ytzi/the-stack-dedup-python-filtered-docstrings

500e861a5d9736a4f8277adb38a02df2b39dbf6e

1,031

py

Python

addons/finetune_style.py

geoffrey0822/multilevellabel_NN

faec3303dac2376d6e8a761632aca31bc3868413

[ "Apache-2.0" ]

null

addons/finetune_style.py

geoffrey0822/multilevellabel_NN

faec3303dac2376d6e8a761632aca31bc3868413

[ "Apache-2.0" ]

null

addons/finetune_style.py

geoffrey0822/multilevellabel_NN

faec3303dac2376d6e8a761632aca31bc3868413

[ "Apache-2.0" ]

null

import os,sys from caffe.proto import caffe_pb2 import google.protobuf.text_format as txtf prototxt_file=sys.argv[1] to_prototxt_file=sys.argv[2] hold_layers=sys.argv[3].split(',') model=caffe_pb2.NetParameter() with open(prototxt_file,'r') as f: txtf.Merge(f.read(),model) f.close() active_layers={} i=0 for layer in model.layer: if len(layer.param)>0: isActive=False for param in layer.param: isActive=param.lr_mult>0 or param.decay_mult>0 if isActive: break active_layers[layer.name]=i i+=1 print active_layers print model.layer[active_layers['cls']] for lname in active_layers.keys(): if lname in hold_layers: continue nParam=len(model.layer[active_layers[lname]].param) for n in range(nParam): model.layer[active_layers[lname]].param[n].lr_mult=0 model.layer[active_layers[lname]].param[n].decay_mult=0 with open(to_prototxt_file,'w') as f: f.write(str(model)) f.close() print 'finished'

25.146341

63

0.675073

import os,sys from caffe.proto import caffe_pb2 import google.protobuf.text_format as txtf prototxt_file=sys.argv[1] to_prototxt_file=sys.argv[2] hold_layers=sys.argv[3].split(',') model=caffe_pb2.NetParameter() with open(prototxt_file,'r') as f: txtf.Merge(f.read(),model) f.close() active_layers={} i=0 for layer in model.layer: if len(layer.param)>0: isActive=False for param in layer.param: isActive=param.lr_mult>0 or param.decay_mult>0 if isActive: break active_layers[layer.name]=i i+=1 print active_layers print model.layer[active_layers['cls']] for lname in active_layers.keys(): if lname in hold_layers: continue nParam=len(model.layer[active_layers[lname]].param) for n in range(nParam): model.layer[active_layers[lname]].param[n].lr_mult=0 model.layer[active_layers[lname]].param[n].decay_mult=0 with open(to_prototxt_file,'w') as f: f.write(str(model)) f.close() print 'finished'

0

d4a79cd608d45cd244b8fc0f37ecdbd2b9b20e1d

6,134

py

Python

docarray/document/mixins/porting.py

qdrant/docarray

acc76ca31e3344cc5dd7e42e6627951b91845ba3

[ "Apache-2.0" ]

591

2022-01-09T14:39:59.000Z

2022-03-31T13:19:39.000Z

docarray/document/mixins/porting.py

qdrant/docarray

acc76ca31e3344cc5dd7e42e6627951b91845ba3

[ "Apache-2.0" ]

210

2022-01-10T07:59:29.000Z

2022-03-31T14:49:18.000Z

docarray/document/mixins/porting.py

qdrant/docarray

acc76ca31e3344cc5dd7e42e6627951b91845ba3

[ "Apache-2.0" ]

40

2022-01-09T14:52:20.000Z

2022-03-31T07:59:45.000Z

import base64 import json import pickle from typing import Optional, TYPE_CHECKING, Type, Dict, Any, Union from ...helper import compress_bytes, decompress_bytes if TYPE_CHECKING: from ...typing import T

34.655367

104

0.604989

import base64 import json import pickle from typing import Optional, TYPE_CHECKING, Type, Dict, Any, Union from ...helper import compress_bytes, decompress_bytes if TYPE_CHECKING: from ...typing import T class PortingMixin: @classmethod def from_dict( cls: Type['T'], obj: Dict, protocol: str = 'jsonschema', **kwargs ) -> 'T': """Convert a dict object into a Document. :param obj: a Python dict object :param protocol: `jsonschema` or `protobuf` :param kwargs: extra key-value args pass to pydantic and protobuf parser. :return: the parsed Document object """ if protocol == 'jsonschema': from ..pydantic_model import PydanticDocument return cls.from_pydantic_model(PydanticDocument.parse_obj(obj, **kwargs)) elif protocol == 'protobuf': from google.protobuf import json_format from ...proto.docarray_pb2 import DocumentProto pb_msg = DocumentProto() json_format.ParseDict(obj, pb_msg, **kwargs) return cls.from_protobuf(pb_msg) else: return cls(obj) @classmethod def from_json( cls: Type['T'], obj: Union[str, bytes, bytearray], protocol: str = 'jsonschema', **kwargs, ) -> 'T': """Convert a JSON string into a Document. :param obj: a valid JSON string :param protocol: `jsonschema` or `protobuf` :param kwargs: extra key-value args pass to pydantic and protobuf parser. :return: the parsed Document object """ if protocol == 'jsonschema': from ..pydantic_model import PydanticDocument return cls.from_pydantic_model(PydanticDocument.parse_raw(obj, **kwargs)) elif protocol == 'protobuf': from google.protobuf import json_format from ...proto.docarray_pb2 import DocumentProto pb_msg = DocumentProto() json_format.Parse(obj, pb_msg, **kwargs) return cls.from_protobuf(pb_msg) else: return cls.from_dict(json.loads(obj), protocol=protocol) def to_dict(self, protocol: str = 'jsonschema', **kwargs) -> Dict[str, Any]: """Convert itself into a Python dict object. :param protocol: `jsonschema` or `protobuf` :param kwargs: extra key-value args pass to pydantic and protobuf dumper. :return: the dumped Document as a dict object """ if protocol == 'jsonschema': return self.to_pydantic_model().dict(**kwargs) elif protocol == 'protobuf': from google.protobuf.json_format import MessageToDict return MessageToDict( self.to_protobuf(), **kwargs, ) else: raise ValueError(f'protocol=`{protocol}` is not supported') def to_bytes( self, protocol: str = 'pickle', compress: Optional[str] = None ) -> bytes: if protocol == 'pickle': bstr = pickle.dumps(self) elif protocol == 'protobuf': bstr = self.to_protobuf().SerializePartialToString() else: raise ValueError( f'protocol={protocol} is not supported. Can be only `protobuf` or pickle protocols 0-5.' ) return compress_bytes(bstr, algorithm=compress) @classmethod def from_bytes( cls: Type['T'], data: bytes, protocol: str = 'pickle', compress: Optional[str] = None, ) -> 'T': """Build Document object from binary bytes :param data: binary bytes :param protocol: protocol to use :param compress: compress method to use :return: a Document object """ bstr = decompress_bytes(data, algorithm=compress) if protocol == 'pickle': return pickle.loads(bstr) elif protocol == 'protobuf': from ...proto.docarray_pb2 import DocumentProto pb_msg = DocumentProto() pb_msg.ParseFromString(bstr) return cls.from_protobuf(pb_msg) else: raise ValueError( f'protocol={protocol} is not supported. Can be only `protobuf` or pickle protocols 0-5.' ) def to_json(self, protocol: str = 'jsonschema', **kwargs) -> str: """Convert itself into a JSON string. :param protocol: `jsonschema` or `protobuf` :param kwargs: extra key-value args pass to pydantic and protobuf dumper. :return: the dumped JSON string """ if protocol == 'jsonschema': return self.to_pydantic_model().json(**kwargs) elif protocol == 'protobuf': from google.protobuf.json_format import MessageToJson return MessageToJson(self.to_protobuf(), **kwargs) else: raise ValueError(f'protocol={protocol} is not supported.') def to_base64( self, protocol: str = 'pickle', compress: Optional[str] = None ) -> str: """Serialize a Document object into as base64 string :param protocol: protocol to use :param compress: compress method to use :return: a base64 encoded string """ return base64.b64encode(self.to_bytes(protocol, compress)).decode('utf-8') @classmethod def from_base64( cls: Type['T'], data: str, protocol: str = 'pickle', compress: Optional[str] = None, ) -> 'T': """Build Document object from binary bytes :param data: a base64 encoded string :param protocol: protocol to use :param compress: compress method to use :return: a Document object """ return cls.from_bytes(base64.b64decode(data), protocol, compress) def _to_stream_bytes(self, protocol, compress) -> bytes: # 4 bytes (uint32) doc_as_bytes = self.to_bytes(protocol=protocol, compress=compress) # variable size bytes len_doc_as_bytes = len(doc_as_bytes).to_bytes(4, 'big', signed=False) return len_doc_as_bytes + doc_as_bytes

765

5,135

23

45f6e0cdfb3d2b0b20d8ce80caa54171ae3cfc46

195

py

Python

tableau_rest_api/methods/metrics.py

Radabaugh/tableau_tools

8630ec4c5614ace8b08246f83dc80c330a8045b4

[ "MIT" ]

179

2016-07-06T19:34:39.000Z

2021-03-16T02:30:27.000Z

tableau_rest_api/methods/metrics.py

Radabaugh/tableau_tools

8630ec4c5614ace8b08246f83dc80c330a8045b4

[ "MIT" ]

75

2016-07-28T16:17:44.000Z

2021-03-29T12:22:32.000Z

tableau_rest_api/methods/metrics.py

chip-felton-montage/tableau_tools

60881a99a32c7d9e03afbb0e1161deefaa854904

[ "MIT" ]

83

2016-08-25T10:32:26.000Z

2021-02-17T18:31:14.000Z

from .rest_api_base import * # First Metrics Methods appear in API 3.9

21.666667

60

0.74359

from .rest_api_base import * # First Metrics Methods appear in API 3.9 class MetricsMethods39(): def __init__(self, rest_api_base: TableauRestApiBase38): self.rest = rest_api_base

69

4

48

d4dfe468b447f835214af93d10dfa13057a2239c

458

py

Python

tests/factories/pipeline/pipeline.py

TheLabbingProject/django_analyses

08cac40a32754a265b37524f08ec6160c69ebea8

[ "Apache-2.0" ]

1

2020-12-30T12:43:34.000Z

tests/factories/pipeline/pipeline.py

TheLabbingProject/django_analyses

08cac40a32754a265b37524f08ec6160c69ebea8

[ "Apache-2.0" ]

59

2019-12-25T13:14:56.000Z

2021-07-22T12:24:46.000Z

tests/factories/pipeline/pipeline.py

TheLabbingProject/django_analyses

08cac40a32754a265b37524f08ec6160c69ebea8

[ "Apache-2.0" ]

2

2020-05-24T06:44:27.000Z

2020-07-09T15:47:31.000Z

from factory import Faker from factory.django import DjangoModelFactory

35.230769

80

0.733624

from factory import Faker from factory.django import DjangoModelFactory class PipelineFactory(DjangoModelFactory): title = Faker("pystr", min_chars=3, max_chars=18) description = Faker("paragraph", nb_sentences=3, variable_nb_sentences=True) created = Faker("date_between", start_date="-30d", end_date="-15d") modified = Faker("date_between", start_date="-14d", end_date="today") class Meta: model = "django_analyses.Pipeline"

0

362

23

916322b1e03ef17f05188b14b907317d24dead00

216

py

Python

src/year2021/day16b.py

lancelote/advent_of_code

06dda6ca034bc1e86addee7798bb9b2a34ff565b

[ "Unlicense" ]

10

2017-12-11T17:54:52.000Z

2021-12-09T20:16:30.000Z

src/year2021/day16b.py

lancelote/advent_of_code

06dda6ca034bc1e86addee7798bb9b2a34ff565b

[ "Unlicense" ]

260

2015-12-09T11:03:03.000Z

2021-12-12T14:32:23.000Z

src/year2021/day16b.py

lancelote/advent_of_code

06dda6ca034bc1e86addee7798bb9b2a34ff565b

[ "Unlicense" ]

null

"""2021 - Day 16 Part 2: Packet Decoder.""" from src.year2021.day16a import BITS

24

43

0.689815

"""2021 - Day 16 Part 2: Packet Decoder.""" from src.year2021.day16a import BITS def solve(task: str) -> int: system = BITS.from_hex(task) top_packet = system.read_packet() return top_packet.evaluate()

111

0

23

e8017922857077673165e16e7b2111a57d751eea

93

py

Python

Chapter 05/Praktikum-2/1-n.py

icaksh/Python-Projects-Protek

dfd56ea5afc637a8850911a9296131652de383c5

[ "MIT" ]

null

Chapter 05/Praktikum-2/1-n.py

icaksh/Python-Projects-Protek

dfd56ea5afc637a8850911a9296131652de383c5

[ "MIT" ]

null

Chapter 05/Praktikum-2/1-n.py

icaksh/Python-Projects-Protek

dfd56ea5afc637a8850911a9296131652de383c5

[ "MIT" ]

null

banyakPerulangan = 10 i = 0 while (i < banyakPerulangan): print('Hello World') i += 1

18.6

29

0.634409

banyakPerulangan = 10 i = 0 while (i < banyakPerulangan): print('Hello World') i += 1

0

251bbbc73707a4d8bb70aeffb163ed0511e6ba61

660

py

Python

matroska_cache/dep/tag.py

kolypto/py-matroska-cache

b40030f97d463aac8e3a6f4b0e0e9f081dfc92b1

[ "MIT" ]

null

matroska_cache/dep/tag.py

kolypto/py-matroska-cache

b40030f97d463aac8e3a6f4b0e0e9f081dfc92b1

[ "MIT" ]

null

matroska_cache/dep/tag.py

kolypto/py-matroska-cache

b40030f97d463aac8e3a6f4b0e0e9f081dfc92b1

[ "MIT" ]

null

from .base import DependencyBase, dataclass @dataclass class Tag(DependencyBase): """ Dependency on an arbitrary tag Usage: use this tag as a named signal to invalidate records Example: update_dashboard_for_admins = Tag('update-dashboard-for-admins') cache.put( 'articles-list', [...], dependencies=[ update_dashboard_for_admins, ... ] ) cache.invalidate(update_dashboard_for_admins) """ name: str __slots__ = 'name', PREFIX = 'tag'

20

72

0.562121

from .base import DependencyBase, dataclass @dataclass class Tag(DependencyBase): """ Dependency on an arbitrary tag Usage: use this tag as a named signal to invalidate records Example: update_dashboard_for_admins = Tag('update-dashboard-for-admins') cache.put( 'articles-list', [...], dependencies=[ update_dashboard_for_admins, ... ] ) cache.invalidate(update_dashboard_for_admins) """ name: str __slots__ = 'name', PREFIX = 'tag' def key(self) -> str: return f'{self.PREFIX}:{self.name}'

44

0

27

4e3ce9f763c7ac23b63142ca0b09def2613ab552

3,644

py

Python

exam_organization/models.py

andaeh/django-exam-organization-tool

fd269402cb74e4028f3aa65978da5a47e4d2f7ae

[ "Apache-2.0" ]

null

exam_organization/models.py

andaeh/django-exam-organization-tool

fd269402cb74e4028f3aa65978da5a47e4d2f7ae

[ "Apache-2.0" ]

2

2021-09-11T13:42:08.000Z

2022-03-12T01:01:37.000Z

exam_organization/models.py

andaeh/django-exam-organization-tool

fd269402cb74e4028f3aa65978da5a47e4d2f7ae

[ "Apache-2.0" ]

null

import os from django.db import models from django.contrib.auth.models import User from django.urls import reverse from django.dispatch import receiver from django.db.models.signals import post_delete, post_save from django.conf import settings from django.core.validators import int_list_validator from taggit.managers import TaggableManager # Create your models here. @receiver(post_save, sender=Task) @receiver(post_delete, sender=Task)

32.828829

156

0.693743

import os from django.db import models from django.contrib.auth.models import User from django.urls import reverse from django.dispatch import receiver from django.db.models.signals import post_delete, post_save from django.conf import settings from django.core.validators import int_list_validator from taggit.managers import TaggableManager # Create your models here. class Faculty(models.Model): short_name = models.CharField(max_length=10) name = models.CharField(max_length=100) class Meta: verbose_name = "Ausbildungsrichtung" verbose_name_plural = "Ausbildungsrichtungen" def get_absolute_url(self): return reverse('exam_organization:overview', kwargs={'model': self._meta.verbose_name_raw}) def __str__(self): return self.name class Grade(models.Model): name = models.CharField(max_length=20) class Meta: verbose_name = "Jahrgangsstufe" verbose_name_plural = "Jahrgangsstufen" def get_absolute_url(self): return reverse('exam_organization:overview', kwargs={'model': self._meta.verbose_name_raw}) def __str__(self): return self.name class Topic(models.Model): short_name = models.CharField(max_length=10) grade = models.ForeignKey( Grade, on_delete=models.SET_NULL, null=True) faculty = models.ForeignKey( Faculty, on_delete=models.SET_NULL, null=True) description = models.CharField(max_length=200) class Meta: verbose_name = "Lernbereich" verbose_name_plural = "Lernbereiche" def __str__(self): return self.faculty.short_name + self.grade.name + ": " + self.description class Task(models.Model): headline = models.CharField(max_length=100, verbose_name="Überschrift") topic = models.ManyToManyField(Topic, verbose_name="Lernbereiche", blank=True) description = models.CharField(max_length=500, verbose_name="Beschreibung") total_BE = models.CharField( max_length=50, blank=True, validators=[int_list_validator], help_text="Pro Teilaufgabe, getrennt durch Kommata", verbose_name="Bewertungseinheiten") task_text = models.TextField(null=True, blank=True, verbose_name="Aufgabentext") slug = models.SlugField(unique=True, max_length=100, editable=True) tags = TaggableManager() created_by = models.ForeignKey( User, on_delete=models.SET_NULL, null=True, related_name="created_by", editable=False) edited_by = models.ForeignKey( User, on_delete=models.SET_NULL, null=True, related_name="edited_by", editable=False) class Meta: verbose_name = "Aufgabe" verbose_name_plural = "Aufgaben" def get_absolute_url(self): return reverse('exam_organization:overview', kwargs={'model': self._meta.verbose_name_raw}) def __str__(self): return self.headline @receiver(post_save, sender=Task) def delete_pdf(sender, instance, *args, **kwargs): try: file = os.path.join(settings.MEDIA_ROOT, 'pdf', str(instance.id) + '.pdf') os.remove(file) except: pass @receiver(post_delete, sender=Task) def delete_pdf(sender, instance, *args, **kwargs): try: file = os.path.join(settings.MEDIA_ROOT, 'pdf', str(instance.id) + '.pdf') os.remove(file) except: pass try: images = os.listdir(os.path.join(settings.MEDIA_ROOT, 'latex')) task_images = [f for f in images if f.startswith(str(instance.id))] for image in task_images: os.remove(os.path.join(settings.MEDIA_ROOT, 'latex', image)) except: pass

1,154

1,905

136

b4825201505425ba7ab5d67d7de5b45c05e857f4

432

py

Python

tests/test_utils.py

suzukey/fitrate

b78625fc6479210b63df93a854d687641b080621

[ "MIT" ]

null

tests/test_utils.py

suzukey/fitrate

b78625fc6479210b63df93a854d687641b080621

[ "MIT" ]

1

2020-09-14T22:42:40.000Z

tests/test_utils.py

suzukey/fitrate

b78625fc6479210b63df93a854d687641b080621

[ "MIT" ]

null

from fitrate.utils import gcd, nthrt, prod

21.6

42

0.548611

from fitrate.utils import gcd, nthrt, prod def test_gcd() -> None: assert gcd(*(300, 400)) == 100 assert gcd(*[200, 400, 1000]) == 200 assert gcd(27, 123, 57, 255) == 3 def test_nthrt() -> None: assert nthrt(25, 2) == 5 assert nthrt(27, 3) == 3 assert nthrt(16, 4) == 2 def test_prod() -> None: assert prod(*[5, 10]) == 50 assert prod(*(3.5, 4.5, 5)) == 78.75 assert prod(3, 5, 2, 2) == 60

317

0

69

ccc8ecdd8c0db0c16f54d60be75aad92f2e60bc2

375

py

Python

modulector/migrations/0010_auto_20201022_0445.py

omics-datascience/modulector

357d8f6f8eab5d04b2357a08d177d75cbdad001a

[ "MIT" ]

2

2021-07-10T20:45:58.000Z

2021-08-18T02:24:58.000Z

modulector/migrations/0010_auto_20201022_0445.py

omics-datascience/modulector

357d8f6f8eab5d04b2357a08d177d75cbdad001a

[ "MIT" ]

3

2021-11-07T23:18:58.000Z

2021-11-22T23:17:35.000Z

modulector/migrations/0010_auto_20201022_0445.py

omics-datascience/modulector

357d8f6f8eab5d04b2357a08d177d75cbdad001a

[ "MIT" ]

null

# Generated by Django 3.0.8 on 2020-10-22 04:45 from django.db import migrations

19.736842

50

0.597333

# Generated by Django 3.0.8 on 2020-10-22 04:45 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('modulector', '0009_mirnadrugs_support'), ] operations = [ migrations.RenameField( model_name='mirnadrugs', old_name='mirna', new_name='mature_mirna', ), ]

0

269

23

8ae50b2a412cd79582d436e2ec46bea671d5277c

468

py

Python

MicroPythonADC/adcloop.py

Roger-random/ESP8266Tests

a1938083879042f53a87f5a8d2ac656a62f77281

[ "MIT" ]

null

MicroPythonADC/adcloop.py

Roger-random/ESP8266Tests

a1938083879042f53a87f5a8d2ac656a62f77281

[ "MIT" ]

null

MicroPythonADC/adcloop.py

Roger-random/ESP8266Tests

a1938083879042f53a87f5a8d2ac656a62f77281

[ "MIT" ]

null

import machine import time

23.4

130

0.570513

import machine import time class adcloop: def __init__(self): self.min = 1025 self.max = 0 self.vals = list() def run(self): while True: val = machine.ADC(0).read() if self.max < val: self.max = val if self.min > val: self.min = val self.vals.append(val) print("Latest: {:4d} Max: {:4d} Min: {:4d} Average: {:4.2f}".format(val, self.max, self.min, sum(self.vals)/len(self.vals))) time.sleep(1)

376

-7

72

5412a958b9304c6f8be80eb4facd2bfcfa635905

648

py

Python

utils/train.py

csyhhu/attention-is-all-you-need-pytorch

5792c9714295b1a33d1ca074206ec223f436b954

[ "MIT" ]

1

2020-10-01T23:57:16.000Z

utils/train.py

csyhhu/attention-is-all-you-need-pytorch

5792c9714295b1a33d1ca074206ec223f436b954

[ "MIT" ]

null

utils/train.py

csyhhu/attention-is-all-you-need-pytorch

5792c9714295b1a33d1ca074206ec223f436b954

[ "MIT" ]

null

import torch import torch.nn as nn from utils.miscellaneous import progress_bar

21.6

65

0.589506

import torch import torch.nn as nn from utils.miscellaneous import progress_bar def evaluate(model, iterator, criterion): model.eval() epoch_loss = 0 with torch.no_grad(): for batch_idx, batch in enumerate(iterator): src = batch.src trg = batch.trg output = model(src, trg, 0) #turn off teacher forcing output = output[1:].view(-1, output.shape[-1]) trg = trg[1:].view(-1) loss = criterion(output, trg) epoch_loss += loss.item() progress_bar(batch_idx, len(iterator), 'Testing...') return epoch_loss / len(iterator)

545

0

23

6de1e62c4a84fc603defbbf1a354ab8b75173011

6,558

py

Python

odin/classes/visualizer_localization.py

rnt-pmi/odin

8cfddf04f964393ef30217aa5f4aa61229d7e811

[ "Apache-2.0" ]

4

2021-01-09T10:46:31.000Z

2021-12-16T14:38:06.000Z

odin/classes/visualizer_localization.py

rnt-pmi/odin

8cfddf04f964393ef30217aa5f4aa61229d7e811

[ "Apache-2.0" ]

null

odin/classes/visualizer_localization.py

rnt-pmi/odin

8cfddf04f964393ef30217aa5f4aa61229d7e811

[ "Apache-2.0" ]

3

2021-01-09T10:46:15.000Z

2021-05-11T01:33:30.000Z

import os import cv2 import numpy as np from matplotlib import pyplot as plt from matplotlib.patches import Polygon from .dataset_localization import DatasetLocalization from odin.utils import Iterator from .visulizer_interface import VisualizerInterface from odin.classes import strings as labels_str

47.868613

199

0.582342

import os import cv2 import numpy as np from matplotlib import pyplot as plt from matplotlib.patches import Polygon from .dataset_localization import DatasetLocalization from odin.utils import Iterator from .visulizer_interface import VisualizerInterface from odin.classes import strings as labels_str class VisualizerLocalization(VisualizerInterface): def __init__(self, dataset: DatasetLocalization): self.dataset = dataset self.__colors = {} category_ids = [] for c in self.dataset.get_categories_names(): self.__colors[c] = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] category_ids.append(self.dataset.get_category_id_from_name(c)) def __show_image(self, image_path, index): im_id = self.__current_images[index]["id"] print("Image with id:{}".format(im_id)) if not os.path.exists(image_path): print("Image path does not exist: " + image_path ) else: plt.figure(figsize=(10, 10)) img = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) plt.imshow(img) if self.__current_category is None: annIds = self.dataset.coco_lib.getAnnIds(imgIds=[im_id]) elif type(self.__current_category) is int: annIds = self.dataset.coco_lib.getAnnIds(imgIds=[im_id], catIds=[self.__current_category]) else: annIds = self.dataset.coco_lib.getAnnIds(imgIds=[im_id], catIds=self.__current_category) if self.__current_meta_anotation != None and self.__meta_annotation_value != None: anns = [ann for ann in self.dataset.coco_lib.loadAnns(annIds) if ann[self.__current_meta_anotation] == self.__meta_annotation_value] else: anns = [ann for ann in self.dataset.coco_lib.loadAnns(annIds)] if len(anns) == 0: plt.show() return 0 if self.dataset.is_segmentation and 'segmentation' in anns[0]: # TODO: move to another function ax = plt.gca() for ann in anns: cat = self.dataset.get_category_name_from_id(ann['category_id']) color = self.__colors[cat] seg_points = ann["segmentation"] for pol in seg_points: poly = [[float(pol[i]), float(pol[i+1])] for i in range(0, len(pol), 2)] np_poly = np.array(poly) ax.add_patch( Polygon(np_poly, linestyle='--', fill=False, facecolor='none', edgecolor=color, linewidth=2)) ax.text(x=seg_points[0][0], y=seg_points[0][1], s=ann['category_id'], color='white', fontsize=9, horizontalalignment='left',verticalalignment='top',bbox=dict(facecolor=color)) plt.imshow(img) plt.axis('off') plt.show() else: # TODO: move to another function ax = plt.gca() for ann in anns: cat = self.dataset.get_category_name_from_id(ann['category_id']) color = self.__colors[cat] bbox_x, bbox_y, bbox_w, bbox_h = ann['bbox'] poly = [[bbox_x, bbox_y], [bbox_x, bbox_y + bbox_h], [bbox_x + bbox_w, bbox_y + bbox_h], [bbox_x + bbox_w, bbox_y]] np_poly = np.array(poly).reshape((4, 2)) ax.add_patch(Polygon(np_poly, linestyle='--', facecolor='none', edgecolor=color, linewidth=3)) ax.text(x=bbox_x, y=bbox_y, s=ann['category_id'], color='white', fontsize=9, horizontalalignment='left',verticalalignment='top',bbox=dict(facecolor=color)) plt.axis('off') plt.show() def visualize_annotations(self, categories=None): categories_ds = self.dataset.get_categories_names() if categories is None: categories = categories_ds images = self.dataset.get_images_id_with_path() else: images = [] for cat in categories: if cat in categories_ds: ii = self.dataset.get_images_id_with_path_for_category(cat) images.extend(ii) else: print(labels_str.warn_incorrect_class) category_ids = [self.dataset.get_category_id_from_name(c) for c in categories] self.__start_iterator( images, category=category_ids) def visualize_annotations_for_property(self, meta_annotation, meta_annotation_value): images = self.dataset.get_images_id_with_path_with_property_value(meta_annotation, meta_annotation_value) self.__start_iterator(images, meta_annotation= meta_annotation, meta_annotation_value=meta_annotation_value) def visualize_annotations_for_class_for_property(self, category, meta_annotation, meta_annotation_value): if self.dataset.is_valid_category(category): images = self.dataset.get_images_id_with_path_for_category_with_property_value(category, meta_annotation, meta_annotation_value) category_id = self.dataset.get_category_id_from_name(category) self.__start_iterator(images, category=category_id, meta_annotation=meta_annotation, meta_annotation_value=meta_annotation_value) else: print(labels_str.warn_incorrect_class) def __start_iterator(self, images, category=None, meta_annotation=None, meta_annotation_value=None): self.__current_category = category self.__current_images = images self.__current_meta_anotation = meta_annotation self.__meta_annotation_value = meta_annotation_value paths = [img["path"] for img in images] if len(paths) == 0: print(labels_str.warn_no_images_criteria) else: iterator = Iterator(paths, show_name=False, image_display_function=self.__show_image) iterator.start_iteration()

6,017

29

209

acc7052fa3ee7bfdc9aa6aafe4fb59c11afa86c5

533

py

Python

NoteBooks/Curso de Python/Python/Data_Managment/Compresion de archivos/Formato_bz2.py

Alejandro-sin/Learning_Notebooks

161d6bed4c7b1d171b45f61c0cc6fa91e9894aad

[ "MIT" ]

1

2021-02-26T13:12:22.000Z

NoteBooks/Curso de Python/Python/Data_Managment/Compresion de archivos/Formato_bz2.py

Alejandro-sin/Learning_Notebooks

161d6bed4c7b1d171b45f61c0cc6fa91e9894aad

[ "MIT" ]

null

NoteBooks/Curso de Python/Python/Data_Managment/Compresion de archivos/Formato_bz2.py

Alejandro-sin/Learning_Notebooks

161d6bed4c7b1d171b45f61c0cc6fa91e9894aad

[ "MIT" ]

null

''' Es un méotod de compresión muy sencillo. Bonzip ''' import bz2 #Cadena binaria cadena = b'Este es eun ejemplo del formato de compresion Bonzip, sirve para comprimir TODO.' cadena_comprimida = bz2.compress(cadena) print(cadena_comprimida)#b"BZh91AY&SY\xbf\xb8:\xfa\x00\x00\x06\x15\x80@\x04\x08\x00.\xa3\xde@ \x00Hj\x9f\xaayL\xd3'\xa8 \xd4\xd3\xd2i\xa0hb)\xd3Aw\x8d2\x12\x8c\xf8\xc6\xa2q\xc2\x15\xf8-\x1a\xad\x85B\xc6`+\xe1\xfdr>\t\x12.\xe4\x8ap\xa1!\x7fpu\xf4" #para descromprimir print(bz2.decompress(cadena_comprimida))

26.65

246

0.752345

''' Es un méotod de compresión muy sencillo. Bonzip ''' import bz2 #Cadena binaria cadena = b'Este es eun ejemplo del formato de compresion Bonzip, sirve para comprimir TODO.' cadena_comprimida = bz2.compress(cadena) print(cadena_comprimida)#b"BZh91AY&SY\xbf\xb8:\xfa\x00\x00\x06\x15\x80@\x04\x08\x00.\xa3\xde@ \x00Hj\x9f\xaayL\xd3'\xa8 \xd4\xd3\xd2i\xa0hb)\xd3Aw\x8d2\x12\x8c\xf8\xc6\xa2q\xc2\x15\xf8-\x1a\xad\x85B\xc6`+\xe1\xfdr>\t\x12.\xe4\x8ap\xa1!\x7fpu\xf4" #para descromprimir print(bz2.decompress(cadena_comprimida))

0

a345ec5ac2fe536c17768881a75d838a62037de6

1,057

py

Python

src/message_based_responses.py

LakshyaShastri/C45DiscordBot

624674ef2b9ef4d147d709fa5f23dafc0b9fcadb

[ "MIT" ]

2

2020-03-31T14:45:11.000Z

2020-03-31T15:20:35.000Z

src/message_based_responses.py

LakshyaShastri/C45DiscordBot

624674ef2b9ef4d147d709fa5f23dafc0b9fcadb

[ "MIT" ]

26

2020-03-30T17:34:35.000Z

2020-10-05T19:26:37.000Z

src/message_based_responses.py

LakshyaShastri/C45DiscordBot

624674ef2b9ef4d147d709fa5f23dafc0b9fcadb

[ "MIT" ]

1

2020-10-02T07:58:18.000Z

import re import random

31.088235

99

0.599811

import re import random def _match_word(word_to_match, message_content): return re.search("^.*(\\w*[\b ]?{}([. !?@]+\\w*| +)?)$".format(word_to_match), message_content) def regex_based_response(message_content): response = [] if _match_word("test", message_content): print('[DEBUG] Test hit!') response.append("Marks out") response.append("?") elif _match_word("papi", message_content): response.append(random.choice([ "UWU DID SOMEBODY SAY P A P I", "Yas daddi 🤪", "Big P A P I Dave 😍" ])) elif _match_word("triggered", message_content): fl = open("./resources/triggered.lol", "r") msg = fl.readlines() index = random.randint(0, len(msg) - 1) response.append(msg[index]) elif _match_word("vim", message_content): f = open("./resources/vim.txt", "r") response.append(f.readline()) elif _match_word("eclipse", message_content): response.append("eclipse kaka, IDE's kaka") return response

991

0

46

2bc8856b29271b763a0ca748604553dbd4283306

10,681

py

Python

test/unit/spiderfoot/test_spiderfootplugin.py

khiemtq-cyber/spiderfoot

66e671918853b0334931fd2fbabad0096d506726

[ "MIT" ]

null

test/unit/spiderfoot/test_spiderfootplugin.py

khiemtq-cyber/spiderfoot

66e671918853b0334931fd2fbabad0096d506726

[ "MIT" ]

null

test/unit/spiderfoot/test_spiderfootplugin.py

khiemtq-cyber/spiderfoot

66e671918853b0334931fd2fbabad0096d506726

[ "MIT" ]

null

# test_spiderfootplugin.py import pytest import unittest from sflib import SpiderFoot from spiderfoot import SpiderFootDb, SpiderFootEvent, SpiderFootPlugin, SpiderFootTarget @pytest.mark.usefixtures class TestSpiderFootPlugin(unittest.TestCase): """ Test SpiderFoot """ def test_init(self): """ Test __init__(self) """ sfp = SpiderFootPlugin() self.assertIsInstance(sfp, SpiderFootPlugin) def test_updateSocket(self): """ Test _updateSocket(self, sock) """ sfp = SpiderFootPlugin() sfp._updateSocket(None) self.assertEqual('TBD', 'TBD') def test_clearListeners(self): """ Test clearListeners(self) """ sfp = SpiderFootPlugin() sfp.clearListeners() self.assertEqual('TBD', 'TBD') def test_setup(self): """ Test setup(self, sf, userOpts=dict()) """ sfp = SpiderFootPlugin() sfp.setup(None) sfp.setup(None, None) self.assertEqual('TBD', 'TBD') def test_enrichTargetargument_target_should_enrih_target(self): """ Test enrichTarget(self, target) """ sfp = SpiderFootPlugin() sfp.enrichTarget(None) self.assertEqual('TBD', 'TBD') def test_setTarget_should_set_a_target(self): """ Test setTarget(self, target) """ sfp = SpiderFootPlugin() target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) get_target = sfp.getTarget().targetValue self.assertIsInstance(get_target, str) self.assertEqual("spiderfoot.net", get_target) def test_setTarget_argument_target_invalid_type_should_raise_TypeError(self): """ Test setTarget(self, target) """ sfp = SpiderFootPlugin() invalid_types = [None, "", list(), dict(), int()] for invalid_type in invalid_types: with self.subTest(invalid_type=invalid_type): with self.assertRaises(TypeError): sfp.setTarget(invalid_type) def test_set_dbhargument_dbh_should_set_database_handle(self): """ Test setDbh(self, dbh) """ sfdb = SpiderFootDb(self.default_options, False) sfp = SpiderFootPlugin() sfp.setDbh(sfdb) self.assertIsInstance(sfp.__sfdb__, SpiderFootDb) def test_setScanId_argument_id_should_set_a_scan_id(self): """ Test setScanId(self, id) """ sfp = SpiderFootPlugin() scan_id = '1234' sfp.setScanId(scan_id) get_scan_id = sfp.getScanId() self.assertIsInstance(get_scan_id, str) self.assertEqual(scan_id, get_scan_id) def test_setScanId_argument_id_invalid_type_should_raise_TypeError(self): """ Test setScanId(self, id) """ sfp = SpiderFootPlugin() invalid_types = [None, list(), dict(), int()] for invalid_type in invalid_types: with self.subTest(invalid_type=invalid_type): with self.assertRaises(TypeError): sfp.setScanId(invalid_type) def test_getScanId_should_return_a_string(self): """ Test getScanId(self) """ sfp = SpiderFootPlugin() scan_id = 'example scan id' sfp.setScanId(scan_id) get_scan_id = sfp.getScanId() self.assertIsInstance(get_scan_id, str) self.assertEqual(scan_id, get_scan_id) def test_getScanId_unitialised_scanid_should_raise_TypeError(self): """ Test getScanId(self) """ sfp = SpiderFootPlugin() with self.assertRaises(TypeError): sfp.getScanId() def test_getTarget_should_return_a_string(self): """ Test getTarget(self) """ sfp = SpiderFootPlugin() target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) get_target = sfp.getTarget().targetValue self.assertIsInstance(get_target, str) self.assertEqual("spiderfoot.net", get_target) def test_getTarget_unitialised_target_should_raise(self): """ Test getTarget(self) """ sfp = SpiderFootPlugin() with self.assertRaises(TypeError): sfp.getTarget() def test_register_listener(self): """ Test registerListener(self, listener) """ sfp = SpiderFootPlugin() sfp.registerListener(None) self.assertEqual('TBD', 'TBD') def test_setOutputFilter_should_set_output_filter(self): """ Test setOutputFilter(self, types) """ sfp = SpiderFootPlugin() output_filter = "test filter" sfp.setOutputFilter("test filter") self.assertEqual(output_filter, sfp.__outputFilter__) def test_tempStorage_should_return_a_dict(self): """ Test tempStorage(self) """ sfp = SpiderFootPlugin() temp_storage = sfp.tempStorage() self.assertIsInstance(temp_storage, dict) def test_notifyListeners_should_notify_listener_modules(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_output_filter_matched_should_notify_listener_modules(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) event_type = 'test event type' event_data = 'test data' module = 'test module' source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.__outputFilter__ = event_type sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_output_filter_unmatched_should_not_notify_listener_modules(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) event_type = 'test event type' event_data = 'test data' module = 'test module' source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.__outputFilter__ = "example unmatched event type" sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_event_type_and_data_same_as_source_event_source_event_should_story_only(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) event_type = 'test event type' event_data = 'test data' module = 'test module' source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_argument_sfEvent_invalid_event_should_raise_TypeError(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() invalid_types = [None, "", list(), dict(), int()] for invalid_type in invalid_types: with self.subTest(invalid_type=invalid_type): with self.assertRaises(TypeError): sfp.notifyListeners(invalid_type) def test_checkForStop(self): """ Test checkForStop(self) """ sfp = SpiderFootPlugin() sfp.__sfdb__ = DatabaseStub() sfp.__scanId__ = 'example scan id' # pseudo-parameterized test scan_statuses = [ (None, False), ("anything", False), ("RUNNING", False), ("ABORT-REQUESTED", True) ] for status, expectedReturnValue in scan_statuses: returnValue = sfp.checkForStop() self.assertEqual(returnValue, expectedReturnValue, status) def test_watchedEvents_should_return_a_list(self): """ Test watchedEvents(self) """ sfp = SpiderFootPlugin() watched_events = sfp.watchedEvents() self.assertIsInstance(watched_events, list) def test_producedEvents_should_return_a_list(self): """ Test producedEvents(self) """ sfp = SpiderFootPlugin() produced_events = sfp.producedEvents() self.assertIsInstance(produced_events, list) def test_handleEvent(self): """ Test handleEvent(self, sfEvent) """ event_type = 'ROOT' event_data = 'example event data' module = '' source_event = '' evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp = SpiderFootPlugin() sfp.handleEvent(evt) def test_start(self): """ Test start(self) """ sf = SpiderFoot(self.default_options) sfp = SpiderFootPlugin() sfp.sf = sf sfp.start()

