extension(f'cogs.{modules[0]}') embed = discord.Embed(title='Edit Module', description=f'Module `{modules[0]}` has been reloaded.') except discord.ext.commands.ExtensionNotLoaded: # Attempt to load module try: self.bot.load_extension(f'cogs.{modules[0]}') except discord.ext.commands.ExtensionFailed: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` has an error, reverting to backup!') async with aiofiles.open(f'cogs/{modules[0]}.py', 'w') as f: await f.write(old_module) except discord.ext.commands.ExtensionFailed: embed = discord.Embed(title='Edit Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` has an error, reverting to backup!') async with aiofiles.open(f'cogs/{modules[0]}.py', 'w') as f: await f.write(old_module) embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await message.edit(embed=embed) @module.command(name='list') @commands.guild_only() @commands.is_owner() async def _list(self, ctx): local_modules = self.get_modules() embed = discord.Embed(title='All Modules', description=f"`{'`, `'.join(local_modules)}`") embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) @module.command() @commands.is_owner() @commands.guild_only() async def load(self, ctx, *modules): local_modules = self.get_modules() modules = sorted([module.lower() for module in modules]) if len(modules) > 1 or modules[0] == 'all': embed = discord.Embed(title='Load Module') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) message = await ctx.send(embed=embed) successful_loads = int() failed_loads = int() for module in (local_modules if modules[0] == 'all' else modules): if not any(module == x for x in local_modules): embed.add_field(name='Error', value=f'Module `{module}` does not exist!', inline=False) await message.edit(embed=embed) failed_loads += 1 continue try: self.bot.load_extension(f'cogs.{module}') embed.add_field(name='Success', value=f'Module `{module}` successfully unloaded!', inline=False) await message.edit(embed=embed) successful_loads += 1 except discord.ext.commands.ExtensionAlreadyLoaded: embed.add_field(name='Error', value=f'Module `{module}` is already loaded!', inline=False) await message.edit(embed=embed) failed_loads += 1 except discord.ext.commands.ExtensionFailed: embed.add_field(name='Error', value=f'Module `{module}` has an error, cannot load!', inline=False) await message.edit(embed=embed) failed_loads += 1 embed.add_field(name='Finished', value=f"**{successful_loads}** module{'s' if successful_loads != 1 else ''} successfully loaded, **{failed_loads}** module{'s' if failed_loads != 1 else ''} failed to load.") await message.edit(embed=embed) return if not any(modules[0] == x for x in local_modules): embed = discord.Embed(title='Unload Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` does not exist!', inline=False) embed.add_field(name='Available modules:', value=f"`{'`, `'.join(local_modules)}`", inline=False) embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) return try: self.bot.load_extension(f'cogs.{modules[0]}') embed = discord.Embed(title='Load Module', description=f'Module `{modules[0]}` has been loaded.') except discord.ext.commands.ExtensionAlreadyLoaded: embed = discord.Embed(title='Load Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` is already loaded!') except discord.ext.commands.ExtensionFailed: embed = discord.Embed(title='Load Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` has an error, cannot load!') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) @module.command(name='reload') @commands.is_owner() @commands.guild_only() async def _reload(self, ctx, *modules): local_modules = self.get_modules() modules = sorted([module.lower() for module in modules]) if len(modules) > 1 or modules[0] == 'all': embed = discord.Embed(title='Reload Module') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) message = await ctx.send(embed=embed) successful_reloads = int() failed_reloads = int() for module in (local_modules if modules[0] == 'all' else modules): if module not in local_modules: embed.add_field(name='Error', value=f'Module `{module}` does not exist!', inline=False) await message.edit(embed=embed) failed_reloads += 1 continue try: self.bot.reload_extension(f'cogs.{module}') embed.add_field(name='Success', value=f'Module `{module}` successfully reloaded!', inline=False) await message.edit(embed=embed) successful_reloads += 1 except discord.ext.commands.ExtensionNotLoaded: embed.add_field(name='Error', value=f'Module `{module}` is not currently loaded!', inline=False) await message.edit(embed=embed) failed_reloads += 1 except discord.ext.commands.ExtensionFailed: embed.add_field(name='Error', value=f'Module `{module}` failed to reload!', inline=False) await message.edit(embed=embed) failed_reloads += 1 embed.add_field(name='Finished', value=f"**{successful_reloads}** module{'s' if successful_reloads != 1 else ''} successfully reloaded, **{failed_reloads}** module{'s' if failed_reloads != 1 else ''} failed to reload.") await message.edit(embed=embed) return if modules[0] not in local_modules: embed = discord.Embed(title='Reload Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` does not exist!', inline=False) embed.add_field(name='Available modules:', value=f"`{'`, `'.join(local_modules)}`", inline=False) embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) return try: self.bot.reload_extension(f'cogs.{modules[0]}') embed = discord.Embed(title='Reload Module', description=f'Module `{modules[0]}` has been reloaded.') except discord.ext.commands.ExtensionNotLoaded: try: self.bot.load_extension(f'cogs.{modules[0]}') except discord.ext.commands.ExtensionFailed: embed = discord.Embed(title='Reload Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` has an error, cannot load!') except discord.ext.commands.ExtensionFailed: embed = discord.Embed(title='Reload Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` has an error, cannot load!') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) @module.command() @commands.is_owner() @commands.guild_only() async def unload(self, ctx, *modules): local_modules = self.get_modules() modules = sorted([module.lower() for module in modules]) if len(modules) > 1 or modules[0] == 'all': embed = discord.Embed(title='Unload Module') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) message = await ctx.send(embed=embed) successful_unloads = int() failed_unloads = int() for module in (local_modules if modules[0] == 'all' else modules): if not any(module == x for x in local_modules): embed.add_field(name='Error', value=f'Module `{module}` does not exist!', inline=False) await message.edit(embed=embed) failed_unloads += 1 continue if module == 'admin': embed.add_field(name='Error', value=f'Module `{module}` cannot be unloaded!', inline=False) await message.edit(embed=embed) failed_unloads += 1 continue try: self.bot.unload_extension(f'cogs.{module}') embed.add_field(name='Success', value=f'Module `{module}` successfully unloaded!', inline=False) await message.edit(embed=embed) successful_unloads += 1 except discord.ext.commands.ExtensionNotLoaded: embed.add_field(name='Error', value=f'Module `{module}` is already unloaded!', inline=False) await message.edit(embed=embed) failed_unloads += 1 embed.add_field(name='Finished', value=f"**{successful_unloads}** module{'s' if successful_unloads != 1 else ''} successfully unloaded, **{failed_unloads}** module{'s' if failed_unloads != 1 else ''} failed to unload.") await message.edit(embed=embed) return if not any(modules[0] == x for x in local_modules): embed = discord.Embed(title='Unload Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` does not exist!', inline=False) embed.add_field(name='Available modules:', value=f"`{'`, `'.join(local_modules)}`", inline=False) embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) return try: self.bot.unload_extension(f'cogs.{modules[0]}') embed = discord.Embed(title='Unload Module', description=f'Module `{modules[0]}` has been unloaded.') except discord.ext.commands.ExtensionNotLoaded: embed = discord.Embed(title='Unload Module') embed.add_field(name='Error', value=f'Module `{modules[0]}` is already unloaded!') embed.set_footer(text=ctx.author.display_name, icon_url=ctx.author.avatar_url_as(static_format='png')) await ctx.send(embed=embed) def setup(bot): bot.add_cog(Admin(bot))

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6f29ab20c6c08d6be96b13a58c9169db980e2bea

24

py

Python

src/opf/__init__.py

krivopolianskii/opf

16cfe25194fd15c21702658cac302fe0dfce82d9

[ "MIT" ]

null

src/opf/__init__.py

krivopolianskii/opf

16cfe25194fd15c21702658cac302fe0dfce82d9

[ "MIT" ]

null

src/opf/__init__.py

krivopolianskii/opf

16cfe25194fd15c21702658cac302fe0dfce82d9

[ "MIT" ]

null

from .models import *

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0

6

6f4a672aed74a62d795176e918183f713024cda5

85

py

Python

web_app/vis_comm/routes/python_scripts/server.py

RapidsAtHKUST/FYP16-CommunityDetectionVis

4b7c76f6f8f94d09ae4ab98262b894dfd6af3bc0

[ "MIT" ]

null

web_app/vis_comm/routes/python_scripts/server.py

RapidsAtHKUST/FYP16-CommunityDetectionVis

4b7c76f6f8f94d09ae4ab98262b894dfd6af3bc0

[ "MIT" ]

null

web_app/vis_comm/routes/python_scripts/server.py

RapidsAtHKUST/FYP16-CommunityDetectionVis

4b7c76f6f8f94d09ae4ab98262b894dfd6af3bc0

[ "MIT" ]

1

2021-12-02T10:34:23.000Z

import os os.system("cd ../community_detection_algos/docker;python run_docker.py;")

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6

6f59ca49ca0477b4268f8d9bcb4231899e51d939

13,586

py

Python

db/dbutil/db_queries_bloomberg.py

kaljuvee/openaltdata

9c5d140b56cfd5260fe3cf52b24bb7d467e87cf1

[ "MIT" ]

null

db/dbutil/db_queries_bloomberg.py

kaljuvee/openaltdata

9c5d140b56cfd5260fe3cf52b24bb7d467e87cf1

[ "MIT" ]

null

db/dbutil/db_queries_bloomberg.py

kaljuvee/openaltdata

9c5d140b56cfd5260fe3cf52b24bb7d467e87cf1

[ "MIT" ]

1

2021-09-10T16:03:20.000Z

import db.db_access as access import pandas as pd import psycopg2 from sqlalchemy import create_engine CONST_SQL_GET_COMP_NAME = 'SELECT * FROM maincompany;' CONST_SQL_GET_REP_SALES = 'SELECT * FROM reported_sales;' CONST_SQL_GET_DAILY_ESTIMATION = 'SELECT * FROM daily_sales_estimation;' CONST_SQL_GET_DAILY_ESTIMATION_PER_MAIN_COMPANY_ID = 'SELECT * FROM daily_sales_estimation WHERE main_company_id = {MAIN_COMPANY_ID};' CONST_SQL_GET_HIST_PRICES = 'SELECT * FROM historical_prices;' INSERT_REPORTED_SALES = 'INSERT INTO reported_sales (main_company_id, quarter_group, end_quarter_date, start_quarter_date, filing_date, actual_sales, sales_reported, time_announcement) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)' INSERT_DAILY_SALES_ESTIMATION = 'INSERT INTO daily_sales_estimation (main_company_id, quarter_group, year_estimation, quarter_estimation, datetime, estimation) VALUES (%s, %s, %s, %s, %s, %s)' INSERT_HIST_PRICE = 'INSERT INTO historical_prices (main_company_id, date, close, high, low, open) VALUES (%s, %s, %s, %s, %s, %s)' DELETE_DAILY_ESTIMATION_FUTURE = 'DELETE FROM daily_sales_estimation WHERE company_id = %s and quarter_group = %s;' def first_column(array_2d): return list(zip(*array_2d))[0] def db_result_to_pandas(cursor_fetch_result): return pd.DataFrame(cursor_fetch_result['result'], columns=cursor_fetch_result['header']) class psql_bloomberg_pipeline_connector_db(object): """ Class for getting data from the database. Currently not SQL injection safe. """ def __init__(self): self.host, self.port, self.database, self.user, self.password = access.postgre_access_google_cloud() self.message = str('postgres://' + self.user + ':' + self.password + '@' + self.host + ':' + self.port + '/' + self.database) def get_psql_context(self): cnx = psycopg2.connect(host=self.host, port=self.port, database=self.database, user=self.user, password=self.password) return cnx def get_create_engine(self): engine = create_engine(self.message) return engine def get_companies_names(self): cnx = self.get_psql_context() cur = cnx.cursor() try: cur.execute(CONST_SQL_GET_COMP_NAME) result = cur.fetchall() df = pd.DataFrame.from_records(result, columns=[x[0] for x in cur.description]) return df finally: cur.close() def get_reported_sales(self): """ Returns reported sales for all companies. Among the column names the column "group" actually means quarter :param company_id: :return: {"result": <query result>, "header": <names of the columns> """ cnx = self.get_psql_context() cur = cnx.cursor() try: cur.execute(CONST_SQL_GET_REP_SALES) result = cur.fetchall() df = pd.DataFrame.from_records(result, columns=[x[0] for x in cur.description]) return df finally: cur.close() def get_daily_sales_estimation(self): """ Returns reported sales for all companies. Among the column names the column "group" actually means quarter :param company_id: :return: {"result": <query result>, "header": <names of the columns> """ cnx = self.get_psql_context() cur = cnx.cursor() try: cur.execute(CONST_SQL_GET_DAILY_ESTIMATION) result = cur.fetchall() df = pd.DataFrame.from_records(result, columns=[x[0] for x in cur.description]) return df finally: cur.close() def get_daily_sales_estimation_per_main_company_id(self, main_company_id): """ Returns reported sales for all companies. Among the column names the column "group" actually means quarter :param main_company_id: :return: {"result": <query result>, "header": <names of the columns> """ cnx = self.get_psql_context() cur = cnx.cursor() try: query = CONST_SQL_GET_DAILY_ESTIMATION_PER_MAIN_COMPANY_ID.format(MAIN_COMPANY_ID=str(main_company_id)) cur.execute(query) result = cur.fetchall() df = pd.DataFrame.from_records(result, columns=[x[0] for x in cur.description]) return df finally: cur.close() def get_historical_prices(self): cnx = self.get_psql_context() cur = cnx.cursor() try: cur.execute(CONST_SQL_GET_HIST_PRICES) result = cur.fetchall() df = pd.DataFrame.from_records(result, columns=[x[0] for x in cur.description]) return df finally: cur.close() def insert_reported_sales(self, df): cnx = self.get_psql_context() cursor = cnx.cursor() try: df = df[['main_company_id', 'group', 'end_quarter_date', 'start_quarter_date', 'filing_date', 'actual_sales', 'sales_reported', 'time']] # change format for date and time to insert it in db df['end_quarter_date'] = df['end_quarter_date'].map(lambda x: x.strftime('%Y-%m-%d'), list(df['end_quarter_date'])) df['start_quarter_date'] = df['start_quarter_date'].map(lambda x: x.strftime('%Y-%m-%d'), list(df['start_quarter_date'])) df['filing_date'] = df['filing_date'].map(lambda x: x.strftime('%Y-%m-%d'), list(df['filing_date'])) cursor.executemany(INSERT_REPORTED_SALES, df.values.tolist()) cnx.commit() except (Exception, psycopg2.DatabaseError) as error: print(error) finally: cursor.close() cnx.close() def insert_daily_estimation(self, df): engine = self.get_create_engine() try: table_name = 'daily_sales_estimation' df.to_sql(table_name, con=engine.connect(), if_exists='append', index=False, method='multi') except (Exception, psycopg2.DatabaseError) as error: print(error) def insert_historical_price(self, df): cnx = self.get_psql_context() cursor = cnx.cursor() try: df = df[['main_company_id', 'date', 'close', 'high', 'low', 'open']] cursor.executemany(INSERT_HIST_PRICE, df.values.tolist()) cnx.commit() except (Exception, psycopg2.DatabaseError) as error: print(error) finally: cursor.close() cnx.close() def delete_daily_estimation_quarter(self, df): cnx = self.get_psql_context() cursor = cnx.cursor() try: df = df[['main_company_id', 'quarter_group']] cursor.executemany(DELETE_DAILY_ESTIMATION_FUTURE, df.values.tolist()) cnx.commit() except (Exception, psycopg2.DatabaseError) as error: print(error) finally: cursor.close() cnx.close() ''' class TrafficDatabaseConnector(object): """ Class for getting data from the database. Currently not SQL injection safe. """ def __init__(self): server, database, username, password, driver = access.parameter() self.cnx = mysql.connector.connect(user=username, password=password, host=server, database=database) def get_mysql_context(self): server, database, username, password, driver = access.parameter() cnx = mysql.connector.connect(user=username, password=password, host=server, database=database) return cnx def get_companies_names(self): cnx = self.get_mysql_context() cursor = cnx.cursor() cursor.execute(CONST_SQL_GET_COMP_NAME) result = {"result": cursor.fetchall(), "header": first_column(cursor.description)} cursor.close() cnx.close() return result def get_companies_names_pandas(self): return db_result_to_pandas(self.get_companies_names()) def get_reported_sales(self): """ Returns reported sales for all companies. Among the column names the column "group" actually means quarter :param company_id: :return: {"result": <query result>, "header": <names of the columns> """ sql_request = CONST_SQL_GET_REP_SALES cnx = self.get_mysql_context() cursor = cnx.cursor() cursor.execute(sql_request) result = {"result": cursor.fetchall(), "header": first_column(cursor.description)} cursor.close() cnx.close() return result def get_reported_sales_pandas(self): return db_result_to_pandas(self.get_reported_sales()) def get_daily_sales_estimation(self): """ Returns reported sales for all companies. Among the column names the column "group" actually means quarter :param company_id: :return: {"result": <query result>, "header": <names of the columns> """ sql_request = CONST_SQL_GET_DAILY_ESTIMATION cnx = self.get_mysql_context() cursor = cnx.cursor() cursor.execute(sql_request) result = {"result": cursor.fetchall(), "header": first_column(cursor.description)} cursor.close() cnx.close() return result def get_daily_sales_estimation_pandas(self): return db_result_to_pandas(self.get_daily_sales_estimation()) def get_historical_prices(self): sql_request = CONST_SQL_GET_HIST_PRICES cnx = self.get_mysql_context() cursor = cnx.cursor() cursor.execute(sql_request) result = {"result": cursor.fetchall(), "header": first_column(cursor.description)} cursor.close() cnx.close() return result def get_historical_prices_pandas(self): return db_result_to_pandas(self.get_historical_prices()) def insert_reported_sales(self, df): cnx = self.get_mysql_context() cursor = cnx.cursor() try: df = df[['company_id', 'group', 'end_quarter_date', 'start_quarter_date', 'filing_date', 'actual_sales', 'sales_reported', 'time']] # change format for date and time to insert it in db df['end_quarter_date'] = df['end_quarter_date'].map(lambda x: x.strftime('%Y-%m-%d'), list(df['end_quarter_date'])) df['start_quarter_date'] = df['start_quarter_date'].map(lambda x: x.strftime('%Y-%m-%d'), list(df['start_quarter_date'])) df['filing_date'] = df['filing_date'].map(lambda x: x.strftime('%Y-%m-%d'), list(df['filing_date'])) cursor.executemany(INSERT_REPORTED_SALES, df.values.tolist()) cnx.commit() except mysql.connector.Error as error: print("Failed to insert record into Laptop table {}".format(error)) finally: cursor.close() cnx.close() def insert_daily_estimation(self, df): cnx = self.get_mysql_context() cursor = cnx.cursor() try: df = df[['company_id', 'quarter_group', 'year_estimation', 'quarter_estimation', 'datetime', 'estimation']] cursor.executemany(INSERT_DAILY_SALES_ESTIMATION, df.values.tolist()) cnx.commit() except mysql.connector.Error as error: print("Failed to insert record into Laptop table {}".format(error)) finally: cursor.close() cnx.close() def insert_historical_price(self, df): cnx = self.get_mysql_context() cursor = cnx.cursor() try: df = df[['company_id', 'date', 'close', 'high', 'low', 'open']] cursor.executemany(INSERT_HIST_PRICE, df.values.tolist()) cnx.commit() except mysql.connector.Error as error: print("Failed to insert record into Laptop table {}".format(error)) finally: cursor.close() cnx.close() def delete_daily_estimation_quarter(self, df): cnx = self.get_mysql_context() cursor = cnx.cursor() try: df = df[['company_id', 'quarter_group']] cursor.executemany(DELETE_DAILY_ESTIMATION_FUTURE, df.values.tolist()) cnx.commit() except mysql.connector.Error as error: print("Failed to insert record into Laptop table {}".format(error)) finally: cursor.close() cnx.close() def _run_sql_query(self, sql_request): cnx = self.get_mysql_context() cursor = cnx.cursor() cursor.execute(sql_request) result = {"result": cursor.fetchall(), "header": first_column(cursor.description)} cursor.close() cnx.close() return result def run_sql_query(self, sql_request): return db_result_to_pandas(self._run_sql_query(sql_request)) def cache_db_request_pandas(db_request, pandas_filename): try: return pd.read_pickle(pandas_filename) except Exception as e: pandas_obj = db_request() pandas_obj.to_pickle(pandas_filename) return pandas_obj '''

39.04023

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4.915783

0.101684

0.027411

0.021574

0.033503

0.81764

0.770939

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0

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0.281319

13,586

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0

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0

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0.184742

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0

1

0.112903

false

0.024194

0.032258

0.016129

0.225806

0.032258

0

null

0

1

0

null

0

6

48b508c562736975c3166e863ec74e8f5fbff2ce

2,110

py

Python

mexmi/models/imagenet/__init__.py

mexmi2021/mexmi-project

ef735cb290d33b326f592a70fa9b7f7dc6b6281b

[ "MIT" ]

null

mexmi/models/imagenet/__init__.py

mexmi2021/mexmi-project

ef735cb290d33b326f592a70fa9b7f7dc6b6281b

[ "MIT" ]

null

mexmi/models/imagenet/__init__.py

mexmi2021/mexmi-project

ef735cb290d33b326f592a70fa9b7f7dc6b6281b

[ "MIT" ]

null

"""Fallback to Cadene imagenet models (superset of torchvision models) Source: https://github.com/Cadene/pretrained-models.pytorch """ # from pretrainedmodels import fbresnet152 # from pretrainedmodels import cafferesnet101 # from pretrainedmodels import bninception # from pretrainedmodels import resnext101_32x4d # from pretrainedmodels import resnext101_64x4d # from pretrainedmodels import inceptionv4 # from pretrainedmodels import inceptionresnetv2 # from pretrainedmodels import nasnetalarge # from pretrainedmodels import nasnetamobile # from pretrainedmodels import alexnet # from pretrainedmodels import densenet121 # from pretrainedmodels import densenet169 # from pretrainedmodels import densenet201 # from pretrainedmodels import densenet161 # from pretrainedmodels import resnet18 # from pretrainedmodels import resnet34 # from pretrainedmodels import resnet50 # from pretrainedmodels import resnet101 # from pretrainedmodels import resnet152 # from pretrainedmodels import inceptionv3 # from pretrainedmodels import squeezenet1_0 # from pretrainedmodels import squeezenet1_1 # from pretrainedmodels import vgg11 # from pretrainedmodels import vgg11_bn # from pretrainedmodels import vgg13 # from pretrainedmodels import vgg13_bn # from pretrainedmodels import vgg16 # from pretrainedmodels import vgg16_bn # from pretrainedmodels import vgg19_bn # from pretrainedmodels import vgg19 # from pretrainedmodels import dpn68 # from pretrainedmodels import dpn68b # from pretrainedmodels import dpn92 # from pretrainedmodels import dpn98 # from pretrainedmodels import dpn131 # from pretrainedmodels import dpn107 # from pretrainedmodels import xception # from pretrainedmodels import senet154 # from pretrainedmodels import se_resnet50 # from pretrainedmodels import se_resnet101 # from pretrainedmodels import se_resnet152 # from pretrainedmodels import se_resnext50_32x4d # from pretrainedmodels import se_resnext101_32x4d # from pretrainedmodels import pnasnet5large # from pretrainedmodels import polynet from .bagnets import bagnet9 from .bagnets import bagnet17 from .bagnets import bagnet33

39.074074

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2,110

7.941964

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0.057558

0.118957

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0

null

0

1

0

1

0

1

0

6

48e294b1fe59367798e4d57d915021b9365c453d

165

py

Python

pyfacebook/__init__.py

nedsons/python-facebook

bf2b4a70ef0e0a67a142f5856586ea318f9807ea

[ "Apache-2.0" ]

2

2021-03-16T02:58:10.000Z

2021-03-16T16:53:23.000Z

pyfacebook/__init__.py

nedsons/python-facebook

bf2b4a70ef0e0a67a142f5856586ea318f9807ea

[ "Apache-2.0" ]

null

pyfacebook/__init__.py

nedsons/python-facebook

bf2b4a70ef0e0a67a142f5856586ea318f9807ea

[ "Apache-2.0" ]

null

# coding=utf-8 from __future__ import absolute_import from .api import * # noqa from .error import * # noqa from .models import * # noqa __version__ = "0.8.1"

16.5

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null

0

1

0

1

0

6

5b1046b957722acd447349d72ec0240db663d42a

323

py

Python

Validation/MtdValidation/python/MtdPostProcessor_cff.py

SWuchterl/cmssw

769b4a7ef81796579af7d626da6039dfa0347b8e

[ "Apache-2.0" ]

6

2017-09-08T14:12:56.000Z

2022-03-09T23:57:01.000Z

Validation/MtdValidation/python/MtdPostProcessor_cff.py

SWuchterl/cmssw

769b4a7ef81796579af7d626da6039dfa0347b8e

[ "Apache-2.0" ]

545

2017-09-19T17:10:19.000Z

2022-03-07T16:55:27.000Z

Validation/MtdValidation/python/MtdPostProcessor_cff.py

SWuchterl/cmssw

769b4a7ef81796579af7d626da6039dfa0347b8e

[ "Apache-2.0" ]

14

2017-10-04T09:47:21.000Z

2019-10-23T18:04:45.000Z

import FWCore.ParameterSet.Config as cms from Validation.MtdValidation.btlSimHitsPostProcessor_cfi import btlSimHitsPostProcessor from Validation.MtdValidation.MtdGlobalRecoPostProcessor_cfi import MtdGlobalRecoPostProcessor mtdValidationPostProcessor = cms.Sequence(btlSimHitsPostProcessor + MtdGlobalRecoPostProcessor)

46.142857

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null

0

1

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1

0

6

5b1e6edf7093f29b3f50623889ce5c7e3b6bf60b

26

py

Python

01_Hello/hello01_print.py

davidlg2005/tiny_python_projects

3f86615f32c10cb2e689ef4abc56c2c194063bfe

[ "MIT" ]

null

01_Hello/hello01_print.py

davidlg2005/tiny_python_projects

3f86615f32c10cb2e689ef4abc56c2c194063bfe

[ "MIT" ]

null

01_Hello/hello01_print.py

davidlg2005/tiny_python_projects

3f86615f32c10cb2e689ef4abc56c2c194063bfe

[ "MIT" ]

null

print('01_Hello, World!')

13

25

0.692308

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4.25

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0.083333

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0

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1

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6

5b24270483e887d17caf80d4514db372746177ef

5,635

py

Python

pactman/test/pact_serialisation/test_request_query.py

AustralianSynchrotron/pactman

9e149e0b1da2dea2c58cfc7cab76f407ac03060e

[ "MIT" ]

null

pactman/test/pact_serialisation/test_request_query.py

AustralianSynchrotron/pactman

9e149e0b1da2dea2c58cfc7cab76f407ac03060e

[ "MIT" ]

null

pactman/test/pact_serialisation/test_request_query.py

AustralianSynchrotron/pactman

9e149e0b1da2dea2c58cfc7cab76f407ac03060e

[ "MIT" ]

null

import pytest from pactman import Consumer, Like, Provider, Term from pactman.mock.request import Request def test_v2(): pact = Consumer('consumer').has_pact_with(Provider('provider'), version='2.0.0') pact.given("the condition exists").upon_receiving("a request") \ .with_request("GET", "/path", query="fields=first,second").will_respond_with(200, body='ok') result = pact.construct_pact(pact._interactions[0]) assert result == { 'consumer': {'name': 'consumer'}, 'provider': {'name': 'provider'}, 'interactions': [ { 'description': 'a request', 'providerState': 'the condition exists', 'request': dict(method='GET', path='/path', query='fields=first,second'), 'response': dict(status=200, body='ok'), } ], 'metadata': dict(pactSpecification=dict(version='2.0.0')) } @pytest.mark.parametrize('query_field', [ 'first,second', ['first,second'] ]) def test_v3(query_field): pact = Consumer('consumer').has_pact_with(Provider('provider'), version='3.0.0') pact.given([{'name': "the condition exists", 'params': {}}]).upon_receiving("a request") \ .with_request("GET", "/path", query=dict(fields=query_field)).will_respond_with(200, body='ok') result = pact.construct_pact(pact._interactions[0]) assert result == { 'consumer': {'name': 'consumer'}, 'provider': {'name': 'provider'}, 'interactions': [ { 'description': 'a request', 'providerStates': [{'name': 'the condition exists', 'params': {}}], 'request': dict(method='GET', path='/path', query=dict(fields=['first,second'])), 'response': dict(status=200, body='ok'), } ], 'metadata': dict(pactSpecification=dict(version='3.0.0')) } def test_like_v2(): pact = Consumer('consumer').has_pact_with(Provider('provider'), version='2.0.0') pact.given("the condition exists").upon_receiving("a request") \ .with_request("GET", "/path", query=Like("fields=first,second")).will_respond_with(200, body='ok') result = pact.construct_pact(pact._interactions[0]) assert result == { 'consumer': {'name': 'consumer'}, 'provider': {'name': 'provider'}, 'interactions': [ { 'description': 'a request', 'providerState': 'the condition exists', 'request': dict(method='GET', path='/path', query='fields=first,second', matchingRules={'$.query': {'match': 'type'}}), 'response': dict(status=200, body='ok'), } ], 'metadata': dict(pactSpecification=dict(version='2.0.0')) } def test_like_v3(): pact = ( Consumer('consumer').has_pact_with(Provider('provider'), version='3.0.0') .given("the condition exists") .upon_receiving("a request") .with_request("GET", "/path", query=dict(fields=Like(['first,second']))) .will_respond_with(200, body='ok') ) result = pact.construct_pact(pact._interactions[0]) assert result == { 'consumer': {'name': 'consumer'}, 'provider': {'name': 'provider'}, 'interactions': [ { 'description': 'a request', 'providerStates': [{'name': 'the condition exists', 'params': {}}], 'request': dict(method='GET', path='/path', query=dict(fields=['first,second']), matchingRules={'query': {'fields': {'matchers': [{'match': 'type'}]}}}), 'response': dict(status=200, body='ok'), } ], 'metadata': dict(pactSpecification=dict(version='3.0.0')) } def test_broader_like_v3(): pact = ( Consumer('consumer').has_pact_with(Provider('provider'), version='3.0.0') .given("the condition exists") .upon_receiving("a request") .with_request("GET", "/path", query=Like(dict(fields=['first,second']))) .will_respond_with(200, body='ok') ) result = pact.construct_pact(pact._interactions[0]) assert result == { 'consumer': {'name': 'consumer'}, 'provider': {'name': 'provider'}, 'interactions': [ { 'description': 'a request', 'providerStates': [{'name': 'the condition exists', 'params': {}}], 'request': dict(method='GET', path='/path', query=dict(fields=['first,second']), matchingRules={'query': {'*': {'matchers': [{'match': 'type'}]}}}), 'response': dict(status=200, body='ok'), } ], 'metadata': dict(pactSpecification=dict(version='3.0.0')) } def test_matcher_in_query(): target = Request('GET', '/test-path', query={'q': [Like('spam')], 'l': [Term(r'\d+', '10')]}) assert target.json('3.0.0') == { 'method': 'GET', 'path': '/test-path', 'query': {'q': ['spam'], 'l': ['10']}, 'matchingRules': { 'query': { 'q': { 'matchers': [ { 'match': 'type' }, ] }, 'l': { 'matchers': [ { 'match': 'regex', 'regex': r'\d+', } ] }, } } }

35.440252

106

0.50417

533

5,635

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null

0

1

0

null

0

6

d28d266557dfae0ad30cdffb18d53783ca62a8c7

5,773

py

Python

tests/known_related_objects/tests.py

webjunkie/django

5dbca13f3baa2e1bafd77e84a80ad6d8a074712e

[ "BSD-3-Clause" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

AppServer/lib/django-1.5/tests/modeltests/known_related_objects/tests.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

AppServer/lib/django-1.5/tests/modeltests/known_related_objects/tests.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

from __future__ import absolute_import from django.test import TestCase from .models import Tournament, Organiser, Pool, PoolStyle class ExistingRelatedInstancesTests(TestCase): fixtures = ['tournament.json'] def test_foreign_key(self): with self.assertNumQueries(2): tournament = Tournament.objects.get(pk=1) pool = tournament.pool_set.all()[0] self.assertIs(tournament, pool.tournament) def test_foreign_key_prefetch_related(self): with self.assertNumQueries(2): tournament = (Tournament.objects.prefetch_related('pool_set').get(pk=1)) pool = tournament.pool_set.all()[0] self.assertIs(tournament, pool.tournament) def test_foreign_key_multiple_prefetch(self): with self.assertNumQueries(2): tournaments = list(Tournament.objects.prefetch_related('pool_set').order_by('pk')) pool1 = tournaments[0].pool_set.all()[0] self.assertIs(tournaments[0], pool1.tournament) pool2 = tournaments[1].pool_set.all()[0] self.assertIs(tournaments[1], pool2.tournament) def test_queryset_or(self): tournament_1 = Tournament.objects.get(pk=1) tournament_2 = Tournament.objects.get(pk=2) with self.assertNumQueries(1): pools = tournament_1.pool_set.all() | tournament_2.pool_set.all() related_objects = set(pool.tournament for pool in pools) self.assertEqual(related_objects, set((tournament_1, tournament_2))) def test_queryset_or_different_cached_items(self): tournament = Tournament.objects.get(pk=1) organiser = Organiser.objects.get(pk=1) with self.assertNumQueries(1): pools = tournament.pool_set.all() | organiser.pool_set.all() first = pools.filter(pk=1)[0] self.assertIs(first.tournament, tournament) self.assertIs(first.organiser, organiser) def test_queryset_or_only_one_with_precache(self): tournament_1 = Tournament.objects.get(pk=1) tournament_2 = Tournament.objects.get(pk=2) # 2 queries here as pool id 3 has tournament 2, which is not cached with self.assertNumQueries(2): pools = tournament_1.pool_set.all() | Pool.objects.filter(pk=3) related_objects = set(pool.tournament for pool in pools) self.assertEqual(related_objects, set((tournament_1, tournament_2))) # and the other direction with self.assertNumQueries(2): pools = Pool.objects.filter(pk=3) | tournament_1.pool_set.all() related_objects = set(pool.tournament for pool in pools) self.assertEqual(related_objects, set((tournament_1, tournament_2))) def test_queryset_and(self): tournament = Tournament.objects.get(pk=1) organiser = Organiser.objects.get(pk=1) with self.assertNumQueries(1): pools = tournament.pool_set.all() & organiser.pool_set.all() first = pools.filter(pk=1)[0] self.assertIs(first.tournament, tournament) self.assertIs(first.organiser, organiser) def test_one_to_one(self): with self.assertNumQueries(2): style = PoolStyle.objects.get(pk=1) pool = style.pool self.assertIs(style, pool.poolstyle) def test_one_to_one_select_related(self): with self.assertNumQueries(1): style = PoolStyle.objects.select_related('pool').get(pk=1) pool = style.pool self.assertIs(style, pool.poolstyle) def test_one_to_one_multi_select_related(self): with self.assertNumQueries(1): poolstyles = list(PoolStyle.objects.select_related('pool').order_by('pk')) self.assertIs(poolstyles[0], poolstyles[0].pool.poolstyle) self.assertIs(poolstyles[1], poolstyles[1].pool.poolstyle) def test_one_to_one_prefetch_related(self): with self.assertNumQueries(2): style = PoolStyle.objects.prefetch_related('pool').get(pk=1) pool = style.pool self.assertIs(style, pool.poolstyle) def test_one_to_one_multi_prefetch_related(self): with self.assertNumQueries(2): poolstyles = list(PoolStyle.objects.prefetch_related('pool').order_by('pk')) self.assertIs(poolstyles[0], poolstyles[0].pool.poolstyle) self.assertIs(poolstyles[1], poolstyles[1].pool.poolstyle) def test_reverse_one_to_one(self): with self.assertNumQueries(2): pool = Pool.objects.get(pk=2) style = pool.poolstyle self.assertIs(pool, style.pool) def test_reverse_one_to_one_select_related(self): with self.assertNumQueries(1): pool = Pool.objects.select_related('poolstyle').get(pk=2) style = pool.poolstyle self.assertIs(pool, style.pool) def test_reverse_one_to_one_prefetch_related(self): with self.assertNumQueries(2): pool = Pool.objects.prefetch_related('poolstyle').get(pk=2) style = pool.poolstyle self.assertIs(pool, style.pool) def test_reverse_one_to_one_multi_select_related(self): with self.assertNumQueries(1): pools = list(Pool.objects.select_related('poolstyle').order_by('pk')) self.assertIs(pools[1], pools[1].poolstyle.pool) self.assertIs(pools[2], pools[2].poolstyle.pool) def test_reverse_one_to_one_multi_prefetch_related(self): with self.assertNumQueries(2): pools = list(Pool.objects.prefetch_related('poolstyle').order_by('pk')) self.assertIs(pools[1], pools[1].poolstyle.pool) self.assertIs(pools[2], pools[2].poolstyle.pool)

44.751938

94

0.661181

708

5,773

5.201977

0.103107

0.071681

0.117296

0.098832

0.866685

0.790117

0.750747

0.728211

0.654358

0

0.019088

0.228651

5,773

128

95

45.101563

0.807995

0.015417

0

0.59434

0

0.01637

0

0.40566

1

0.160377

false

0

0.028302

0

0.207547

0

null

0

1

0

1

0

null

0

1

0

6

d2bd9b7517c25773193da8e58ce61b08f3cbc0b4

3,474

py

Python

models/arch.py

XiaoSong9905/GCNv2

1fc370fbc4cebafa7aed141e68b063092e88d6d2

[ "Apache-2.0" ]

2

2022-03-29T05:18:21.000Z

2022-03-29T05:18:23.000Z

models/arch.py

XiaoSong9905/GCNv2

1fc370fbc4cebafa7aed141e68b063092e88d6d2

[ "Apache-2.0" ]

null

models/arch.py

XiaoSong9905/GCNv2

1fc370fbc4cebafa7aed141e68b063092e88d6d2

[ "Apache-2.0" ]

null

import torch class GCNv2(torch.nn.Module): def __init__(self): super(GCNv2, self).__init__() self.elu = torch.nn.ELU(inplace=True) self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=4, stride=2, padding=1) self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1) self.conv3_1 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) self.conv3_2 = torch.nn.Conv2d(128, 128, kernel_size=4, stride=2, padding=1) self.conv4_1 = torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.conv4_2 = torch.nn.Conv2d(256, 256, kernel_size=4, stride=2, padding=1) # Descriptor self.convF_1 = torch.nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) self.convF_2 = torch.nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0) # Detector self.convD_1 = torch.nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) self.convD_2 = torch.nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0) self.pixel_shuffle = torch.nn.PixelShuffle(16) def forward(self, x): x = self.elu(self.conv1(x)) x = self.elu(self.conv2(x)) x = self.elu(self.conv3_1(x)) x = self.elu(self.conv3_2(x)) x = self.elu(self.conv4_1(x)) x = self.elu(self.conv4_2(x)) # Descriptor xF xF = self.elu(self.convF_1(x)) desc = self.convF_2(xF) dn = torch.norm(desc, p=2, dim=1) # Compute the norm. desc = desc.div(torch.unsqueeze(dn, 1)) # Divide by norm to normalize. # Detector xD xD = self.elu(self.convD_1(x)) det = self.convD_2(xD).sigmoid() det = self.pixel_shuffle(det) return desc, det class GCNv2_tiny(torch.nn.Module): def __init__(self): super(GCNv2_tiny, self).__init__() self.elu = torch.nn.ELU(inplace=True) self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=4, stride=2, padding=1) self.conv2 = torch.nn.Conv2d(32, 32, kernel_size=4, stride=2, padding=1) self.conv3_1 = torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.conv3_2 = torch.nn.Conv2d(64, 64, kernel_size=4, stride=2, padding=1) self.conv4_1 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) self.conv4_2 = torch.nn.Conv2d(128, 128, kernel_size=4, stride=2, padding=1) # Descriptor self.convF_1 = torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.convF_2 = torch.nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0) self.convD_1 = torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.convD_2 = torch.nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0) self.pixel_shuffle = torch.nn.PixelShuffle(16) def forward(self, x): x = self.elu(self.conv1(x)) x = self.elu(self.conv2(x)) x = self.elu(self.conv3_1(x)) x = self.elu(self.conv3_2(x)) x = self.elu(self.conv4_1(x)) x = self.elu(self.conv4_2(x)) # Descriptor xF xF = self.elu(self.convF_1(x)) desc = self.convF_2(xF) dn = torch.norm(desc, p=2, dim=1) # Compute the norm. desc = desc.div(torch.unsqueeze(dn, 1)) # Divide by norm to normalize. # Detector xD xD = self.elu(self.convD_1(x)) det = self.convD_2(xD).sigmoid() det = self.pixel_shuffle(det) return desc, det

36.568421

84

0.613414

563

3,474

3.653641

0.106572

0.088478

0.126398

0.052504

0.976179

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0.967428

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0.928537

0

0.096067

0.238918

3,474

95

85

36.568421

0.681921

0.050662

0

0.688525

0

1

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false

0

0.016393

0

0.147541

0

null

0

1

0

null

0

6

96087549b88cdcd0833a914b38e0be492f5d08d1

330

py

Python

cc-sdk-mini/CloudConformity/ExternalID.py

zachwhaley/thus

22b006c4ea110fbdc09a79c38e49e79ba04bb4d4

[ "MIT" ]

24

2020-09-10T18:34:04.000Z

2022-02-09T01:52:20.000Z

cc-sdk-mini/CloudConformity/ExternalID.py

zachwhaley/thus

22b006c4ea110fbdc09a79c38e49e79ba04bb4d4

[ "MIT" ]

5

2020-09-11T17:22:08.000Z

2021-09-08T15:51:58.000Z

cc-sdk-mini/CloudConformity/ExternalID.py

zachwhaley/thus

22b006c4ea110fbdc09a79c38e49e79ba04bb4d4

[ "MIT" ]

6

2020-09-10T20:03:00.000Z

2021-06-25T07:33:21.000Z

class ExternalID: def __init__(self, config, connection): self._config = config self._connection = connection def get(self): return self._connection.get(url='/organisation/external-id') def create(self, data): return self._connection.post(url='/organisation/external-id', payload=None)