28.712366

107

0.614175

# test_spiderfootplugin.py import pytest import unittest from sflib import SpiderFoot from spiderfoot import SpiderFootDb, SpiderFootEvent, SpiderFootPlugin, SpiderFootTarget @pytest.mark.usefixtures class TestSpiderFootPlugin(unittest.TestCase): """ Test SpiderFoot """ def test_init(self): """ Test __init__(self) """ sfp = SpiderFootPlugin() self.assertIsInstance(sfp, SpiderFootPlugin) def test_updateSocket(self): """ Test _updateSocket(self, sock) """ sfp = SpiderFootPlugin() sfp._updateSocket(None) self.assertEqual('TBD', 'TBD') def test_clearListeners(self): """ Test clearListeners(self) """ sfp = SpiderFootPlugin() sfp.clearListeners() self.assertEqual('TBD', 'TBD') def test_setup(self): """ Test setup(self, sf, userOpts=dict()) """ sfp = SpiderFootPlugin() sfp.setup(None) sfp.setup(None, None) self.assertEqual('TBD', 'TBD') def test_enrichTargetargument_target_should_enrih_target(self): """ Test enrichTarget(self, target) """ sfp = SpiderFootPlugin() sfp.enrichTarget(None) self.assertEqual('TBD', 'TBD') def test_setTarget_should_set_a_target(self): """ Test setTarget(self, target) """ sfp = SpiderFootPlugin() target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) get_target = sfp.getTarget().targetValue self.assertIsInstance(get_target, str) self.assertEqual("spiderfoot.net", get_target) def test_setTarget_argument_target_invalid_type_should_raise_TypeError(self): """ Test setTarget(self, target) """ sfp = SpiderFootPlugin() invalid_types = [None, "", list(), dict(), int()] for invalid_type in invalid_types: with self.subTest(invalid_type=invalid_type): with self.assertRaises(TypeError): sfp.setTarget(invalid_type) def test_set_dbhargument_dbh_should_set_database_handle(self): """ Test setDbh(self, dbh) """ sfdb = SpiderFootDb(self.default_options, False) sfp = SpiderFootPlugin() sfp.setDbh(sfdb) self.assertIsInstance(sfp.__sfdb__, SpiderFootDb) def test_setScanId_argument_id_should_set_a_scan_id(self): """ Test setScanId(self, id) """ sfp = SpiderFootPlugin() scan_id = '1234' sfp.setScanId(scan_id) get_scan_id = sfp.getScanId() self.assertIsInstance(get_scan_id, str) self.assertEqual(scan_id, get_scan_id) def test_setScanId_argument_id_invalid_type_should_raise_TypeError(self): """ Test setScanId(self, id) """ sfp = SpiderFootPlugin() invalid_types = [None, list(), dict(), int()] for invalid_type in invalid_types: with self.subTest(invalid_type=invalid_type): with self.assertRaises(TypeError): sfp.setScanId(invalid_type) def test_getScanId_should_return_a_string(self): """ Test getScanId(self) """ sfp = SpiderFootPlugin() scan_id = 'example scan id' sfp.setScanId(scan_id) get_scan_id = sfp.getScanId() self.assertIsInstance(get_scan_id, str) self.assertEqual(scan_id, get_scan_id) def test_getScanId_unitialised_scanid_should_raise_TypeError(self): """ Test getScanId(self) """ sfp = SpiderFootPlugin() with self.assertRaises(TypeError): sfp.getScanId() def test_getTarget_should_return_a_string(self): """ Test getTarget(self) """ sfp = SpiderFootPlugin() target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) get_target = sfp.getTarget().targetValue self.assertIsInstance(get_target, str) self.assertEqual("spiderfoot.net", get_target) def test_getTarget_unitialised_target_should_raise(self): """ Test getTarget(self) """ sfp = SpiderFootPlugin() with self.assertRaises(TypeError): sfp.getTarget() def test_register_listener(self): """ Test registerListener(self, listener) """ sfp = SpiderFootPlugin() sfp.registerListener(None) self.assertEqual('TBD', 'TBD') def test_setOutputFilter_should_set_output_filter(self): """ Test setOutputFilter(self, types) """ sfp = SpiderFootPlugin() output_filter = "test filter" sfp.setOutputFilter("test filter") self.assertEqual(output_filter, sfp.__outputFilter__) def test_tempStorage_should_return_a_dict(self): """ Test tempStorage(self) """ sfp = SpiderFootPlugin() temp_storage = sfp.tempStorage() self.assertIsInstance(temp_storage, dict) def test_notifyListeners_should_notify_listener_modules(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_output_filter_matched_should_notify_listener_modules(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) event_type = 'test event type' event_data = 'test data' module = 'test module' source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.__outputFilter__ = event_type sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_output_filter_unmatched_should_not_notify_listener_modules(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) target = SpiderFootTarget("spiderfoot.net", "INTERNET_NAME") sfp.setTarget(target) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) event_type = 'test event type' event_data = 'test data' module = 'test module' source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.__outputFilter__ = "example unmatched event type" sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_event_type_and_data_same_as_source_event_source_event_should_story_only(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() sfdb = SpiderFootDb(self.default_options, False) sfp.setDbh(sfdb) event_type = 'ROOT' event_data = 'test data' module = 'test module' source_event = None evt = SpiderFootEvent(event_type, event_data, module, source_event) event_type = 'test event type' event_data = 'test data' module = 'test module' source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) source_event = evt evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp.notifyListeners(evt) self.assertEqual('TBD', 'TBD') def test_notifyListeners_argument_sfEvent_invalid_event_should_raise_TypeError(self): """ Test notifyListeners(self, sfEvent) """ sfp = SpiderFootPlugin() invalid_types = [None, "", list(), dict(), int()] for invalid_type in invalid_types: with self.subTest(invalid_type=invalid_type): with self.assertRaises(TypeError): sfp.notifyListeners(invalid_type) def test_checkForStop(self): """ Test checkForStop(self) """ sfp = SpiderFootPlugin() class DatabaseStub: def scanInstanceGet(self, scanId): return [None, None, None, None, None, status] sfp.__sfdb__ = DatabaseStub() sfp.__scanId__ = 'example scan id' # pseudo-parameterized test scan_statuses = [ (None, False), ("anything", False), ("RUNNING", False), ("ABORT-REQUESTED", True) ] for status, expectedReturnValue in scan_statuses: returnValue = sfp.checkForStop() self.assertEqual(returnValue, expectedReturnValue, status) def test_watchedEvents_should_return_a_list(self): """ Test watchedEvents(self) """ sfp = SpiderFootPlugin() watched_events = sfp.watchedEvents() self.assertIsInstance(watched_events, list) def test_producedEvents_should_return_a_list(self): """ Test producedEvents(self) """ sfp = SpiderFootPlugin() produced_events = sfp.producedEvents() self.assertIsInstance(produced_events, list) def test_handleEvent(self): """ Test handleEvent(self, sfEvent) """ event_type = 'ROOT' event_data = 'example event data' module = '' source_event = '' evt = SpiderFootEvent(event_type, event_data, module, source_event) sfp = SpiderFootPlugin() sfp.handleEvent(evt) def test_start(self): """ Test start(self) """ sf = SpiderFoot(self.default_options) sfp = SpiderFootPlugin() sfp.sf = sf sfp.start()

75

-2

65

7c20b7d1b3b1ead52022d57946dc953d553868a4

4,989

py

Python

pytorch_lightning/utilities/seed.py

GabrielePicco/pytorch-lightning

0d6dfd42d8965347a258e3d20e83bddd344e718f

[ "Apache-2.0" ]

1

2022-01-08T14:06:36.000Z

pytorch_lightning/utilities/seed.py

GabrielePicco/pytorch-lightning

0d6dfd42d8965347a258e3d20e83bddd344e718f

[ "Apache-2.0" ]

null

pytorch_lightning/utilities/seed.py

GabrielePicco/pytorch-lightning

0d6dfd42d8965347a258e3d20e83bddd344e718f

[ "Apache-2.0" ]

1

2022-01-08T14:06:27.000Z

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Helper functions to help with reproducibility of models. """ import logging import os import random from typing import Optional import numpy as np import torch from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_7, rank_zero_warn from pytorch_lightning.utilities.distributed import rank_zero_only log = logging.getLogger(__name__) def seed_everything(seed: Optional[int] = None, workers: bool = False) -> int: """ Function that sets seed for pseudo-random number generators in: pytorch, numpy, python.random In addition, sets the following environment variables: - `PL_GLOBAL_SEED`: will be passed to spawned subprocesses (e.g. ddp_spawn backend). - `PL_SEED_WORKERS`: (optional) is set to 1 if ```workers=True``. Args: seed: the integer value seed for global random state in Lightning. If `None`, will read seed from `PL_GLOBAL_SEED` env variable or select it randomly. workers: if set to ``True``, will properly configure all dataloaders passed to the Trainer with a ``worker_init_fn``. If the user already provides such a function for their dataloaders, setting this argument will have no influence. See also: :func:`~pytorch_lightning.utilities.seed.pl_worker_init_function`. """ max_seed_value = np.iinfo(np.uint32).max min_seed_value = np.iinfo(np.uint32).min try: if seed is None: seed = os.environ.get("PL_GLOBAL_SEED") seed = int(seed) except (TypeError, ValueError): seed = _select_seed_randomly(min_seed_value, max_seed_value) rank_zero_warn(f"No correct seed found, seed set to {seed}") if not (min_seed_value <= seed <= max_seed_value): rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}") seed = _select_seed_randomly(min_seed_value, max_seed_value) # using `log.info` instead of `rank_zero_info`, # so users can verify the seed is properly set in distributed training. log.info(f"Global seed set to {seed}") os.environ["PL_GLOBAL_SEED"] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) os.environ["PL_SEED_WORKERS"] = f"{int(workers)}" return seed def reset_seed() -> None: """ Reset the seed to the value that :func:`pytorch_lightning.utilities.seed.seed_everything` previously set. If :func:`pytorch_lightning.utilities.seed.seed_everything` is unused, this function will do nothing. """ seed = os.environ.get("PL_GLOBAL_SEED", None) workers = os.environ.get("PL_SEED_WORKERS", False) if seed is not None: seed_everything(int(seed), workers=bool(workers)) def pl_worker_init_function(worker_id: int, rank: Optional = None) -> None: # pragma: no cover """ The worker_init_fn that Lightning automatically adds to your dataloader if you previously set set the seed with ``seed_everything(seed, workers=True)``. See also the PyTorch documentation on `randomness in DataLoaders <https://pytorch.org/docs/stable/notes/randomness.html#dataloader>`_. """ # implementation notes: https://github.com/pytorch/pytorch/issues/5059#issuecomment-817392562 global_rank = rank if rank is not None else rank_zero_only.rank process_seed = torch.initial_seed() # back out the base seed so we can use all the bits base_seed = process_seed - worker_id log.debug( f'Initializing random number generators of process {global_rank} worker {worker_id} with base seed {base_seed}' ) ss = np.random.SeedSequence([base_seed, worker_id, global_rank]) # use 128 bits (4 x 32-bit words) np.random.seed(ss.generate_state(4)) # Spawn distinct SeedSequences for the PyTorch PRNG and the stdlib random module torch_ss, stdlib_ss = ss.spawn(2) # PyTorch 1.7 and above takes a 64-bit seed dtype = np.uint64 if _TORCH_GREATER_EQUAL_1_7 else np.uint32 torch.manual_seed(torch_ss.generate_state(1, dtype=dtype)[0]) # use 128 bits expressed as an integer stdlib_seed = (stdlib_ss.generate_state(2, dtype=np.uint64).astype(object) * [1 << 64, 1]).sum() random.seed(stdlib_seed)

42.279661

119

0.719583

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Helper functions to help with reproducibility of models. """ import logging import os import random from typing import Optional import numpy as np import torch from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_7, rank_zero_warn from pytorch_lightning.utilities.distributed import rank_zero_only log = logging.getLogger(__name__) def seed_everything(seed: Optional[int] = None, workers: bool = False) -> int: """ Function that sets seed for pseudo-random number generators in: pytorch, numpy, python.random In addition, sets the following environment variables: - `PL_GLOBAL_SEED`: will be passed to spawned subprocesses (e.g. ddp_spawn backend). - `PL_SEED_WORKERS`: (optional) is set to 1 if ```workers=True``. Args: seed: the integer value seed for global random state in Lightning. If `None`, will read seed from `PL_GLOBAL_SEED` env variable or select it randomly. workers: if set to ``True``, will properly configure all dataloaders passed to the Trainer with a ``worker_init_fn``. If the user already provides such a function for their dataloaders, setting this argument will have no influence. See also: :func:`~pytorch_lightning.utilities.seed.pl_worker_init_function`. """ max_seed_value = np.iinfo(np.uint32).max min_seed_value = np.iinfo(np.uint32).min try: if seed is None: seed = os.environ.get("PL_GLOBAL_SEED") seed = int(seed) except (TypeError, ValueError): seed = _select_seed_randomly(min_seed_value, max_seed_value) rank_zero_warn(f"No correct seed found, seed set to {seed}") if not (min_seed_value <= seed <= max_seed_value): rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}") seed = _select_seed_randomly(min_seed_value, max_seed_value) # using `log.info` instead of `rank_zero_info`, # so users can verify the seed is properly set in distributed training. log.info(f"Global seed set to {seed}") os.environ["PL_GLOBAL_SEED"] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) os.environ["PL_SEED_WORKERS"] = f"{int(workers)}" return seed def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int: return random.randint(min_seed_value, max_seed_value) def reset_seed() -> None: """ Reset the seed to the value that :func:`pytorch_lightning.utilities.seed.seed_everything` previously set. If :func:`pytorch_lightning.utilities.seed.seed_everything` is unused, this function will do nothing. """ seed = os.environ.get("PL_GLOBAL_SEED", None) workers = os.environ.get("PL_SEED_WORKERS", False) if seed is not None: seed_everything(int(seed), workers=bool(workers)) def pl_worker_init_function(worker_id: int, rank: Optional = None) -> None: # pragma: no cover """ The worker_init_fn that Lightning automatically adds to your dataloader if you previously set set the seed with ``seed_everything(seed, workers=True)``. See also the PyTorch documentation on `randomness in DataLoaders <https://pytorch.org/docs/stable/notes/randomness.html#dataloader>`_. """ # implementation notes: https://github.com/pytorch/pytorch/issues/5059#issuecomment-817392562 global_rank = rank if rank is not None else rank_zero_only.rank process_seed = torch.initial_seed() # back out the base seed so we can use all the bits base_seed = process_seed - worker_id log.debug( f'Initializing random number generators of process {global_rank} worker {worker_id} with base seed {base_seed}' ) ss = np.random.SeedSequence([base_seed, worker_id, global_rank]) # use 128 bits (4 x 32-bit words) np.random.seed(ss.generate_state(4)) # Spawn distinct SeedSequences for the PyTorch PRNG and the stdlib random module torch_ss, stdlib_ss = ss.spawn(2) # PyTorch 1.7 and above takes a 64-bit seed dtype = np.uint64 if _TORCH_GREATER_EQUAL_1_7 else np.uint32 torch.manual_seed(torch_ss.generate_state(1, dtype=dtype)[0]) # use 128 bits expressed as an integer stdlib_seed = (stdlib_ss.generate_state(2, dtype=np.uint64).astype(object) * [1 << 64, 1]).sum() random.seed(stdlib_seed)

122

0

23

97928beddf03c0eb483c6b3faad2fde0285120b9

4,790

py

Python

src/experiments/train_common.py

prakashchhipa/Depth-Contrast-Self-Supervised-Method

c68f2ea85063be3a63216985fbe806621174889b

[ "Apache-2.0" ]

null

src/experiments/train_common.py

prakashchhipa/Depth-Contrast-Self-Supervised-Method

c68f2ea85063be3a63216985fbe806621174889b

[ "Apache-2.0" ]

null

src/experiments/train_common.py

prakashchhipa/Depth-Contrast-Self-Supervised-Method

c68f2ea85063be3a63216985fbe806621174889b

[ "Apache-2.0" ]

null

from distutils.log import error import errno import numpy as np import json import argparse import time from tqdm import tqdm import cv2 import logging import sys, os import torch import torchvision import torch.nn as nn from torch import optim from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from torchvision.transforms import transforms from sklearn.metrics import f1_score from sampler import BalancedBatchSampler from models import Resnext_Model, Densenet_Model from dataloader import MBV_Dataset sys.path.append(os.path.dirname(__file__)) from train_util import Train_Util sys.path.append(os.path.dirname(__file__)) from utils import * from mbv_config import MBV_Config import mbv_config import argparse if __name__ == "__main__": train()

41.293103

320

0.746764

from distutils.log import error import errno import numpy as np import json import argparse import time from tqdm import tqdm import cv2 import logging import sys, os import torch import torchvision import torch.nn as nn from torch import optim from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from torchvision.transforms import transforms from sklearn.metrics import f1_score from sampler import BalancedBatchSampler from models import Resnext_Model, Densenet_Model from dataloader import MBV_Dataset sys.path.append(os.path.dirname(__file__)) from train_util import Train_Util sys.path.append(os.path.dirname(__file__)) from utils import * from mbv_config import MBV_Config import mbv_config import argparse def train(): parser = argparse.ArgumentParser(description='PyTorch MBV Training') parser.add_argument('--lr', default=0.00001, type=float, help='learning rate') parser.add_argument('--wd', default=5e-3, type=float, help='weight decay') parser.add_argument('--architecture', default="resnext", type=str, help='architecture - resnext | densenet') parser.add_argument('--machine', default=7, type=int, help='define gpu no.') parser.add_argument('--patience', default=10, type=int, help='patience for learning rate change') parser.add_argument('--batch_size', default=16, type=int, help='batch size') parser.add_argument('--input_size', default=225, type=int, help='input image') parser.add_argument('--epochs', default=100, type=int, help='epochs') parser.add_argument('--description', default="fine_tune", type=str, help='experiment name | description') parser.add_argument('--data_path', default="fine_tune", type=str, help=' path for data of specifc fold - Fold 0|1|2|3|4 ') args = parser.parse_args() batch_size = args.batch_size image_size = args.input_size LR = args.lr patience = args.patience weight_decay = args.wd fold_root = args.data_path device = torch.device(f"cuda:{args.machine}") epochs = args.epochs experiment_description = args.description architecture = args.architecture raw_train_transform = transforms.Compose([ transforms.RandomCrop((image_size,image_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485], std=[0.229]) ]) ref_train_transform = transforms.Compose([ transforms.RandomCrop((image_size,image_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485], std=[0.229]) ]) val_transform = transforms.Compose([ transforms.Resize((image_size*4,image_size*4)), transforms.ToTensor(), transforms.Normalize(mean=[0.485], std=[0.229]) ]) train_raw_image_file = fold_root + 'X_raw_train.npy' train_ref_image_file = fold_root + 'X_ref_train.npy' train_label_file = fold_root + 'Y_train.npy' val_raw_image_file = fold_root + 'X_raw_val.npy' val_ref_image_file = fold_root + 'X_ref_val.npy' val_label_file = fold_root + 'Y_val.npy' train_dataset = MBV_Dataset(raw_train_file_path = train_raw_image_file , reflectance_train_file_path=train_ref_image_file, label_file_path=train_label_file, transform= [raw_train_transform, ref_train_transform]) train_loader = DataLoader(train_dataset, batch_size = batch_size, shuffle=True, sampler=None) #, sampler = BalancedBatchSampler(train_dataset) val_dataset = MBV_Dataset(raw_train_file_path = val_raw_image_file , reflectance_train_file_path=val_ref_image_file, label_file_path=val_label_file, transform= [val_transform]) val_loader = DataLoader(val_dataset, batch_size = batch_size, shuffle=False, sampler=None) if architecture == "resnext": downstream_task_model = Resnext_Model( pretrained=True) elif architecture == "densenet": downstream_task_model = Densenet_Model(pretrained=True) else: raise error ("invalid architecture name") downstream_task_model = downstream_task_model.to(device) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(downstream_task_model.parameters(), lr=LR, weight_decay= weight_decay) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.1 ,patience=patience, min_lr= 5e-3) writer = SummaryWriter(log_dir=mbv_config.tensorboard_path+experiment_description) train_util = Train_Util(experiment_description = experiment_description,image_type=mbv_config.image_both, epochs = epochs, model=downstream_task_model, device=device, train_loader=train_loader, val_loader=val_loader, optimizer=optimizer, criterion=criterion, batch_size=batch_size,scheduler=scheduler, writer=writer) train_util.train_and_evaluate() if __name__ == "__main__": train()

3,970

0

23

b36a0b7dd2d9576d02a33fbac3594480be303cda

1,140

py

Python

spplib/cli/commands/cmd_vm.py

sppautomation/sppclient

d6d5f2c27877f03f68ef2716b204503b7764fe75

[ "Apache-2.0" ]

7

2018-06-01T21:53:35.000Z

2021-06-02T13:50:56.000Z

spplib/cli/commands/cmd_vm.py

sppautomation/sppclient

d6d5f2c27877f03f68ef2716b204503b7764fe75

[ "Apache-2.0" ]

7

2018-11-29T05:15:26.000Z

2021-03-31T13:39:30.000Z

spplib/cli/commands/cmd_vm.py

sppautomation/sppclient

d6d5f2c27877f03f68ef2716b204503b7764fe75

[ "Apache-2.0" ]

8

2018-05-08T16:07:09.000Z

2020-04-05T09:39:39.000Z

import json import click from tabulate import tabulate from spplib.cli import util @click.group() @util.pass_context def cli(ctx, **kwargs): """VM resource. """ pass @cli.command() @util.pass_context @click.argument('pattern')

22.8

95

0.647368

import json import click from tabulate import tabulate from spplib.cli import util @click.group() @util.pass_context def cli(ctx, **kwargs): """VM resource. """ pass def get_sla_info(spp_session): sla_policies = spp_session.get(restype='sla')['slapolicies'] return dict([(int(x['id']), x['name']) for x in sla_policies]) @cli.command() @util.pass_context @click.argument('pattern') def search(ctx, pattern): if not pattern: raise Exception('VM pattern is required. ') qparams = {'resourceType': 'vm', 'from': 'hlo', 'pageSize': '500'} data = {'name': pattern, 'hypervisorType': 'vmware'} sla_info = get_sla_info(ctx.spp_session) resp = ctx.spp_session.post(restype='hypervisor', path='search', data=data, params=qparams) table_info = [] for vm in resp['vms']: sla_ids = set([x['storageProfileId'] for x in vm['copies']]) slas = set([sla_info[sla_id] for sla_id in sla_ids]) table_info.append((vm['name'], slas)) if not table_info: return print click.echo_via_pager(tabulate(table_info, headers=["Name", "SLAs"])) print

847

0

46

2b8daba4bc8dba84de9f41bfcd785d9de888127b

344

py

Python

immobilien/admin.py

sahin88/Django_Rest_Framework_Redux_React_Estate_App_FullStack

10e31c4071bcebc0e4401f42084211d170b2ea56

[ "Unlicense" ]

null

immobilien/admin.py

sahin88/Django_Rest_Framework_Redux_React_Estate_App_FullStack

10e31c4071bcebc0e4401f42084211d170b2ea56

[ "Unlicense" ]

null

immobilien/admin.py

sahin88/Django_Rest_Framework_Redux_React_Estate_App_FullStack

10e31c4071bcebc0e4401f42084211d170b2ea56

[ "Unlicense" ]

null

from django.contrib import admin from .models import immobilien admin.site.register(immobilien, immobilienAdmin) # Register your models here.

21.5

61

0.718023

from django.contrib import admin from .models import immobilien class immobilienAdmin(admin.ModelAdmin): list_display = ('id', 'name', 'date_joined', 'topseller') list_display_links = ('id', 'name') search_fields = ('name',) list_per_page = 24 admin.site.register(immobilien, immobilienAdmin) # Register your models here.

0

175

23

d74b93e88cabf9aa66652003dbd1b9b073dd3701

289

py

Python

css/email.py

Pipefehecar/repo_prueba

2d1f8c531e40dc3071d17da1cdb1dbfb98448537

[ "Apache-2.0" ]

2

2018-04-13T02:04:25.000Z

2018-04-13T02:04:27.000Z

css/email.py

Pipefehecar/repo_prueba

2d1f8c531e40dc3071d17da1cdb1dbfb98448537

[ "Apache-2.0" ]

null

css/email.py

Pipefehecar/repo_prueba

2d1f8c531e40dc3071d17da1cdb1dbfb98448537

[ "Apache-2.0" ]

null

class Alarma(): """clase del objeto alarma"""

16.055556

30

0.698962

class Alarma(): """clase del objeto alarma""" def __init__(self,fecha): global fecha, destinatarios self.fecha = fecha pass def adjuntar_archivo(): pass def agregar_borrador(): pass def corregir_error(): pass def leer_email(): pass def verificar_recibido(): pass

102

0

138

2feb18d20046f1329505a5aa39be610306b9d37c

1,339

py

Python

nova/compute/opts.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/compute/opts.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/compute/opts.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

2

2017-07-20T17:31:34.000Z

2020-07-24T02:42:19.000Z

17.166667

87

0.655713

0

e003d37ef95a2f23468b50954b5d783dca79c69a

7,620

py

Python

scratch/rowe_model_opt3.py

Lefebvrelab/SpectralNeuralModels

35d34095f573713ea8beb061f0ec13929ce579e9

[ "MIT" ]

null

scratch/rowe_model_opt3.py

Lefebvrelab/SpectralNeuralModels

35d34095f573713ea8beb061f0ec13929ce579e9

[ "MIT" ]

8

2018-05-03T16:09:01.000Z

2019-04-20T18:44:46.000Z

scratch/rowe_model_opt3.py

Lefebvrelab/SpectralNeuralModels

35d34095f573713ea8beb061f0ec13929ce579e9

[ "MIT" ]

null

from matplotlib import pyplot as plt import numpy as np import pandas as pd from numpy import pi,abs,exp,log,log10 from scipy import optimize class RoweOptimization(): ''' Optimizing the Rowe Model onto a training set. The key parameters to adjust are as follows: - G_ee - G_ei - G_ese - G_esre - G_srs - alpha - beta - t0 - A_EMG - f_EMG ''' def optimize(self, param_list, tol=None): ''' Fits the model using the listed parameters. ''' # Define the function w.r.t. the parameters. The vector P has the same # length as params, with 1-1 coordinate correspondance. EEG_fun = lambda P: self.mod.update_and_compute_P(P, param_list, self.freqs) chi_fun = lambda P: sum(((EEG_fun(P) - self.output) / self.output)**2) # Get initial parameter values P0 = [] for j in range(len(param_list)): P0.append(getattr(self.mod, param_list[j])) P0 = np.array(P0) # Obtain the bounds for the optimization procedure w.r.t. the selected # parameters. bounds_list = [] for k in range(len(param_list)): bound_attr_str = 'bound_' + param_list[k] # Check if model has the bound attribute. if not hasattr(self.mod, bound_attr_str): bounds_list.append((None,None)) else: bounds_list.append(tuple(getattr(self.mod, bound_attr_str))) bounds_tuple = tuple(bounds_list) # Initiate the optimization result = optimize.minimize(chi_fun, P0, bounds=bounds_list, tol=tol) return result if __name__ == '__main__': task = 'optimize' if task == 'optimize': # Get training data text_file = np.loadtxt('EEG_data.csv', skiprows=1, delimiter=',') freqs = text_file[1:,0] powers = text_file[1:,1]*10**24 N = min(len(freqs), len(powers)) train_data = [(freqs[k], powers[k]) for k in range(N)] rowe_opt = RoweOptimization(train=train_data) param_list = ['G_ee', 'G_ei', 'G_ese', 'G_esre', 'G_srs', 'alpha', 'beta', 't0', 'A_EMG' ] result = rowe_opt.optimize(param_list, tol=5) model_powers = rowe_opt.mod.compute_P(freqs) plt.plot(freqs, powers, 'r--', freqs, model_powers) plt.show() elif task == 'graph': freqs = np.linspace(0.2,100, num=50) mod = Rowe2015Model() EEG = mod.compute_P(freqs) - mod.compute_P_EEG(freqs) df_EEG = pd.DataFrame(np.squeeze(EEG)) df_EEG.abs().plot(logx=True,logy=True)

28.973384

89

0.465354

from matplotlib import pyplot as plt import numpy as np import pandas as pd from numpy import pi,abs,exp,log,log10 from scipy import optimize class Rowe2015Model(): def __init__(self): # Constants self.gamma_e = 116 # s^-1 self.r_e = 86 # mm self.Q_max = 340 # s^-1 self.theta = 12.9 # mV self.sigma = 3.8 #mV self.phi_n = 10**-5 # s^-1 self.k0 = 10 self.G_rs = 0.1 # from Abeysuria 2015 self.G_re = 0.2 # from Abeysuria 2015 self.G_sn = 1 # Random <3 John self.l_x = self.l_y = 0.5 self.fmax = 50 self.freq_min = 5. self.freq_max = 100. # Variable parameters self.G_ee = 5.4 self.G_ei = -7. self.G_ese = 5.6 self.G_esre = -2.8 self.G_srs = -0.6 self.alpha = 75 #s^-1 self.beta = 75*3.8 #s^-1 self.t0 = 84 # ms self.A_EMG = 0.5E-12 #s^-1 self.f_EMG = 40 # Hz # Variable bounds self.bound_G_ee = [0., 20.] self.bound_G_ei = [-40., 0.] self.bound_G_ese = [0., 40.] self.bound_G_esre = [-40., 0.] self.bound_G_srs = [-14., -0.1] self.bound_alpha = [10., 100.] self.bound_beta = [100., 800.] self.bound_t0 = [75., 140.] self.bound_A_EMG = [0., 1E-12] self.bound_f_EMG = [10., 50.] def compute_L(self, omega): alpha, beta = self.alpha, self.beta L = (1 - 1j*omega/alpha)**-1 * (1 - 1j*omega/beta)**-1 return L def compute_q2r2(self, omega): gamma_e = self.gamma_e G_ei, G_ee = self.G_ei, self.G_ee G_ese, G_esre, G_srs = self.G_ese, self.G_esre, self.G_srs t0 = self.t0 L = self.compute_L(omega) term1 = (1 - 1j*omega / gamma_e)**2 coeff2 = (1 - G_ei * L)**-1 term2_1 = L * G_ee term2_2 = (L**2 * G_ese + L**3 * G_esre) * exp(1j*omega*t0) / (1 - L**2 * G_srs) term2 = term2_1 + term2_2 q2r2 = term1 - coeff2 * term2 return q2r2 def compute_k2r2(self, m, n): k_x = 2*pi*m / self.l_x k_y = 2*pi*n / self.l_y k2r2 = (k_x**2 + k_y**2)*self.r_e**2 return k2r2 def compute_P_EEG(self, omega): G_ei, G_ee = self.G_ei, self.G_ee G_ese, G_esre, G_srs = self.G_ese, self.G_esre, self.G_srs t0 = self.t0 r_e = self.r_e k0 = self.k0 phi_n = self.phi_n # Other Gs G_sr = G_srs / self.G_rs G_es = G_esre / (G_sr * self.G_re) G_sn = self.G_sn L = self.compute_L(omega) q2r2 = self.compute_q2r2(omega) term1 = G_es * G_sn * phi_n * L**2 * exp(1j*omega*t0/2) term2 = (1 - G_srs * L**2) * (1 - G_ei * L) term3 = 0 k_x = 2 * pi / self.l_x k_y = 2 * pi / self.l_y fmax = self.fmax for m in np.arange(-fmax,fmax): for n in np.arange(-fmax,fmax): k2r2 = self.compute_k2r2(m,n) k2 = k2r2 / r_e Fk = exp(-k2 / k0**2) term3 += abs(k2r2 + q2r2)**-2 * Fk * k_x * k_y P_EEG = abs(term1)**2 * abs(term2)**2 * term3 return P_EEG def compute_P(self, omega): ''' Compute the power spectrum. ''' A_EMG, f_EMG = self.A_EMG, self.f_EMG mod_omega = omega / (2 * pi * f_EMG) P_EMG = A_EMG * (mod_omega)**2 / (1 + mod_omega**2)**2 P_EEG = self.compute_P_EEG(omega) return P_EEG + P_EMG def update_and_compute_P(self, values, param_list, omega): N = min(len(values), len(param_list)) for k in range(N): setattr(self, param_list[k], values[k]) return self.compute_P(omega) class RoweOptimization(): ''' Optimizing the Rowe Model onto a training set. The key parameters to adjust are as follows: - G_ee - G_ei - G_ese - G_esre - G_srs - alpha - beta - t0 - A_EMG - f_EMG ''' def __init__(self, train=[]): self.train = train # Get frequencies self.freqs = np.array([train[k][0] for k in range(len(train))]) self.output = np.array([train[k][1] for k in range(len(train))]) self.mod = Rowe2015Model() def optimize(self, param_list, tol=None): ''' Fits the model using the listed parameters. ''' # Define the function w.r.t. the parameters. The vector P has the same # length as params, with 1-1 coordinate correspondance. EEG_fun = lambda P: self.mod.update_and_compute_P(P, param_list, self.freqs) chi_fun = lambda P: sum(((EEG_fun(P) - self.output) / self.output)**2) # Get initial parameter values P0 = [] for j in range(len(param_list)): P0.append(getattr(self.mod, param_list[j])) P0 = np.array(P0) # Obtain the bounds for the optimization procedure w.r.t. the selected # parameters. bounds_list = [] for k in range(len(param_list)): bound_attr_str = 'bound_' + param_list[k] # Check if model has the bound attribute. if not hasattr(self.mod, bound_attr_str): bounds_list.append((None,None)) else: bounds_list.append(tuple(getattr(self.mod, bound_attr_str))) bounds_tuple = tuple(bounds_list) # Initiate the optimization result = optimize.minimize(chi_fun, P0, bounds=bounds_list, tol=tol) return result if __name__ == '__main__': task = 'optimize' if task == 'optimize': # Get training data text_file = np.loadtxt('EEG_data.csv', skiprows=1, delimiter=',') freqs = text_file[1:,0] powers = text_file[1:,1]*10**24 N = min(len(freqs), len(powers)) train_data = [(freqs[k], powers[k]) for k in range(N)] rowe_opt = RoweOptimization(train=train_data) param_list = ['G_ee', 'G_ei', 'G_ese', 'G_esre', 'G_srs', 'alpha', 'beta', 't0', 'A_EMG' ] result = rowe_opt.optimize(param_list, tol=5) model_powers = rowe_opt.mod.compute_P(freqs) plt.plot(freqs, powers, 'r--', freqs, model_powers) plt.show() elif task == 'graph': freqs = np.linspace(0.2,100, num=50) mod = Rowe2015Model() EEG = mod.compute_P(freqs) - mod.compute_P_EEG(freqs) df_EEG = pd.DataFrame(np.squeeze(EEG)) df_EEG.abs().plot(logx=True,logy=True)

3,761

611

57

d3530afd4b1ee97d2fb3a9b0e9d548a215b5bb8c

1,384

py

Python

src/graph_visualize.py

sawyerWeld/PrefGAN

295a165947fc300b2dd0754607076c780fbcafa1

[ "MIT" ]

null

src/graph_visualize.py

sawyerWeld/PrefGAN

295a165947fc300b2dd0754607076c780fbcafa1

[ "MIT" ]

null

src/graph_visualize.py

sawyerWeld/PrefGAN

295a165947fc300b2dd0754607076c780fbcafa1

[ "MIT" ]

null

#graph_visualize.py from graphviz import Digraph import pairwise # Given a vector of the form generated in pairwise.py for # easy reading into NNs, produce a diagram of the represented graph

32.952381

81

0.609827

#graph_visualize.py from graphviz import Digraph import pairwise # Given a vector of the form generated in pairwise.py for # easy reading into NNs, produce a diagram of the represented graph def vec_to_graph(vec, name='no_name_graph', save=False, fromTorch=True): matrix = None if fromTorch: matrix = pairwise.vec_to_matrix(vec.numpy()) else: matrix = pairwise.vec_to_matrix(vec) n_cands = len(matrix[0]) dot = Digraph(comment='Preference Graph',format='png') # init nodes for i, row in enumerate(matrix): dot.node(chr(i+97), 'alt {}'.format(i+1)) # init edges for i, row in enumerate(matrix): # only care about the upper triangluar part li = row[i+1:] for j, alt in enumerate(li): # math got confusing a = i+1 b = i+j+2 p_a = chr(a+96) p_b = chr(b+96) if alt == 1: dot.edge(p_a, p_b) elif alt == -1: dot.edge(p_b, p_a) file_output = '../diagrams/graph_views/{}'.format(name) if save: dot.render(file_output,view=False) return dot def vote_to_graph(vote, name='no_name_graph', save=False): if 0 in vote: raise Exception('There should be no 0 values in vote vector') return vec_to_graph(pairwise.process_vote(vote), name, save, fromTorch=False)

1,145

0

45

2d116e962789c83860d1307c9a4a5d5c894be102

3,449

py

Python

tests/corpora/test_parallel_text_corpus.py

johnml1135/machine.py

7bfb668b81d6af476f7ee797900e111c3027d542

[ "MIT" ]

2

2021-09-14T15:41:14.000Z

2021-09-14T15:53:46.000Z

tests/corpora/test_parallel_text_corpus.py

johnml1135/machine.py

7bfb668b81d6af476f7ee797900e111c3027d542

[ "MIT" ]

2

2021-11-04T09:12:26.000Z

2021-11-08T08:35:36.000Z

tests/corpora/test_parallel_text_corpus.py

johnml1135/machine.py

7bfb668b81d6af476f7ee797900e111c3027d542

[ "MIT" ]

1

2021-11-03T14:45:11.000Z

from machine.corpora import ( DictionaryTextAlignmentCorpus, DictionaryTextCorpus, MemoryText, MemoryTextAlignmentCollection, ParallelTextCorpus, )

45.381579

103

0.76138

from machine.corpora import ( DictionaryTextAlignmentCorpus, DictionaryTextCorpus, MemoryText, MemoryTextAlignmentCollection, ParallelTextCorpus, ) def test_texts_no_texts() -> None: source_corpus = DictionaryTextCorpus() target_corpus = DictionaryTextCorpus() parallel_corpus = ParallelTextCorpus(source_corpus, target_corpus) assert not any(parallel_corpus.texts) def test_texts_no_missing_texts() -> None: source_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text2"), MemoryText("text3")) target_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text2"), MemoryText("text3")) alignment_corpus = DictionaryTextAlignmentCorpus( MemoryTextAlignmentCollection("text1"), MemoryTextAlignmentCollection("text2"), MemoryTextAlignmentCollection("text3"), ) parallel_corpus = ParallelTextCorpus(source_corpus, target_corpus, alignment_corpus) texts = parallel_corpus.texts assert [t.id for t in texts] == ["text1", "text2", "text3"] def test_texts_missing_text() -> None: source_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text2"), MemoryText("text3")) target_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text3")) alignment_corpus = DictionaryTextAlignmentCorpus( MemoryTextAlignmentCollection("text1"), MemoryTextAlignmentCollection("text3") ) parallel_corpus = ParallelTextCorpus(source_corpus, target_corpus, alignment_corpus) texts = parallel_corpus.texts assert [t.id for t in texts] == ["text1", "text3"] def test_get_texts_missing_target_text_all_source_segments() -> None: source_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text2"), MemoryText("text3")) target_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text3")) alignment_corpus = DictionaryTextAlignmentCorpus( MemoryTextAlignmentCollection("text1"), MemoryTextAlignmentCollection("text3") ) parallel_corpus = ParallelTextCorpus(source_corpus, target_corpus, alignment_corpus) texts = parallel_corpus.get_texts(all_source_segments=True) assert [t.id for t in texts] == ["text1", "text2", "text3"] def test_get_texts_missing_source_text_all_target_segments() -> None: source_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text3")) target_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text2"), MemoryText("text3")) alignment_corpus = DictionaryTextAlignmentCorpus( MemoryTextAlignmentCollection("text1"), MemoryTextAlignmentCollection("text3") ) parallel_corpus = ParallelTextCorpus(source_corpus, target_corpus, alignment_corpus) texts = parallel_corpus.get_texts(all_target_segments=True) assert [t.id for t in texts] == ["text1", "text2", "text3"] def test_get_texts_missing_source_and_target_text_all_source_and_target_segments() -> None: source_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text3")) target_corpus = DictionaryTextCorpus(MemoryText("text1"), MemoryText("text2")) alignment_corpus = DictionaryTextAlignmentCorpus(MemoryTextAlignmentCollection("text1")) parallel_corpus = ParallelTextCorpus(source_corpus, target_corpus, alignment_corpus) texts = parallel_corpus.get_texts(all_source_segments=True, all_target_segments=True) assert [t.id for t in texts] == ["text1", "text2", "text3"]

3,137

0

138

5e7f49dd4d34480adfda849b4671baf126b95da5

523

py

Python

tests/test_vote.py

james-muriithi/pitches

e7da364f9525847c2ed8d88aea9a81a87c4c113b

[ "Unlicense" ]

null

tests/test_vote.py

james-muriithi/pitches

e7da364f9525847c2ed8d88aea9a81a87c4c113b

[ "Unlicense" ]

null

tests/test_vote.py

james-muriithi/pitches

e7da364f9525847c2ed8d88aea9a81a87c4c113b

[ "Unlicense" ]

null

from unicodedata import name import unittest from app.models import Vote, User

32.6875

88

0.6826

from unicodedata import name import unittest from app.models import Vote, User class TestVote(unittest.TestCase): def setUp(self): self.user = User(username="james", id=1, name="James Muriithi", email="m@a.com") self.vote = Vote(vote=1, user=self.user, pitch_id=1) def test_instance(self): self.assertTrue(isinstance(self.vote, Vote)) def test_vote_user(self): self.assertTrue(isinstance(self.vote.user, User)) self.assertEquals(self.vote.user.id, self.user.id)

324

13

107

cedaee8ae0d46d7b6db7e6e560962ac51696012d

2,076

py

Python

7/challenge2.py

roryeiffe/Adent-of-Code

80f123663fcf04bf5f0d6733807b4a2dd53bc68c

[ "MIT" ]

null

7/challenge2.py

roryeiffe/Adent-of-Code

80f123663fcf04bf5f0d6733807b4a2dd53bc68c

[ "MIT" ]

null

7/challenge2.py

roryeiffe/Adent-of-Code

80f123663fcf04bf5f0d6733807b4a2dd53bc68c

[ "MIT" ]

null

import sys # recursive search through bag dictionary: # given a bag string, return only the name of the bag: # ex: 1 light blue returns light blue # given a bag string, return the quantity: # ex: 1 light blue returns 1 f = open(sys.argv[1],"r") L = [] for item in f: L.append(item.strip()) # dictionary where key is bag and values are which bags can be stored: bag_contains = dict() for rule in L: rule_list = rule.split(" ") # this bag is always going to be the first 2 words: this_bag = rule_list[0] + " " + rule_list[1] # insert into dictionary: if this_bag not in bag_contains: bag_contains[this_bag] = [] i = 3 # loop through rest of list: while i < len(rule_list): word = rule_list[i].strip(",").strip(".") # if we find a bag, grab the last 2 words (these will be the color) if (word == "bag" or word == "bags"): current_bag = rule_list[i-3] + " " + rule_list[i-2] + " " + rule_list[i-1] # this means no bags if get_bag(current_bag) != "no other": bag_contains[this_bag].append(current_bag) i += 1 print(search(bag_contains,"2 dark green")) print(search(bag_contains,"1 shiny gold"))

29.239437

77

0.69316

import sys # recursive search through bag dictionary: def search(bag_contains, current): children = bag_contains[get_bag(current)] # base cases # if this bag does not contain any other bags: # each level is how many bags are in this current bag (including children) # base case: since there are no children, we are just counting how many bags # are in our current bag, and no children if len(children) == 0: print(current) return int(get_num(current)) recursion = [] for child in children: grandchildren = bag_contains[get_bag(child)] if len(grandchildren) == 0: recursion.append(int(get_num(child))) else: recursion.append(search(bag_contains,child)) print(get_bag(current), "has", recursion, "inner bags") # make sure we count this current bag: return int(get_num(current)) + int(get_num(current))*sum(recursion) # given a bag string, return only the name of the bag: # ex: 1 light blue returns light blue def get_bag(bag_string): return bag_string.split(" ")[1] + " " + bag_string.split(" ")[2] # given a bag string, return the quantity: # ex: 1 light blue returns 1 def get_num(bag_string): return bag_string.split(" ")[0] f = open(sys.argv[1],"r") L = [] for item in f: L.append(item.strip()) # dictionary where key is bag and values are which bags can be stored: bag_contains = dict() for rule in L: rule_list = rule.split(" ") # this bag is always going to be the first 2 words: this_bag = rule_list[0] + " " + rule_list[1] # insert into dictionary: if this_bag not in bag_contains: bag_contains[this_bag] = [] i = 3 # loop through rest of list: while i < len(rule_list): word = rule_list[i].strip(",").strip(".") # if we find a bag, grab the last 2 words (these will be the color) if (word == "bag" or word == "bags"): current_bag = rule_list[i-3] + " " + rule_list[i-2] + " " + rule_list[i-1] # this means no bags if get_bag(current_bag) != "no other": bag_contains[this_bag].append(current_bag) i += 1 print(search(bag_contains,"2 dark green")) print(search(bag_contains,"1 shiny gold"))

877

0

66

1bdbba7f4e63c6bef0d5a716b758d10956003075

326

py

Python

samples/WebApplication/OneUserScenario.py

jproudlo/PyModel

2ab0e2cf821807206725adaa425409b0c28929b7

[ "BSD-3-Clause" ]

61

2015-01-29T16:18:51.000Z

2021-09-28T10:14:02.000Z

samples/WebApplication/OneUserScenario.py

vikstr/PyModel

4fff616fe0fd8342c91a42d9db5d4097a179dff8

[ "BSD-3-Clause" ]

2

2015-02-04T11:57:53.000Z

2021-07-18T20:59:55.000Z

samples/WebApplication/OneUserScenario.py

vikstr/PyModel

4fff616fe0fd8342c91a42d9db5d4097a179dff8

[ "BSD-3-Clause" ]

34

2015-02-04T12:00:29.000Z

2022-03-14T07:41:25.000Z

"One user repeatedly logs in, logs out. Allow interleaving with other actions" from WebModel import Login, Logout actions = (Login, Logout) # just these to allow interleaving initial = 0 accepting = (0,) graph = ((0, (Login, ( 'VinniPuhh', 'Correct' ), 'Success'), 1), (1, (Logout, ( 'VinniPuhh', ), None), 0))

25.076923

79

0.650307

"One user repeatedly logs in, logs out. Allow interleaving with other actions" from WebModel import Login, Logout actions = (Login, Logout) # just these to allow interleaving initial = 0 accepting = (0,) graph = ((0, (Login, ( 'VinniPuhh', 'Correct' ), 'Success'), 1), (1, (Logout, ( 'VinniPuhh', ), None), 0))

0

cf94635475a33564a16e558dee889bd78cf10571

542

py

Python

tests/test_statics.py

beastbikes/django-only-admin

c89b782b92edbb1f75151e71163c0708afacd4f9

[ "MIT" ]

32

2016-11-24T08:33:10.000Z

2017-12-18T00:25:00.000Z

tests/test_statics.py

beastbikes/django-only-admin

c89b782b92edbb1f75151e71163c0708afacd4f9

[ "MIT" ]

15

2016-11-30T08:28:56.000Z

2017-09-20T15:54:18.000Z

tests/test_statics.py

beastbikes/django-only-admin

c89b782b92edbb1f75151e71163c0708afacd4f9

[ "MIT" ]

9

2016-11-25T02:14:24.000Z

2017-12-06T13:22:51.000Z

import os from django.test import TestCase from django.contrib.staticfiles import finders

27.1

62

0.710332

import os from django.test import TestCase from django.contrib.staticfiles import finders class StaticTestCase(TestCase): def test_font_awesome(self): path = 'plugins/font-awesome/css/font-awesome.min.css' absolute_path = finders.find(path) assert absolute_path is not None assert os.path.exists(absolute_path) path = 'plugins/font-awesome/fonts/FontAwesome.otf' absolute_path = finders.find(path) assert absolute_path is not None assert os.path.exists(absolute_path)

389

10

50

0c9884ea394c7fd5bab4fbd1dc9bebd53080457b

4,264

py

Python

FaceSwap/zad2.py

i-khan/FaceSwap

e9f5b0a95af7ba62ce6978a5c7a4511086948a82

[ "MIT" ]

null

FaceSwap/zad2.py

i-khan/FaceSwap

e9f5b0a95af7ba62ce6978a5c7a4511086948a82

[ "MIT" ]

null

FaceSwap/zad2.py

i-khan/FaceSwap

e9f5b0a95af7ba62ce6978a5c7a4511086948a82

[ "MIT" ]

null

import dlib import cv2 import numpy as np import sys import models import NonLinearLeastSquares import ImageProcessing from drawing import * import FaceRendering import utils import os import subprocess print "Press T to draw the keypoints and the 3D model" print "Press R to start recording to a video file" #you need to download shape_predictor_68_face_landmarks.dat from the link below and unpack it where the solution file is #http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2 #loading the keypoint detection model, the image and the 3D model predictor_path = "../shape_predictor_68_face_landmarks.dat" face_cvv_detector_path ="../mmod_human_face_detector.dat" image_name = "../bnl/images/"+sys.argv[1] #the smaller this value gets the faster the detection will work #if it is too small, the user's face might not be detected maxImageSizeForDetection = 960 #detector = dlib.cnn_face_detection_model_v1(face_cvv_detector_path) detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_path) mean3DShape, blendshapes, mesh, idxs3D, idxs2D = utils.load3DFaceModel("../candide.npz") projectionModel = models.OrthographicProjectionBlendshapes(blendshapes.shape[0]) modelParams = None lockedTranslation = False drawOverlay = False cap = cv2.VideoCapture("../data/"+sys.argv[2]+".mp4") fourcc = cv2.VideoWriter_fourcc(*'XVID') # Check if camera opened successfully if (cap.isOpened()== False): print("Error opening video stream or file") cameraImg = cap.read()[1] writer = None if writer is None: print "Starting video writer" writer = cv2.VideoWriter("../bnl/videos/"+sys.argv[1]+"-out.avi", fourcc, 25,(cameraImg.shape[1], cameraImg.shape[0])) if writer.isOpened(): print "Writer succesfully opened" else: writer = None print "Writer opening failed" textureImg = cv2.imread(image_name) textureCoords = utils.getFaceTextureCoords(textureImg, mean3DShape, blendshapes, idxs2D, idxs3D, detector, predictor) renderer = FaceRendering.FaceRenderer(cameraImg, textureImg, textureCoords, mesh) while True: cameraImg = cap.read()[1] try: shapes2D = utils.getFaceKeypoints(cameraImg, detector, predictor, maxImageSizeForDetection) if shapes2D is not None: for shape2D in shapes2D: #3D model parameter initialization modelParams = projectionModel.getInitialParameters(mean3DShape[:, idxs3D], shape2D[:, idxs2D]) #3D model parameter optimization modelParams = NonLinearLeastSquares.GaussNewton(modelParams, projectionModel.residual, projectionModel.jacobian, ([mean3DShape[:, idxs3D], blendshapes[:, :, idxs3D]], shape2D[:, idxs2D]), verbose=0) #rendering the model to an image shape3D = utils.getShape3D(mean3DShape, blendshapes, modelParams) renderedImg = renderer.render(shape3D) #blending of the rendered face with the image mask = np.copy(renderedImg[:, :, 0]) renderedImg = ImageProcessing.colorTransfer(cameraImg, renderedImg, mask) cameraImg = ImageProcessing.blendImages(renderedImg, cameraImg, mask,0.1) #drawing of the mesh and keypoints if drawOverlay: drawPoints(cameraImg, shape2D.T) drawProjectedShape(cameraImg, [mean3DShape, blendshapes], projectionModel, mesh, modelParams, lockedTranslation) if writer is not None: writer.write(cameraImg) cv2.imshow('image', cameraImg) key = cv2.waitKey(1) if key == 27: break if key == ord('t'): drawOverlay = not drawOverlay except: print("An exception occurred") break print "Stopping video writer" writer.release() writer = None os.chdir('C://Users/Asus/') # ffmpeg -i 1569831566308.jpeg-out.avi -i ../../data/superVideo2.mp4 -map 0:0 -map 1:1 -shortest 1569831566308.jpeg-out.mp4 subprocess.call(["ffmpeg", "-i", "../bnl/videos/"+sys.argv[1]+"-out.avi", "-i ", "../data/"+sys.argv[2]+".mp4", "-map ", "0:0", "-map ", "1:1", "-shortest", "../bnl/videos/"+sys.argv[1]+"-out.mp4" ])

36.135593

214

0.692542

import dlib import cv2 import numpy as np import sys import models import NonLinearLeastSquares import ImageProcessing from drawing import * import FaceRendering import utils import os import subprocess print "Press T to draw the keypoints and the 3D model" print "Press R to start recording to a video file" #you need to download shape_predictor_68_face_landmarks.dat from the link below and unpack it where the solution file is #http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2 #loading the keypoint detection model, the image and the 3D model predictor_path = "../shape_predictor_68_face_landmarks.dat" face_cvv_detector_path ="../mmod_human_face_detector.dat" image_name = "../bnl/images/"+sys.argv[1] #the smaller this value gets the faster the detection will work #if it is too small, the user's face might not be detected maxImageSizeForDetection = 960 #detector = dlib.cnn_face_detection_model_v1(face_cvv_detector_path) detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_path) mean3DShape, blendshapes, mesh, idxs3D, idxs2D = utils.load3DFaceModel("../candide.npz") projectionModel = models.OrthographicProjectionBlendshapes(blendshapes.shape[0]) modelParams = None lockedTranslation = False drawOverlay = False cap = cv2.VideoCapture("../data/"+sys.argv[2]+".mp4") fourcc = cv2.VideoWriter_fourcc(*'XVID') # Check if camera opened successfully if (cap.isOpened()== False): print("Error opening video stream or file") cameraImg = cap.read()[1] writer = None if writer is None: print "Starting video writer" writer = cv2.VideoWriter("../bnl/videos/"+sys.argv[1]+"-out.avi", fourcc, 25,(cameraImg.shape[1], cameraImg.shape[0])) if writer.isOpened(): print "Writer succesfully opened" else: writer = None print "Writer opening failed" textureImg = cv2.imread(image_name) textureCoords = utils.getFaceTextureCoords(textureImg, mean3DShape, blendshapes, idxs2D, idxs3D, detector, predictor) renderer = FaceRendering.FaceRenderer(cameraImg, textureImg, textureCoords, mesh) while True: cameraImg = cap.read()[1] try: shapes2D = utils.getFaceKeypoints(cameraImg, detector, predictor, maxImageSizeForDetection) if shapes2D is not None: for shape2D in shapes2D: #3D model parameter initialization modelParams = projectionModel.getInitialParameters(mean3DShape[:, idxs3D], shape2D[:, idxs2D]) #3D model parameter optimization modelParams = NonLinearLeastSquares.GaussNewton(modelParams, projectionModel.residual, projectionModel.jacobian, ([mean3DShape[:, idxs3D], blendshapes[:, :, idxs3D]], shape2D[:, idxs2D]), verbose=0) #rendering the model to an image shape3D = utils.getShape3D(mean3DShape, blendshapes, modelParams) renderedImg = renderer.render(shape3D) #blending of the rendered face with the image mask = np.copy(renderedImg[:, :, 0]) renderedImg = ImageProcessing.colorTransfer(cameraImg, renderedImg, mask) cameraImg = ImageProcessing.blendImages(renderedImg, cameraImg, mask,0.1) #drawing of the mesh and keypoints if drawOverlay: drawPoints(cameraImg, shape2D.T) drawProjectedShape(cameraImg, [mean3DShape, blendshapes], projectionModel, mesh, modelParams, lockedTranslation) if writer is not None: writer.write(cameraImg) cv2.imshow('image', cameraImg) key = cv2.waitKey(1) if key == 27: break if key == ord('t'): drawOverlay = not drawOverlay except: print("An exception occurred") break print "Stopping video writer" writer.release() writer = None os.chdir('C://Users/Asus/') # ffmpeg -i 1569831566308.jpeg-out.avi -i ../../data/superVideo2.mp4 -map 0:0 -map 1:1 -shortest 1569831566308.jpeg-out.mp4 subprocess.call(["ffmpeg", "-i", "../bnl/videos/"+sys.argv[1]+"-out.avi", "-i ", "../data/"+sys.argv[2]+".mp4", "-map ", "0:0", "-map ", "1:1", "-shortest", "../bnl/videos/"+sys.argv[1]+"-out.mp4" ])

0

29895e9f595bd0e2bfb7657a094cd535ad76784c

586

py

Python

Desafios/Desafio98.py

Felix-xilef/Curso-de-Python

cdff7c7f3850e6326e274c8c1987b9e1a18ce910

[ "MIT" ]

null

Desafios/Desafio98.py

Felix-xilef/Curso-de-Python

cdff7c7f3850e6326e274c8c1987b9e1a18ce910

[ "MIT" ]

null

Desafios/Desafio98.py

Felix-xilef/Curso-de-Python

cdff7c7f3850e6326e274c8c1987b9e1a18ce910

[ "MIT" ]

null

from auxiliar import receberInt from time import sleep # main contador(1, 10, 1) contador(10, 0, 2) contador(receberInt('Digite o inicio: '), receberInt('Digite o fim: '), receberInt('Digite o passo: ')) input('\n\nPressione <enter> para continuar')

23.44

103

0.583618

from auxiliar import receberInt from time import sleep def contador(inicio, fim, passo): print('-'*30) print(f'Contagem de {inicio} até {fim} de {passo} em {passo}') if inicio > fim: passo *= -1 fim -= 1 else: fim += 1 for i in range(inicio, fim, passo): sleep(0.5) print(f'{i} -> ', end='') print('FIM') print('-'*30) # main contador(1, 10, 1) contador(10, 0, 2) contador(receberInt('Digite o inicio: '), receberInt('Digite o fim: '), receberInt('Digite o passo: ')) input('\n\nPressione <enter> para continuar')

311

0

23

31311b055c16c8db5e97123b97dbdb3720180513

368

py

Python

blog/migrations/0030_auto_20190705_1244.py

Labbit-kw/hologram-project

708b773e932f6ad0f92d1d9e2e57cfbd8b17b933

[ "MIT" ]

null

blog/migrations/0030_auto_20190705_1244.py

Labbit-kw/hologram-project

708b773e932f6ad0f92d1d9e2e57cfbd8b17b933

[ "MIT" ]

null

blog/migrations/0030_auto_20190705_1244.py

Labbit-kw/hologram-project

708b773e932f6ad0f92d1d9e2e57cfbd8b17b933

[ "MIT" ]

null

# Generated by Django 2.2.2 on 2019-07-05 12:44 from django.db import migrations

19.368421

47

0.589674

# Generated by Django 2.2.2 on 2019-07-05 12:44 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('blog', '0029_auto_20190705_1240'), ] operations = [ migrations.RenameField( model_name='uploadboard', old_name='file_title', new_name='title', ), ]

0

262

23

7af1d31414874444295da4f2cff38f7fc177697e

4,889

py

Python

tx_rx_list_creator_milp.py

WiSig-dataset/wisig-subset-creation

bc8c6bd352a8c184a571e6b4c2d7a890cdfff3d8

[ "BSD-3-Clause" ]

1

2022-03-17T07:44:24.000Z

tx_rx_list_creator_milp.py

WiSig-dataset/wisig-subset-creation

bc8c6bd352a8c184a571e6b4c2d7a890cdfff3d8

[ "BSD-3-Clause" ]

null

tx_rx_list_creator_milp.py

WiSig-dataset/wisig-subset-creation

bc8c6bd352a8c184a571e6b4c2d7a890cdfff3d8

[ "BSD-3-Clause" ]

null

import pickle import numpy as np import gurobipy as gp from gurobipy import GRB from tqdm.notebook import trange, tqdm

41.786325

141

0.599305

import pickle import numpy as np import gurobipy as gp from gurobipy import GRB from tqdm.notebook import trange, tqdm def solve_for_lists_milp(num_tx = None, min_sig = None, min_sig_low = None, satisfaction=1.0, verbose=False): with open('data_summary.pkl', 'rb') as f: d=pickle.load(f) capture_date_list=d['capture_date_list'] tx_list= d['tx_list'] rx_list= d['rx_list'] mat_date=np.array(d['mat_date']) mat_date_eq=np.array(d['mat_date_eq']) n_tx = len(tx_list) n_rx = len(rx_list) num_days = 4; if(num_tx is None): raise ValueError("num_tx must be specified. Exiting...") return if(min_sig is None): raise ValueError("min_sig must be specified. Exiting...") return if(min_sig_low is None): min_sig_low = 0; M=3000 # Upper-bound to num_signals for any rx-tx pair mat_date_thresh = np.double(mat_date > min_sig); mat_date_eq_thresh = np.double(mat_date_eq > min_sig); m = gp.Model("mip_dual_tol") if(verbose): m.params.outputflag = 1 else: m.params.outputflag = 0 T = m.addMVar(shape=n_tx, vtype=GRB.BINARY, name="T") R = m.addMVar(shape=(n_rx, 1), vtype=GRB.BINARY, name="R") Y = m.addMVar(shape=(n_rx, n_tx), vtype=GRB.BINARY, name="Y") min_sig_var = m.addMVar(shape = 1, vtype=GRB.INTEGER, name="min_sig_var") Z = m.addMVar(shape=(n_rx, n_tx), vtype=GRB.INTEGER, name="Z") Q = m.addMVar(shape=(num_days, n_rx, n_tx), vtype=GRB.BINARY, name="Q") Q_eq = m.addMVar(shape=(num_days, n_rx, n_tx), vtype=GRB.BINARY, name="Q_eq") for rxid in range(n_rx): for txid in range(n_tx): # Y[rxid][txid] = R[rxid] AND T[txid] m.addConstr(Y[rxid][txid] <= T[txid]) m.addConstr(Y[rxid][txid] <= R[rxid, 0]) m.addConstr(Y[rxid][txid] >= T[txid] + R[rxid, 0] - 1) # Z[rxid][txid] = 0 if Y[rxid][txid] == False. Z[rxid][txid] = min_sig if Y[rxid][txid] == True. m.addConstr(Z[rxid][txid] <= Y[rxid][txid] * M) m.addConstr(Z[rxid][txid] >= 0) m.addConstr(Z[rxid][txid] <= min_sig_var[0]) m.addConstr(Z[rxid][txid] >= min_sig_var[0] - (1-Y[rxid][txid]) * M) for day_id in range(num_days): # Q[rxid][txid] = Y[rxid][txid] AND (mat_date[rxid][txid] > min_sig) m.addConstr(Q[day_id][rxid][txid] <= Y[rxid][txid]) m.addConstr(Q[day_id][rxid][txid] <= mat_date_thresh[day_id][rxid][txid]) m.addConstr(Q[day_id][rxid][txid] >= Y[rxid][txid] + mat_date_thresh[day_id][rxid][txid] - 1) # Q_eq[rxid][txid] = Y[rxid][txid] AND (mat_date_eq[rxid][txid] > min_sig) m.addConstr(Q_eq[day_id][rxid][txid] <= Y[rxid][txid]) m.addConstr(Q_eq[day_id][rxid][txid] <= mat_date_eq_thresh[day_id][rxid][txid]) m.addConstr(Q_eq[day_id][rxid][txid] >= Y[rxid][txid] + mat_date_eq_thresh[day_id][rxid][txid] - 1) # mat_date[day_id][rxid][txid] >= min_sig, if R[rxid] == True AND T[txid] == True m.addConstr(mat_date[day_id][rxid][txid] * Y[rxid][txid] - Z[rxid][txid] >= 0) # mat_date_eq[day_id][rxid][txid] >= min_sig, if R[rxid] == True AND T[txid] == True m.addConstr(mat_date_eq[day_id][rxid][txid] * Y[rxid][txid] - Z[rxid][txid] >= 0) m.addConstr(T.sum() == num_tx) # This can be made m.addConstr(T.sum() >= num_tx) at the expense of larger solve times, but larger dataset m.addConstr(min_sig_var >= min_sig_low) for i in range(n_rx): for day_id in range(num_days): m.addConstr(Q[day_id, i, :].sum() >= R[i] * num_tx * satisfaction) m.addConstr(Q_eq[day_id, i, :].sum() >= R[i] * num_tx * satisfaction) # dual-objective optimization. maximixing n_rx has higher priority over maximizing min_sig m.setObjectiveN(R.sum(), 0, 10) m.setObjectiveN(min_sig_var - min_sig_low, 1, 0) m.ModelSense = GRB.MAXIMIZE m.optimize() if m.status == GRB.OPTIMAL: min_sig_value = min_sig_var.X; R_V, T_V = np.array(R.X, dtype=bool).squeeze(), np.array(T.X, dtype=bool) # min_satisfaction = min(np.min((mat_date[:,:,T_V][:,R_V,:]>satisfaction).sum(axis=2) / np.sum(T_V)), # np.min((mat_date_eq[:,:,T_V][:,R_V,:]>satisfaction).sum(axis=2) / np.sum(T_V))); print(f"Found optimal solution with {np.sum(R_V)} Rx") else: print("Could not find optimal solution.") T_V = np.array(T.X, dtype=bool) R_V = np.array(R.X, dtype=bool).squeeze() op_tx_list = apply_list(tx_list,T_V) op_rx_list = apply_list(rx_list,R_V) return op_tx_list,op_rx_list def apply_list(lst, bool_list): return [lst[ii] for ii in range(bool_list.size) if bool_list[ii]]

4,719

0

46

b84078c4cb83dfb19f774e9116faa6561ca0090b

15,948

py

Python

alphapept/matching.py

PatrickvanZalm/alphapept

58dbe8b75c1a5384f970893c7cc9b7c929a1cfe2

[ "Apache-2.0" ]

null

alphapept/matching.py

PatrickvanZalm/alphapept

58dbe8b75c1a5384f970893c7cc9b7c929a1cfe2

[ "Apache-2.0" ]

null

alphapept/matching.py

PatrickvanZalm/alphapept

58dbe8b75c1a5384f970893c7cc9b7c929a1cfe2

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/09_matching.ipynb (unless otherwise specified). __all__ = ['calculate_distance', 'calib_table', 'align', 'calculate_deltas', 'align_files', 'align_datasets', 'get_probability', 'match_datasets'] # Cell import pandas as pd import numpy as np def calculate_distance(table_1: pd.DataFrame, table_2: pd.DataFrame, offset_dict: dict, calib: bool = False) -> (list, int): """Calculate the distance between two precursors for different columns Distance can either be relative or absolute. An example for a minimal offset_dict is: offset_dict = {'mass':'absolute'} Args: table_1 (pd.DataFrame): Dataframe with precusor data. table_2 (pd.DataFrame): Dataframe with precusor data. offset_dict (dict): Dictionary with column names and how the distance should be calculated. calib (bool): Flag to indicate that distances should be calculated on calibrated columns. Defaults to False. Raises: KeyError: If either table_1 or table_2 is not indexed by precursor """ if table_1.index.name != 'precursor': raise KeyError('table_1 is not indexed by precursor') if table_2.index.name != 'precursor': raise KeyError('table_2 is not indexed by precursor') shared_precursors = list(set(table_1.index).intersection(set(table_2.index))) table_1_ = table_1.loc[shared_precursors] table_2_ = table_2.loc[shared_precursors] table_1_ = table_1_.groupby('precursor').mean() table_2_ = table_2_.groupby('precursor').mean() deltas = [] for col in offset_dict: if calib: col_ = col+'_calib' else: col_ = col if offset_dict[col] == 'absolute': deltas.append(np.nanmedian(table_1_[col_] - table_2_[col_])) elif offset_dict[col] == 'relative': deltas.append(np.nanmedian((table_1_[col_] - table_2_[col_]) / (table_1_[col_] + table_2_[col_]) * 2)) else: raise NotImplementedError(f"Calculating delta for {offset_dict[col_]} not implemented.") return deltas, len(shared_precursors) # Cell def calib_table(table: pd.DataFrame, delta: pd.Series, offset_dict: dict): """ Apply offset to a table. Different operations for offsets exist. Offsets will be saved with a '_calib'-suffix. If this does not already exist, it will be created. Args: table_1 (pd.DataFrame): Dataframe with data. delta (pd.Series): Series cotaining the offset. offset_dict (dict): Dictionary with column names and how the distance should be calculated. Raises: NotImplementedError: If the type of vonversion is not implemented. """ for col in offset_dict: if (col not in table.columns) and (col+'_apex' in table.columns): col_ = col+'_apex' else: col_ = col if offset_dict[col] == 'absolute': table[col+'_calib'] = table[col_]-delta[col] elif offset_dict[col] == 'relative': table[col+'_calib'] = (1-delta[col_])*table[col] else: raise NotImplementedError(offset_dict[col]) # Cell import logging from sklearn.linear_model import LinearRegression def align(deltas: pd.DataFrame, filenames: list, weights:np.ndarray=None) -> np.ndarray: """Align multiple datasets. This function creates a matrix to represent the shifts from each dataset to another. This effectively is an overdetermined equation system and is solved with a linear regression. Args: deltas (pd.DataFrame): Distances from each dataset to another. filenames (list): The filenames of the datasts that were compared. weights (np.ndarray, optional): Distances can be weighted by their number of shared elements. Defaults to None. Returns: np.ndarray: alignment values. """ matrix = [] for i in range(len(deltas)): start, end = deltas.index[i] start_idx = filenames.index(start) end_idx = filenames.index(end) lines = np.zeros(len(filenames)-1) lines[start_idx:end_idx] = 1 matrix.append(lines) # Remove nan values not_nan = ~deltas.isnull().any(axis=1) matrix = np.array(matrix) matrix = matrix[not_nan] deltas_ = deltas[not_nan] if len(deltas) < matrix.shape[1]: logging.info('Low overlap between datasets detected. Alignment may fail.') if weights is not None: reg = LinearRegression(fit_intercept=False).fit(matrix, deltas_.values, sample_weight = weights[not_nan]) score= reg.score(matrix, deltas_.values) else: reg = LinearRegression(fit_intercept=False).fit(matrix, deltas_.values) score= reg.score(matrix, deltas_.values) logging.info(f"Regression score is {score}") x = reg.predict(np.eye(len(filenames)-1)) return x # Cell import alphapept.io import os from typing import Callable def calculate_deltas(combos: list, calib:bool = False, callback:Callable=None) -> (pd.DataFrame, np.ndarray, dict): """Wrapper function to calculate the distances of multiple files. In here, we define the offset_dict to make a relative comparison for mz and mobility and absolute for rt. TODO: This function could be speed-up by parallelization Args: combos (list): A list containing tuples of filenames that should be compared. calib (bool): Boolean flag to indicate distance should be calculated on calibrated data. callback (Callable): A callback function to track progress. Returns: pd.DataFrame: Dataframe containing the deltas of the files np.ndarray: Numpy array containing the weights of each comparison (i.e. number of shared elements) dict: Offset dictionary whicch was used for comparing. """ offset_dict = {} deltas = pd.DataFrame() weights = [] for i, combo in enumerate(combos): file1 = os.path.splitext(combo[0])[0] + '.ms_data.hdf' file2 = os.path.splitext(combo[1])[0] + '.ms_data.hdf' df_1 = alphapept.io.MS_Data_File(file1).read(dataset_name="peptide_fdr").set_index('precursor') df_2 = alphapept.io.MS_Data_File(file2).read(dataset_name="peptide_fdr").set_index('precursor') if not offset_dict: offset_dict = {'mz':'relative', 'rt':'absolute'} if 'mobility' in df_1.columns: logging.info("Also using mobility for calibration.") offset_dict['mobility'] = 'relative' cols = list(offset_dict.keys()) if len(deltas) == 0: deltas = pd.DataFrame(columns = cols) dists, weight = calculate_distance(df_1, df_2, offset_dict, calib = calib) deltas = deltas.append(pd.DataFrame([dists], columns = cols, index=[combo])) weights.append(weight) if callback: callback((i+1)/len(combos)) return deltas, np.array(weights), offset_dict # Cell import pandas as pd from itertools import combinations import numpy as np import os import functools #There is no unit test for align_files and align_datasets as they are wrappers and should be covered by the quick_test def align_files(filenames: list, alignment: pd.DataFrame, offset_dict: dict): """ Wrapper function that aligns a list of files. Args: filenames (list): A list with raw file names. alignment (pd.DataFrame): A pandas dataframe containing the alignment information. offset_dict (dict): Dictionary with column names and how the distance should be calculated. """ for idx, filename in enumerate(filenames): file = os.path.splitext(filename)[0] + '.ms_data.hdf' for column in ['peptide_fdr', 'feature_table']: df = alphapept.io.MS_Data_File(file).read(dataset_name=column) calib_table(df, alignment.iloc[idx], offset_dict) logging.info(f"Saving {file} - {column}.") ms_file = alphapept.io.MS_Data_File(file, is_overwritable=True) ms_file.write(df, dataset_name=column) def align_datasets(settings:dict, callback:callable=None): """ Wrapper function that aligns all experimental files specified a settings file. Args: settings (dict): A list with raw file names. callback (Callable): Callback function to indicate progress. """ filenames = settings['experiment']['file_paths'] if callback: cb = functools.partial(progress_wrapper, 0, 2) else: cb = None if len(filenames) > 1: combos = list(combinations(filenames, 2)) deltas, weights, offset_dict = calculate_deltas(combos, callback=cb) cols = list(offset_dict.keys()) before_sum = deltas.abs().sum().to_dict() before_mean = deltas.abs().mean().to_dict() logging.info(f'Total deviation before calibration {before_sum}') logging.info(f'Mean deviation before calibration {before_mean}') logging.info(f'Solving equation system') alignment = pd.DataFrame(align(deltas, filenames, weights), columns = cols) alignment = pd.concat([pd.DataFrame(np.zeros((1, alignment.shape[1])), columns= cols), alignment]) alignment -= alignment.mean() logging.info(f'Solving equation system complete.') logging.info(f'Applying offset') align_files(filenames, -alignment, offset_dict) if cb: cb = functools.partial(progress_wrapper, 1, 2) deltas, weights, offset_dict = calculate_deltas(combos, calib=True, callback=cb) after_sum = deltas.abs().sum().to_dict() after_mean = deltas.abs().mean().to_dict() logging.info(f'Total deviation after calibration {after_sum}') logging.info(f'Mean deviation after calibration {after_mean}') change_sum = {k:v/before_sum[k] for k,v in after_sum.items()} change_mean = {k:v/before_mean[k] for k,v in after_mean.items()} logging.info(f'Change (after/before) total deviation {change_sum}') logging.info(f'Change (after/before) mean deviation {change_mean}') else: logging.info('Only 1 dataset present. Skipping alignment.') # Cell from scipy import stats def get_probability(df: pd.DataFrame, ref: pd.DataFrame, sigma:pd.DataFrame, index:int)-> float: """Probablity estimate of a transfered identification using the Mahalanobis distance. The function calculates the probability that a feature is a reference feature. The reference features containing std deviations so that a probability can be estimated. It is required that the data frames are matched, meaning that the first entry in df matches to the first entry in ref. Args: df (pd.DataFrame): Dataset containing transferered features ref (pd.DataFrame): Dataset containing reference features sigma (pd.DataFrame): Dataset containing the standard deviations of the reference features index (int): Index to the datframes that should be compared Returns: float: Mahalanobis distance """ sigma = sigma.iloc[index].values sigma = sigma*np.eye(len(sigma)) mu = ref.iloc[index].values x = df.iloc[index].values try: m_dist_x = np.dot((x-mu).transpose(), np.linalg.inv(sigma)) m_dist_x = np.dot(m_dist_x, (x-mu)) _ = stats.chi2.cdf(m_dist_x, len(mu)) except Exception as e: _ = np.nan return _ # Cell from sklearn.neighbors import KDTree from .utils import assemble_df # This function is a wrapper function and has currently has no unit test # The function will be revised when implementing issue #255: https://github.com/MannLabs/alphapept/issues/255 def match_datasets(settings:dict, callback:Callable = None): """Match datasets: Wrapper function to match datasets based on a settings file. Args: settings (dict): Dictionary containg specifications of the run callback (Callable): Callback function to indicate progress. """ if len(settings['experiment']['file_paths']) > 2: xx = alphapept.utils.assemble_df(settings, field='peptide_fdr') base_col = ['precursor'] alignment_cols = ['mz_calib','rt_calib'] extra_cols = ['score','decoy','target'] if 'mobility' in xx.columns: alignment_cols += ['mobility_calib'] use_mobility = True else: use_mobility = False grouped = xx[base_col + alignment_cols + extra_cols].groupby('precursor').mean() std_ = xx[base_col + alignment_cols].groupby('precursor').std() grouped[[_+'_std' for _ in alignment_cols]] = std_ std_range = np.nanmedian(std_.values, axis=0) match_p_min = settings['matching']['match_p_min'] match_d_min = settings['matching']['match_d_min'] filenames = settings['experiment']['file_paths'] lookup_dict = xx.set_index('precursor')[['sequence']].to_dict() for idx, filename in enumerate(filenames): file = os.path.splitext(filename)[0] + '.ms_data.hdf' df = alphapept.io.MS_Data_File(file).read(dataset_name='peptide_fdr') features = alphapept.io.MS_Data_File(file).read(dataset_name='feature_table') features['feature_idx'] = features.index matching_set = set(grouped.index) - set(df['precursor']) logging.info(f'Trying to match file {file} with database of {len(matching_set):,} unidentified candidates') mz_range = std_range[0] rt_range = std_range[1] tree_points = features[alignment_cols].values tree_points[:,0] = tree_points[:,0]/mz_range tree_points[:,1] = tree_points[:,1]/rt_range query_points = grouped.loc[matching_set][alignment_cols].values query_points[:,0] = query_points[:,0]/mz_range query_points[:,1] = query_points[:,1]/rt_range if use_mobility: logging.info("Using mobility") i_range = std_range[2] tree_points[:,2] = tree_points[:,2]/i_range query_points[:,2] = query_points[:,2]/i_range matching_tree = KDTree(tree_points, metric="minkowski") dist, idx = matching_tree.query(query_points, k=1) matched = features.iloc[idx[:,0]] for _ in extra_cols: matched[_] = grouped.loc[matching_set, _].values to_keep = dist < match_d_min matched = matched[to_keep] ref = grouped.loc[matching_set][alignment_cols][to_keep] sigma = std_.loc[matching_set][to_keep] logging.info(f'{len(matched):,} possible features for matching based on distance of {match_d_min}') matched['matching_p'] = [get_probability(matched[alignment_cols], ref, sigma, i) for i in range(len(matched))] matched['precursor'] = grouped.loc[matching_set][to_keep].index.values matched = matched[matched['matching_p']< match_p_min] logging.info(f'{len(matched):,} possible features for matching based on probability of {match_p_min}') matched['type'] = 'matched' for _ in lookup_dict.keys(): matched[_] = [lookup_dict[_][x] for x in matched['precursor']] df['type'] = 'msms' df['matching_p'] = np.nan shared_columns = set(matched.columns).intersection(set(df.columns)) df_ = pd.concat([df, matched[shared_columns]], ignore_index=True) logging.info(f"Saving {file} - peptide_fdr.") ms_file = alphapept.io.MS_Data_File(file, is_overwritable=True) ms_file.write(df_, dataset_name='peptide_fdr') else: logging.info('Less than 3 datasets present. Skipping matching.')

36.746544

124

0.658891

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/09_matching.ipynb (unless otherwise specified). __all__ = ['calculate_distance', 'calib_table', 'align', 'calculate_deltas', 'align_files', 'align_datasets', 'get_probability', 'match_datasets'] # Cell import pandas as pd import numpy as np def calculate_distance(table_1: pd.DataFrame, table_2: pd.DataFrame, offset_dict: dict, calib: bool = False) -> (list, int): """Calculate the distance between two precursors for different columns Distance can either be relative or absolute. An example for a minimal offset_dict is: offset_dict = {'mass':'absolute'} Args: table_1 (pd.DataFrame): Dataframe with precusor data. table_2 (pd.DataFrame): Dataframe with precusor data. offset_dict (dict): Dictionary with column names and how the distance should be calculated. calib (bool): Flag to indicate that distances should be calculated on calibrated columns. Defaults to False. Raises: KeyError: If either table_1 or table_2 is not indexed by precursor """ if table_1.index.name != 'precursor': raise KeyError('table_1 is not indexed by precursor') if table_2.index.name != 'precursor': raise KeyError('table_2 is not indexed by precursor') shared_precursors = list(set(table_1.index).intersection(set(table_2.index))) table_1_ = table_1.loc[shared_precursors] table_2_ = table_2.loc[shared_precursors] table_1_ = table_1_.groupby('precursor').mean() table_2_ = table_2_.groupby('precursor').mean() deltas = [] for col in offset_dict: if calib: col_ = col+'_calib' else: col_ = col if offset_dict[col] == 'absolute': deltas.append(np.nanmedian(table_1_[col_] - table_2_[col_])) elif offset_dict[col] == 'relative': deltas.append(np.nanmedian((table_1_[col_] - table_2_[col_]) / (table_1_[col_] + table_2_[col_]) * 2)) else: raise NotImplementedError(f"Calculating delta for {offset_dict[col_]} not implemented.") return deltas, len(shared_precursors) # Cell def calib_table(table: pd.DataFrame, delta: pd.Series, offset_dict: dict): """ Apply offset to a table. Different operations for offsets exist. Offsets will be saved with a '_calib'-suffix. If this does not already exist, it will be created. Args: table_1 (pd.DataFrame): Dataframe with data. delta (pd.Series): Series cotaining the offset. offset_dict (dict): Dictionary with column names and how the distance should be calculated. Raises: NotImplementedError: If the type of vonversion is not implemented. """ for col in offset_dict: if (col not in table.columns) and (col+'_apex' in table.columns): col_ = col+'_apex' else: col_ = col if offset_dict[col] == 'absolute': table[col+'_calib'] = table[col_]-delta[col] elif offset_dict[col] == 'relative': table[col+'_calib'] = (1-delta[col_])*table[col] else: raise NotImplementedError(offset_dict[col]) # Cell import logging from sklearn.linear_model import LinearRegression def align(deltas: pd.DataFrame, filenames: list, weights:np.ndarray=None) -> np.ndarray: """Align multiple datasets. This function creates a matrix to represent the shifts from each dataset to another. This effectively is an overdetermined equation system and is solved with a linear regression. Args: deltas (pd.DataFrame): Distances from each dataset to another. filenames (list): The filenames of the datasts that were compared. weights (np.ndarray, optional): Distances can be weighted by their number of shared elements. Defaults to None. Returns: np.ndarray: alignment values. """ matrix = [] for i in range(len(deltas)): start, end = deltas.index[i] start_idx = filenames.index(start) end_idx = filenames.index(end) lines = np.zeros(len(filenames)-1) lines[start_idx:end_idx] = 1 matrix.append(lines) # Remove nan values not_nan = ~deltas.isnull().any(axis=1) matrix = np.array(matrix) matrix = matrix[not_nan] deltas_ = deltas[not_nan] if len(deltas) < matrix.shape[1]: logging.info('Low overlap between datasets detected. Alignment may fail.') if weights is not None: reg = LinearRegression(fit_intercept=False).fit(matrix, deltas_.values, sample_weight = weights[not_nan]) score= reg.score(matrix, deltas_.values) else: reg = LinearRegression(fit_intercept=False).fit(matrix, deltas_.values) score= reg.score(matrix, deltas_.values) logging.info(f"Regression score is {score}") x = reg.predict(np.eye(len(filenames)-1)) return x # Cell import alphapept.io import os from typing import Callable def calculate_deltas(combos: list, calib:bool = False, callback:Callable=None) -> (pd.DataFrame, np.ndarray, dict): """Wrapper function to calculate the distances of multiple files. In here, we define the offset_dict to make a relative comparison for mz and mobility and absolute for rt. TODO: This function could be speed-up by parallelization Args: combos (list): A list containing tuples of filenames that should be compared. calib (bool): Boolean flag to indicate distance should be calculated on calibrated data. callback (Callable): A callback function to track progress. Returns: pd.DataFrame: Dataframe containing the deltas of the files np.ndarray: Numpy array containing the weights of each comparison (i.e. number of shared elements) dict: Offset dictionary whicch was used for comparing. """ offset_dict = {} deltas = pd.DataFrame() weights = [] for i, combo in enumerate(combos): file1 = os.path.splitext(combo[0])[0] + '.ms_data.hdf' file2 = os.path.splitext(combo[1])[0] + '.ms_data.hdf' df_1 = alphapept.io.MS_Data_File(file1).read(dataset_name="peptide_fdr").set_index('precursor') df_2 = alphapept.io.MS_Data_File(file2).read(dataset_name="peptide_fdr").set_index('precursor') if not offset_dict: offset_dict = {'mz':'relative', 'rt':'absolute'} if 'mobility' in df_1.columns: logging.info("Also using mobility for calibration.") offset_dict['mobility'] = 'relative' cols = list(offset_dict.keys()) if len(deltas) == 0: deltas = pd.DataFrame(columns = cols) dists, weight = calculate_distance(df_1, df_2, offset_dict, calib = calib) deltas = deltas.append(pd.DataFrame([dists], columns = cols, index=[combo])) weights.append(weight) if callback: callback((i+1)/len(combos)) return deltas, np.array(weights), offset_dict # Cell import pandas as pd from itertools import combinations import numpy as np import os import functools #There is no unit test for align_files and align_datasets as they are wrappers and should be covered by the quick_test def align_files(filenames: list, alignment: pd.DataFrame, offset_dict: dict): """ Wrapper function that aligns a list of files. Args: filenames (list): A list with raw file names. alignment (pd.DataFrame): A pandas dataframe containing the alignment information. offset_dict (dict): Dictionary with column names and how the distance should be calculated. """ for idx, filename in enumerate(filenames): file = os.path.splitext(filename)[0] + '.ms_data.hdf' for column in ['peptide_fdr', 'feature_table']: df = alphapept.io.MS_Data_File(file).read(dataset_name=column) calib_table(df, alignment.iloc[idx], offset_dict) logging.info(f"Saving {file} - {column}.") ms_file = alphapept.io.MS_Data_File(file, is_overwritable=True) ms_file.write(df, dataset_name=column) def align_datasets(settings:dict, callback:callable=None): """ Wrapper function that aligns all experimental files specified a settings file. Args: settings (dict): A list with raw file names. callback (Callable): Callback function to indicate progress. """ filenames = settings['experiment']['file_paths'] if callback: def progress_wrapper(current, step, n_steps): callback(step+current/n_steps) cb = functools.partial(progress_wrapper, 0, 2) else: cb = None if len(filenames) > 1: combos = list(combinations(filenames, 2)) deltas, weights, offset_dict = calculate_deltas(combos, callback=cb) cols = list(offset_dict.keys()) before_sum = deltas.abs().sum().to_dict() before_mean = deltas.abs().mean().to_dict() logging.info(f'Total deviation before calibration {before_sum}') logging.info(f'Mean deviation before calibration {before_mean}') logging.info(f'Solving equation system') alignment = pd.DataFrame(align(deltas, filenames, weights), columns = cols) alignment = pd.concat([pd.DataFrame(np.zeros((1, alignment.shape[1])), columns= cols), alignment]) alignment -= alignment.mean() logging.info(f'Solving equation system complete.') logging.info(f'Applying offset') align_files(filenames, -alignment, offset_dict) if cb: cb = functools.partial(progress_wrapper, 1, 2) deltas, weights, offset_dict = calculate_deltas(combos, calib=True, callback=cb) after_sum = deltas.abs().sum().to_dict() after_mean = deltas.abs().mean().to_dict() logging.info(f'Total deviation after calibration {after_sum}') logging.info(f'Mean deviation after calibration {after_mean}') change_sum = {k:v/before_sum[k] for k,v in after_sum.items()} change_mean = {k:v/before_mean[k] for k,v in after_mean.items()} logging.info(f'Change (after/before) total deviation {change_sum}') logging.info(f'Change (after/before) mean deviation {change_mean}') else: logging.info('Only 1 dataset present. Skipping alignment.') # Cell from scipy import stats def get_probability(df: pd.DataFrame, ref: pd.DataFrame, sigma:pd.DataFrame, index:int)-> float: """Probablity estimate of a transfered identification using the Mahalanobis distance. The function calculates the probability that a feature is a reference feature. The reference features containing std deviations so that a probability can be estimated. It is required that the data frames are matched, meaning that the first entry in df matches to the first entry in ref. Args: df (pd.DataFrame): Dataset containing transferered features ref (pd.DataFrame): Dataset containing reference features sigma (pd.DataFrame): Dataset containing the standard deviations of the reference features index (int): Index to the datframes that should be compared Returns: float: Mahalanobis distance """ sigma = sigma.iloc[index].values sigma = sigma*np.eye(len(sigma)) mu = ref.iloc[index].values x = df.iloc[index].values try: m_dist_x = np.dot((x-mu).transpose(), np.linalg.inv(sigma)) m_dist_x = np.dot(m_dist_x, (x-mu)) _ = stats.chi2.cdf(m_dist_x, len(mu)) except Exception as e: _ = np.nan return _ # Cell from sklearn.neighbors import KDTree from .utils import assemble_df # This function is a wrapper function and has currently has no unit test # The function will be revised when implementing issue #255: https://github.com/MannLabs/alphapept/issues/255 def match_datasets(settings:dict, callback:Callable = None): """Match datasets: Wrapper function to match datasets based on a settings file. Args: settings (dict): Dictionary containg specifications of the run callback (Callable): Callback function to indicate progress. """ if len(settings['experiment']['file_paths']) > 2: xx = alphapept.utils.assemble_df(settings, field='peptide_fdr') base_col = ['precursor'] alignment_cols = ['mz_calib','rt_calib'] extra_cols = ['score','decoy','target'] if 'mobility' in xx.columns: alignment_cols += ['mobility_calib'] use_mobility = True else: use_mobility = False grouped = xx[base_col + alignment_cols + extra_cols].groupby('precursor').mean() std_ = xx[base_col + alignment_cols].groupby('precursor').std() grouped[[_+'_std' for _ in alignment_cols]] = std_ std_range = np.nanmedian(std_.values, axis=0) match_p_min = settings['matching']['match_p_min'] match_d_min = settings['matching']['match_d_min'] filenames = settings['experiment']['file_paths'] lookup_dict = xx.set_index('precursor')[['sequence']].to_dict() for idx, filename in enumerate(filenames): file = os.path.splitext(filename)[0] + '.ms_data.hdf' df = alphapept.io.MS_Data_File(file).read(dataset_name='peptide_fdr') features = alphapept.io.MS_Data_File(file).read(dataset_name='feature_table') features['feature_idx'] = features.index matching_set = set(grouped.index) - set(df['precursor']) logging.info(f'Trying to match file {file} with database of {len(matching_set):,} unidentified candidates') mz_range = std_range[0] rt_range = std_range[1] tree_points = features[alignment_cols].values tree_points[:,0] = tree_points[:,0]/mz_range tree_points[:,1] = tree_points[:,1]/rt_range query_points = grouped.loc[matching_set][alignment_cols].values query_points[:,0] = query_points[:,0]/mz_range query_points[:,1] = query_points[:,1]/rt_range if use_mobility: logging.info("Using mobility") i_range = std_range[2] tree_points[:,2] = tree_points[:,2]/i_range query_points[:,2] = query_points[:,2]/i_range matching_tree = KDTree(tree_points, metric="minkowski") dist, idx = matching_tree.query(query_points, k=1) matched = features.iloc[idx[:,0]] for _ in extra_cols: matched[_] = grouped.loc[matching_set, _].values to_keep = dist < match_d_min matched = matched[to_keep] ref = grouped.loc[matching_set][alignment_cols][to_keep] sigma = std_.loc[matching_set][to_keep] logging.info(f'{len(matched):,} possible features for matching based on distance of {match_d_min}') matched['matching_p'] = [get_probability(matched[alignment_cols], ref, sigma, i) for i in range(len(matched))] matched['precursor'] = grouped.loc[matching_set][to_keep].index.values matched = matched[matched['matching_p']< match_p_min] logging.info(f'{len(matched):,} possible features for matching based on probability of {match_p_min}') matched['type'] = 'matched' for _ in lookup_dict.keys(): matched[_] = [lookup_dict[_][x] for x in matched['precursor']] df['type'] = 'msms' df['matching_p'] = np.nan shared_columns = set(matched.columns).intersection(set(df.columns)) df_ = pd.concat([df, matched[shared_columns]], ignore_index=True) logging.info(f"Saving {file} - peptide_fdr.") ms_file = alphapept.io.MS_Data_File(file, is_overwritable=True) ms_file.write(df_, dataset_name='peptide_fdr') else: logging.info('Less than 3 datasets present. Skipping matching.')