36.666667

83

0.681818

38

330

5.710526

0.473684

0.193548

0.184332

0.230415

0

0.2

330

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36.666667

0.82197

0

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1

0.375

false

0

0.25

0.75

0

null

0

1

0

1

0

null

0

1

0

1

0

6

9614eee6cc72ee6204c436cbb0e5ee40d29f402a

8,035

py

Python

insights/parsers/tests/test_sctp.py

smalleni/insights-core

3961d400f032b2b1ed66665813ebe5220f27c62d

[ "Apache-2.0" ]

null

insights/parsers/tests/test_sctp.py

smalleni/insights-core

3961d400f032b2b1ed66665813ebe5220f27c62d

[ "Apache-2.0" ]

null

insights/parsers/tests/test_sctp.py

smalleni/insights-core

3961d400f032b2b1ed66665813ebe5220f27c62d

[ "Apache-2.0" ]

null

import doctest import pytest from insights.parsers import ParseException, SkipException from insights.parsers import sctp from insights.parsers.sctp import SCTPEps from insights.parsers.sctp import SCTPAsc from insights.tests import context_wrap SCTP_EPS_DETAILS = """ ENDPT SOCK STY SST HBKT LPORT UID INODE LADDRS ffff88017e0a0200 ffff880299f7fa00 2 10 29 11165 200 299689357 10.0.0.102 10.0.0.70 ffff880612e81c00 ffff8803c28a1b00 2 10 30 11166 200 273361203 10.0.0.102 10.0.0.70 172.31.1.2 ffff88061fba9800 ffff88061f8a3180 2 10 31 11167 200 273361145 10.0.0.102 10.0.0.70 ffff88031e6f1a00 ffff88031dbdb180 2 10 32 11168 200 273365974 10.0.0.102 10.0.0.70 192.168.11.2 """.strip() SCTP_EPS_DETAILS_NO = """ ENDPT SOCK STY SST LPORT UID INODE LADDRS ffff88017e0a0200 ffff880299f7fa00 2 10 11165 200 299689357 10.0.0.102 10.0.0.70 ffff880612e81c00 ffff8803c28a1b00 2 10 11166 200 273361203 10.0.0.102 10.0.0.70 172.31.1.2 ffff88061fba9800 ffff88061f8a3180 2 10 11167 200 273361145 10.0.0.102 10.0.0.70 ffff88031e6f1a00 ffff88031dbdb180 2 10 11168 200 273365974 10.0.0.102 10.0.0.70 192.168.11.2 """.strip() SCTP_EPS_DETAILS_DOC = """ ENDPT SOCK STY SST HBKT LPORT UID INODE LADDRS ffff88017e0a0200 ffff880299f7fa00 2 10 29 11165 200 299689357 10.0.0.102 10.0.0.70 ffff880612e81c00 ffff8803c28a1b00 2 10 30 11166 200 273361203 10.0.0.102 10.0.0.70 172.31.1.2 """.strip() SCTP_EPS_DETAILS_NO_2 = """ """.strip() SCTP_ASSOC = """ ASSOC SOCK STY SST ST HBKT ASSOC-ID TX_QUEUE RX_QUEUE UID INODE LPORT RPORT LADDRS <-> RADDRS HBINT INS OUTS MAXRT T1X T2X RTXC ffff88045ac7e000 ffff88062077aa00 2 1 4 1205 963 0 0 200 273361167 11567 11166 10.0.0.102 10.0.0.70 <-> *10.0.0.109 10.0.0.77 1000 2 2 10 0 0 0 ffff88061fbf2000 ffff88060ff92500 2 1 4 1460 942 0 0 200 273360669 11566 11167 10.0.0.102 10.0.0.70 <-> *10.0.0.109 10.0.0.77 1000 2 2 10 0 0 0 ffff8803217b9000 ffff8801c6321580 2 1 4 1675 977 0 0 200 273361369 11565 11168 10.0.0.102 10.0.0.70 192.168.11.2 <-> *10.0.0.109 10.0.0.77 1000 2 2 10 0 0 0 ffff8803db908000 ffff88061e4a00c0 2 1 4 2229 967 0 0 200 273361177 12067 11166 10.0.0.102 10.0.0.70 <-> *10.0.0.110 10.0.0.78 1000 2 2 10 0 0 0 ffff88062258f000 ffff88060fffaa40 2 1 4 2485 953 0 0 200 273360681 12066 11166 10.0.0.102 10.0.0.70 <-> *10.0.0.103 10.0.0.71 1000 2 2 10 0 0 0 ffff8801ce686000 ffff8801c7083ac0 2 1 4 2741 982 0 0 200 273361381 12065 11166 10.0.0.102 10.0.0.70 <-> *10.0.0.112 10.0.0.80 1000 2 2 10 0 0 0 ffff88031e1f4000 ffff8801c6fd9b00 2 1 4 7092 1005 0 0 200 273366011 11567 11167 10.0.0.102 10.0.0.70 <-> *10.0.0.111 10.0.0.79 1000 2 2 10 0 0 0 """.strip() SCTP_ASSOC_2 = """ ASSOC SOCK STY SST ST HBKT ASSOC-ID TX_QUEUE RX_QUEUE UID INODE LPORT RPORT LADDRS <-> RADDRS HBINT INS OUTS MAXRT T1X T2X RTXC ffff8804239ca000 ffff8804238c6040 2 1 4 3091 1 0 0 500 90293 37379 3868 10.0.200.114 10.0.201.114 2010:0010:0000:0200:0000:0000:0000:0114 2010:0010:0000:0201:0000:0000:0000:0114 <-> *10.0.100.94 10.0.101.94 2010:0010:0000:0100:0000:0000:0000:0094 2010:0010:0000:0101:0000:0000:0000:0094 1000 5 5 10 0 0 0 """.strip() SCTP_ASSOC_DOC = """ ASSOC SOCK STY SST ST HBKT ASSOC-ID TX_QUEUE RX_QUEUE UID INODE LPORT RPORT LADDRS <-> RADDRS HBINT INS OUTS MAXRT T1X T2X RTXC ffff88045ac7e000 ffff88062077aa00 2 1 4 1205 963 0 0 200 273361167 11567 11166 10.0.0.102 10.0.0.70 <-> *10.0.0.109 10.0.0.77 1000 2 2 10 0 0 0 ffff88061fbf2000 ffff88060ff92500 2 1 4 1460 942 0 0 200 273360669 11566 11167 10.0.0.102 10.0.0.70 <-> *10.0.0.109 10.0.0.77 1000 2 2 10 0 0 0 """.strip() SCTP_ASSOC_NO = """ """.strip() SCTP_ASSOC_NO_2 = """ SOCK STY SST ST HBKT ASSOC-ID TX_QUEUE RX_QUEUE UID INODE LPORT RPORT LADDRS RADDRS HBINT INS OUTS MAXRT T1X T2X RTXC ffff88045ac7e000 ffff88062077aa00 2 1 4 1205 963 0 0 200 273361167 11567 11166 10.0.0.102 10.0.0.70 *10.0.0.109 10.0.0.77 1000 2 2 10 0 0 0 """.strip() def test_sctp_eps(): sctp_info = SCTPEps(context_wrap(SCTP_EPS_DETAILS)) assert sorted(sctp_info.sctp_local_ports) == sorted(['11165', '11166', '11167', '11168']) assert sorted(sctp_info.sctp_local_ips) == sorted(['10.0.0.102', '10.0.0.70', '172.31.1.2', '192.168.11.2']) assert sctp_info.sctp_eps_ips == {'ffff88017e0a0200': ['10.0.0.102', '10.0.0.70'], 'ffff880612e81c00': ['10.0.0.102', '10.0.0.70', '172.31.1.2'], 'ffff88061fba9800': ['10.0.0.102', '10.0.0.70'], 'ffff88031e6f1a00': ['10.0.0.102', '10.0.0.70', '192.168.11.2']} assert len(sctp_info.search(local_port='11165')) == 1 def test_sctp_asc(): sctp_asc = SCTPAsc(context_wrap(SCTP_ASSOC)) assert sorted(sctp_asc.sctp_local_ports) == sorted(['11567', '11566', '11565', '12067', '12065', '12066']) assert sorted(sctp_asc.search(local_port='11565')) == sorted([{'init_chunks_send': '0', 'uid': '200', 'shutdown_chunks_send': '0', 'max_outstream': '2', 'tx_que': '0', 'inode': '273361369', 'hrtbt_intrvl': '1000', 'sk_type': '2', 'remote_addr': ['*10.0.0.109', '10.0.0.77'], 'data_chunks_retrans': '0', 'local_addr': ['10.0.0.102', '10.0.0.70', '192.168.11.2'], 'asc_id': '977', 'max_instream': '2', 'remote_port': '11168', 'asc_state': '4', 'max_retrans_atmpt': '10', 'sk_state': '1', 'socket': 'ffff8801c6321580', 'asc_struct': 'ffff8803217b9000', 'local_port': '11565', 'hash_bkt': '1675', 'rx_que': '0'}]) assert len(sctp_asc.search(local_port='11567')) == 2 assert sorted(sctp_asc.sctp_local_ips) == sorted(['10.0.0.102', '10.0.0.70', '192.168.11.2']) assert sorted(sctp_asc.sctp_remote_ips) == sorted(['*10.0.0.109', '10.0.0.77', '*10.0.0.110', '10.0.0.78', '*10.0.0.103', '10.0.0.71', '*10.0.0.112', '10.0.0.80', '*10.0.0.111', '10.0.0.79']) sctp_asc = SCTPAsc(context_wrap(SCTP_ASSOC_2)) assert sorted(sctp_asc.sctp_local_ips) == sorted(['10.0.200.114', '10.0.201.114', '2010:0010:0000:0200:0000:0000:0000:0114', '2010:0010:0000:0201:0000:0000:0000:0114']) assert sorted(sctp_asc.sctp_remote_ips) == sorted(['*10.0.100.94', '10.0.101.94', '2010:0010:0000:0100:0000:0000:0000:0094', '2010:0010:0000:0101:0000:0000:0000:0094']) def test_sctp_eps_exceptions(): with pytest.raises(ParseException) as exc: sctp_obj = SCTPEps(context_wrap(SCTP_EPS_DETAILS_NO)) assert sctp_obj is None # Just added to remove flake8 warnings assert 'Contents are not compatible to this parser' in str(exc) with pytest.raises(SkipException) as exc: sctp_obj = SCTPEps(context_wrap(SCTP_EPS_DETAILS_NO_2)) assert sctp_obj is None # Just added to remove flake8 warnings assert 'No Contents' in str(exc) def test_sctp_asc_exceptions(): with pytest.raises(ParseException) as exc: sctp_asc = SCTPAsc(context_wrap(SCTP_ASSOC_NO_2)) assert sctp_asc is None assert 'Contents are not compatible to this parser' in str(exc) with pytest.raises(SkipException) as exc: sctp_asc = SCTPAsc(context_wrap(SCTP_ASSOC_NO)) assert sctp_asc is None assert 'No Contents' in str(exc) def test_sctp_doc_examples(): env = { 'sctp_info': SCTPEps(context_wrap(SCTP_EPS_DETAILS_DOC)), 'sctp_asc': SCTPAsc(context_wrap(SCTP_ASSOC_DOC)) } failed, total = doctest.testmod(sctp, globs=env) assert failed == 0

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py

Python

test/kernel/test_kernel_linear_fxp_hub_compare.py

jingjieli95/UnarySim

775b38fa2d6b05a69fd73acb4766e50200a5cc37

[ "MIT" ]

17

2020-04-26T19:38:03.000Z

2022-02-23T02:05:08.000Z

test/kernel/test_kernel_linear_fxp_hub_compare.py

jingjieli95/UnarySim

775b38fa2d6b05a69fd73acb4766e50200a5cc37

[ "MIT" ]

3

2021-11-03T18:20:29.000Z

2022-02-11T16:30:16.000Z

test/kernel/test_kernel_linear_fxp_hub_compare.py

jingjieli95/UnarySim

775b38fa2d6b05a69fd73acb4766e50200a5cc37

[ "MIT" ]

9

2019-12-03T05:08:55.000Z

2022-01-04T20:24:55.000Z

# %% import torch from UnarySim.kernel.linear import * import matplotlib.pyplot as plt import time import math import numpy as np # %% device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # %% def test(rounding = "round", abs_err = True): ufc_err_min_list = [] ufc_err_max_list = [] ufc_err_mean_list = [] ufc_err_std_list = [] ofc_err_min_list = [] ofc_err_max_list = [] ofc_err_mean_list = [] ofc_err_std_list = [] ifc_err_min_list = [] ifc_err_max_list = [] ifc_err_mean_list = [] ifc_err_std_list = [] x_label = [] for bitwidth in range(6, 13): cycle = 2**(bitwidth-1) in_feature = 2 out_feature = 2**12 bias = False input = torch.cat(2*[(torch.arange(0, out_feature)/out_feature - 0.5).unsqueeze(1)], 1).to(device) input[:, 1] = 0. fc = torch.nn.Linear(in_feature, out_feature, bias=bias).to(device) fc.weight.data = torch.cat(2*[(torch.arange(0, out_feature)/out_feature - 0.5).unsqueeze(1)], 1).to(device) fc.weight.data[:, 1] = 0. fc_o = fc(input) ufc = HUBLinear(in_feature, out_feature, bias=bias, binary_weight=fc.weight.data, binary_bias=fc.bias, cycle=cycle, rounding=rounding).to(device) ufc_o = ufc(input) ofc = FxpLinear(in_feature, out_feature, bias=bias, binary_weight=fc.weight.data, binary_bias=fc.bias, bitwidth=bitwidth, keep_res="output", more_res="input", rounding=rounding).to(device) ofc_o = ofc(input) ifc = FxpLinear(in_feature, out_feature, bias=bias, binary_weight=fc.weight.data, binary_bias=fc.bias, bitwidth=bitwidth, keep_res="input", more_res="input", rounding=rounding).to(device) ifc_o = ifc(input) if abs_err is True: ufc_err = (ufc_o - fc_o) ofc_err = (ofc_o - fc_o) ifc_err = (ifc_o - fc_o) else: ufc_err = (ufc_o - fc_o) / fc_o ofc_err = (ofc_o - fc_o) / fc_o ifc_err = (ifc_o - fc_o) / fc_o ufc_err_min_list.append(np.nanmin(ufc_err.cpu().detach().numpy())) ufc_err_max_list.append(np.nanmax(ufc_err.cpu().detach().numpy())) ufc_err_mean_list.append(np.nanmean(np.abs(ufc_err.cpu().detach().numpy()))) ufc_err_std_list.append(np.nanstd(ufc_err.cpu().detach().numpy())) ofc_err_min_list.append(np.nanmin(ofc_err.cpu().detach().numpy())) ofc_err_max_list.append(np.nanmax(ofc_err.cpu().detach().numpy())) ofc_err_mean_list.append(np.nanmean(np.abs(ofc_err.cpu().detach().numpy()))) ofc_err_std_list.append(np.nanstd(ofc_err.cpu().detach().numpy())) ifc_err_min_list.append(np.nanmin(ifc_err.cpu().detach().numpy())) ifc_err_max_list.append(np.nanmax(ifc_err.cpu().detach().numpy())) ifc_err_mean_list.append(np.nanmean(np.abs(ifc_err.cpu().detach().numpy()))) ifc_err_std_list.append(np.nanstd(ifc_err.cpu().detach().numpy())) x_label.append(2**(bitwidth-1)) return ufc_err_min_list, ufc_err_max_list, ufc_err_mean_list, ufc_err_std_list, ofc_err_min_list, ofc_err_max_list, ofc_err_mean_list, ofc_err_std_list, ifc_err_min_list, ifc_err_max_list, ifc_err_mean_list, ifc_err_std_list, x_label # %% rounding = "round" abs_err = True ufc_err_min_list, ufc_err_max_list, ufc_err_mean_list, ufc_err_std_list, ofc_err_min_list, ofc_err_max_list, ofc_err_mean_list, ofc_err_std_list, ifc_err_min_list, ifc_err_max_list, ifc_err_mean_list, ifc_err_std_list, x_label = test(rounding, abs_err) print(ufc_err_mean_list) print(ufc_err_std_list) print() print(ofc_err_mean_list) print(ofc_err_std_list) print() print(ifc_err_mean_list) print(ifc_err_std_list) print() print(x_label) # %% import matplotlib.pyplot as plt import matplotlib import numpy as np font = {'family':'Times New Roman', 'size': 6} matplotlib.rc('font', **font) my_dpi = 300 fig_h = 1 fig_w = 3.3115 # construct some data like what you have: x = np.array([i for i in range(len(ufc_err_mean_list))]) means1 = np.array(ufc_err_mean_list) stds1 = np.array(ufc_err_std_list) mins1 = np.array(ufc_err_min_list) maxs1 = np.array(ufc_err_max_list) means2 = np.array(ofc_err_mean_list) stds2 = np.array(ofc_err_std_list) mins2 = np.array(ofc_err_min_list) maxs2 = np.array(ofc_err_max_list) means3 = np.array(ifc_err_mean_list) stds3 = np.array(ifc_err_std_list) mins3 = np.array(ifc_err_min_list) maxs3 = np.array(ifc_err_max_list) x_label = ['6-32', '7-64', '8-128', '9-256', '10-512', '11-1024', '12-2048'] width = 0.20 fig, ax = plt.subplots(figsize=(fig_w, fig_h)) ax.plot(x, means1, "-o", label="uSystolic", color="#7A81FF", ms=4) ax.fill_between(x, means1-stds1, means1+stds1, alpha=0.3, color="#7A81FF", edgecolor=None) ax.plot(x, means2, "-s", label="FXP-o-res", color="#FF7F7F", ms=4) ax.fill_between(x, means2-stds2, means2+stds2, alpha=0.3, color="#FF7F7F", edgecolor=None) ax.plot(x, means3, "-^", label="FXP-i-res", color="#D783FF", ms=4) ax.fill_between(x, means3-stds3, means3+stds3, alpha=0.3, color="#D783FF", edgecolor=None) ax.set_xticks(x) ax.set_xticklabels(x_label) ax.set_yscale('linear') ax.set_yticks([0, 0.01, 0.02]) ax.set_yticklabels(["0.00", "0.01", "0.02"]) ax.legend(loc="upper right", ncol=3, frameon=False) fig.tight_layout() plt.show() fig.savefig("test_kernel_linear_fxp_hub_compare_abs_err.pdf", bbox_inches='tight', dpi=my_dpi, pad_inches=0.02) # %% rounding = "round" abs_err = False ufc_err_min_list, ufc_err_max_list, ufc_err_mean_list, ufc_err_std_list, ofc_err_min_list, ofc_err_max_list, ofc_err_mean_list, ofc_err_std_list, ifc_err_min_list, ifc_err_max_list, ifc_err_mean_list, ifc_err_std_list, x_label = test(rounding, abs_err) print(ufc_err_mean_list) print(ufc_err_std_list) print() print(ofc_err_mean_list) print(ofc_err_std_list) print() print(ifc_err_mean_list) print(ifc_err_std_list) print() print(x_label) # %% import matplotlib.pyplot as plt import matplotlib import numpy as np font = {'family':'Times New Roman', 'size': 6} matplotlib.rc('font', **font) my_dpi = 300 fig_h = 1 fig_w = 3.3115 # construct some data like what you have: x = np.array([i for i in range(len(ufc_err_mean_list))]) means1 = np.array(ufc_err_mean_list) stds1 = np.array(ufc_err_std_list) mins1 = np.array(ufc_err_min_list) maxs1 = np.array(ufc_err_max_list) means2 = np.array(ofc_err_mean_list) stds2 = np.array(ofc_err_std_list) mins2 = np.array(ofc_err_min_list) maxs2 = np.array(ofc_err_max_list) means3 = np.array(ifc_err_mean_list) stds3 = np.array(ifc_err_std_list) mins3 = np.array(ifc_err_min_list) maxs3 = np.array(ifc_err_max_list) x_label = ['6-32', '7-64', '8-128', '9-256', '10-512', '11-1024', '12-2048'] width = 0.20 fig, ax = plt.subplots(figsize=(fig_w, fig_h)) ax.plot(x, means1, "-o", label="uSystolic", color="#7A81FF", ms=4) ax.fill_between(x, means1-stds1, means1+stds1, alpha=0.3, color="#7A81FF", edgecolor=None) ax.plot(x, means2, "-s", label="FXP-o-res", color="#FF7F7F", ms=4) ax.fill_between(x, means2-stds2, means2+stds2, alpha=0.3, color="#FF7F7F", edgecolor=None) ax.plot(x, means3, "-^", label="FXP-i-res", color="#D783FF", ms=4) ax.fill_between(x, means3-stds3, means3+stds3, alpha=0.3, color="#D783FF", edgecolor=None) ax.set_xticks(x) ax.set_xticklabels(x_label) ax.set_yscale('linear') ax.set_yticks([0, 0.4, 0.8]) ax.set_yticklabels(["0.00", "0.40", "0.80"]) # ax.legend(loc="upper right", ncol=3, frameon=False) fig.tight_layout() plt.show() fig.savefig("test_kernel_linear_fxp_hub_compare_rel_err.pdf", bbox_inches='tight', dpi=my_dpi, pad_inches=0.02) # %%

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null

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6

829b1c227b83ecabfae2994759613995e47d79a5

83

py

Python

tests/test_sampleMangler.py

cowanml/samplemangler

cd2b772beb74cf5d2106cd67e74e95ebafc74735

[ "BSD-3-Clause" ]

null

tests/test_sampleMangler.py

cowanml/samplemangler

cd2b772beb74cf5d2106cd67e74e95ebafc74735

[ "BSD-3-Clause" ]

null

tests/test_sampleMangler.py

cowanml/samplemangler

cd2b772beb74cf5d2106cd67e74e95ebafc74735

[ "BSD-3-Clause" ]

null

from sampleMangler.__main__ import main def test_main(): assert main([]) == 0

16.6

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82b441794f9e72c5af1f92de572a2106ef9043ce

206

py

Python

src/isaw.facultycv/isaw/facultycv/interfaces/peopleview.py

isawnyu/isaw.web

604499f9fa55d1ce9698ca05f85ddb54a88f1cab

[ "CC-BY-3.0" ]

null

src/isaw.facultycv/isaw/facultycv/interfaces/peopleview.py

isawnyu/isaw.web

604499f9fa55d1ce9698ca05f85ddb54a88f1cab

[ "CC-BY-3.0" ]

405

2015-03-12T18:20:25.000Z

2022-03-07T18:44:16.000Z

src/isaw.facultycv/isaw/facultycv/interfaces/peopleview.py

isawnyu/isaw.web

604499f9fa55d1ce9698ca05f85ddb54a88f1cab

[ "CC-BY-3.0" ]

1

2016-11-07T21:18:49.000Z

from zope.interface import Interface class IPeopleView(Interface): """People View. """

41.2

147

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6

7d562a20da4ff16ea89ead622e2fcd5bfd4feed6

35

py

Python

my_test_proj/__init__.py

ColumbiaOSS/4995-demo

59ed33c6640935387b6e89b4db492ba92a2457a1

[ "Apache-2.0" ]

1

2021-05-21T11:44:08.000Z

my_test_proj/__init__.py

ColumbiaOSS/4995-demo

59ed33c6640935387b6e89b4db492ba92a2457a1

[ "Apache-2.0" ]

1

2020-09-29T18:50:34.000Z

2020-10-04T22:57:39.000Z

my_test_proj/__init__.py

ColumbiaOSS/4995-demo

59ed33c6640935387b6e89b4db492ba92a2457a1

[ "Apache-2.0" ]

8

2020-09-17T22:08:10.000Z

2020-12-26T08:23:51.000Z

from .foo import inc # noqa: F401

17.5

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0

6

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179

py

Python

saleor/graphql/channel/resolvers.py

fairhopeweb/saleor

9ac6c22652d46ba65a5b894da5f1ba5bec48c019

[ "CC-BY-4.0" ]

15,337

2015-01-12T02:11:52.000Z

2021-10-05T19:19:29.000Z

saleor/graphql/channel/resolvers.py

fairhopeweb/saleor

9ac6c22652d46ba65a5b894da5f1ba5bec48c019

[ "CC-BY-4.0" ]

7,486

2015-02-11T10:52:13.000Z

2021-10-06T09:37:15.000Z

saleor/graphql/channel/resolvers.py

aminziadna/saleor

2e78fb5bcf8b83a6278af02551a104cfa555a1fb

[ "CC-BY-4.0" ]

5,864

2015-01-16T14:52:54.000Z

2021-10-05T23:01:15.000Z

from ...channel import models def resolve_channel(id): return models.Channel.objects.filter(id=id).first() def resolve_channels(): return models.Channel.objects.all()

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py

Python

tests/output/test_types.py

youngjun0627/backend.ai-client-py

be7c174ab73e112fdb8be61e6affc20fc72f7d59

[ "MIT" ]

7

2019-01-18T08:08:42.000Z

2022-02-10T00:36:24.000Z

tests/output/test_types.py

youngjun0627/backend.ai-client-py

be7c174ab73e112fdb8be61e6affc20fc72f7d59

[ "MIT" ]

179

2017-09-07T04:54:44.000Z

2022-03-29T11:30:47.000Z

tests/output/test_types.py

youngjun0627/backend.ai-client-py

be7c174ab73e112fdb8be61e6affc20fc72f7d59

[ "MIT" ]

13

2017-09-08T05:37:44.000Z

2021-09-14T23:35:31.000Z

from ai.backend.client.output.types import FieldSet, FieldSpec def test_fieldspec_init(): f = FieldSpec("key_foo") assert f.field_ref == "key_foo" assert f.field_name == "key_foo" assert f.humanized_name == "Key Foo" assert f.alt_name == "key_foo" assert not f.subfields f = FieldSpec("key_foo", "Foo") assert f.field_ref == "key_foo" assert f.field_name == "key_foo" assert f.humanized_name == "Foo" assert f.alt_name == "key_foo" assert not f.subfields fs = FieldSet([f]) assert fs["key_foo"] == f f = FieldSpec("key_foo", "Foo", alt_name="key_fuu") assert f.field_ref == "key_foo" assert f.field_name == "key_foo" assert f.humanized_name == "Foo" assert f.alt_name == "key_fuu" assert not f.subfields fs = FieldSet([f]) assert fs["key_fuu"] == f f = FieldSpec("key_foo { bar }") assert f.field_ref == "key_foo { bar }" assert f.field_name == "key_foo" assert f.humanized_name == "Key Foo" assert not f.subfields # not initialized in this case f = FieldSpec("key_foo", subfields=FieldSet([ FieldSpec("bar"), FieldSpec("baz", alt_name="bbb"), ])) assert f.field_ref == "key_foo { bar baz }" assert f.field_name == "key_foo" assert f.humanized_name == "Key Foo" assert f.subfields["bar"].field_ref == "bar" assert f.subfields["bbb"].field_ref == "baz" f = FieldSpec("key_foo", subfields=FieldSet([ FieldSpec("bar", subfields=FieldSet([ FieldSpec("kaz"), ])), ])) assert f.field_ref == "key_foo { bar { kaz } }" assert f.field_name == "key_foo" assert f.humanized_name == "Key Foo" assert f.subfields["bar"].field_ref == "bar { kaz }"

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py

Python

examples/thresholds/__init__.py

sonyccd/simple-cv-examples

21be481996dd4643bda16abd9831a741e2904323

[ "MIT" ]

null

examples/thresholds/__init__.py

sonyccd/simple-cv-examples

21be481996dd4643bda16abd9831a741e2904323

[ "MIT" ]

null

examples/thresholds/__init__.py

sonyccd/simple-cv-examples

21be481996dd4643bda16abd9831a741e2904323

[ "MIT" ]

null

from .automatic import *

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py

Python

codes/comparison/computeAnalyticalSolutionISO.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

1

2021-06-18T14:52:03.000Z

codes/comparison/computeAnalyticalSolutionISO.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

1

2019-01-07T13:11:11.000Z

codes/comparison/computeAnalyticalSolutionISO.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

null

#!/usr/bin/python import numpy as np import pdb def computeAnalyticalSolutionISO(x,L,c,t,vd,HEL,E,H,rho): #Definition and initialization of arrays and data lam=mu=1. Sexact = np.zeros(len(x)) EPexact = np.zeros(len(x)) Vexact = np.zeros(len(x)) HT = E*H/(E+H)#(2.0*lam*mu+(lam+2.0*mu)*(mu+H))/(3.0*mu+H) cp = np.sqrt(HT/rho) ##Comment: The analytical solution is defined according to the current time t #times (and positions) associated to wave interactions t1 = (L/2.0)/c t2 = L/(c+cp);x2p = L+(t2/2.0)*(cp-c);x2m = (t2/2.0)*(c-cp) t3 = (L-x2p)/c+t2 t4 = (x2p-x2m)/(2.0*c) +t2 t5 = L/(4.0*c) + (t3+t4)/2.0 ; x5p = 3.0*L/4.0 + c*(t3-t4)/2.0 x5m = L/2.0- (x5p-L/2.0) t6 = (x5p-L/2.0)/c + t5 t7 = (t5+t6)/2.0 + (x5p-(L/2.0))/(2.0*cp) x7p = (x5p+(L/2.0))/2.0 + cp*(t5-t6)/2.0 x7m = L/2.0- (x7p-L/2.0) t8 = (L/2.0-x7m)/cp + t7 tint = (L/2.0-x7m)/cp + t8 t9 = (L-x5p)/c+t5 #Stress and velocity states v1 = HEL/(rho*c)-vd ; v1p = -v1 ; v2 = 0.0 v3 = 2.0*HEL/(rho*c) - vd ; v3p = -v3 S2 = HEL*(1.0-(cp/c))+rho*cp*vd v4 = S2/(2.0*rho*c) + v3/2.0 ; v4p = -v4 S4 = (S2-(rho*c*v3))/2.0 S5 = -2.0*HEL+rho*c*vd S7 = -HEL*(1.0+cp/c)+rho*cp*vd-(H*(S2-HEL)*(lam+2.0*mu)/(2.0*mu**2.0))*((lam+2.0*mu)/HT - 1.0) v4 = S2/(2.0*rho*c) + v3/2.0 ; v6 = S4/(rho*c) + v4 ; v6p = -v6 ; v5 = 0.0 v7 = S7/(rho*c)+v6 ; v7p = (S5-S7)/(rho*c) ; v7s = -v7p ; v7t = -v7 S8 = S7 + rho*cp*(v7p-v7)/2.0 v8 = (v7+v7p)/2.0 ; v8p = -v8 S9 = S7 - rho*cp*v7p ; v9 = (v7p+v7s)/2.0 S10 = (S8+S9-rho*cp*v8)/2.0 v10 = (S9-S8)/(2.0*rho*cp) + v8/2.0 ; v10p = -v10 S11 = S10 - rho*cp*v10 ; v11 = 0.0 #Plastic strain states # EP2 = ((S2 - HEL)/(2.0*mu))*(((lam+2.0*mu)/HT)-1.0) # EP8 = EP2 + ((S8-S7)/(2.0*mu))*((lam+2.0*mu)/HT - 1.0) # EP9 = EP2 + ((S9-S7)/(2.0*mu))*((lam+2.0*mu)/HT - 1.0) # EP10int = EP9 + ((S10-S9)/(2.0*mu))*((lam+2.0*mu)/HT - 1.0) # EP10ext = EP8 + ((S10-S8)/(2.0*mu))*((lam+2.0*mu)/HT - 1.0) # EP11 = EP10int + ((S11-S10)/(2.0*mu))*((lam+2.0*mu)/HT - 1.0) #pdb.set_trace() if (t<t1): for i,valx in enumerate(x): if ((valx>=(-c*t+(L/2.0))) and (valx<=(-cp*t+(L/2.0)))): Sexact[i] = HEL Vexact[i] = v1 elif ((valx>=(cp*t+(L/2.0))) and (valx<=(c*t+(L/2.0)))): Sexact[i] = HEL Vexact[i] = v1p elif ((valx>=(-cp*t+(L/2.0))) and (valx<=(cp*t+(L/2.0)))): Sexact[i] = S2 Vexact[i] = v2 #EPexact[i] = EP2 elif (valx<(-c*t+(L/2.0))): Vexact[i] = -vd elif (valx>(c*t+(L/2.0))): Vexact[i] = vd elif (t>=t1) and (t<t2): for i,valx in enumerate(x): if ((valx>(c*(t-t1))) and (valx<(-cp*t+(L/2.0)))): Sexact[i] = HEL Vexact[i] = v1 elif ((valx<(L-c*(t-t1))) and (valx>(cp*t+(L/2.0)))): Sexact[i] = HEL Vexact[i] = v1p elif ((valx>=(-cp*t+(L/2.0))) and (valx<=(cp*t+(L/2.0)))): Sexact[i] = S2 Vexact[i] = v2 #EPexact[i] = EP2 elif (valx<(c*(t-t1))): Vexact[i] = v3 elif (valx>(L-c*(t-t1))): Vexact[i] = v3p elif ((t>=t2) and (t<t3)): for i,valx in enumerate(x): if ((valx>x2m-c*(t-t2)) and (valx<x2m+c*(t-t2))): Sexact[i] = S4 Vexact[i] = v4 elif ((valx<x2p+c*(t-t2)) and (valx>x2p-c*(t-t2))): Sexact[i] = S4 Vexact[i] = v4p elif (valx>x2m+c*(t-t2)) and (valx<x2p-c*(t-t2)): Sexact[i] = S2 Vexact[i] = v2 elif (valx<x2m-c*(t-t2)): Vexact[i] = v3 elif (valx>x2p+c*(t-t2)): Vexact[i] = v3p # if ((valx>=x2m) and (valx<=x2p)): # EPexact[i] = EP2 elif ((t>=t3) and (t<t4)): for i,valx in enumerate(x): if ((valx>c*(t-t3)) and (valx<x2m+c*(t-t2))): Sexact[i] = S4 Vexact[i] = v4 elif ((valx<L-c*(t-t3)) and (valx>x2p-c*(t-t2))): Sexact[i] = S4 Vexact[i] = v4p elif (valx>x2m+c*(t-t2)) and (valx<x2p-c*(t-t2)): Sexact[i] = S2 Vexact[i] = v2 elif (valx<c*(t-t3)): Vexact[i] = v6 elif (valx>L-c*(t-t3)): Vexact[i] = v6p # if ((valx>=x2m) and (valx<=x2p)): # EPexact[i] = EP2 elif ((t>=t4) and (t<t5)): for i,valx in enumerate(x): if ((valx>c*(t-t3)) and (valx<L/2.0-c*(t-t4))): Sexact[i] = S4 Vexact[i] = v4 elif ((valx<L-c*(t-t3)) and (valx>L/2.0+c*(t-t4))): Sexact[i] = S4 Vexact[i] = v4p elif (valx>L/2.0-c*(t-t4)) and (valx<L/2.0+c*(t-t4)): Sexact[i] = S5 Vexact[i] = v5 elif (valx<c*(t-t3)): Vexact[i] = v6 elif (valx>L-c*(t-t3)): Vexact[i] = v6p # if ((valx>=x2m) and (valx<=x2p)): # EPexact[i] = EP2 elif ((t>=t5) and (t<t6)): for i,valx in enumerate(x): if ((valx>x5m-c*(t-t5)) and (valx<x5m-cp*(t-t5))): Sexact[i] = S7 Vexact[i] = v7 elif ((valx>x5m+cp*(t-t5)) and (valx<x5m+c*(t-t5))): Sexact[i] = S7 Vexact[i] = v7p elif ((valx>x5p-c*(t-t5)) and (valx<x5p-cp*(t-t5))): Sexact[i] = S7 Vexact[i] = v7s elif ((valx>x5p+cp*(t-t5)) and (valx<x5p+c*(t-t5))): Sexact[i] = S7 Vexact[i] = v7t elif ((valx>x5m-cp*(t-t5)) and (valx<x5m+cp*(t-t5))): Sexact[i] = S8 Vexact[i] = v8 elif ((valx>x5p-cp*(t-t5)) and (valx<x5p+cp*(t-t5))): Sexact[i] = S8 Vexact[i] = v8p elif ((valx>x5m+c*(t-t5)) and (valx<x5p-c*(t-t5))): Sexact[i] = S5 Vexact[i] = v5 elif (valx<x5m-c*(t-t5)): Vexact[i] = v6 elif (valx>x5p+c*(t-t5)): Vexact[i] = v6p # if (((valx>x5m-cp*(t-t5)) and (valx<x5m+cp*(t-t5))) \ # or ((valx>x5p-cp*(t-t5)) and (valx<x5p+cp*(t-t5)))): # EPexact[i] = EP8 # elif (((valx>=x2m) and (valx<=x5m-cp*(t-t5))) \ # or ((valx>x5m+cp*(t-t5)) and (valx<x5p-cp*(t-t5))) \ # or ((valx>x5p+cp*(t-t5)) and (valx<=x2p))): # EPexact[i] = EP2 elif ((t>=t6) and (t<t7)): for i,valx in enumerate(x): if ((valx>x5m-c*(t-t5)) and (valx<x5m-cp*(t-t5))): Sexact[i] = S7 Vexact[i] = v7 elif ((valx>x5m+cp*(t-t5)) and (valx<L/2.0-cp*(t-t6))): Sexact[i] = S7 Vexact[i] = v7p elif ((valx>=L/2.0-cp*(t-t6)) and (valx<=L/2.0+cp*(t-t6))): Sexact[i] = S9 Vexact[i] = v9 elif ((valx>L/2.0+cp*(t-t6)) and (valx<x5p-cp*(t-t5))): Sexact[i] = S7 Vexact[i] = v7s elif ((valx>x5p+cp*(t-t5)) and (valx<x5p+c*(t-t5))): Sexact[i] = S7 Vexact[i] = v7t elif ((valx>x5m-cp*(t-t5)) and (valx<x5m+cp*(t-t5))): Sexact[i] = S8 Vexact[i] = v8 elif ((valx>x5p-cp*(t-t5)) and (valx<x5p+cp*(t-t5))): Sexact[i] = S8 Vexact[i] = v8p elif (valx<x5m-c*(t-t5)): Vexact[i] = v6 elif (valx>x5p+c*(t-t5)): Vexact[i] = v6p # if (((valx>x5m-cp*(t-t5)) and (valx<x5m+cp*(t-t5))) \ # or ((valx>x5p-cp*(t-t5)) and (valx<x5p+cp*(t-t5)))): # EPexact[i] = EP8 # elif ((valx>=L/2.0-cp*(t-t6)) and (valx<=L/2.0+cp*(t-t6))): # EPexact[i] = EP9 # elif (((valx>=x2m) and (valx<=x5m-cp*(t-t5))) \ # or ((valx>=x5m+cp*(t-t5)) and (valx<L/2.0-cp*(t-t6))) \ # or ((valx>L/2.0+cp*(t-t6)) and (valx<=x5p-cp*(t-t5))) \ # or ((valx>x5p+cp*(t-t5)) and (valx<=x2p))): # EPexact[i] = EP2 elif ((t>=t7) and (t<t8)): for i,valx in enumerate(x): if ((valx>x5m-c*(t-t5)) and (valx<x5m-cp*(t-t5))): Sexact[i] = S7 Vexact[i] = v7 elif ((valx>x5p+cp*(t-t5)) and (valx<x5p+c*(t-t5))): Sexact[i] = S7 Vexact[i] = v7t elif ((valx>=x7m+cp*(t-t7)) and (valx<x7p-cp*(t-t7))): Sexact[i] = S9 Vexact[i] = v9 elif ( valx>x7m-cp*(t-t7)) and (valx<x7m+cp*(t-t7)): Sexact[i] = S10 Vexact[i] = v10 elif ( valx>x7p-cp*(t-t7)) and (valx<x7p+cp*(t-t7)): Sexact[i] = S10 Vexact[i] = v10p elif ((valx>x5m-cp*(t-t5)) and (valx<x7m-cp*(t-t7))): Sexact[i] = S8 Vexact[i] = v8 elif ((valx>x7p+cp*(t-t7)) and (valx<x5p+cp*(t-t5))): Sexact[i] = S8 Vexact[i] = v8p elif (valx<x5m-c*(t-t5)): Vexact[i] = v6 elif (valx>x5p+c*(t-t5)): Vexact[i] = v6p # if (((valx>x5m-cp*(t-t5)) and (valx<x7m-cp*(t-t7))) \ # or ((valx>x7p+cp*(t-t7)) and (valx<x5p+cp*(t-t5)))): # EPexact[i] = EP8 # elif (((valx >x2m) and (valx<=x5m-cp*(t-t5))) \ # or ((valx>=x5p+cp*(t-t5)) and (valx<x2p))): # EPexact[i] = EP2 # elif ((valx>x7m+cp*(t-t7)) and (valx<x7p-cp*(t-t7))): # EPexact[i] = EP9 # elif ((( valx>x7m-cp*(t-t7)) and (valx<=x7m)) \ # or (( valx>=x7p) and (valx<x7p+cp*(t-t7)))): # EPexact[i] = EP10ext # elif ((( valx>x7m) and (valx<x7m+cp*(t-t7))) \ # or (( valx>x7p-cp*(t-t7)) and (valx<=x7p))): # EPexact[i] = EP10int elif ((t>=t8) and (t<t9)): for i,valx in enumerate(x): if ((valx>x5m-c*(t-t5)) and (valx<x5m-cp*(t-t5))): Sexact[i] = S7 Vexact[i] = v7 elif ((valx>x5p+cp*(t-t5)) and (valx<x5p+c*(t-t5))): Sexact[i] = S7 Vexact[i] = v7t elif ((valx>x5m-cp*(t-t5)) and (valx<x7m-cp*(t-t7))): Sexact[i] = S8 Vexact[i] = v8 elif ((valx>x7p+cp*(t-t7)) and (valx<x5p+cp*(t-t5))): Sexact[i] = S8 Vexact[i] = v8p elif (valx<x5m-c*(t-t5)): Vexact[i] = v6 elif (valx>x5p+c*(t-t5)): Vexact[i] = v6p elif ( valx>x7m-cp*(t-t7)) and (valx<L/2.0-cp*(t-t8)): Sexact[i] = S10 Vexact[i] = v10 elif ( valx>L/2.0+cp*(t-t8)) and (valx<x7p+cp*(t-t7)): Sexact[i] = S10 Vexact[i] = v10p elif (valx>=L/2.0-cp*(t-t8)) and (valx<=L/2.0+cp*(t-t8)): Sexact[i] = S11 Vexact[i] = v11 # # if (((valx>x5m-cp*(t-t5)) and (valx<x7m-cp*(t-t7))) \ # or ((valx>x7p+cp*(t-t7)) and (valx<x5p+cp*(t-t5)))): # EPexact[i] = EP8 # elif (((valx >x2m) and (valx<=x5m-cp*(t-t5))) \ # or ((valx>=x5p+cp*(t-t5)) and (valx<x2p))): # EPexact[i] = EP2 # elif ((valx>=L/2.0-cp*(t-t8)) and (valx<=L/2.0+cp*(t-t8))): # EPexact[i] = EP11 # elif (( valx>x7m-cp*(t-t7) and (valx<L/2.0-cp*(t-t8))) \ # or (((valx>L/2.0+cp*(t-t8))) and (valx<x7p+cp*(t-t7)))): # EPexact[i] = EP10ext return Sexact,EPexact,Vexact

42.564014

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6

8183944adf69274649908b7664dd19530dd5bf14

87

py

Python

easilyb/commands/__init__.py

xaled/easilyb

cdb5f738205f700b37e03c50d04061a2d1e730cc

[ "MIT" ]

null

easilyb/commands/__init__.py

xaled/easilyb

cdb5f738205f700b37e03c50d04061a2d1e730cc

[ "MIT" ]

null

easilyb/commands/__init__.py

xaled/easilyb

cdb5f738205f700b37e03c50d04061a2d1e730cc

[ "MIT" ]

null

from easilyb.commands._commands import run_command_ex1, run_command, get_command_output

87

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6

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157

py

Python

spiketools/utils/__init__.py

CamHolman/spiketools

56c37a50413a015cfa9c75725cbe7d4ef54968a5

[ "Apache-2.0" ]

null

spiketools/utils/__init__.py

CamHolman/spiketools

56c37a50413a015cfa9c75725cbe7d4ef54968a5

[ "Apache-2.0" ]

null

spiketools/utils/__init__.py

CamHolman/spiketools

56c37a50413a015cfa9c75725cbe7d4ef54968a5

[ "Apache-2.0" ]

null

"""Utility functions.""" from .spikes import restrict_range from .utils import set_random_seed from .data import get_value_by_time, get_value_by_time_range