67

0

30

e20006aa1bcdf9766118cc8327dfb3893735dd0f

5,147

py

Python

web_journal/service/filesystem.py

pete88b/web_journal

7a6cd88d13f109836118c2a025a1c69d6172283e

[ "Apache-2.0" ]

null

web_journal/service/filesystem.py

pete88b/web_journal

7a6cd88d13f109836118c2a025a1c69d6172283e

[ "Apache-2.0" ]

null

web_journal/service/filesystem.py

pete88b/web_journal

7a6cd88d13f109836118c2a025a1c69d6172283e

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: 40b_service_filesystem.ipynb (unless otherwise specified). __all__ = ['sort_posts', 'posts_list_to_dict', 'migrate', 'ServiceFilesystem', 'before_request', 'after_request', 'init_service'] # Cell import json,uuid,datetime,re from pathlib import Path from operator import attrgetter # Cell # Cell def posts_list_to_dict(posts,key='id'): "Convert a list of dictionaries to a dictionary of dictionaries" return {post[key]:post for post in posts} # Cell # Cell # TODO: DRY # Cell # Cell # Cell

37.569343

113

0.631436

# AUTOGENERATED! DO NOT EDIT! File to edit: 40b_service_filesystem.ipynb (unless otherwise specified). __all__ = ['sort_posts', 'posts_list_to_dict', 'migrate', 'ServiceFilesystem', 'before_request', 'after_request', 'init_service'] # Cell import json,uuid,datetime,re from pathlib import Path from operator import attrgetter # Cell def sort_posts(posts): return sorted(posts, key=lambda post: (post['created'],post['last_updated'],post['id']), reverse=True) # Cell def posts_list_to_dict(posts,key='id'): "Convert a list of dictionaries to a dictionary of dictionaries" return {post[key]:post for post in posts} # Cell def migrate(data_dir,output_dir=None): posts_file_re=re.compile(r'posts-(?:\d{13}).json') data_dir=Path(data_dir) output_dir=data_dir if output_dir is None else Path(output_dir) for f_name in data_dir.iterdir(): if not posts_file_re.fullmatch(f_name.name): continue with open(f_name) as f: posts = json.load(f) if not posts: continue print('migrating',f_name,'to',output_dir) if not 'status' in posts[0]: for post in posts: post['last_updated']=post['created'] post['status']=50 if post['is_deleted']==0 else 20 del post['is_deleted'] for post in posts: post['id']=str(post['id']) # subsequent migrations might do something like # if not 'other_key' in posts[0]: ... with open(output_dir/f_name.name,'w') as f: json.dump(posts,f) # Cell class ServiceFilesystem: # TODO: DRY def __init__(self,data_dir): self.data_dir=Path(data_dir) self.data_dir.mkdir(parents=True,exist_ok=True) def read_user_by_id(self,id): if (self.data_dir/'users.json').is_file(): with open(self.data_dir/'users.json') as f: for user in json.load(f): if user['id']==id: return user return None def read_user_by_username(self,username): if (self.data_dir/'users.json').is_file(): with open(self.data_dir/'users.json') as f: for user in json.load(f): if user['username']==username: return user return None def create_user(self,username,password): users=[] if (self.data_dir/'users.json').is_file(): with open(self.data_dir/'users.json') as f: users=json.load(f) id=uuid.uuid4().hex users.append(dict(id=id,username=username,password=password)) with open(self.data_dir/'users.json','w') as f: json.dump(users,f) return id def _add_username(self,post): # TODO: check how slow this is ... user=self.read_user_by_id(post['author_id']) post['username']='Unknown user' if user is None else user['username'] return post def _posts(self,author_id): if (self.data_dir/f'posts-{author_id}.json').is_file(): with open(self.data_dir/f'posts-{author_id}.json') as f: return json.load(f) return [] def read_posts_by_author_id(self,author_id): return [self._add_username(p) for p in self._posts(author_id) if p['status']>30] def read_post_by_id(self,author_id,id): for post in self._posts(author_id): if post['id']==id: return self._add_username(post) return None def _now(self): return datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S') def create_post(self,author_id,title,body): posts=self._posts(author_id) id,_now=uuid.uuid4().hex,self._now() posts.insert(0,dict(id=id,author_id=author_id,title=title,body=body, created=_now,last_updated=_now,status=50)) with open(self.data_dir/f'posts-{author_id}.json','w') as f: json.dump(posts,f) return id def update_post_by_id(self,author_id,id,keys,values): posts=self._posts(author_id) for post in posts: if post['id']==id: for key,value in zip(keys,values): post[key]=value post['last_updated']=self._now() with open(self.data_dir/f'posts-{author_id}.json','w') as f: json.dump(posts,f) return post return None def prepare_posts_file_by_author_id(self,author_id): if (self.data_dir/f'posts-{author_id}.json').is_file(): return self.data_dir,f'posts-{author_id}.json' return None,None def upload_posts_from_file(self,author_id,file): # TODO: handle non-json formats with open(file) as f: posts=json.load(f) for post in posts: post['author_id']=author_id posts=posts_list_to_dict(posts) posts.update(posts_list_to_dict(self._posts(author_id))) posts=sort_posts(posts.values()) with open(self.data_dir/f'posts-{author_id}.json','w') as f: json.dump(posts,f) # Cell def before_request(app): return ServiceFilesystem(app.config['DATA_DIR']) # Cell def after_request(app,service): pass # Cell def init_service(app): print('service.filesystem.init_service')

4,097

3

482

cf4b895823276a02926235217abb1d4e591964cc

1,468

py

Python

core_dev/_collections/graph/graph.py

alexzanderr/_core-dev

831f69dad524e450c4243b1dd88f26de80e1d444

[ "MIT" ]

null

core_dev/_collections/graph/graph.py

alexzanderr/_core-dev

831f69dad524e450c4243b1dd88f26de80e1d444

[ "MIT" ]

null

core_dev/_collections/graph/graph.py

alexzanderr/_core-dev

831f69dad524e450c4243b1dd88f26de80e1d444

[ "MIT" ]

null

""" graph.py useful graph collection author: @alexzander """ # python import os # core package from core.system import * from core.json__ import * from exceptions import * from core.path__ import * # from exceptions import * (same thing, it works)

27.185185

97

0.583787

""" graph.py useful graph collection author: @alexzander """ # python import os # core package from core.system import * from core.json__ import * from exceptions import * from core.path__ import * # from exceptions import * (same thing, it works) class Graph: def __init__(self, edges_json: list): self.nodes = [] self.graph_adj_dict = {} self.edges = list(map(tuple, edges_json)) for edge in edges_json: if edge[0] in self.graph_adj_dict: self.graph_adj_dict[edge[0]].append(edge[1]) else: self.graph_adj_dict[edge[0]] = [edge[1]] self.nodes.append(edge[0]) def degree(self, node: int): for __node, adj_nodes in self.graph_adj_dict.items(): if __node == node: return len(adj_nodes) raise VertexNotFoundError def get_adj_list_represenation(self): graph_representation = "" for index, vertex in enumerate(self.graph_adj_dict.keys()): if index == len(self.graph_adj_dict.keys()) - 1: graph_representation += "{} -> {}".format(vertex, self.graph_adj_dict[vertex]) else: graph_representation += "{} -> {}\n".format(vertex, self.graph_adj_dict[vertex]) return graph_representation def __str__(self): return self.get_adj_list_represenation()

1,047

-9

138

1e33a26347f208b9cd6f46e08a1083723e916c43

1,262

py

Python

tools/lib/formats/default/lineHandler.py

pfloos/QUESTDB_website

720fb41c42f50e3614cf406fa6cade594f9dd526

[ "BSD-3-Clause" ]

2

2020-10-29T19:41:52.000Z

2021-08-12T04:28:33.000Z

tools/lib/formats/default/lineHandler.py

rdguerrerom/QUESTDB_website

bebcfdd9596ca90f9c1ca210a68569b767fdfbce

[ "BSD-3-Clause" ]

1

2020-11-20T10:06:44.000Z

tools/lib/formats/default/lineHandler.py

rdguerrerom/QUESTDB_website

bebcfdd9596ca90f9c1ca210a68569b767fdfbce

[ "BSD-3-Clause" ]

2

2020-11-16T14:46:02.000Z

2020-11-24T15:56:47.000Z

from ..formatHandlerBase import formatHandlerBase from ..formatName import formatName from ...data import dataFileBase,DataType,method,excitationValue,datafileSelector from ...utils import getValFromCell import numpy as np @formatName("line")

38.242424

119

0.666403

from ..formatHandlerBase import formatHandlerBase from ..formatName import formatName from ...data import dataFileBase,DataType,method,excitationValue,datafileSelector from ...utils import getValFromCell import numpy as np @formatName("line") class lineHandler(formatHandlerBase): def readFromTable(self,table): datalist=list() for col in range(1,np.size(table,1)): col=table[:,col] mymolecule=str(col[0]) mymethod=method(str(col[2]),str(col[1])) initialState=self.TexOps.initialStates[mymolecule] finsts=dataFileBase.convertState(table[3:,0],initialState,default=self.TexOps.defaultType,commands=self.Commands) datacls=dict() for index,cell in enumerate(col[3:]): if str(cell)!="": val,unsafe=getValFromCell(cell) finst=finsts[index] dt=finst[1] if dt in datacls: data=datacls[dt] else: cl=datafileSelector(dt) data=cl() datacls[dt]=data data.molecule=mymolecule data.method=mymethod data.excitations.append(excitationValue(initialState,finst[0],val,type=finst[2],isUnsafe=unsafe)) for value in datacls.values(): datalist.append(value) return datalist

958

16

46

72b828a699b17bd111dc3d200cb7028c6164204b

1,356

py

Python

setup.py

karstenw/FMPasteBox

c84aa860401051ed369d8559afa83f572a2bd729

[ "BSD-2-Clause" ]

2

2021-07-14T10:07:13.000Z

2021-11-14T17:59:18.000Z

setup.py

karstenw/FMPasteBox

c84aa860401051ed369d8559afa83f572a2bd729

[ "BSD-2-Clause" ]

null

setup.py

karstenw/FMPasteBox

c84aa860401051ed369d8559afa83f572a2bd729

[ "BSD-2-Clause" ]

null

""" Script for building FMPasteBox Usage: python setup.py py2app """ from distutils.core import setup from setuptools.extension import Extension import py2app import FMPasteBoxVersion setup( name = FMPasteBoxVersion.appname, version = FMPasteBoxVersion.version, description = FMPasteBoxVersion.description, long_description = FMPasteBoxVersion.longdescription, author = FMPasteBoxVersion.author, app=[{ 'script': "FMPasteBox.py", "plist": { "NSPrincipalClass": 'NSApplication', "CFBundleIdentifier": FMPasteBoxVersion.bundleID, "CFBundleName": FMPasteBoxVersion.appnameshort, "CFBundleSignature": FMPasteBoxVersion.creator, "CFBundleShortVersionString": FMPasteBoxVersion.version, "CFBundleGetInfoString": FMPasteBoxVersion.description, "NSHumanReadableCopyright": FMPasteBoxVersion.copyright, } }], data_files=[ "English.lproj/MainMenu.nib", "English.lproj/Preferences.nib", #"English.lproj/FMPasteBoxDocument.nib", "+icon/FMPasteBox.icns", #"+icon/FMPasteBoxFile.icns", ], options={ "py2app": { "iconfile": "+icon/FMPasteBox.icns", # "packages": [], "excludes": ["TkInter", 'Tcl', 'Tk'], } } )

27.12

68

0.634218

""" Script for building FMPasteBox Usage: python setup.py py2app """ from distutils.core import setup from setuptools.extension import Extension import py2app import FMPasteBoxVersion setup( name = FMPasteBoxVersion.appname, version = FMPasteBoxVersion.version, description = FMPasteBoxVersion.description, long_description = FMPasteBoxVersion.longdescription, author = FMPasteBoxVersion.author, app=[{ 'script': "FMPasteBox.py", "plist": { "NSPrincipalClass": 'NSApplication', "CFBundleIdentifier": FMPasteBoxVersion.bundleID, "CFBundleName": FMPasteBoxVersion.appnameshort, "CFBundleSignature": FMPasteBoxVersion.creator, "CFBundleShortVersionString": FMPasteBoxVersion.version, "CFBundleGetInfoString": FMPasteBoxVersion.description, "NSHumanReadableCopyright": FMPasteBoxVersion.copyright, } }], data_files=[ "English.lproj/MainMenu.nib", "English.lproj/Preferences.nib", #"English.lproj/FMPasteBoxDocument.nib", "+icon/FMPasteBox.icns", #"+icon/FMPasteBoxFile.icns", ], options={ "py2app": { "iconfile": "+icon/FMPasteBox.icns", # "packages": [], "excludes": ["TkInter", 'Tcl', 'Tk'], } } )

0

0c0e45b33a5297c3f61ad03da499611f67da31e8

9,755

py

Python

src/visualization/visualize.py

siebeniris/superresolution

2eec93029c1332720ba17d5747ec9aee19bc0c63

[ "MIT" ]

null

src/visualization/visualize.py

siebeniris/superresolution

2eec93029c1332720ba17d5747ec9aee19bc0c63

[ "MIT" ]

null

src/visualization/visualize.py

siebeniris/superresolution

2eec93029c1332720ba17d5747ec9aee19bc0c63

[ "MIT" ]

null

from __future__ import print_function import argparse import os import sys, glob, itertools, math from typing import List, Dict from PIL import Image, ImageFont, ImageDraw import torch from ..srcnn.srcnn_run import SRCNNPreProcessor, SRCNNPreProcessorGen from src.srgan.srgan_module import Generator as SRGANGenerator from ..srcnn.srcnn_module import * supportedModels = { "srcnn": SRCNN, "srcnn-bnorm": SRCNNBatchNorm, "srcnn-residual": SRCNNR, "srgan": lambda: SRGANGenerator(scale_factor=2), } class PytorchSRVisulizator: """ Visualizes pytorch nn.models: * model files should contain only the state dict, because the Model classes can't be pickled at runtime. * model dir must follow this hierarch: modeldir | model.pth #final model file | checkpoints | model_epoch_{}.pth # '{}' is replaced with epoch number """ @staticmethod if __name__ == "__main__": parser = argparse.ArgumentParser("SRCNN visualizer") PytorchSRVisulizator.add_arguments_to(parser) config = parser.parse_args() print(config) visualizer = PytorchSRVisulizator(config) visualizer.visualize()

32.088816

108

0.526602

from __future__ import print_function import argparse import os import sys, glob, itertools, math from typing import List, Dict from PIL import Image, ImageFont, ImageDraw import torch from ..srcnn.srcnn_run import SRCNNPreProcessor, SRCNNPreProcessorGen from src.srgan.srgan_module import Generator as SRGANGenerator from ..srcnn.srcnn_module import * supportedModels = { "srcnn": SRCNN, "srcnn-bnorm": SRCNNBatchNorm, "srcnn-residual": SRCNNR, "srgan": lambda: SRGANGenerator(scale_factor=2), } class PytorchSRVisulizator: """ Visualizes pytorch nn.models: * model files should contain only the state dict, because the Model classes can't be pickled at runtime. * model dir must follow this hierarch: modeldir | model.pth #final model file | checkpoints | model_epoch_{}.pth # '{}' is replaced with epoch number """ def __init__(self, config): self.config = config @staticmethod def add_arguments_to(parser: argparse.ArgumentParser): parser.add_argument( "-i", "--input", nargs="?", type=argparse.FileType("r"), default=sys.stdin, help="List of input images, will be read by default from stdin.", ) parser.add_argument( "-t", "--model_type", required=True, default="srcnn", choices=supportedModels.keys(), ) parser.add_argument( "-m", "--model_dir", type=str, required=True, help="Model directory to use" ) parser.add_argument( "-s", "--scale_factor", type=int, default=2.0, help="Factor by which super resolution is needed", ) parser.add_argument( "-l", "--inc_lowres", type=bool, const=True, nargs="?", default=False, help="Whether lowres images should be included.", ) parser.add_argument( "-o", "--inc_highres", type=bool, const=True, nargs="?", default=False, help="Whether highres images should be included.", ) parser.add_argument( "--merge_to", type=str, default=None, help="Merges all generated images to specified filename.", ) parser.add_argument( "-c", "--columns", type=int, default=None, help="Generated images per line. Defaults to one line per input image.", ) parser.add_argument( "--checkpoints", type=str, nargs="?", const="::", default=None, help="Slice used checkpoints using start:stop:step python syntax.", ) parser.add_argument( "-a", "--annotate", type=bool, const=True, nargs="?", default=False, help="Whether images should be labeled.", ) parser.add_argument( "-out", "--outdir", type=str, help="output directory of the images" ) parser.add_argument( '-no', '--no_downscale', nargs='?', const=True, default=False, help='Whether downscaling preprocessing should not be applied.' ) return parser def model_final(self, model_dir): return model_dir + "model.pth" def model_checkpoints(self, model_dir): return (glob.glob(model_dir + "checkpoints/*.pth"),) def epoch_from_model_checkpoint_name(self): return lambda x: int( os.path.basename(x).replace("model_epoch_", "").replace(".pth", "") ) def visualize(self): images_dict = { line.strip(): [Image.open(line.strip()).convert('RGB')] for line in self.config.input } if self.config.checkpoints: models = sorted( glob.glob(self.config.model_dir + "checkpoints/*.pth"), key=self.epoch_from_model_checkpoint_name(), ) sliced = slice( *[ None if x is "" else int(x) for x in self.config.checkpoints.split(":") ] ) models = models[sliced] else: models = [self.model_final(self.config.model_dir)] model_constr = supportedModels[config.model_type] print("Using models with {!s}:".format(model_constr())) for m in models: print("\t" + m) # we assume images have all the same size and need to be downscaled first target_size = list(images_dict.values())[0][0].size[0] if config.no_downscale: target_size = int(target_size*config.scale_factor) print("Scaling images to size {!s}:".format(target_size)) print("\n".join(["\t" + img for img in images_dict.keys()])) if self.config.inc_lowres: proc = SRCNNPreProcessorGen( model_constr(), models[0], self.config.scale_factor, not config.no_downscale, use_gpu=torch.cuda.is_available(), target_size=target_size, ) for key, images in images_dict.items(): low_image = proc.lowres(images[0]) images_dict[key].append((low_image)) for model in models: proc = SRCNNPreProcessorGen( model_constr(), model, self.config.scale_factor, not config.no_downscale, use_gpu=torch.cuda.is_available(), target_size=target_size, srgan=config.model_type == 'srgan' ) for key, images in images_dict.items(): result_image = proc.apply(images[0]) images_dict[key].append(result_image) if not self.config.inc_highres: images_dict = {key: value[1:] for key, value in images_dict.items()} def combine_to_single_image(images, labels: List[str] = []) -> Image: if config.columns: columns = config.columns else: columns = len(models) + len( list( filter( lambda x: x, [self.config.inc_lowres, self.config.inc_highres], ) ) ) rows = math.ceil(len(images) / float(columns)) height, width = rows * target_size, columns * target_size result = Image.new("RGB", (width, height)) font = ImageFont.truetype("reports/B612Mono-Regular.ttf", 32) draw = ImageDraw.Draw(result) image_slots = [ (w, h) for h, w in itertools.product( range(0, target_size * rows, target_size), range(0, target_size * columns, target_size), ) ][ : len(images) ] # take only n slots for image, (w_index, h_index), label in itertools.zip_longest( images, image_slots, labels ): print(*image.size) result.paste(image, (w_index, h_index)) if label: draw.text((w_index, h_index), label, (255, 0, 0), font=font) return result def gen_labels(file, images): labels = list(len(images) * [""]) labels[0] += "\n".join(os.path.basename(file).split("-")) + "\n" index = 0 if config.inc_highres: labels[index] += "high_res " index += 1 if config.inc_lowres: labels[index] += "low_res " index += 1 if config.checkpoints: for i, model in enumerate(models, index): labels[i] += "e" + str( self.epoch_from_model_checkpoint_name()(model) ) return labels if not self.config.outdir: out_dir = self.config.model_dir + "results/" else: out_dir = self.config.outdir if not os.path.exists(out_dir): os.makedirs(out_dir) if self.config.merge_to is not None: fin_images = [] fin_labels = [] for file, images in images_dict.items(): fin_images.extend(images) if self.config.annotate: fin_labels.extend(gen_labels(file, images)) result_image = combine_to_single_image(fin_images, labels=fin_labels) result_image.save(out_dir + config.merge_to) print("Saved image to '{}'.".format(out_dir + config.merge_to)) else: for file, images in images_dict.items(): out_file = out_dir + os.path.basename(file) combine_params = [images] if config.annotate: combine_params.append(gen_labels(file, images)) result_image = combine_to_single_image(*combine_params) result_image.save(out_file) print("Saved images to '{}'.".format(out_dir)) if __name__ == "__main__": parser = argparse.ArgumentParser("SRCNN visualizer") PytorchSRVisulizator.add_arguments_to(parser) config = parser.parse_args() print(config) visualizer = PytorchSRVisulizator(config) visualizer.visualize()

8,426

0

161

bb3a71e89f46206d090a6a89fb38b7d1c5ab5c34

4,883

py

Python

tests/test_store2hdf5.py

Matioz/humblerl

8000cf60f2baa8b80927275c15147b6de199d75a

[ "MIT" ]

null

tests/test_store2hdf5.py

Matioz/humblerl

8000cf60f2baa8b80927275c15147b6de199d75a

[ "MIT" ]

null

tests/test_store2hdf5.py

Matioz/humblerl

8000cf60f2baa8b80927275c15147b6de199d75a

[ "MIT" ]

null

import h5py import numpy as np from .. import Transition from ..callbacks import StoreStates2Hdf5 HDF5_PATH = "/tmp/test_humblerl_callback.hdf5" class TestStoreTransitions2Hdf5(object): """Test callback on 3D (e.g. images) and continuous states."""

40.02459

94

0.584067

import h5py import numpy as np from .. import Transition from ..callbacks import StoreStates2Hdf5 HDF5_PATH = "/tmp/test_humblerl_callback.hdf5" class TestStoreTransitions2Hdf5(object): """Test callback on 3D (e.g. images) and continuous states.""" def test_images_states(self): ACTION_SPACE = np.array([1, 2, 3]) STATE_SPACE = np.zeros((8, 8, 3, 2)) STATE_SPACE[:] = np.array([0, 255]) STATE_SPACE_SHAPE = STATE_SPACE.shape[:-1] MIN_TRANSITIONS = 96 CHUNK_SIZE = 48 N_TRANSITIONS = 1024 callback = StoreStates2Hdf5(STATE_SPACE_SHAPE, HDF5_PATH, shuffle=False, min_transitions=MIN_TRANSITIONS, chunk_size=CHUNK_SIZE, dtype=np.uint8) transitions = [] for idx in range(N_TRANSITIONS): transition = Transition( state=np.random.randint(0, 256, size=(8, 8, 3)), action=np.random.choice(ACTION_SPACE), reward=np.random.normal(0, 1), next_state=np.random.randint(0, 256, size=(8, 8, 3)), is_terminal=(idx + 1) % 16 == 0 ) transitions.append(transition) callback.on_step_taken(idx, transition, None) callback.on_loop_end(False) h5py_file = h5py.File(HDF5_PATH, "r") assert h5py_file.attrs["N_TRANSITIONS"] == N_TRANSITIONS assert h5py_file.attrs["N_GAMES"] == N_TRANSITIONS // 16 assert h5py_file.attrs["CHUNK_SIZE"] == CHUNK_SIZE assert np.all(h5py_file.attrs["STATE_SHAPE"] == STATE_SPACE_SHAPE) for idx, transition in enumerate(transitions): assert np.all(h5py_file['states'][idx] == transition.state) def test_continous_states(self): ACTION_SPACE = np.array([1, 2, 3]) STATE_SPACE = np.zeros((4, 2)) STATE_SPACE[:] = np.array([-1, 1]) STATE_SPACE_SHAPE = STATE_SPACE.shape[:-1] MIN_TRANSITIONS = 96 CHUNK_SIZE = 48 N_TRANSITIONS = 1024 callback = StoreStates2Hdf5(STATE_SPACE_SHAPE, HDF5_PATH, shuffle=False, min_transitions=MIN_TRANSITIONS, chunk_size=CHUNK_SIZE, dtype=np.float) transitions = [] for idx in range(N_TRANSITIONS): transition = Transition( state=np.random.uniform(STATE_SPACE.T[0], STATE_SPACE.T[1]), action=np.random.choice(ACTION_SPACE), reward=np.random.normal(0, 1), next_state=np.random.uniform(STATE_SPACE.T[0], STATE_SPACE.T[1]), is_terminal=(idx + 1) % 16 == 0 ) transitions.append(transition) callback.on_step_taken(idx, transition, None) callback.on_loop_end(False) h5py_file = h5py.File(HDF5_PATH, "r") assert h5py_file.attrs["N_TRANSITIONS"] == N_TRANSITIONS assert h5py_file.attrs["N_GAMES"] == N_TRANSITIONS // 16 assert h5py_file.attrs["CHUNK_SIZE"] == CHUNK_SIZE assert np.all(h5py_file.attrs["STATE_SHAPE"] == STATE_SPACE_SHAPE) for idx, transition in enumerate(transitions): assert np.all(h5py_file['states'][idx] == transition.state) def test_shuffle_chunks(self): ACTION_SPACE = np.array([1, 2, 3]) STATE_SPACE = np.zeros((4, 2)) STATE_SPACE[:] = np.array([-1, 1]) STATE_SPACE_SHAPE = STATE_SPACE.shape[:-1] MIN_TRANSITIONS = 48 CHUNK_SIZE = 48 N_TRANSITIONS = 48 callback = StoreStates2Hdf5(STATE_SPACE_SHAPE, HDF5_PATH, shuffle=True, min_transitions=MIN_TRANSITIONS, chunk_size=CHUNK_SIZE, dtype=np.float) states = [] next_states = [] transitions = [] for idx in range(N_TRANSITIONS): states.append(np.random.uniform(STATE_SPACE.T[0], STATE_SPACE.T[1]).tolist()) next_states.append(np.random.uniform(STATE_SPACE.T[0], STATE_SPACE.T[1]).tolist()) transitions.append((np.random.choice(ACTION_SPACE), np.random.normal(0, 1), 0)) callback.on_step_taken(idx, Transition( state=states[-1], action=transitions[-1][0], reward=transitions[-1][1], next_state=next_states[-1], is_terminal=transitions[-1][2] ), None) in_order = True h5py_file = h5py.File(HDF5_PATH, "r") for idx in range(N_TRANSITIONS): state = h5py_file['states'][idx] idx_target = states.index(state.tolist()) if idx != idx_target: in_order = False assert np.all(h5py_file['states'][idx] == states[idx_target]) assert not in_order, "Data isn't shuffled!"

4,545

0

81

c84164ddec73f5647fd324062b3b890f6b6bfb9b

3,362

py

Python

tests/test_accounts.py

zcking/oink

95f07567ecda214172c29684d0ee5f67da5b99cf

[ "MIT" ]

3

2017-10-15T21:24:14.000Z

2021-06-02T21:15:28.000Z

tests/test_accounts.py

zach-king/oink

95f07567ecda214172c29684d0ee5f67da5b99cf

[ "MIT" ]

1

2018-06-26T03:48:55.000Z

tests/test_accounts.py

zach-king/oink

95f07567ecda214172c29684d0ee5f67da5b99cf

[ "MIT" ]

null

''' File: test_accounts.py Author: Zachary King Defines unit tests for accounts.py. Tests all account features. ''' from __future__ import print_function import unittest import os from oink import accounts, db class TestAccounts(unittest.TestCase): '''Defines unit tests for adding and removing accounts.''' def setUp(self): '''Setup testing databse.''' # Creates the testing database os.mkdir('testdb') with open(r'testdb\oink.db', 'w') as fout: pass db.connect('testdb') accounts.setup() def tearDown(self): '''Destroys the testing database.''' db.disconnect() os.remove(r'testdb\oink.db') os.rmdir('testdb') def test_add_new_account(self): '''Test for adding a new account.''' cur = db.cursor() # Add a new account accounts.add_account(12345, 'TestAddAccount', 100.00, '2017-1-1') self.assertEqual(cur.execute('SELECT COUNT(*) FROM accounts').fetchone()[0], 1) accounts.add_account(54321, 'TestAddAccount2', 0.01, '2017-1-1') self.assertEqual(cur.execute('SELECT COUNT(*) FROM accounts').fetchone()[0], 2) def test_remove_account(self): '''Test to remove an existing account.''' cur = db.cursor() # Insert a new account cur.execute('INSERT INTO accounts VALUES (024, "TestRemoveAccount", 0.00, "2017-1-1")') self.assertEqual(cur.rowcount, 1) # Remove the accont accounts.delete('TestRemoveAccount') cur.execute('SELECT COUNT(*) FROM accounts') self.assertEqual(cur.fetchone()[0], 0) def test_add_null_account_number(self): '''Tests NOT NULL constraint of database for account number''' # Try to insert NULL as account number with self.assertRaises(ValueError): accounts.add_account(None, 'TestNullNumAccount', 0.0, '2017-1-1') with self.assertRaises(ValueError): accounts.add_account('', 'TestNullNumAccount', 0.0, '2017-1-1') def test_add_null_account_name(self): '''Tests NOT NULL constraint of database for account name''' # Try to insert NULL as account name with self.assertRaises(ValueError): accounts.add_account(987, None, 0.0, '2017-1-1') with self.assertRaises(ValueError): accounts.add_account(789, '', 0.0, '2017-1-1') def test_add_null_start_balance(self): '''Tests NOT NULL constraint of databse for account starting balance''' # Try to insert NULL as account starting balance with self.assertRaises(ValueError): accounts.add_account(111, 'TestNullStartBalanceAccount', None, '2017-1-1') def test_add_negative_start_balance(self): '''Tests inserting a negative starting balace for a new account''' with self.assertRaises(ValueError): accounts.add_account(222, 'TestNegativeStartingBalance', -100.0, '2017-1-1') def test_add_null_created_date(self): '''Tests NOT NULL constraint for account created_on''' with self.assertRaises(ValueError): accounts.add_account(333, 'TestNullCreatedOn', 0.0, '') with self.assertRaises(ValueError): accounts.add_account(333, 'TestNullCreatedOn', 0.0, None) if __name__ == '__main__': unittest.main()

33.959596

95

0.646936

''' File: test_accounts.py Author: Zachary King Defines unit tests for accounts.py. Tests all account features. ''' from __future__ import print_function import unittest import os from oink import accounts, db class TestAccounts(unittest.TestCase): '''Defines unit tests for adding and removing accounts.''' def setUp(self): '''Setup testing databse.''' # Creates the testing database os.mkdir('testdb') with open(r'testdb\oink.db', 'w') as fout: pass db.connect('testdb') accounts.setup() def tearDown(self): '''Destroys the testing database.''' db.disconnect() os.remove(r'testdb\oink.db') os.rmdir('testdb') def test_add_new_account(self): '''Test for adding a new account.''' cur = db.cursor() # Add a new account accounts.add_account(12345, 'TestAddAccount', 100.00, '2017-1-1') self.assertEqual(cur.execute('SELECT COUNT(*) FROM accounts').fetchone()[0], 1) accounts.add_account(54321, 'TestAddAccount2', 0.01, '2017-1-1') self.assertEqual(cur.execute('SELECT COUNT(*) FROM accounts').fetchone()[0], 2) def test_remove_account(self): '''Test to remove an existing account.''' cur = db.cursor() # Insert a new account cur.execute('INSERT INTO accounts VALUES (024, "TestRemoveAccount", 0.00, "2017-1-1")') self.assertEqual(cur.rowcount, 1) # Remove the accont accounts.delete('TestRemoveAccount') cur.execute('SELECT COUNT(*) FROM accounts') self.assertEqual(cur.fetchone()[0], 0) def test_add_null_account_number(self): '''Tests NOT NULL constraint of database for account number''' # Try to insert NULL as account number with self.assertRaises(ValueError): accounts.add_account(None, 'TestNullNumAccount', 0.0, '2017-1-1') with self.assertRaises(ValueError): accounts.add_account('', 'TestNullNumAccount', 0.0, '2017-1-1') def test_add_null_account_name(self): '''Tests NOT NULL constraint of database for account name''' # Try to insert NULL as account name with self.assertRaises(ValueError): accounts.add_account(987, None, 0.0, '2017-1-1') with self.assertRaises(ValueError): accounts.add_account(789, '', 0.0, '2017-1-1') def test_add_null_start_balance(self): '''Tests NOT NULL constraint of databse for account starting balance''' # Try to insert NULL as account starting balance with self.assertRaises(ValueError): accounts.add_account(111, 'TestNullStartBalanceAccount', None, '2017-1-1') def test_add_negative_start_balance(self): '''Tests inserting a negative starting balace for a new account''' with self.assertRaises(ValueError): accounts.add_account(222, 'TestNegativeStartingBalance', -100.0, '2017-1-1') def test_add_null_created_date(self): '''Tests NOT NULL constraint for account created_on''' with self.assertRaises(ValueError): accounts.add_account(333, 'TestNullCreatedOn', 0.0, '') with self.assertRaises(ValueError): accounts.add_account(333, 'TestNullCreatedOn', 0.0, None) if __name__ == '__main__': unittest.main()

0

8c4c0dc6efacc5b204fcaf8a7dd7a84ae0e43270

466

py

Python

rpna/core/migrations/0007_profile_change_default.py

code-for-good-wm/rooseveltparkna-alerts

bb22150141dd9448004a04df66d9ce0f353a3c10

[ "MIT" ]

1

2021-11-07T19:00:32.000Z

rpna/core/migrations/0007_profile_change_default.py

code-for-good-wm/rooseveltparkna-alerts

bb22150141dd9448004a04df66d9ce0f353a3c10

[ "MIT" ]

null

rpna/core/migrations/0007_profile_change_default.py

code-for-good-wm/rooseveltparkna-alerts

bb22150141dd9448004a04df66d9ce0f353a3c10

[ "MIT" ]

null

# Generated by Django 3.2.9 on 2021-11-07 02:05 from django.db import migrations, models

22.190476

65

0.590129

# Generated by Django 3.2.9 on 2021-11-07 02:05 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("core", "0006_profile_fix_nulls"), ] operations = [ migrations.AlterField( model_name="profile", name="neighborhood_updates", field=models.BooleanField( default=True, verbose_name="Neighborhood Updates" ), ), ]

0

352

23

c66c4b547d80fddf7c83780bd317fd0de4bbb554

1,174

py

Python

siptrackweb/views/device/utils.py

lalusvipi/siptrackweb

e4d2882595a40d3dbb1bb74c1838937988726d8e

[ "BSD-2-Clause" ]

38

2015-03-18T08:05:35.000Z

2021-11-09T10:57:54.000Z

siptrackweb/views/device/utils.py

lalusvipi/siptrackweb

e4d2882595a40d3dbb1bb74c1838937988726d8e

[ "BSD-2-Clause" ]

29

2015-06-25T11:28:07.000Z

2019-11-30T21:15:30.000Z

siptrackweb/views/device/utils.py

lalusvipi/siptrackweb

e4d2882595a40d3dbb1bb74c1838937988726d8e

[ "BSD-2-Clause" ]

13

2015-03-18T06:57:46.000Z

2021-06-22T10:38:49.000Z

from siptrackweb.views import helpers

46.96

116

0.539182

from siptrackweb.views import helpers def make_device_association_list(device): ret = [] for assoc in device.listAssociations(include = ['device', 'device category']): if assoc.class_name == 'device category': path = [{'path': '/display/%s/' % assoc.oid, 'name': assoc.attributes.get('name', '[device category]')}] else: path = helpers.make_browsable_path(assoc, ['device category', 'device tree'], include_root = False) ent = {'obj': assoc, 'path': path, 'type': 'association'} ret.append(ent) for ref in device.listReferences(include = ['device', 'device category']): if ref.class_name == 'device category': path = [{'path': '/display/%s/' % ref.oid, 'name': ref.attributes.get('name', '[device category]')}] else: path = helpers.make_browsable_path(ref, ['device category', 'device tree'], include_root = False) ent = {'obj': ref, 'path': path, 'type': 'reference'} ret.append(ent) ret.sort() return ret

1,113

0

23

3f1716b49e21337ee8e79019daeb46f75cdd36f3

1,316

py

Python

setup.py

WaltWh/w2n

be5157949e6d0e07d13084ae3dcee0267c6f1711

[ "MIT" ]

null

setup.py

WaltWh/w2n

be5157949e6d0e07d13084ae3dcee0267c6f1711

[ "MIT" ]

null

setup.py

WaltWh/w2n

be5157949e6d0e07d13084ae3dcee0267c6f1711

[ "MIT" ]

null

import re from setuptools import setup version = "" with open("w2n/__init__.py") as f: search = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE) if search is not None: version = search.group(1) else: raise RuntimeError("Could not grab version string") if not version: raise RuntimeError("version is not set") with open("README.rst") as f: readme = f.read() setup( name='longsphinx-word2number', packages=['w2n'], version=version, license='MIT', description='Convert number words eg. three hundred and forty two to numbers (342). ' 'Forked for https://github.com/WaltWh/LongSphinx, not intended for public use.', author='Akshay Nagpal & Walt Whiteside', author_email='tinman@mage.city', url='https://github.com/WaltWh/w2n', # use the URL to the GitHub repo download_url='https://github.com/WaltWh/w2n/tarball/1.2', keywords=['numbers', 'convert', 'words'], # arbitrary keywords classifiers=[ 'Intended Audience :: Developers', 'Programming Language :: Python :: 3.10' ], long_description=readme, long_description_content_type="text/x-rst", python_requires=">=3.10.0", # Probably not, but I haven't tested anything else test_suite="unit_testing" )

31.333333

96

0.651216

import re from setuptools import setup version = "" with open("w2n/__init__.py") as f: search = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE) if search is not None: version = search.group(1) else: raise RuntimeError("Could not grab version string") if not version: raise RuntimeError("version is not set") with open("README.rst") as f: readme = f.read() setup( name='longsphinx-word2number', packages=['w2n'], version=version, license='MIT', description='Convert number words eg. three hundred and forty two to numbers (342). ' 'Forked for https://github.com/WaltWh/LongSphinx, not intended for public use.', author='Akshay Nagpal & Walt Whiteside', author_email='tinman@mage.city', url='https://github.com/WaltWh/w2n', # use the URL to the GitHub repo download_url='https://github.com/WaltWh/w2n/tarball/1.2', keywords=['numbers', 'convert', 'words'], # arbitrary keywords classifiers=[ 'Intended Audience :: Developers', 'Programming Language :: Python :: 3.10' ], long_description=readme, long_description_content_type="text/x-rst", python_requires=">=3.10.0", # Probably not, but I haven't tested anything else test_suite="unit_testing" )

0

e0f36937cc4182603ec4de89ec2b3cd9d5f9e53c

5,995

py

Python

regression/train.py

Mariappan/examples

2c75018b743a26b6495ddba70100e557aeda11b8

[ "Apache-2.0" ]

null

regression/train.py

Mariappan/examples

2c75018b743a26b6495ddba70100e557aeda11b8

[ "Apache-2.0" ]

null

regression/train.py

Mariappan/examples

2c75018b743a26b6495ddba70100e557aeda11b8

[ "Apache-2.0" ]

null

import joblib import logging from typing import Tuple from catboost import CatBoostRegressor from environs import Env import numpy as np import pandas as pd from sklearn import metrics from bedrock_client.bedrock.analyzer import ModelTask, ModelTypes from bedrock_client.bedrock.analyzer.model_analyzer import ModelAnalyzer from bedrock_client.bedrock.api import BedrockApi from bedrock_client.bedrock.metrics.collector import ( BaselineMetricCollector, FeatureHistogramCollector, InferenceHistogramCollector ) from bedrock_client.bedrock.metrics.encoder import MetricEncoder env = Env() OUTPUT_MODEL_PATH = env("OUTPUT_MODEL_PATH") TRAIN_DATA_PATH = env("TRAIN_DATA_PATH") TEST_DATA_PATH = env("TEST_DATA_PATH") CONFIG_FAI = { "large_rings": { "group_a": [1], "group_a_name": "Large", "group_b": [0], "group_b_name": "Small" } } def load_dataset(filepath: str, target: str) -> Tuple[pd.core.frame.DataFrame, np.ndarray]: """ Loads the dataset and returns the features as a pandas dataframe and the target variable as a numpy array. :param filepath: Path to load the data :type filepath: str :param target: Target variable :type target: str :return: The features pandas dataframe and the target numpy array :rtype: tuple[pandas.core.frame.DataFrame, numpy.ndarray] """ df = pd.read_csv(filepath).drop('Type', axis=1) # Removes 'Type' column df['large_rings'] = (df['Rings'] > 10).astype(int) # Ensure nothing missing original_len = len(df) df.dropna(how="any", axis=0, inplace=True) num_rows_dropped = original_len - len(df) if num_rows_dropped > 0: print(f"Warning - dropped {num_rows_dropped} rows with NA data.") y = df[target].values df.drop(target, axis=1, inplace=True) return df, y def train_catboost_model(X: pd.core.frame.DataFrame, y: np.ndarray, verbose: bool = False) -> CatBoostRegressor: """ Scales the features and trains a logistic regression model. :param X: Features for training :type X: pandas.core.frame.DataFrame :param y: Target variable :type y: numpy.ndarray :param verbose: Whether to print additional info :type verbose: bool :return: Trained CatBoostRegressor model :rtype: catboost.CatBoostRegressor """ verbose and print('\nTRAIN\nScaling...') model = CatBoostRegressor(iterations=100, learning_rate=0.1, depth=5) verbose and print('Fitting...') model.fit(X, y) verbose and print('Done training.') return model def compute_log_metrics(model: CatBoostRegressor, x_test: pd.core.frame.DataFrame, y_test: np.ndarray): """ Computes, prints and log metrics. :param model: Trained CatBoostRegressor model :type model: catboost.CatBoostRegressor :param x_test: Features for testing :type x_test: pandas.core.frame.DataFrame :param y_test: Target variable data for testing :type y_test: numpy.ndarray :return: Test predicted probability and predictions :rtype: tuple[numpy.ndarray, numpy.ndarray] """ y_pred = model.predict(x_test) mae = metrics.mean_absolute_error(y_test, y_pred) mse = metrics.mean_squared_error(y_test, y_pred) r2_score = metrics.r2_score(y_test, y_pred) print("\nEVALUATION\n" f"\tMean absolute error = {mae:.4f}\n" f"\tMean squared error = {mse:.4f}\n" f"\tR2 regression score function = {r2_score:.4f}\n") # Bedrock Logger: captures model metrics bedrock = BedrockApi(logging.getLogger(__name__)) bedrock.log_metric("MAE", mae) bedrock.log_metric("MSE", mse) bedrock.log_metric("R2", r2_score) return y_pred if __name__ == '__main__': main()

30.431472

76

0.639867

import joblib import logging from typing import Tuple from catboost import CatBoostRegressor from environs import Env import numpy as np import pandas as pd from sklearn import metrics from bedrock_client.bedrock.analyzer import ModelTask, ModelTypes from bedrock_client.bedrock.analyzer.model_analyzer import ModelAnalyzer from bedrock_client.bedrock.api import BedrockApi from bedrock_client.bedrock.metrics.collector import ( BaselineMetricCollector, FeatureHistogramCollector, InferenceHistogramCollector ) from bedrock_client.bedrock.metrics.encoder import MetricEncoder env = Env() OUTPUT_MODEL_PATH = env("OUTPUT_MODEL_PATH") TRAIN_DATA_PATH = env("TRAIN_DATA_PATH") TEST_DATA_PATH = env("TEST_DATA_PATH") CONFIG_FAI = { "large_rings": { "group_a": [1], "group_a_name": "Large", "group_b": [0], "group_b_name": "Small" } } def load_dataset(filepath: str, target: str) -> Tuple[pd.core.frame.DataFrame, np.ndarray]: """ Loads the dataset and returns the features as a pandas dataframe and the target variable as a numpy array. :param filepath: Path to load the data :type filepath: str :param target: Target variable :type target: str :return: The features pandas dataframe and the target numpy array :rtype: tuple[pandas.core.frame.DataFrame, numpy.ndarray] """ df = pd.read_csv(filepath).drop('Type', axis=1) # Removes 'Type' column df['large_rings'] = (df['Rings'] > 10).astype(int) # Ensure nothing missing original_len = len(df) df.dropna(how="any", axis=0, inplace=True) num_rows_dropped = original_len - len(df) if num_rows_dropped > 0: print(f"Warning - dropped {num_rows_dropped} rows with NA data.") y = df[target].values df.drop(target, axis=1, inplace=True) return df, y def train_catboost_model(X: pd.core.frame.DataFrame, y: np.ndarray, verbose: bool = False) -> CatBoostRegressor: """ Scales the features and trains a logistic regression model. :param X: Features for training :type X: pandas.core.frame.DataFrame :param y: Target variable :type y: numpy.ndarray :param verbose: Whether to print additional info :type verbose: bool :return: Trained CatBoostRegressor model :rtype: catboost.CatBoostRegressor """ verbose and print('\nTRAIN\nScaling...') model = CatBoostRegressor(iterations=100, learning_rate=0.1, depth=5) verbose and print('Fitting...') model.fit(X, y) verbose and print('Done training.') return model def compute_log_metrics(model: CatBoostRegressor, x_test: pd.core.frame.DataFrame, y_test: np.ndarray): """ Computes, prints and log metrics. :param model: Trained CatBoostRegressor model :type model: catboost.CatBoostRegressor :param x_test: Features for testing :type x_test: pandas.core.frame.DataFrame :param y_test: Target variable data for testing :type y_test: numpy.ndarray :return: Test predicted probability and predictions :rtype: tuple[numpy.ndarray, numpy.ndarray] """ y_pred = model.predict(x_test) mae = metrics.mean_absolute_error(y_test, y_pred) mse = metrics.mean_squared_error(y_test, y_pred) r2_score = metrics.r2_score(y_test, y_pred) print("\nEVALUATION\n" f"\tMean absolute error = {mae:.4f}\n" f"\tMean squared error = {mse:.4f}\n" f"\tR2 regression score function = {r2_score:.4f}\n") # Bedrock Logger: captures model metrics bedrock = BedrockApi(logging.getLogger(__name__)) bedrock.log_metric("MAE", mae) bedrock.log_metric("MSE", mse) bedrock.log_metric("R2", r2_score) return y_pred def main(): x_train, y_train = load_dataset( filepath=TRAIN_DATA_PATH, target='ShellWeight' ) x_test, y_test = load_dataset( filepath=TEST_DATA_PATH, target='ShellWeight' ) print('X (train)') print(x_train) model = train_catboost_model(x_train, y_train, verbose=True) # Save trained model feature_names = x_train.columns.tolist() print("\nSAMPLE FEATURES") print({ feature_name: str(x_train[feature_name][0]) for feature_name in feature_names }) joblib.dump([feature_names, model], OUTPUT_MODEL_PATH) print('\nSaved feature names and catboost regression model.') y_pred = compute_log_metrics(model, x_test, y_test) # Save feature and inferance distribution train_predicted = model.predict(x_train).flatten().tolist() collectors = [ FeatureHistogramCollector( data=x_train.iteritems(), discrete={6, 7}, # Specify which column indices are discrete ), InferenceHistogramCollector(data=train_predicted, is_discrete=False) # Specify inference as discrete ] encoder = MetricEncoder(collectors=collectors) with open(BaselineMetricCollector.DEFAULT_HISTOGRAM_PATH, "wb") as f: f.write(encoder.as_text()) print('Saved feature and inference distribution.') # Train Shap model and calculate xafai metrics analyzer = ( ModelAnalyzer(model, model_name='catboost_model', model_type=ModelTypes.TREE, model_task=ModelTask.REGRESSION) .test_features(x_test) ) ( analyzer .fairness_config(CONFIG_FAI) .test_labels(y_test) .test_inference(y_pred) ) analyzer.analyze() print('Saved Shap model and XAI for regression.') if __name__ == '__main__': main()

1,994

0

23

2df7f47663759c2dc57b8f7fa714d0205f4b1432

10,059

py

Python

Scripts/GenCode_CNN_301.py

ShepherdCode/Soars2021

ab4f304eaa09e52d260152397a6c53d7a05457da

[ "MIT" ]

1

2021-08-16T14:49:04.000Z

Scripts/GenCode_CNN_301.py

ShepherdCode/Soars2021

ab4f304eaa09e52d260152397a6c53d7a05457da

[ "MIT" ]

null

Scripts/GenCode_CNN_301.py

ShepherdCode/Soars2021

ab4f304eaa09e52d260152397a6c53d7a05457da

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # # PC/NC classification by CNN # # The convolutional neural network (CNN) was invented for image processing. # We can use Conv1D layers for processing string sequences. # How well does CNN work on human RNA as a binary classifier of protein-coding/non-coding? # # Assume user downloaded files from GenCode 38 [FTP](http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/) # to a subdirectory called data. # # The learning curve is strange: up then down. I suspect the CNN is learning the fact that we padded sequences with T to make them uniform length. # In[1]: import time show_time() # In[2]: PC_FILENAME='gencode.v38.pc_transcripts.fa.gz' NC_FILENAME='gencode.v38.lncRNA_transcripts.fa.gz' # In[3]: PC_SEQUENCES=20000 # how many protein-coding sequences NC_SEQUENCES=20000 # how many non-coding sequences PC_TESTS=1000 NC_TESTS=1000 BASES=1000 # how long is each sequence ALPHABET=4 # how many different letters are possible INPUT_SHAPE_2D = (BASES,ALPHABET,1) # Conv2D needs 3D inputs INPUT_SHAPE = (BASES,ALPHABET) # Conv1D needs 2D inputs FILTERS = 32 # how many different patterns the model looks for NEURONS = 32 DROP_RATE = 0.2 WIDTH = 3 # how wide each pattern is, in bases STRIDE_2D = (1,1) # For Conv2D how far in each direction STRIDE = 1 # For Conv1D, how far between pattern matches, in bases EPOCHS=50 # how many times to train on all the data SPLITS=5 # SPLITS=3 means train on 2/3 and validate on 1/3 FOLDS=3 # train the model this many times (range 1 to SPLITS) # In[4]: import sys import csv try: from google.colab import drive IN_COLAB = True print("On Google CoLab, mount cloud-local file, get our code from GitHub.") PATH='/content/drive/' #drive.mount(PATH,force_remount=True) # hardly ever need this drive.mount(PATH) # Google will require login credentials DATAPATH=PATH+'My Drive/data/' # must end in "/" import requests r = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_gen.py') with open('RNA_gen.py', 'w') as f: f.write(r.text) from RNA_gen import * r = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_describe.py') with open('RNA_describe.py', 'w') as f: f.write(r.text) from RNA_describe import * r = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_prep.py') with open('RNA_prep.py', 'w') as f: f.write(r.text) from RNA_prep import * except: print("CoLab not working. On my PC, use relative paths.") IN_COLAB = False DATAPATH='../data/' # must end in "/" sys.path.append("..") # append parent dir in order to use sibling dirs from SimTools.RNA_gen import * from SimTools.RNA_describe import * from SimTools.RNA_prep import * MODELPATH="BestModel" # saved on cloud instance and lost after logout #MODELPATH=DATAPATH+MODELPATH # saved on Google Drive but requires login if not assert_imported_RNA_gen(): print("ERROR: Cannot use RNA_gen.") if not assert_imported_RNA_prep(): print("ERROR: Cannot use RNA_prep.") # In[5]: from os import listdir #from zipfile import ZipFile import gzip import numpy as np import pandas as pd from scipy import stats # mode from sklearn.preprocessing import StandardScaler from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from keras.models import Sequential from keras.layers import Dense,Embedding,Dropout from keras.layers import Conv1D,Conv2D from keras.layers import Flatten,MaxPooling1D,MaxPooling2D from keras.losses import BinaryCrossentropy # tf.keras.losses.BinaryCrossentropy import matplotlib.pyplot as plt from matplotlib import colors mycmap = colors.ListedColormap(['red','blue']) # list color for label 0 then 1 np.set_printoptions(precision=2) # In[6]: # In[7]: PC_FULLPATH=DATAPATH+PC_FILENAME NC_FULLPATH=DATAPATH+NC_FILENAME pcdf=load_gencode(PC_FULLPATH,1) print("PC seqs loaded:",len(pcdf)) ncdf=load_gencode(NC_FULLPATH,0) print("NC seqs loaded:",len(ncdf)) # In[8]: # In[9]: from sklearn.model_selection import train_test_split pc_all = pcdf['sequence'] nc_all = ncdf['sequence'] # The split function also shuffles pc_train,pc_test=train_test_split(pc_all,test_size=0.10,random_state=1234) nc_train,nc_test=train_test_split(nc_all,test_size=0.10,random_state=1234) # In[10]: # Use code from our SimTools library. UNIFORM_LENGTH=1000 MAXIMUM_LENGTH=2000 pc_seqs=uniform_length(pc_train,UNIFORM_LENGTH,MAXIMUM_LENGTH-500) print("PC seqs ready:",len(pc_seqs)) nc_seqs=uniform_length(nc_train,UNIFORM_LENGTH,MAXIMUM_LENGTH+2000) print("NC seqs ready:",len(nc_seqs)) X,y = prepare_inputs_len_x_alphabet(pc_seqs,nc_seqs,ALPHABET) # shuffles print("Data ready") # In[11]: model = make_DNN() print(model.summary()) # In[12]: from keras.callbacks import ModelCheckpoint # In[13]: show_time() do_cross_validation(X,y) show_time() # In[13]:

32.344051

146

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#!/usr/bin/env python # coding: utf-8 # # PC/NC classification by CNN # # The convolutional neural network (CNN) was invented for image processing. # We can use Conv1D layers for processing string sequences. # How well does CNN work on human RNA as a binary classifier of protein-coding/non-coding? # # Assume user downloaded files from GenCode 38 [FTP](http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/) # to a subdirectory called data. # # The learning curve is strange: up then down. I suspect the CNN is learning the fact that we padded sequences with T to make them uniform length. # In[1]: import time def show_time(): t = time.time() s = time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t)) print(s) show_time() # In[2]: PC_FILENAME='gencode.v38.pc_transcripts.fa.gz' NC_FILENAME='gencode.v38.lncRNA_transcripts.fa.gz' # In[3]: PC_SEQUENCES=20000 # how many protein-coding sequences NC_SEQUENCES=20000 # how many non-coding sequences PC_TESTS=1000 NC_TESTS=1000 BASES=1000 # how long is each sequence ALPHABET=4 # how many different letters are possible INPUT_SHAPE_2D = (BASES,ALPHABET,1) # Conv2D needs 3D inputs INPUT_SHAPE = (BASES,ALPHABET) # Conv1D needs 2D inputs FILTERS = 32 # how many different patterns the model looks for NEURONS = 32 DROP_RATE = 0.2 WIDTH = 3 # how wide each pattern is, in bases STRIDE_2D = (1,1) # For Conv2D how far in each direction STRIDE = 1 # For Conv1D, how far between pattern matches, in bases EPOCHS=50 # how many times to train on all the data SPLITS=5 # SPLITS=3 means train on 2/3 and validate on 1/3 FOLDS=3 # train the model this many times (range 1 to SPLITS) # In[4]: import sys import csv try: from google.colab import drive IN_COLAB = True print("On Google CoLab, mount cloud-local file, get our code from GitHub.") PATH='/content/drive/' #drive.mount(PATH,force_remount=True) # hardly ever need this drive.mount(PATH) # Google will require login credentials DATAPATH=PATH+'My Drive/data/' # must end in "/" import requests r = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_gen.py') with open('RNA_gen.py', 'w') as f: f.write(r.text) from RNA_gen import * r = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_describe.py') with open('RNA_describe.py', 'w') as f: f.write(r.text) from RNA_describe import * r = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_prep.py') with open('RNA_prep.py', 'w') as f: f.write(r.text) from RNA_prep import * except: print("CoLab not working. On my PC, use relative paths.") IN_COLAB = False DATAPATH='../data/' # must end in "/" sys.path.append("..") # append parent dir in order to use sibling dirs from SimTools.RNA_gen import * from SimTools.RNA_describe import * from SimTools.RNA_prep import * MODELPATH="BestModel" # saved on cloud instance and lost after logout #MODELPATH=DATAPATH+MODELPATH # saved on Google Drive but requires login if not assert_imported_RNA_gen(): print("ERROR: Cannot use RNA_gen.") if not assert_imported_RNA_prep(): print("ERROR: Cannot use RNA_prep.") # In[5]: from os import listdir #from zipfile import ZipFile import gzip import numpy as np import pandas as pd from scipy import stats # mode from sklearn.preprocessing import StandardScaler from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from keras.models import Sequential from keras.layers import Dense,Embedding,Dropout from keras.layers import Conv1D,Conv2D from keras.layers import Flatten,MaxPooling1D,MaxPooling2D from keras.losses import BinaryCrossentropy # tf.keras.losses.BinaryCrossentropy import matplotlib.pyplot as plt from matplotlib import colors mycmap = colors.ListedColormap(['red','blue']) # list color for label 0 then 1 np.set_printoptions(precision=2) # In[6]: def load_gencode(filename,label): DEFLINE='>' DELIM='|' EMPTY='' labels=[] # usually 1 for protein-coding or 0 for non-coding seqs=[] # usually string of ACGT lens=[] # sequence length ids=[] # GenCode transcript ID, always starts ENST one_seq = EMPTY one_id = None # Use gzip 'r' mode to open file in read-only mode. # Use gzip 't' mode to read each line of text as type string. with gzip.open (filename,'rt') as infile: for line in infile: if line[0]==DEFLINE: # Save the previous sequence if one exists. if not one_seq == EMPTY: labels.append(label) seqs.append(one_seq) lens.append(len(one_seq)) ids.append(one_id) # Get ready to read the next sequence. # Parse a GenCode defline that is formatted like this: # >transcript_ID|gene_ID|other_fields other_info|other_info one_id = line[1:].split(DELIM)[0] one_seq = EMPTY else: # Continue loading sequence lines till next defline. additional = line.rstrip() one_seq = one_seq + additional # Don't forget to save the last sequence after end-of-file. if not one_seq == EMPTY: labels.append(label) seqs.append(one_seq) lens.append(len(one_seq)) ids.append(one_id) df1=pd.DataFrame(ids,columns=['tid']) df2=pd.DataFrame(labels,columns=['class']) df3=pd.DataFrame(seqs,columns=['sequence']) df4=pd.DataFrame(lens,columns=['seqlen']) df=pd.concat((df1,df2,df3,df4),axis=1) return df # In[7]: PC_FULLPATH=DATAPATH+PC_FILENAME NC_FULLPATH=DATAPATH+NC_FILENAME pcdf=load_gencode(PC_FULLPATH,1) print("PC seqs loaded:",len(pcdf)) ncdf=load_gencode(NC_FULLPATH,0) print("NC seqs loaded:",len(ncdf)) # In[8]: def uniform_length(seqs,hardlen,too_big): newseqs=[] pad='T'*hardlen too_small=200 ## by definition of long non-coding RNA for seq in seqs: size=len(seq) if (size>too_small and size<too_big): big=seq+pad little=big[:hardlen] newseqs.append(little) return newseqs # In[9]: from sklearn.model_selection import train_test_split pc_all = pcdf['sequence'] nc_all = ncdf['sequence'] # The split function also shuffles pc_train,pc_test=train_test_split(pc_all,test_size=0.10,random_state=1234) nc_train,nc_test=train_test_split(nc_all,test_size=0.10,random_state=1234) # In[10]: # Use code from our SimTools library. UNIFORM_LENGTH=1000 MAXIMUM_LENGTH=2000 pc_seqs=uniform_length(pc_train,UNIFORM_LENGTH,MAXIMUM_LENGTH-500) print("PC seqs ready:",len(pc_seqs)) nc_seqs=uniform_length(nc_train,UNIFORM_LENGTH,MAXIMUM_LENGTH+2000) print("NC seqs ready:",len(nc_seqs)) X,y = prepare_inputs_len_x_alphabet(pc_seqs,nc_seqs,ALPHABET) # shuffles print("Data ready") # In[11]: def make_DNN(): print("make_DNN") print("input shape:",INPUT_SHAPE) dnn = Sequential() #dnn.add(Embedding(input_dim=INPUT_SHAPE,output_dim=INPUT_SHAPE)) dnn.add(Conv1D(filters=FILTERS,kernel_size=WIDTH,strides=STRIDE,padding="same", input_shape=INPUT_SHAPE)) dnn.add(Conv1D(filters=FILTERS,kernel_size=WIDTH,strides=STRIDE,padding="same")) dnn.add(MaxPooling1D()) dnn.add(Conv1D(filters=FILTERS,kernel_size=WIDTH,strides=STRIDE,padding="same")) dnn.add(Conv1D(filters=FILTERS,kernel_size=WIDTH,strides=STRIDE,padding="same")) dnn.add(MaxPooling1D()) dnn.add(Flatten()) dnn.add(Dense(NEURONS,activation="sigmoid",dtype=np.float32)) dnn.add(Dropout(DROP_RATE)) dnn.add(Dense(1,activation="sigmoid",dtype=np.float32)) dnn.compile(optimizer='adam', loss=BinaryCrossentropy(from_logits=False), metrics=['accuracy']) # add to default metrics=loss dnn.build(input_shape=INPUT_SHAPE) #ln_rate = tf.keras.optimizers.Adam(learning_rate = LN_RATE) #bc=tf.keras.losses.BinaryCrossentropy(from_logits=False) #model.compile(loss=bc, optimizer=ln_rate, metrics=["accuracy"]) return dnn model = make_DNN() print(model.summary()) # In[12]: from keras.callbacks import ModelCheckpoint def do_cross_validation(X,y): cv_scores = [] fold=0 mycallbacks = [ModelCheckpoint( filepath=MODELPATH, save_best_only=True, monitor='val_accuracy', mode='max')] splitter = KFold(n_splits=SPLITS) # this does not shuffle for train_index,valid_index in splitter.split(X): if fold < FOLDS: fold += 1 X_train=X[train_index] # inputs for training y_train=y[train_index] # labels for training X_valid=X[valid_index] # inputs for validation y_valid=y[valid_index] # labels for validation print("MODEL") # Call constructor on each CV. Else, continually improves the same model. model = model = make_DNN() print("FIT") # model.fit() implements learning start_time=time.time() history=model.fit(X_train, y_train, epochs=EPOCHS, verbose=1, # ascii art while learning callbacks=mycallbacks, # called at end of each epoch validation_data=(X_valid,y_valid)) end_time=time.time() elapsed_time=(end_time-start_time) print("Fold %d, %d epochs, %d sec"%(fold,EPOCHS,elapsed_time)) # print(history.history.keys()) # all these keys will be shown in figure pd.DataFrame(history.history).plot(figsize=(8,5)) plt.grid(True) plt.gca().set_ylim(0,1) # any losses > 1 will be off the scale plt.show() # In[13]: show_time() do_cross_validation(X,y) show_time() # In[13]:

4,856

0

114

5d00cd12b85bb0a5d6ff531e4050eaf2b25a92f6

137

py

Python

setup.py

perillaseed/PyPunchP2P

a85fb41cc6be96463a8fa3f9ffd7c37f378c5910

[ "MIT" ]

1

2015-04-25T07:49:37.000Z

setup.py

perillaseed/PyPunchP2P

a85fb41cc6be96463a8fa3f9ffd7c37f378c5910

[ "MIT" ]

null

setup.py

perillaseed/PyPunchP2P

a85fb41cc6be96463a8fa3f9ffd7c37f378c5910

[ "MIT" ]

null

from distutils.core import setup import py2exe setup(console=['client.py']) setup(console=['server.py']) setup(console=['stun.py'])

22.833333

33

0.715328

from distutils.core import setup import py2exe setup(console=['client.py']) setup(console=['server.py']) setup(console=['stun.py'])

0

ed617c413c97581a929329b03f0040b599be7555

1,088

py

Python

examples/pyramid_backbone_redis_chat/chatter3/scripts/populate.py

benthomasson/gevent-socketio

0f9bd2744af033b7cba57bfd5b82106592e9f667

[ "BSD-3-Clause" ]

625

2015-01-05T04:11:59.000Z

2022-03-14T13:29:59.000Z

examples/pyramid_backbone_redis_chat/chatter3/scripts/populate.py

benthomasson/gevent-socketio

0f9bd2744af033b7cba57bfd5b82106592e9f667

[ "BSD-3-Clause" ]

53

2015-01-30T07:55:45.000Z

2021-02-28T10:50:34.000Z

examples/pyramid_backbone_redis_chat/chatter3/scripts/populate.py

benthomasson/gevent-socketio

0f9bd2744af033b7cba57bfd5b82106592e9f667

[ "BSD-3-Clause" ]

213

2015-01-05T10:18:51.000Z

2022-01-23T08:57:38.000Z

#!/usr/bin/env python import os import sys from pyramid.config import Configurator from sqlalchemy import engine_from_config from sqlalchemy.orm import sessionmaker from sqlalchemy.orm import scoped_session from chatter3.models import Base from pyramid.paster import ( get_appsettings, setup_logging, ) DBSession = scoped_session(sessionmaker()) here = os.path.dirname(__file__) if __name__ == "__main__": # pragma: no cover main()

20.923077

57

0.693934

#!/usr/bin/env python import os import sys from pyramid.config import Configurator from sqlalchemy import engine_from_config from sqlalchemy.orm import sessionmaker from sqlalchemy.orm import scoped_session from chatter3.models import Base from pyramid.paster import ( get_appsettings, setup_logging, ) DBSession = scoped_session(sessionmaker()) here = os.path.dirname(__file__) def usage(argv): # pragma: no cover cmd = os.path.basename(argv[0]) print('usage: %s <config_uri>\n' '(example: "%s development.ini")' % (cmd, cmd)) sys.exit(1) def main(argv=sys.argv): # pragma: no cover if len(argv) != 2: usage(argv) config_uri = argv[1] setup_logging(config_uri) settings = get_appsettings(config_uri) config = Configurator( settings=settings ) config.include('chatter.models') engine = engine_from_config(settings, 'sqlalchemy.') Base.metadata.bind = engine Base.metadata.drop_all(engine) Base.metadata.create_all(engine) if __name__ == "__main__": # pragma: no cover main()

589

0

46

5ab2de7a3b4f2a0c3e272c15eeead137590295a0

1,630

py

Python

toolbox/custom_metrics.py

dear-anastasia/oil-field-modelling

fd5000225de4554564d184c5129322cda27958fa

[ "BSD-3-Clause" ]

6

2020-08-27T14:37:36.000Z

2022-02-28T04:43:37.000Z

toolbox/custom_metrics.py

dear-anastasia/oil-field-modelling

fd5000225de4554564d184c5129322cda27958fa

[ "BSD-3-Clause" ]

null

toolbox/custom_metrics.py

dear-anastasia/oil-field-modelling

fd5000225de4554564d184c5129322cda27958fa

[ "BSD-3-Clause" ]

3

2020-11-08T15:13:43.000Z

2022-01-05T21:24:37.000Z

import numpy as np import tensorflow as tf

33.265306

78

0.552147

import numpy as np import tensorflow as tf def _mean_iou(y_true, y_pred): prec = [] for t in np.arange(0.5, 1.0, 0.05): tensor = y_pred > t y_pred_ = tf.cast(tensor, tf.int32) score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2) K.get_session().run(tf.local_variables_initializer()) with tf.control_dependencies([up_opt]): score = tf.identity(score) prec.append(score) return K.mean(K.stack(prec), axis=0) def _get_iou_vector(A, B): batch_size = A.shape[0] metric = [] for batch in range(batch_size): t, p = A[batch] > 0, B[batch] > 0 # if np.count_nonzero(t) == 0 and np.count_nonzero(p) > 0: # metric.append(0) # continue # if np.count_nonzero(t) >= 1 and np.count_nonzero(p) == 0: # metric.append(0) # continue # if np.count_nonzero(t) == 0 and np.count_nonzero(p) == 0: # metric.append(1) # continue intersection = np.logical_and(t, p) union = np.logical_or(t, p) iou = (np.sum(intersection > 0) + 1e-10) / (np.sum(union > 0) + 1e-10) thresholds = np.arange(0.5, 1, 0.05) s = [] for thresh in thresholds: s.append(iou > thresh) metric.append(np.mean(s)) return np.mean(metric) def my_iou_metric(label, pred): return tf.py_func(get_iou_vector, [label, pred > 0.5], tf.float64) def my_iou_metric_2(label, pred): return tf.py_func(get_iou_vector, [label, pred > 0], tf.float64)

1,493

0

92

b5ec4357a8609ecfac4ad2393bae54409aeac7a2

30,691

py

Python

_JNJ/Media_Defaults.py

Tapyr/tapyr

4235fba6dce169fe747cce4d17d88dcf4a3f9f1d

[ "BSD-3-Clause" ]

6

2016-12-10T17:51:10.000Z

2021-10-11T07:51:48.000Z

_JNJ/Media_Defaults.py

Tapyr/tapyr

4235fba6dce169fe747cce4d17d88dcf4a3f9f1d

[ "BSD-3-Clause" ]

null

_JNJ/Media_Defaults.py

Tapyr/tapyr

4235fba6dce169fe747cce4d17d88dcf4a3f9f1d

[ "BSD-3-Clause" ]

3

2020-03-29T07:37:03.000Z

2021-01-21T16:08:40.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2011-2019 Mag. Christian Tanzer All rights reserved # Glasauergasse 32, A--1130 Wien, Austria. tanzer@swing.co.at # **************************************************************************** # This module is part of the package JNJ. # # This module is licensed under the terms of the BSD 3-Clause License # <http://www.c-tanzer.at/license/bsd_3c.html>. # **************************************************************************** # #++ # Name # JNJ.Media_Defaults # # Purpose # Provide defaults for media fragments in html/*.media # # Revision Dates # 1-Jan-2011 (CT) Creation # 2-Jan-2011 (CT) Creation continued # 4-Jan-2011 (CT) Creation continued.. # 14-Jan-2011 (CT) Use `GTW.Parameters.Definition` to allow lazy # references with `P` and `P_dict` # 22-Mar-2011 (CT) `afs` # 22-Mar-2011 (CT) `afs` continued # 29-Mar-2011 (CT) `afs` continued.. # 30-Mar-2011 (CT) `afs` continued... # 19-May-2011 (CT) `afs` continued.... # 16-Oct-2011 (MG) `Debugger` added # 24-Oct-2011 (CT) `color_spec_heading_rev` added # 24-Oct-2011 (CT) `tablesorter` added # 24-Nov-2011 (CT) Add `color_spec_selected_rev` # 23-Feb-2012 (CT) Add `color_status_{bad,good,missing}`, `afs.status_size` # 23-Feb-2012 (CT) Add `color_bg_bad` and `color_bg_missing` # 1-Mar-2012 (CT) Change `pg_main_max_width` from `Em (50)` to `Em (45)` # 8-Mar-2012 (CT) Add `color_bg_menu` # 10-Aug-2012 (CT) Add `color_spec_sub_heading`, `color_spec_term` # 7-Mar-2013 (CT) Add `border_added` and `border_deleted` # 3-Apr-2013 (CT) Add `Rule`, `Rule.clearfix` # 8-Apr-2013 (CT) Add lots of rules to `Rule`, e.g., `disabled`, `focus`... # 7-Jan-2014 (CT) Add some more muted colors # 21-Jan-2014 (CT) Add `breakpoint` # 20-Feb-2014 (CT) Add `Rule.pg_nav_show`, `.pg_nav_show_a` # 20-Feb-2014 (CT) Add `breakpoint.broad` and `.very_narrow` # 8-Apr-2014 (CT) Improve `clearfix` # 12-Apr-2014 (CT) Use `Border.P`, not `P_Border` # 15-May-2014 (CT) Add `color_lightest_grey` # 15-May-2014 (CT) Add `breakpoint.quite_narrow` # 9-Jul-2014 (CT) Add `Rule.rotate_45_left` # 26-Aug-2014 (CT) Add `pure` parameters # 3-Sep-2014 (CT) Add `Rule.hidden` # 3-Dec-2014 (CT) Add `color.alphabet_max_contrast_colors` and # `color.kellys_max_contrast_colors` # 16-Jan-2015 (CT) Change `nav_col.width` and `col_padding` to unit `Rem` # 23-Jan-2015 (CT) Add `color_jnd_grey`, reduce contrast of `color_spec_row1` # 23-Jan-2015 (CT) Factor `line_height*` parameters # 15-Feb-2015 (CT) Add `menu_icon` # 24-Mar-2015 (CT) Add `Rule.input_focus` # 8-Apr-2015 (CT) Change `font_family_print` to "serif" to void font # substitution by the printer # 2-Dec-2015 (CT) Fix `Rule.visited` # 2-Dec-2015 (CT) Change `nav_col.color_spec_link_current.background_color` # 31-Dec-2015 (CT) Change `pg_nav_show` to allow embedded rel-nav buttons # 11-Oct-2016 (CT) Import from `CHJ`, not `GTW` # 27-Dec-2016 (CT) Add `breakpoint.really_narrow` # 12-Jan-2017 (CT) Add `breakpoint.supports_two_column` # 13-Jan-2017 (CT) Add `body_margin`, `hidden_collapse`, `target_visible`, # `visible` # 16-Jan-2017 (CT) Add `border_double`, `border_simple_light` # 16-Jan-2017 (CT) Change `nav_col` to `nav.main`; modernize nav styling # 16-Jan-2017 (CT) Add `nav.header`, `nav.rel` # 19-Jan-2017 (CT) Add `Rule.main_nav_off`, `.main_nav_on` # 10-May-2017 (CT) Add system-specific fonts to `font_family_normal` # 28-May-2017 (CT) Add missing `"` in `font_family_normal` # 29-Mar-2018 (CT) Add `system-ui` to `font_family_normal` # 15-Apr-2019 (CT) Add `nord colors` # ««revision-date»»··· #-- from _CHJ._CSS.import_CSS import * from _CHJ.Parameters import \ ( Definition, P, P_dict, Rule_Definition , Rule, Rule_Attr, Rule_Child, Rule_Class, Rule_Pseudo, Rule_Sibling ) Color.formatter = RGB_X class Media_Defaults (Definition) : """Provide defaults for CSS fragments in html/*.css""" # end class color color_bg_bad = RGB_X ("#FFEEEE") color_bg_menu = RGB_X ("#DDEEFF") color_bg_missing = RGB_X ("#FFFFBB") color_desc = RGB_X ("#666666") color_focus = SVG_Color ("yellow") color_heading = RGB_X ("#34444D") color_heading_closed = RGB_X ("#56666E") color_heading_sub = RGB_X ("#78888F") color_jnd_grey = RGB_X ("#F8F8F8") color_lightest_grey = RGB_X ("#F6F6F6") color_lighter_grey = RGB_X ("#EDEDED") color_light_grey = RGB_X ("#DEDEDE") color_border_grey = RGB_X ("#CCCCCC") color_medium_grey = RGB_X ("#BEBEBE") color_half_grey = RGB_X ("#888888") color_dark_grey = RGB_X ("#444444") color_darker_grey = RGB_X ("#222222") color_selected = RGB_X ("#FF6633") color_status_bad = RGB_X ("#FF6666") color_status_good = RGB_X ("#AAEEAA") color_status_missing = RGB_X ("#FFDD00") color_target = RGB_X ("#FF6633") css_arrow_color = SVG_Color ("red") css_arrow_width = Em (1./2) block_margin_bottom = Em (1./2) body_margin = TRBL (Em (0.2), Em (0.5)) border_added = "1px solid " + P.color.m_red border_button = "2px outset " + P.color_medium_grey border_deleted = "1px solid " + P.color.m_blue border_double = "2px solid " + P.color_medium_grey border_double_light = "2px solid " + P.color_light_grey border_selected = "2px solid " + P.color_selected border_simple = "1px solid " + P.color_medium_grey border_simple_light = "1px solid " + P.color_light_grey button_spec = P_dict \ ( border = P.border_button , cursor = "pointer" , margin = TRBL0 (t = Em (1./4), b = Em (3./4)) , padding = TRBL (Em (0.5), Em (1.5)) , ** Border (radius = Px (10)) ) col_padding = Rem (0.3125) ### Px (5) background_color = P.color.p_white link_color = RGB_X ("#0000EE") no_link_color = RGB_X ("#333333") text_color = RGB_X ("#000033") visited_color = RGB_X ("#551A8B") # end class afs class breakpoint (Definition) : """Breakpoints for responsive rules""" really_narrow = P_dict \ ( max_width = Px (360) ) very_narrow = P_dict \ ( max_width = Px (420) ) quite_narrow = P_dict \ ( max_width = Px (480) ) narrow = P_dict \ ( max_width = Px (680) ) small_device = P_dict \ ( max_device_width = Px (767) ) supports_two_column = P_dict \ ( min_width = Px (768) ) broad = P_dict \ ( min_width = Px (1280) ) wide = P_dict \ ( min_width = Px (1600) ) # end class breakpoint # end class cal # end class menu_icon # end class nav class pure (Definition) : """Parameters of `pure` css as of version v0.4.2.""" input_focus_border_color = RGB_X ("#129FEA") label_width = Em (10.0) label_margin_right = Em (1.0) aside_indent = label_width + label_margin_right # end class pure # end class tablesorter # end class Rule color_spec_error = P_dict \ ( background_color = P.background_color , color = P.color_status_bad ) color_spec_gallery_heading = P_dict \ ( background_color = RGB_P (50, 75, 100) , color = P.nav.main.background_color ) color_spec_heading = P_dict \ ( background_color = P.background_color , color = P.color_selected ) color_spec_normal = P_dict \ ( background_color = P.background_color , color = P.text_color ) color_spec_pg_head = P_dict \ ( background_color = P.background_color , color = RGB_X ("#0200DE") ) color_spec_row1 = color_spec_meta = P_dict \ ( background_color = "transparent" , color = P.text_color ) color_spec_row2 = color_spec_message = P_dict \ ( background_color = P.color_lightest_grey , color = P.text_color ) color_spec_selected = P_dict \ ( background_color = SVG_Color ("yellow") , color = SVG_Color ("red") ) color_spec_selected_rev= P_dict \ ( background_color = SVG_Color ("red") , color = SVG_Color ("yellow") ) color_spec_strong = P_dict \ ( background_color = P.background_color , color = SVG_Color ("blue") ) color_spec_sub_heading = P_dict \ ( background_color = P.background_color , color = P.color_half_grey ) color_spec_term = P_dict \ ( background_color = RGB_X ("#E6E6E6") , color = P.color_dark_grey ) del_spec = P_dict \ ( text_decoration = "line-through" # XXX ??? ) font_family_normal = \ ( """"system-ui","-apple-system", BlinkMacSystemFont, """ """"Segoe UI", Roboto, "Fira Sans", """ """"Lucida Grande", verdana, sans-serif""" ) font_family_pre = \ """"Lucida Sans Typewriter", "Lucida Console", "Courier New", Courier, monospace""" font_family_print = "serif" ### Don't use specific fonts for `print` because font substitution done ### by a printer can look terribly ugly font_spec_normal = P_dict \ ( font_family = P.font_family_normal , font_style = "normal" , font_weight = "normal" , line_height = P.line_height_normal ) font_spec_print = P_dict \ ( font_spec_normal , font_family = P.font_family_print ) font_spec_em = P_dict \ ( font_spec_normal , font_weight = "bold" ) font_spec_pre = P_dict \ ( font_spec_normal , font_family = P.font_family_pre ) grid_table_border = "3px ridge gray" h1_font_size = Percent (125) h1_font_weight = "bold" hbox_spec = P_dict \ ( display = "block" , overflow = "hidden" ) hr_spec = P_dict \ ( border = 0 , border_top = "1px solid #CCC" , display = "block" , height = Px (1) , margin = TRBL (Em (1), 0) , padding = 0 ) input_margin = TRBL (Em (0.1), 0) input_padding = Em (0.2) ins_spec = P_dict \ ( text_decoration = "none" # XXX ??? ) line_height_heading = 2.00 line_height_larger = 1.875 line_height_normal = 1.44 line_height_input = 1.143 outline_focus = "2px solid " + P.color_focus outline_target = "2px dotted " + P.color_target pg_body_margin_lr = Em (3.6) pg_head_height = "auto" pg_header_nav_height = Rem (3.5) pg_main_max_width = Em (45) pg_main_min_width = Em (15) pg_short_nav_font_size = Rem (1.20) pg_short_nav_top = Em (3) thumbnail_size = Px (155) thumbnail_selected_color = P.color_selected # end class Debugger # end class Media_Defaults ### __END__ JNJ.Media_Defaults