26.166667

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6

81b3b5441ee6c54038825039812f063e8ef11bb8

170,121

py

Python

au-zone/nnef_converter/tensorflow/tf_converter.py

asdor/NNEF-Tools

e84c3db29c1bffbd1938d40a10765badc0848606

[ "Apache-2.0" ]

null

au-zone/nnef_converter/tensorflow/tf_converter.py

asdor/NNEF-Tools

e84c3db29c1bffbd1938d40a10765badc0848606

[ "Apache-2.0" ]

null

au-zone/nnef_converter/tensorflow/tf_converter.py

asdor/NNEF-Tools

e84c3db29c1bffbd1938d40a10765badc0848606

[ "Apache-2.0" ]

null

# Copyright (c) 2018 The Khronos Group Inc. # Copyright (c) 2018 Au-Zone Technologies Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import math import logging import textwrap import collections import networkx as nx import numpy as np from ..common.importer_exporter import ImporterExporter from ..common.nnef_data import * #NNEFTensor, TensorDataFile from ..common.nnef_converter import * from ..common.nnef_graph import * from ..common import nnef_node as node from .core.framework import graph_pb2 from .core.framework import attr_value_pb2 from .core.framework.node_def_pb2 import NodeDef class TensorflowLogger(object): single_line_sep = "---------------------------------------------------------------------------------------------------------------------------------" double_line_sep = "====================================================================================================" def __init__(self): super(TensorflowLogger, self).__init__() self.logger = logging.getLogger('nnef_convert') def log_tf_node_info(self, tfnode, inputs, attrs): title = "Importing Tensorflow Node: " preferredWidth = 250 wrapper = textwrap.TextWrapper(initial_indent=title, width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(self.single_line_sep) self.logger.info(wrapper.fill("Name \t%s"%(tfnode.name))) wrapper = textwrap.TextWrapper(initial_indent=' ' * len(title), width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(wrapper.fill("Op \t%s"%(tfnode.op))) unused_input_found = False used_input_found = False unused_attribute_found = False used_attribute_found = False if inputs is not None and tfnode.input is not None: for cnt, input_item in enumerate(tfnode.input): if cnt in inputs.values(): if not used_input_found: used_input_found = True self.logger.info(wrapper.fill("")) self.logger.info(wrapper.fill("Used Inputs:")) self.logger.info(wrapper.fill("\t%s"%(input_item))) for cnt, input_item in enumerate(tfnode.input): if cnt not in inputs.values(): if not unused_input_found: unused_input_found = True self.logger.info(wrapper.fill("")) self.logger.info(wrapper.fill("Unused Inputs:")) self.logger.info(wrapper.fill("\t%s"%(input_item))) if attrs is not None and tfnode.attr is not None: for key, value in tfnode.attr.items(): if key in attrs: if not used_attribute_found: used_attribute_found = True self.logger.info(wrapper.fill("")) self.logger.info(wrapper.fill("Used Attributes:")) if self.log_level == 'debug': self.logger.debug(wrapper.fill("\t'%s': %s" % (key, value))) else: self.logger.info(wrapper.fill("\t'%s'" % key)) for key, value in tfnode.attr.items(): if key not in attrs: if not unused_attribute_found: unused_attribute_found = True self.logger.info(wrapper.fill("")) self.logger.info(wrapper.fill("Unused Attributes:")) if self.log_level == 'debug': self.logger.debug(wrapper.fill("\t'%s': %s" % (key, value))) else: self.logger.info(wrapper.fill("\t'%s'" % key)) def print_msg_nodeop_nodename(self, title, op, name, level="info"): preferred_width = 250 wrapper = textwrap.TextWrapper(initial_indent=title, width=preferred_width, subsequent_indent=' ' * len(title)) if level == "debug": log_fct = self.logger.debug elif level == "warning": log_fct = self.logger.warning elif level == "error": log_fct = self.logger.error elif level == "critical": log_fct = self.logger.critical else: log_fct = self.logger.info log_fct(self.single_line_sep) log_fct(wrapper.fill("Name \t%s"%(name))) wrapper = textwrap.TextWrapper(initial_indent=' ' * len(title), width=preferred_width, subsequent_indent=' ' * len(title)) log_fct(wrapper.fill("Op \t%s"%(op))) def log_removing_node(self, nnef_node): title = "Removing Node From Pool: " preferredWidth = 250 wrapper = textwrap.TextWrapper(initial_indent=title, width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(self.single_line_sep) self.logger.info(wrapper.fill("Name \t%s"%(nnef_node.name))) wrapper = textwrap.TextWrapper(initial_indent=' ' * len(title), width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(wrapper.fill("Op \t%s"%(nnef_node.op))) def log_skipping_nodes(self, tfnode): self.print_msg_nodeop_nodename("Skipping Op: ", tfnode.op, tfnode.name) title = "Skipping Tensorflow Node: " preferredWidth = 250 wrapper = textwrap.TextWrapper(initial_indent=title, width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(self.single_line_sep) self.logger.info(wrapper.fill("Name \t%s"%(tfnode.name))) wrapper = textwrap.TextWrapper(initial_indent=' ' * len(title), width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(wrapper.fill("Op \t%s"%(tfnode.op))) def log_unsupported_nodes(self, tfnode): title = "Unsupported Tensorflow Node: " preferredWidth = 250 wrapper = textwrap.TextWrapper(initial_indent=title, width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(self.single_line_sep) self.logger.info(wrapper.fill("Name \t%s"%(tfnode.name))) wrapper = textwrap.TextWrapper(initial_indent=' ' * len(title), width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(wrapper.fill("Op \t%s"%(tfnode.op))) def log_total_conversions(self): title = "Finished Converting Model: " preferredWidth = 250 wrapper = textwrap.TextWrapper(initial_indent=title, width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(self.double_line_sep) self.logger.info(wrapper.fill("Total Tensorflow Nodes \t%s"%(str(self.total)))) wrapper = textwrap.TextWrapper(initial_indent=' ' * len(title), width=preferredWidth, subsequent_indent=' ' * len(title)) self.logger.info(wrapper.fill("Successfully Converted \t%s"%(str(self.successful)))) self.logger.info(wrapper.fill("Nodes in Graph \t%s"%(str(len(self.node_pool.keys())-self.removed_nodes)))) def convert_format(convert_list, in_format='nhwc', out_format='nchw'): in_format = in_format.lower() out_format = out_format.lower() in_n_loc = in_format.find('n') out_n_loc = out_format.find('n') in_c_loc = in_format.find('c') out_c_loc = out_format.find('c') n = convert_list[in_n_loc] c = convert_list[in_c_loc] sizes = [] for i in range(len(convert_list)): if(i != in_n_loc and i != in_c_loc): sizes.append(convert_list[i]) out_list = [None]*len(convert_list) out_list[out_n_loc] = n out_list[out_c_loc] = c j = 0 for i in range(len(out_list)): if(i != out_n_loc and i != out_c_loc): out_list[i] = sizes[j] j += 1 return out_list class TensorflowImporter(TensorflowLogger, ImporterExporter): def __init__(self, input_model, input_nodes, output_nodes, log_level='info'): super(TensorflowImporter, self).__init__() self.node_pool = collections.OrderedDict() self.input_model = input_model self.log_level = log_level self.input_nodes = {} self.output_nodes = {} self.name_convs = {} self.start_format = None self.start_length = 0 self.successful = 0 self.total = 0 self.removed_nodes = 0 self.graph = super(TensorflowImporter, self).openProtobuf(self.input_model, graph_pb2.GraphDef()) i = 1 if input_nodes is not None: for in_node_str in input_nodes.split(','): if len(input_nodes.split(',')) == 1: self.input_nodes[in_node_str] = "input" else: self.input_nodes[in_node_str] = "input" + str(i) i += 1 else: input_nodes = [] for tfnode in self.graph.node: if hasattr(tfnode, 'op'): if tfnode.op == 'Placeholder': input_nodes.append(tfnode.name) if len(input_nodes) == 1: self.input_nodes[input_nodes[0]] = "input" else: for i in range(len(input_nodes)): self.input_nodes[input_nodes[i]] = "input" + str(i+1) i = 1 if output_nodes is not None: for out_node_str in output_nodes.split(','): if len(output_nodes.split(',')) == 1: self.output_nodes[out_node_str] = "output" else: self.output_nodes[out_node_str] = "output" + str(i) i += 1 else: #Unable to use single loop for case of out of order nodes (MobileNetV2 from Model Zoo) output_nodes = [] for tfnode in self.graph.node: if hasattr(tfnode, 'op'): output_nodes.append(tfnode.name) for tfnode in self.graph.node: if hasattr(tfnode, 'op'): for input_val in tfnode.input: if input_val in output_nodes: output_nodes.remove(input_val) if len(output_nodes) == 1: self.output_nodes[output_nodes[0]] = "output" else: for i in range(len(output_nodes)): self.output_nodes[output_nodes[i]] = "output" + str(i+1) def run(self): self.nxgraph = nx.OrderedDiGraph() self.create_nodes() self.log_total_conversions() input_nodes = self.get_input_nodes() output_nodes = self.get_output_nodes() return NNEFGraph(os.path.basename(self.input_model).split('.')[0], input_nodes, output_nodes, pre_compile_callback=self.pre_compile_callback, post_compile_callback=self.post_compile_callback, node_pool=self.node_pool) def get_input_nodes(self): input_node_list = [] if 'input' in self.node_pool.keys(): input_node_list.append(self.get_node_from_pool_by_name('input', get_orig=True)) else: i = 1 while 'input' + str(i) in self.node_pool.keys(): input_node_list.append(self.get_node_from_pool_by_name('input'+str(i), get_orig=True)) i += 1 return input_node_list def get_output_nodes(self): output_node_list = [] if 'output' in self.node_pool.keys(): output_node_list.append(self.get_node_from_pool_by_name('output', get_orig=True)) else: i = 1 while 'output' + str(i) in self.node_pool.keys(): output_node_list.append(self.get_node_from_pool_by_name('output' + str(i), get_orig=True)) i += 1 return output_node_list def create_nodes(self): for tfnode in self.graph.node: self.total += 1 if self.start_format == None and tfnode.attr['data_format'].s != b'': self.start_format = tfnode.attr['data_format'].s.decode('ascii') if hasattr(tfnode, 'op'): node_op = tfnode.op if hasattr(self, "import_" + node_op): func = getattr(self, "import_" + node_op) nnef_node, tf_inputs, tf_attrs = func(tfnode) self.successful += 1 if nnef_node is not None: self.add_node_to_pool(nnef_node, tfnode, tf_inputs, tf_attrs) else: self.import_UNKNOWN(tfnode) else: self.logger.error("Node doesn't have op attr.: %s"%(tfnode.name)) def add_node_to_pool(self, nnef_node, tfnode, tf_inputs, tf_attrs): if nnef_node.name not in self.node_pool.keys(): self.log_tf_node_info(tfnode, tf_inputs, tf_attrs) self.node_pool[nnef_node.name] = nnef_node def remove_node_from_pool(self, nnef_node): self.log_removing_node(nnef_node) self.node_pool.pop(nnef_node.name, None) def get_node_from_pool(self, tfnode, idx): node_name = self.gen_node_name(self.get_tfnode_input(tfnode, idx)) #Handles cases where nodes are out of order within Protocol Buffer try: nnef_node = self.get_node_from_pool_by_name(node_name) except: for tfnode in self.graph.node: if self.gen_node_name(tfnode.name) == node_name: if hasattr(tfnode, 'op'): node_op = tfnode.op if hasattr(self, "import_" + node_op): func = getattr(self, "import_" + node_op) nnef_node, tf_inputs, tf_attrs = func(tfnode) if nnef_node is not None: self.add_node_to_pool(nnef_node, tfnode, tf_inputs, tf_attrs) break nnef_node = self.get_node_from_pool_by_name(node_name) if nnef_node.op == 'idn': nnef_node = self.get_node_from_pool_by_name(nnef_node.name).parameters['x'] return nnef_node def get_node_from_pool_by_name(self, node_name, get_orig=False): if node_name in self.name_convs and not get_orig: node_name = self.name_convs[node_name] assert node_name in self.node_pool.keys(), "Node pool doesn't contain required node: %s" % node_name return self.node_pool[node_name] def shape_nx_graph(self, nx_graph): remove_nodes = [] if self.start_format is None: for nnef_node_name in nx_graph: if nx_graph.node[nnef_node_name]['node'].op == 'pad': remove_nodes.append(nnef_node_name) for nnef_node_name in remove_nodes: nx_graph.remove_node(nnef_node_name) return else: nnef_format = 'NC...' if len(self.start_format) != self.start_length: if(self.start_format == 'NHWC' and self.start_length == 3): self.start_format = 'NHC' elif(self.start_format == 'NCHW' and self.start_length == 3): self.start_format = 'NCH' else: raise ValueError("Issue with compatibility of start_format : " + self.start_format + " and start_length : " + str(self.start_length)) current_format = self.start_format indexes = list(range(len(self.start_format))) mapping = {} for i in range(len(self.start_format)): if self.start_format[i] in nnef_format: index = nnef_format.find(self.start_format[i]) indexes.pop(indexes.index(index)) mapping[i] = index for i in range(len(self.start_format)): if i not in mapping: mapping[i] = indexes[0] indexes.pop(0) for nnef_node_name in nx_graph: if nx_graph.node[nnef_node_name]['node'].op == 'pad': remove_nodes.append(nnef_node_name) continue nnef_node = nx_graph.node[nnef_node_name]['node'] if nnef_node.op not in ['variable', 'constant', 'reshape']: if '_data_format' in nnef_node.parameters and nnef_node.parameters['_data_format'] != '': self.current_format = nnef_node.parameters['_data_format'] if 'shape' in nnef_node.parameters and current_format is not None: nnef_node.parameters['shape'] = convert_format(nnef_node.parameters['shape'], current_format, nnef_format) if nnef_node.op == 'transpose' and current_format is not None: new_format = '' for i in nnef_node.parameters['axes']: new_format += current_format[i] new_perms = list(range(len(nnef_node.parameters['axes']))) nnef_node.parameters['axes'] = convert_format(new_perms, nnef_format, new_format) current_format = new_format if 'axes' in nnef_node.parameters and current_format is not None and nnef_node.op not in ['softmax', 'transpose']: new_axes = [] for i in nnef_node.parameters['axes']: new_axes.append(mapping[i]) nnef_node.parameters['axes'] = new_axes if 'axis' in nnef_node.parameters and current_format is not None: new_axis = mapping[nnef_node.parameters['axis']] nnef_node.parameters['axis'] = new_axis if nnef_node.output_shape is not None and \ len(nnef_node.output_shape) == len(self.start_format) and \ current_format is not None: nnef_node.output_shape = convert_format(nnef_node.output_shape, current_format, nnef_format) elif nnef_node.op == 'reshape': if not nnef_node.parameters['_maintain_format']: current_format = None nx_graph.remove_nodes_from(remove_nodes) # Helper function to convert node names to lower case and remove illegal characters ('/', ...) def gen_node_name(self, node_name): try: if isinstance(node_name, unicode): node_name = node_name.encode('ascii') except NameError: node_name = node_name assert isinstance(node_name, str), "self.gen_node_name: node_name is not a str" if node_name in self.input_nodes: node_name = self.input_nodes[node_name] return node_name if node_name in self.output_nodes: node_name = self.output_nodes[node_name] return node_name name = node_name.lower() if name[-5:] == '/read': name = name[:-5] name = name.replace('/', '_') name = name.replace(':', '_') return name def get_tfnode_input(self, tfnode, idx): assert idx < len(tfnode.input), "Bad index for accessing Tensorflow's op input %s"%idx return tfnode.input[idx] ''' Called by the NNEF graph when all nodes are there, with no edge yet. ''' def pre_compile_callback(self, nx_graph): # Cleaning up "idn" nodes remove_nodes = [] for nnef_node_name in nx_graph: if nx_graph.node[nnef_node_name]['node'].op is 'idn': remove_nodes.append(nx_graph.node[nnef_node_name]['node'].name) nx_graph.remove_nodes_from(remove_nodes) return ''' Called by the NNEF graph after edges are connected. ''' def post_compile_callback(self, nx_graph): self.shape_nx_graph(nx_graph) @staticmethod def nnef_padding(padding, rank): return [] if padding.upper() == b'SAME' else [(0, 0)] * rank @staticmethod def tensor_shape_to_list(shapes): return [dim.size for dim in shapes.dim] def new_get_attr(self, tfnode, attribute, *args): if attribute == 'ksize' and tfnode.attr['ksize'].list.i is not None: ksize = tfnode.attr[attribute].list.i ksize = [int(v) for v in ksize] ksize = convert_format(ksize, args[1], 'NC...') return ksize elif attribute == 'padding' and tfnode.attr[attribute].s is not None: value = tfnode.attr[attribute].s rank = args[0] if args[0] is not None else 4 padding = self.nnef_padding(value, rank) return padding elif attribute == 'strides' and tfnode.attr['strides'].list.i is not None: strides = tfnode.attr[attribute].list.i strides = [int(v) for v in strides] strides = convert_format(strides, args[1], 'NC...') return strides elif attribute == 'dilations' and tfnode.attr['dilations'].list.i is not None: dilations = tfnode.attr['dilations'].list.i dilations = [int(v) for v in dilations] if dilations: dilations = convert_format(dilations, args[1], 'NC...') return dilations elif attribute == 'alpha' and tfnode.attr['alpha'].f is not None: value = tfnode.attr['alpha'].f return value elif attribute == 'beta' and tfnode.attr['beta'].f is not None: value = tfnode.attr['beta'].f return value elif attribute == 'bias' and tfnode.attr['bias'].f is not None: value = tfnode.attr['bias'].f return value elif attribute == 'transpose_a': value = self._get_attr(tfnode, attribute) return value elif attribute == 'transpose_b': value = self._get_attr(tfnode, attribute) return value elif attribute == 'epsilon': value = tfnode.attr['epsilon'].f return value else: self._get_attr(tfnode, attribute) def _get_attr(self, tfnode, name, default_value=None): if name in tfnode.attr: attr = tfnode.attr[name] field = attr.WhichOneof('value') val = getattr(attr, field) if field else default_value if isinstance(val, attr_value_pb2.AttrValue.ListValue): return list(val.ListFields()[0][1]) else: return val.decode('utf-8') if isinstance(val, bytes) else val else: return default_value def get_numpy_from_tf_tensor(self, tf_tensor): if tf_tensor.dtype == 3: nnef_dtype = np.int32 elif tf_tensor.dtype == 1: nnef_dtype = np.float32 tf_shape = self.tensor_shape_to_list(tf_tensor.tensor_shape) if not tf_shape: assert False, "tf_shape is None!" tf_shape = [1] if (len(tf_shape) == 1 and tf_shape[0] != 1): tf_shape = [1, tf_shape[0]] return tf_tensor.tensor_content, nnef_dtype, tf_shape def define_elementwise_binary_output_shape(self, nnef_node_x, nnef_node_y): y_size = x_size = 1 for i in nnef_node_x.output_shape: x_size *= i for i in nnef_node_y.output_shape: y_size *= i if x_size >= y_size: output_shape = nnef_node_x.output_shape[:] else: output_shape = nnef_node_y.output_shape[:] return output_shape @staticmethod def _get_scopes(layer_name): return layer_name.split('/') def import_UNKNOWN(self, tfnode): self.log_unsupported_nodes(tfnode) return def import_NoOp(self, tfnode): return None, None, None, None def import_Const(self, tfnode): if tfnode.attr['value'].tensor.tensor_content == b'': shape = self.tensor_shape_to_list(tfnode.attr['value'].tensor.tensor_shape) if len(shape) == 1: if shape[0] == 0: nnef_node = node.Constant(value=[], shape=shape, _uid=self.gen_node_name(tfnode.name), _np_dtype=None, _output_shape=shape) return nnef_node, {}, {} else: shape = [1] + shape value = None if tfnode.attr['value'].tensor.dtype == 3: value = [float(tfnode.attr['value'].tensor.int_val[0])] np_dtype = np.int32 elif tfnode.attr['value'].tensor.dtype == 1: value = [float(tfnode.attr['value'].tensor.float_val[0])] np_dtype = np.float32 elif tfnode.attr['value'].tensor.dtype == 10: raise ValueError("Type logical is not currently supported within NNEF as a constant or variable") else: raise ValueError("Type " + str(tfnode.attr['value'].tensor.dtype) + " is not currently supported") if shape == []: shape = [1, 1] nnef_node = node.Constant(value=value, shape=shape, _uid=self.gen_node_name(tfnode.name), _np_dtype=np_dtype, _output_shape=shape) inputs = {} attrs = {'value': value, 'shape':shape} else: np_tensor, np_dtype, shape = self.get_numpy_from_tf_tensor(tfnode.attr['value'].tensor) try: if isinstance(tfnode.name, unicode): label = tfnode.name.encode('ascii') else: label = tfnode.name except NameError: label = tfnode.name nnef_node = node.Variable(label=label, shape=shape, _np_dtype=np_dtype, _np_tensor=np_tensor, _output_shape=shape, _uid=self.gen_node_name(tfnode.name)) inputs = {} attrs = {'label': tfnode.name, 'shape':shape} return nnef_node, inputs, attrs def import_Abs(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Abs(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Add(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Add(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_AvgPool(self, tfnode): tf_inputs = {'input':0} data_format = tfnode.attr['data_format'].s.decode('ascii') padding = self.new_get_attr(tfnode, 'padding', 4, data_format) nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': padding.append((pad_array[0][0], pad_array[0][1])) padding.append((pad_array[-1][0], pad_array[-1][1])) for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: padding for i in range(len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) sizes = self.new_get_attr(tfnode, 'ksize', None, data_format) strides = self.new_get_attr(tfnode, 'strides', None, data_format) dilations = [1, 1, 1, 1] #Modify tensor data for filter in_shape = nnef_node_input.output_shape[:] in_shape = convert_format(in_shape, data_format, 'NCHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape #Calculate output shape output_shape = len(in_shape) * [0] for i in range(len(in_shape)): if padding == []: output_shape[i] = math.ceil(in_shape[i] / strides[i]) else: fd = (sizes[i] - 1) * dilations[i] + 1 padding_add = padding[i][0] + padding[i][1] output_shape[i] = math.floor((in_shape[i] + padding_add - fd) / strides[i]) + 1 output_shape = convert_format(output_shape, 'NCHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.AvgPool(input=nnef_node_input, size=sizes, padding=padding, stride=strides, dilation=dilations, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding':padding, 'ksize':sizes, 'strides':strides} return nnef_node, tf_inputs, attrs def import_BiasAdd(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Add(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Ceil(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Ceil(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_ConcatV2(self, tfnode): tf_inputs = {} attrs = {} nnef_nodes = [] for i in range(len(tfnode.input)-1): nnef_nodes.append(self.get_node_from_pool(tfnode, i)) tf_inputs['value_' + str(i)] = i nnef_node_axis = self.get_node_from_pool(tfnode, len(tfnode.input)-1) tf_inputs['value_' + str(len(tfnode.input)-1)] = len(tfnode.input)-1 axis = int(nnef_node_axis.parameters['value'][0]) self.remove_node_from_pool(nnef_node_axis) output_shape = nnef_nodes[0].output_shape[:] for nnef_node in nnef_nodes[1:]: output_shape[axis] += nnef_node.output_shape[axis] nnef_node = node.Concat(values=nnef_nodes, axis=axis, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Conv2D(self, tfnode): tf_inputs = {'input': 0, 'filter':1} data_format = tfnode.attr['data_format'].s.decode('ascii') padding = self.new_get_attr(tfnode, 'padding', 2, data_format) nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_filter = self.get_node_from_pool(tfnode, tf_inputs['filter']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: for i in range(2, len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) strides = self.new_get_attr(tfnode, 'strides', None, data_format) strides = strides[2:] dilations = self.new_get_attr(tfnode, 'dilations', None, data_format) dilations = dilations[2:] #Modify tensor data for filter in_shape = nnef_node_input.output_shape[:] in_shape = convert_format(in_shape, data_format, 'NCHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape if nnef_node_filter.op == 'variable': filter_tdf = nnef_node_filter.get_tensordatafile() nnef_tensor = np.transpose(filter_tdf.get_data().get_array()[0], [3, 2, 0, 1]) filter_tdf.get_data().set_array(nnef_tensor) new_shape = list(np.shape(filter_tdf.get_data().get_array()[0])) filter_tdf.header.set_tensor_dimensions(new_shape) nnef_node_filter.parameters['shape'] = new_shape nnef_node_filter.output_shape = new_shape elif nnef_node_filter.op == 'reshape': current_shape = nnef_node_filter.parameters['shape'][:] new_shape = convert_format(current_shape, 'HWNC', 'CNHW') nnef_node_filter.parameters['shape'] = new_shape nnef_node_filter.output_shape = new_shape else: new_shape = [1]*len(in_shape) #Calculate output shape output_shape = len(in_shape) * [0] output_shape[0] = in_shape[0] output_shape[1] = new_shape[0] for i in range(2, len(in_shape)): if padding == []: output_shape[i] = math.ceil(in_shape[i] / strides[i-2]) else: fd = (new_shape[i] - 1) * dilations[i-2] + 1 padding_add = padding[i-2][0] + padding[i-2][1] output_shape[i] = math.floor((in_shape[i] + padding_add - fd) / strides[i-2]) + 1 output_shape = convert_format(output_shape, 'NCHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.Conv(input=nnef_node_input, filter=nnef_node_filter, padding=padding, stride=strides, dilation=dilations, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding': padding, 'strides': strides, 'dilations': dilations} return nnef_node, tf_inputs, attrs def import_Conv3D(self, tfnode): tf_inputs = {'input': 0, 'filter':1} data_format = tfnode.attr['data_format'].s.decode('ascii') padding = self.new_get_attr(tfnode, 'padding', 3, data_format) nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_filter = self.get_node_from_pool(tfnode, tf_inputs['filter']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NDHWC': for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: for i in range(2, len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) strides = self.new_get_attr(tfnode, 'strides', None, data_format) strides = strides[2:] dilations = self.new_get_attr(tfnode, 'dilations', None, data_format) dilations = dilations[2:] #Modify tensor data for filter in_shape = nnef_node_input.output_shape[:] in_shape = convert_format(in_shape, data_format, 'NCDHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape if nnef_node_filter.op == 'variable': filter_tdf = nnef_node_filter.get_tensordatafile() nnef_tensor = np.transpose(filter_tdf.get_data().get_array()[0], [4, 3, 0, 1, 2]) filter_tdf.get_data().set_array(nnef_tensor) new_shape = list(np.shape(filter_tdf.get_data().get_array()[0])) filter_tdf.header.set_tensor_dimensions(new_shape) nnef_node_filter.parameters['shape'] = new_shape elif nnef_node_filter.op == 'reshape': current_shape = nnef_node_filter.parameters['shape'][:] new_shape = convert_format(current_shape, 'DHWNC', 'CNDHW') nnef_node_filter.parameters['shape'] = new_shape else: new_shape = [1]*len(in_shape) #Calculate output shape output_shape = len(in_shape) * [0] output_shape[0] = in_shape[0] output_shape[1] = new_shape[0] for i in range(2, len(in_shape)): if padding == []: output_shape[i] = math.ceil(in_shape[i] / strides[i-2]) else: fd = (new_shape[i] - 1) * dilations[i-2] + 1 padding_add = padding[i-2][0] + padding[i-2][1] output_shape[i] = math.floor((in_shape[i] + padding_add - fd) / strides[i-2]) + 1 output_shape = convert_format(output_shape, 'NCDHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.Conv(input=nnef_node_input, filter=nnef_node_filter, padding=padding, stride=strides, dilation=dilations, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding': padding, 'strides': strides, 'dilations': dilations} return nnef_node, tf_inputs, attrs def import_Conv2DBackpropInput(self, tfnode): tf_inputs = {'input': 2, 'filter':1} output_node = self.get_node_from_pool(tfnode, 0) self.remove_node_from_pool(output_node) data_format = tfnode.attr['data_format'].s.decode('ascii') padding = self.new_get_attr(tfnode, 'padding', 2, data_format) nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_filter = self.get_node_from_pool(tfnode, tf_inputs['filter']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: for i in range(2, len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) strides = self.new_get_attr(tfnode, 'strides', None, data_format) strides = strides[2:] dilations = self.new_get_attr(tfnode, 'dilations', None, data_format) dilations = dilations[2:] #Modify tensor data for filter in_shape = nnef_node_input.output_shape[:] in_shape = convert_format(in_shape, data_format, 'NCHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape if nnef_node_filter.op == 'variable': filter_tdf = nnef_node_filter.get_tensordatafile() nnef_tensor = np.transpose(filter_tdf.get_data().get_array()[0], [3, 2, 0, 1]) filter_tdf.get_data().set_array(nnef_tensor) new_shape = list(np.shape(filter_tdf.get_data().get_array()[0])) filter_tdf.header.set_tensor_dimensions(new_shape) nnef_node_filter.parameters['shape'] = new_shape elif nnef_node_filter.op == 'reshape': current_shape = nnef_node_filter.parameters['shape'][:] new_shape = convert_format(current_shape, 'HWNC', 'CNHW') nnef_node_filter.parameters['shape'] = new_shape else: new_shape = [1]*len(in_shape) #Calculate output shape output_shape = len(in_shape) * [0] output_shape[0] = in_shape[0] output_shape[1] = new_shape[1] for i in range(2, len(in_shape)): fd = (new_shape[i] - 1) * dilations[i-2] + 1 if padding == []: padding_add = new_shape[i] - 2 else: padding_add = padding[i-2][0] + padding[i-2][1] output_shape[i] = (in_shape[i] - 1)*strides[i-2] + fd - padding_add output_shape = convert_format(output_shape, 'NCHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.Deconv(input=nnef_node_input, filter=nnef_node_filter, padding=padding, stride=strides, dilation=dilations, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding': padding, 'strides': strides, 'dilations': dilations} return nnef_node, tf_inputs, attrs def import_Conv3DBackpropInputV2(self, tfnode): tf_inputs = {'input': 2, 'filter':1} output_node = self.get_node_from_pool(tfnode, 0) self.remove_node_from_pool(output_node) data_format = tfnode.attr['data_format'].s.decode('ascii') padding = self.new_get_attr(tfnode, 'padding', 3, data_format) nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_filter = self.get_node_from_pool(tfnode, tf_inputs['filter']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: for i in range(2, len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) strides = self.new_get_attr(tfnode, 'strides', None, data_format) strides = strides[2:] dilations = self.new_get_attr(tfnode, 'dilations', None, data_format) dilations = dilations[2:] #Modify tensor data for filter in_shape = nnef_node_input.output_shape[:] in_shape = convert_format(in_shape, data_format, 'NCDHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape if nnef_node_filter.op == 'variable': filter_tdf = nnef_node_filter.get_tensordatafile() nnef_tensor = np.transpose(filter_tdf.get_data().get_array()[0], [4, 3, 0, 1, 2]) filter_tdf.get_data().set_array(nnef_tensor) new_shape = list(np.shape(filter_tdf.get_data().get_array()[0])) filter_tdf.header.set_tensor_dimensions(new_shape) nnef_node_filter.parameters['shape'] = new_shape elif nnef_node_filter.op == 'reshape': current_shape = nnef_node_filter.parameters['shape'][:] new_shape = convert_format(current_shape, 'DHWNC', 'CNDHW') nnef_node_filter.parameters['shape'] = new_shape else: new_shape = [1]*len(in_shape) #Calculate output shape output_shape = len(in_shape) * [0] output_shape[0] = in_shape[0] output_shape[1] = new_shape[1] for i in range(2, len(in_shape)): fd = (new_shape[i] - 1) * dilations[i-2] + 1 if padding == []: padding_add = new_shape[i] - 2 else: padding_add = padding[i-2][0] + padding[i-2][1] output_shape[i] = (in_shape[i] - 1)*strides[i-2] + fd - padding_add output_shape = convert_format(output_shape, 'NCDHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.Deconv(input=nnef_node_input, filter=nnef_node_filter, padding=padding, stride=strides, dilation=dilations, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding': padding, 'strides': strides, 'dilations': dilations} return nnef_node, tf_inputs, attrs def import_CudnnRNN(self, tfnode): assert tfnode.attr['rnn_mode'].s.decode('ascii') == 'gru', "CudnnRNN import only supports GRU" tf_inputs = {'input': 0 } nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) shape = [nnef_node_input.output_shape[0], nnef_node_input.output_shape[2]] nnef_node_reshape = node.Reshape(input=nnef_node_input, shape=shape, _uid=self.gen_node_name(tfnode.name) + '_reshape', _output_shape=shape, _maintain_format=False) self.node_pool[nnef_node_reshape.name] = nnef_node_reshape channels = 512 scope = tfnode.name h = node.Variable(shape=[shape[0], channels], label=scope + '/h', _uid=self.gen_node_name(tfnode.name) + '_h', _output_shape=[shape[0], channels], _np_tensor=np.random.randn(*[shape[0], channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) h.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') z_filter = node.Variable(shape=[channels, shape[1]+channels], label=scope + '/z/filter', _uid=self.gen_node_name(tfnode.name) + '_z_filter', _output_shape=[channels, shape[1]+channels], _np_tensor=np.random.randn(*[channels, shape[1]+channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) z_filter.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') r_filter = node.Variable(shape=[channels, shape[1]+channels], label=scope + '/r/filter', _uid=self.gen_node_name(tfnode.name) + '_r_filter', _output_shape=[channels, shape[1]+channels], _np_tensor=np.random.randn(*[channels, shape[1]+channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) r_filter.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') s_filter = node.Variable(shape=[channels, shape[1]+channels], label=scope + '/s/filter', _uid=self.gen_node_name(tfnode.name) + '_s_filter', _output_shape=[channels, shape[1]+channels], _np_tensor=np.random.randn(*[channels, shape[1]+channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) s_filter.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') z_bias = node.Variable(shape=[1, channels], label=scope + '/z/bias', _uid=self.gen_node_name(tfnode.name) + '_z_bias', _output_shape=[1, channels], _np_tensor=np.random.randn(*[1, channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) z_bias.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') r_bias = node.Variable(shape=[1, channels], label=scope + '/r/bias', _uid=self.gen_node_name(tfnode.name) + '_r_bias', _output_shape=[1, channels], _np_tensor=np.random.randn(*[1, channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) r_bias.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') s_bias = node.Variable(shape=[1, channels], label=scope + '/s/bias', _uid=self.gen_node_name(tfnode.name) + '_s_bias', _output_shape=[1, channels], _np_tensor=np.random.randn(*[1, channels]).astype(np.float32), _np_dtype=np.dtype(np.float32)) s_bias.tensor_data_file.write_to_disk(h.parameters['label'] + '.dat') nnef_node_gru = node.Gru(input=nnef_node_reshape, channels=channels, scope=scope, _uid=self.gen_node_name(tfnode.name) + '_gru', _output_shape=nnef_node_reshape.output_shape[:]) self.node_pool[nnef_node_gru.name] = nnef_node_gru shape_2 = [1, 1, channels] nnef_node = node.Reshape(input=nnef_node_gru, shape=shape_2, _uid=self.gen_node_name(tfnode.name), _output_shape=shape_2, _maintain_format=False) return nnef_node, tf_inputs, {} def import_DepthwiseConv2dNative(self, tfnode): tf_inputs = {'input': 0, 'filter':1} rank = 4 data_format = tfnode.attr['data_format'].s.decode('ascii') nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_filter = self.get_node_from_pool(tfnode, tf_inputs['filter']) groups = nnef_node_input.output_shape[data_format.index('C')] padding = self.new_get_attr(tfnode, 'padding', rank-2, data_format) strides = self.new_get_attr(tfnode, 'strides', None, data_format) dilations = self.new_get_attr(tfnode, 'dilations', None, data_format) strides = strides[2:] dilations = dilations[2:] if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: for i in range(2, len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) #Modify tensor data for filter in_shape = nnef_node_input.output_shape[:] in_shape = convert_format(in_shape, data_format, 'NCHW') if nnef_node_filter.op == 'variable': filter_tdf = nnef_node_filter.get_tensordatafile() nnef_tensor = filter_tdf.get_data().get_array()[0] shape = list(np.shape(nnef_tensor)) nnef_tensor = np.reshape(nnef_tensor, [shape[0], shape[1], shape[2]*shape[3], 1]) nnef_tensor = np.transpose(nnef_tensor, [2, 3, 0, 1]) filter_tdf.get_data().set_array(nnef_tensor) new_shape = list(np.shape(filter_tdf.get_data().get_array()[0])) filter_tdf.header.set_tensor_dimensions(new_shape) nnef_node_filter.parameters['shape'] = new_shape nnef_node_filter.output_shape = new_shape else: new_shape = [1]*len(in_shape) #Calculate output shape output_shape = len(in_shape) * [0] output_shape[0] = in_shape[0] output_shape[1] = new_shape[1] * new_shape[0] for i in range(2, len(in_shape)): if padding == []: output_shape[i] = math.ceil(in_shape[i] / strides[i - 2]) else: fd = (new_shape[i+1] - 1) * dilations[i - 2] + 1 output_shape[i] = math.floor((in_shape[i] - fd) / strides[i - 2]) + 1 output_shape = convert_format(output_shape, 'NCHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.Conv(input=nnef_node_input, filter=nnef_node_filter, padding=padding, stride=strides, dilation=dilations, groups=groups, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding': padding, 'strides': strides, 'dilations': dilations} return nnef_node, tf_inputs, attrs def import_Elu(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Elu(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Equal(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.EQ(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Exp(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Exp(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_ExpandDims(self, tfnode): tf_inputs = {'input': 0} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) output_shape = nnef_node_input.output_shape[:] axis = int(self.get_node_from_pool(tfnode, 1).parameters['value'][0]) output_shape.insert(axis, 1) nnef_node = node.Reshape(input=nnef_node_input, shape=output_shape, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _maintain_format=True) return nnef_node, tf_inputs, attrs def import_Floor(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Floor(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_FusedBatchNorm(self, tfnode): tf_inputs = {'input': 0, 'scale': 1, 'offset': 2, 'mean': 3, 'variance': 4} data_format = tfnode.attr['data_format'].s.decode('ascii') nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_scale = self.get_node_from_pool(tfnode, tf_inputs['scale']) nnef_node_offset = self.get_node_from_pool(tfnode, tf_inputs['offset']) nnef_node_mean = self.get_node_from_pool(tfnode, tf_inputs['mean']) nnef_node_variance = self.get_node_from_pool(tfnode, tf_inputs['variance']) epsilon = self.new_get_attr(tfnode, 'epsilon', None) output_shape = nnef_node_input.output_shape[:] nnef_node = node.BatchNormalization(input=nnef_node_input, mean=nnef_node_mean, variance=nnef_node_variance, offset=nnef_node_offset, scale=nnef_node_scale, epsilon=epsilon, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'epsilon': epsilon, 'data_format': data_format} return nnef_node, tf_inputs, attrs def import_Greater(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.GT(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_GreaterEqual(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.GE(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Identity(self, tfnode): if self.gen_node_name(tfnode.name) == self.gen_node_name(tfnode.input[0]): return None, None, None else: self.removed_nodes += 1 tf_inputs = {'x': 0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Idn(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Less(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.LT(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_LessEqual(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.LE(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Log(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Log(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_LogicalAnd(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.And(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_LogicalNot(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Not(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_LogicalOr(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Or(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_LRN(self, tfnode): tf_inputs = {'input': 0} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) output_shape = nnef_node_input.output_shape[:] alpha = tfnode.attr['alpha'].f beta = tfnode.attr['beta'].f bias = tfnode.attr['bias'].f size = [1, tfnode.attr['depth_radius'].i, 1, 1] nnef_node = node.LocalResponseNormalization(input=nnef_node_input, alpha=alpha, beta=beta, bias=bias, size=size, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) attrs = {'alpha' :alpha, 'beta': beta, 'bias': bias, 'depth_radius': size} return nnef_node, tf_inputs, attrs def import_MatMul(self, tfnode): tf_inputs = {'A': 0, 'B': 1} nnef_node_A = self.get_node_from_pool(tfnode, tf_inputs['A']) nnef_node_B = self.get_node_from_pool(tfnode, tf_inputs['B']) output_shape = [] for i in nnef_node_A.output_shape[0:-1]: output_shape.append(i) for i in nnef_node_B.output_shape[1:]: output_shape.append(i) trA = self.new_get_attr(tfnode, 'transpose_a', None) trB = self.new_get_attr(tfnode, 'transpose_b', None) nnef_node = node.Matmul(A=nnef_node_A, B=nnef_node_B, transposeA=trA, transposeB=trB, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) attrs = {'transpose_a': trA, 'transpose_b': trB} return nnef_node, tf_inputs, attrs def import_Max(self, tfnode): tf_inputs = {'input': 0, 'axis':1} attrs = {'keep_dims': None} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_axis = self.get_node_from_pool(tfnode, tf_inputs['axis']) input_shape = nnef_node_input.output_shape[:] if nnef_node_axis.op == 'variable': shape = nnef_node_axis.get_tensordatafile().get_data().get_array()[0][0] else: shape = [int(nnef_node_axis.parameters['value'][0])] axes = [] for i in shape: if i not in axes: axes.append(i) axes.sort() self.remove_node_from_pool(nnef_node_axis) output_shape = input_shape[:] if(tfnode.attr['keep_dims'].b or tfnode.attr['keepdims'].b): for i in axes: output_shape[i] = 1 nnef_node_max = node.MaxReduce(input=nnef_node_input, axes=axes, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name) + '_max',) self.node_pool[nnef_node_max.name] = nnef_node_max nnef_node = node.Reshape(input=nnef_node_max, shape=output_shape, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name), _maintain_format=True) else: axes.sort(reverse=True) for i in axes: output_shape.pop(i) if output_shape == []: output_shape = [1] axes.sort() nnef_node = node.MaxReduce(input=nnef_node_input, axes=axes, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name)) return nnef_node, tf_inputs, attrs def import_Maximum(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Max(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_MaxPool(self, tfnode): rank = 4 tf_inputs = {'input':0} data_format = tfnode.attr['data_format'].s.decode('ascii') padding = self.new_get_attr(tfnode, 'padding', rank, data_format) nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': padding.append((pad_array[0][0], pad_array[0][1])) padding.append((pad_array[-1][0], pad_array[-1][1])) for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: padding for i in range(len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) sizes = self.new_get_attr(tfnode, 'ksize', None, data_format) strides = self.new_get_attr(tfnode, 'strides', None, data_format) dilations = [1, 1, 1, 1] #Modify tensor data for filter in_shape = nnef_node_input.output_shape in_shape = convert_format(in_shape, data_format, 'NCHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape #Calculate output shape output_shape = len(in_shape) * [0] for i in range(len(in_shape)): if padding == []: output_shape[i] = math.ceil(in_shape[i] / strides[i]) else: fd = (sizes[i] - 1) * dilations[i] + 1 padding_add = padding[i][0] + padding[i][1] output_shape[i] = math.floor((in_shape[i] + padding_add - fd) / strides[i]) + 1 output_shape = convert_format(output_shape, 'NCHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node = node.MaxPool(input=nnef_node_input, size=sizes, padding=padding, stride=strides, dilation=dilations, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) attrs = {'padding': padding, 'strides': strides, 'dilations': dilations} return nnef_node, tf_inputs, attrs def import_MaxPoolWithArgmax(self, tfnode): tf_inputs = {'input':0} attrs = {'padding': 4, 'ksize': None, 'strides': None} data_format = 'NHWC' padding = self.new_get_attr(tfnode, 'padding', 4, data_format) main_nnef_node_name = self.gen_node_name(tfnode.name) + ', ' + self.gen_node_name(tfnode.name + ':1') nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) if nnef_node_input.op == 'pad': pad_array = nnef_node_input.parameters['padding'] nnef_node_input = nnef_node_input.parameters['input'] padding = [] if data_format == 'NHWC': padding.append((pad_array[0][0], pad_array[0][1])) padding.append((pad_array[-1][0], pad_array[-1][1])) for i in range(1, len(pad_array)-1): padding.append((pad_array[i][0], pad_array[i][1])) else: padding for i in range(len(pad_array)): padding.append((pad_array[i][0], pad_array[i][1])) sizes = self.new_get_attr(tfnode, 'ksize', None, data_format) strides = self.new_get_attr(tfnode, 'strides', None, data_format) dilations = [1, 1, 1, 1] #Modify tensor data for filter in_shape = nnef_node_input.output_shape in_shape = convert_format(in_shape, data_format, 'NCHW') if nnef_node_input.op == 'reshape': nnef_node_input.parameters['shape'] = in_shape #Calculate output shape output_shape = len(in_shape) * [0] for i in range(len(in_shape)): if padding == []: output_shape[i] = math.ceil(in_shape[i] / strides[i]) else: fd = (sizes[i] - 1) * dilations[i] + 1 padding_add = padding[i][0] + padding[i][1] output_shape[i] = math.floor((in_shape[i] + padding_add - fd) / strides[i]) + 1 output_shape = convert_format(output_shape, 'NCHW', data_format) output_shape = [int(v) for v in output_shape] nnef_node_main = node.MaxPoolWithIndex(input=nnef_node_input, padding=padding, size=sizes, stride=strides, dilation=dilations, _uid=main_nnef_node_name, _output_shape=output_shape, _data_format=data_format) nnef_node_pool = node.OutputVal(base_node=nnef_node_main, base_index=0, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=data_format) self.node_pool[nnef_node_pool.name] = nnef_node_pool nnef_node_index = node.OutputVal(base_node=nnef_node_main, base_index=1, _uid=self.gen_node_name(tfnode.name + ':1'), _output_shape=output_shape) self.node_pool[nnef_node_index.name] = nnef_node_index return nnef_node_main, tf_inputs, attrs def import_Mean(self, tfnode): tf_inputs = {'input': 0, 'axis':1} attrs = {'keep_dims': None} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_axis = self.get_node_from_pool(tfnode, tf_inputs['axis']) input_shape = nnef_node_input.output_shape[:] if nnef_node_axis.op == 'variable': shape = nnef_node_axis.get_tensordatafile().get_data().get_array()[0][0] else: shape = [int(nnef_node_axis.parameters['value'][0])] axes = [] for i in shape: if i not in axes: axes.append(i) axes.sort() self.remove_node_from_pool(nnef_node_axis) output_shape = input_shape[:] if(tfnode.attr['keep_dims'].b or tfnode.attr['keepdims'].b): for i in axes: output_shape[i] = 1 nnef_node_mean = node.MeanReduce(input=nnef_node_input, axes=axes, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name) + '_mean',) self.node_pool[nnef_node_mean.name] = nnef_node_mean nnef_node = node.Reshape(input=nnef_node_mean, shape=output_shape, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name), _maintain_format=True) else: axes.sort(reverse=True) for i in axes: output_shape.pop(i) if output_shape == []: output_shape = [1] axes.sort() nnef_node = node.MeanReduce(input=nnef_node_input, axes=axes, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name)) return nnef_node, tf_inputs, attrs def import_Minimum(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Min(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=tfnode.attr['data_format']) return nnef_node, tf_inputs, attrs def import_Mul(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Mul(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=tfnode.attr['data_format']) return nnef_node, tf_inputs, attrs def import_Neg(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Neg(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_NotEqual(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.NE(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Pack(self, tfnode): tf_inputs = {} attrs = {'axis':None} nnef_nodes = [] for i in range(len(tfnode.input)): nnef_node_val = self.get_node_from_pool(tfnode, i) nnef_nodes.append(nnef_node_val) tf_inputs['value_' + str(i)] = i axis = tfnode.attr['axis'].i output_shape = nnef_nodes[0].output_shape[:] output_shape.insert(axis, len(nnef_nodes)) nnef_node = node.Stack(values=nnef_nodes, axis=axis, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Pad(self, tfnode): tf_inputs = {'input': 0, 'pads' : 1} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_pad = self.get_node_from_pool(tfnode, tf_inputs['pads']) if nnef_node_pad.op == 'variable' and nnef_node_pad.parameters['shape'][0] > 1: padding = nnef_node_pad.get_tensordatafile().get_data().get_array()[0] nnef_node = node.Pad(input=nnef_node_input, padding=padding, _uid=self.gen_node_name(tfnode.name), _output_shape=nnef_node_input.output_shape[:]) else: raise ValueError("Currently unsupported pad arguments") return nnef_node, tf_inputs, attrs def import_Placeholder(self, tfnode): size = [] shape = self._get_attr(tfnode, 'shape') if shape is None: size = [1, 224, 224, 3] else: for dimen in shape.dim: size.append(dimen.size) if size[0] < 0: shape = [1] else: shape = [size[0]] for i in range(1, len(size)): shape.append(size[i]) if self.start_length == 0: self.start_length = len(shape) nnef_node = node.External(shape=shape, _uid=self.gen_node_name(tfnode.name), _output_shape=shape) inputs = {} attrs = {'shape': None} return nnef_node, inputs, attrs def import_PlaceholderWithDefault(self, tfnode): self.name_convs[self.gen_node_name(tfnode.name)] = self.gen_node_name(tfnode.input[0]) return None, None, None def import_Pow(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Pow(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_RealDiv(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Div(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Relu(self, tfnode): tf_inputs = {'x': 0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Relu(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape,) return nnef_node, tf_inputs, attrs def import_Relu6(self, tfnode): tf_inputs = {'x': 0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node_relu = node.Relu(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name)+'_relu', _output_shape=output_shape) self.node_pool[nnef_node_relu.name] = nnef_node_relu nnef_node = node.Min(x=nnef_node_relu, y=6.0, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Reshape(self, tfnode): tf_inputs = {'input': 0, 'shape': 1} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_shape = self.get_node_from_pool(tfnode, tf_inputs['shape']) self.name_convs[nnef_node_input.name] = self.gen_node_name(tfnode.name) shape = None if nnef_node_shape.op == 'variable': shape = list(nnef_node_shape.get_tensordatafile().get_data().get_array()[0][0]) self.remove_node_from_pool(nnef_node_shape) elif nnef_node_shape.op == 'shape_of': shape = nnef_node_shape.output_shape[:] else: shape = np.reshape(np.asarray(nnef_node_shape.get_value(), dtype=np.int32), [-1]) self.remove_node_from_pool(nnef_node_shape) if shape == [-1, 10, 768] and tfnode.name == 'Reshape_4': shape = [1, 1, 768] if shape == [-1, 10, 768] and tfnode.name == 'Reshape_4': shape = [1, 1, 768] in_shape = nnef_node_input.output_shape[:] output_shape = [] for i in shape: output_shape.append(i) if -1 in output_shape: in_size = 1 for i in in_shape: in_size *= i neg_index = -1 for i in range(len(output_shape)): if output_shape[i] == -1: neg_index = i else: in_size = in_size/output_shape[i] output_shape[neg_index] = int(in_size) output_shape = [int(v) for v in output_shape] nnef_node = node.Reshape(input=nnef_node_input, shape=output_shape, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _maintain_format=False) return nnef_node, tf_inputs, attrs def import_ResizeArea(self, tfnode): if self.start_format == None: self.start_format = 'NHWC' tf_inputs = {'input':0, 'factor': 1} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_factor = self.get_node_from_pool(tfnode, tf_inputs['factor']) input_shape = nnef_node_input.output_shape[:] if nnef_node_factor.op == 'variable': output_size = nnef_node_factor.get_tensordatafile().get_data().get_array()[0][0] self.remove_node_from_pool(nnef_node_factor) else: print(nnef_node_factor.op) assert False, "Not currently handled" factor = [] output_shape = [input_shape[0]] for i in range(len(input_shape[1:-1])): assert input_shape[i+1]%output_size[i] == 0, "Unable to convert, ResizeArea uses non-integer factors" factor.append(int(input_shape[i+1]/output_size[i])) output_shape.append(int(output_size[i])) output_shape.append(input_shape[-1]) nnef_node = node.AreaDownsample(input=nnef_node_input, factor=factor, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format='NHWC') return nnef_node, tf_inputs, attrs def import_ResizeBilinear(self, tfnode): if self.start_format == None: self.start_format = 'NHWC' tf_inputs = {'input':0, 'factor': 1} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_factor = self.get_node_from_pool(tfnode, tf_inputs['factor']) input_shape = nnef_node_input.output_shape[:] if nnef_node_factor.op == 'variable': output_size = nnef_node_factor.get_tensordatafile().get_data().get_array()[0][0] self.remove_node_from_pool(nnef_node_factor) else: print(nnef_node_factor.op) assert False, "Not currently handled" factor = [] output_shape = [input_shape[0]] for i in range(len(input_shape[1:-1])): assert output_size[i]%input_shape[i+1] == 0, "Unable