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0.470105

# -*- coding: utf-8 -*- # Copyright (C) 2011-2019 Mag. Christian Tanzer All rights reserved # Glasauergasse 32, A--1130 Wien, Austria. tanzer@swing.co.at # **************************************************************************** # This module is part of the package JNJ. # # This module is licensed under the terms of the BSD 3-Clause License # <http://www.c-tanzer.at/license/bsd_3c.html>. # **************************************************************************** # #++ # Name # JNJ.Media_Defaults # # Purpose # Provide defaults for media fragments in html/*.media # # Revision Dates # 1-Jan-2011 (CT) Creation # 2-Jan-2011 (CT) Creation continued # 4-Jan-2011 (CT) Creation continued.. # 14-Jan-2011 (CT) Use `GTW.Parameters.Definition` to allow lazy # references with `P` and `P_dict` # 22-Mar-2011 (CT) `afs` # 22-Mar-2011 (CT) `afs` continued # 29-Mar-2011 (CT) `afs` continued.. # 30-Mar-2011 (CT) `afs` continued... # 19-May-2011 (CT) `afs` continued.... # 16-Oct-2011 (MG) `Debugger` added # 24-Oct-2011 (CT) `color_spec_heading_rev` added # 24-Oct-2011 (CT) `tablesorter` added # 24-Nov-2011 (CT) Add `color_spec_selected_rev` # 23-Feb-2012 (CT) Add `color_status_{bad,good,missing}`, `afs.status_size` # 23-Feb-2012 (CT) Add `color_bg_bad` and `color_bg_missing` # 1-Mar-2012 (CT) Change `pg_main_max_width` from `Em (50)` to `Em (45)` # 8-Mar-2012 (CT) Add `color_bg_menu` # 10-Aug-2012 (CT) Add `color_spec_sub_heading`, `color_spec_term` # 7-Mar-2013 (CT) Add `border_added` and `border_deleted` # 3-Apr-2013 (CT) Add `Rule`, `Rule.clearfix` # 8-Apr-2013 (CT) Add lots of rules to `Rule`, e.g., `disabled`, `focus`... # 7-Jan-2014 (CT) Add some more muted colors # 21-Jan-2014 (CT) Add `breakpoint` # 20-Feb-2014 (CT) Add `Rule.pg_nav_show`, `.pg_nav_show_a` # 20-Feb-2014 (CT) Add `breakpoint.broad` and `.very_narrow` # 8-Apr-2014 (CT) Improve `clearfix` # 12-Apr-2014 (CT) Use `Border.P`, not `P_Border` # 15-May-2014 (CT) Add `color_lightest_grey` # 15-May-2014 (CT) Add `breakpoint.quite_narrow` # 9-Jul-2014 (CT) Add `Rule.rotate_45_left` # 26-Aug-2014 (CT) Add `pure` parameters # 3-Sep-2014 (CT) Add `Rule.hidden` # 3-Dec-2014 (CT) Add `color.alphabet_max_contrast_colors` and # `color.kellys_max_contrast_colors` # 16-Jan-2015 (CT) Change `nav_col.width` and `col_padding` to unit `Rem` # 23-Jan-2015 (CT) Add `color_jnd_grey`, reduce contrast of `color_spec_row1` # 23-Jan-2015 (CT) Factor `line_height*` parameters # 15-Feb-2015 (CT) Add `menu_icon` # 24-Mar-2015 (CT) Add `Rule.input_focus` # 8-Apr-2015 (CT) Change `font_family_print` to "serif" to void font # substitution by the printer # 2-Dec-2015 (CT) Fix `Rule.visited` # 2-Dec-2015 (CT) Change `nav_col.color_spec_link_current.background_color` # 31-Dec-2015 (CT) Change `pg_nav_show` to allow embedded rel-nav buttons # 11-Oct-2016 (CT) Import from `CHJ`, not `GTW` # 27-Dec-2016 (CT) Add `breakpoint.really_narrow` # 12-Jan-2017 (CT) Add `breakpoint.supports_two_column` # 13-Jan-2017 (CT) Add `body_margin`, `hidden_collapse`, `target_visible`, # `visible` # 16-Jan-2017 (CT) Add `border_double`, `border_simple_light` # 16-Jan-2017 (CT) Change `nav_col` to `nav.main`; modernize nav styling # 16-Jan-2017 (CT) Add `nav.header`, `nav.rel` # 19-Jan-2017 (CT) Add `Rule.main_nav_off`, `.main_nav_on` # 10-May-2017 (CT) Add system-specific fonts to `font_family_normal` # 28-May-2017 (CT) Add missing `"` in `font_family_normal` # 29-Mar-2018 (CT) Add `system-ui` to `font_family_normal` # 15-Apr-2019 (CT) Add `nord colors` # ««revision-date»»··· #-- from _CHJ._CSS.import_CSS import * from _CHJ.Parameters import \ ( Definition, P, P_dict, Rule_Definition , Rule, Rule_Attr, Rule_Child, Rule_Class, Rule_Pseudo, Rule_Sibling ) Color.formatter = RGB_X class Media_Defaults (Definition) : """Provide defaults for CSS fragments in html/*.css""" class color (Definition) : ### color alphabet ### http://eleanormaclure.files.wordpress.com/2011/03/colour-coding.pdf ### ### The RGB color values were taken with gcolor2's color picker from ### colour-coding.pdf abc_a = RGB_X ("#C5A1CA") abc_b = RGB_X ("#486EB5") abc_c = RGB_X ("#82411D") abc_d = RGB_X ("#401A57") abc_e = RGB_X ("#1F1E1E") abc_f = RGB_X ("#335A36") abc_g = RGB_X ("#78C259") abc_h = RGB_X ("#EDC99A") abc_i = RGB_X ("#7F8080") abc_j = RGB_X ("#BEDEAE") abc_k = RGB_X ("#877A2F") abc_l = RGB_X ("#A5C43A") abc_m = RGB_X ("#9B247F") abc_n = RGB_X ("#253777") abc_o = RGB_X ("#E1A131") abc_p = RGB_X ("#E6A4B5") abc_q = RGB_X ("#50662E") abc_r = RGB_X ("#CF2128") abc_s = RGB_X ("#A4DBDF") abc_t = RGB_X ("#56968C") abc_u = RGB_X ("#E0E77B") abc_v = RGB_X ("#584EA0") abc_w = RGB_X ("#7D1416") abc_x = RGB_X ("#F3F190") abc_y = RGB_X ("#ECDA43") abc_z = RGB_X ("#D55428") ### muted colors m_aqua = RGB_X ("#7FDBFF") m_black = RGB_X ("#111111") m_blue = RGB_X ("#0088DD") m_grey = RGB_X ("#AAAAAA") m_fuchsia = RGB_X ("#F012BE") m_green = RGB_X ("#00AA00") m_lime = RGB_X ("#01FF70") m_maroon = RGB_X ("#85144B") m_navy = RGB_X ("#001F3F") m_red = RGB_X ("#CC3333") m_olive = RGB_X ("#3D9970") m_orange = RGB_X ("#FFA022") m_pink = RGB_X ("#DD4499") m_purple = RGB_X ("#AA33BB") m_silver = RGB_X ("#DDDDDD") m_teal = RGB_X ("#33CCCC") m_white = RGB_X ("#EEEEEE") m_yellow = RGB_X ("#FFF00F") ### nord colors ### https://www.nordtheme.com/docs/colors-and-palettes nord0 = n_polar_light_0 = RGB_X ("#2e3440") nord1 = n_polar_light_1 = RGB_X ("#3b4252") nord2 = n_polar_light_2 = RGB_X ("#434c5e") nord3 = n_polar_light_3 = RGB_X ("#4c566a") nord4 = n_snow_storm_0 = RGB_X ("#d8dee9") nord5 = n_snow_storm_1 = RGB_X ("#e5e9f0") nord6 = n_snow_storm_2 = RGB_X ("#eceff4") nord7 = n_frost_0 = RGB_X ("#8fbcbb") nord8 = n_frost_1 = RGB_X ("#88c0d0") nord9 = n_frost_2 = RGB_X ("#81a1c1") nord10 = n_frost_3 = RGB_X ("#5e81ac") nord11 = n_aurora_0 = RGB_X ("#bf616a") nord12 = n_aurora_1 = RGB_X ("#d08770") nord13 = n_aurora_2 = RGB_X ("#ebcb8b") nord14 = n_aurora_3 = RGB_X ("#a3be8c") nord15 = n_aurora_4 = RGB_X ("#b48ead") ### pure colors p_black = SVG_Color ("black") p_blue = SVG_Color ("blue") p_cyan = SVG_Color ("cyan") p_gray = p_grey = SVG_Color ("gray") p_green = SVG_Color ("green") p_lime = SVG_Color ("lime") p_magenta = SVG_Color ("magenta") p_maroon = SVG_Color ("maroon") p_navy = SVG_Color ("navy") p_olive = SVG_Color ("olive") p_purple = SVG_Color ("purple") p_red = SVG_Color ("red") p_teal = SVG_Color ("teal") p_white = SVG_Color ("white") p_yellow = SVG_Color ("yellow") ### Kelly's 22 colors of maximum contrast ### http://www.iscc.org/pdf/PC54_1724_001.pdf ### http://burgess-studio.co.uk/colour/ ### http://eleanormaclure.files.wordpress.com/2011/03/colour-coding.pdf ### ### The order of colors in Kelly's list was planned so that there ### would be maximum contrast between colors in a set if the required ### number of colors were always selected in order from the top. So a ### set of five colors should be white, black, yellow, purple, and ### orange. And if seven colors were required, light blue and red ### should be added. Kelly took care of the needs of people with ### defective color vision. The first nine colors would be maximally ### different for such people as well as for people with normal ### vision. These nine colors are also readily distinguishable by ### color name. ### ### The RGB color values were taken with gcolor2's color picker from ### colour-coding.pdf k_white = RGB_X ("#FFFFFF") # 1 k_black = RGB_X ("#1F1E1E") # 2 k_yellow = RGB_X ("#EBCD3F") # 3 k_purple = RGB_X ("#6F308B") # 4 k_orange = RGB_X ("#DB6A28") # 5 k_light_blue = RGB_X ("#98CEE6") # 6 k_red = RGB_X ("#B91F36") # 7 k_buff = RGB_X ("#C1BC82") # 8 k_gray = k_grey = RGB_X ("#7F8080") # 9 ——————————— k_green = RGB_X ("#62A647") # 10 k_purplish_pink = RGB_X ("#D386B1") # 11 k_blue = RGB_X ("#4578B4") # 12 k_yellowish_pink = RGB_X ("#DD8565") # 13 k_violet = RGB_X ("#493896") # 14 k_orange_yellow = RGB_X ("#E1A131") # 15 k_purplish_red = RGB_X ("#91278B") # 16 k_greenish_yellow = RGB_X ("#E9E857") # 17 k_reddish_brown = RGB_X ("#7D1716") # 18 k_yellow_green = RGB_X ("#93AD3C") # 19 k_yellowish_brown = RGB_X ("#6E3515") # 20 k_reddish_orange = RGB_X ("#D12D27") # 21 k_olive_green = RGB_X ("#2C3617") # 22 kellys_max_contrast_colors = \ [ k_white , k_black , k_yellow , k_purple , k_orange , k_light_blue , k_red , k_buff , k_gray , k_green , k_purplish_pink , k_blue , k_yellowish_pink , k_violet , k_orange_yellow , k_purplish_red , k_greenish_yellow , k_reddish_brown , k_yellow_green , k_yellowish_brown , k_reddish_orange , k_olive_green ] ### The order in the following list tries to maximize contrast, ### analogously to Kelly's list alphabet_max_contrast_colors = \ [ abc_e , abc_y , abc_d , abc_z , abc_s , abc_r , abc_x , abc_i , abc_g , abc_p , abc_b , abc_h , abc_v , abc_o , abc_m , abc_u , abc_w , abc_l , abc_c , abc_a , abc_q , abc_j , abc_n , abc_k , abc_f , abc_t ] # end class color color_bg_bad = RGB_X ("#FFEEEE") color_bg_menu = RGB_X ("#DDEEFF") color_bg_missing = RGB_X ("#FFFFBB") color_desc = RGB_X ("#666666") color_focus = SVG_Color ("yellow") color_heading = RGB_X ("#34444D") color_heading_closed = RGB_X ("#56666E") color_heading_sub = RGB_X ("#78888F") color_jnd_grey = RGB_X ("#F8F8F8") color_lightest_grey = RGB_X ("#F6F6F6") color_lighter_grey = RGB_X ("#EDEDED") color_light_grey = RGB_X ("#DEDEDE") color_border_grey = RGB_X ("#CCCCCC") color_medium_grey = RGB_X ("#BEBEBE") color_half_grey = RGB_X ("#888888") color_dark_grey = RGB_X ("#444444") color_darker_grey = RGB_X ("#222222") color_selected = RGB_X ("#FF6633") color_status_bad = RGB_X ("#FF6666") color_status_good = RGB_X ("#AAEEAA") color_status_missing = RGB_X ("#FFDD00") color_target = RGB_X ("#FF6633") css_arrow_color = SVG_Color ("red") css_arrow_width = Em (1./2) block_margin_bottom = Em (1./2) body_margin = TRBL (Em (0.2), Em (0.5)) border_added = "1px solid " + P.color.m_red border_button = "2px outset " + P.color_medium_grey border_deleted = "1px solid " + P.color.m_blue border_double = "2px solid " + P.color_medium_grey border_double_light = "2px solid " + P.color_light_grey border_selected = "2px solid " + P.color_selected border_simple = "1px solid " + P.color_medium_grey border_simple_light = "1px solid " + P.color_light_grey button_spec = P_dict \ ( border = P.border_button , cursor = "pointer" , margin = TRBL0 (t = Em (1./4), b = Em (3./4)) , padding = TRBL (Em (0.5), Em (1.5)) , ** Border (radius = Px (10)) ) col_padding = Rem (0.3125) ### Px (5) background_color = P.color.p_white link_color = RGB_X ("#0000EE") no_link_color = RGB_X ("#333333") text_color = RGB_X ("#000033") visited_color = RGB_X ("#551A8B") class afs (Definition) : block_margin_bottom = Em (0.1) border_spec_input = Border.P \ ( color = P.R.color_dark_grey , style = "solid" , width = Px (1) ) border_spec_readonly = Border.P \ ( color = P.R.color_medium_grey , style = "solid" , width = Px (2) ) border_spec_section = Border.P \ ( color = P.R.color_darker_grey , style = "solid" , width = TRBL0 (l = Px (2), default = Px (1)) ) color_spec_desc = P_dict \ ( background_color = P.R.color_desc , color = P.R.background_color ) color_spec_heading = P_dict \ ( background_color = P.R.color_heading , color = P.R.background_color ) color_spec_heading_closed = P_dict \ ( background_color = P.R.color_heading_closed , color = P.R.background_color ) color_spec_heading_sub = P_dict \ ( background_color = P.R.color_heading_sub , color = P.R.background_color ) color_spec_label = P_dict \ ( background_color = "inherit" , color = P.R.text_color ) color_spec_optional = P.R.color_spec_normal color_spec_necessary = P_dict \ ( background_color = P.R.color_light_grey , color = P.R.text_color ) color_spec_readonly = P_dict \ ( background_color = P.R.color_light_grey , color = P.R.color_dark_grey ) color_spec_required = P_dict \ ( background_color = P.R.color_medium_grey , color = P.R.text_color ) header_padding = TRBL (Em (0.2), Em (0.2), Em (0.3), Em (0.5)) status_size = Px (12) # end class afs class breakpoint (Definition) : """Breakpoints for responsive rules""" really_narrow = P_dict \ ( max_width = Px (360) ) very_narrow = P_dict \ ( max_width = Px (420) ) quite_narrow = P_dict \ ( max_width = Px (480) ) narrow = P_dict \ ( max_width = Px (680) ) small_device = P_dict \ ( max_device_width = Px (767) ) supports_two_column = P_dict \ ( min_width = Px (768) ) broad = P_dict \ ( min_width = Px (1280) ) wide = P_dict \ ( min_width = Px (1600) ) # end class breakpoint class cal (Definition) : date_bg = P.R.color_medium_grey date_padding = TRBL0 (r = Em (1./4), b = Em (1./10)) event_bg = RGB_X ("#FFF8AF") font_size = Em (0.7) line_height = 1.5 holiday_bg = RGB_X ("#CCFFFF") month_color = RGB_X ("#777777") weekend_color = P.R.color.m_blue week_bg = P.R.color_heading week_color = RGB_8 (255, 153, 0) week_height = Em (8) # end class cal class menu_icon (Definition) : color = P.R.color_selected line_width = Rem (0.1875) margin_ab = Em (0.4375) margin_m = Em (0.125) width = Em (1.25) # end class menu_icon class nav (Definition) : border_radius = Em (0.625) ### Px (10) class header (Definition) : a_color = P.T.color_target a_margin = 0 a_padding = Em (1) hbt_sep = Em (1) # end class header class main (Definition) : a_font_size = "small" a_font_size_current = Em (0.875) background_color = P.T.background_color button_spec = P_dict \ ( border = P.T.border_button , color = P.link_color , margin = 0 , text_align = "center" ) color_no_link = P.T.color_heading color_spec_heading = P_dict \ ( background_color = P.T.color_heading , color = P.background_color ) color_spec_heading_rev = P_dict \ ( background_color = P.background_color , color = P.T.color_heading ) color_spec_label = P_dict \ ( background_color = P.background_color , color = P.T.text_color ) color_spec_link = P_dict \ ( background_color = P.background_color , color = P.link_color ) color_spec_link_current = P_dict \ ( background_color = P.T.color_lighter_grey , color = P.T.color_heading ) color_spec_no_link = P_dict \ ( background_color = P.background_color , color = P.color_no_link , opacity = 0.7 ) color_spec_section_current = P_dict \ ( background_color = P.background_color , color = P.T.color_heading ) color_spec_visited = P_dict \ ( color_spec_link , color = P.visited_color ) color_spec_web_link_hover = P_dict \ ( background_color = P.link_color , color = P.background_color ) li_left = Em (0.75) line_height_larger = 1.50 line_height_normal = 1.35 link_color = P.T.color_selected mark_color_link = P.T.css_arrow_color mark_color_section = P.T.color_heading mark_width = Em (0.40) max_button_width = Em (20) padding = TRBL (Rem (1./4), Rem (0.625), 0) visited_color = P.T.color_heading vert_padding = Rem (0.2) width = Rem (11.875) + 3 * P.T.col_padding # end class main class rel (Definition) : a_color = P.T.color_target a_margin = Em (0.5) a_padding = Em (1) # end class rel # end class nav class pure (Definition) : """Parameters of `pure` css as of version v0.4.2.""" input_focus_border_color = RGB_X ("#129FEA") label_width = Em (10.0) label_margin_right = Em (1.0) aside_indent = label_width + label_margin_right # end class pure class tablesorter (Definition) : color_marker = P.R.background_color margin_top = Px (8) opacity = 0.75 width = Px (5) # end class tablesorter class Rule (Rule_Definition) : clearfix = Rule_Pseudo \ ( "after" , clear = "both" , content = "' '" , display = "table" # http://nicolasgallagher.com/micro-clearfix-hack/ ) disabled = Rule_Class \ ( "disabled" , opacity = 0.5 ) focus = Rule_Pseudo \ ( "focus" , P.R.color_spec_selected ) focus_outline = Rule_Pseudo \ ( "focus" , P.R.color_spec_selected , outline = P.R.outline_focus ) hidden = Rule \ ( display = "none" , visibility = "hidden" ) hidden_collapse = Rule \ ( display = "none" , visibility = "collapse" ) hover = Rule_Pseudo \ ( "hover" , P.R.color_spec_selected ) hover_rev = Rule_Pseudo \ ( "hover" , P.R.color_spec_selected_rev ) input_focus = Rule_Pseudo \ ( "focus" , background_color = "inherit" , border_color = P.R.pure.input_focus_border_color , color = RGB_X ("#0078E7") , outline = P.R.pure.input_focus_border_color ) link = Rule_Pseudo \ ( "link" , color = P.R.link_color ) main_nav_off = Rule \ ( height = 0 , padding = 0 , width = 0 ) main_nav_on = Rule \ ( height = "auto" , padding = P.R.nav.main.padding , width = "auto" ) rotate_45_left = Rule \ ( Transform ("rotate(-45deg)") , display = "inline-block" ) row_even = Rule_Pseudo \ ( "nth-child(2n)" , P.R.color_spec_row2 ) row_odd = Rule_Pseudo \ ( "nth-child(2n+1)" , P.R.color_spec_row1 ) target = Rule_Pseudo \ ( "target" , P.R.color_spec_selected ) target_outline = Rule_Pseudo \ ( "target" , P.R.color_spec_selected , outline = P.R.outline_focus ) target_visible = Rule_Pseudo \ ( "target" , visibility = "visible" ) target_visible_block = Rule_Pseudo \ ( "target" , display = "block" , visibility = "visible" ) visible = Rule_Pseudo \ ( "target" , visibility = "visible" ) visible_block = Rule_Pseudo \ ( display = "block" , visibility = "visible" ) visited = Rule_Pseudo \ ( "visited" , color = P.R.visited_color ) # end class Rule color_spec_error = P_dict \ ( background_color = P.background_color , color = P.color_status_bad ) color_spec_gallery_heading = P_dict \ ( background_color = RGB_P (50, 75, 100) , color = P.nav.main.background_color ) color_spec_heading = P_dict \ ( background_color = P.background_color , color = P.color_selected ) color_spec_normal = P_dict \ ( background_color = P.background_color , color = P.text_color ) color_spec_pg_head = P_dict \ ( background_color = P.background_color , color = RGB_X ("#0200DE") ) color_spec_row1 = color_spec_meta = P_dict \ ( background_color = "transparent" , color = P.text_color ) color_spec_row2 = color_spec_message = P_dict \ ( background_color = P.color_lightest_grey , color = P.text_color ) color_spec_selected = P_dict \ ( background_color = SVG_Color ("yellow") , color = SVG_Color ("red") ) color_spec_selected_rev= P_dict \ ( background_color = SVG_Color ("red") , color = SVG_Color ("yellow") ) color_spec_strong = P_dict \ ( background_color = P.background_color , color = SVG_Color ("blue") ) color_spec_sub_heading = P_dict \ ( background_color = P.background_color , color = P.color_half_grey ) color_spec_term = P_dict \ ( background_color = RGB_X ("#E6E6E6") , color = P.color_dark_grey ) del_spec = P_dict \ ( text_decoration = "line-through" # XXX ??? ) font_family_normal = \ ( """"system-ui","-apple-system", BlinkMacSystemFont, """ """"Segoe UI", Roboto, "Fira Sans", """ """"Lucida Grande", verdana, sans-serif""" ) font_family_pre = \ """"Lucida Sans Typewriter", "Lucida Console", "Courier New", Courier, monospace""" font_family_print = "serif" ### Don't use specific fonts for `print` because font substitution done ### by a printer can look terribly ugly font_spec_normal = P_dict \ ( font_family = P.font_family_normal , font_style = "normal" , font_weight = "normal" , line_height = P.line_height_normal ) font_spec_print = P_dict \ ( font_spec_normal , font_family = P.font_family_print ) font_spec_em = P_dict \ ( font_spec_normal , font_weight = "bold" ) font_spec_pre = P_dict \ ( font_spec_normal , font_family = P.font_family_pre ) grid_table_border = "3px ridge gray" h1_font_size = Percent (125) h1_font_weight = "bold" hbox_spec = P_dict \ ( display = "block" , overflow = "hidden" ) hr_spec = P_dict \ ( border = 0 , border_top = "1px solid #CCC" , display = "block" , height = Px (1) , margin = TRBL (Em (1), 0) , padding = 0 ) input_margin = TRBL (Em (0.1), 0) input_padding = Em (0.2) ins_spec = P_dict \ ( text_decoration = "none" # XXX ??? ) line_height_heading = 2.00 line_height_larger = 1.875 line_height_normal = 1.44 line_height_input = 1.143 outline_focus = "2px solid " + P.color_focus outline_target = "2px dotted " + P.color_target pg_body_margin_lr = Em (3.6) pg_head_height = "auto" pg_header_nav_height = Rem (3.5) pg_main_max_width = Em (45) pg_main_min_width = Em (15) pg_short_nav_font_size = Rem (1.20) pg_short_nav_top = Em (3) thumbnail_size = Px (155) thumbnail_selected_color = P.color_selected class Debugger (Definition) : background_color = P.R.background_color console_border_color = P.R.color_border_grey console_background_color = P.R.color_lightest_grey console_text_color = P.R.color.m_black form_text_color = P.R.color_dark_grey traceback_background_color = P.R.background_color traceback_text_color = P.R.background_color # end class Debugger # end class Media_Defaults ### __END__ JNJ.Media_Defaults

0

18,191

216

7e8520464ef78923e2bcd35f840d3b3c36c23689

76

py

Python

Exercise_4_9.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

Exercise_4_9.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

Exercise_4_9.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

#list comprehension cubes = [cube**3 for cube in range(1,11)] print(cubes)

15.2

41

0.710526

#list comprehension cubes = [cube**3 for cube in range(1,11)] print(cubes)

0

00380a4e96afe9b21aff8fb8b7107dd165f0876b

38,092

py

Python

src/gui.py

mgely/scriptq

7339abfdefac8a1df6f3c0928e6f22bae9620e05

[ "MIT" ]

null

src/gui.py

mgely/scriptq

7339abfdefac8a1df6f3c0928e6f22bae9620e05

[ "MIT" ]

1

2020-06-10T08:08:24.000Z

src/gui.py

mgely/scriptq

7339abfdefac8a1df6f3c0928e6f22bae9620e05

[ "MIT" ]

null

import tkinter as tk from tkinter import messagebox, filedialog, simpledialog, scrolledtext from tkinter import PhotoImage from tkinter import ttk import subprocess from threading import Thread import time import tempfile from os import path import sys try: from . import settings except ImportError: # Running from source import settings # Contains the graphics featured on the buttons graphics_directory = path.join(path.dirname(__file__), "graphics") ''' Hiearchy of Tkinter frames: GuiWindow.master = tk.Tk() GuiWindow(ttk.Frame) BatchingFrame.frame(ttk.Frame) BatchingFrame.vbar(ttk.Scrollbar) BatchingFrame(tk.Canvas) BatchingFrame.canvas_content(tk.Frame) Note: this is an amalgamation of code from different projects. It could probably be simplified dramatically. ''' class GuiWindow(ttk.Frame): ''' Highest level window and frame. Here we set the name and size of the window, and prepare for hosting the actual content of the program. If unittest is set to True, we don't start the mainloop, but wait for a script to trigger events and manually update the GUI. ''' class BatchingFrame(tk.Canvas): ''' This is the place where the widgets corresponding to scripts live. It is also the brain of the program, and controls the state of the different scripts, the launching of scripts, etc... ''' def remove_all(self): ''' Removes all the Scripts, from the last to the first, excluding the topmost InsertWidget ''' for position in range(len(self.scripts) - 1, 0, -1): self.remove(position) def build_output_window(self): ''' Shows the output window which contains the continuously updated output of the currently running script (stdout and stderr). Or, if no script is running, contains the content of the latest run script. ''' if self.output_window_visible: # the output is already visible # in this case bring the window to the top self.output_window.lift() self.output_window.attributes("-topmost", True) self.output_window.attributes("-topmost", False) return # Open up the output window self.output_window = tk.Toplevel(self.master) self.output_window.title("Script queuer | Output") self.output_window.geometry("400x400") # Keep track of the window being visible self.output_window_visible = True # Window size cannot be reduced beyond # a certain size self.output_window.minsize(200, 150) # When closing the window run self.on_closing_output_window self.output_window.protocol("WM_DELETE_WINDOW", self.on_closing_output_window) # Put a scrollable text region in it, and make is stretchable self.output_text_widget = ScrolledLabel(self.output_window) self.output_text_widget.grid(column=0, row=0, sticky='news') self.output_window.rowconfigure(0, weight=1) self.output_window.columnconfigure(0, weight=1) # Add a button to toggle following the output / autoscrolling b = ToggleAutoscrollButton(self.output_window, text='Autoscroll') b.grid(column=0, row=1, sticky='nws') if self.running_script is not None: # Is there is no running script, # show the log of the last run script self.output_text_widget.insert(self.running_script.log) self.scroll_output_window_down() def on_closing_output_window(self): ''' Function called when the output window is closed ''' # Keep track of the window state self.output_window_visible = False # Close the window self.output_window.destroy() def insert(self, position, script_path=None): ''' Will insert a new script after the row indicated by the input integer `position`. Optionally one can specify the script path (for unittesting purposes). ''' if script_path is None: # If no script path was provided # prompt user for file name if self.latest_searched_directory == None: script_path = filedialog.askopenfilename() else: # If a script was already inserted, # open the file prompt at the same directory script_path = filedialog.askopenfilename( initialdir=self.latest_searched_directory) if script_path == "": # User cancelled return # keep track of the directory the user navigated to self.latest_searched_directory = path.dirname(script_path) # Creates a new script widget, by default it will be queued sw = ScriptWidget(self, script_path=script_path, state='queued') # add it to the list of scripts self.scripts.insert(position + 1, sw) # update the scripts states and graphical information self.update_script_widgets() def move(self, position, new_position=None): ''' Move a script from a position (row `position`) to a new position (after row `new_position`). The new position will be chosen in a popup window by the user, or given as a kwarg (for unittesting purposes). ''' if new_position is None: # No postion was given: prompt user # with a popup window # Determine message to be displayed in popup if self.state == 'running': # If running, do not allow script to be placed in first postion # (above the script which is running) message = " 1 = place below row 1\n2 = place below row 2\n etc..." minvalue = 1 else: # If stopped message = " 0 = place first \n 1 = place below row 1\n etc..." minvalue = 0 # Open popup window new_position = tk.simpledialog.askinteger("Move to", message, parent=self.master, minvalue=minvalue, maxvalue=len( self.scripts)) if new_position is None: # User cancelled return # the position the user sees does not # take into account the rows of "done" scripts # position_0 is the position of the first "not done" # script. new_position += self.position_0 # Insert the script at the new position self.scripts.insert(new_position, self.scripts[position]) # Remove the script at the old position if new_position > position: self.scripts.pop(position) else: # If the script is moved up, then # the old position is actually +1 self.scripts.pop(position + 1) # Update script states and graphical information self.update_script_widgets() def remove(self, position): ''' Remove a script from a position. ''' # Destroy the ScriptWidget object self.scripts[position].destroy() # Remove it from the self.scripts list self.scripts.pop(position) # Update script states and graphical information self.update_script_widgets() def run(self, position): ''' Run the script located at row `position` ''' # Useful information about the script to be run self.running_script = self.scripts[position] script_path = self.scripts[position].script_path self.running_script_position = position self.running_script.log = '' # Delete the contents of the output window if self.output_window_visible: self.output_text_widget.clear() # Start the script and # setup the communication # with subprocess self.start_script_process(script_path) # Start the periodic monitoring of the script, # to capture the output, but also detect the end/error self.after(self.t_output_monitoring, self.monitor_script_process) # Update the states of this object and the script self.state = 'running' self.running_script.state = 'running' # Update script states and graphical information self.update_script_widgets() def start_script_process(self, script): ''' Start the script subprocess --- the -u option foces stdout, stderr streams to be unbuffered, which allows us to collect these outputs in real tim, rather than wait to the end of the scripts --- the cwd is chosen to be the folder in which the script is located ''' self.script_process = subprocess.Popen(['python', '-u', script], cwd=path.dirname(script), stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=1) ''' This list will be populated with contents of the subprocess `stdout` by the `reader` function in a seperate thread. It's the bridge between the subprocess and the log file and output window. ''' self.line_buffer = [] # in a seperate thread, collect the output # of the subprocess and load it into # line_buffer list self.buffer_filling_thread = Thread(target=reader, args=(self.script_process.stdout, self.line_buffer)) self.buffer_filling_thread.daemon = True self.buffer_filling_thread.start() def write_to_output(self, to_write): ''' write `to_write` both to the output window and to the log file of the running scripts ''' if self.output_window_visible: self.output_text_widget.insert(to_write) self.running_script.log += to_write def monitor_script_process(self): ''' Whilst the script is running, copy what the `reader` function has put into the `self.line_buffer` and write it to the output and log files. This function will detect when the running script crashed or ended, append the output/log accordingly, and run the next queued script. ''' # write all contents of the `self.line_buffer` list # to the output/log while self.line_buffer: self.write_to_output(self.line_buffer.pop(0).decode("utf-8")) # if autoscroll is activated, scroll the output window # to the latest written if self.output_window.follow: self.scroll_output_window_down() # poll checks on the status of the subprocess poll = self.script_process.poll() if poll is None: # Hasnt crashed or ended # monitor again in a time `self.t_output_monitoring` self.after(self.t_output_monitoring, self.monitor_script_process) else: self.treat_end_of_script(poll) self.treat_next_queued_script(poll) def treat_end_of_script(self, poll): ''' Called whenever a script crashes or ends. Appends the output/log to give maximum information to the user about causes of crashes. ''' if poll != 0: # Something went wrong while True: # Get Error Log and write to output/log line = self.script_process.stderr.readline() if not line: break else: self.write_to_output(line.decode("utf-8")) # Scroll the output window to the bottom self.scroll_output_window_down() # If `self.state` is stopped, then it's the user # who interrupted the script, write this into the output\log if self.state == 'stopped': self.write_to_output(self.interrupted_error_message) # Scroll the output window to the bottom self.scroll_output_window_down() def treat_next_queued_script(self, poll): ''' Called when a script crashes or ends, to carry out the actions which follow: - starting a new queued script if the script ended/crashed on its own - stopping the run if the user forced a stop - notifying the user via email if enabled ''' if poll != 0 and self.state == 'stopped': # User interrupted the script # The script is stopped and made ready to go again self.running_script.state = 'ready' # It is also duplicated and marked above as a # stopped script, so that the user may also inspect the # logging file stopped = self.running_script duplicate = ScriptWidget(self, script_path=stopped.script_path, state='ended') duplicate.success = 'stopped' duplicate.log = stopped.log self.scripts.insert(self.running_script_position, duplicate) # Update script states and graphical information self.update_script_widgets() else: if poll != 0: # Script stopped because of an error self.running_script.state = 'ended' self.running_script.success = 'failed' elif poll == 0: # Script successfully ended self.running_script.state = 'ended' self.running_script.success = 'done' if settings.gmail_notifications['enable']: self.gmail_notify() if self.running_script_position + 1 < len(self.scripts): # more scripts are queued: run the next one self.run(position=self.running_script_position + 1) else: # no more scripts to be run: just update visual information self.state = 'stopped' self.update_script_widgets() def stop(self): ''' Triggered by a user clicking the stop button all one needs to do is set the state to `stopped` and force the script to stop, the automatic monitoring of the running script in `monitor_script_process` will take care of the following actions ''' self.state = 'stopped' # Interrupt process self.script_process.kill() def update_script_widgets(self): ''' Updates the states of the ScriptWidget objects and updates the graphical information displayed. All is determined by the `self.states` list and the `self.state` variable. ''' # The self.scripts list should never be empty # as a failsafe we always populate it in that case # with the insert widget if len(self.scripts) == 0: self.scripts = [InsertWidget(self)] return # The row is a property of the non-done scripts # it is displayed in the GUI starting from 1 row = 1 for i, s in enumerate(self.scripts): # All scripts are given a position, running from 0 upwards # this is not necessarily the same as the row and acts # as a unique identifier of the script s.position = i # Scripts which are done are given no row information s.row = None if s.state in ['running', 'ready', 'queued'] or row > 1: if row == 1: # First script running/to-run # Helps in converting rows given by the user # to the position identifier of a script self.position_0 = i # Since this is the first script which has not already been run # it should be either running or stopped if self.state == 'running': s.state = 'running' self.running_script = s self.running_script_position = i elif self.state == 'stopped': s.state = 'ready' elif row > 1: # this script is lower down the queue: # if they were just moved for example, we should # adjust their state accordingly s.state = 'queued' # These non-done scripts are given a row s.row = row row += 1 for i, s in enumerate(self.scripts): # Place the script in the grid s.grid(row=i, column=0, sticky='news') # Populate it with buttons etc... s.add_widgets() # Adjust the scrollable region of the GUI self.update() self.config(scrollregion=self.bbox("all")) def build_gridframe(self): """ This frame will be divided into a grid hosting the canvas, scrollbars, (and potentially a menubar in the future if needed) """ self.frame = ttk.Frame() # Places the Frame widget self.frame in the parent # in a grid self.frame.grid() # Configure the frames grid self.frame.grid(sticky="nswe") # make frame container sticky self.frame.rowconfigure(0, weight=1) # make canvas expandable in x self.frame.columnconfigure(0, weight=1) # make canvas expandable in y def build_menubar(self): """ Builds the File, Edit, ... menu bar situated at the top of the window. Not used for the moment... """ # initialize the menubar object self.menubar = tk.Menu(self.frame) #################################### # FILE cascade menu build #################################### # add new item to the menubar menu = tk.Menu(self.menubar, tearoff=0) self.menubar.add_cascade(label="File", menu=menu) #################################### # VIEW cascade menu build #################################### # add new item to the menubar menu = tk.Menu(self.menubar, tearoff=0) self.menubar.add_cascade(label="View", menu=menu) # add cascade menu items menu.add_command(label="Output", command=self.build_output_window) # Add the menubar to the application self.master.config(menu=self.menubar) def build_scrollbars(self): """ Builds a vertical scrollbars and places it in the window """ self.vbar = ttk.Scrollbar(self.frame, orient="vertical") self.vbar.grid(row=0, column=1, sticky="ns") def build_canvas(self): """ Initializes the canvas from which this object inherits and places it in the grid of our frame """ tk.Canvas.__init__( self, self.frame, bd=0, highlightthickness=0, yscrollcommand=self.vbar.set, confine=False, bg="white", ) self.grid(row=0, column=0, sticky="nswe") def configure_scrollbars(self): """ Define what functions the scrollbars should call when we interact with them, and make scrolling on the mouse do something similar """ self.vbar.configure(command=self.scroll_y) self.bind("<MouseWheel>", self.scroll_y_wheel) def scroll_y(self, *args, **kwargs): """ Is called when the user interacts with the vertical scroll bar """ # stop from scolling up beyond a certain point if float(args[1]) < 0: args = (args[0], "0") # shift canvas vertically self.yview(*args) time.sleep(0.01) # Update scrollable area self.update() self.config(scrollregion=self.bbox("all")) def scroll_y_wheel(self, event): """ Triggered by the user scrolling (in combination with no particular key presses). """ # Determine which direction the user is scrolling # if using windows, then event.delta has also a different # amplitude depending on how fast the user is scrolling, # but we ignore that if event.num == 5 or event.delta < 0: direction = 1 if event.num == 4 or event.delta > 0: direction = -1 # Move the canvas appropriately, and stop # the user from scrolling to far out if direction == 1: if self.canvasy(self.winfo_height()) < 2 * self.bbox("all")[3]: self.yview_scroll(direction, tk.UNITS) elif direction == -1: if self.canvasy(0) > self.bbox("all")[1]: self.yview_scroll(direction, tk.UNITS) self.update() # if we scroll above the top row, move a little down.. if self.canvasy(0) < self.bbox("all")[1]: self.yview_moveto(0) # Update the scrollable region self.update() self.config(scrollregion=self.bbox("all")) def build_canvas_content(self): ''' Build a window which will contain the widgets ''' self.canvas_content = tk.Frame(self) self.create_window((0, 0), window=self.canvas_content, anchor='nw', width=1000) self.canvas_content.columnconfigure(0, weight=1) class ScriptWidget(tk.Frame): ''' Widget (tkinter frame) in which are stored all the graphical elements and information about a script. ''' def next_script_state(self): ''' Returns the state of the script below the current one. Returns None is this is the last script. ''' try: return self.parent.scripts[self.position + 1].state except IndexError: # This script is last in line return None def add_widgets(self): ''' Builds all graphical elements depending on the state and information about the script. ''' # remove all previously bult graphical elements for w in self.all_widgets: w.destroy() self.all_widgets = [] ################## # INSERT BUTTON ################## if self.next_script_state() in ['ready', 'queued', None]: b = ImageButton( self, image='insert.gif', command=(lambda: self.parent.insert(self.position))) else: b = ImageButton(self, image='half_blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=0, sticky='swe', padx=(5, 0)) self.all_widgets.append(b) ################## # ROW LABEL ################## if self.state == 'ended': l = ImageLabel(self, image='blank.gif', compound=tk.CENTER) else: l = ImageLabel(self, image='blank.gif', compound=tk.CENTER, text=self.row) l.grid(row=0, column=1, sticky='new') self.all_widgets.append(l) ################## # STATE LABEL ################## if self.state == 'ended': text = self.success else: text = self.state b = ImageLabel(self, text=text, image='label_' + self.state + self.success + ".gif", compound=tk.CENTER) b.grid(row=0, column=2, sticky='new') self.all_widgets.append(b) ################## # REMOVE BUTTON ################## if self.state == 'running': b = ImageButton(self, image='blank.gif') b.config(state=tk.DISABLED) else: b = ImageButton( self, image='remove.gif', command=(lambda: self.parent.remove(self.position))) b.grid(row=0, column=3, sticky='new', pady=self.pady) self.all_widgets.append(b) ################## # MOVE BUTTON ################## if self.state in ['queued', 'ready']: b = ImageButton(self, image='move.gif', command=(lambda: self.parent.move(self.position))) else: b = ImageButton(self, image='blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=4, sticky='new', pady=self.pady) self.all_widgets.append(b) ################## # RUN/STOP BUTTON ################## if self.state == 'running': b = ImageButton(self, image='stop.gif', command=self.parent.stop) elif self.state == 'ready': b = ImageButton(self, image='run.gif', command=(lambda: self.parent.run(self.position))) else: b = ImageButton(self, image='blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=5, sticky='new', pady=self.pady) self.all_widgets.append(b) ################## # LOG/OUTPUT BUTTON ################## if self.state == 'ended': b = ImageButton(self, text="view log", command=self.view_log, image='blank.gif', compound=tk.CENTER) elif self.state in ['running', 'ready']: b = ImageButton(self, text="view output", command=self.parent.build_output_window, image='blank.gif', compound=tk.CENTER) else: b = ImageButton(self, text="", command=self.parent.build_output_window, image='blank.gif', compound=tk.CENTER) b.config(state=tk.DISABLED) self.all_widgets.append(b) b.grid(row=0, column=6, sticky='ne', pady=self.pady, padx=(2, 10)) ################## # SCRIPT PATH LABEL ################## b = tk.Label( self, text=self.script_path, anchor=tk.W, ) b.grid(row=0, column=7, columnspan=1, sticky='new', pady=self.pady, padx=(0, 40)) self.columnconfigure(7, weight=1) self.all_widgets.append(b) self.update() # Wrap the path text b.config(wraplength=b.winfo_width() - 50) class InsertWidget(ScriptWidget): '''Like Script Widget, but with just an insert button. ''' def add_widgets(self): ''' Add the graphical elements of the widget ''' if self.next_script_state() in ['ready', 'queued', None]: b = ImageButton( self, image='insert.gif', command=(lambda: self.parent.insert(self.position))) else: b = ImageButton(self, image='half_blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=0, sticky='swe', padx=(5, 0)) class ImageButton(ttk.Button): '''Wrapper around the ttk.Button class which automizes the importation of the buttons picture. ''' class ImageLabel(ttk.Label): """docstring for ImageButton""" class ToggleAutoscrollButton(tk.Radiobutton): """Button which turns auto scrolling on and off. """ def click(self): ''' Called upon clicking the button ''' if self.state.get(): # If autoscrolling is on self.config(value=False) self.parent.follow = False else: # If autoscrolling is off self.config(value=True) self.state.set(True) self.parent.follow = True class ScrolledLabel(scrolledtext.ScrolledText): """wrapper around scrolledtext, to make the text read-only """ def reader(f, buffer): '''Utility function runing in a thread which transfers any lines from the pipe `f` into the list `buffer` ''' while True: line = f.readline() if line: buffer.append(line) else: break if __name__ == '__main__': GuiWindow()