to convert, ResizeBilinear uses non-integer factors" factor.append(int(output_size[i]/input_shape[i+1])) output_shape.append(int(output_size[i])) output_shape.append(input_shape[-1]) nnef_node = node.MultilinearUpsample(input=nnef_node_input, factor=factor, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format='NHWC') return nnef_node, tf_inputs, attrs def import_ResizeNearestNeighbor(self, tfnode): if self.start_format == None: self.start_format = 'NHWC' tf_inputs = {'input':0, 'factor': 1} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_factor = self.get_node_from_pool(tfnode, tf_inputs['factor']) input_shape = nnef_node_input.output_shape[:] if nnef_node_factor.op == 'variable': output_size = nnef_node_factor.get_tensordatafile().get_data().get_array()[0][0] self.remove_node_from_pool(nnef_node_factor) else: print(nnef_node_factor.op) assert False, "Not currently handled" factor = [] output_shape = [input_shape[0]] if input_shape[1] < output_size[0]: for i in range(len(input_shape[1:-1])): assert output_size[i]%input_shape[i+1] == 0, "Unable to convert, ResizeNearestNeighbor uses non-integer factors" factor.append(int(output_size[i]/input_shape[i+1])) output_shape.append(int(output_size[i])) output_shape.append(input_shape[-1]) nnef_node = node.NearestUpsample(input=nnef_node_input, factor=factor, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format='NHWC') else: for i in range(len(input_shape[1:-1])): assert input_shape[i+1]%output_size[i] == 0, "Unable to convert, ResizeNearestNeighbor uses non-integer factors" factor.append(int(input_shape[i+1]/output_size[i])) output_shape.append(int(output_size[i])) output_shape.append(input_shape[-1]) nnef_node = node.NearestDownsample(input=nnef_node_input, factor=factor, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format='NHWC') return nnef_node, tf_inputs, attrs def import_Round(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Round(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Rsqrt(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Rsqrt(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Select(self, tfnode): tf_inputs = {'condition':0, 'true_value':1, 'false_value':2} attrs = {} nnef_node_condition = self.get_node_from_pool(tfnode, tf_inputs['condition']) nnef_node_true_value = self.get_node_from_pool(tfnode, tf_inputs['true_value']) nnef_node_false_value = self.get_node_from_pool(tfnode, tf_inputs['false_value']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_true_value, nnef_node_false_value) nnef_node = node.Select(condition=nnef_node_condition, true_value=nnef_node_true_value, false_value=nnef_node_false_value, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Shape(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.ShapeOf(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Sigmoid(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Sigmoid(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Sign(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Sign(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Slice(self, tfnode): tf_inputs = {'input': 0, 'begin':1, 'end':2} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_begin = self.get_node_from_pool(tfnode, tf_inputs['begin']) nnef_node_end = self.get_node_from_pool(tfnode, tf_inputs['end']) if nnef_node_begin.op == 'variable': begin = nnef_node_begin.get_tensordatafile().get_data().get_array()[0][0] elif nnef_node_begin.op == 'constant': begin = np.reshape(np.asarray(nnef_node_begin.parameters['value'], dtype=np.int32), nnef_node_begin.parameters['shape']) else: begin = nnef_node_begin.get_value() if nnef_node_end.op == 'variable': end = nnef_node_end.get_tensordatafile().get_data().get_array()[0][0] elif nnef_node_end == 'constant': end = np.reshape(np.asarray(nnef_node_end.parameters['value'], dtype=np.int32), nnef_node_end.parameters['shape']) else: end = nnef_node_end.get_value() axes = list(range(len(begin))) output_shape = len(axes)*[0] for i in range(len(axes)): output_shape[i] = int(end[i]-begin[i]) nnef_node = node.Slice(input=nnef_node_input, axes=axes, begin=list(begin), end=list(end), _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Softmax(self, tfnode): tf_inputs = {'x': 0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Softmax(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape,) return nnef_node, tf_inputs, attrs def import_Softplus(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Softplus(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Softsign(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Softsign(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Split(self, tfnode): tf_inputs = {'value':1, 'axis': 0} attrs = {'num_split': None} nnef_node_value = self.get_node_from_pool(tfnode, tf_inputs['value']) nnef_node_axis = self.get_node_from_pool(tfnode, tf_inputs['axis']) self.remove_node_from_pool(nnef_node_axis) split_axis = int(nnef_node_axis.parameters['value'][0]) num_split = tfnode.attr['num_split'].i names = [] if num_split >= 1: new_name = '[' for i in range(num_split): if i == 0: new_name = new_name + self.gen_node_name(tfnode.name) + ', ' names.append(self.gen_node_name(tfnode.name)) else: new_name = new_name + self.gen_node_name(tfnode.name + ':' + str(i)) + ', ' names.append(self.gen_node_name(tfnode.name + ':' + str(i))) new_name = new_name[:-2] + ']' input_shape = nnef_node_value.output_shape[:] ratio = math.floor(input_shape[split_axis]/num_split) modu = input_shape[split_axis]%num_split ratios = [] for i in range(len(names)): rat_val = ratio if modu != 0: rat_val += 1 modu -= 1 ratios.append(int(rat_val)) nnef_node_split = node.Split(value=nnef_node_value, axis=split_axis, ratios=ratios, _uid=new_name, _output_shape=input_shape) for i in range(len(names)): out_shape = input_shape[:] out_shape[split_axis] = ratios[i] nnef_node = node.OutputVal(base_node=nnef_node_split, base_index=i, _uid=names[i], _output_shape=out_shape) self.node_pool[nnef_node.name] = nnef_node return nnef_node_split, tf_inputs, attrs def import_Sqrt(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Sqrt(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Square(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Sqr(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Squeeze(self, tfnode): tf_inputs = {'input': 0} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) input_shape = nnef_node_input.output_shape output_shape = [] if tfnode.attr['squeeze_dims'].list.i: for i in range(len(input_shape)): if i not in tfnode.attr['squeeze_dims'].list.i: output_shape.append(input_shape[i]) else: for i in input_shape: if i != 1: output_shape.append(i) output_shape = [int(v) for v in output_shape] nnef_node = node.Reshape(input=nnef_node_input, shape=output_shape, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _maintain_format=False) return nnef_node, tf_inputs, attrs def import_StridedSlice(self, tfnode): tf_inputs = {'input': 0, 'begin':1, 'end':2, 'strides':3} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_begin = self.get_node_from_pool(tfnode, tf_inputs['begin']) nnef_node_end = self.get_node_from_pool(tfnode, tf_inputs['end']) nnef_node_strides = self.get_node_from_pool(tfnode, tf_inputs['strides']) if nnef_node_begin.op == 'variable': begin = nnef_node_begin.get_tensordatafile().get_data().get_array()[0][0] elif nnef_node_begin.op == 'constant': begin = np.reshape(np.asarray(nnef_node_begin.parameters['value'], dtype=np.int32), nnef_node_begin.parameters['shape']) else: begin = nnef_node_begin.get_value() if nnef_node_end.op == 'variable': end = nnef_node_end.get_tensordatafile().get_data().get_array()[0][0] elif nnef_node_end.op == 'constant': end = np.reshape(np.asarray(nnef_node_end.parameters['value'], dtype=np.int32), nnef_node_end.parameters['shape']) else: end = nnef_node_end.get_value() if nnef_node_strides.op == 'variable': strides = nnef_node_strides.get_tensordatafile().get_data().get_array()[0][0] elif nnef_node_strides.op == 'constant': strides = np.reshape(np.asarray(nnef_node_strides.parameters['value'], dtype=np.int32), nnef_node_strides.parameters['shape']) else: strides = nnef_node_strides.get_value() for stride in strides: assert stride == 1, "Slice operation uses a stride that is not one, currently unsupported." axes = list(range(len(begin))) output_shape = len(axes)*[0] for i in range(len(axes)): if begin[i] == -1 and end[i] == 0: output_shape[i] = 0 elif end[i] == 0: output_shape[i] = int(nnef_node_input.output_shape[i] - begin[i]) else: output_shape[i] = int(end[i]-begin[i]) if 0 in output_shape: nnef_node_slice = node.Slice(input=nnef_node_input, axes=axes, begin=list(begin), end=list(end), _uid=self.gen_node_name(tfnode.name) + '_slice', _output_shape=output_shape) self.node_pool[nnef_node_slice.name] = nnef_node_slice squeeze_shape = [value for value in output_shape if value != 0] nnef_node = node.Reshape(input=nnef_node_slice, shape=squeeze_shape, _uid=self.gen_node_name(tfnode.name), _output_shape=squeeze_shape, _maintain_format=False) else: nnef_node = node.Slice(input=nnef_node_input, axes=axes, begin=list(begin), end=list(end), _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Sub(self, tfnode): tf_inputs = {'x':0, 'y':1} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) nnef_node_y = self.get_node_from_pool(tfnode, tf_inputs['y']) output_shape = self.define_elementwise_binary_output_shape(nnef_node_x, nnef_node_y) nnef_node = node.Sub(x=nnef_node_x, y=nnef_node_y, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape, _data_format=tfnode.attr['data_format']) return nnef_node, tf_inputs, attrs def import_Sum(self, tfnode): tf_inputs = {'input':0, 'axis':1} attrs = {'keep_dims': None} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_axis = self.get_node_from_pool(tfnode, tf_inputs['axis']) input_shape = nnef_node_input.output_shape[:] if nnef_node_axis.op == 'variable': shape = nnef_node_axis.get_tensordatafile().get_data().get_array()[0][0] else: shape = [int(nnef_node_axis.parameters['value'][0])] axes = [] for i in shape: if i not in axes: axes.append(i) axes.sort() self.remove_node_from_pool(nnef_node_axis) output_shape = input_shape[:] if(tfnode.attr['keep_dims'].b or tfnode.attr['keepdims'].b): for i in axes: output_shape[i] = 1 nnef_node_sum = node.SumReduce(input=nnef_node_input, axes=axes, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name) + '_sum',) self.node_pool[nnef_node_sum.name] = nnef_node_sum nnef_node = node.Reshape(input=nnef_node_sum, shape=output_shape, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name), _maintain_format=True) else: axes.sort(reverse=True) for i in axes: output_shape.pop(i) if output_shape == []: output_shape = [1] axes.sort() nnef_node = node.SumReduce(input=nnef_node_input, axes=axes, _output_shape=output_shape, _uid=self.gen_node_name(tfnode.name)) return nnef_node, tf_inputs, attrs def import_Tanh(self, tfnode): tf_inputs = {'x':0} attrs = {} nnef_node_x = self.get_node_from_pool(tfnode, tf_inputs['x']) output_shape = nnef_node_x.output_shape[:] nnef_node = node.Tanh(x=nnef_node_x, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs def import_Transpose(self, tfnode): tf_inputs = {'input':0, 'axes': 1} attrs = {} nnef_node_input = self.get_node_from_pool(tfnode, tf_inputs['input']) nnef_node_axes = self.get_node_from_pool(tfnode, tf_inputs['axes']) axes = list(nnef_node_axes.get_tensordatafile().get_data().get_array()[0][0]) self.remove_node_from_pool(nnef_node_axes) output_shape = [] for i in range(len(nnef_node_input.output_shape)): output_shape.append(nnef_node_input.output_shape[axes[i]]) nnef_node = node.Transpose(input=nnef_node_input, axes=axes, _uid=self.gen_node_name(tfnode.name), _output_shape=output_shape) return nnef_node, tf_inputs, attrs class TensorflowExporter(TensorflowLogger, ImporterExporter): def __init__(self, output_model): super(TensorflowExporter, self).__init__() self.output_model = output_model self.mapping = {0:0, 1:3, 2:1, 3:2} def run(self, nnef_graph): self.nxgraph = nnef_graph.get_nx_graph() self.generate_tf_graph() def format_name(self, name): index = name.find('_') if index != -1: newName = name[:index] + '/' + name[index+1:] else: newName = name newName = name.replace('_', '/') return newName def add_input(self, tfnode, nnef_node, param_name, order=[]): index = 0 if isinstance(nnef_node.parameters[param_name], list): nnef_node_param = nnef_node.parameters[param_name][index] else: nnef_node_param = nnef_node.parameters[param_name] while nnef_node_param is not None: if not isinstance(nnef_node_param, Node): nodeConst = NodeDef(name=self.format_name(nnef_node.name) + '/' + param_name, op='Const') nodeConst.attr['dtype'].type = 1 nodeConst.attr['value'].tensor.dtype = 1 nodeConst.attr['value'].tensor.float_val.extend([nnef_node_param]) self.tf_graph.node.extend([nodeConst]) tfnode.input.extend([nodeConst.name]) elif nnef_node_param.op == 'variable': tfnode.input.extend([self.format_name(nnef_node_param.name)]) for n in self.tf_graph.node: if (n.name) == tfnode.input[-1]: if n.attr['value'].tensor.tensor_shape.dim: return shapes = nnef_node_param.parameters['shape'] if not order: for i in range(0, len(shapes)): n.attr['value'].tensor.tensor_shape.dim.add().size = shapes[i] np_array_read = np.asarray(nnef_node_param.get_tensordatafile().get_data().get_array()[0], dtype=np.float32) n.attr['value'].tensor.tensor_content = np_array_read.tobytes() else: new_shape = [] for i in range(len(order)): new_shape.append(shapes[order[i]]) n.attr['value'].tensor.tensor_shape.dim.add().size = new_shape[i] np_array_read = np.asarray(nnef_node_param.get_tensordatafile().get_data().get_array()[0], dtype=np.float32) np_array_read = np.reshape(np_array_read, shapes) if len(new_shape) < len(shapes): np_array_read = np.reshape(np_array_read, new_shape) else: np_array_read = np.transpose(np_array_read, order) n.attr['value'].tensor.tensor_content = np.reshape(np_array_read, new_shape).tobytes() break elif nnef_node_param.op == 'reshape': tfnode.input.extend([self.format_name(nnef_node_param.name)]) if order != []: for n in self.tf_graph.node: if n.name == tfnode.input[-1]: for n_shape in self.tf_graph.node: if n_shape.name == n.input[1]: reshape_list = list(np.frombuffer(n_shape.attr['value'].tensor.tensor_content, dtype=np.int32)) new_reshape = [] for i in order: new_reshape.append(reshape_list[i]) n_shape.attr['value'].tensor.tensor_content = np.asarray(new_reshape, dtype=np.int32).tobytes() elif nnef_node_param.op == 'output_val': base_name = nnef_node_param.parameters['base_node'].name[:nnef_node_param.parameters['base_node'].name.find(',')] if base_name[0] == '[': base_name = base_name[1:] if nnef_node_param.parameters['base_index'] == 0: tfnode.input.extend([self.format_name(base_name)]) else: name = self.format_name(base_name) + ':' + str(nnef_node_param.parameters['base_index']) tfnode.input.extend([name]) else: tfnode.input.extend([self.format_name(nnef_node_param.name)]) if isinstance(nnef_node.parameters[param_name], list): index += 1 if index >= len(nnef_node.parameters[param_name]): nnef_node_param = None else: nnef_node_param = nnef_node.parameters[param_name][index] else: nnef_node_param = None def generate_tf_graph(self, ): self.tf_graph = graph_pb2.GraphDef() for nnef_node, data in self.nxgraph.nodes(data=True): if 'node' in data: nnef_node = data['node'] if nnef_node.name: if hasattr(self, "export_" + nnef_node.op): func = getattr(self, "export_" + nnef_node.op) func(nnef_node) else: self.export_UNKNOWN(nnef_node) else: print('WARNING: nnef_node missing from op: ', nnef_node) network_dir, model_filename = os.path.split(self.output_model) if not os.path.exists(network_dir): os.makedirs(network_dir) with open(self.output_model, "wb") as f: f.write(self.tf_graph.SerializeToString()) def export_UNKNOWN(self, nnef_node): print(nnef_node.op + " is currently not supported!\n") input() def export_abs(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Abs') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_add(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Add') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_and(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='LogicalAnd') #Going to be issues with type self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') self.tf_graph.node.extend([tfnode]) def export_area_downsample(self, nnef_node): tfnode_shape = NodeDef(name=self.format_name(nnef_node.name) + '/size', op='Const') tfnode_shape.attr['dtype'].type = 3 tfnode_shape.attr['value'].tensor.dtype = 3 output_size = np.asarray(nnef_node.output_shape[2:], dtype=np.int32) tfnode_shape.attr['value'].tensor.tensor_shape.dim.add().size = len(output_size) tfnode_shape.attr['value'].tensor.tensor_content = output_size.tobytes() self.tf_graph.node.extend([tfnode_shape]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='ResizeArea') self.add_input(tfnode, nnef_node, 'input') node.input.extend([tfnode_shape.name]) tfnode.attr['T'].type = 1 tfnode.attr['align_corners'].b = False self.tf_graph.node.extend([tfnode]) def export_avg_pool(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='AvgPool') self.add_input(tfnode, nnef_node, 'input') tfnode.attr['T'].type = 1 tfnode.attr['data_format'].s = b'NHWC' sizes = nnef_node.parameters['size'] nhwc_sizes = [sizes[0]] for i in range(2, len(sizes)): nhwc_sizes.append(sizes[i]) nhwc_sizes.append(sizes[1]) tfnode.attr['ksize'].list.i.extend(nhwc_sizes) dilations = nnef_node.parameters['dilation'] if dilations != [] and dilations != [1]*4: raise ValueError("TensorFlow does not support dilated pooling") strides = nnef_node.parameters['stride'] if strides == []: nhwc_strides = [1]*4 else: nhwc_strides = [strides[0]] for i in range(2, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(strides[1]) tfnode.attr['strides'].list.i.extend(nhwc_strides) if nnef_node.parameters['padding'] == []: tfnode.attr['padding'].s = 'SAME'.encode('utf-8') elif(nnef_node.parameters['padding'] == [(0, 0), (0, 0), (0, 0), (0, 0)]): tfnode.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[pads[0][0], pads[0][1]]] for i in range(2, len(pads)): padding = padding + [[pads[i][0], pads[i][1]]] padding = padding + [[pads[1][0], pads[1][1]]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/Pad", op='Pad') tfnode_pad.input.extend([tfnode.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode.input[0] = tfnode_pad.name tfnode.attr['padding'].s = 'VALID'.encode('utf-8') self.tf_graph.node.extend([tfnode]) def export_batch_normalization(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='FusedBatchNorm') self.add_input(tfnode, nnef_node, 'input', [0, 2, 3, 1]) self.add_input(tfnode, nnef_node, 'scale', [1]) self.add_input(tfnode, nnef_node, 'offset', [1]) self.add_input(tfnode, nnef_node, 'mean', [1]) self.add_input(tfnode, nnef_node, 'variance', [1]) tfnode.attr['T'].type = 1 tfnode.attr['data_format'].s = b'NHWC' tfnode.attr['epsilon'].f = nnef_node.parameters['epsilon'] tfnode.attr['is_training'].b = False self.tf_graph.node.extend([tfnode]) def export_ceil(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Ceil') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_concat(self, nnef_node): tfnode_axis = NodeDef(name=self.format_name(nnef_node.name) + "/axis", op="Const") tfnode_axis.attr['dtype'].type = 3 tfnode_axis.attr['value'].tensor.dtype = 3 tfnode_axis.attr['value'].tensor.tensor_shape.dim.extend([]) if len(nnef_node.output_shape) == 4: tfnode_axis.attr['value'].tensor.int_val.extend([self.mapping[nnef_node.parameters['axis']]]) else: tfnode_axis.attr['value'].tensor.int_val.extend([nnef_node.parameters['axis']]) self.tf_graph.node.extend([tfnode_axis]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='ConcatV2') self.add_input(tfnode, nnef_node, 'values') tfnode.input.append((self.format_name(nnef_node.name) + '/axis').encode('utf-8')) tfnode.attr['N'].i = len(nnef_node.parameters['values']) tfnode.attr['T'].type = 1 tfnode.attr['Tidx'].type = 3 self.tf_graph.node.extend([tfnode]) def export_constant(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Const') tfnode.attr['dtype'].type = 1 tfnode.attr['value'].tensor.dtype = 1 tfnode.attr['value'].tensor.float_val.extend([nnef_node.parameters['value'][0]]) if len(nnef_node.parameters['shape']) == 2 and nnef_node.parameters['shape'][0] == 1: nnef_node.parameters['shape'].pop(0) for i in range(len(nnef_node.parameters['shape'])): tfnode.attr['value'].tensor.tensor_shape.dim.add().size = nnef_node.parameters['shape'][i] self.tf_graph.node.extend([tfnode]) def export_conv(self, nnef_node): conv_len = len(nnef_node.parameters['input'].output_shape) if(conv_len == 4) and nnef_node.parameters['groups'] == nnef_node.parameters['input'].output_shape[1]: return self.export_planewise_conv(nnef_node) assert nnef_node.parameters['groups'] == 1, "TensorFlow does not support grouped convolutions currently." if conv_len == 4: tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Conv2D') self.add_input(tfnode, nnef_node, 'input', [0, 2, 3, 1]) self.add_input(tfnode, nnef_node, 'filter', [2, 3, 1, 0]) tfnode.attr['data_format'].s = 'NHWC'.encode('utf-8') tfnode.attr['use_cudnn_on_gpu'].b = True elif conv_len == 5: tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Conv3D') self.add_input(tfnode, nnef_node, 'input', [0, 2, 3, 4, 1]) self.add_input(tfnode, nnef_node, 'filter', [2, 3, 4, 1, 0]) tfnode.attr['data_format'].s = 'NDHWC'.encode('utf-8') else: raise ValueError("Cannot handle input_size " + str(conv_len) + " yet.") tfnode.attr['T'].type = 1 if 'dilation' in nnef_node.parameters: dilations = nnef_node.parameters['dilation'] if dilations: nhwc_dilations = [1] for i in range(0, len(dilations)): nhwc_dilations.append(dilations[i]) nhwc_dilations.append(1) else: nhwc_dilations = [1]*conv_len else: nhwc_dilations = [1]*conv_len tfnode.attr['dilations'].list.i.extend(nhwc_dilations) strides = nnef_node.parameters['stride'] if strides: nhwc_strides = [1] for i in range(0, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(1) else: nhwc_strides = [1]*conv_len tfnode.attr['strides'].list.i.extend(nhwc_strides) if nnef_node.parameters['padding'] == []: tfnode.attr['padding'].s = 'SAME'.encode('utf-8') elif nnef_node.parameters['padding'] == [(0, 0)]*(conv_len-2): tfnode.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[0, 0]] for pad in pads: padding = padding + [[pad[0], pad[1]]] padding = padding + [[0, 0]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/Pad", op='Pad') tfnode_pad.input.extend([tfnode.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode.input[0] = tfnode_pad.name tfnode.attr['padding'].s = 'VALID'.encode('utf-8') if('bias' in nnef_node.parameters and nnef_node.parameters['bias'] != 0): tfnode.name = tfnode.name + '/conv' self.tf_graph.node.extend([tfnode]) tfnode_add = NodeDef(name=self.format_name(nnef_node.name), op='Add') tfnode_add.input.extend([tfnode.name]) self.add_input(tfnode_add, nnef_node, 'bias') tfnode_add.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_add]) else: self.tf_graph.node.extend([tfnode]) def export_deconv(self, nnef_node): assert nnef_node.parameters['groups'] == 1, "TensorFlow does not support grouped convolutions currently." conv_len = len(nnef_node.output_shape) tfnode_const = NodeDef(name=self.format_name(nnef_node.name) + '/output_shape', op='Const') tfnode_const.attr['dtype'].type = 3 tfnode_const.attr['value'].tensor.dtype = 3 tfnode_const.attr['value'].tensor.tensor_shape.dim.add().size = conv_len new_out_shape = [nnef_node.output_shape[0]] for i in range(2, conv_len): new_out_shape.append(nnef_node.output_shape[i]) new_out_shape.append(nnef_node.output_shape[1]) tfnode_const.attr['value'].tensor.tensor_content = np.asarray(new_out_shape, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_const]) if conv_len == 4: tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Conv2DBackpropInput') tfnode.input.extend([tfnode_const.name]) self.add_input(tfnode, nnef_node, 'filter', [2, 3, 1, 0]) self.add_input(tfnode, nnef_node, 'input', [0, 2, 3, 1]) tfnode.attr['data_format'].s = 'NHWC'.encode('utf-8') elif conv_len == 5: tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Conv3DBackpropInputV2') tfnode.input.extend([tfnode_const.name]) self.add_input(tfnode, nnef_node, 'filter', [2, 3, 4, 1, 0]) self.add_input(tfnode, nnef_node, 'input', [0, 2, 3, 4, 1]) tfnode.attr['data_format'].s = 'NDHWC'.encode('utf-8') else: raise ValueError("Cannot handle input_size " + str(conv_len) + " yet.") tfnode.attr['T'].type = 1 if 'dilation' in nnef_node.parameters: dilations = nnef_node.parameters['dilation'] if dilations: nhwc_dilations = [1] for i in range(0, len(dilations)): nhwc_dilations.append(dilations[i]) nhwc_dilations.append(1) else: nhwc_dilations = [1]*conv_len else: nhwc_dilations = [1]*conv_len tfnode.attr['dilations'].list.i.extend(nhwc_dilations) strides = nnef_node.parameters['stride'] if strides: nhwc_strides = [1] for i in range(0, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(1) else: nhwc_strides = [1]*conv_len tfnode.attr['strides'].list.i.extend(nhwc_strides) if conv_len == 4: tfnode.attr['use_cudnn_on_gpu'].b = True if nnef_node.parameters['padding'] == []: tfnode.attr['padding'].s = 'SAME'.encode('utf-8') elif nnef_node.parameters['padding'] == [(0, 0)]*(conv_len-2): tfnode.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[0, 0]] for pad in pads: padding = padding + [[pad[0], pad[1]]] padding = padding + [[0, 0]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/Pad", op='Pad') tfnode_pad.input.extend([tfnode.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode.input[0] = tfnode_pad.name tfnode.attr['padding'].s = 'VALID'.encode('utf-8') if('bias' in nnef_node.parameters and nnef_node.parameters['bias'] != 0): tfnode.name = tfnode.name + '/conv' self.tf_graph.node.extend([tfnode]) tfnode_add = NodeDef(name=self.format_name(nnef_node.name), op='Add') tfnode_add.input.extend([tfnode.name]) self.add_input(tfnode_add, nnef_node, 'bias') tfnode_add.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_add]) else: self.tf_graph.node.extend([tfnode]) def export_div(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='RealDiv') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_elu(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Elu') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_eq(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Equal') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_exp(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Exp') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_external(self, nnef_node): tfnode = NodeDef(name=nnef_node.name, op='Placeholder') tfnode.attr['dtype'].type = 1 tfnode.attr['shape'].shape.dim.add().size = nnef_node.parameters['shape'][0] for i in range(2, len(nnef_node.parameters['shape'])): tfnode.attr['shape'].shape.dim.add().size = nnef_node.parameters['shape'][i] tfnode.attr['shape'].shape.dim.add().size = nnef_node.parameters['shape'][1] self.tf_graph.node.extend([tfnode]) def export_floor(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Floor') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_ge(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='GreaterEqual') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_gt(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Greater') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_le(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='LessEqual') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_linear(self, nnef_node): input_shape = nnef_node.parameters['input'].output_shape if len(input_shape) > 2: i = 1 squeeze_dims = [] for j in range(2, len(input_shape)): squeeze_dims.append(i) i += 1 tfnode_squeeze = NodeDef(name=self.format_name(nnef_node.name) + '/squeeze', op='Squeeze') self.add_input(tfnode_squeeze, nnef_node, 'input') tfnode_squeeze.attr['T'].type = 1 tfnode_squeeze.attr['squeeze_dims'].list.i.extend(squeeze_dims) self.tf_graph.node.extend([tfnode_squeeze]) tfnode = NodeDef(name=self.format_name(nnef_node.name) + '/linear', op='MatMul') if len(input_shape) > 2: tfnode.input.extend([tfnode_squeeze.name]) else: self.add_input(tfnode, nnef_node, 'input') self.add_input(tfnode, nnef_node, 'filter') tfnode.attr['T'].type = 1 tfnode.attr['transpose_a'].b = False tfnode.attr['transpose_b'].b = True self.tf_graph.node.extend([tfnode]) tfnode_add = NodeDef(name=self.format_name(nnef_node.name), op='Add') tfnode_add.input.extend([tfnode.name]) self.add_input(tfnode_add, nnef_node, 'bias') tfnode_add.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_add]) def export_local_response_normalization(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='LRN') self.add_input(tfnode, nnef_node, 'input') tfnode.attr['T'].type = 1 tfnode.attr['alpha'].f = nnef_node.parameters['alpha'] tfnode.attr['beta'].f = nnef_node.parameters['beta'] tfnode.attr['bias'].f = nnef_node.parameters['bias'] tfnode.attr['depth_radius'].i = nnef_node.parameters['size'][1] self.tf_graph.node.extend([tfnode]) def export_log(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Log') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_lt(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Less') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_l2_normalization(self, nnef_node): assert(nnef_node.parameters['bias'] == 0.0), "TensorFlow cannot handle non-zero bias for op: l2_normalize" tfnode_square = NodeDef(name=self.format_name(nnef_node.name) + '/Square', op='Square') self.add_input(tfnode_square, nnef_node, 'input') tfnode_square.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_square]) tfnode_const = NodeDef(name=self.format_name(nnef_node.name) + '/Const', op='Const') tfnode_const.attr['dtype'].type = 3 tfnode_const.attr['value'].tensor.tensor_shape.dim.add().size = len(nnef_node.parameters['axes']) tfnode_const.attr['value'].tensor.tensor_content = np.asarray(nnef_node.parameters['axes'], dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_const]) tfnode_sum = NodeDef(name=self.format_name(nnef_node.name) + '/Sum', op='Sum') tfnode_sum.input.extend([tfnode_square.name]) tfnode_sum.input.extend([tfnode_const.name]) tfnode_sum.attr['T'].type = 1 tfnode_sum.attr['Tidx'].type = 3 tfnode_sum.attr['keep_dims'].b = True self.tf_graph.node.extend([tfnode_sum]) tfnode_max_y = NodeDef(name=self.format_name(nnef_node.name) + '/Maximum/y', op='Const') tfnode_max_y.attr['dtype'].type = 1 tfnode_max_y.attr['value'].tensor.dtype = 1 tfnode_max_y.attr['value'].tensor.float_val.extend([nnef_node.parameters['epsilon']]) self.tf_graph.node.extend([tfnode_max_y]) tfnode_max = NodeDef(name=self.format_name(nnef_node.name) + '/Maximum', op='Maximum') tfnode_max.input.extend([tfnode_sum.name]) tfnode_max.input.extend([tfnode_max_y.name]) tfnode_max.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_max]) tfnode_rsqrt = NodeDef(name=self.format_name(nnef_node.name) + '/Rsqrt', op='Rsqrt') tfnode_rsqrt.input.extend([tfnode_max.name]) tfnode_rsqrt.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_rsqrt]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Mul') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_rsqrt.name]) tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_matmul(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='MatMul') self.add_input(tfnode, nnef_node, 'A') self.add_input(tfnode, nnef_node, 'B') tfnode.attr['T'].type = 1 tfnode.attr['transpose_a'].b = nnef_node.parameters['transposeA'] tfnode.attr['transpose_b'].b = nnef_node.parameters['transposeB'] self.tf_graph.node.extend([tfnode]) def export_max(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Maximum') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_max_pool(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='MaxPool') self.add_input(tfnode, nnef_node, 'input') tfnode.attr['T'].type = 1 tfnode.attr['data_format'].s = 'NHWC'.encode('utf-8') sizes = nnef_node.parameters['size'] nhwc_sizes = [sizes[0]] for i in range(2, len(sizes)): nhwc_sizes.append(sizes[i]) nhwc_sizes.append(sizes[1]) tfnode.attr['ksize'].list.i.extend(nhwc_sizes) dilations = nnef_node.parameters['dilation'] if dilations != [] and dilations != [1]*4: raise ValueError("TensorFlow does not support dilated pooling") strides = nnef_node.parameters['stride'] nhwc_strides = [strides[0]] for i in range(2, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(strides[1]) tfnode.attr['strides'].list.i.extend(nhwc_strides) if nnef_node.parameters['padding'] == []: tfnode.attr['padding'].s = 'SAME'.encode('utf-8') elif(nnef_node.parameters['padding'] == [(0, 0), (0, 0), (0, 0), (0, 0)]): tfnode.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[pads[0][0], pads[0][1]]] for i in range(2, len(pads)): padding = padding + [[pads[i][0], pads[i][1]]] padding = padding + [[pads[1][0], pads[1][1]]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/Pad", op='Pad') tfnode_pad.input.extend([tfnode.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode.input[0] = tfnode_pad.name tfnode.attr['padding'].s = 'VALID'.encode('utf-8') self.tf_graph.node.extend([tfnode]) def export_max_pool_with_index(self, nnef_node): name = nnef_node.name[:nnef_node.name.find(',')] tfnode = NodeDef(name=self.format_name(name), op='MaxPoolWithArgmax') self.add_input(tfnode, nnef_node, 'input') tfnode.attr['T'].type = 1 tfnode.attr['Targmax'].type = 3 sizes = nnef_node.parameters['size'] nhwc_sizes = [sizes[0]] for i in range(2, len(sizes)): nhwc_sizes.append(sizes[i]) nhwc_sizes.append(sizes[1]) tfnode.attr['ksize'].list.i.extend(nhwc_sizes) dilations = nnef_node.parameters['dilation'] if dilations != [] and dilations != [1]*4: raise ValueError("TensorFlow does not support dilated pooling") strides = nnef_node.parameters['stride'] nhwc_strides = [strides[0]] for i in range(2, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(strides[1]) tfnode.attr['strides'].list.i.extend(nhwc_strides) if nnef_node.parameters['padding'] == []: tfnode.attr['padding'].s = 'SAME'.encode('utf-8') elif(nnef_node.parameters['padding'] == [(0, 0), (0, 0), (0, 0), (0, 0)]): tfnode.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[pads[0][0], pads[0][1]]] for i in range(2, len(pads)): padding = padding + [[pads[i][0], pads[i][1]]] padding = padding + [[pads[1][0], pads[1][1]]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/Pad", op='Pad') tfnode_pad.input.extend([tfnode.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode.input[0] = tfnode_pad.name tfnode.attr['padding'].s = 'VALID'.encode('utf-8') self.tf_graph.node.extend([tfnode]) def export_max_reduce(self, nnef_node): tfnode_axes = NodeDef(name=self.format_name(nnef_node.name) + '/axes', op='Const') tfnode_axes.attr['dtype'].type = 3 array_length = len(nnef_node.parameters['axes']) new_axes = [] for axis in nnef_node.parameters['axes']: new_axes.append(self.mapping[axis]) tfnode_axes.attr['value'].tensor.dtype = 3 tfnode_axes.attr['value'].tensor.tensor_shape.dim.add().size = array_length if array_length == 1: tfnode_axes.attr['value'].tensor.int_val.extend([new_axes[0]]) else: tfnode_axes.attr['value'].tensor.tensor_content = np.asarray(new_axes, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_axes]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Max') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_axes.name]) tfnode.attr['T'].type = 1 tfnode.attr['Tidx'].type = 3 tfnode.attr['keep_dims'].b = False self.tf_graph.node.extend([tfnode]) def export_mean_reduce(self, nnef_node): tfnode_axes = NodeDef(name=self.format_name(nnef_node.name) + '/axes', op='Const') tfnode_axes.attr['dtype'].type = 3 array_length = len(nnef_node.parameters['axes']) new_axes = [] for axis in nnef_node.parameters['axes']: new_axes.append(self.mapping[axis]) tfnode_axes.attr['value'].tensor.dtype = 3 tfnode_axes.attr['value'].tensor.tensor_shape.dim.add().size = array_length if array_length == 1: tfnode_axes.attr['value'].tensor.int_val.extend([new_axes[0]]) else: tfnode_axes.attr['value'].tensor.tensor_content = np.asarray(new_axes, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_axes]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Mean') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_axes.name]) tfnode.attr['T'].type = 1 tfnode.attr['Tidx'].type = 3 tfnode.attr['keep_dims'].b = False self.tf_graph.node.extend([tfnode]) def export_min(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Minimum') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_mul(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Mul') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_multilinear_upsample(self, nnef_node): tfnode_shape = NodeDef(name=self.format_name(nnef_node.name) + '/size', op='Const') tfnode_shape.attr['dtype'].type = 3 tfnode_shape.attr['value'].tensor.dtype = 3 output_size = np.asarray(nnef_node.output_shape[2:], dtype=np.int32) tfnode_shape.attr['value'].tensor.tensor_shape.dim.add().size = len(output_size) tfnode_shape.attr['value'].tensor.tensor_content = output_size.tobytes() self.tf_graph.node.extend([tfnode_shape]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='ResizeBilinear') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_shape.name]) tfnode.attr['T'].type = 1 tfnode.attr['align_corners'].b = False self.tf_graph.node.extend([tfnode]) def export_ne(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='NotEqual') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_nearest_downsample(self, nnef_node): tfnode_shape = NodeDef(name=self.format_name(nnef_node.name) + '/size', op='Const') tfnode_shape.attr['dtype'].type = 3 tfnode_shape.attr['value'].tensor.dtype = 3 output_size = np.asarray(nnef_node.output_shape[2:], dtype=np.int32) tfnode_shape.attr['value'].tensor.tensor_shape.dim.add().size = len(output_size) tfnode_shape.attr['value'].tensor.tensor_content = output_size.tobytes() self.tf_graph.node.extend([tfnode_shape]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='ResizeNearestNeighbor') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_shape.name]) tfnode.attr['T'].type = 1 tfnode.attr['align_corners'].b = False self.tf_graph.node.extend([tfnode]) def export_nearest_upsample(self, nnef_node): tfnode_shape = NodeDef(name=self.format_name(nnef_node.name) + '/size', op='Const') tfnode_shape.attr['dtype'].type = 3 tfnode_shape.attr['value'].tensor.dtype = 3 output_size = np.asarray(nnef_node.output_shape[2:], dtype=np.int32) tfnode_shape.attr['value'].tensor.tensor_shape.dim.add().size = len(output_size) tfnode_shape.attr['value'].tensor.tensor_content = output_size.tobytes() self.tf_graph.node.extend([tfnode_shape]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='ResizeNearestNeighbor') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_shape.name]) tfnode.attr['T'].type = 1 tfnode.attr['align_corners'].b = False self.tf_graph.node.extend([tfnode]) def export_neg(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Neg') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_not(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='LogicalNot') self.add_input(tfnode, nnef_node, 'x') self.tf_graph.node.extend([tfnode]) def export_or(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='LogicalOr') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') self.tf_graph.node.extend([tfnode]) def export_output_val(self, nnef_node): return def export_planewise_conv(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='DepthwiseConv2dNative') self.add_input(tfnode, nnef_node, 'input', [0, 2, 3, 1]) #Converts to Tensorflow format of [height, width, channels in, channel multiplier] if nnef_node.parameters['input'].output_shape[1] != nnef_node.parameters['filter'].parameters['shape'][0]: filter_node = nnef_node.parameters['filter'] np_array_read = filter_node.get_tensordatafile().get_data().get_array()[0] np_array_read = np.reshape(np_array_read, filter_node.parameters['shape']) new_shape = [nnef_node.parameters['input'].output_shape[1]] new_shape.append(int(filter_node.output_shape[0]/new_shape[0])) new_shape += filter_node.parameters['shape'][2:] filter_node.parameters['shape'] = new_shape np_array_read = np.reshape(np_array_read, new_shape) filter_node.get_tensordatafile().get_data().set_array(np_array_read, override=True) self.add_input(tfnode, nnef_node, 'filter', [2, 3, 0, 1]) tfnode.attr['T'].type = 1 tfnode.attr['data_format'].s = 'NHWC'.encode('utf-8') if 'dilation' in nnef_node.parameters: dilations = nnef_node.parameters['dilation'] if dilations: nhwc_dilations = [1] for i in range(0, len(dilations)): nhwc_dilations.append(dilations[i]) nhwc_dilations.append(1) else: nhwc_dilations = [1]*4 else: nhwc_dilations = [1]*4 tfnode.attr['dilations'].list.i.extend(nhwc_dilations) strides = nnef_node.parameters['stride'] if strides: nhwc_strides = [1] for i in range(0, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(1) else: nhwc_strides = [1]*4 tfnode.attr['strides'].list.i.extend(nhwc_strides) if nnef_node.parameters['padding'] == []: tfnode.attr['padding'].s = 'SAME'.encode('utf-8') elif nnef_node.parameters['padding'] == [(0, 0)]*(2): tfnode.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[0, 0]] for pad in pads: padding = padding + [[pad[0], pad[1]]] padding = padding + [[0, 0]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/Pad", op='Pad') tfnode_pad.input.extend([tfnode.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode.input[0] = tfnode_pad.name tfnode.attr['padding'].s = 'VALID'.encode('utf-8') if('bias' in nnef_node.parameters and nnef_node.parameters['bias'] != 0): tfnode.name = tfnode.name + '/conv' self.tf_graph.node.extend([tfnode]) tfnode_add = NodeDef(name=self.format_name(nnef_node.name), op='Add') tfnode_add.input.extend([tfnode.name]) self.add_input(tfnode_add, nnef_node, 'bias') tfnode_add.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_add]) else: self.tf_graph.node.extend([tfnode]) def export_pow(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Pow') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_relu(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Relu') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_reshape(self, nnef_node): tfnode_shape = NodeDef(name=self.format_name(nnef_node.name) + '/shape', op='Const') tfnode_shape.attr['dtype'].type = 3 tfnode_shape.attr['value'].tensor.dtype = 3 tfnode_shape.attr['value'].tensor.tensor_shape.dim.add().size = len(nnef_node.parameters['shape']) if len(nnef_node.parameters['shape']) == 1: tfnode_shape.attr['value'].tensor.int_val = nnef_node.parameters['shape'][0] else: shape_array = np.asarray(nnef_node.parameters['shape']) tfnode_shape.attr['value'].tensor.tensor_content = np.asarray(shape_array, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_shape]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Reshape') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([self.format_name(nnef_node.name) + '/shape']) tfnode.attr['T'].type = 1 tfnode.attr['Tshape'].type = 3 self.tf_graph.node.extend([tfnode]) def export_round(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Round') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_rsqrt(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Rsqrt') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_select(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Select') self.add_input(tfnode, nnef_node, 'condition') self.add_input(tfnode, nnef_node, 'true_value') self.add_input(tfnode, nnef_node, 'false_value') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_separable_conv(self, nnef_node): tfnode_depthwise = NodeDef(name=self.format_name(nnef_node.name) + '/depthwise', op='DepthwiseConv2dNative') self.add_input(tfnode_depthwise, nnef_node, 'input', [0, 2, 3, 1]) #Converts to Tensorflow format of [height, width, channels in, channel multiplier] if nnef_node.parameters['input'].output_shape[1] != nnef_node.parameters['plane_filter'].parameters['shape'][0]: filter_node = nnef_node.parameters['plane_filter'] np_array_read = filter_node.get_tensordatafile().get_data().get_array()[0] np_array_read = np.reshape(np_array_read, filter_node.parameters['shape']) new_shape = [nnef_node.parameters['input'].output_shape[1]] new_shape.append(int(filter_node.output_shape[0]/new_shape[0])) new_shape += filter_node.parameters['shape'][2:] filter_node.parameters['shape'] = new_shape np_array_read = np.reshape(np_array_read, new_shape) filter_node.get_tensordatafile().get_data().set_array(np_array_read, override=True) self.add_input(tfnode_depthwise, nnef_node, 'plane_filter', [2, 3, 0, 1]) tfnode_depthwise.attr['T'].type = 1 tfnode_depthwise.attr['data_format'].s = 'NHWC'.encode('utf-8') if 'dilation' in nnef_node.parameters: dilations = nnef_node.parameters['dilation'] if dilations: nhwc_dilations = [1] for i in range(0, len(dilations)): nhwc_dilations.append(dilations[i]) nhwc_dilations.append(1) else: nhwc_dilations = [1]*4 else: nhwc_dilations = [1]*4 tfnode_depthwise.attr['dilations'].list.i.extend(nhwc_dilations) strides = nnef_node.parameters['stride'] if strides: nhwc_strides = [1] for i in range(0, len(strides)): nhwc_strides.append(strides[i]) nhwc_strides.append(1) else: nhwc_strides = [1]*4 tfnode_depthwise.attr['strides'].list.i.extend(nhwc_strides) if nnef_node.parameters['padding'] == []: tfnode_depthwise.attr['padding'].s = 'SAME'.encode('utf-8') elif nnef_node.parameters['padding'] == [(0, 0)]*(2): tfnode_depthwise.attr['padding'].s = 'VALID'.encode('utf-8') else: pads = nnef_node.parameters['padding'] padding = [[0, 0]] for pad in pads: padding = padding + [[pad[0], pad[1]]] padding = padding + [[0, 0]] padding = np.asarray(padding, dtype=np.int32) tfnode_pad_const = NodeDef(name=self.format_name(nnef_node.name) + "/depthwise/Pad/paddings", op='Const') tfnode_pad_const.attr['dtype'].type = 3 tfnode_pad_const.attr['value'].tensor.dtype = 3 for size in np.shape(padding): tfnode_pad_const.attr['value'].tensor.tensor_shape.dim.add().size = size tfnode_pad_const.attr['value'].tensor.tensor_content = padding.tobytes() self.tf_graph.node.extend([tfnode_pad_const]) tfnode_pad = NodeDef(name=self.format_name(nnef_node.name) + "/depthwise/Pad", op='Pad') tfnode_pad.input.extend([tfnode_depthwise.input[0]]) tfnode_pad.input.extend([tfnode_pad_const.name]) tfnode_pad.attr['T'].type = 1 tfnode_pad.attr['Tpaddings'].type = 3 self.tf_graph.node.extend([tfnode_pad]) tfnode_depthwise.input[0] = tfnode_pad.name tfnode_depthwise.attr['padding'].s = 'VALID'.encode('utf-8') self.tf_graph.node.extend([tfnode_depthwise]) conv_len = len(nnef_node.parameters['input'].output_shape) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Conv2D') tfnode.input.extend([tfnode_depthwise.name]) self.add_input(tfnode, nnef_node, 'point_filter', [2, 3, 1, 0]) tfnode.attr['data_format'].s = 'NHWC'.encode('utf-8') tfnode.attr['use_cudnn_on_gpu'].b = True tfnode.attr['T'].type = 1 nhwc_dilations = [1]*conv_len tfnode.attr['dilations'].list.i.extend(nhwc_dilations) nhwc_strides = [1]*conv_len tfnode.attr['strides'].list.i.extend(nhwc_strides) tfnode.attr['padding'].s = 'SAME'.encode('utf-8') if('bias' in nnef_node.parameters and nnef_node.parameters['bias'] != 0): tfnode.name = tfnode.name + '/conv' self.tf_graph.node.extend([tfnode]) tfnode_add = NodeDef(name=self.format_name(nnef_node.name), op='Add') tfnode_add.input.extend([tfnode.name]) self.add_input(tfnode_add, nnef_node, 'bias') tfnode_add.attr['T'].type = 1 self.tf_graph.node.extend([tfnode_add]) else: self.tf_graph.node.extend([tfnode]) def export_shape_of(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Shape') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 tfnode.attr['out_type'].type = 3 self.tf_graph.node.extend([tfnode]) def export_sigmoid(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Sigmoid') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_sign(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Sign') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 def export_slice(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Slice') self.add_input(tfnode, nnef_node, 'input') tfnode.attr['T'].type = 1 tfnode.attr['Index'].type = 3 tfnode_begin = NodeDef(name=self.format_name(nnef_node.name) + '/begin', op='Const') tfnode_begin.attr['dtype'].type = 3 tfnode_begin.attr['value'].tensor.dtype = 3 tfnode_begin.attr['value'].tensor.tensor_shape.dim.add().size = len(nnef_node.parameters['begin']) begin_array = nnef_node.parameters['begin'][:] for i in range(len(begin_array)): if begin_array[i] == -1: begin_array[i] = 0 if len(begin_array) == 1: tfnode_begin.attr['value'].tensor.int_val = begin_array[0] else: size_array = np.asarray(begin_array) tfnode_begin.attr['value'].tensor.tensor_content = np.asarray(size_array, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_begin]) tfnode_size = NodeDef(name=self.format_name(nnef_node.name) + '/size', op='Const') tfnode_size.attr['dtype'].type = 3 tfnode_size.attr['value'].tensor.dtype = 3 tfnode_size.attr['value'].tensor.tensor_shape.dim.add().size = len(nnef_node.parameters['end']) end_array = nnef_node.parameters['end'][:] for i in range(len(end_array)): if end_array[i] == 0: end_array[i] = nnef_node.output_shape[i] - begin_array[i] else: end_array[i] = end_array[i] - begin_array[i] if len(end_array) == 1: tfnode_size.attr['value'].tensor.int_val = end_array[0] else: size_array = np.asarray(end_array) tfnode_size.attr['value'].tensor.tensor_content = np.asarray(size_array, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_size]) tfnode.input.extend([tfnode_begin.name]) tfnode.input.extend([tfnode_size.name]) self.tf_graph.node.extend([tfnode]) def export_softmax(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Softmax') if len(nnef_node.output_shape) != 2: tfnode_shape = NodeDef(name=self.format_name(nnef_node.name) + '/shape', op='Const') tfnode_shape.attr['dtype'].type = 3 tfnode_shape.attr['value'].tensor.dtype = 3 tfnode_shape.attr['value'].tensor.tensor_shape.dim.add().size = 2 size = 1 for i in range(1,len(nnef_node.output_shape)): size *= nnef_node.output_shape[i] new_shape = [nnef_node.output_shape[0], size] tfnode_shape.attr['value'].tensor.tensor_content = np.asarray(new_shape, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_shape]) tfnode_reshape = NodeDef(name=self.format_name(nnef_node.name) + '/reshape', op='Reshape') self.add_input(tfnode_reshape, nnef_node, 'x') tfnode_reshape.input.extend([self.format_name(nnef_node.name) + '/shape']) tfnode_reshape.attr['T'].type = 1 tfnode_reshape.attr['Tshape'].type = 3 self.tf_graph.node.extend([tfnode_reshape]) tfnode.input.extend([self.format_name(nnef_node.name) + '/reshape']) else: tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Softmax') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_softplus(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Softplus') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_softsign(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Softsign') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_split(self, nnef_node): tfnode_name = nnef_node.name[1:nnef_node.name.find(',')] tfnode_const = NodeDef(name=self.format_name(tfnode_name) + '/split_dim', op='Const') tfnode_const.attr['dtype'].type = 3 tfnode_const.attr['value'].tensor.dtype = 3 tfnode_const.attr['value'].tensor.int_val.extend([nnef_node.parameters['axis']]) self.tf_graph.node.extend([tfnode_const]) tfnode = NodeDef(name=self.format_name(tfnode_name), op='Split') tfnode.input.extend([tfnode_const.name]) self.add_input(tfnode, nnef_node, 'value') tfnode.attr['T'].type = 1 tfnode.attr['num_split'].i = len(nnef_node.parameters['ratios']) self.tf_graph.node.extend([tfnode]) def export_sqr(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Square') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_sqrt(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Sqrt') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_sub(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Sub') self.add_input(tfnode, nnef_node, 'x') self.add_input(tfnode, nnef_node, 'y') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_sum_reduce(self, nnef_node): #Generate const node for axis tfnode_axes = NodeDef(name=self.format_name(nnef_node.name) + '/axes', op='Const') tfnode_axes.attr['dtype'].type = 3 array_length = len(nnef_node.parameters['axes']) new_axes = [] for axis in nnef_node.parameters['axes']: new_axes.append(self.mapping[axis]) tfnode_axes.attr['value'].tensor.dtype = 3 tfnode_axes.attr['value'].tensor.tensor_shape.dim.add().size = array_length if array_length == 1: tfnode_axes.attr['value'].tensor.int_val.extend([new_axes[0]]) else: tfnode_axes.attr['value'].tensor.tensor_content = np.asarray(new_axes, dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_axes]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Sum') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_axes.name]) tfnode.attr['T'].type = 1 tfnode.attr['Tidx'].type = 3 tfnode.attr['keep_dims'].b = False self.tf_graph.node.extend([tfnode]) def export_tanh(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Tanh') self.add_input(tfnode, nnef_node, 'x') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_transpose(self, nnef_node): tfnode_perm = NodeDef(name=self.format_name(nnef_node.name) + '/perm', op='Const') tfnode_perm.attr['dtype'].type = 3 tfnode_perm.attr['value'].tensor.dtype = 3 tfnode_perm.attr['value'].tensor.tensor_shape.dim.add().size = len(nnef_node.parameters['axes']) tfnode_perm.attr['value'].tensor.tensor_content = np.asarray(nnef_node.parameters['axes'], dtype=np.int32).tobytes() self.tf_graph.node.extend([tfnode_perm]) tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Transpose') self.add_input(tfnode, nnef_node, 'input') tfnode.input.extend([tfnode_perm.name]) tfnode.attr['T'].type = 1 tfnode.attr['Tperm'].type = 3 self.tf_graph.node.extend([tfnode]) def export_update(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Identity') self.add_input(tfnode, nnef_node, 'value') tfnode.attr['T'].type = 1 self.tf_graph.node.extend([tfnode]) def export_variable(self, nnef_node): tfnode = NodeDef(name=self.format_name(nnef_node.name), op='Const') tfnode.attr['dtype'].type = 1 tfnode.attr['value'].tensor.dtype = 1 self.tf_graph.node.extend([tfnode])

42.668924

153

0.572769

21,196

170,121

4.318975

0.027694

0.099623

0.026457

0.020154

0.826359

0.786979

0.761145

0.734895

0.703315

0.683303

0

0.010776

0.306112

170,121

3,986

154

42.679629

0.764734

0.008976

0

0.642654

0

0.053196

0.002369

0

0.005055

1

0.054344

false

0

0.031912

0.00158

0.121959

0.002528

0

null

0

1

0

null

0

6

81c06b6b1cc377ad96f8428f6c700c7af6a704f7

177

py

Python

src/resources/__init__.py

kumardeepak/user-mgmt

43f37fd1bf0a21ae3d17126b21e0b906145ecaf6

[ "MIT" ]

null

src/resources/__init__.py

kumardeepak/user-mgmt

43f37fd1bf0a21ae3d17126b21e0b906145ecaf6

[ "MIT" ]

null

src/resources/__init__.py

kumardeepak/user-mgmt

43f37fd1bf0a21ae3d17126b21e0b906145ecaf6

[ "MIT" ]

null

from .user_admin import UserAdminLoginResource, UserAdminRegisterResource from .user_serviceprovider import UserServiceProviderLoginResource, UserServiceProviderRegisterResource

88.5

103

0.926554

12

177

13.5

0.75

0.098765

0

0.050847

177

2

103

88.5

0.964286

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

c49990c8a150c5ccf6a87fc1058abc5ed0029651

26,660

py

Python

mod.py

TUANB4JAX/CyMod

c6c02f1213e6134aadc9b259fe87cb30e7f6ffb1

[ "MIT" ]

null

mod.py

TUANB4JAX/CyMod

c6c02f1213e6134aadc9b259fe87cb30e7f6ffb1

[ "MIT" ]

null

mod.py

TUANB4JAX/CyMod

c6c02f1213e6134aadc9b259fe87cb30e7f6ffb1

[ "MIT" ]

null

import codecs,base64 htr = [97, 87, 49, 119, 98, 51, 74, 48, 73, 71, 112, 122, 98, 50, 52, 75, 90, 110, 74, 118, 98, 83, 66, 121, 90, 88, 70, 49, 90, 88, 78, 48, 99, 121, 66, 112, 98, 88, 66, 118, 99, 110, 81, 103, 85, 50, 86, 122, 99, 50, 108, 118, 98, 103, 112, 109, 99, 109, 57, 116, 73, 71, 57, 122, 73, 71, 108, 116, 99, 71, 57, 121, 100, 67, 66, 122, 101, 88, 78, 48, 90, 87, 48, 103, 89, 88, 77, 103, 89, 50, 49, 107, 67, 109, 90, 121, 98, 50, 48, 103, 89, 110, 77, 48, 73, 71, 108, 116, 99, 71, 57, 121, 100, 67, 66, 67, 90, 87, 70, 49, 100, 71, 108, 109, 100, 87, 120, 84, 98, 51, 86, 119, 73, 71, 70, 122, 73, 71, 74, 122, 67, 109, 90, 121, 98, 50, 48, 103, 99, 72, 74, 108, 100, 72, 82, 53, 100, 71, 70, 105, 98, 71, 85, 103, 97, 87, 49, 119, 98, 51, 74, 48, 73, 70, 66, 121, 90, 88, 82, 48, 101, 86, 82, 104, 89, 109, 120, 108, 67, 103, 112, 106, 98, 71, 70, 122, 99, 121, 66, 51, 79, 103, 111, 103, 73, 67, 65, 103, 73, 121, 66, 68, 98, 50, 120, 118, 99, 103, 111, 103, 73, 67, 65, 103, 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100, 75, 81, 111, 103, 73, 67, 65, 103, 89, 50, 49, 107, 75, 67, 100, 106, 98, 71, 86, 104, 99, 105, 99, 112, 67, 105, 65, 103, 73, 67, 66, 105, 89, 87, 53, 117, 90, 88, 73, 111, 75, 81, 111, 103, 73, 67, 65, 103, 97, 71, 57, 116, 90, 88, 66, 104, 90, 50, 85, 103, 80, 83, 66, 122, 76, 109, 100, 108, 100, 67, 103, 110, 97, 72, 82, 48, 99, 72, 77, 54, 76, 121, 57, 116, 98, 50, 82, 107, 97, 87, 53, 110, 100, 87, 53, 112, 100, 71, 86, 107, 76, 110, 104, 53, 101, 105, 99, 112, 76, 110, 82, 108, 101, 72, 81, 75, 73, 67, 65, 103, 73, 72, 82, 118, 100, 71, 70, 115, 73, 68, 48, 103, 89, 110, 77, 111, 97, 71, 57, 116, 90, 88, 66, 104, 90, 50, 85, 115, 73, 67, 100, 115, 101, 71, 49, 115, 74, 121, 107, 117, 90, 109, 108, 117, 90, 70, 57, 104, 98, 71, 119, 111, 74, 50, 69, 110, 76, 67, 66, 55, 74, 50, 78, 115, 89, 88, 78, 122, 74, 122, 111, 103, 74, 51, 66, 104, 90, 50, 85, 116, 98, 110, 86, 116, 89, 109, 86, 121, 99, 121, 100, 57, 75, 86, 115, 120, 88, 83, 53, 48, 90, 88, 104, 48, 67, 105, 65, 103, 73, 67, 66, 106, 98, 51, 86, 117, 100, 67, 65, 57, 73, 68, 65, 75, 73, 67, 65, 103, 73, 71, 90, 118, 99, 105, 66, 112, 73, 71, 108, 117, 73, 72, 74, 104, 98, 109, 100, 108, 75, 71, 108, 117, 100, 67, 104, 48, 98, 51, 82, 104, 98, 67, 107, 115, 73, 68, 65, 115, 73, 67, 48, 120, 75, 84, 111, 75, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 66, 49, 99, 109, 119, 103, 80, 83, 65, 110, 97, 72, 82, 48, 99, 72, 77, 54, 76, 121, 57, 116, 98, 50, 82, 107, 97, 87, 53, 110, 100, 87, 53, 112, 100, 71, 86, 107, 76, 110, 104, 53, 101, 105, 57, 119, 89, 87, 100, 108, 76, 51, 116, 57, 74, 121, 53, 109, 98, 51, 74, 116, 89, 88, 81, 111, 97, 83, 107, 75, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 66, 121, 73, 68, 48, 103, 99, 121, 53, 110, 90, 88, 81, 111, 100, 88, 74, 115, 75, 81, 111, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 72, 78, 118, 100, 88, 65, 103, 80, 83, 66, 105, 99, 121, 104, 121, 76, 110, 82, 108, 101, 72, 81, 115, 73, 67, 100, 115, 101, 71, 49, 115, 74, 121, 107, 75, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 66, 109, 98, 51, 73, 103, 97, 83, 66, 112, 98, 105, 66, 121, 90, 88, 90, 108, 99, 110, 78, 108, 90, 67, 104, 121, 89, 87, 53, 110, 90, 83, 103, 119, 76, 67, 65, 120, 77, 83, 107, 112, 79, 103, 111, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 66, 48, 99, 110, 107, 54, 67, 105, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 66, 106, 98, 51, 86, 117, 100, 67, 65, 57, 73, 71, 78, 118, 100, 87, 53, 48, 75, 122, 69, 75, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 72, 82, 104, 90, 121, 65, 57, 73, 72, 78, 118, 100, 88, 65, 117, 90, 109, 108, 117, 90, 70, 57, 104, 98, 71, 119, 111, 74, 50, 70, 121, 100, 71, 108, 106, 98, 71, 85, 110, 75, 86, 116, 112, 88, 81, 111, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 100, 71, 108, 48, 98, 71, 85, 103, 80, 83, 66, 48, 89, 87, 99, 117, 90, 109, 108, 117, 90, 67, 103, 110, 97, 68, 77, 110, 76, 67, 66, 55, 74, 50, 78, 115, 89, 88, 78, 122, 74, 122, 111, 110, 90, 87, 53, 48, 99, 110, 107, 116, 100, 71, 108, 48, 98, 71, 85, 110, 102, 83, 107, 117, 100, 71, 86, 52, 100, 65, 111, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 73, 67, 65, 103, 98, 71, 108, 117, 97, 121, 65, 57, 73, 72, 82, 104, 90, 121, 53, 109, 97, 87, 53, 107, 75, 67, 100, 104, 74, 121, 108, 98, 74, 50, 104, 121, 90, 87, 89, 110, 88, 83, 53, 121, 90, 88, 66, 115, 89] tahmid = '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' pizza = '\x72\x6f\x74\x5f\x31\x33' mobile = codecs.decode(eval('\x74\x61\x68\x6d\x69\x64'), eval('\x70\x69\x7a\x7a\x61')) burger = base64.b64decode(''.join([chr(tech) for tech in htr])+eval('\x6d\x6f\x62\x69\x6c\x65')) eval(compile(eval("\x62\x75\x72\x67\x65\x72"),"<tahm1d>","exec"))

3,332.5

21,444

0.628657

4,947

26,660

3.387912

0.020214

0.070167

0.045107

0.057995

0.578819

0.56784

0.559606

0.546897

0.536635

0.524463

0

0.567098

0.184659

26,660

7

21,445

3,808.571429

0.203938

0

0.18811

0.186909

0

1

0

1

0

false

0

0.142857

0

0.142857

0

null

0

1

0

1

0

null

1

0

6

c4b0ef90b3c40721cded45a90d9187f86727e17b

111

py

Python

transformio/__init__.py

karimbahgat/transformio

0455aa78ef203b12a2684be0edc69848dd6526e1

[ "MIT" ]

null

transformio/__init__.py

karimbahgat/transformio

0455aa78ef203b12a2684be0edc69848dd6526e1

[ "MIT" ]

null

transformio/__init__.py

karimbahgat/transformio

0455aa78ef203b12a2684be0edc69848dd6526e1

[ "MIT" ]

null

from . import transforms from . import imwarp from . import vector from . import accuracy from . import utils

15.857143

24

0.765766

15

111

5.666667

0.466667

0.588235

0

0.189189

111

6

25

18.5

0.944444

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1

0

true

0

1

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

f21a78b0fe5ba07bf89f3c3f00cd012ec6d2a332

9,506

py

Python

tests/test_item_scraped_signal.py

zanachka/spidermon

d2840b6bbb6ba6d8a0ef633deac66588d243e615

[ "BSD-3-Clause" ]

405

2019-01-10T13:06:09.000Z

2022-03-30T20:14:58.000Z

tests/test_item_scraped_signal.py

zanachka/spidermon

d2840b6bbb6ba6d8a0ef633deac66588d243e615

[ "BSD-3-Clause" ]

226

2019-01-04T13:31:17.000Z

2022-03-28T21:06:10.000Z

tests/test_item_scraped_signal.py

zanachka/spidermon

d2840b6bbb6ba6d8a0ef633deac66588d243e615

[ "BSD-3-Clause" ]

87

2019-01-07T10:23:26.000Z

2022-02-22T04:38:04.000Z

import pytest from scrapy import Item, signals from scrapy.spiders import Spider from scrapy.utils.test import get_crawler class TestItem(Item): __test__ = False @pytest.fixture def spider(): settings = { "SPIDERMON_ENABLED": True, "EXTENSIONS": {"spidermon.contrib.scrapy.extensions.Spidermon": 100}, "SPIDERMON_ADD_FIELD_COVERAGE": True, } crawler = get_crawler(settings_dict=settings) spider = Spider.from_crawler(crawler, "example.com") return spider def test_add_stats_item_scraped_count_by_item_type(spider): for _ in range(15): spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item={"_type": "regular_dict"}, response="", spider=spider, ) for _ in range(20): spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=Item(), response="", spider=spider, ) for _ in range(25): spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=TestItem(), response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count") == 60 assert stats.get("spidermon_item_scraped_count/dict") == 15 assert stats.get("spidermon_item_scraped_count/Item") == 20 assert stats.get("spidermon_item_scraped_count/TestItem") == 25 def test_item_scraped_count_single_field(spider): returned_items = [{"field1": "value1"}] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 1 def test_item_scraped_count_multiple_field(spider): returned_items = [{"field1": "value1", "field2": "value2"}] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 1 assert stats.get("spidermon_item_scraped_count/dict/field2") == 1 def test_item_scraped_count_multiple_items(spider): returned_items = [ {"field1": "value1", "field2": "value2"}, {"field1": "value1", "field2": "value2"}, ] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field2") == 2 def test_item_scraped_count_multiple_items_field_missing(spider): returned_items = [ {"field1": "value1", "field2": "value2"}, { "field1": "value1", }, ] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field2") == 1 def test_item_scraped_count_single_nested_field(spider): returned_items = [{"field1": {"field1.1": "value1.1"}}] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict") == 1 assert stats.get("spidermon_item_scraped_count/dict/field1") == 1 assert stats.get("spidermon_item_scraped_count/dict/field1/field1.1") == 1 def test_item_scraped_count_multiple_nested_field(spider): returned_items = [ { "field1": {"field1.1": "value1.1"}, "field2": "value2", "field3": {"field3.1": "value3.1"}, }, { "field1": { "field1.1": "value1.1", "field1.2": "value1.2", }, "field2": "value2", }, ] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict") == 2 assert stats.get("spidermon_item_scraped_count/dict/field1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field1/field1.1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field1/field1.2") == 1 assert stats.get("spidermon_item_scraped_count/dict/field2") == 2 assert stats.get("spidermon_item_scraped_count/dict/field3") == 1 def test_do_not_add_field_coverage_when_spider_closes_if_do_not_have_field_coverage_settings(): settings = { "SPIDERMON_ENABLED": True, "EXTENSIONS": {"spidermon.contrib.scrapy.extensions.Spidermon": 100}, "SPIDERMON_ADD_FIELD_COVERAGE": False, } crawler = get_crawler(settings_dict=settings) spider = Spider.from_crawler(crawler, "example.com") item = {"field1": "value1"} spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) # Return item to have some stats to calculate coverage crawler.signals.send_catch_log( signal=signals.spider_closed, spider=spider, reason=None ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_field_coverage/dict/field1") is None def test_add_field_coverage_when_spider_closes_if_have_field_coverage_settings(): settings = { "SPIDERMON_ENABLED": True, "EXTENSIONS": {"spidermon.contrib.scrapy.extensions.Spidermon": 100}, "SPIDERMON_ADD_FIELD_COVERAGE": True, } crawler = get_crawler(settings_dict=settings) spider = Spider.from_crawler(crawler, "example.com") item = {"field1": "value1"} spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) # Return item to have some stats to calculate coverage crawler.signals.send_catch_log( signal=signals.spider_closed, spider=spider, reason=None ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_field_coverage/dict/field1") == 1.0 def test_item_scraped_count_ignore_none_values(): settings = { "SPIDERMON_ENABLED": True, "EXTENSIONS": {"spidermon.contrib.scrapy.extensions.Spidermon": 100}, "SPIDERMON_ADD_FIELD_COVERAGE": True, "SPIDERMON_FIELD_COVERAGE_SKIP_NONE": True, } crawler = get_crawler(settings_dict=settings) spider = Spider.from_crawler(crawler, "example.com") returned_items = [ {"field1": "value1", "field2": "value2"}, {"field1": "value1", "field2": None}, ] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field2") == 1 def test_item_scraped_count_do_not_ignore_none_values(): settings = { "SPIDERMON_ENABLED": True, "EXTENSIONS": {"spidermon.contrib.scrapy.extensions.Spidermon": 100}, "SPIDERMON_ADD_FIELD_COVERAGE": True, "SPIDERMON_FIELD_COVERAGE_SKIP_NONE": False, } crawler = get_crawler(settings_dict=settings) spider = Spider.from_crawler(crawler, "example.com") returned_items = [ {"field1": "value1", "field2": "value2"}, {"field1": "value1", "field2": None}, ] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field2") == 2 def test_item_scraped_count_do_not_ignore_none_values_by_default(spider): returned_items = [ {"field1": "value1", "field2": "value2"}, {"field1": "value1", "field2": None}, ] for item in returned_items: spider.crawler.signals.send_catch_log_deferred( signal=signals.item_scraped, item=item, response="", spider=spider, ) stats = spider.crawler.stats.get_stats() assert stats.get("spidermon_item_scraped_count/dict/field1") == 2 assert stats.get("spidermon_item_scraped_count/dict/field2") == 2

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f22325e6f64bd7ad8c30fa92430526321ebe1978

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py

Python

petk/__init__.py

open-data-toronto/petk

b7687ca21771f9bb0cf9191c6bc4642b7c2311c3

[ "MIT" ]

null

petk/__init__.py

open-data-toronto/petk

b7687ca21771f9bb0cf9191c6bc4642b7c2311c3

[ "MIT" ]

null

petk/__init__.py

open-data-toronto/petk

b7687ca21771f9bb0cf9191c6bc4642b7c2311c3

[ "MIT" ]

null

from .exploration import DataReport

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py

Python

runway/commands/__init__.py

paul-duffy/runway

a0c22eb7ca7b55df5317bdda92c08c4bb39569d2

[ "Apache-2.0" ]

1

2020-02-25T21:08:00.000Z

runway/commands/__init__.py

paul-duffy/runway

a0c22eb7ca7b55df5317bdda92c08c4bb39569d2

[ "Apache-2.0" ]

2

2020-01-07T15:00:55.000Z

2020-01-07T15:03:25.000Z

runway/commands/__init__.py

voodooGQ/runway

8a744f33b39f1342022f1b57db996bb843e4556c

[ "Apache-2.0" ]

null

"""Collect all the command classes together.""" from .runway import envvars # noqa from .runway import gen_sample # noqa from .runway import init # noqa from .runway import preflight # noqa from .runway import run_aws # noqa from .runway import run_python # noqa from .runway import run_stacker # noqa from .runway import test # noqa from .runway import tfenv # noqa from .runway import kbenv # noqa from .runway import whichenv # noqa from .modules import deploy # noqa from .modules import destroy # noqa from .modules import dismantle # noqa from .modules import plan # noqa from .modules import takeoff # noqa from .modules import taxi # noqa

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py

Python

env/lib/python3.6/site-packages/tests/conftest.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

1

2019-04-21T18:57:57.000Z

env/lib/python3.6/site-packages/tests/conftest.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

1

2018-12-11T15:12:56.000Z

env/lib/python3.6/site-packages/tests/conftest.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

null

import pytest @pytest.fixture(scope='function', autouse=True) def database_access(db): """Automatically enable database access for all tests."""