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import tkinter as tk from tkinter import messagebox, filedialog, simpledialog, scrolledtext from tkinter import PhotoImage from tkinter import ttk import subprocess from threading import Thread import time import tempfile from os import path import sys try: from . import settings except ImportError: # Running from source import settings # Contains the graphics featured on the buttons graphics_directory = path.join(path.dirname(__file__), "graphics") ''' Hiearchy of Tkinter frames: GuiWindow.master = tk.Tk() GuiWindow(ttk.Frame) BatchingFrame.frame(ttk.Frame) BatchingFrame.vbar(ttk.Scrollbar) BatchingFrame(tk.Canvas) BatchingFrame.canvas_content(tk.Frame) Note: this is an amalgamation of code from different projects. It could probably be simplified dramatically. ''' class GuiWindow(ttk.Frame): ''' Highest level window and frame. Here we set the name and size of the window, and prepare for hosting the actual content of the program. If unittest is set to True, we don't start the mainloop, but wait for a script to trigger events and manually update the GUI. ''' def __init__(self, unittesting=False): # Initialize the frame, inside the root window (tk.Tk()) ttk.Frame.__init__(self, master=tk.Tk()) # Set the name to appear in the title bar self.master.title("Script queuer") # Set the initial size of the window in pixels self.master.geometry("1000x400") # Only resizable in the y direction self.master.resizable(False, True) # Make the frame an expandable grid self.master.rowconfigure(0, weight=1) self.master.columnconfigure(0, weight=1) # Populate that grid with the batching frame self.bf = BatchingFrame(self.master) # Bring scriptq to the front self.master.lift() self.master.attributes("-topmost", True) self.master.attributes("-topmost", False) if unittesting: self.update() else: try: self.mainloop() except Exception as e: messagebox.showinfo( "Oops.. The script queuer crashed with error\n" + str(e)) class BatchingFrame(tk.Canvas): ''' This is the place where the widgets corresponding to scripts live. It is also the brain of the program, and controls the state of the different scripts, the launching of scripts, etc... ''' def __init__(self, master, **kwargs): # Master is the highest level, # root window here tk.Tk() self.master = master # When opening a script, this is # where the window will open to by default self.latest_searched_directory = None # currently or latest running ScriptWidget self.running_script = None # Build another frame, which will contain # the canvas and the scrollbar self.build_gridframe() # We may want to have a menubar to add functionality # in the future, so I'm keeping this commented out. # self.build_menubar() # Add the vertical scrollbar self.build_scrollbars() # Add the canvas which will host the # Frame containing the script widgets # Note: a canvas is necessary here to make the # whole GUI scrollable. # (there's probably a simpler alternative though..) self.build_canvas() # configure vertical scrollbar self.configure_scrollbars() # build a window in the canvas which # will contain the ScriptWidgets self.build_canvas_content() # if True, the output window is built and visible self.output_window_visible = False # Build the output window self.build_output_window() # This determines how oftem we collect and display # the output of a running script self.t_output_monitoring = 100 #ms # Either 'stopped' or 'running' self.state = 'stopped' # When we press stop, this message is appended # to the log of the script output self.interrupted_error_message = 'INTERRUPTED BY SCRIPT QUEUER' # Default opening screen # We start just with the insertion widget self.scripts = [ InsertWidget(self), ] # Useful function which goes through the list # self.scripts, and displays the corresponding graphical content self.update_script_widgets() def remove_all(self): ''' Removes all the Scripts, from the last to the first, excluding the topmost InsertWidget ''' for position in range(len(self.scripts) - 1, 0, -1): self.remove(position) def build_output_window(self): ''' Shows the output window which contains the continuously updated output of the currently running script (stdout and stderr). Or, if no script is running, contains the content of the latest run script. ''' if self.output_window_visible: # the output is already visible # in this case bring the window to the top self.output_window.lift() self.output_window.attributes("-topmost", True) self.output_window.attributes("-topmost", False) return # Open up the output window self.output_window = tk.Toplevel(self.master) self.output_window.title("Script queuer | Output") self.output_window.geometry("400x400") # Keep track of the window being visible self.output_window_visible = True # Window size cannot be reduced beyond # a certain size self.output_window.minsize(200, 150) # When closing the window run self.on_closing_output_window self.output_window.protocol("WM_DELETE_WINDOW", self.on_closing_output_window) # Put a scrollable text region in it, and make is stretchable self.output_text_widget = ScrolledLabel(self.output_window) self.output_text_widget.grid(column=0, row=0, sticky='news') self.output_window.rowconfigure(0, weight=1) self.output_window.columnconfigure(0, weight=1) # Add a button to toggle following the output / autoscrolling b = ToggleAutoscrollButton(self.output_window, text='Autoscroll') b.grid(column=0, row=1, sticky='nws') if self.running_script is not None: # Is there is no running script, # show the log of the last run script self.output_text_widget.insert(self.running_script.log) self.scroll_output_window_down() def on_closing_output_window(self): ''' Function called when the output window is closed ''' # Keep track of the window state self.output_window_visible = False # Close the window self.output_window.destroy() def insert(self, position, script_path=None): ''' Will insert a new script after the row indicated by the input integer `position`. Optionally one can specify the script path (for unittesting purposes). ''' if script_path is None: # If no script path was provided # prompt user for file name if self.latest_searched_directory == None: script_path = filedialog.askopenfilename() else: # If a script was already inserted, # open the file prompt at the same directory script_path = filedialog.askopenfilename( initialdir=self.latest_searched_directory) if script_path == "": # User cancelled return # keep track of the directory the user navigated to self.latest_searched_directory = path.dirname(script_path) # Creates a new script widget, by default it will be queued sw = ScriptWidget(self, script_path=script_path, state='queued') # add it to the list of scripts self.scripts.insert(position + 1, sw) # update the scripts states and graphical information self.update_script_widgets() def move(self, position, new_position=None): ''' Move a script from a position (row `position`) to a new position (after row `new_position`). The new position will be chosen in a popup window by the user, or given as a kwarg (for unittesting purposes). ''' if new_position is None: # No postion was given: prompt user # with a popup window # Determine message to be displayed in popup if self.state == 'running': # If running, do not allow script to be placed in first postion # (above the script which is running) message = " 1 = place below row 1\n2 = place below row 2\n etc..." minvalue = 1 else: # If stopped message = " 0 = place first \n 1 = place below row 1\n etc..." minvalue = 0 # Open popup window new_position = tk.simpledialog.askinteger("Move to", message, parent=self.master, minvalue=minvalue, maxvalue=len( self.scripts)) if new_position is None: # User cancelled return # the position the user sees does not # take into account the rows of "done" scripts # position_0 is the position of the first "not done" # script. new_position += self.position_0 # Insert the script at the new position self.scripts.insert(new_position, self.scripts[position]) # Remove the script at the old position if new_position > position: self.scripts.pop(position) else: # If the script is moved up, then # the old position is actually +1 self.scripts.pop(position + 1) # Update script states and graphical information self.update_script_widgets() def remove(self, position): ''' Remove a script from a position. ''' # Destroy the ScriptWidget object self.scripts[position].destroy() # Remove it from the self.scripts list self.scripts.pop(position) # Update script states and graphical information self.update_script_widgets() def run(self, position): ''' Run the script located at row `position` ''' # Useful information about the script to be run self.running_script = self.scripts[position] script_path = self.scripts[position].script_path self.running_script_position = position self.running_script.log = '' # Delete the contents of the output window if self.output_window_visible: self.output_text_widget.clear() # Start the script and # setup the communication # with subprocess self.start_script_process(script_path) # Start the periodic monitoring of the script, # to capture the output, but also detect the end/error self.after(self.t_output_monitoring, self.monitor_script_process) # Update the states of this object and the script self.state = 'running' self.running_script.state = 'running' # Update script states and graphical information self.update_script_widgets() def start_script_process(self, script): ''' Start the script subprocess --- the -u option foces stdout, stderr streams to be unbuffered, which allows us to collect these outputs in real tim, rather than wait to the end of the scripts --- the cwd is chosen to be the folder in which the script is located ''' self.script_process = subprocess.Popen(['python', '-u', script], cwd=path.dirname(script), stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=1) ''' This list will be populated with contents of the subprocess `stdout` by the `reader` function in a seperate thread. It's the bridge between the subprocess and the log file and output window. ''' self.line_buffer = [] # in a seperate thread, collect the output # of the subprocess and load it into # line_buffer list self.buffer_filling_thread = Thread(target=reader, args=(self.script_process.stdout, self.line_buffer)) self.buffer_filling_thread.daemon = True self.buffer_filling_thread.start() def write_to_output(self, to_write): ''' write `to_write` both to the output window and to the log file of the running scripts ''' if self.output_window_visible: self.output_text_widget.insert(to_write) self.running_script.log += to_write def scroll_output_window_down(self): if self.output_window_visible: self.output_text_widget.see("end") def monitor_script_process(self): ''' Whilst the script is running, copy what the `reader` function has put into the `self.line_buffer` and write it to the output and log files. This function will detect when the running script crashed or ended, append the output/log accordingly, and run the next queued script. ''' # write all contents of the `self.line_buffer` list # to the output/log while self.line_buffer: self.write_to_output(self.line_buffer.pop(0).decode("utf-8")) # if autoscroll is activated, scroll the output window # to the latest written if self.output_window.follow: self.scroll_output_window_down() # poll checks on the status of the subprocess poll = self.script_process.poll() if poll is None: # Hasnt crashed or ended # monitor again in a time `self.t_output_monitoring` self.after(self.t_output_monitoring, self.monitor_script_process) else: self.treat_end_of_script(poll) self.treat_next_queued_script(poll) def treat_end_of_script(self, poll): ''' Called whenever a script crashes or ends. Appends the output/log to give maximum information to the user about causes of crashes. ''' if poll != 0: # Something went wrong while True: # Get Error Log and write to output/log line = self.script_process.stderr.readline() if not line: break else: self.write_to_output(line.decode("utf-8")) # Scroll the output window to the bottom self.scroll_output_window_down() # If `self.state` is stopped, then it's the user # who interrupted the script, write this into the output\log if self.state == 'stopped': self.write_to_output(self.interrupted_error_message) # Scroll the output window to the bottom self.scroll_output_window_down() def treat_next_queued_script(self, poll): ''' Called when a script crashes or ends, to carry out the actions which follow: - starting a new queued script if the script ended/crashed on its own - stopping the run if the user forced a stop - notifying the user via email if enabled ''' if poll != 0 and self.state == 'stopped': # User interrupted the script # The script is stopped and made ready to go again self.running_script.state = 'ready' # It is also duplicated and marked above as a # stopped script, so that the user may also inspect the # logging file stopped = self.running_script duplicate = ScriptWidget(self, script_path=stopped.script_path, state='ended') duplicate.success = 'stopped' duplicate.log = stopped.log self.scripts.insert(self.running_script_position, duplicate) # Update script states and graphical information self.update_script_widgets() else: if poll != 0: # Script stopped because of an error self.running_script.state = 'ended' self.running_script.success = 'failed' elif poll == 0: # Script successfully ended self.running_script.state = 'ended' self.running_script.success = 'done' if settings.gmail_notifications['enable']: self.gmail_notify() if self.running_script_position + 1 < len(self.scripts): # more scripts are queued: run the next one self.run(position=self.running_script_position + 1) else: # no more scripts to be run: just update visual information self.state = 'stopped' self.update_script_widgets() def gmail_notify(self): try: import smtplib message = 'Subject: [scriptq] script %s\n\n' % self.running_script.success message += "Path -- %s\n" % self.running_script.script_path message += "Status -- %s\n" % self.running_script.success message += "Log -- \n%s" % self.running_script.log # creates SMTP session s = smtplib.SMTP('smtp.gmail.com', 587) # start TLS for security s.starttls() # Authentication s.login(settings.gmail_notifications['sender_email'], settings.gmail_notifications['sender_password']) # sending the mail s.sendmail(settings.gmail_notifications['sender_email'], settings.gmail_notifications['receiver_emails'], message) # terminating the session s.quit() except Exception as e: messagebox.showinfo( "Sending notification email failed with error:\n" + str(e)) def stop(self): ''' Triggered by a user clicking the stop button all one needs to do is set the state to `stopped` and force the script to stop, the automatic monitoring of the running script in `monitor_script_process` will take care of the following actions ''' self.state = 'stopped' # Interrupt process self.script_process.kill() def update_script_widgets(self): ''' Updates the states of the ScriptWidget objects and updates the graphical information displayed. All is determined by the `self.states` list and the `self.state` variable. ''' # The self.scripts list should never be empty # as a failsafe we always populate it in that case # with the insert widget if len(self.scripts) == 0: self.scripts = [InsertWidget(self)] return # The row is a property of the non-done scripts # it is displayed in the GUI starting from 1 row = 1 for i, s in enumerate(self.scripts): # All scripts are given a position, running from 0 upwards # this is not necessarily the same as the row and acts # as a unique identifier of the script s.position = i # Scripts which are done are given no row information s.row = None if s.state in ['running', 'ready', 'queued'] or row > 1: if row == 1: # First script running/to-run # Helps in converting rows given by the user # to the position identifier of a script self.position_0 = i # Since this is the first script which has not already been run # it should be either running or stopped if self.state == 'running': s.state = 'running' self.running_script = s self.running_script_position = i elif self.state == 'stopped': s.state = 'ready' elif row > 1: # this script is lower down the queue: # if they were just moved for example, we should # adjust their state accordingly s.state = 'queued' # These non-done scripts are given a row s.row = row row += 1 for i, s in enumerate(self.scripts): # Place the script in the grid s.grid(row=i, column=0, sticky='news') # Populate it with buttons etc... s.add_widgets() # Adjust the scrollable region of the GUI self.update() self.config(scrollregion=self.bbox("all")) def build_gridframe(self): """ This frame will be divided into a grid hosting the canvas, scrollbars, (and potentially a menubar in the future if needed) """ self.frame = ttk.Frame() # Places the Frame widget self.frame in the parent # in a grid self.frame.grid() # Configure the frames grid self.frame.grid(sticky="nswe") # make frame container sticky self.frame.rowconfigure(0, weight=1) # make canvas expandable in x self.frame.columnconfigure(0, weight=1) # make canvas expandable in y def build_menubar(self): """ Builds the File, Edit, ... menu bar situated at the top of the window. Not used for the moment... """ # initialize the menubar object self.menubar = tk.Menu(self.frame) #################################### # FILE cascade menu build #################################### # add new item to the menubar menu = tk.Menu(self.menubar, tearoff=0) self.menubar.add_cascade(label="File", menu=menu) #################################### # VIEW cascade menu build #################################### # add new item to the menubar menu = tk.Menu(self.menubar, tearoff=0) self.menubar.add_cascade(label="View", menu=menu) # add cascade menu items menu.add_command(label="Output", command=self.build_output_window) # Add the menubar to the application self.master.config(menu=self.menubar) def build_scrollbars(self): """ Builds a vertical scrollbars and places it in the window """ self.vbar = ttk.Scrollbar(self.frame, orient="vertical") self.vbar.grid(row=0, column=1, sticky="ns") def build_canvas(self): """ Initializes the canvas from which this object inherits and places it in the grid of our frame """ tk.Canvas.__init__( self, self.frame, bd=0, highlightthickness=0, yscrollcommand=self.vbar.set, confine=False, bg="white", ) self.grid(row=0, column=0, sticky="nswe") def configure_scrollbars(self): """ Define what functions the scrollbars should call when we interact with them, and make scrolling on the mouse do something similar """ self.vbar.configure(command=self.scroll_y) self.bind("<MouseWheel>", self.scroll_y_wheel) def scroll_y(self, *args, **kwargs): """ Is called when the user interacts with the vertical scroll bar """ # stop from scolling up beyond a certain point if float(args[1]) < 0: args = (args[0], "0") # shift canvas vertically self.yview(*args) time.sleep(0.01) # Update scrollable area self.update() self.config(scrollregion=self.bbox("all")) def scroll_y_wheel(self, event): """ Triggered by the user scrolling (in combination with no particular key presses). """ # Determine which direction the user is scrolling # if using windows, then event.delta has also a different # amplitude depending on how fast the user is scrolling, # but we ignore that if event.num == 5 or event.delta < 0: direction = 1 if event.num == 4 or event.delta > 0: direction = -1 # Move the canvas appropriately, and stop # the user from scrolling to far out if direction == 1: if self.canvasy(self.winfo_height()) < 2 * self.bbox("all")[3]: self.yview_scroll(direction, tk.UNITS) elif direction == -1: if self.canvasy(0) > self.bbox("all")[1]: self.yview_scroll(direction, tk.UNITS) self.update() # if we scroll above the top row, move a little down.. if self.canvasy(0) < self.bbox("all")[1]: self.yview_moveto(0) # Update the scrollable region self.update() self.config(scrollregion=self.bbox("all")) def build_canvas_content(self): ''' Build a window which will contain the widgets ''' self.canvas_content = tk.Frame(self) self.create_window((0, 0), window=self.canvas_content, anchor='nw', width=1000) self.canvas_content.columnconfigure(0, weight=1) class ScriptWidget(tk.Frame): ''' Widget (tkinter frame) in which are stored all the graphical elements and information about a script. ''' def __init__(self, parent, script_path=None, state=None, success=''): super(ScriptWidget, self).__init__(parent.canvas_content) # A reference to the canvas in which # the widget is placed self.parent = parent ''' string representing the state of the script, can be one of: - ended - ready (waiting for user to click run) - running - queued ''' self.state = state ''' Is not None only if the script is ended. Can then be one of: - done (ran successfully) - failed (there was an error in the script) - stopped (the user interrupted the script) ''' self.success = success # Full, absolute path to the script self.script_path = script_path # Row of the script displayed in the GUI # None if the script has ended, # 1 and above if not self.row = None # Position of the script regardless of the state # Goes from 0 up self.position = None # Vertical padding of the graphical elements self.pady = (1, 1) # Stores all the widgets displayed self.all_widgets = [] def next_script_state(self): ''' Returns the state of the script below the current one. Returns None is this is the last script. ''' try: return self.parent.scripts[self.position + 1].state except IndexError: # This script is last in line return None def add_widgets(self): ''' Builds all graphical elements depending on the state and information about the script. ''' # remove all previously bult graphical elements for w in self.all_widgets: w.destroy() self.all_widgets = [] ################## # INSERT BUTTON ################## if self.next_script_state() in ['ready', 'queued', None]: b = ImageButton( self, image='insert.gif', command=(lambda: self.parent.insert(self.position))) else: b = ImageButton(self, image='half_blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=0, sticky='swe', padx=(5, 0)) self.all_widgets.append(b) ################## # ROW LABEL ################## if self.state == 'ended': l = ImageLabel(self, image='blank.gif', compound=tk.CENTER) else: l = ImageLabel(self, image='blank.gif', compound=tk.CENTER, text=self.row) l.grid(row=0, column=1, sticky='new') self.all_widgets.append(l) ################## # STATE LABEL ################## if self.state == 'ended': text = self.success else: text = self.state b = ImageLabel(self, text=text, image='label_' + self.state + self.success + ".gif", compound=tk.CENTER) b.grid(row=0, column=2, sticky='new') self.all_widgets.append(b) ################## # REMOVE BUTTON ################## if self.state == 'running': b = ImageButton(self, image='blank.gif') b.config(state=tk.DISABLED) else: b = ImageButton( self, image='remove.gif', command=(lambda: self.parent.remove(self.position))) b.grid(row=0, column=3, sticky='new', pady=self.pady) self.all_widgets.append(b) ################## # MOVE BUTTON ################## if self.state in ['queued', 'ready']: b = ImageButton(self, image='move.gif', command=(lambda: self.parent.move(self.position))) else: b = ImageButton(self, image='blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=4, sticky='new', pady=self.pady) self.all_widgets.append(b) ################## # RUN/STOP BUTTON ################## if self.state == 'running': b = ImageButton(self, image='stop.gif', command=self.parent.stop) elif self.state == 'ready': b = ImageButton(self, image='run.gif', command=(lambda: self.parent.run(self.position))) else: b = ImageButton(self, image='blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=5, sticky='new', pady=self.pady) self.all_widgets.append(b) ################## # LOG/OUTPUT BUTTON ################## if self.state == 'ended': b = ImageButton(self, text="view log", command=self.view_log, image='blank.gif', compound=tk.CENTER) elif self.state in ['running', 'ready']: b = ImageButton(self, text="view output", command=self.parent.build_output_window, image='blank.gif', compound=tk.CENTER) else: b = ImageButton(self, text="", command=self.parent.build_output_window, image='blank.gif', compound=tk.CENTER) b.config(state=tk.DISABLED) self.all_widgets.append(b) b.grid(row=0, column=6, sticky='ne', pady=self.pady, padx=(2, 10)) ################## # SCRIPT PATH LABEL ################## b = tk.Label( self, text=self.script_path, anchor=tk.W, ) b.grid(row=0, column=7, columnspan=1, sticky='new', pady=self.pady, padx=(0, 40)) self.columnconfigure(7, weight=1) self.all_widgets.append(b) self.update() # Wrap the path text b.config(wraplength=b.winfo_width() - 50) def view_log(self): # Open up the output window self.log_window = tk.Toplevel(self.parent.master) self.log_window.title("Script queuer | Log | " + self.script_path) # Opening size of the window self.log_window.geometry("400x400") # Minimum size of the window self.log_window.minsize(200, 150) # Put a scrollable text region in it self.log_text_widget = ScrolledLabel(self.log_window) self.log_text_widget.grid(column=0, row=0, sticky='news') # Add the log text self.log_text_widget.insert(self.log) # Scroll all the way down to the end self.log_text_widget.see("end") # Make the scrollable text stretch with the window self.log_window.rowconfigure(0, weight=1) self.log_window.columnconfigure(0, weight=1) class InsertWidget(ScriptWidget): '''Like Script Widget, but with just an insert button. ''' def __init__(self, parent): super(InsertWidget, self).__init__(parent, script_path=None, state=None, success=None) def add_widgets(self): ''' Add the graphical elements of the widget ''' if self.next_script_state() in ['ready', 'queued', None]: b = ImageButton( self, image='insert.gif', command=(lambda: self.parent.insert(self.position))) else: b = ImageButton(self, image='half_blank.gif') b.config(state=tk.DISABLED) b.grid(row=0, column=0, sticky='swe', padx=(5, 0)) class ImageButton(ttk.Button): '''Wrapper around the ttk.Button class which automizes the importation of the buttons picture. ''' def __init__(self, *args, image=None, **kwargs): # Import image image = PhotoImage(file=path.join(graphics_directory, image)) # Make two times smaller image = image.subsample(2, 2) super(ImageButton, self).__init__(*args, image=image, **kwargs) # This is necessary otherwise the picture dosnt appear somehow self.image = image class ImageLabel(ttk.Label): """docstring for ImageButton""" def __init__(self, *args, image=None, **kwargs): # Import image image = PhotoImage(file=path.join(graphics_directory, image)) # Make two times smaller image = image.subsample(2, 2) super(ImageLabel, self).__init__(*args, image=image, **kwargs) # This is necessary otherwise the picture dosnt appear somehow self.image = image class ToggleAutoscrollButton(tk.Radiobutton): """Button which turns auto scrolling on and off. """ def __init__(self, parent, text): self.parent = parent # The button is checked when this variable is set to True self.state = tk.BooleanVar() self.state.set(True) # The auto-scrolling is activated in the parent widget # when this variable is set to True self.parent.follow = True super(ToggleAutoscrollButton, self).__init__(parent, text=text, variable=self.state, value=True, command=self.click) def click(self): ''' Called upon clicking the button ''' if self.state.get(): # If autoscrolling is on self.config(value=False) self.parent.follow = False else: # If autoscrolling is off self.config(value=True) self.state.set(True) self.parent.follow = True class ScrolledLabel(scrolledtext.ScrolledText): """wrapper around scrolledtext, to make the text read-only """ def __init__(self, *args, **kwargs): super(ScrolledLabel, self).__init__(*args, **kwargs) self.configure(state='disabled') def insert(self, text): self.configure(state='normal') super(ScrolledLabel, self).insert(tk.INSERT, text) self.configure(state='disabled') def clear(self): self.configure(state='normal') self.delete("1.0", "end") self.configure(state='disabled') def reader(f, buffer): '''Utility function runing in a thread which transfers any lines from the pipe `f` into the list `buffer` ''' while True: line = f.readline() if line: buffer.append(line) else: break if __name__ == '__main__': GuiWindow()

8,293

0

351

e083ad3a3c94ca1b1fe9170e1f7a73ca987df66a

250

py

Python

Thesis_WebCrawler/coursera/scrape_coursera.py

cdhekne/My_Thesis

7f60dc48ea7de68c66a702816271ce94832b8fa0

[ "Apache-2.0" ]

null

Thesis_WebCrawler/coursera/scrape_coursera.py

cdhekne/My_Thesis

7f60dc48ea7de68c66a702816271ce94832b8fa0

[ "Apache-2.0" ]

1

2016-10-25T23:21:17.000Z

Thesis_WebCrawler/coursera/scrape_coursera.py

GradThesis/Thesis

7f60dc48ea7de68c66a702816271ce94832b8fa0

[ "Apache-2.0" ]

null

import coursera_requests as c import sys try: # c.getCourseraCategories() c.getCourseraCourses() # c.getCourseraInstructors() # c.getCourseraSessions() # c.getCourseraUniversities() except: print ("Unexpected error:", sys.exc_info()[0]) raise

20.833333

47

0.756

import coursera_requests as c import sys try: # c.getCourseraCategories() c.getCourseraCourses() # c.getCourseraInstructors() # c.getCourseraSessions() # c.getCourseraUniversities() except: print ("Unexpected error:", sys.exc_info()[0]) raise

0

97daa73929b125d81963d3faf813e0c563052bb2

599

py

Python

make_log_table.py

kevinko/rabin

ad80bb12a455c25ded958f0ad86564d9465175cf

[ "BSD-3-Clause" ]

17

2015-04-11T16:54:30.000Z

2021-11-12T05:15:04.000Z

make_log_table.py

kevinko/rabin

ad80bb12a455c25ded958f0ad86564d9465175cf

[ "BSD-3-Clause" ]

3

2015-07-24T04:46:42.000Z

2019-11-27T11:40:31.000Z

make_log_table.py

kevinko/rabin

ad80bb12a455c25ded958f0ad86564d9465175cf

[ "BSD-3-Clause" ]

6

2015-06-24T14:50:29.000Z

2019-05-29T07:08:46.000Z

#!/usr/bin/python # Copyright 2012, Kevin Ko <kevin@faveset.com>. All rights reserved. # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. # # Generates an 8-bit log base 2 table. # Print out an inverse 8-bit log table print "-1, 0, 1, 1," # log(i) for i in 0, 1, 2, 3 print "2, 2, 2, 2," # ... 4, 5, 6, 7 print "3, 3, 3, 3, 3, 3, 3, 3," # ... 8-15 lt(4) # 16-31 # 32-63 [lt(5) for i in xrange(2)] # 64-127 [lt(6) for i in xrange(4)] # 128-255 [lt(7) for i in xrange(8)]

23.038462

69

0.589316

#!/usr/bin/python # Copyright 2012, Kevin Ko <kevin@faveset.com>. All rights reserved. # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. # # Generates an 8-bit log base 2 table. def lt(n): print (",".join([str(n) for i in xrange(16)]) + ",") # Print out an inverse 8-bit log table print "-1, 0, 1, 1," # log(i) for i in 0, 1, 2, 3 print "2, 2, 2, 2," # ... 4, 5, 6, 7 print "3, 3, 3, 3, 3, 3, 3, 3," # ... 8-15 lt(4) # 16-31 # 32-63 [lt(5) for i in xrange(2)] # 64-127 [lt(6) for i in xrange(4)] # 128-255 [lt(7) for i in xrange(8)]

44

0

23

7ae29eddfd970a4827e122bd3cce94f398f44f32

46

py

Python

LibSerial4/__init__.py

thiagolemedasilva/LibSerial4

29b155ae4096f5e9e4bd18a2dd33018347298e1e

[ "MIT" ]

null

LibSerial4/__init__.py

thiagolemedasilva/LibSerial4

29b155ae4096f5e9e4bd18a2dd33018347298e1e

[ "MIT" ]

null

LibSerial4/__init__.py

thiagolemedasilva/LibSerial4

29b155ae4096f5e9e4bd18a2dd33018347298e1e

[ "MIT" ]

null

from LibSerial4.LibSerial4 import Serial, Uart

46

0.869565

from LibSerial4.LibSerial4 import Serial, Uart

0

3369a1c91cb44fe5318448852ed32ffe37ae2507

3,825

py

Python

authors/apps/articles/migrations/0001_initial.py

andela/ah-backend-stark

c38810e221f95567262034b860ee0512cf15f102

[ "BSD-3-Clause" ]

null

authors/apps/articles/migrations/0001_initial.py

andela/ah-backend-stark

c38810e221f95567262034b860ee0512cf15f102

[ "BSD-3-Clause" ]

29

2018-09-25T13:53:06.000Z

2021-06-10T20:51:58.000Z

authors/apps/articles/migrations/0001_initial.py

andela/ah-backend-stark

c38810e221f95567262034b860ee0512cf15f102

[ "BSD-3-Clause" ]

2

2019-08-02T12:23:24.000Z

2019-11-05T12:22:23.000Z

# Generated by Django 2.1.2 on 2018-11-22 17:40 from django.conf import settings from django.db import migrations, models import django.db.models.deletion

47.222222

145

0.585359

# Generated by Django 2.1.2 on 2018-11-22 17:40 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('profiles', '0001_initial'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Article', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=255)), ('slug', models.SlugField(blank=True, max_length=255, unique=True)), ('description', models.CharField(max_length=500)), ('body', models.TextField()), ('tagList', models.CharField(blank=True, max_length=2000)), ('image', models.URLField(blank=True)), ('createdAt', models.DateTimeField(auto_now_add=True)), ('updatedAt', models.DateTimeField(auto_now_add=True)), ('rating', models.FloatField(default=0)), ('ratingsCount', models.IntegerField(default=0)), ('read_time', models.CharField(max_length=100)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'ordering': ['createdAt'], }, ), migrations.CreateModel( name='ArticlesRead', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('slug', models.TextField()), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='profiles.Profile')), ], ), migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('body', models.TextField()), ('timestamp', models.DateTimeField(auto_now_add=True)), ('article', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='articles.Article')), ('parent_comment', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='articles.Comment')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'ordering': ['timestamp'], }, ), migrations.CreateModel( name='Favourite', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('article', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='articles.Article')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Likes', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('action', models.BooleanField()), ('action_at', models.DateTimeField(auto_now_add=True)), ('action_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('article', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='articles.Article')), ], ), ]

0

3,645

23

84de6959d1cf90895494230c52ee66aae1877921

95

py

Python

Chapter 3/stateless.py

PacktPublishing/Machine-Learning-Model-Serving-Patterns-and-Best-Practices

390ae5868e023f0e417f5dca23eab69e848c5f91

[ "MIT" ]

null

Chapter 3/stateless.py

PacktPublishing/Machine-Learning-Model-Serving-Patterns-and-Best-Practices

390ae5868e023f0e417f5dca23eab69e848c5f91

[ "MIT" ]

null

Chapter 3/stateless.py

PacktPublishing/Machine-Learning-Model-Serving-Patterns-and-Best-Practices

390ae5868e023f0e417f5dca23eab69e848c5f91

[ "MIT" ]

null

if __name__ == "__main__": print(fun(5)) # Always returns 25

15.833333

37

0.6

def fun(x): return x * x if __name__ == "__main__": print(fun(5)) # Always returns 25

7

0

22

5013fdff381c0cc0e6850f21d14cefe9667b2bba

908

py

Python

plot.py

Aravind-Suresh/non-uniform-betweenness-centrality

4473ea157d93322a8680c1cfc18bfa69d00bfc56

[ "MIT" ]

null

plot.py

Aravind-Suresh/non-uniform-betweenness-centrality

4473ea157d93322a8680c1cfc18bfa69d00bfc56

[ "MIT" ]

null

plot.py

Aravind-Suresh/non-uniform-betweenness-centrality

4473ea157d93322a8680c1cfc18bfa69d00bfc56

[ "MIT" ]

null

import matplotlib.pyplot as plt import sys """ Utility script to plot Execution time Vs Number of vertices based on the log-files obtained by running tests/*.sh """ lines = open(sys.argv[1]).readlines()[1::2] x = map(lambda xx: 5*eval(xx.strip().split('\t')[1].split(' ')[1]), lines[::2]) # x = map(lambda xx: 400*eval(xx.strip().split(' ')[1].split('\t')[0]), lines[::2]) y = map(lambda xx: eval(xx.split(' ')[2]), lines[1::2]) t = {} c = {} for xx in x: c[xx] = 0 t[xx] = 0 for i in range(len(y)): xx = x[i] t[xx] += y[i] c[xx] += 1 y_plot = [] for i in range(len(y)): xx = x[i] y_plot.append(t[xx]*0.0001*25/c[xx]) # y_plot.append(t[xx]*14.711*0.6*0.5*10*16/c[xx]) print x print y_plot # print t[700]*10.0/c[700] # print y_plot plt.xlabel("Edges | Number of vertices = 5000") # plt.xlabel("Vertices") plt.ylabel("Execution time ( s )") plt.plot(x,y_plot,'ro') plt.show()

25.942857

113

0.598018

import matplotlib.pyplot as plt import sys """ Utility script to plot Execution time Vs Number of vertices based on the log-files obtained by running tests/*.sh """ lines = open(sys.argv[1]).readlines()[1::2] x = map(lambda xx: 5*eval(xx.strip().split('\t')[1].split(' ')[1]), lines[::2]) # x = map(lambda xx: 400*eval(xx.strip().split(' ')[1].split('\t')[0]), lines[::2]) y = map(lambda xx: eval(xx.split(' ')[2]), lines[1::2]) t = {} c = {} for xx in x: c[xx] = 0 t[xx] = 0 for i in range(len(y)): xx = x[i] t[xx] += y[i] c[xx] += 1 y_plot = [] for i in range(len(y)): xx = x[i] y_plot.append(t[xx]*0.0001*25/c[xx]) # y_plot.append(t[xx]*14.711*0.6*0.5*10*16/c[xx]) print x print y_plot # print t[700]*10.0/c[700] # print y_plot plt.xlabel("Edges | Number of vertices = 5000") # plt.xlabel("Vertices") plt.ylabel("Execution time ( s )") plt.plot(x,y_plot,'ro') plt.show()

0

ec87cbc371454a77008029d3d80e1d5a2d9cde1b

1,236

py

Python

microcosm_metrics/main.py

globality-corp/microcosm-metrics

ca63c7c7ef67c637c6f20cd37eac301f07e32c6d

[ "Apache-2.0" ]

1

2021-05-27T20:17:51.000Z

microcosm_metrics/main.py

globality-corp/microcosm-metrics

ca63c7c7ef67c637c6f20cd37eac301f07e32c6d

[ "Apache-2.0" ]

1

2018-10-09T13:16:17.000Z

microcosm_metrics/main.py

globality-corp/microcosm-metrics

ca63c7c7ef67c637c6f20cd37eac301f07e32c6d

[ "Apache-2.0" ]

1

2019-03-17T03:46:28.000Z

""" Test CLI for metric integration. """ from argparse import ArgumentParser from getpass import getuser from time import sleep from microcosm.api import create_object_graph from microcosm.loaders import load_from_dict from microcosm_metrics.naming import name_for def publish(): """ Publish a metric (for testing). """ args = parse_args() statsd = create_statsd_client(args) if args.action == "increment": statsd.increment(name_for(getuser(), args.action)) elif args.action == "histogram": statsd.histogram(name_for(getuser(), args.action), 1.0) try: # wait a little to allow the delivery of the metric before we exit sleep(1.0) except KeyboardInterrupt: pass

23.320755

92

0.674757

""" Test CLI for metric integration. """ from argparse import ArgumentParser from getpass import getuser from time import sleep from microcosm.api import create_object_graph from microcosm.loaders import load_from_dict from microcosm_metrics.naming import name_for def parse_args(): parser = ArgumentParser() parser.add_argument("--host", default="localhost") parser.add_argument("--action", choices=["increment", "histogram"], default="increment") return parser.parse_args() def create_statsd_client(args): loader = load_from_dict(dict( metrics=dict( host=args.host, ), )) graph = create_object_graph("example", loader=loader) graph.use("metrics") graph.lock() return graph.metrics def publish(): """ Publish a metric (for testing). """ args = parse_args() statsd = create_statsd_client(args) if args.action == "increment": statsd.increment(name_for(getuser(), args.action)) elif args.action == "histogram": statsd.histogram(name_for(getuser(), args.action), 1.0) try: # wait a little to allow the delivery of the metric before we exit sleep(1.0) except KeyboardInterrupt: pass

443

0

46

e8dab8438c4953bc3ee00db7c8f6087572852097

2,854

py

Python

test_metrics_maes.py

maximdanilchenko/fusionBasedRecSys

42b47a6cb2691967aad8c65932b932f8ee5e231d

[ "MIT" ]

null

test_metrics_maes.py

maximdanilchenko/fusionBasedRecSys

42b47a6cb2691967aad8c65932b932f8ee5e231d

[ "MIT" ]

null

test_metrics_maes.py

maximdanilchenko/fusionBasedRecSys

42b47a6cb2691967aad8c65932b932f8ee5e231d

[ "MIT" ]

null

from metrics import * from itembased_recommender_system import * import shelve import matplotlib.pyplot as plt genData('base','u2.base') genData('test','u2.test') print("data ready") base = transform(shelve.open('base')) test = transform(shelve.open('test')) ##base = {'max':{'odin doma':3,'labirint straha':5,'detektiv':2,'komnata':4}, ## 'dima':{'odin doma':5,'labirint straha':1,'detektiv':5}, ## 'alex':{'odin doma':5,'pila':2,'komnata':3,'grabim bank':3,'labirint straha':1,'detektiv':4,'dom s privideniamy':3}, ## 'den':{'odin doma':2,'grabim bank':3,'labirint straha':5,'dom s privideniamy':5}, ## 'kirill':{'grabim bank':3,'labirint straha':4,'detektiv':1,'dom s privideniamy':5}, ## 'olga':{'odin doma':3,'pila':4,'detektiv':4,'komnata':1,'dom s privideniamy':3}, ## 'lera':{'odin doma':4,'pila':3,'grabim bank':4,'labirint straha':1}, ## 'anna':{'pila':4,'grabim bank':2,'labirint straha':5,'komnata':4,'detektiv':4,'dom s privideniamy':4}} ## ##test = {'max':{'pila':4,'dom s privideniamy':3}, ## 'dima':{'pila':2,'dom s privideniamy':1}, ## 'kirill':{'odin doma':3,'pila':4}, ## 'olga':{'grabim bank':4,'labirint straha':1}} print("opened with size %d"%len(base)) tr = transform(base) print("transformed") ns = [30,100,200,300,500,0] ##ns = [1,2,3,4,5,6] cls = ['b','g','r','c','m','y'] markers = ['.',',','o','v','^','<','>','p','*','+'] cls_n = 0 for n in ns: metrix = [JMSD,PCC,CPCC,SPCC,Jaccard,MSD,COS,ACOS] maes = {} for sim in metrix: if (n != 0): itMtr = itemMatrix(tr,n,sim) else: itMtr = itemMatrix(tr,n,sim,False) orig_recs = {} test_recs = {} for user in test: orig_recs[user] = {} test_recs[user] = {} for item in test[user]: rec = recommendOne(base,tr,itMtr,item,user) if (rec != 200): orig_recs[user][item] = rec test_recs[user][item] = test[user][item] mae = MAE(test_recs,orig_recs) print("Mae for %s is %f"%(sim.__name__,mae)) maes[sim.__name__] = mae print(maes) labels = sorted(maes) x = [0,1,2,3,4,5,6,7] y = [maes[i] for i in labels] plt.plot(x,y,color = cls[cls_n],marker = markers[cls_n],linewidth=1.5,label = 'k = '+str(n)) ## plt.plot(x,y,'ko') cls_n += 1 plt.title('maes, user-based') plt.axis([-1,8,0.7,1.1]) plt.ylabel('MAE') plt.xlabel('metrics') plt.xticks(x,labels,rotation='vertical') plt.subplots_adjust(bottom=0.15) plt.legend() ##for i,j in zip(x,y): ## plt.annotate(str(round(j,4)),xy=(i-0.5,j+0.01)) plt.show() ##plt.savefig('E:/Diploma/results/maes-item-based_n'+str(n)+'.png')