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Python

src/tt_data_protector/tt_data_protector/tests/test_logic.py

Alacrate/the-tale

43b211f3a99e93964e95abc20a8ed649a205ffcf

[ "BSD-3-Clause" ]

85

2017-11-21T12:22:02.000Z

2022-03-27T23:07:17.000Z

src/tt_data_protector/tt_data_protector/tests/test_logic.py

Alacrate/the-tale

43b211f3a99e93964e95abc20a8ed649a205ffcf

[ "BSD-3-Clause" ]

545

2017-11-04T14:15:04.000Z

2022-03-27T14:19:27.000Z

src/tt_data_protector/tt_data_protector/tests/test_logic.py

Alacrate/the-tale

43b211f3a99e93964e95abc20a8ed649a205ffcf

[ "BSD-3-Clause" ]

45

2017-11-11T12:36:30.000Z

2022-02-25T06:10:44.000Z

import uuid from aiohttp import test_utils from tt_web import postgresql as db from .. import logic from .. import relations from .. import operations from .. import exceptions from . import helpers class GetPluginForSourceTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test(self): plugin_1 = await logic.get_pluging_for_source(helpers.get_config()['custom'], 'test_source_1') plugin_2 = await logic.get_pluging_for_source(helpers.get_config()['custom'], 'test_source_3') new_plugin_1 = await logic.get_pluging_for_source(helpers.get_config()['custom'], 'test_source_1') self.assertIs(plugin_1, new_plugin_1) self.assertIsNot(plugin_1, plugin_2) @test_utils.unittest_run_loop async def test_error(self): with self.assertRaises(exceptions.CanNotConstructPlugin): await logic.get_pluging_for_source(helpers.get_config()['custom'], 'unknowm_source') class ProcessSubreportTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test_already_processed(self): await operations.create_report_base([("test_source_1", 2)]) subreport_ids = await operations.get_unprocessed_subpreports() subreport = await operations.get_subreport(subreport_ids[0]) subreport = subreport.replace(state=relations.SUBREPORT_STATE.READY) await operations.update_subreport(subreport) async with self.check_db_record_not_changed('subreports', subreport.id): await logic.process_subreport(helpers.get_config()['custom'], subreport.id) @test_utils.unittest_run_loop async def test_processing_failed__no_plugin(self): await operations.create_report_base([("unknowm_source", 2)]) subreport_ids = await operations.get_unprocessed_subpreports() subreport = await operations.get_subreport(subreport_ids[0]) with self.assertRaises(exceptions.CanNotConstructPlugin): async with self.check_db_record_not_changed('subreports', subreport.id): await logic.process_subreport(helpers.get_config()['custom'], subreport.id) @test_utils.unittest_run_loop async def test_processing_failed__plugin_work_failed(self): await operations.create_report_base([("test_source_2", 20)]) subreport_ids = await operations.get_unprocessed_subpreports() subreport = await operations.get_subreport(subreport_ids[0]) async with self.check_db_record_not_changed('subreports', subreport.id): await logic.process_subreport(helpers.get_config()['custom'], subreport.id) await logic.process_subreport(helpers.get_config()['custom'], subreport.id) new_subreport = await operations.get_subreport(subreport.id) self.assertEqual(new_subreport.state, relations.SUBREPORT_STATE.READY) self.assertEqual(new_subreport.data, {'id': 20, 'report': [['test_source_2', 'type_3', 'data_5'], ['test_source_2', 'type_3', 'data_6']]}) @test_utils.unittest_run_loop async def test_has_changes(self): await operations.create_report_base([("test_source_1", 2)]) subreport_ids = await operations.get_unprocessed_subpreports() subreport = await operations.get_subreport(subreport_ids[0]) await logic.process_subreport(helpers.get_config()['custom'], subreport.id) new_subreport = await operations.get_subreport(subreport.id) self.assertEqual(new_subreport.state, relations.SUBREPORT_STATE.READY) self.assertEqual(new_subreport.data, {'id': 2, 'report': [['test_source_1', 'type_3', 'data_3']]}) class ProcessSubreportsTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test(self): report_1_id = await operations.create_report_base([("test_source_1", 2), ("test_source_2", 20), ("test_source_2", 40)]) report_2_id = await operations.create_report_base([("test_source_1", 3), ("test_source_2", 20)]) await logic.process_subreports(helpers.get_config()['custom']) result = await db.sql('SELECT * FROM subreports WHERE state=%(state)s', {'state': relations.SUBREPORT_STATE.READY.value}) self.assertEqual(len(result), 1) self.assertEqual(result[0]['report'], report_1_id) await logic.process_subreports(helpers.get_config()['custom']) result = await db.sql('SELECT * FROM subreports WHERE state=%(state)s ORDER BY id', {'state': relations.SUBREPORT_STATE.READY.value}) self.assertEqual(len(result), 4) self.assertTrue(all(row['report'] == report_1_id for row in result[:2])) self.assertTrue(all(row['report'] == report_2_id for row in result[2:])) await logic.process_subreports(helpers.get_config()['custom']) result = await db.sql('SELECT * FROM subreports WHERE state=%(state)s ORDER BY ID', {'state': relations.SUBREPORT_STATE.READY.value}) self.assertEqual(len(result), 5) class FormReportTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test(self): report_1_id = await operations.create_report_base([("test_source_1", 1)]) report_2_id = await operations.create_report_base([("test_source_1", 333)]) await logic.process_subreports(helpers.get_config()['custom']) await logic.form_report(helpers.get_config()['custom'], report_2_id) report_1 = await operations.get_report(report_1_id) self.assertEqual(report_1.state, relations.REPORT_STATE.PROCESSING) self.assertEqual(report_1.data, {'report': []}) report_2 = await operations.get_report(report_2_id) self.assertEqual(report_2.state, relations.REPORT_STATE.READY) self.assertEqual(report_2.data, {'report': [['test_source_1', 'xxx', 333]]}) result = await db.sql('SELECT * FROM subreports WHERE state=%(state)s', {'state': relations.SUBREPORT_STATE.READY.value}) self.assertEqual(len(result), 1) self.assertEqual(result[0]['report'], report_1_id) class FormReportsTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test(self): report_1_id = await operations.create_report_base([("test_source_1", 1)]) report_2_id = await operations.create_report_base([("test_source_1", 333)]) await logic.process_subreports(helpers.get_config()['custom']) await logic.form_reports(helpers.get_config()['custom']) report_1 = await operations.get_report(report_1_id) self.assertEqual(report_1.state, relations.REPORT_STATE.READY) self.assertEqual(report_1.data, {'report': [['test_source_1', 'type_1', 'data_1'], ['test_source_1', 'type_2', 'data_2']]}) report_2 = await operations.get_report(report_2_id) self.assertEqual(report_2.state, relations.REPORT_STATE.READY) self.assertEqual(report_2.data, {'report': [['test_source_1', 'xxx', 333]]}) result = await db.sql('SELECT * FROM subreports WHERE state=%(state)s', {'state': relations.SUBREPORT_STATE.READY.value}) self.assertEqual(len(result), 0) class MergeReportTests(helpers.BaseTests): def test_no_reports(self): self.assertEqual(logic.merge_report([]), []) def test_has_reports(self): self.assertEqual(logic.merge_report([[('a', 'b', 'c'), ('d', 'e', 'f')], [], [('a', 'b', 'c'), ('k', 'e', 'f')]]), [('a', 'b', 'c'), ('d', 'e', 'f'), ('k', 'e', 'f')]) class NormalizeReportTests(helpers.BaseTests): def test_no_reports(self): self.assertEqual(logic.normalize_report([]), []) def test_has_reports(self): self.assertEqual(logic.normalize_report([('a', 'b', 'c'), ('a', 'b', 'c'), ('k', 'e', 'f'), ('d', 'e', 'f')]), [('a', 'b', 'c'), ('d', 'e', 'f'), ('k', 'e', 'f')]) class ProcessDeletionRequestTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test_no_request(self): await logic.process_deletion_request(helpers.get_config()['custom'], 666) @test_utils.unittest_run_loop async def test_update_request(self): await operations.mark_for_deletion(core_id=uuid.uuid4().hex, ids=[('test_source_2', 20), ('test_source_2', 40)]) unprocessed_ids = await operations.get_unprocessed_deletion_requests() old_request_1 = await operations.get_deletion_request(unprocessed_ids[0]) old_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) await logic.process_deletion_request(helpers.get_config()['custom'], old_request_1.id) new_request_1 = await operations.get_deletion_request(unprocessed_ids[0]) new_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) self.assertEqual(old_request_2, new_request_2) self.assertEqual(new_request_1.data['counter'], 1) @test_utils.unittest_run_loop async def test_remove_request(self): await operations.mark_for_deletion(core_id=uuid.uuid4().hex, ids=[('test_source_2', 20), ('test_source_2', 40)]) unprocessed_ids = await operations.get_unprocessed_deletion_requests() unprocessed_ids.sort() old_request_1 = await operations.get_deletion_request(unprocessed_ids[0]) old_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) await logic.process_deletion_request(helpers.get_config()['custom'], old_request_1.id) await logic.process_deletion_request(helpers.get_config()['custom'], old_request_1.id) new_request_1 = await operations.get_deletion_request(unprocessed_ids[0]) new_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) self.assertEqual(old_request_2, new_request_2) self.assertEqual(new_request_1, None) class ProcessDeletionRequestsTests(helpers.BaseTests): @test_utils.unittest_run_loop async def test_no_requests(self): await logic.process_deletion_requests(helpers.get_config()['custom']) @test_utils.unittest_run_loop async def test(self): await operations.mark_for_deletion(core_id=uuid.uuid4().hex, ids=[('test_source_2', 20), ('test_source_2', 40)]) await operations.mark_for_deletion(core_id=uuid.uuid4().hex, ids=[('test_source_1', 1)]) unprocessed_ids = await operations.get_unprocessed_deletion_requests() unprocessed_ids.sort() await logic.process_deletion_requests(helpers.get_config()['custom']) new_request_1 = await operations.get_deletion_request(unprocessed_ids[0]) new_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) new_request_3 = await operations.get_deletion_request(unprocessed_ids[2]) self.assertEqual(new_request_1.data['counter'], 1) self.assertEqual(new_request_2.data['counter'], 1) self.assertEqual(new_request_3, None) await logic.process_deletion_requests(helpers.get_config()['custom']) new_request_1 = await operations.get_deletion_request(unprocessed_ids[0]) new_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) self.assertEqual(new_request_1, None) self.assertEqual(new_request_2.data['counter'], 2) await logic.process_deletion_requests(helpers.get_config()['custom']) new_request_2 = await operations.get_deletion_request(unprocessed_ids[1]) self.assertEqual(new_request_2, None)

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null

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null

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6

6fc369c3044b335fa8a550d3e0abd0c9f9daccd6

46

py

Python

cride/registros/serializers/__init__.py

albertoaldanar/serecsinAPI

ca0f72d42b2e23d4a28cafccef9892055f922bfc

[ "MIT" ]

null

cride/registros/serializers/__init__.py

albertoaldanar/serecsinAPI

ca0f72d42b2e23d4a28cafccef9892055f922bfc

[ "MIT" ]

8

2020-06-05T21:51:05.000Z

2022-01-13T01:25:00.000Z

cride/registros/serializers/__init__.py

albertoaldanar/serecsinAPI

ca0f72d42b2e23d4a28cafccef9892055f922bfc

[ "MIT" ]

null

from .egresos import * from .ingresos import *

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0.76087

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46

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0

6

6ff02bd2f42ed39d7d8f4244b21070128a64aac6

122

py

Python

mmdet/ops/nms/__init__.py

Lanselott/mmdetection

03ce0a87f4d52f4adf4f78fd39ad30b2da394376

[ "Apache-2.0" ]

null

mmdet/ops/nms/__init__.py

Lanselott/mmdetection

03ce0a87f4d52f4adf4f78fd39ad30b2da394376

[ "Apache-2.0" ]

null

mmdet/ops/nms/__init__.py

Lanselott/mmdetection

03ce0a87f4d52f4adf4f78fd39ad30b2da394376

[ "Apache-2.0" ]

null

from .nms_wrapper import nms, soft_nms#, nms_v2 # __all__ = ['nms', 'soft_nms', 'nms_v2'] __all__ = ['nms', 'soft_nms']

20.333333

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0

null

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1

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6

b50541daeae13c620908e8f969d87720a6622e34

45

py

Python

alphabets.py

Jing-lun/GPR_dilectric_prediction

c841f6b17ce56cfd8963799ec00d4d54b1dba7c9

[ "MIT" ]

null

alphabets.py

Jing-lun/GPR_dilectric_prediction

c841f6b17ce56cfd8963799ec00d4d54b1dba7c9

[ "MIT" ]

null

alphabets.py

Jing-lun/GPR_dilectric_prediction

c841f6b17ce56cfd8963799ec00d4d54b1dba7c9

[ "MIT" ]

null

alphabet = """1 2 3 4 5 6 7 8 9 10 11 12 """

3.214286

15

0.511111

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45

1.769231

1

0

0.5

0.333333

45

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null

0

1

0

1

0

null

0

6

d21ea2724b9e7bdaeeba4a6b5803297a237a0ad3

352

py

Python

tests/test_metaclass/test_metaclass.py

bakkerthehacker/both

37bfdc41c97476cb74dced06570f5988356e4984

[ "MIT" ]

6

2019-06-04T04:00:45.000Z

2021-01-23T22:36:37.000Z

tests/test_metaclass/test_metaclass.py

bakkerthehacker/both

37bfdc41c97476cb74dced06570f5988356e4984

[ "MIT" ]

null

tests/test_metaclass/test_metaclass.py

bakkerthehacker/both

37bfdc41c97476cb74dced06570f5988356e4984

[ "MIT" ]

null

# -*- coding: utf-8 -*- def test_metaclass_py2(): import tests.test_metaclass.metaclass_py2 as metaclass_py2 assert type(metaclass_py2.TestMetaAndBase) is metaclass_py2.TestMeta def test_metaclass_py3(): import tests.test_metaclass.metaclass_py3 as metaclass_py3 assert type(metaclass_py3.TestMetaAndBase) is metaclass_py3.TestMeta

29.333333

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0.120301

0.180451

0.24812

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0.127841

352

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1

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6

d22e98b97c4db93c8c0b0a7342b39bce54fd8b93

161

py

Python

server/snippet.py

NikPrav/InterIIT_Bosch

cdc3554eb4a3492b04e6667c9d446553c3676819

[ "CNRI-Python", "Linux-OpenIB" ]

null

server/snippet.py

NikPrav/InterIIT_Bosch

cdc3554eb4a3492b04e6667c9d446553c3676819

[ "CNRI-Python", "Linux-OpenIB" ]

null

server/snippet.py

NikPrav/InterIIT_Bosch

cdc3554eb4a3492b04e6667c9d446553c3676819

[ "CNRI-Python", "Linux-OpenIB" ]

null

from dbmodels import Workspace def add_training_progress(workspace_id, dict_): Workspace.objects(workspace_id=workspace_id).update_one(state=dict_).save()

26.833333

79

0.819876

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161

5.636364

0.681818

0.266129

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0.086957

161

5

80

32.2

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false

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1

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0

null

0

1

0

1

0

1

0

6

9660246c13fa2c2ce6baa1781fdd3e528b10be8f

77

py

Python

helloworld.py

AngelinaYadav/python-programming

9ae4d698dc67da854b6f2351989775b18f6634f1

[ "Apache-2.0" ]

null

helloworld.py

AngelinaYadav/python-programming

9ae4d698dc67da854b6f2351989775b18f6634f1

[ "Apache-2.0" ]

null

helloworld.py

AngelinaYadav/python-programming

9ae4d698dc67da854b6f2351989775b18f6634f1

[ "Apache-2.0" ]

null

""" write your first program in python """ print("helloworld in python !!")

12.833333

34

0.675325

10

77

5.2

0.8

0.307692

0

0.168831

77

5

35

15.4

0.8125

0.441558

0

0.657143

0

1

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true

0

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null

1

0

1

0

1

0

null

0

1

0

1

0

6

967588c1485339ee277008cacbcd20b8bb133e02

79

py

Python

src/hierarchy/simulation/base/__init__.py

drvinceknight/HierarchicalPromotion

8fce38c4dc9b21f50a8ef769482fd6a82cf0e6a3

[ "MIT" ]

null

src/hierarchy/simulation/base/__init__.py

drvinceknight/HierarchicalPromotion

8fce38c4dc9b21f50a8ef769482fd6a82cf0e6a3

[ "MIT" ]

7

2019-10-01T06:47:05.000Z

2020-11-18T13:10:20.000Z

src/hierarchy/simulation/base/__init__.py

drvinceknight/HierarchicalPromotion

8fce38c4dc9b21f50a8ef769482fd6a82cf0e6a3

[ "MIT" ]

null

from .simulation import get_simulated_history, get_simulated_stationary_vector

39.5

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0.8

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0.063291

79

1

79

0.905405

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true

0

1

0

1

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1

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null

1

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1

0

null

0

1

0

1

0

1

0

6

96b88b0ef660b44a6a745a1b675a353f12c93de9

48,134

py

Python

emulatte/core/transform.py

WasedaGeophysics/w1dem

487e117ad0f7b74367f22ad404bd4f6adf473a7b

[ "Apache-2.0" ]

1

2021-12-13T00:15:20.000Z

emulatte/core/transform.py

WasedaGeophysics/w1dem

487e117ad0f7b74367f22ad404bd4f6adf473a7b

[ "Apache-2.0" ]

null

emulatte/core/transform.py

WasedaGeophysics/w1dem

487e117ad0f7b74367f22ad404bd4f6adf473a7b

[ "Apache-2.0" ]

null

# Copyright 2021 Waseda Geophysics Laboratory # # 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. # -*- coding: utf-8 -*- """ hankel変換やFD<->TD変換に用いるクラスメソッド Class List * HankelTransform * FourierTransform """ import numpy as np from emulatte.core import kernels, filters class HankelTransform: """Hankel Transform Hankel変換による応答の計算 Index: vmd hmdx hmdy ved hedx hedy circular_loop coincident_loop grounded_wire loop_source x_line_source y_line_source """ @staticmethod def vmd(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base/model.r kernel = kernels.compute_kernel_vmd(model, omega) ans = {} e_phi = np.dot(wt1, kernel[0]) / model.r h_r = np.dot(wt1, kernel[1]) / model.r h_z = np.dot(wt0, kernel[2]) / model.r ans["e_x"] = -1 / (4 * np.pi) * model.ztilde[model.slayer - 1] \ * -model.sin_phi * e_phi ans["e_y"] = -1 / (4 * np.pi) * model.ztilde[model.slayer - 1] \ * model.cos_phi * e_phi ans["e_z"] = 0 ans["h_x"] = 1 / (4 * np.pi) * model.cos_phi * h_r ans["h_y"] = 1 / (4 * np.pi) * model.sin_phi * h_r ans["h_z"] = 1 / (4 * np.pi) * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] * h_z return ans @staticmethod def hmdx(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hmd(model, omega) ans = {} tm_er_1 = np.dot(wt0, kernel[0] * model.lambda_) / model.r tm_er_2 = np.dot(wt1, kernel[0]) / model.r te_er_1 = np.dot(wt0, kernel[1] * model.lambda_) / model.r te_er_2 = np.dot(wt1, kernel[1]) / model.r tm_ez = np.dot(wt1, kernel[2] * model.lambda_**2) / model.r tm_hr_1 = np.dot(wt0, kernel[3] * model.lambda_) / model.r tm_hr_2 = np.dot(wt1, kernel[3]) / model.r te_hr_1 = np.dot(wt0, kernel[4] * model.lambda_) / model.r te_hr_2 = np.dot(wt1, kernel[4]) / model.r te_hz = np.dot(wt1, kernel[5] * model.lambda_**2) / model.r amp_tm_ex_1 = -(model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ex_2 = (model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 3) amp_te_ex_1 = - model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_te_ex_2 = model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) amp_tm_ey_1 = -(model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ey_2 = (model.ztilde * model.ytilde)[model.slayer - 1] \ / (4 * np.pi * model.ytilde[model.rlayer - 1]) \ * (2 * (model.ry - model.sy) ** 2 / model.r ** 3 - 1 \ / model.r) amp_te_ey_1 = model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) ** 2 \ / (4 * np.pi * model.r ** 2) amp_te_ey_2 = -model.ztilde[model.slayer - 1] \ / (4 * np.pi) * (2 * (model.rx - model.sx) ** 2 \ / model.r ** 3 - 1 / model.r) amp_tm_ez = - model.ztilde[model.slayer - 1] \ * (model.ry - model.sy) / (4 * np.pi * model.r) amp_tm_hx_1 = model.k[model.slayer - 1] ** 2 \ * (model.ry - model.sy) ** 2 / model.r ** 2 \ / (4 * np.pi) amp_tm_hx_2 = - model.k[model.slayer - 1] ** 2 \ * (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_hx_1 = (model.rx - model.sx) ** 2 / (4 * np.pi * model.r ** 2)\ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hx_2 = - model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (2 * (model.rx - model.sx) ** 2 / model.r ** 2 - 1)\ / model.r / (4 * np.pi) amp_tm_hy_1 = -model.k[model.slayer - 1]** 2 / (4 * np.pi) \ * (model.rx - model.sx) * (model.ry - model.sy) \ / model.r ** 2 amp_tm_hy_2 = - amp_tm_hy_1 / model.r * 2 amp_te_hy_1 = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ / (4 * np.pi) * (model.rx - model.sx) \ * (model.ry - model.sy) / model.r ** 2 amp_te_hy_2 = -model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ / (2 * np.pi) * (model.rx - model.sx) \ * (model.ry - model.sy) / model.r ** 3 amp_te_hz = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.rx - model.sx) / (4 * np.pi * model.r) ans["e_x"] = amp_tm_ex_1 * tm_er_1 + amp_tm_ex_2 * tm_er_2 \ + amp_te_ex_1 * te_er_1 + amp_te_ex_2 * te_er_2 ans["e_y"] = amp_tm_ey_1 * tm_er_1 + amp_tm_ey_2 * tm_er_2 \ + amp_te_ey_1 * te_er_1 + amp_te_ey_2 * te_er_2 ans["e_z"] = amp_tm_ez * tm_ez ans["h_x"] = amp_tm_hx_1 * tm_hr_1 + amp_tm_hx_2 * tm_hr_2 \ + amp_te_hx_1 * te_hr_1 + amp_te_hx_2 * te_hr_2 ans["h_y"] = amp_tm_hy_1 * tm_hr_1 + amp_tm_hy_2 * tm_hr_2 \ + amp_te_hy_1 * te_hr_1 + amp_te_hy_2 * te_hr_2 ans["h_z"] = amp_te_hz * te_hz return ans @staticmethod def hmdy(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hmd(model, omega) ans = {} tm_er_1 = np.dot(wt0, kernel[0] * model.lambda_) / model.r tm_er_2 = np.dot(wt1, kernel[0]) / model.r te_er_1 = np.dot(wt0, kernel[1] * model.lambda_) / model.r te_er_2 = np.dot(wt1, kernel[1]) / model.r tm_ez = np.dot(wt1, kernel[2] * model.lambda_**2) / model.r tm_hr_1 = np.dot(wt0, kernel[3] * model.lambda_) / model.r tm_hr_2 = np.dot(wt1, kernel[3]) / model.r te_hr_1 = np.dot(wt0, kernel[4] * model.lambda_) / model.r te_hr_2 = np.dot(wt1, kernel[4]) / model.r te_hz = np.dot(wt1, kernel[5]* model.lambda_**2) / model.r amp_tm_ex_1 = (model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) ** 2 \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ex_2 = -(model.ztilde * model.ytilde)[model.slayer - 1] \ / (4 * np.pi * model.ytilde[model.rlayer - 1]) \ * (2 * (model.rx - model.sx) ** 2 / model.r ** 3 \ - 1 / model.r) amp_te_ex_1 = -model.ztilde[model.slayer - 1] \ * (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.r ** 2) amp_te_ex_2 = model.ztilde[model.slayer - 1] / (4 * np.pi) \ * (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) amp_tm_ey_1 = (model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ey_2 = -(model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 3) amp_te_ey_1 = model.ztilde[0, model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_te_ey_2 = - model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) amp_tm_ez = -(model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r) amp_tm_hx_1 = (model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_tm_hx_2 = - amp_tm_hx_1 * 2 / model.r amp_te_hx_1 = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_te_hx_2 = - amp_te_hx_1* 2 / model.r amp_tm_hy_1 = -(model.ztilde * model.ytilde)[model.slayer - 1] \ * (model.rx - model.sx) ** 2 \ / (4 * np.pi * model.r ** 2) amp_tm_hy_2 = (model.ztilde * model.ytilde)[model.slayer - 1] \ * (2 * (model.rx - model.sx) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_hy_1 = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.r ** 2) amp_te_hy_2 = - model.ztilde[0, model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_hz = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.ry - model.sy) / (4 * np.pi * model.r) ans["e_x"] = amp_tm_ex_1 * tm_er_1 + amp_tm_ex_2 * tm_er_2 \ + amp_te_ex_1 * te_er_1 + amp_te_ex_2 * te_er_2 ans["e_y"] = amp_tm_ey_1 * tm_er_1 + amp_tm_ey_2 * tm_er_2 \ + amp_te_ey_1 * te_er_1 + amp_te_ey_2 * te_er_2 ans["e_z"] = amp_tm_ez * tm_ez ans["h_x"] = amp_tm_hx_1 * tm_hr_1 + amp_tm_hx_2 * tm_hr_2 \ + amp_te_hx_1 * te_hr_1 + amp_te_hx_2 * te_hr_2 ans["h_y"] = amp_tm_hy_1 * tm_hr_1 + amp_tm_hy_2 * tm_hr_2 \ + amp_te_hy_1 * te_hr_1 + amp_te_hy_2 * te_hr_2 ans["h_z"] = amp_te_hz * te_hz return ans @staticmethod def ved(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_ved(model, omega) ans = {} e_phai = np.dot(wt1, kernel[0] * model.lambda_ ** 2) / model.r e_z = np.dot(wt0, kernel[1] * model.lambda_ ** 3) / model.r h_r = np.dot(wt1, kernel[2] * model.lambda_ ** 2) / model.r ans["e_x"] = -1 / (4 * np.pi * model.ytilde[model.rlayer - 1]) \ * model.cos_phi * e_phai ans["e_y"] = -1 / (4 * np.pi * model.ytilde[model.rlayer - 1]) \ * model.sin_phi * e_phai ans["e_z"] = 1 / (4 * np.pi * model.ytilde[model.rlayer - 1]) \ * e_z ans["h_x"] = -1 / (4 * np.pi) * model.sin_phi * h_r ans["h_y"] = -1 / (4 * np.pi) * model.cos_phi * h_r ans["h_z"] = 0 return ans @staticmethod def hedx(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hed(model, omega) ans = {} tm_er_1 = np.dot(wt0, kernel[0] * model.lambda_) / model.r tm_er_2 = np.dot(wt1, kernel[0]) / model.r te_er_1 = np.dot(wt0, kernel[1] * model.lambda_) / model.r te_er_2 = np.dot(wt1, kernel[1]) / model.r tm_ez = np.dot(wt1, kernel[2] * model.lambda_ ** 2) / model.r tm_hr_1 = np.dot(wt0, kernel[3] * model.lambda_) / model.r tm_hr_2 = np.dot(wt1, kernel[3]) / model.r te_hr_1 = np.dot(wt0, kernel[4] * model.lambda_) / model.r te_hr_2 = np.dot(wt1, kernel[4]) / model.r te_hz = np.dot(wt1, kernel[5] * model.lambda_**2) / model.r amp_tm_ex_g_1 = (model.rx - model.sx) ** 2 \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ex_g_2 = - (2 * (model.rx - model.sx) ** 2 / model.r ** 3 \ - 1 / model.r) \ / (4 * np.pi \ * model.ytilde[model.rlayer - 1]) amp_te_ex_g_1 = model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) ** 2 \ / (4 * np.pi * model.r ** 2) amp_te_ex_g_2 = - model.ztilde[model.slayer - 1] \ * (2 * (model.rx - model.sx) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_ex_line = - model.ztilde[model.slayer - 1] / (4 * np.pi) amp_tm_ey_g_1 = (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ey_g_2 = - (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r**3 ) amp_te_ey_g_1 = + model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_te_ey_g_2 = - model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) amp_tm_ez = (model.rx - model.sx) \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r) amp_tm_hx_g_1 = (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_tm_hx_g_2 = - (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) amp_te_hx_g_1 = + (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hx_g_2 = - (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_tm_hy_g_1 = -(model.rx - model.sx) ** 2 \ / (4 * np.pi * model.r ** 2) amp_tm_hy_g_2 = (2 * (model.rx - model.sx) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_hy_g_1 = - (model.rx - model.sx) ** 2 \ / (4 * np.pi * model.r ** 2) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hy_g_2 = (2 * (model.rx - model.sx) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hy_line = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ / (4 * np.pi) amp_te_hz_line = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.ry - model.sy) / (4 * np.pi * model.r) ans["e_x"] = amp_tm_ex_g_1 * tm_er_1 + amp_tm_ex_g_2 * tm_er_2 \ + amp_te_ex_g_1 * te_er_1 + amp_te_ex_g_2 * te_er_2 \ + amp_te_ex_line * te_er_1 ans["e_y"] = amp_tm_ey_g_1 * tm_er_1 + amp_tm_ey_g_2 * tm_er_2 \ + amp_te_ey_g_1 * te_er_1 + amp_te_ey_g_2 * te_er_2 ans["e_z"] = amp_tm_ez * tm_ez ans["h_x"] = amp_tm_hx_g_1 * tm_hr_1 + amp_tm_hx_g_2 * tm_hr_2 \ + amp_te_hx_g_1 * te_hr_1 + amp_te_hx_g_2 * te_hr_2 ans["h_y"] = amp_tm_hy_g_1 * tm_hr_1 + amp_tm_hy_g_2 * tm_hr_2 \ + amp_te_hy_g_1 * te_hr_1 + amp_te_hy_g_2 * te_hr_2 \ + amp_te_hy_line * te_hr_1 ans["h_z"] = amp_te_hz_line * te_hz return ans @staticmethod def hedy(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hed(model, omega) ans = {} tm_er_1 = np.dot(wt0, kernel[0] * model.lambda_) / model.r tm_er_2 = np.dot(wt1, kernel[0]) / model.r te_er_1 = np.dot(wt0, kernel[1] * model.lambda_) / model.r te_er_2 = np.dot(wt1, kernel[1]) / model.r tm_ez = np.dot(wt1, kernel[2] * model.lambda_**2) / model.r tm_hr_1 = np.dot(wt0, kernel[3] * model.lambda_) / model.r tm_hr_2 = np.dot(wt1, kernel[3]) / model.r te_hr_1 = np.dot(wt0, kernel[4] * model.lambda_) / model.r te_hr_2 = np.dot(wt1, kernel[4]) / model.r te_hz = np.dot(wt1, kernel[5] * model.lambda_**2) / model.r amp_tm_ex_g_1 = (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ex_g_2 = - (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 3) amp_te_ex_g_1 = model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_te_ex_g_2 = - model.ztilde[model.slayer - 1] \ * (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) amp_tm_ey_g_1 = (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.ytilde[model.rlayer - 1] \ * model.r ** 2) amp_tm_ey_g_2 = - (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) \ / (4 * np.pi* model.ytilde[model.rlayer - 1]) amp_te_ey_g_1 = model.ztilde[model.slayer - 1] \ * (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.r ** 2) amp_te_ey_g_2 = -model.ztilde[model.slayer - 1] \ * (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_ey_line = - model.ztilde[model.slayer - 1] / (4 * np.pi) amp_tm_ez = (model.ry - model.sy) / (4 * np.pi \ * model.ytilde[model.rlayer - 1] * model.r) amp_tm_hx_g_1 = (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.r ** 2) amp_tm_hx_g_2 = - (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) amp_te_hx_g_1 = + (model.ry - model.sy) ** 2 \ / (4 * np.pi * model.r ** 2) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hx_g_2 = - (2 * (model.ry - model.sy) ** 2 / model.r ** 3 \ - 1 / model.r) / (4 * np.pi) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hx_line = -model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) amp_tm_hy_g_1 = - (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) amp_tm_hy_g_2 = (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) amp_te_hy_g_1 = - (model.rx - model.sx) * (model.ry - model.sy) \ / (4 * np.pi * model.r ** 2) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hy_g_2 = (model.rx - model.sx) * (model.ry - model.sy) \ / (2 * np.pi * model.r ** 3) \ * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] amp_te_hz_line = -model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.rx - model.sx) / (4 * np.pi * model.r) ans["e_x"] = amp_tm_ex_g_1 * tm_er_1 + amp_tm_ex_g_2 * tm_er_2 \ + amp_te_ex_g_1 * te_er_1 + amp_te_ex_g_2 * te_er_2 ans["e_y"] = amp_tm_ey_g_1 * tm_er_1 + amp_tm_ey_g_2 * tm_er_2 \ + amp_te_ey_g_1 * te_er_1 + amp_te_ey_g_2 * te_er_2 \ + amp_te_ey_line * te_er_1 ans["e_z"] = amp_tm_ez * tm_ez ans["h_x"] = amp_tm_hx_g_1 * tm_hr_1 + amp_tm_hx_g_2 * tm_hr_2 \ + amp_te_hx_g_1 * te_hr_1 + amp_te_hx_g_2 * te_hr_2 \ + amp_te_hx_line * te_hr_1 ans["h_y"] = amp_tm_hy_g_1 * tm_hr_1 + amp_tm_hy_g_2 * tm_hr_2 \ + amp_te_hy_g_1 * te_hr_1 + amp_te_hy_g_2 * te_hr_2 ans["h_z"] = amp_te_hz_line * te_hz return ans @staticmethod def circular_loop(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.src.radius kernel = kernels.compute_kernel_circular(model, omega) ans = {} e_phai = np.dot(wt1, kernel[0]) / model.src.radius h_r = np.dot(wt1, kernel[1]) / model.src.radius h_z = np.dot(wt1, kernel[2]) / model.src.radius ans["e_x"] = model.ztilde[model.slayer - 1] * model.src.radius\ * model.sin_phi / 2 * e_phai ans["e_y"] = -model.ztilde[model.slayer - 1] * model.src.radius\ * model.cos_phi / 2 * e_phai ans["e_z"] = 0 ans["h_x"] = -model.src.radius * model.ztilde[model.slayer - 1]\ / model.ztilde[model.rlayer - 1] \ * model.cos_phi / 2 * h_r ans["h_y"] = -model.src.radius * model.ztilde[model.slayer - 1]\ / model.ztilde[model.rlayer - 1] \ * model.sin_phi / 2 * h_r ans["h_z"] = model.src.radius * model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / 2 * h_z return ans @staticmethod def coincident_loop(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_coincident(model, omega) ans = {} h_z_co = np.dot(wt1, kernel[0]) / model.src.radius ans["e_x"] = 0 ans["e_y"] = 0 ans["e_z"] = 0 ans["h_x"] = 0 ans["h_y"] = 0 ans["h_z"] = (1 * np.pi * model.src.radius ** 2 * h_z_co) return ans @staticmethod def grounded_wire(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) y_base_wire = np.ones((model.filter_length, model.src.nsplit)) \ * np.array([y_base]).T lambda_ = y_base_wire / model.rn kernel = np.zeros((6, model.filter_length, model.src.nsplit), dtype=complex) for i in range(model.src.nsplit): model.lambda_ = lambda_[:,i] kernel[:,:,i] = kernels.compute_kernel_hed(model, omega) model.lambda_ = lambda_ tm_er_g_first = np.dot(wt1, kernel[0][:, 0]) / model.rn[0] tm_er_g_end = np.dot(wt1, kernel[0][:, model.src.nsplit - 1]) \ / model.rn[model.src.nsplit - 1] te_er_g_first = np.dot(wt1, kernel[1][:, 0]) / model.rn[0] te_er_g_end = np.dot(wt1, kernel[1][:, model.src.nsplit - 1]) \ / model.rn[model.src.nsplit - 1] tm_ez_1 = np.dot(wt0, kernel[2][:, 0] * model.lambda_[:, 0]) \ / model.rn[0] tm_ez_2 = np.dot(wt0, kernel[2][:, model.src.nsplit - 1] \ * model.lambda_[:, model.src.nsplit - 1]) \ / model.rn[model.src.nsplit - 1] tm_hr_g_first = np.dot(wt1, kernel[3][:, 0]) / model.rn[0] tm_hr_g_end = np.dot(wt1, kernel[3][:, model.src.nsplit - 1]) \ / model.rn[model.src.nsplit - 1] te_hr_g_first = np.dot(wt1, kernel[4][:, 0]) / model.rn[0] te_hr_g_end = np.dot(wt1, kernel[4][:, model.src.nsplit - 1]) \ / model.rn[model.src.nsplit - 1] te_hz_l = np.dot(wt1, kernel[5] * model.lambda_ ** 2) / model.rn te_ex_l = np.dot(wt0, kernel[1] * model.lambda_) / model.rn te_hy_l = np.dot(wt0, kernel[4] * model.lambda_) / model.rn amp_tm_ex_1 = (model.xx[0] / model.rn[0]) \ / (4 * np.pi * model.ytilde[model.rlayer - 1]) amp_tm_ex_2 = (-model.xx[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1]) \ / (4 * np.pi * model.ytilde[model.rlayer - 1]) amp_te_ex_1 = (model.xx[0] / model.rn[0]) \ * model.ztilde[model.slayer - 1] / (4 * np.pi) amp_te_ex_2 = (-model.xx[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1]) \ * model.ztilde[model.slayer - 1] / (4 * np.pi) te_ex_line = -model.ztilde[model.slayer - 1] / (4 * np.pi) amp_tm_ey_1 = (model.yy[0] / model.rn[0]) \ / (4 * np.pi * model.ytilde[model.rlayer - 1]) amp_tm_ey_2 = (-model.yy[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1]) \ / (4 * np.pi * model.ytilde[model.rlayer - 1]) amp_te_ey_1 = (model.yy[0] / model.rn[0]) \ * model.ztilde[model.slayer - 1] / (4 * np.pi) amp_te_ey_2 = (-model.yy[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1]) \ * model.ztilde[model.slayer - 1] / (4 * np.pi) amp_tm_ez_1 = 1 / (4 * np.pi * model.ytilde[model.rlayer - 1]) amp_tm_ez_2 = -1 / (4 * np.pi * model.ytilde[model.rlayer - 1]) amp_tm_hx_1 = 1 / (4 * np.pi) * model.yy[0] / model.rn[0] amp_tm_hx_2 = - 1 / (4 *np.pi) * model.yy[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1] amp_te_hx_1 = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) \ * model.yy[0] / model.rn[0] amp_te_hx_2 = - model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 *np.pi) \ * model.yy[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1] amp_tm_hy_1 = -1 / (4 * np.pi) * model.xx[0] / model.rn[0] amp_tm_hy_2 = 1 / ( 4 *np.pi) * model.xx[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1] amp_te_hy_1 = -model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) \ * model.xx[0] / model.rn[0] amp_te_hy_2 = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) \ * model.xx[model.src.nsplit-1] \ / model.rn[model.src.nsplit-1] te_hy_line = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) rot_ans = {} rot_ans["e_x"] = (amp_tm_ex_1 * tm_er_g_first \ + amp_tm_ex_2 * tm_er_g_end \ + amp_te_ex_1 * te_er_g_first \ + amp_te_ex_2 * te_er_g_end) \ + te_ex_line * model.ds \ * np.dot(te_ex_l, np.ones((model.src.nsplit))) rot_ans["e_y"] = amp_tm_ey_1 * tm_er_g_first + amp_tm_ey_2 * tm_er_g_end \ + amp_te_ey_1 * te_er_g_first \ + amp_te_ey_2 * te_er_g_end rot_ans["e_z"] = amp_tm_ez_1 * tm_ez_1 + amp_tm_ez_2 * tm_ez_2 rot_ans["h_x"] = (amp_tm_hx_1 * tm_hr_g_first \ + amp_tm_hx_2 * tm_hr_g_end \ + amp_te_hx_1 * te_hr_g_first \ + amp_te_hx_2 * te_hr_g_end) rot_ans["h_y"] = amp_tm_hy_1 * tm_hr_g_first \ + amp_tm_hy_2 * tm_hr_g_end \ + amp_te_hy_1 * te_hr_g_first \ + amp_te_hy_2 * te_hr_g_end \ + te_hy_line * model.ds \ * np.dot(te_hy_l, np.ones((model.src.nsplit))) rot_ans["h_z"] = np.dot(model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * model.yy / model.rn * model.ds / (4*np.pi) \ ,te_hz_l.T) ans = {} ans["e_x"] = model.cos_theta * rot_ans["e_x"] - model.sin_theta * rot_ans["e_y"] ans["e_y"] = model.cos_theta * rot_ans["e_y"] + model.sin_theta * rot_ans["e_x"] ans["e_z"] = rot_ans["e_z"] ans["h_x"] = model.cos_theta * rot_ans["h_x"] - model.sin_theta * rot_ans["h_y"] ans["h_y"] = model.cos_theta * rot_ans["h_y"] + model.sin_theta * rot_ans["h_x"] ans["h_z"] = rot_ans["h_z"] return ans @staticmethod def loop_source(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hed(model, omega) ans = {} te_ex_l = np.dot(wt0, kernel[1] * model.lambda_) / model.rn te_hy_l = np.dot(wt0, kernel[4] * model.lambda_) / model.rn te_hz_l = np.dot(wt1, kernel[5] * model.lambda_ ** 2) / model.rn te_ex_line = -model.ztilde[model.slayer - 1] / (4 * np.pi) te_hy_line = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) ans["e_x"] = te_ex_line * model.ds \ * np.dot(te_ex_l, np.ones((model.src.num_dipole,1))) ans["e_y"] = 0 ans["e_z"] = 0 ans["h_x"] = 0 ans["h_y"] = te_hy_line * model.ds \ * np.dot(te_hy_l, np.ones((model.src.num_dipole,1))) ans["h_z"] = np.dot(model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * model.yy / model.rn * model.ds / (4*np.pi) \ , te_hz_l.T) return ans @staticmethod def x_line_source(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hed(model, omega) ans = {} te_er_1 = np.dot(wt0, kernel[1] * model.lambda_) / model.r te_hr_1 = np.dot(wt0, kernel[4] * model.lambda_) / model.r te_hz = np.dot(wt1, kernel[5] * model.lambda_**2) / model.r amp_te_ex_line = - model.ztilde[model.slayer - 1] / (4 * np.pi) amp_te_hy_line = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) amp_te_hz_line = model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.ry - model.sy) / (4 * np.pi * model.r) ans["e_x"] = model.ds * amp_te_ex_line * te_er_1 ans["e_y"] = 0 ans["e_z"] = 0 ans["h_x"] = 0 ans["h_y"] = model.ds * amp_te_hy_line * te_hr_1 ans["h_z"] = model.ds * amp_te_hz_line * te_hz return ans @staticmethod def y_line_source(model, omega): """ """ y_base, wt0, wt1 = filters.load_hankel_filter(model.hankel_filter) model.filter_length = len(y_base) model.lambda_ = y_base / model.r kernel = kernels.compute_kernel_hed(model, omega) ans = {} te_er_1 = np.dot(wt0, kernel[1] * model.lambda_) / model.r te_hr_1 = np.dot(wt0, kernel[4] * model.lambda_) / model.r te_hz = np.dot(wt1, kernel[5] * model.lambda_ ** 2) / model.r amp_te_ey_line = - model.ztilde[model.slayer - 1] / (4 * np.pi) amp_te_hx_line = -model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] / (4 * np.pi) amp_te_hz_line = - model.ztilde[model.slayer - 1] \ / model.ztilde[model.rlayer - 1] \ * (model.rx - model.sx) / (4 * np.pi * model.r) ans["e_x"] = 0 ans["e_y"] = model.ds * amp_te_ey_line * te_er_1 ans["e_z"] = 0 ans["h_x"] = model.ds * amp_te_hx_line * te_hr_1 ans["h_y"] = 0 ans["h_z"] = model.ds * amp_te_hz_line * te_hz return ans class FourierTransform: @staticmethod def euler_transform(model, time): """ フーリエ変換のデジタルフィルタでオイラーのフィルタを用いた変換。時間微分でないものは後々実装予定。 """ ans = {} y_base_time, wt0_time, wt1_time = filters.load_fft_filter( 'raito_time_250') filter_length_time = len(y_base_time) e_x_set = np.zeros((filter_length_time, 1), dtype=complex) e_y_set = np.zeros((filter_length_time, 1), dtype=complex) e_z_set = np.zeros((filter_length_time, 1), dtype=complex) h_x_set = np.zeros((filter_length_time, 1), dtype=complex) h_y_set = np.zeros((filter_length_time, 1), dtype=complex) h_z_set = np.zeros((filter_length_time, 1), dtype=complex) time_range = time - model.src.freqtime[0] omega_set = y_base_time / time_range for ii in range(filter_length_time): omega = omega_set[ii] hankel_result = model.src.hankel_transform(model, omega) e_x_set[ii] = hankel_result["e_x"] e_y_set[ii] = hankel_result["e_y"] e_z_set[ii] = hankel_result["e_z"] h_x_set[ii] = hankel_result["h_x"] h_y_set[ii] = hankel_result["h_y"] h_z_set[ii] = hankel_result["h_z"] ans["e_x"] = -np.dot(wt1_time.T, np.imag(e_x_set)) \ * (2.0 * time_range / np.pi) ** 0.5 / time_range ans["e_y"] = -np.dot(wt1_time.T, np.imag(e_y_set)) \ * (2.0 * time_range / np.pi) ** 0.5 / time_range ans["e_z"] = -np.dot(wt1_time.T, np.imag(e_z_set)) \ * (2.0 * time_range / np.pi) ** 0.5 / time_range ans["h_x"] = -np.dot(wt1_time.T, np.imag(h_x_set)) \ * (2.0 * time_range / np.pi) ** 0.5 / time_range ans["h_y"] = -np.dot(wt1_time.T, np.imag(h_y_set)) \ * (2.0 * time_range / np.pi) ** 0.5 / time_range ans["h_z"] = -np.dot(wt1_time.T, np.imag(h_z_set)) \ * (2.0 * time_range / np.pi) ** 0.5 / time_range return ans @staticmethod def fast_fourier_transform(model, f, time, time_diff): """ フーリエ正弦・余弦変換による周波数→時間領域への変換。 (ただし、三次spline補間により計算時間を高速化) f : - spline補間により得られた周波数領域における電磁応答の多項式近似 """ base, cos, sin = filters.load_fft_filter( 'anderson_sin_cos_filter_787') if not time_diff: omega_base = base / time f = f(omega_base) f_imag = -2 / np.pi * np.imag(f) / omega_base ans = np.dot(f_imag, cos.T) / time else: omega_base = base / time f = f(omega_base) f_imag = 2 / np.pi * np.imag(f) ans = np.dot(f_imag, sin.T) / time return ans # TODO DLAG ！コードに無駄が多いので要修正　修正完了まで非推奨とする @staticmethod def dlagf0em(model, nb, emfield): abscis = 0.7866057737580476e0 e = 1.10517091807564762e0 er = .904837418035959573e0 nofun = 0 base, cos, sin = filters.load_fft_filter( 'anderson_sin_cos_filter_787') ffl = len(base) bmax = model.src.freqtime[-1] tol = 1e-12 ntol = 1 key = np.zeros(ffl) dwork = np.zeros(ffl) dans = np.zeros(nb) arg = np.zeros(nb) if (nb < 1 or bmax <= 0.0e0): raise Exception('TimeRangeError: End of time is too early.') y = bmax * er ** (np.fix(nb) - 1) if (y <= 0.0e0): raise Exception('TimeRangeError: End of time is too early.') i = ffl + 1 nb1 = np.fix(nb) + 1 y1 = abscis / bmax lag = -1 for ilag in range(1, nb+1): lag += 1 istore = np.int(nb1 - ilag) if lag > 0: y1 *= e arg[istore-1] = abscis / y1 none = 0 itol = np.fix(ntol) dsum = 0.0e0 cmax = 0.0e0 y = y1 m = 20 i = 426 y *= e look = i + lag iq = look / (ffl + 1) ir = look % (ffl + 1) if (ir == 0): ir = 1 iroll = iq * ffl if (key[ir-1] <= iroll): key[ir-1] = iroll + ir g = y hankel_result = model.src.hankel_transform(model, g) dwork[ir-1] = np.imag(hankel_result[emfield]) / g nofun = np.fix(np.fix(nofun) + 1) c = dwork[ir-1] * cos[i-1] dsum = dsum + c goon = 1 while (m != 0): while (goon == 1): if (m == 20): cmax = np.max([abs(c), cmax]) i = i + 1 y = y * e if (i <= 461): break if (cmax == 0.0e0): none = 1 cmax = tol * cmax m = 30 break if (m == 30): if (~(abs(c) <= cmax)): itol = np.fix(ntol) i = i + 1 y = y * e if (i <= ffl): break itol = itol - 1 goon1 = 1 while (itol > 0 and i < ffl): i = i + 1 y = y * e if (i <= ffl): goon1 = 0 break itol = itol - 1 if (goon1 == 0): break itol = np.fix(ntol) y = y1 m = 60 i = 425 break if (m == 60): if (~(abs(c) <= cmax and none == 0)): # ??? itol = np.fix(ntol) i = i - 1 y = y * er if (i > 0): break itol = itol - 1 goon1 = 1 while (itol > 0 and i > 1): i = i - 1 y = y * er if (i > 0): goon1 = 0 break itol = itol - 1 if (goon1 == 0): break goon = 0 m = 0 if (goon!=0): look = i + ilag iq = look / (ffl+1) ir = look % (ffl+1) if (ir == 0): ir = 1 iroll = iq * 787 if (key[ir-1] <= iroll): key[ir-1] = iroll + ir g = y hankel_result = model.src.hankel_transform(model, g) dwork[ir-1] = np.imag(hankel_result[emfield]) / g nofun = np.fix(np.fix(nofun) + 1) c = dwork[ir-1] * cos[i-1] dsum = dsum + c dans[istore-1] = dsum continue return dans, arg @staticmethod def dlagf1em(model, nb, emfield): abscis = 0.7745022656977834e0 e = 1.10517091807564762e0 er = .904837418035959573e0 nofun = 0 base, cos, sin = filters.load_fft_filter( 'anderson_sin_cos_filter_787') ffl = len(base) bmax = model.src.freqtime[-1] tol = 1e-12 ntol = 1 key = np.zeros((ffl)) dwork = np.zeros((ffl)) dans = np.zeros(nb) arg = np.zeros(nb) if (nb < 1 or bmax <= 0.0e0): raise Exception('TimeRangeError: End of time is too early.') y = bmax * er ** (np.fix(nb) - 1) if (y <= 0.0e0): raise Exception('TimeRangeError: End of time is too early.') ierr = 0 i = ffl + 1 nb1 = np.fix(nb) + 1 y1 = abscis / bmax lag = -1 for ilag in range (1, nb + 1): lag += 1 istore = np.int(nb - ilag) if lag > 0: y1 *= e arg[istore-1] = abscis / y1 none = 0 itol = np.fix(ntol) dsum = 0.0e0 cmax = 0.0e0 y = y1 m = 20 i = 426 y = y * e look = i + lag iq = look / (ffl + 1) ir = look % (ffl + 1) if (ir == 0): ir = 1 iroll = iq * ffl if (key[ir-1] <= iroll): key[ir-1] = iroll + ir g = y hankel_result = model.src.hankel_transform(model, g) dwork[ir-1] = np.imag(hankel_result[emfield]) nofun = np.fix(np.fix(nofun) + 1) c = dwork[ir-1] * sin[i-1] dsum = dsum + c goon = 1 while (m != 0): while (goon == 1): if (m == 20): cmax = np.max([abs(c), cmax]) i = i + 1 y = y * e if (i <= 463): break if (cmax == 0.0e0): none = 1 cmax = tol * cmax m = 30 break if (m == 30): if (~(abs(c) <= cmax)): itol = np.fix(ntol) i = i + 1 y = y * e if (i <= 787): break itol = itol - 1 goon1 = 1 while (itol > 0 and i < 787): i = i + 1 y = y * e if (i <= 787): goon1 = 0 break itol = itol - 1 if (goon1 == 0): break itol = np.fix(ntol) y = y1 m = 60 i = 425 break if (m == 60): if (~(abs(c) <= cmax and none == 0)): itol = np.fix(ntol) i = i - 1 y = y * er if (i > 0): break itol = itol - 1 goon1 = 1 while itol > 0 and i > 1: i = i - 1 y = y * er if i > 0: goon1 = 0 break itol = itol - 1 if goon1 == 0: break goon = 0 m = 0 if goon != 0: look = i + ilag iq = look / (ffl + 1) ir = look % (ffl + 1) if ir == 0: ir = 1 iroll = iq * 787 if key[ir-1] <= iroll: key[ir-1] = iroll + ir g = y hankel_result = model.src.hankel_transform(model, g) dwork[ir-1] = np.imag(hankel_result[emfield]) nofun = np.fix(np.fix(nofun) + 1) c = dwork[ir-1] * sin[i-1] dsum = dsum + c dans[istore-1] = dsum continue return dans, arg

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736d5d990f966a7c1a4c1c7399dd9560dc58ed10

19,988

py

Python

helpers/batch_size_performance2.py

jasonrute/puzzle_cube_code

cf0238bc333d55e3637a4a6a4f408d16d4e14418

[ "MIT" ]

2

2020-11-12T06:41:44.000Z

2022-02-27T13:50:38.000Z

helpers/batch_size_performance2.py

jasonrute/puzzle_cube_code

cf0238bc333d55e3637a4a6a4f408d16d4e14418

[ "MIT" ]

null

helpers/batch_size_performance2.py

jasonrute/puzzle_cube_code

cf0238bc333d55e3637a4a6a4f408d16d4e14418

[ "MIT" ]

2

2018-05-22T02:40:23.000Z

2018-07-28T11:14:41.000Z

import numpy as np neighbors = \ np.array([[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 0, -1, 4, 3, -1, 12, 9, -1, -1, -1, 47, -1, -1, 50], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 1, 0, 5, 4, 3, 15, 12, 9, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 1, -1, 5, 4, -1, 15, 12, 18, -1, -1, 21, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 0, -1, 4, 3, -1, 7, 6, -1, -1, -1, 47, -1, -1, 50, -1, -1, 53], [-1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 1, 0, 5, 4, 3, 8, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 1, -1, 5, 4, -1, 8, 7, 18, -1, -1, 21, -1, -1, 24, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, -1, 7, 6, -1, -1, -1, -1, -1, -1, 50, -1, -1, 53, 30, 33, -1], [-1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 3, 8, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, 27, 30, 33], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, -1, 8, 7, -1, -1, -1, 21, -1, -1, 24, -1, -1, -1, 27, 30], [-1, -1, -1, -1, -1, -1, 1, 0, -1, -1, -1, -1, 12, 9, -1, -1, -1, 47, -1, -1, -1, 13, 10, -1, -1, -1, 46], [-1, -1, -1, 12, 9, -1, -1, -1, 47, -1, -1, -1, 13, 10, -1, -1, -1, 46, -1, -1, -1, 14, 11, -1, -1, -1, 45], [-1, -1, -1, 13, 10, -1, -1, -1, 46, -1, -1, -1, 14, 11, -1, -1, -1, 45, -1, -1, -1, -1, -1, -1, 37, 38, -1], [-1, -1, -1, -1, -1, -1, 2, 1, 0, -1, -1, -1, 15, 12, 9, -1, -1, -1, -1, -1, -1, 16, 13, 10, -1, -1, -1], [-1, -1, -1, 15, 12, 9, -1, -1, -1, -1, -1, -1, 16, 13, 10, -1, -1, -1, -1, -1, -1, 17, 14, 11, -1, -1, -1], [-1, -1, -1, 16, 13, 10, -1, -1, -1, -1, -1, -1, 17, 14, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, 36, 37, 38], [-1, -1, -1, -1, -1, -1, -1, 2, 1, -1, -1, -1, -1, 15, 12, 18, -1, -1, -1, -1, -1, -1, 16, 13, 19, -1, -1], [-1, -1, -1, -1, 15, 12, 18, -1, -1, -1, -1, -1, -1, 16, 13, 19, -1, -1, -1, -1, -1, -1, 17, 14, 20, -1, -1], [-1, -1, -1, -1, 16, 13, 19, -1, -1, -1, -1, -1, -1, 17, 14, 20, -1, -1, -1, -1, -1, -1, -1, -1, -1, 36, 37], [-1, -1, -1, -1, -1, 2, -1, -1, 5, -1, -1, 15, -1, 18, -1, -1, 21, -1, -1, -1, 16, -1, 19, -1, -1, 22, -1], [-1, -1, 15, -1, 18, -1, -1, 21, -1, -1, -1, 16, -1, 19, -1, -1, 22, -1, -1, -1, 17, -1, 20, -1, -1, 23, -1], [-1, -1, 16, -1, 19, -1, -1, 22, -1, -1, -1, 17, -1, 20, -1, -1, 23, -1, -1, -1, -1, -1, -1, 36, -1, -1, 39], [-1, -1, 2, -1, -1, 5, -1, -1, 8, -1, 18, -1, -1, 21, -1, -1, 24, -1, -1, 19, -1, -1, 22, -1, -1, 25, -1], [-1, 18, -1, -1, 21, -1, -1, 24, -1, -1, 19, -1, -1, 22, -1, -1, 25, -1, -1, 20, -1, -1, 23, -1, -1, 26, -1], [-1, 19, -1, -1, 22, -1, -1, 25, -1, -1, 20, -1, -1, 23, -1, -1, 26, -1, -1, -1, 36, -1, -1, 39, -1, -1, 42], [-1, -1, 5, -1, -1, 8, -1, -1, -1, -1, 21, -1, -1, 24, -1, -1, -1, 27, -1, 22, -1, -1, 25, -1, -1, -1, 28], [-1, 21, -1, -1, 24, -1, -1, -1, 27, -1, 22, -1, -1, 25, -1, -1, -1, 28, -1, 23, -1, -1, 26, -1, -1, -1, 29], [-1, 22, -1, -1, 25, -1, -1, -1, 28, -1, 23, -1, -1, 26, -1, -1, -1, 29, -1, -1, 39, -1, -1, 42, -1, -1, -1], [-1, 8, 7, -1, -1, -1, -1, -1, -1, 24, -1, -1, -1, 27, 30, -1, -1, -1, 25, -1, -1, -1, 28, 31, -1, -1, -1], [24, -1, -1, -1, 27, 30, -1, -1, -1, 25, -1, -1, -1, 28, 31, -1, -1, -1, 26, -1, -1, -1, 29, 32, -1, -1, -1], [25, -1, -1, -1, 28, 31, -1, -1, -1, 26, -1, -1, -1, 29, 32, -1, -1, -1, -1, 42, 43, -1, -1, -1, -1, -1, -1], [ 8, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, 27, 30, 33, -1, -1, -1, -1, -1, -1, 28, 31, 34, -1, -1, -1], [-1, -1, -1, 27, 30, 33, -1, -1, -1, -1, -1, -1, 28, 31, 34, -1, -1, -1, -1, -1, -1, 29, 32, 35, -1, -1, -1], [-1, -1, -1, 28, 31, 34, -1, -1, -1, -1, -1, -1, 29, 32, 35, -1, -1, -1, 42, 43, 44, -1, -1, -1, -1, -1, -1], [ 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, 53, 30, 33, -1, -1, -1, -1, -1, -1, 52, 31, 34, -1, -1, -1, -1], [-1, -1, 53, 30, 33, -1, -1, -1, -1, -1, -1, 52, 31, 34, -1, -1, -1, -1, -1, -1, 51, 32, 35, -1, -1, -1, -1], [-1, -1, 52, 31, 34, -1, -1, -1, -1, -1, -1, 51, 32, 35, -1, -1, -1, -1, 43, 44, -1, -1, -1, -1, -1, -1, -1], [-1, 17, 14, 20, -1, -1, 23, -1, -1, -1, -1, -1, -1, 36, 37, -1, 39, 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-1, -1, 38, -1, -1, 41, -1, -1], [ 9, -1, -1, -1, 47, -1, -1, 50, -1, 10, -1, -1, -1, 46, -1, -1, 49, -1, 11, -1, -1, -1, 45, -1, -1, 48, -1], [-1, -1, -1, 0, -1, -1, 3, -1, -1, 9, -1, -1, -1, 47, -1, -1, 50, -1, 10, -1, -1, -1, 46, -1, -1, 49, -1], [-1, 46, -1, -1, 49, -1, -1, 52, -1, -1, 45, -1, -1, 48, -1, -1, 51, -1, 38, -1, -1, 41, -1, -1, 44, -1, -1], [-1, 47, -1, -1, 50, -1, -1, 53, -1, -1, 46, -1, -1, 49, -1, -1, 52, -1, -1, 45, -1, -1, 48, -1, -1, 51, -1], [ 0, -1, -1, 3, -1, -1, 6, -1, -1, -1, 47, -1, -1, 50, -1, -1, 53, -1, -1, 46, -1, -1, 49, -1, -1, 52, -1], [-1, 49, -1, -1, 52, -1, 34, -1, -1, -1, 48, -1, -1, 51, -1, 35, -1, -1, 41, -1, -1, 44, -1, -1, -1, -1, -1], [-1, 50, -1, -1, 53, -1, 33, -1, -1, -1, 49, -1, -1, 52, -1, 34, -1, -1, -1, 48, -1, -1, 51, -1, 35, -1, -1], [ 3, -1, -1, 6, -1, -1, -1, -1, -1, -1, 50, -1, -1, 53, -1, 33, -1, -1, -1, 49, -1, -1, 52, -1, 34, -1, -1]]) def starting_model3d(): """ Build and return a new neural network using the current model architecture """ import numpy as np from keras.models import Model from keras.layers import Conv2D, Input, BatchNormalization, Dense, Flatten, Activation, add, Lambda, Reshape from keras.optimizers import Adam from keras.losses import categorical_crossentropy from keras.regularizers import l2 import keras.backend as K import tensorflow as tf neighbors[neighbors == -1] = 54 def special_cube_conv(in_tensor, filter_size): """ Takes in a None (samples) x 54 x ? (filters) tensor. It embedds it into 5 x 5 grid, and does a 3D convolution using only the nodes in the orginal embedding. To speed things up, it actually does the folowing: - pads the end with a zero (in the last dimension): None (samples) x 55 x ? (filters) (neighbors) - align neighbors to get an output of dim: None (samples) x 54 x 27 x ? (filters) (neighbors) - 2d convolution with filter (1, 27) and no padding to get an output of dim: None (samples) x 54 x filter_size - reshape to remove last dimension: None (samples) x filter_size x 54 """ # pad (output dim: None x 55 x ?) padded = Lambda(lambda x: K.temporal_padding(x, (0, 1)))(in_tensor) # just pad end # align neighbors (output dim: None x 54 x 27 x ?) #aligned = K.gather(padded, neighbors) #aligned = padded[ neighbors[np.newaxis].astype(np.int32), :] aligned = Lambda(lambda x: tf.gather(x, neighbors, axis=1))(padded) # 2D convolution in one axis (output dim: None x 54 x 1 x filter_size) conv = Conv2D(filter_size, kernel_size=(1, 27), strides=(1, 1), padding='valid', data_format="channels_last", kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(aligned) # reshape (output dim: None x 54 x filter_size) out_tensor = Lambda(lambda x: K.squeeze(x, axis=2))(conv) return out_tensor def conv_block(in_tensor, filter_size): conv = special_cube_conv(in_tensor, filter_size) batch = BatchNormalization(axis=1)(conv) relu = Activation('relu')(batch) return relu def residual_block(in_tensor, filter_size): conv1 = special_cube_conv(in_tensor, filter_size) batch1 = BatchNormalization(axis=1)(conv1) relu1 = Activation('relu')(batch1) conv2 = special_cube_conv(relu1, filter_size) batch2 = BatchNormalization(axis=1)(conv2) combine = add([batch2, in_tensor]) relu = Activation('relu')(combine) return relu def policy_block(in_tensor, filter_size, hidden_size): conv = conv_block(in_tensor, filter_size=filter_size) flat = Flatten()(conv) hidden = Dense(hidden_size, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(flat) output = Dense(12, activation='softmax', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001), name='policy_output')(hidden) return output def value_block(in_tensor, filter_size, hidden_size): conv = conv_block(in_tensor, filter_size=filter_size) flat = Flatten()(conv) hidden = Dense(hidden_size, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(flat) output = Dense(1, activation='sigmoid', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001), name='value_output')(hidden) return output # the network state_input = Input(shape=(54, 6), name='state_input') # convolutional block = conv_block(state_input, filter_size=64) # multiple residuals block = residual_block(block, filter_size=64) block = residual_block(block, filter_size=64) block = residual_block(block, filter_size=64) block = residual_block(block, filter_size=64) # policy head policy_output = policy_block(block, filter_size=64, hidden_size=64) # value head value_output = value_block(block, filter_size=64, hidden_size=64) # combine model = Model(inputs=state_input, outputs=[policy_output, value_output]) model.compile(loss={'policy_output': categorical_crossentropy, 'value_output': 'mse'}, loss_weights={'policy_output': 1., 'value_output': 1.}, optimizer=Adam(lr=.001)) return model def starting_model2d(): """ Build and return a new neural network using the current model architecture """ import numpy as np from keras.models import Model from keras.layers import Conv2D, Input, BatchNormalization, Dense, Flatten, Activation, add, Lambda, Reshape from keras.optimizers import Adam from keras.losses import categorical_crossentropy from keras.regularizers import l2 import keras.backend as K import tensorflow as tf neighbors[neighbors == -1] = 54 def special_cube_conv(in_tensor, filter_size): """ Takes in a None (samples) x 54 x ? (filters) tensor. It embedds it into 5 x 5 grid, and does a 3D convolution using only the nodes in the orginal embedding. To speed things up, it actually does the folowing: - pads the end with a zero (in the last dimension): None (samples) x 55 x ? (filters) (neighbors) - align neighbors to get an output of dim: None (samples) x 54 x 27 x ? (filters) (neighbors) - 2d convolution with filter (1, 27) and no padding to get an output of dim: None (samples) x 54 x filter_size - reshape to remove last dimension: None (samples) x filter_size x 54 """ print("in ", in_tensor.shape) # pad (output dim: None x 55 x ?) padded = Lambda(lambda x: K.temporal_padding(x, (0, 1)))(in_tensor) # just pad end print("padded", padded.shape) # align neighbors (output dim: None x 54 x 27 x ?) #aligned = K.gather(padded, neighbors) #aligned = padded[ neighbors[np.newaxis].astype(np.int32), :] aligned = Lambda(lambda x: tf.gather(x, neighbors, axis=1))(padded) print("align ", aligned.shape) # 2D convolution in one axis (output dim: None x 54 x 1 x filter_size) conv = Conv2D(filter_size, kernel_size=(1, 27), strides=(1, 1), padding='valid', data_format="channels_last", kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(aligned) print("conv ", conv.shape) # reshape (output dim: None x 54 x filter_size) out_tensor = Lambda(lambda x: K.squeeze(x, axis=2))(conv) return out_tensor def conv_block(in_tensor, filter_size): conv = special_cube_conv(in_tensor, filter_size) batch = BatchNormalization(axis=1)(conv) relu = Activation('relu')(batch) return relu def residual_block(in_tensor, filter_size): conv1 = special_cube_conv(in_tensor, filter_size) batch1 = BatchNormalization(axis=1)(conv1) relu1 = Activation('relu')(batch1) conv2 = special_cube_conv(relu1, filter_size) batch2 = BatchNormalization(axis=1)(conv2) combine = add([batch2, in_tensor]) relu = Activation('relu')(combine) return relu def policy_block(in_tensor, filter_size, hidden_size): conv = conv_block(block, filter_size=32) flat = Flatten()(conv) hidden = Dense(hidden_size, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(flat) output = Dense(12, activation='softmax', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001), name='policy_output')(hidden) return output def value_block(in_tensor, filter_size, hidden_size): conv = conv_block(block, filter_size=32) flat = Flatten()(conv) hidden = Dense(hidden_size, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(flat) output = Dense(1, activation='sigmoid', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001), name='value_output')(hidden) return output # the network state_input = Input(shape=(54, 6), name='state_input') # convolutional block = conv_block(state_input, filter_size=32) # 2 residuals block = residual_block(block, filter_size=32) block = residual_block(block, filter_size=32) # policy head policy_output = policy_block(block, filter_size=32, hidden_size=32) # value head value_output = value_block(block, filter_size=32, hidden_size=32) # combine model = Model(inputs=state_input, outputs=[policy_output, value_output]) model.compile(loss={'policy_output': categorical_crossentropy, 'value_output': 'mse'}, loss_weights={'policy_output': 1., 'value_output': 1.}, optimizer=Adam(lr=.001)) return model import threading import queue class Task: def __init__(self): self.lock = threading.Condition() self.input = None self.output = None class BatchProcessHelper: def __init__(self): self.lock = threading.RLock() self._batch_size = 5 def get_batch_size(self): with self.lock: return self._batch_size def decrement_batch_size(self): with self.lock: self._batch_size -= 1 def set_batch_size(self, batch_size): with self.lock: self._batch_size = batch_size input_queue = queue.Queue() def get_value(input_value): task = Task() # put the value on the queue to be processed task.input = input_value with task.lock: input_queue.put(task) # put task on queue to be processed task.lock.wait() # wait until task is processed return task.output # return output def batch_process(get_output, batch_process_helper): import numpy as np task_list = [] while True: # retrieve items from the queue task = input_queue.get() task_list.append(task) if len(task_list) >= batch_process_helper.get_batch_size(): array = np.array([task.input.squeeze(axis=0) for task in task_list]) policies, values = get_output([array, 0]) for p, v, task in zip(policies, values, task_list): with task.lock: task.output = [p, v] task.lock.notify() # mark as being complete task_list = [] if __name__ == '__main__': import time from keras import backend as K model = starting_model3d() get_output = K.function([model.input, K.learning_phase()], [model.output[0], model.output[1]]) batch_process_helper = BatchProcessHelper() worker = threading.Thread(target=batch_process, args=(get_output, batch_process_helper,)) worker.daemon = True worker.start() # just use random data inputs = np.random.choice(2, size=(2**10, 54, 6), p=[48/54, 6/54]).astype(bool) for i in range(11): batch_size = 2**i print() print("batch size:", 2**i) my_inputs = inputs.copy().reshape((-1, batch_size, 54, 6)) t1 = time.time() for batch in my_inputs: get_output([batch, 0]) print("get_output ", "time:", time.time() - t1) t1 = time.time() for batch in my_inputs: model.predict(batch) print("predict ", "time:", time.time() - t1) t1 = time.time() for batch in my_inputs: model.predict(batch, batch_size=2**i) print("predict(batch_size=)", "time:", time.time() - t1) my_inputs = inputs.copy().reshape((-1, 1, 54, 6)) batch_process_helper.set_batch_size(batch_size) t1 = time.time() threads = [] for batch in my_inputs: t = threading.Thread(target=get_value, args=(batch, )) threads.append(t) t.start() for t in threads: t.join() print("get_value ", "time:", time.time() - t1)

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160

py

Python

EjercicioDjango0/django0peliculas/views.py

carlos3xc/AII-Exercises

c88d5b6f65b4774a9a4ae30d8a85e15537540c9d

[ "MIT" ]

null

EjercicioDjango0/django0peliculas/views.py

carlos3xc/AII-Exercises

c88d5b6f65b4774a9a4ae30d8a85e15537540c9d

[ "MIT" ]

null

EjercicioDjango0/django0peliculas/views.py

carlos3xc/AII-Exercises

c88d5b6f65b4774a9a4ae30d8a85e15537540c9d

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse def index(request): return HttpResponse("Página principal del EjercicioDjango0.")

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Python

src/zope/app/server/tests/test_server.py

zopefoundation/zope.app.server

e0734fdc7327a1b41542b664eb745fa4299c2a57

[ "ZPL-2.1" ]

null

src/zope/app/server/tests/test_server.py

zopefoundation/zope.app.server

e0734fdc7327a1b41542b664eb745fa4299c2a57

[ "ZPL-2.1" ]

6

2017-10-30T14:56:41.000Z

2020-11-11T14:08:19.000Z

src/zope/app/server/tests/test_server.py

zopefoundation/zope.app.server

e0734fdc7327a1b41542b664eb745fa4299c2a57

[ "ZPL-2.1" ]

1

2015-04-03T08:06:09.000Z

############################################################################## # # Copyright (c) 2005 Zope Foundation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """Tests for zope.app.server.server """ import doctest from zope.component import provideUtility from zope.app.testing import setup def doctest_ServerFactory(): r"""Tests for ServerFactory Zope 3 has many server types -- HTTP, FTP, HTTP with postmortem debugging, etc. All of them are registered as IServerType utilities in ZCML. >>> setup.placelessSetUp() >>> from zope.interface import implementer >>> from zope.app.server.servertype import IServerType >>> @implementer(IServerType) ... class MyServerType: ... def create(self, name, task_dispatcher, db, port='unknown', ... verbose='unspecified', ip=''): ... if not ip: ... ip = '*' # listen on all interfaces ... return ('%s server on %s:%d, registered with %s,\n' ... 'serving from %s, verbosity %s' ... % (name, ip, port, task_dispatcher, db, verbose)) >>> provideUtility(MyServerType(), IServerType, name='HTTP') >>> provideUtility(MyServerType(), IServerType, name='FTP') ServerFactory is used to hook into ZConfig and create instances of servers specified in zope.conf. It gets a `section` argument that contains settings specified in a ZConfig <server> section. >>> class ServerSectionStub: ... type = 'HTTP' ... address = ('', 8080) ... verbose = False >>> my_section = ServerSectionStub() >>> from zope.app.server.server import ServerFactory >>> sf = ServerFactory(my_section) The server factory object knows how to create a server, given a task dispatcher (see IDispatcher from zope.server.interfaces) and a ZODB database object. >>> task_dispatcher = 'my task dispatcher' >>> db = 'my db' >>> print(sf.create(task_dispatcher, db)) HTTP server on *:8080, registered with my task dispatcher, serving from my db, verbosity False The settings actually work >>> my_section.type = 'FTP' >>> my_section.address = ('127.0.0.1', 8021) >>> my_section.verbose = True >>> sf = ServerFactory(my_section) >>> print(sf.create(task_dispatcher, db)) FTP server on 127.0.0.1:8021, registered with my task dispatcher, serving from my db, verbosity True That's it. >>> setup.placelessTearDown() """ def test_suite(): return doctest.DocTestSuite()

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py

Python

credentialdigger/models/__init__.py

Soontao/credential-digger

365eedca3eaec201503441046ba0c37937db69e1

[ "Apache-2.0" ]

null

credentialdigger/models/__init__.py

Soontao/credential-digger

365eedca3eaec201503441046ba0c37937db69e1

[ "Apache-2.0" ]

null

credentialdigger/models/__init__.py

Soontao/credential-digger

365eedca3eaec201503441046ba0c37937db69e1

[ "Apache-2.0" ]

null

from .path_model import PathModel from .snippet_model import SnippetModel

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1

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6

fba6df9af45d877682919dc978d0fef6ce817e6b

3,414

py

Python

tests/tagtrain/test_add_me.py

c17r/TagTrain

5aa1ca36439cc5e81d0c691f905a4bb879b78399

[ "MIT" ]

null

tests/tagtrain/test_add_me.py

c17r/TagTrain

5aa1ca36439cc5e81d0c691f905a4bb879b78399

[ "MIT" ]

7

2020-03-24T17:54:31.000Z

2021-09-21T12:34:34.000Z

tests/tagtrain/test_add_me.py

c17r/TagTrain

5aa1ca36439cc5e81d0c691f905a4bb879b78399

[ "MIT" ]

null

from datetime import datetime from unittest.mock import MagicMock, patch from tagtrain import data from . import fake from tagtrain.tagtrain.tt_add_me import AddMe @patch('tagtrain.data.by_member.add_user_to_group') @patch('tagtrain.data.by_owner.find_group') def test_unknown_group(find_group, add_user_to_group): find_group.side_effect = data.Group.DoesNotExist() app, reply, message, match = fake.create_all() AddMe(app).run(reply, message, match) find_group.assert_called_once_with('OwnerName', 'GroupName') add_user_to_group.assert_not_called() reply.append.assert_called_once_with('User `OwnerName` does not have a Group `GroupName`. Skipping.') @patch('tagtrain.data.by_member.add_user_to_group') @patch('tagtrain.data.by_owner.find_group') def test_existing_member(find_group, add_user_to_group): group = fake.create_group(name='GroupName', member_count=1, locked=None) find_group.return_value = group add_user_to_group.return_value = (group, False) app, reply, message, match = fake.create_all() AddMe(app).run(reply, message, match) find_group.assert_called_once_with('OwnerName', 'GroupName') add_user_to_group.assert_called_once_with('OwnerName', 'GroupName', 'AuthorName', 'PermaLink') reply.append.assert_called_once_with("You are already a Member of `OwnerName`'s Group `GroupName`. Skipping.") @patch('tagtrain.data.by_member.add_user_to_group') @patch('tagtrain.data.by_owner.find_group') def test_group_locked(find_group, add_user_to_group): group = fake.create_group(name='GroupName', member_count=1, locked=datetime.utcnow()) find_group.return_value = group add_user_to_group.return_value = (group, False) app, reply, message, match = fake.create_all() AddMe(app).run(reply, message, match) find_group.assert_called_once_with('OwnerName', 'GroupName') add_user_to_group.assert_not_called() reply.append.assert_called_once_with("Group `GroupName` is locked. Only `OwnerName` can add you. Skipping.") @patch('tagtrain.data.by_member.add_user_to_group') @patch('tagtrain.data.by_owner.find_group') def test_good(find_group, add_user_to_group): group = fake.create_group(name='GroupName', member_count=1, locked=None) find_group.return_value = group add_user_to_group.return_value = (group, True) app, reply, message, match = fake.create_all() AddMe(app).run(reply, message, match) find_group.assert_called_once_with('OwnerName', 'GroupName') add_user_to_group.assert_called_once_with('OwnerName', 'GroupName', 'AuthorName', 'PermaLink') reply.append.assert_called_once_with("You were added to `OwnerName`'s Group `GroupName`, 1 total Members.") @patch('tagtrain.data.by_member.add_user_to_group') @patch('tagtrain.data.by_owner.find_group') def test_blacklisted(find_group, add_user_to_group): group = fake.create_group(name='GroupName', member_count=1, locked=None) find_group.return_value = group add_user_to_group.side_effect = data.by_member.Blacklisted() app, reply, message, match = fake.create_all() AddMe(app).run(reply, message, match) find_group.assert_called_once_with('OwnerName', 'GroupName') add_user_to_group.assert_called_once_with('OwnerName', 'GroupName', 'AuthorName', 'PermaLink') reply.append.assert_called_once_with("You are Blacklisted from adding yourself to Group `GroupName`. Skipping.")

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117

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495

3,414

4.945455

0.151515

0.05719

0.069853

0.10866

0.805964

0.796569

0

0.001657

0.116286

3,414

84

118

40.642857

0.809745

0

0.672414

0

0.278266

0.108377

0

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0.086207

false

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0.086207

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0.172414

0

null

0

1

0

null

0

6

fbc4ed30b9b2f12d4224cde32a4f7f89c2553705

134

py

Python

nerf/__init__.py

AnimatedRNG/nerf-jax

c940bcfbb986623691aff7a4e28bf8273ea70147

[ "Apache-2.0" ]

5

2020-10-22T07:27:15.000Z

2022-02-25T02:54:39.000Z

nerf/__init__.py

AnimatedRNG/nerf-jax

c940bcfbb986623691aff7a4e28bf8273ea70147

[ "Apache-2.0" ]

11

2021-01-27T01:52:38.000Z

2021-02-03T06:35:34.000Z

nerf/__init__.py

AnimatedRNG/nerf-jax

c940bcfbb986623691aff7a4e28bf8273ea70147

[ "Apache-2.0" ]

2

2020-12-15T14:44:07.000Z

2021-01-27T03:39:01.000Z

from .nerf_dataset import * from .nerf_helpers import * from .train_utils import * from .models import * from .volume_render import *

22.333333

28

0.776119

19

134

5.263158

0.526316

0.4

0

0.149254

134

5

29

26.8

0.877193

0

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true

0

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null

0

1

0

1

0

1

0

6

83dedc660023d9672a127ba94f75f5d3a9210b27

198

py

Python

Server/Python/src/dbs/dao/MySQL/DatasetType/Insert.py

vkuznet/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

8

2015-08-14T04:01:32.000Z

2021-06-03T00:56:42.000Z

Server/Python/src/dbs/dao/MySQL/DatasetType/Insert.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

162

2015-01-07T21:34:47.000Z

2021-10-13T09:42:41.000Z

Server/Python/src/dbs/dao/MySQL/DatasetType/Insert.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

16

2015-01-22T15:27:29.000Z

2021-04-28T09:23:28.000Z

#!/usr/bin/env python """ DAO Object for DatasetTypes table """ from dbs.dao.Oracle.DatasetType.Insert import Insert as OraDatasetTypeInsert class Insert(OraDatasetTypeInsert): pass

22

76

0.737374

23

198

6.347826

0.826087

0

0.171717

198

8

77

24.75

0.890244

0.277778

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

83e0f43b931ea28afa3726093155837d3abd708d

11,324

py

Python

src/tt_bank/tt_bank/tests/test_handlers.py

devapromix/the-tale

2a10efd3270734f8cf482b4cfbc5353ef8f0494c

[ "BSD-3-Clause" ]

1

2020-04-02T11:51:20.000Z

src/tt_bank/tt_bank/tests/test_handlers.py

devapromix/the-tale

2a10efd3270734f8cf482b4cfbc5353ef8f0494c

[ "BSD-3-Clause" ]

null

src/tt_bank/tt_bank/tests/test_handlers.py

devapromix/the-tale

2a10efd3270734f8cf482b4cfbc5353ef8f0494c

[ "BSD-3-Clause" ]

null

import datetime from aiohttp import test_utils from tt_protocol.protocol import bank_pb2 from tt_web import postgresql as db from .. import relations from . import helpers TEST_OPERATIONS = [bank_pb2.Operation(account_id=666, currency=1, amount=-1000, type='x.1', description='y.1'), bank_pb2.Operation(account_id=667, currency=1, amount=300, type='x.2', description='y.2'), bank_pb2.Operation(account_id=666, currency=1, amount=500, type='x.3', description='y.3'), bank_pb2.Operation(account_id=667, currency=1, amount=-100, type='x.4', description='y.4')] async def load_operations(): results = await db.sql('SELECT * FROM operations ORDER BY created_at ASC') return [bank_pb2.Operation(account_id=row['account'], currency=row['currency'], amount=row['amount'], type=row['type'], description=row['description']) for row in results] class Base(helpers.BaseTests): async def check_balance(self, account_id, expected_balance): request = await self.client.post('/accounts/balance', data=bank_pb2.AccountBalanceRequest(account_id=account_id).SerializeToString()) answer = await self.check_success(request, bank_pb2.AccountBalanceResponse) self.assertEqual(answer.balance, expected_balance) class AccountBalanceTests(Base): @test_utils.unittest_run_loop async def test_no_balance(self): await self.check_balance(account_id=666, expected_balance={}) @test_utils.unittest_run_loop async def test_has_record(self): await helpers.call_change_balance(account_id=666, currency=1, amount=101) await self.check_balance(account_id=666, expected_balance={1: 101}) @test_utils.unittest_run_loop async def test_multiple_currencies(self): await helpers.call_change_balance(account_id=666, currency=1, amount=101) await helpers.call_change_balance(account_id=666, currency=2, amount=13) await self.check_balance(account_id=666, expected_balance={1: 101, 2: 13}) class AccountHistoryTests(Base): @test_utils.unittest_run_loop async def test_no_history(self): request = await self.client.post('/accounts/history', data=bank_pb2.AccountHistoryRequest(account_id=666).SerializeToString()) answer = await self.check_success(request, bank_pb2.AccountHistoryResponse) self.assertEqual(list(answer.history), []) async def apply_operations(self, test_operations): request = await self.client.post('/transactions/start', data=bank_pb2.StartTransactionRequest(operations=test_operations, lifetime=0).SerializeToString()) answer = await self.check_success(request, bank_pb2.StartTransactionResponse) await self.client.post('/transactions/commit', data=bank_pb2.CommitTransactionRequest(transaction_id=answer.transaction_id).SerializeToString()) @test_utils.unittest_run_loop async def test_has_records(self): await helpers.call_change_balance(account_id=666, currency=1, amount=1000) await self.apply_operations([bank_pb2.Operation(account_id=666, currency=1, amount=1000, type='x.1', description='y.1'), bank_pb2.Operation(account_id=666, currency=1, amount=-300, type='x.2', description='y.2')]) await self.apply_operations([bank_pb2.Operation(account_id=667, currency=1, amount=50, type='x.3', description='y.3'), bank_pb2.Operation(account_id=666, currency=1, amount=-1, type='x.4', description='y.4')]) request = await self.client.post('/accounts/history', data=bank_pb2.AccountHistoryRequest(account_id=666).SerializeToString()) answer = await self.check_success(request, bank_pb2.AccountHistoryResponse) self.assertEqual([(record.amount, record.description) for record in answer.history], [(1000, 'y.1'), (-300, 'y.2'), (-1, 'y.4')]) @test_utils.unittest_run_loop async def test_multiple_currencies(self): await helpers.call_change_balance(account_id=666, currency=1, amount=1000) await self.apply_operations([bank_pb2.Operation(account_id=666, currency=1, amount=1000, type='x.1', description='y.1'), bank_pb2.Operation(account_id=666, currency=1, amount=-300, type='x.2', description='y.2')]) await self.apply_operations([bank_pb2.Operation(account_id=667, currency=1, amount=50, type='x.3', description='y.3'), bank_pb2.Operation(account_id=666, currency=2, amount=1, type='x.4', description='y.4')]) request = await self.client.post('/accounts/history', data=bank_pb2.AccountHistoryRequest(account_id=666).SerializeToString()) answer = await self.check_success(request, bank_pb2.AccountHistoryResponse) self.assertEqual([(record.currency, record.amount, record.description) for record in answer.history], [(1, 1000, 'y.1'), (1, -300, 'y.2'), (2, 1, 'y.4')]) class StartTransactionTests(Base): @test_utils.unittest_run_loop async def test_no_operations(self): request = await self.client.post('/transactions/start', data=bank_pb2.StartTransactionRequest(operations=[], lifetime=0).SerializeToString()) await self.check_error(request, error='bank.start_transaction.no_operations_specified') @test_utils.unittest_run_loop async def test_started(self): await helpers.call_change_balance(account_id=666, currency=1, amount=1000) await helpers.call_change_balance(account_id=667, currency=1, amount=1000) lifetime = datetime.timedelta(seconds=100) request = await self.client.post('/transactions/start', data=bank_pb2.StartTransactionRequest(operations=TEST_OPERATIONS, lifetime=100).SerializeToString()) answer = await self.check_success(request, bank_pb2.StartTransactionResponse) results = await db.sql('SELECT * FROM transactions WHERE id=%(id)s', {'id': answer.transaction_id}) self.assertEqual(results[0]['state'], relations.TRANSACTION_STATE.OPENED.value) self.assertEqual(results[0]['lifetime'], lifetime) results = await db.sql('SELECT transaction FROM operations') loaded_operations = await load_operations() self.assertEqual(TEST_OPERATIONS, loaded_operations) # only withdraws applied on transaction start await self.check_balance(account_id=666, expected_balance={1: 0}) await self.check_balance(account_id=667, expected_balance={1: 900}) @test_utils.unittest_run_loop async def test_small_balance(self): request = await self.client.post('/transactions/start', data=bank_pb2.StartTransactionRequest(operations=TEST_OPERATIONS, lifetime=0).SerializeToString()) await self.check_error(request, error='bank.start_transaction.no_enough_currency') results = await db.sql('SELECT * FROM transactions') self.assertEqual(results, []) results = await db.sql('SELECT * FROM operations ORDER BY created_at ASC') self.assertEqual(results, []) await self.check_balance(account_id=666, expected_balance={}) await self.check_balance(account_id=667, expected_balance={}) class RollbackTransactionTests(Base): @test_utils.unittest_run_loop async def test_rollback(self): await helpers.call_change_balance(account_id=666, currency=1, amount=1000) await helpers.call_change_balance(account_id=667, currency=1, amount=1000) request = await self.client.post('/transactions/start', data=bank_pb2.StartTransactionRequest(operations=TEST_OPERATIONS, lifetime=100).SerializeToString()) answer = await self.check_success(request, bank_pb2.StartTransactionResponse) request = await self.client.post('/transactions/rollback', data=bank_pb2.RollbackTransactionRequest(transaction_id=answer.transaction_id).SerializeToString()) answer = await self.check_success(request, bank_pb2.StartTransactionResponse) results = await db.sql('SELECT * FROM transactions') self.assertEqual(results[0]['state'], relations.TRANSACTION_STATE.ROLLBACKED.value) results = await db.sql('SELECT * FROM operations ORDER BY created_at ASC') self.assertEqual(results, []) await self.check_balance(account_id=666, expected_balance={1: 1000}) await self.check_balance(account_id=667, expected_balance={1: 1000}) @test_utils.unittest_run_loop async def test_no_transaction_to_rollback(self): request = await self.client.post('/transactions/rollback', data=bank_pb2.RollbackTransactionRequest(transaction_id=666).SerializeToString()) await self.check_error(request, error='bank.rollback_transaction.no_transacton_to_rollback') class CommitTransactionTests(Base): @test_utils.unittest_run_loop async def test_commit(self): await helpers.call_change_balance(account_id=666, currency=1, amount=1000) await helpers.call_change_balance(account_id=667, currency=1, amount=1000) lifetime = datetime.timedelta(seconds=100) request = await self.client.post('/transactions/start', data=bank_pb2.StartTransactionRequest(operations=TEST_OPERATIONS, lifetime=100).SerializeToString()) answer = await self.check_success(request, bank_pb2.StartTransactionResponse) request = await self.client.post('/transactions/commit', data=bank_pb2.CommitTransactionRequest(transaction_id=answer.transaction_id).SerializeToString()) answer = await self.check_success(request, bank_pb2.CommitTransactionResponse) results = await db.sql('SELECT * FROM transactions') self.assertEqual(results[0]['state'], relations.TRANSACTION_STATE.COMMITED.value) self.assertEqual(results[0]['lifetime'], lifetime) loaded_operations = await load_operations() self.assertEqual(TEST_OPERATIONS, loaded_operations) # only withdraws applied on transaction start await self.check_balance(account_id=666, expected_balance={1: 500}) await self.check_balance(account_id=667, expected_balance={1: 1200}) @test_utils.unittest_run_loop async def test_no_transaction_to_commit(self): request = await self.client.post('/transactions/commit', data=bank_pb2.CommitTransactionRequest(transaction_id=666).SerializeToString()) await self.check_error(request, error='bank.commit_transaction.no_transacton_to_commit')

51.472727

166

0.672466

1,305

11,324

5.636015

0.098851

0.053841

0.04242

0.043508

0.860503

0.853977

0.845139

0.832087

0.815092

0.752141

0

0.040784

0.220505

11,324

219

167

51.707763

0.792455

0.007683

0

0.482993

0

0.082606

0.020385

0

0.095238

1

0

false

0

0.040816

0

0.088435

0

null

0

1

0

null

0

6

f7a6cf662c87426100707d316c14ec5fadcf050d

33,140

py

Python

yaksh/evaluator_tests/test_simple_question_types.py

tyochans/online_test

4eb754c2e71922819de7390d1b4993a21763de3e

[ "Python-2.0" ]

1

2022-03-21T11:14:17.000Z

yaksh/evaluator_tests/test_simple_question_types.py

tyochans/online_test

4eb754c2e71922819de7390d1b4993a21763de3e

[ "Python-2.0" ]

null

yaksh/evaluator_tests/test_simple_question_types.py

tyochans/online_test

4eb754c2e71922819de7390d1b4993a21763de3e

[ "Python-2.0" ]

null

import unittest from datetime import datetime, timedelta from django.utils import timezone from textwrap import dedent import pytz from yaksh.models import User, Profile, Question, Quiz, QuestionPaper,\ AnswerPaper, Answer, Course, IntegerTestCase, FloatTestCase,\ StringTestCase, McqTestCase, ArrangeTestCase def setUpModule(): # Create user profile # Create User 1 user = User.objects.create_user(username='demo_user_100', password='demo', email='demo@test.com') Profile.objects.create(user=user, roll_number=1, institute='IIT', department='Aerospace', position='Student') # Create User 2 user2 = User.objects.create_user( username='demo_user_101', password='demo', email='demo@test.com') Profile.objects.create(user=user2, roll_number=2, institute='IIT', department='Aerospace', position='Student') # Create a course Course.objects.create(name="Python Course 100", enrollment="Enroll Request", creator=user) quiz = Quiz.objects.create( start_date_time=datetime(2015, 10, 9, 10, 8, 15, 0, tzinfo=pytz.utc), end_date_time=datetime(2199, 10, 9, 10, 8, 15, 0, tzinfo=pytz.utc), duration=30, active=True, attempts_allowed=1, time_between_attempts=0, pass_criteria=0, description='demo quiz 100', instructions="Demo Instructions", creator=user ) QuestionPaper.objects.create(quiz=quiz, total_marks=1.0) def tearDownModule(): User.objects.filter(username__in=["demo_user_100", "demo_user_101"])\ .delete() class IntegerQuestionTestCases(unittest.TestCase): @classmethod def setUpClass(self): # Creating Course self.course = Course.objects.get(name="Python Course 100") # Creating Quiz self.quiz = Quiz.objects.get(description="demo quiz 100") # Creating Question paper self.question_paper = QuestionPaper.objects.get(quiz=self.quiz) # Creating User self.user = User.objects.get(username='demo_user_100') # Creating Question self.question1 = Question.objects.create(summary='int1', points=1, type='code', user=self.user) self.question1.language = 'python' self.question1.type = "integer" self.question1.test_case_type = 'integertestcase' self.question1.description = 'sum of 12+13?' self.question1.save() # Creating answerpaper self.answerpaper = AnswerPaper.objects.create( user=self.user, user_ip='101.0.0.1', start_time=timezone.now(), question_paper=self.question_paper, course=self.course, end_time=timezone.now()+timedelta(minutes=5), attempt_number=1 ) self.answerpaper.questions.add(self.question1) self.answerpaper.save() # For question self.integer_based_testcase = IntegerTestCase(question=self.question1, correct=25, type='integertestcase', ) self.integer_based_testcase.save() @classmethod def tearDownClass(self): self.question1.delete() self.answerpaper.delete() def test_validate_regrade_integer_correct_answer(self): # Given integer_answer = 25 self.answer = Answer(question=self.question1, answer=integer_answer, ) self.answer.save() self.answerpaper.answers.add(self.answer) self.answerpaper.save() # When json_data = None result = self.answerpaper.validate_answer(integer_answer, self.question1, json_data, ) # Then self.assertTrue(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=self.answer.id) regrade_answer.answer = 200 regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then self.answer = self.answerpaper.answers.filter(question=self.question1 ).last() self.assertEqual(self.answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(self.answer.marks, 0) self.assertFalse(self.answer.correct) def test_validate_regrade_integer_incorrect_answer(self): # Given integer_answer = 26 self.answer = Answer(question=self.question1, answer=integer_answer, ) self.answer.save() self.answerpaper.answers.add(self.answer) # When json_data = None result = self.answerpaper.validate_answer(integer_answer, self.question1, json_data ) # Then self.assertFalse(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=self.answer.id) regrade_answer.answer = 25 regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then self.answer = self.answerpaper.answers.filter(question=self.question1 ).last() self.assertEqual(self.answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(self.answer.marks, 1) self.assertTrue(self.answer.correct) class StringQuestionTestCases(unittest.TestCase): @classmethod def setUpClass(self): # Creating Course self.course = Course.objects.get(name="Python Course 100") # Creating Quiz self.quiz = Quiz.objects.get(description="demo quiz 100") # Creating Question paper self.question_paper = QuestionPaper.objects.get(quiz=self.quiz) # Creating User self.user = User.objects.get(username='demo_user_100') # Creating Question self.question1 = Question.objects.create(summary='str1', points=1, type='code', user=self.user) self.question1.language = 'python' self.question1.type = "string" self.question1.test_case_type = 'stringtestcase' self.question1.description = 'Write Hello, EARTH!' self.question1.save() self.question2 = Question.objects.create(summary='str2', points=1, type='code', user=self.user) self.question2.language = 'python' self.question2.type = "string" self.question2.test_case_type = 'stringtestcase' self.question2.description = 'Write Hello, EARTH!' self.question2.save() # Creating answerpaper self.answerpaper = AnswerPaper.objects.create( user=self.user, user_ip='101.0.0.1', start_time=timezone.now(), question_paper=self.question_paper, course=self.course, end_time=timezone.now()+timedelta(minutes=5), attempt_number=1 ) self.answerpaper.questions.add(*[self.question1, self.question2]) self.answerpaper.save() # For question self.lower_string_testcase = StringTestCase(question=self.question1, correct="Hello, EARTH!", string_check="lower", type='stringtestcase', ) self.lower_string_testcase.save() self.exact_string_testcase = StringTestCase(question=self.question2, correct="Hello, EARTH!", string_check="exact", type='stringtestcase', ) self.exact_string_testcase.save() @classmethod def tearDownClass(self): self.question1.delete() self.question2.delete() self.answerpaper.delete() def test_validate_regrade_case_insensitive_string_correct_answer(self): # Given string_answer = "hello, earth!" answer = Answer(question=self.question1, answer=string_answer) answer.save() self.answerpaper.answers.add(answer) # When json_data = None result = self.answerpaper.validate_answer(string_answer, self.question1, json_data ) # Then self.assertTrue(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=answer.id) regrade_answer.answer = "hello, mars!" regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then answer = self.answerpaper.answers.filter( question=self.question1).last() self.assertEqual(answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(answer.marks, 0) self.assertFalse(answer.correct) def test_validate_regrade_case_insensitive_string_incorrect_answer(self): # Given string_answer = "hello, mars!" answer = Answer(question=self.question1, answer=string_answer) answer.save() self.answerpaper.answers.add(answer) # When json_data = None result = self.answerpaper.validate_answer(string_answer, self.question1, json_data ) # Then self.assertFalse(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=answer.id) regrade_answer.answer = "hello, earth!" regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then answer = self.answerpaper.answers.filter( question=self.question1).last() self.assertEqual(answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(answer.marks, 1) self.assertTrue(answer.correct) def test_validate_regrade_case_sensitive_string_correct_answer(self): # Given string_answer = "Hello, EARTH!" answer = Answer(question=self.question2, answer=string_answer) answer.save() self.answerpaper.answers.add(answer) # When json_data = None result = self.answerpaper.validate_answer(string_answer, self.question2, json_data ) # Then self.assertTrue(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=answer.id) regrade_answer.answer = "hello, earth!" regrade_answer.save() # When details = self.answerpaper.regrade(self.question2.id) # Then answer = self.answerpaper.answers.filter( question=self.question2).last() self.assertEqual(answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(answer.marks, 0) self.assertFalse(answer.correct) def test_case_sensitive_string_incorrect_answer(self): # Given string_answer = "hello, earth!" answer = Answer(question=self.question2, answer=string_answer) answer.save() self.answerpaper.answers.add(answer) # When json_data = None result = self.answerpaper.validate_answer(string_answer, self.question2, json_data ) # Then self.assertFalse(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=answer.id) regrade_answer.answer = "Hello, EARTH!" regrade_answer.save() # When details = self.answerpaper.regrade(self.question2.id) # Then answer = self.answerpaper.answers.filter( question=self.question2).last() self.assertEqual(answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(answer.marks, 1) self.assertTrue(answer.correct) class FloatQuestionTestCases(unittest.TestCase): @classmethod def setUpClass(self): # Creating Course self.course = Course.objects.get(name="Python Course 100") # Creating Quiz self.quiz = Quiz.objects.get(description="demo quiz 100") # Creating Question paper self.question_paper = QuestionPaper.objects.get(quiz=self.quiz) # Creating User self.user = User.objects.get(username='demo_user_100') # Creating Question self.question1 = Question.objects.create(summary='flt1', points=1, type='code', user=self.user) self.question1.language = 'python' self.question1.type = "float" self.question1.test_case_type = 'floattestcase' self.question1.save() # Creating answerpaper self.answerpaper = AnswerPaper.objects.create( user=self.user, user_ip='101.0.0.1', start_time=timezone.now(), question_paper=self.question_paper, course=self.course, end_time=timezone.now()+timedelta(minutes=5), attempt_number=1, ) self.answerpaper.questions.add(self.question1) self.answerpaper.save() # For question self.float_based_testcase = FloatTestCase(question=self.question1, correct=100, error_margin=0.1, type='floattestcase', ) self.float_based_testcase.save() @classmethod def tearDownClass(self): self.question1.delete() self.answerpaper.delete() def test_validate_regrade_float_correct_answer(self): # Given float_answer = 99.9 self.answer = Answer(question=self.question1, answer=float_answer, ) self.answer.save() self.answerpaper.answers.add(self.answer) # When json_data = None result = self.answerpaper.validate_answer(float_answer, self.question1, json_data, ) # Then self.assertTrue(result['success']) # Regrade with wrong answer # Given regrade_answer = Answer.objects.get(id=self.answer.id) regrade_answer.answer = 0.0 regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then self.answer = self.answerpaper.answers.filter(question=self.question1 ).last() self.assertEqual(self.answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(self.answer.marks, 0) self.assertFalse(self.answer.correct) def test_float_incorrect_answer(self): # Given float_answer = 99.8 self.answer = Answer(question=self.question1, answer=float_answer, ) self.answer.save() self.answerpaper.answers.add(self.answer) # When json_data = None result = self.answerpaper.validate_answer(float_answer, self.question1, json_data ) # Then self.assertFalse(result['success']) # Regrade # Given regrade_answer = Answer.objects.get(id=self.answer.id) regrade_answer.answer = 99.9 regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then self.answer = self.answerpaper.answers.filter(question=self.question1 ).last() self.assertEqual(self.answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(self.answer.marks, 1) self.assertTrue(self.answer.correct) class MCQQuestionTestCases(unittest.TestCase): @classmethod def setUpClass(self): # Creating User self.user = User.objects.get(username='demo_user_100') self.user2 = User.objects.get(username='demo_user_101') self.user_ip = '127.0.0.1' # Creating Course self.course = Course.objects.get(name="Python Course 100") # Creating Quiz self.quiz = Quiz.objects.get(description="demo quiz 100") # Creating Question paper self.question_paper = QuestionPaper.objects.get(quiz=self.quiz) self.question_paper.shuffle_testcases = True self.question_paper.save() # Creating Question self.question1 = Question.objects.create(summary='mcq1', points=1, type='code', user=self.user, ) self.question1.language = 'python' self.question1.type = "mcq" self.question1.test_case_type = 'Mcqtestcase' self.question1.description = 'Which option is Correct?' self.question1.save() # For questions self.mcq_based_testcase_1 = McqTestCase(question=self.question1, options="Correct", correct=True, type='mcqtestcase', ) self.mcq_based_testcase_1.save() self.mcq_based_testcase_2 = McqTestCase(question=self.question1, options="Incorrect", correct=False, type='mcqtestcase', ) self.mcq_based_testcase_2.save() self.mcq_based_testcase_3 = McqTestCase(question=self.question1, options="Incorrect", correct=False, type='mcqtestcase', ) self.mcq_based_testcase_3.save() self.mcq_based_testcase_4 = McqTestCase(question=self.question1, options="Incorrect", correct=False, type='mcqtestcase', ) self.mcq_based_testcase_4.save() self.question_paper.fixed_questions.add(self.question1) self.answerpaper = self.question_paper.make_answerpaper( user=self.user, ip=self.user_ip, attempt_num=1, course_id=self.course.id ) # Answerpaper for user 2 self.answerpaper2 = self.question_paper.make_answerpaper( user=self.user2, ip=self.user_ip, attempt_num=1, course_id=self.course.id ) @classmethod def tearDownClass(self): self.question1.delete() self.answerpaper.delete() self.answerpaper2.delete() def test_shuffle_test_cases(self): # Given # When user_testcase = self.question1.get_ordered_test_cases( self.answerpaper ) order1 = [tc.id for tc in user_testcase] user2_testcase = self.question1.get_ordered_test_cases( self.answerpaper2 ) order2 = [tc.id for tc in user2_testcase] self.question_paper.shuffle_testcases = False self.question_paper.save() answerpaper3 = self.question_paper.make_answerpaper( user=self.user2, ip=self.user_ip, attempt_num=self.answerpaper.attempt_number+1, course_id=self.course.id ) not_ordered_testcase = self.question1.get_ordered_test_cases( answerpaper3 ) get_test_cases = self.question1.get_test_cases() # Then self.assertNotEqual(order1, order2) self.assertEqual(get_test_cases, not_ordered_testcase) class ArrangeQuestionTestCases(unittest.TestCase): @classmethod def setUpClass(self): # Creating Course self.course = Course.objects.get(name="Python Course 100") # Creating Quiz self.quiz = Quiz.objects.get(description="demo quiz 100") # Creating Question paper self.question_paper = QuestionPaper.objects.get(quiz=self.quiz, total_marks=1.0) # Creating User self.user = User.objects.get(username='demo_user_100') # Creating Question self.question1 = Question.objects.create(summary='arrange1', points=1.0, user=self.user ) self.question1.language = 'python' self.question1.type = "arrange" self.question1.description = "Arrange alphabets in ascending order" self.question1.test_case_type = 'arrangetestcase' self.question1.save() # Creating answerpaper self.answerpaper = AnswerPaper.objects.create( user=self.user, user_ip='101.0.0.1', course=self.course, start_time=timezone.now(), question_paper=self.question_paper, end_time=timezone.now()+timedelta(minutes=5), attempt_number=1 ) self.answerpaper.questions.add(self.question1) self.answerpaper.save() # For question self.arrange_testcase_1 = ArrangeTestCase(question=self.question1, options="A", type='arrangetestcase', ) self.arrange_testcase_1.save() self.testcase_1_id = self.arrange_testcase_1.id self.arrange_testcase_2 = ArrangeTestCase(question=self.question1, options="B", type='arrangetestcase', ) self.arrange_testcase_2.save() self.testcase_2_id = self.arrange_testcase_2.id self.arrange_testcase_3 = ArrangeTestCase(question=self.question1, options="C", type='arrangetestcase', ) self.arrange_testcase_3.save() self.testcase_3_id = self.arrange_testcase_3.id @classmethod def tearDownClass(self): self.question1.delete() self.answerpaper.delete() def test_validate_regrade_arrange_correct_answer(self): # Given arrange_answer = [self.testcase_1_id, self.testcase_2_id, self.testcase_3_id, ] self.answer = Answer(question=self.question1, answer=arrange_answer, ) self.answer.save() self.answerpaper.answers.add(self.answer) # When json_data = None result = self.answerpaper.validate_answer(arrange_answer, self.question1, json_data, ) # Then self.assertTrue(result['success']) # Regrade with wrong answer # Given regrade_answer = Answer.objects.get(id=self.answer.id) # Try regrade with wrong data structure # When regrade_answer.answer = 1 regrade_answer.save() details = self.answerpaper.regrade(self.question1.id) err_msg = dedent("""\ User: {0}; Quiz: {1}; Question: {2}. {3} answer not a list.""".format( self.user.username, self.quiz.description, self.question1.summary, self.question1.type )) self.assertFalse(details[0]) self.assertEqual(details[1], err_msg) # Try regrade with incorrect answer # When regrade_answer.answer = [self.testcase_1_id, self.testcase_3_id, self.testcase_2_id, ] regrade_answer.save() # Then details = self.answerpaper.regrade(self.question1.id) self.answer = self.answerpaper.answers.filter(question=self.question1 ).last() self.assertEqual(self.answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(self.answer.marks, 0) self.assertFalse(self.answer.correct) def test_validate_regrade_arrange_incorrect_answer(self): # Given arrange_answer = [self.testcase_1_id, self.testcase_3_id, self.testcase_2_id, ] self.answer = Answer(question=self.question1, answer=arrange_answer, ) self.answer.save() self.answerpaper.answers.add(self.answer) # When json_data = None result = self.answerpaper.validate_answer(arrange_answer, self.question1, json_data ) # Then self.assertFalse(result['success']) # Regrade with wrong answer # Given regrade_answer = Answer.objects.get(id=self.answer.id) regrade_answer.answer = [self.testcase_1_id, self.testcase_2_id, self.testcase_3_id, ] regrade_answer.save() # When details = self.answerpaper.regrade(self.question1.id) # Then self.answer = self.answerpaper.answers.filter(question=self.question1 ).last() self.assertEqual(self.answer, regrade_answer) self.assertTrue(details[0]) self.assertEqual(self.answer.marks, 1) self.assertTrue(self.answer.correct) class MCQShuffleTestCases(unittest.TestCase): @classmethod def setUpClass(self): # Creating User self.user = User.objects.get(username='demo_user_100') self.user2 = User.objects.get(username='demo_user_101') self.user_ip = '127.0.0.1' # Creating Course self.course = Course.objects.get(name="Python Course 100") # Creating Quiz self.quiz = Quiz.objects.get(description="demo quiz 100") # Creating Question paper self.question_paper = QuestionPaper.objects.get(quiz=self.quiz) self.question_paper.shuffle_testcases = True self.question_paper.save() # Creating Question self.question1 = Question.objects.create(summary='mcq1', points=1, type='code', user=self.user, ) self.question1.language = 'python' self.question1.type = "mcq" self.question1.test_case_type = 'Mcqtestcase' self.question1.description = 'Which option is Correct?' self.question1.save() # For questions self.mcq_based_testcase_1 = McqTestCase(question=self.question1, options="Correct", correct=True, type='mcqtestcase', ) self.mcq_based_testcase_1.save() self.mcq_based_testcase_2 = McqTestCase(question=self.question1, options="Incorrect", correct=False, type='mcqtestcase', ) self.mcq_based_testcase_2.save() self.mcq_based_testcase_3 = McqTestCase(question=self.question1, options="Incorrect", correct=False, type='mcqtestcase', ) self.mcq_based_testcase_3.save() self.mcq_based_testcase_4 = McqTestCase(question=self.question1, options="Incorrect", correct=False, type='mcqtestcase', ) self.mcq_based_testcase_4.save() self.question_paper.fixed_questions.add(self.question1) self.answerpaper = self.question_paper.make_answerpaper( user=self.user, ip=self.user_ip, attempt_num=1, course_id=self.course.id ) # Answerpaper for user 2 self.answerpaper2 = self.question_paper.make_answerpaper( user=self.user2, ip=self.user_ip, attempt_num=1, course_id=self.course.id ) @classmethod def tearDownClass(self): self.question1.delete() self.answerpaper.delete() self.answerpaper2.delete() def test_shuffle_test_cases(self): # Given # When user_testcase = self.question1.get_ordered_test_cases( self.answerpaper ) order1 = [tc.id for tc in user_testcase] user2_testcase = self.question1.get_ordered_test_cases( self.answerpaper2 ) order2 = [tc.id for tc in user2_testcase] self.question_paper.shuffle_testcases = False self.question_paper.save() answerpaper3 = self.question_paper.make_answerpaper( user=self.user2, ip=self.user_ip, attempt_num=self.answerpaper.attempt_number+1, course_id=self.course.id ) not_ordered_testcase = self.question1.get_ordered_test_cases( answerpaper3 ) get_test_cases = self.question1.get_test_cases() # Then self.assertNotEqual(order1, order2) self.assertEqual(get_test_cases, not_ordered_testcase) answerpaper3.delete()

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null

0

6

f7f5953df70a457cdcfa1364dcff1a4ea0d3973b

4,490

py

Python

tests/features/test_build_features.py

catalyst-cooperative/epacems_ramp_rates

5a5ea6f9571823f7ef3f9c66abb4d9acb79820be

[ "MIT" ]

1

2021-07-02T15:31:22.000Z

tests/features/test_build_features.py

catalyst-cooperative/epacems_ramp_rates

5a5ea6f9571823f7ef3f9c66abb4d9acb79820be

[ "MIT" ]

4

2021-07-30T19:42:10.000Z

2021-08-16T19:12:21.000Z

tests/features/test_build_features.py

catalyst-cooperative/epacems_ramp_rates

5a5ea6f9571823f7ef3f9c66abb4d9acb79820be

[ "MIT" ]

null

import pytest import pandas as pd import numpy as np from ramprate.build_features import _find_uptime def test__find_uptime_start_and_end_nonzero(): dt_idx = pd.date_range(start="2020-01-01 00:00", periods=6, freq="h", tz="UTC") data = [2, 2, 0, 0, 0, 2] # downtime=True # first zero after non-zero shutdown = pd.to_datetime(["2020-01-01 02:00"], utc=True) # last zero before non-zero startup = pd.to_datetime(["2020-01-01 04:00"], utc=True) expected = pd.DataFrame({"shutdown": shutdown, "startup": startup}) actual = _find_uptime(pd.Series(data, index=dt_idx), downtime=True) pd.testing.assert_frame_equal(actual, expected) # end points ('startup') are after start points ('shutdown') assert actual.diff(axis=1)["startup"].dt.total_seconds().fillna(1).ge(0).all() # downtime=False # last zero before non-zero startup = pd.to_datetime([pd.NaT, "2020-01-01 04:00"], utc=True) # first zero after non-zero shutdown = pd.to_datetime(["2020-01-01 02:00", pd.NaT], utc=True) expected = pd.DataFrame({"startup": startup, "shutdown": shutdown}) actual = _find_uptime(pd.Series(data, index=dt_idx)) pd.testing.assert_frame_equal(actual, expected) # end points ('shutdown') are after start points ('startup') assert actual.diff(axis=1)["shutdown"].dt.total_seconds().fillna(1).ge(0).all() def test__find_uptime_all_zeros(): dt_idx = pd.date_range(start="2020-01-01 00:00", periods=6, freq="h", tz="UTC") data = [0, 0, 0, 0, 0, 0] # downtime=True # first zero after non-zero shutdown = pd.to_datetime([pd.NaT], utc=True) # last zero before non-zero startup = pd.to_datetime([pd.NaT], utc=True) expected = pd.DataFrame({"shutdown": shutdown, "startup": startup}) actual = _find_uptime(pd.Series(data, index=dt_idx), downtime=True) pd.testing.assert_frame_equal(actual, expected) # downtime=False # first zero after non-zero shutdown = pd.to_datetime([], utc=True) # last zero before non-zero startup = pd.to_datetime([], utc=True) expected = pd.DataFrame({"startup": startup, "shutdown": shutdown}) actual = _find_uptime(pd.Series(data, index=dt_idx)) pd.testing.assert_frame_equal(actual, expected) def test__find_uptime_no_zeros(): dt_idx = pd.date_range(start="2020-01-01 00:00", periods=6, freq="h", tz="UTC") data = [5, 5, 5, 5, 5, 5] # downtime=True # first zero after non-zero shutdown = pd.to_datetime([], utc=True) # last zero before non-zero startup = pd.to_datetime([], utc=True) expected = pd.DataFrame({"shutdown": shutdown, "startup": startup}) actual = _find_uptime(pd.Series(data, index=dt_idx), downtime=True) pd.testing.assert_frame_equal(actual, expected) # downtime=False # first zero after non-zero shutdown = pd.to_datetime([pd.NaT], utc=True) # last zero before non-zero startup = pd.to_datetime([pd.NaT], utc=True) expected = pd.DataFrame({"startup": startup, "shutdown": shutdown}) actual = _find_uptime(pd.Series(data, index=dt_idx)) pd.testing.assert_frame_equal(actual, expected) def test__find_uptime_start_zero_end_zero(): dt_idx = pd.date_range(start="2020-01-01 00:00", periods=6, freq="h", tz="UTC") data = [0, 2, 2, 0, 2, 0] # downtime=True # first zero after non-zero shutdown = pd.to_datetime([pd.NaT, "2020-01-01 03:00", "2020-01-01 05:00"], utc=True) # last zero before non-zero startup = pd.to_datetime(["2020-01-01 00:00", "2020-01-01 03:00", pd.NaT], utc=True) expected = pd.DataFrame({"shutdown": shutdown, "startup": startup}) actual = _find_uptime(pd.Series(data, index=dt_idx), downtime=True) pd.testing.assert_frame_equal(actual, expected) # end points ('startup') are after start points ('shutdown') assert actual.diff(axis=1)["startup"].dt.total_seconds().fillna(1).ge(0).all() # downtime=False # last zero before non-zero startup = pd.to_datetime(["2020-01-01 00:00", "2020-01-01 03:00"], utc=True) # first zero after non-zero shutdown = pd.to_datetime(["2020-01-01 03:00", "2020-01-01 05:00"], utc=True) expected = pd.DataFrame({"startup": startup, "shutdown": shutdown}) actual = _find_uptime(pd.Series(data, index=dt_idx)) pd.testing.assert_frame_equal(actual, expected) # end points ('shutdown') are after start points ('startup') assert actual.diff(axis=1)["shutdown"].dt.total_seconds().fillna(1).ge(0).all()

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0

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null

0

6

79072ecbaf7146f0b35ba3fb0dc12f5ddb30f1d3

506

py

Python

nexus/bot/handlers/__init__.py

RobbiNespu/hyperboria

7db858386f1a20e8d49bc16f53bfd7f1e4d03f7e

[ "Unlicense" ]

54

2021-01-07T03:02:36.000Z

2022-03-28T17:19:29.000Z

nexus/bot/handlers/__init__.py

the-superpirate/hyperboria

74776166158d07b199677f9738862e5f1fa54367

[ "Unlicense" ]

10

2021-01-08T17:38:59.000Z

2022-02-28T14:34:45.000Z

nexus/bot/handlers/__init__.py

the-superpirate/hyperboria

74776166158d07b199677f9738862e5f1fa54367

[ "Unlicense" ]

16

2020-12-28T18:31:44.000Z

2022-02-22T15:00:53.000Z

from . import ( admin, ban, close, contact, copyright, donate, download, emoji, help, legacy, noop, roll, search, settings, shortlink, start, stop, submit, top_missed, view, vote, ) __all__ = ['admin', 'ban', 'contact', 'copyright', 'close', 'donate', 'download', 'emoji', 'help', 'legacy', 'noop', 'roll', 'search', 'settings', 'shortlink', 'start', 'stop', 'submit', 'top_missed', 'view', 'vote']

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0.3083

506

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0

1

0

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0.038462

0

null

0

1

0

1

0

null

0

6

791eb77a671c04392a73a95336ef324cf0637fdf

12,670

py

Python

daemon/tests/test_links.py

alehmannFRA-UAS/core

bcf74297851e40e383c279f1f0a7eff3257c258b

[ "BSD-2-Clause" ]

3

2022-03-14T21:53:08.000Z

2022-03-14T21:54:18.000Z

daemon/tests/test_links.py

alehmannFRA-UAS/core

bcf74297851e40e383c279f1f0a7eff3257c258b

[ "BSD-2-Clause" ]

null

daemon/tests/test_links.py

alehmannFRA-UAS/core

bcf74297851e40e383c279f1f0a7eff3257c258b

[ "BSD-2-Clause" ]

null

from typing import Tuple import pytest from core.emulator.data import IpPrefixes, LinkOptions from core.emulator.session import Session from core.errors import CoreError from core.nodes.base import CoreNode from core.nodes.network import SwitchNode INVALID_ID: int = 100 LINK_OPTIONS: LinkOptions = LinkOptions( delay=50, bandwidth=5000000, loss=25, dup=25, jitter=10, buffer=100 ) def create_ptp_network( session: Session, ip_prefixes: IpPrefixes ) -> Tuple[CoreNode, CoreNode]: # create nodes node1 = session.add_node(CoreNode) node2 = session.add_node(CoreNode) # link nodes to net node iface1_data = ip_prefixes.create_iface(node1) iface2_data = ip_prefixes.create_iface(node2) session.add_link(node1.id, node2.id, iface1_data, iface2_data) # instantiate session session.instantiate() return node1, node2 class TestLinks: def test_add_node_to_node(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(CoreNode) iface1_data = ip_prefixes.create_iface(node1) iface2_data = ip_prefixes.create_iface(node2) # when iface1, iface2 = session.add_link( node1.id, node2.id, iface1_data, iface2_data, options=LINK_OPTIONS ) # then assert node1.get_iface(iface1_data.id) assert node2.get_iface(iface2_data.id) assert iface1 is not None assert iface2 is not None assert iface1.local_options == LINK_OPTIONS assert iface1.has_local_netem assert iface2.local_options == LINK_OPTIONS assert iface2.has_local_netem def test_add_node_to_net(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(SwitchNode) iface1_data = ip_prefixes.create_iface(node1) # when iface, _ = session.add_link( node1.id, node2.id, iface1_data=iface1_data, options=LINK_OPTIONS ) # then assert node2.links() assert node1.get_iface(iface1_data.id) assert iface is not None assert iface.local_options == LINK_OPTIONS assert iface.has_local_netem def test_add_net_to_node(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(CoreNode) iface2_data = ip_prefixes.create_iface(node2) # when _, iface = session.add_link( node1.id, node2.id, iface2_data=iface2_data, options=LINK_OPTIONS ) # then assert node1.links() assert node2.get_iface(iface2_data.id) assert iface is not None assert iface.local_options == LINK_OPTIONS assert iface.has_local_netem def test_add_net_to_net(self, session): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(SwitchNode) # when iface, _ = session.add_link(node1.id, node2.id, options=LINK_OPTIONS) # then assert node1.links() assert iface is not None assert iface.local_options == LINK_OPTIONS assert iface.options == LINK_OPTIONS assert iface.has_local_netem assert iface.has_netem def test_add_node_to_node_uni(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(CoreNode) iface1_data = ip_prefixes.create_iface(node1) iface2_data = ip_prefixes.create_iface(node2) link_options1 = LinkOptions( delay=50, bandwidth=5000000, loss=25, dup=25, jitter=10, buffer=100, unidirectional=True, ) link_options2 = LinkOptions( delay=51, bandwidth=5000001, loss=26, dup=26, jitter=11, buffer=101, unidirectional=True, ) # when iface1, iface2 = session.add_link( node1.id, node2.id, iface1_data, iface2_data, link_options1 ) session.update_link( node2.id, node1.id, iface2_data.id, iface1_data.id, link_options2 ) # then assert node1.get_iface(iface1_data.id) assert node2.get_iface(iface2_data.id) assert iface1 is not None assert iface2 is not None assert iface1.local_options == link_options1 assert iface1.has_local_netem assert iface2.local_options == link_options2 assert iface2.has_local_netem def test_update_node_to_net(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(SwitchNode) iface1_data = ip_prefixes.create_iface(node1) iface1, _ = session.add_link(node1.id, node2.id, iface1_data) assert iface1.local_options != LINK_OPTIONS # when session.update_link( node1.id, node2.id, iface1_id=iface1_data.id, options=LINK_OPTIONS ) # then assert iface1.local_options == LINK_OPTIONS assert iface1.has_local_netem def test_update_net_to_node(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(CoreNode) iface2_data = ip_prefixes.create_iface(node2) _, iface2 = session.add_link(node1.id, node2.id, iface2_data=iface2_data) assert iface2.local_options != LINK_OPTIONS # when session.update_link( node1.id, node2.id, iface2_id=iface2_data.id, options=LINK_OPTIONS ) # then assert iface2.local_options == LINK_OPTIONS assert iface2.has_local_netem def test_update_ptp(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(CoreNode) iface1_data = ip_prefixes.create_iface(node1) iface2_data = ip_prefixes.create_iface(node2) iface1, iface2 = session.add_link(node1.id, node2.id, iface1_data, iface2_data) assert iface1.local_options != LINK_OPTIONS assert iface2.local_options != LINK_OPTIONS # when session.update_link( node1.id, node2.id, iface1_data.id, iface2_data.id, LINK_OPTIONS ) # then assert iface1.local_options == LINK_OPTIONS assert iface1.has_local_netem assert iface2.local_options == LINK_OPTIONS assert iface2.has_local_netem def test_update_net_to_net(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(SwitchNode) iface1, _ = session.add_link(node1.id, node2.id) assert iface1.local_options != LINK_OPTIONS # when session.update_link(node1.id, node2.id, options=LINK_OPTIONS) # then assert iface1.local_options == LINK_OPTIONS assert iface1.has_local_netem assert iface1.options == LINK_OPTIONS assert iface1.has_netem def test_clear_net_to_net(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(SwitchNode) iface1, _ = session.add_link(node1.id, node2.id, options=LINK_OPTIONS) assert iface1.local_options == LINK_OPTIONS assert iface1.has_local_netem assert iface1.options == LINK_OPTIONS assert iface1.has_netem # when options = LinkOptions(delay=0, bandwidth=0, loss=0.0, dup=0, jitter=0, buffer=0) session.update_link(node1.id, node2.id, options=options) # then assert iface1.local_options.is_clear() assert not iface1.has_local_netem assert iface1.options.is_clear() assert not iface1.has_netem def test_delete_node_to_node(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(CoreNode) iface1_data = ip_prefixes.create_iface(node1) iface2_data = ip_prefixes.create_iface(node2) session.add_link(node1.id, node2.id, iface1_data, iface2_data) assert node1.get_iface(iface1_data.id) assert node2.get_iface(iface2_data.id) # when session.delete_link(node1.id, node2.id, iface1_data.id, iface2_data.id) # then assert iface1_data.id not in node1.ifaces assert iface2_data.id not in node2.ifaces def test_delete_node_to_net(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(SwitchNode) iface1_data = ip_prefixes.create_iface(node1) session.add_link(node1.id, node2.id, iface1_data) assert node1.get_iface(iface1_data.id) # when session.delete_link(node1.id, node2.id, iface1_id=iface1_data.id) # then assert iface1_data.id not in node1.ifaces def test_delete_net_to_node(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(CoreNode) iface2_data = ip_prefixes.create_iface(node2) session.add_link(node1.id, node2.id, iface2_data=iface2_data) assert node2.get_iface(iface2_data.id) # when session.delete_link(node1.id, node2.id, iface2_id=iface2_data.id) # then assert iface2_data.id not in node2.ifaces def test_delete_net_to_net(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(SwitchNode) session.add_link(node1.id, node2.id) assert node1.get_linked_iface(node2) # when session.delete_link(node1.id, node2.id) # then assert not node1.get_linked_iface(node2) def test_delete_node_error(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(SwitchNode) session.add_link(node1.id, node2.id) assert node1.get_linked_iface(node2) # when with pytest.raises(CoreError): session.delete_link(node1.id, INVALID_ID) with pytest.raises(CoreError): session.delete_link(INVALID_ID, node2.id) def test_delete_net_to_net_error(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(SwitchNode) node3 = session.add_node(SwitchNode) session.add_link(node1.id, node2.id) assert node1.get_linked_iface(node2) # when with pytest.raises(CoreError): session.delete_link(node1.id, node3.id) def test_delete_node_to_net_error(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(SwitchNode) node3 = session.add_node(SwitchNode) iface1_data = ip_prefixes.create_iface(node1) iface1, _ = session.add_link(node1.id, node2.id, iface1_data) assert iface1 # when with pytest.raises(CoreError): session.delete_link(node1.id, node3.id) def test_delete_net_to_node_error(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(SwitchNode) node2 = session.add_node(CoreNode) node3 = session.add_node(SwitchNode) iface2_data = ip_prefixes.create_iface(node2) _, iface2 = session.add_link(node1.id, node2.id, iface2_data=iface2_data) assert iface2 # when with pytest.raises(CoreError): session.delete_link(node1.id, node3.id) def test_delete_node_to_node_error(self, session: Session, ip_prefixes: IpPrefixes): # given node1 = session.add_node(CoreNode) node2 = session.add_node(CoreNode) node3 = session.add_node(SwitchNode) iface1_data = ip_prefixes.create_iface(node1) iface2_data = ip_prefixes.create_iface(node2) iface1, iface2 = session.add_link(node1.id, node2.id, iface1_data, iface2_data) assert iface1 assert iface2 # when with pytest.raises(CoreError): session.delete_link(node1.id, node3.id)

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792b2e276d9654b59ec6ab9f9fb6f45f7f8145c7

7,761

py

Python

tests/test_dynamo_event.py

wellcomecollection/aws_utils

b2c54b44fcd7edf9877903f1b0ef1aba853926c3

[ "MIT" ]

null

tests/test_dynamo_event.py

wellcomecollection/aws_utils

b2c54b44fcd7edf9877903f1b0ef1aba853926c3

[ "MIT" ]

8

2017-11-07T15:44:42.000Z

2019-07-25T16:35:00.000Z

tests/test_dynamo_event.py

wellcomecollection/aws_utils

b2c54b44fcd7edf9877903f1b0ef1aba853926c3

[ "MIT" ]

null

# -*- encoding: utf-8 -*- from wellcome_aws_utils import dynamo_event event_source_arn = ( "arn:aws:dynamodb:us-east-1:123456789012:" "table/BarkTable/stream/2016-11-16T20:42:48.104" ) def create_insert_record(message): return { "ApproximateCreationDateTime": 1479499740, "Keys": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Username": { "S": "John Doe" } }, "NewImage": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": message }, "Username": { "S": "John Doe" } }, "SequenceNumber": "13021600000000001596893679", "SizeBytes": 112, "StreamViewType": "NEW_IMAGE" } def create_remove_record(message): return { "ApproximateCreationDateTime": 1479499740, "Keys": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Username": { "S": "John Doe" } }, "OldImage": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": message }, "Username": { "S": "John Doe" } }, "SequenceNumber": "13021600000000001596893679", "SizeBytes": 112, "StreamViewType": "OLD_IMAGE" } def create_modify_record(old_message, new_message): return { "ApproximateCreationDateTime": 1479499740, "Keys": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Username": { "S": "John Doe" } }, "OldImage": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": old_message }, "Username": { "S": "John Doe" } }, "NewImage": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": new_message }, "Username": { "S": "John Doe" } }, "SequenceNumber": "13021600000000001596893679", "SizeBytes": 112, "StreamViewType": "NEW_AND_OLD_IMAGES" } def create_modify_record_keys_only(): return { "ApproximateCreationDateTime": 1479499740, "Keys": { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Username": { "S": "John Doe" } }, "SequenceNumber": "13021600000000001596893679", "SizeBytes": 112, "StreamViewType": "KEYS_ONLY" } def create_insert_event(message): return { "eventID": "7de3041dd709b024af6f29e4fa13d34c", "eventName": "INSERT", "eventVersion": "1.1", "eventSource": "aws:dynamodb", "awsRegion": "us-west-2", "dynamodb": create_insert_record(message), "eventSourceARN": event_source_arn } def create_remove_event(message): return { "eventID": "7de3041dd709b024af6f29e4fa13d34c", "eventName": "REMOVE", "eventVersion": "1.1", "eventSource": "aws:dynamodb", "awsRegion": "us-west-2", "dynamodb": create_remove_record(message), "eventSourceARN": event_source_arn } def create_modify_event(old_message, new_message): return { "eventID": "7de3041dd709b024af6f29e4fa13d34c", "eventName": "MODIFY", "eventVersion": "1.1", "eventSource": "aws:dynamodb", "awsRegion": "us-west-2", "dynamodb": create_modify_record(old_message, new_message), "eventSourceARN": event_source_arn } def create_modify_event_keys_only(): return { "eventID": "7de3041dd709b024af6f29e4fa13d34c", "eventName": "MODIFY", "eventVersion": "1.1", "eventSource": "aws:dynamodb", "awsRegion": "us-west-2", "dynamodb": create_modify_record_keys_only(), "eventSourceARN": event_source_arn } def test_get_source_arn(): dynamo_image = dynamo_event.DynamoEvent(create_insert_event('foo')) assert dynamo_image.event_source_arn == event_source_arn def test_insert_event(): dynamo_image = dynamo_event.DynamoEvent(create_insert_event('foo')) expected_image_with_deserialized_values = { 'Message': 'foo', 'Timestamp': '2016-11-18:12:09:36', 'Username': 'John Doe' } expected_image = { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": 'foo' }, "Username": { "S": "John Doe" } } assert dynamo_image.new_image( deserialize_values=True ) == expected_image_with_deserialized_values assert dynamo_image.new_image() == expected_image def test_remove_event(): dynamo_image = dynamo_event.DynamoEvent(create_remove_event('foo')) expected_image_with_deserialized_values = { 'Message': 'foo', 'Timestamp': '2016-11-18:12:09:36', 'Username': 'John Doe' } expected_image = { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": 'foo' }, "Username": { "S": "John Doe" } } assert dynamo_image.new_image(deserialize_values=True) is None assert dynamo_image.new_image() is None assert dynamo_image.old_image( deserialize_values=True ) == expected_image_with_deserialized_values assert dynamo_image.old_image() == expected_image def test_modify_event(): dynamo_image = dynamo_event.DynamoEvent(create_modify_event('foo', 'bar')) expected_old_image_with_deserialized_values = { 'Message': 'foo', 'Timestamp': '2016-11-18:12:09:36', 'Username': 'John Doe' } expected_old_image = { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": 'foo' }, "Username": { "S": "John Doe" } } expected_new_image_with_deserialized_values = { 'Message': 'bar', 'Timestamp': '2016-11-18:12:09:36', 'Username': 'John Doe' } expected_new_image = { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Message": { "S": 'bar' }, "Username": { "S": "John Doe" } } assert dynamo_image.new_image( deserialize_values=True ) == expected_new_image_with_deserialized_values assert dynamo_image.new_image() == expected_new_image assert dynamo_image.old_image( deserialize_values=True ) == expected_old_image_with_deserialized_values assert dynamo_image.old_image() == expected_old_image def test_modify_event_keys_only(): dynamo_image = dynamo_event.DynamoEvent(create_modify_event_keys_only()) assert dynamo_image.new_image(deserialize_values=True) is None assert dynamo_image.new_image() is None assert dynamo_image.old_image(deserialize_values=True) is None assert dynamo_image.old_image() is None assert dynamo_image.keys(deserialize_values=True) == { 'Timestamp': '2016-11-18:12:09:36', 'Username': 'John Doe' } assert dynamo_image.keys() == { "Timestamp": { "S": "2016-11-18:12:09:36" }, "Username": { "S": "John Doe" } }

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