38.567568

131

0.546952

from metrics import * from itembased_recommender_system import * import shelve import matplotlib.pyplot as plt genData('base','u2.base') genData('test','u2.test') print("data ready") base = transform(shelve.open('base')) test = transform(shelve.open('test')) ##base = {'max':{'odin doma':3,'labirint straha':5,'detektiv':2,'komnata':4}, ## 'dima':{'odin doma':5,'labirint straha':1,'detektiv':5}, ## 'alex':{'odin doma':5,'pila':2,'komnata':3,'grabim bank':3,'labirint straha':1,'detektiv':4,'dom s privideniamy':3}, ## 'den':{'odin doma':2,'grabim bank':3,'labirint straha':5,'dom s privideniamy':5}, ## 'kirill':{'grabim bank':3,'labirint straha':4,'detektiv':1,'dom s privideniamy':5}, ## 'olga':{'odin doma':3,'pila':4,'detektiv':4,'komnata':1,'dom s privideniamy':3}, ## 'lera':{'odin doma':4,'pila':3,'grabim bank':4,'labirint straha':1}, ## 'anna':{'pila':4,'grabim bank':2,'labirint straha':5,'komnata':4,'detektiv':4,'dom s privideniamy':4}} ## ##test = {'max':{'pila':4,'dom s privideniamy':3}, ## 'dima':{'pila':2,'dom s privideniamy':1}, ## 'kirill':{'odin doma':3,'pila':4}, ## 'olga':{'grabim bank':4,'labirint straha':1}} print("opened with size %d"%len(base)) tr = transform(base) print("transformed") ns = [30,100,200,300,500,0] ##ns = [1,2,3,4,5,6] cls = ['b','g','r','c','m','y'] markers = ['.',',','o','v','^','<','>','p','*','+'] cls_n = 0 for n in ns: metrix = [JMSD,PCC,CPCC,SPCC,Jaccard,MSD,COS,ACOS] maes = {} for sim in metrix: if (n != 0): itMtr = itemMatrix(tr,n,sim) else: itMtr = itemMatrix(tr,n,sim,False) orig_recs = {} test_recs = {} for user in test: orig_recs[user] = {} test_recs[user] = {} for item in test[user]: rec = recommendOne(base,tr,itMtr,item,user) if (rec != 200): orig_recs[user][item] = rec test_recs[user][item] = test[user][item] mae = MAE(test_recs,orig_recs) print("Mae for %s is %f"%(sim.__name__,mae)) maes[sim.__name__] = mae print(maes) labels = sorted(maes) x = [0,1,2,3,4,5,6,7] y = [maes[i] for i in labels] plt.plot(x,y,color = cls[cls_n],marker = markers[cls_n],linewidth=1.5,label = 'k = '+str(n)) ## plt.plot(x,y,'ko') cls_n += 1 plt.title('maes, user-based') plt.axis([-1,8,0.7,1.1]) plt.ylabel('MAE') plt.xlabel('metrics') plt.xticks(x,labels,rotation='vertical') plt.subplots_adjust(bottom=0.15) plt.legend() ##for i,j in zip(x,y): ## plt.annotate(str(round(j,4)),xy=(i-0.5,j+0.01)) plt.show() ##plt.savefig('E:/Diploma/results/maes-item-based_n'+str(n)+'.png')

0

a2bcc15bc5cd3222f1d3285db315bac3a27b1e83

1,289

py

Python

qasm/circuits/combined_constraints_v2.py

JamesGopsill/ICED21-Quantum-Design

bbb3d60639f0dbb81aa18165647eb4a0769d1a26

[ "MIT" ]

null

qasm/circuits/combined_constraints_v2.py

JamesGopsill/ICED21-Quantum-Design

bbb3d60639f0dbb81aa18165647eb4a0769d1a26

[ "MIT" ]

null

qasm/circuits/combined_constraints_v2.py

JamesGopsill/ICED21-Quantum-Design

bbb3d60639f0dbb81aa18165647eb4a0769d1a26

[ "MIT" ]

null

from qiskit.circuit.quantumcircuit import QuantumCircuit, QuantumRegister, ClassicalRegister

18.955882

92

0.6827

from qiskit.circuit.quantumcircuit import QuantumCircuit, QuantumRegister, ClassicalRegister def combined_constraints_v2() -> QuantumCircuit: # Initialise the quantum register qreg = QuantumRegister(10, "q") creg = ClassicalRegister(4, "c") # Initialise the circuit circuit = QuantumCircuit(qreg, creg) circuit.h(qreg[0]) circuit.h(qreg[1]) circuit.h(qreg[2]) circuit.h(qreg[3]) circuit.barrier() circuit.ccx(qreg[0], qreg[1], qreg[4]) circuit.cx(qreg[4], qreg[0]) circuit.barrier() circuit.ccx(qreg[2], qreg[3], qreg[5]) circuit.cx(qreg[5], qreg[2]) circuit.barrier() circuit.ccx(qreg[0], qreg[2], qreg[6]) circuit.x(qreg[0]) circuit.x(qreg[2]) circuit.barrier() circuit.ccx(qreg[0], qreg[2], qreg[6]) circuit.x(qreg[0]) circuit.x(qreg[2]) circuit.barrier() circuit.ccx(qreg[1], qreg[3], qreg[7]) circuit.x(qreg[1]) circuit.x(qreg[3]) circuit.barrier() circuit.ccx(qreg[1], qreg[3], qreg[7]) circuit.x(qreg[1]) circuit.x(qreg[3]) circuit.barrier() circuit.ccx(qreg[6], qreg[7], qreg[8]) circuit.cx(qreg[8], qreg[3]) circuit.barrier() circuit.ccx(qreg[2], qreg[3], qreg[9]) circuit.cx(qreg[9], qreg[2]) circuit.barrier() circuit.measure(0, 0) circuit.measure(1, 1) circuit.measure(2, 2) circuit.measure(3, 3) return circuit

1,174

0

23

1167c2e8ebd9cd5d4995f31fedeb5bf1dfb82d45

2,492

py

Python

meiduo_mall/meiduo_mall/apps/oauth/utils.py

bapewing/MeiduoMall

2b1634de2b7bc9eec80716770894824d668ec314

[ "MIT" ]

null

meiduo_mall/meiduo_mall/apps/oauth/utils.py

bapewing/MeiduoMall

2b1634de2b7bc9eec80716770894824d668ec314

[ "MIT" ]

null

meiduo_mall/meiduo_mall/apps/oauth/utils.py

bapewing/MeiduoMall

2b1634de2b7bc9eec80716770894824d668ec314

[ "MIT" ]

null

import json import logging from urllib.parse import urlencode, parse_qs from urllib.request import urlopen from django.conf import settings from oauth.exceptions import QQAPIException logger = logging.getLogger('django') class OauthQQ(object): """ QQ认证工具类 """ def get_auth_url(self): """ 生成访问qq的拼接路径 :return: """ parameters = { 'response_type': 'code', 'client_id': self.app_id, 'redirect_uri': self.redirect_uri, 'state': self.state, 'scope': 'get_user_info', } return 'https://graph.qq.com/oauth2.0/authorize?' + urlencode(parameters) def get_access_token(self, code): """ 获取QQ的access_token :param code: 重定向域路径中code :return: access_token """ parameters = { 'grant_type': 'authorization_code', 'client_id': self.app_id, 'client_secret': self.app_key, 'code': code, 'redirect_uri': self.redirect_uri } url = 'https://graph.qq.com/oauth2.0/token?' + urlencode(parameters) # access_token=FE04************************CCE2&expires_in=7776000&refresh_token=88E4************************BE14 try: response = urlopen(url).read().decode() response_dict = parse_qs(response) access_token = response_dict.get("access_token")[0] except Exception as e: logger.error(e) raise QQAPIException('获取access_token异常') return access_token def get_openid(self, access_token): """ 获取QQ用户的openid :param access_token: 服务器获取的access_token :return: openid """ url = 'https://graph.qq.com/oauth2.0/me?access_token=' + access_token # callback( {"client_id":"YOUR_APPID","openid":"YOUR_OPENID"} ); try: reponse = urlopen(url).read().decode() # TODO:可以换种方式操作字符串 reponse_dict = json.loads(reponse[10:-4]) except Exception as e: logger.error(e) raise QQAPIException('获取openid异常') return reponse_dict.get('openid')

30.390244

121

0.58748

import json import logging from urllib.parse import urlencode, parse_qs from urllib.request import urlopen from django.conf import settings from oauth.exceptions import QQAPIException logger = logging.getLogger('django') class OauthQQ(object): """ QQ认证工具类 """ def __init__(self, app_id=None, app_key=None, redirect_uri=None, state=None): self.app_id = app_id or settings.QQ_APP_ID self.app_key = app_key or settings.QQ_APP_KEY self.redirect_uri = redirect_uri or settings.QQ_REDIRECT_URI self.state = state or settings.QQ_STATE def get_auth_url(self): """ 生成访问qq的拼接路径 :return: """ parameters = { 'response_type': 'code', 'client_id': self.app_id, 'redirect_uri': self.redirect_uri, 'state': self.state, 'scope': 'get_user_info', } return 'https://graph.qq.com/oauth2.0/authorize?' + urlencode(parameters) def get_access_token(self, code): """ 获取QQ的access_token :param code: 重定向域路径中code :return: access_token """ parameters = { 'grant_type': 'authorization_code', 'client_id': self.app_id, 'client_secret': self.app_key, 'code': code, 'redirect_uri': self.redirect_uri } url = 'https://graph.qq.com/oauth2.0/token?' + urlencode(parameters) # access_token=FE04************************CCE2&expires_in=7776000&refresh_token=88E4************************BE14 try: response = urlopen(url).read().decode() response_dict = parse_qs(response) access_token = response_dict.get("access_token")[0] except Exception as e: logger.error(e) raise QQAPIException('获取access_token异常') return access_token def get_openid(self, access_token): """ 获取QQ用户的openid :param access_token: 服务器获取的access_token :return: openid """ url = 'https://graph.qq.com/oauth2.0/me?access_token=' + access_token # callback( {"client_id":"YOUR_APPID","openid":"YOUR_OPENID"} ); try: reponse = urlopen(url).read().decode() # TODO:可以换种方式操作字符串 reponse_dict = json.loads(reponse[10:-4]) except Exception as e: logger.error(e) raise QQAPIException('获取openid异常') return reponse_dict.get('openid')

278

0

27

bbc176cb39896e1eb962622b41c7feb57f5208ed

17,306

py

Python

src/state_base.py

akolishchak/doom-net-pytorch

96bad5b15c9c5267d494cd5791481801cd6d2107

[ "MIT" ]

143

2017-01-30T01:43:58.000Z

2021-11-15T07:53:22.000Z

src/state_base.py

akolishchak/doom-net-pytorch

96bad5b15c9c5267d494cd5791481801cd6d2107

[ "MIT" ]

7

2017-12-28T02:42:08.000Z

2020-05-23T23:12:33.000Z

src/state_base.py

akolishchak/doom-net-pytorch

96bad5b15c9c5267d494cd5791481801cd6d2107

[ "MIT" ]

27

2017-02-03T09:20:10.000Z

2020-07-19T21:35:28.000Z

# # aac_state_base.py, doom-net # # Created by Andrey Kolishchak on 01/21/17. # import os import datetime import glob from multiprocessing.pool import ThreadPool from threading import Thread import time import h5py import bisect import torch import torch.nn.functional as F import torch.optim as optim from torch.utils.data import Dataset, DataLoader import numpy as np from device import device from model import Model from state_model import StateModel from state_controller import AdvantageActorCriticController import vizdoom

45.067708

229

0.570322

# # aac_state_base.py, doom-net # # Created by Andrey Kolishchak on 01/21/17. # import os import datetime import glob from multiprocessing.pool import ThreadPool from threading import Thread import time import h5py import bisect import torch import torch.nn.functional as F import torch.optim as optim from torch.utils.data import Dataset, DataLoader import numpy as np from device import device from model import Model from state_model import StateModel from state_controller import AdvantageActorCriticController import vizdoom class StateBase: def __init__(self, args): super().__init__() def run_train(self, args): for iter in range(100): self.generate_data(args) self.train_state_model(args) self.train_controller(args) def generate_data(self, args): print("Generate data...") def worker(id, args): state_model = Model.create(StateModel, args, args.state_model) state_model.eval() controller = Model.create(AdvantageActorCriticController, args, args.checkpoint_file) controller.eval() new_controller = not os.path.isfile(args.checkpoint_file) game = args.instance_class(args.vizdoom_config, args.wad_path, args.skiprate, actions=args.action_set, id=id) state = args.instance_class.NormalizedState(screen=None, depth=None, labels=None, variables=None) state.screen = torch.Tensor(1, *args.screen_size) state.variables = torch.Tensor(1, args.variable_num) action_onehot = torch.zeros(1, args.button_num, device=device) cells = StateModel.get_cells(1) episode_num = 2 max_step = 1000 for episode in range(episode_num): step_state = game.get_state_normalized() episode_screens = [] episode_variables = [] episode_actions = [] episode_vars = [] action = 0 for step in range(max_step): # convert state to torch tensors state.screen[0, :] = torch.from_numpy(step_state.screen) state.variables[0, :] = torch.from_numpy(step_state.variables) # compute an action if not new_controller: with torch.set_grad_enabled(False): observation = state_model.features(state.screen.to(device), state.variables.to(device)) action = controller.forward(observation, cells[-2]) cells, pred = state_model(observation, action_onehot.zero_().scatter_(-1, action, 1), cells) else: action = torch.randint(0, args.button_num, (1, 1), dtype=torch.long, device=device) action_onehot.zero_().scatter_(-1, action, 1) episode_screens.append(step_state.screen) episode_variables.append(step_state.variables) episode_actions.append(action_onehot.cpu().numpy()[0]) # render step_state, _, finished, vars = game.step_normalized(action[0, 0]) episode_vars.append(vars) if finished: print("episode return: {}".format(game.get_episode_return())) cells = state_model.set_nonterminal(cells, torch.zeros(1, 1)) break # # save episodes data to file # filename = os.path.join(args.h5_path, '{:%Y-%m-%d %H-%M-%S}-{}-{}.hd5'.format(datetime.datetime.now(), id, episode)) print(filename) file = h5py.File(filename, 'w') file.create_dataset('screens', data=episode_screens, dtype='float32', compression='gzip') file.create_dataset('variables', data=episode_variables, dtype='float32', compression='gzip') file.create_dataset('actions', data=episode_actions, dtype='float32', compression='gzip') file.create_dataset('vars', data=episode_vars, dtype='float32', compression='gzip') game.new_episode() threads = [] for i in range(5): thread = Thread(target=worker, args=(i, args)) thread.start() threads.append(thread) for thread in threads: thread.join() def train_state_model(self, args): print("Train state model...") state_model = Model.create(StateModel, args, args.state_model) state_model.train() def data_generator(args): batch_size = args.batch_size episode_size = args.episode_size screens = [] variables = [] actions = [] vars = [] for filename in glob.glob(os.path.join(args.h5_path, '*.hd5')): file = h5py.File(filename, 'r') screens.append(file['screens']) variables.append(file['variables']) actions.append(file['actions']) vars.append(file['vars']) # episodes_num = len(screens) step_screens = np.ndarray(shape=(batch_size, *screens[0].shape[1:]), dtype=np.float32) step_variables = np.ndarray(shape=(batch_size, *variables[0].shape[1:]), dtype=np.float32) step_actions = np.ndarray(shape=(batch_size, *actions[0].shape[1:]), dtype=np.float32) step_vars = np.ndarray(shape=(batch_size, *vars[0].shape[1:]), dtype=np.int) step_nonterminals = np.ones(shape=(batch_size, 1), dtype=np.float32) # select episodes for the initial batch batch_episodes = np.random.randint(episodes_num, size=batch_size) batch_episodes_length = np.array([len(actions[episode]) for episode in batch_episodes]) batch_episodes_step = np.zeros(batch_size, dtype=np.int) iter_num = batch_episodes_length.mean().astype(np.int)*episodes_num//batch_size for iter in range(iter_num): for i in range(batch_size): episode = batch_episodes[i] step = batch_episodes_step[i] length = batch_episodes_length[i] step_screens[i, :] = screens[episode][step] step_variables[i, :] = variables[episode][step] step_actions[i, :] = actions[episode][step] step_vars[i, :] = vars[episode][step]+1 batch_episodes_step[i] += 1 if batch_episodes_step[i] >= length: step_nonterminals[i] = 0.0 # reached terminal state, select a new episode episode = np.random.randint(episodes_num) batch_episodes[i] = episode batch_episodes_step[i] = 0 else: if step_variables[i, -1] == 0: step_nonterminals[i] = 1.0 else: step_nonterminals[i] = 0.0 yield torch.from_numpy(step_screens), \ torch.from_numpy(step_variables), \ torch.from_numpy(step_actions), \ torch.from_numpy(step_vars), \ torch.from_numpy(step_nonterminals) training_data_loader = data_generator(args) optimizer = optim.AdamW(state_model.parameters(), lr=5e-4, weight_decay=1e-4, amsgrad=True) cells = StateModel.get_cells(args.batch_size) epoch_num = 1 for epoch in range(epoch_num): mean_loss = 0 mean_accuracy = 0 updates = 0 batch_time = time.time() for batch, (screens, variables, actions, vars, nonterminals) in enumerate(training_data_loader): screens, variables, actions, vars, nonterminals = \ screens.to(device), variables.to(device), actions.to(device), vars.to(device), nonterminals.to(device) observation = state_model.features(screens, variables) cells, pred = state_model(observation, actions, cells) cells = state_model.set_nonterminal(cells, nonterminals) loss = F.nll_loss(pred, vars) mean_loss += loss.item() updates += 1 _, pred_vars = pred.max(1) mean_accuracy += (pred_vars == vars).float().mean() if batch % args.episode_size == args.episode_size - 1: loss.backward() grads = [] weights = [] for p in state_model.parameters(): if p.grad is not None: grads.append(p.grad.data.view(-1)) weights.append(p.data.view(-1)) grads = torch.cat(grads, 0) grads_norm = grads.norm() weights = torch.cat(weights, 0) weights_norm = weights.norm() assert grads_norm == grads_norm optimizer.step() optimizer.zero_grad() cells = state_model.reset(cells) mean_loss /= updates mean_accuracy /= updates print("episode loss = {:f}, accuracy = {:f}, grads_norm = {:f}, weights_norm = {:f} train_time = {:.3f}".format(mean_loss, mean_accuracy, grads_norm, weights_norm, time.time() - batch_time)) mean_loss = 0 mean_accuracy = 0 updates = 0 batch_time = time.time() if batch >= 5000: break torch.save(state_model.state_dict(), args.state_model) def train_controller(self, args): print("Controller training...") controller = Model.create(AdvantageActorCriticController, args) #, args.load) controller.train() optimizer = optim.AdamW(controller.parameters(), lr=5e-4, amsgrad=True) #if args.load is not None and os.path.isfile(args.load + '_optimizer.pth'): # optimizer_dict = torch.load(args.load+'_optimizer.pth') # optimizer.load_state_dict(optimizer_dict) assert args.state_model is not None state_model = Model.create(StateModel, args, args.state_model) state_model.eval() optimizer.zero_grad() state = args.instance_class.NormalizedState(screen=None, depth=None, labels=None, variables=None) state.screen = torch.Tensor(args.batch_size, *args.screen_size) state.variables = torch.Tensor(args.batch_size, args.variable_num) vars = torch.Tensor(args.batch_size, args.variable_num).long() reward = torch.Tensor(args.batch_size, 1) nonterminal = torch.Tensor(args.batch_size, 1) action_onehot = torch.zeros(args.batch_size, len(args.action_set), device=device) cells = StateModel.get_cells(args.batch_size) games = [] for i in range(args.batch_size): games.append(args.instance_class(args.vizdoom_config, args.wad_path, args.skiprate, actions=args.action_set, id=i)) pool = ThreadPool() def get_state(game): id = game.get_id() normalized_state = game.get_state_normalized() state.screen[id, :] = torch.from_numpy(normalized_state.screen) state.variables[id, :] = torch.from_numpy(normalized_state.variables) pool.map(get_state, games) # start training for episode in range(args.episode_num): batch_time = time.time() rewards = [] nonterminals = [] episode_return = 0 episode_accuracy = 0 for step in range(args.episode_size): # get action with torch.set_grad_enabled(False): observation = state_model.features(state.screen.to(device), state.variables.to(device)) action = controller.forward(observation, cells[-2]) with torch.set_grad_enabled(False): cells, pred = state_model(observation, action_onehot.zero_().scatter_(-1, action, 1), cells) action = action.cpu() #print(action.squeeze()) # step and get new state def step_game(game): id = game.get_id() normalized_state, step_reward, finished, step_vars = game.step_normalized(action[id, 0]) state.screen[id, :] = torch.from_numpy(normalized_state.screen) state.variables[id, :] = torch.from_numpy(normalized_state.variables) reward[id, 0] = step_reward vars[id] = torch.from_numpy(step_vars+1) if finished: #episode_return[id] = float(game.get_episode_return()) # cut rewards from future actions nonterminal[id] = 0 else: nonterminal[id] = 1 pool.map(step_game, games) #rewards.append(reward.clone()) # mse as reward for exploration policy _, pred_vars = pred.max(1) episode_accuracy += (pred_vars == vars.to(device)).float().mean() exploration_reward = F.nll_loss(pred, vars.to(device), reduce=False).mean(dim=-1) exploration_reward = exploration_reward[:, None].cpu()*0.1 episode_return += exploration_reward.mean() rewards.append(exploration_reward) noterminal_copy = nonterminal.clone() nonterminals.append(noterminal_copy) cells = state_model.set_nonterminal(cells, noterminal_copy) # update model controller.backward(rewards, nonterminals) grads = [] weights = [] for p in controller.parameters(): if p.grad is not None: grads.append(p.grad.view(-1)) weights.append(p.view(-1)) grads = torch.cat(grads, 0) weights = torch.cat(weights, 0) grads_norm = grads.norm() weights_norm = weights.norm() assert grads_norm == grads_norm optimizer.step() optimizer.zero_grad() episode_accuracy /= args.episode_size episode_return /= args.episode_size if episode % 1 == 0: print("{}: mean_return = {:f}, mean_accuracy= {:f}, grads_norm = {:f}, weights_norm = {:f}, batch_time = {:.3f}".format(episode, episode_return, episode_accuracy, grads_norm, weights_norm, time.time()-batch_time)) if episode % args.checkpoint_rate == 0: torch.save(controller.state_dict(), args.checkpoint_file) #torch.save(optimizer.state_dict(), args.checkpoint_file+'_optimizer.pth') # terminate games pool.map(lambda game: game.release(), games) torch.save(controller.state_dict(), args.checkpoint_file) #torch.save(optimizer.state_dict(), args.checkpoint_file+'_optimizer.pth') def run_test(self, args): print("testing...") controller = Model.create(AdvantageActorCriticController, args, args.load) controller.eval() assert args.state_model is not None state_model = Model.create(StateModel, args, args.state_model) state_model.eval() game = args.instance_class( args.vizdoom_config, args.wad_path, args.skiprate, visible=True, mode=vizdoom.Mode.ASYNC_PLAYER, actions=args.action_set) step_state = game.get_state_normalized() state = args.instance_class.NormalizedState(screen=None, depth=None, labels=None, variables=None) state.screen = torch.Tensor(1, *args.screen_size) state.variables = torch.Tensor(1, args.variable_num) action_onehot = torch.zeros(1, len(args.action_set), device=device) cells = StateModel.get_cells(1) while True: # convert state to torch tensors state.screen[0, :] = torch.from_numpy(step_state.screen) state.variables[0, :] = torch.from_numpy(step_state.variables) # compute an action with torch.set_grad_enabled(False): observation = state_model.features(state.screen.to(device), state.variables.to(device)) action = controller.forward(observation, cells[-2]) cells, pred = state_model(observation, action_onehot.zero_().scatter_(-1, action, 1), cells) action = action.cpu() print(action) # render step_state, _, finished, _ = game.step_normalized(action[0, 0]) if finished: print("episode return: {}".format(game.get_episode_return())) cells = state_model.set_nonterminal(torch.zeros(1, 1))

16,596

-5

184

d85c96f9a4ab5c97f33e2f69851fd5509bf3d8bc

1,027

py

Python

dataset/TransNAS-Bench-101/models/utils/utils.py

mindspore-ai/contrib

85dccac7a2ba6e962092ecd51aefd962d7f2aeac

[ "Apache-2.0" ]

2

2021-11-10T06:16:55.000Z

2022-02-22T11:30:04.000Z

dataset/TransNAS-Bench-101/models/utils/utils.py

mindspore-ai/contrib

85dccac7a2ba6e962092ecd51aefd962d7f2aeac

[ "Apache-2.0" ]

null

dataset/TransNAS-Bench-101/models/utils/utils.py

mindspore-ai/contrib

85dccac7a2ba6e962092ecd51aefd962d7f2aeac

[ "Apache-2.0" ]

1

2022-03-22T06:03:15.000Z

# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """utils""" import sys from pathlib import Path lib_dir = (Path(__file__).parent / '..').resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) ############################ # operations for all tasks # ############################

30.205882

78

0.627069

# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """utils""" import sys from pathlib import Path lib_dir = (Path(__file__).parent / '..').resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) ############################ # operations for all tasks # ############################ def merge_list(lists): merged = [] for li in lists: merged += li return merged

77

0

23

a155bdc515a1f2882b5a75befd2139acb09c7731

1,410

py

Python

tests/classes/test_channel_group_registry.py

vkottler/vtelem

3908f2445249ddb5f2057fe3a7f46c204b677bbb

[ "MIT" ]

3

2021-02-03T01:14:44.000Z

2022-02-27T00:14:13.000Z

tests/classes/test_channel_group_registry.py

vkottler/vtelem

3908f2445249ddb5f2057fe3a7f46c204b677bbb

[ "MIT" ]

27

2021-05-04T21:19:41.000Z

2022-02-27T00:49:18.000Z

tests/classes/test_channel_group_registry.py

vkottler/vtelem

3908f2445249ddb5f2057fe3a7f46c204b677bbb

[ "MIT" ]

null

""" vtelem - Test the channel-group registry's correctness. """ # module under test from vtelem.enums.primitive import Primitive from vtelem.channel.group_registry import ChannelGroupRegistry from vtelem.telemetry.environment import TelemetryEnvironment # internal from . import EnumA def test_group_registry_basic(): """Test simple functionality of a group registry.""" env = TelemetryEnvironment(2 ** 8) assert env.add_from_enum(EnumA) >= 0 reg = ChannelGroupRegistry(env) groups = [ reg.create_group("a"), reg.create_group("b"), reg.create_group("c"), ] # add channels to each group for group in groups: reg.add_channel(group, "a", Primitive.UINT32, 1.0) reg.add_channel(group, "b", Primitive.UINT32, 1.0) reg.add_channel(group, "c", Primitive.UINT32, 1.0) reg.add_enum_channel(group, "test_enum", "enum_a", 1.0) # write channels in each group for group in groups: with reg.group(group) as data: data["a"] = 1 data["b"] = 2 data["c"] = 3 data["test_enum"] = "b" # read channels in each group to make sure correct values were written for group in groups: with reg.group(group) as data: assert data["a"] == 1 assert data["b"] == 2 assert data["c"] == 3 assert data["test_enum"] == "b"

29.375

74

0.61844

""" vtelem - Test the channel-group registry's correctness. """ # module under test from vtelem.enums.primitive import Primitive from vtelem.channel.group_registry import ChannelGroupRegistry from vtelem.telemetry.environment import TelemetryEnvironment # internal from . import EnumA def test_group_registry_basic(): """Test simple functionality of a group registry.""" env = TelemetryEnvironment(2 ** 8) assert env.add_from_enum(EnumA) >= 0 reg = ChannelGroupRegistry(env) groups = [ reg.create_group("a"), reg.create_group("b"), reg.create_group("c"), ] # add channels to each group for group in groups: reg.add_channel(group, "a", Primitive.UINT32, 1.0) reg.add_channel(group, "b", Primitive.UINT32, 1.0) reg.add_channel(group, "c", Primitive.UINT32, 1.0) reg.add_enum_channel(group, "test_enum", "enum_a", 1.0) # write channels in each group for group in groups: with reg.group(group) as data: data["a"] = 1 data["b"] = 2 data["c"] = 3 data["test_enum"] = "b" # read channels in each group to make sure correct values were written for group in groups: with reg.group(group) as data: assert data["a"] == 1 assert data["b"] == 2 assert data["c"] == 3 assert data["test_enum"] == "b"

0

6eaccc68cf3c4a05bd6471980fcbe35ac2f815a9

775

py

Python

extra_scripts/plot_naive_expansion_length.py

wdecoster/exrede

3e1f81d55365c360ee0d2d620c5239b5f3488825

[ "MIT" ]

null

extra_scripts/plot_naive_expansion_length.py

wdecoster/exrede

3e1f81d55365c360ee0d2d620c5239b5f3488825

[ "MIT" ]

null

extra_scripts/plot_naive_expansion_length.py

wdecoster/exrede

3e1f81d55365c360ee0d2d620c5239b5f3488825

[ "MIT" ]

null

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt res = [line.strip().split('\t') for line in open("sizes.txt").readlines()] a = [] for line in res: sizes = [int(i) for i in line[1].split(',')] strands = [j for j in line[2].split(',')] for si, st in zip(sizes, strands): a.append((line[0], si, st)) df = pd.DataFrame(a, columns=['name', 'length', 'strand1']) df.loc[df.strand1 == "True", 'strand'] = '-' df.loc[df.strand1 == "False", 'strand'] = '+' df.replace(dict(d6843='Patient1', d5945='Patient2'), regex=True, inplace=True ) plt.close("all") sns.swarmplot(x="name", y="length", data=df, hue="strand") plt.legend(loc='upper left', title="strand", frameon=False) plt.show()

27.678571

74

0.590968

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt res = [line.strip().split('\t') for line in open("sizes.txt").readlines()] a = [] for line in res: sizes = [int(i) for i in line[1].split(',')] strands = [j for j in line[2].split(',')] for si, st in zip(sizes, strands): a.append((line[0], si, st)) df = pd.DataFrame(a, columns=['name', 'length', 'strand1']) df.loc[df.strand1 == "True", 'strand'] = '-' df.loc[df.strand1 == "False", 'strand'] = '+' df.replace(dict(d6843='Patient1', d5945='Patient2'), regex=True, inplace=True ) plt.close("all") sns.swarmplot(x="name", y="length", data=df, hue="strand") plt.legend(loc='upper left', title="strand", frameon=False) plt.show()

0

582bb6d45d5243b53f6b225ab85affb63b8d114d

11,102

py

Python

src/pyetllib/etllib/tools/streamtools.py

slouchart/pyetllib

133df36a1628f413cd60a86e4c7eac2738844d17

[ "MIT" ]

2

2020-04-01T10:08:02.000Z

2021-03-07T15:18:14.000Z

src/pyetllib/etllib/tools/streamtools.py

slouchart/pyetllib

133df36a1628f413cd60a86e4c7eac2738844d17

[ "MIT" ]

null

src/pyetllib/etllib/tools/streamtools.py

slouchart/pyetllib

133df36a1628f413cd60a86e4c7eac2738844d17

[ "MIT" ]

1

2020-10-13T13:23:02.000Z

from itertools import starmap, filterfalse, zip_longest from itertools import chain import functools from functools import reduce as reduce_ import operator from collections import namedtuple import toolz from toolz import pipe as pipe_, compose as compose_ from ._iterators import _iterators_controller, _controlled_iterator """ designing a lookup scheme. accepts any Iterable for each item in this iterable - computes a key value from the item data using a provided callable - checks if a provided lookup map contains this key if yes: - outputs this item optionally enriched with the lookup data using another provided callable if no: if rejects are enabled: - outputs this item in a separate iterator """ @stream_converter.dispatch(tuple, tuple) @stream_converter.dispatch(dict, dict) @stream_converter.dispatch(namedtuple, namedtuple) @stream_converter.dispatch(tuple, dict, key_type=str) @stream_converter.dispatch(tuple, dict, key_type=int) @stream_converter.dispatch(tuple, namedtuple) @stream_converter.dispatch(dict, tuple) @stream_converter.dispatch(dict, namedtuple) @stream_converter.dispatch(namedtuple, dict) @stream_converter.dispatch(namedtuple, tuple) def xargs(g, funcs, as_iterable=False): """returns a function that accepts a tuple as an arguments and then maps each element of this tuple to one of the funcs generating another tuple in the process. Finally, the function g is called with the tuple elements as arguments. If the tuple does not contain enough elements to map all the funcs, the last element is repeated to provide an argument to the remaining funcs """ return inner

26.245863

96

0.580796

from itertools import starmap, filterfalse, zip_longest from itertools import chain import functools from functools import reduce as reduce_ import operator from collections import namedtuple import toolz from toolz import pipe as pipe_, compose as compose_ from ._iterators import _iterators_controller, _controlled_iterator def aggregate(aggregator, groupings): if aggregator is None: aggregator = lambda x: x # noqa: E731 for k, g in dict(groupings).items(): yield k, aggregator(g) def call_next(iterable): it = iter(iterable) def inner(): try: return next(it) except StopIteration: return None return inner def call_next_starred(*items): return call_next(iter(items)) def compose(*funcs): return compose_(*funcs) def filtertruefalse(predicate, iterable): src_1, src_2 = replicate(iterable) return filter(predicate, src_1), filterfalse(predicate, src_2) def groupby(key, iterable): return toolz.groupby(key, iterable) def join(*iterables, fill_value=None): iterators = [iter(it) for it in iterables] stopped_iterators = {it: False for it in iterators} while True: result = [] for it in iterators: try: item = next(it) except StopIteration: item = fill_value stopped_iterators[it] = True result.append(item) if all(stopped_iterators.values()): return else: yield tuple(result) """ designing a lookup scheme. accepts any Iterable for each item in this iterable - computes a key value from the item data using a provided callable - checks if a provided lookup map contains this key if yes: - outputs this item optionally enriched with the lookup data using another provided callable if no: if rejects are enabled: - outputs this item in a separate iterator """ def lookup(iterable, key=lambda x: x, lookup_map=None, merge=False, enable_rejects=False): if lookup_map is None: lookup_map = {} if merge and callable(merge): func_merge = merge elif merge: def func_merge(a, b): return a, b else: def func_merge(a, _): return a def lookup_(it): def map_merge(e): return func_merge(e, lookup_map[key(e)]) return map( map_merge, filter( lambda x: key(x) in lookup_map, it ) ) if enable_rejects: src1, src2 = replicate(iterable) return lookup_(iter(src1)), \ filterfalse( lambda x: key(x) in lookup_map, iter(src2) ) else: return lookup_(iter(iterable)) def mcompose(*funcs): def _composer(f, g): def inner(*args, **kwargs): return f(*g(*args, **kwargs)) return inner return reduce_( _composer, funcs ) class pipable(object): def __init__(self, callable_): self._callable = callable_ assert callable(self._callable) def __call__(self, *args, **kwargs): result = self._callable(*args, **kwargs) if callable(result): return pipable(result) else: return result def __or__(self, other): return pipable(pipeline(self, other)) def pipeline(*funcs): return compose_(*reversed(funcs)) def pipe_data_through(data, *steps): return pipe_(data, *steps) def reduce(function, iterable, initial=None): return functools.reduce( function, iterable, initial ) class replicate(_iterators_controller): def create_controlled_iterators(self, n=2): return tuple( _controlled_iterator(self) for _ in range(n) ) def dispatch_item(self, item, requester): for it in self._iterators: it.send(item) def select(predicates, iterable, strict=False): if predicates is None or len(predicates) == 0: clauses = ( lambda x: bool(x), lambda x: not bool(x) ) # works either if strict is True else: def or_(f1, f2): return xargs( operator.or_, (f1, f2) ) def and_not_(f1, f2): return xargs( operator.and_, ( f1, compose_( operator.not_, f2 ) ) ) last_clause = lambda a: False # noqa: E731 if strict: clauses = list() for predicate in predicates: last_clause = and_not_( predicate, last_clause ) clauses.append(last_clause) else: clauses = list(predicates) else_ = compose_( operator.not_, functools.reduce( or_, predicates, last_clause ) ) clauses = tuple(clauses) + (else_, ) iterators = replicate( iter(iterable), len(clauses) ) return tuple( starmap( filter, zip( clauses, iterators ) ) ) class split(_iterators_controller): def create_controlled_iterators(self, expected_length=-1): if expected_length > 0: nb_splitter = expected_length else: try: first = next(self._it) nb_splitter = len(self._splitter_func(first)) self._it = chain([first], self._it) # reset the main iterator except StopIteration: nb_splitter = 0 if nb_splitter: return tuple( _controlled_iterator(self) for _ in range(nb_splitter) ) else: return tuple() def __init__(self, func, *args, expected_length=-1, **kwargs): self._splitter_func = func assert callable(self._splitter_func) super().__init__(*args, expected_length=expected_length, **kwargs) def dispatch_item(self, item, requester): try: split_item = self._splitter_func(item) except Exception: raise RuntimeError("Exception in the splitting function") for index, t in enumerate(split_item): try: self._iterators[index].send(t) except IndexError: raise ValueError( "Encountered a tuple with length " "exceeding the number of output " "iterators: " + f"{len(split_item)} > " f"{len(self._iterators)}" ) class stream_converter: _dispatcher = dict() def __init__(self, from_, to_, *args, **kwargs): key_type = kwargs.pop('key_type', None) if (from_, to_, key_type) in stream_converter._dispatcher: meth_factory = stream_converter._dispatcher[ (from_, to_, key_type) ] # a kind of goofy name isn't it? self._method = meth_factory(*args, **kwargs) # be insured no meth has been cooked here, only methODs :) else: raise KeyError(f"Type association " f"'({from_.__name__}, {to_.__name__})' " f"unsupported by stream_converter") def __call__(self, data): return self._method(data) def __or__(self, other): # chain converters using | assert callable(other) return pipable(pipeline(self, other)) @classmethod def dispatch(cls, from_, to_, key_type=None): def decorator(func): @functools.wraps(func) def wrapper(*args, **kwargs): return func(*args, **kwargs) assert (from_, to_, key_type) not in cls._dispatcher, \ f"A dispatch entry already exists for " \ f"({from_.__name__}, {to_.__name__}, " \ f"{key_type.__name__ if key_type is not None else str(key_type)})" # noqa: E501 cls._dispatcher[(from_, to_, key_type)] = wrapper return wrapper return decorator @stream_converter.dispatch(tuple, tuple) @stream_converter.dispatch(dict, dict) @stream_converter.dispatch(namedtuple, namedtuple) def _identity(): return toolz.identity @stream_converter.dispatch(tuple, dict, key_type=str) def _convert_tuple_to_dict_with_keys(keys): if keys and len(keys): return lambda d: dict(zip(keys, d)) else: raise ValueError("Converter requires non-empty 'keys' argument") @stream_converter.dispatch(tuple, dict, key_type=int) def _convert_tuple_to_dict(): return lambda t: dict(enumerate(t)) @stream_converter.dispatch(tuple, namedtuple) def _convert_tuple_to_named_tuple(keys, typename='DataStructure'): if keys and len(keys): return namedtuple(typename, keys)._make else: raise ValueError("Converter requires non-empty 'keys' argument") @stream_converter.dispatch(dict, tuple) def _convert_dict_to_tuple(): return lambda d: tuple(d.values()) @stream_converter.dispatch(dict, namedtuple) def _convert_dict_to_namedtuple(typename='DataStructure'): return lambda d: namedtuple(typename, d.keys())._make(d.values()) @stream_converter.dispatch(namedtuple, dict) def _convert_namedtuple_to_dict(): return lambda nt: dict(nt._asdict()) @stream_converter.dispatch(namedtuple, tuple) def _convert_namedtuple_to_tuple(): return lambda nt: tuple(nt) def stream_generator(keys, funcs, nb_items): key_func = {} for k, f in zip_longest(keys, funcs, fillvalue=lambda: None): if callable(f): key_func[k] = f else: key_func[k] = lambda: f # noqa: E731 def make_data(): return { k_: f_() for k_, f_ in key_func.items() } if nb_items >= 0: for _ in range(nb_items): yield make_data() elif nb_items < 0: while True: yield make_data() def xargs(g, funcs, as_iterable=False): """returns a function that accepts a tuple as an arguments and then maps each element of this tuple to one of the funcs generating another tuple in the process. Finally, the function g is called with the tuple elements as arguments. If the tuple does not contain enough elements to map all the funcs, the last element is repeated to provide an argument to the remaining funcs """ def inner(*args): evaluated_funcs = tuple( starmap( lambda f, arg: f(arg), zip_longest(funcs, args, fillvalue=args[-1]) ) ) return g(evaluated_funcs) if as_iterable else g(*evaluated_funcs) return inner

8,379

186

831

5d4a5045147d1f57d04c1ce247f1b2f1b13ede17

2,309

py

Python

tests/mocks.py

DJRavinszkha/pykeen

d79fe39f83bc2831137f22be6421b37568694cf4

[ "MIT" ]

null

tests/mocks.py

DJRavinszkha/pykeen

d79fe39f83bc2831137f22be6421b37568694cf4

[ "MIT" ]

null

tests/mocks.py

DJRavinszkha/pykeen

d79fe39f83bc2831137f22be6421b37568694cf4

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Mocks for tests.""" from typing import Optional, Tuple import torch from torch import nn from pykeen.models import EntityRelationEmbeddingModel, Model from pykeen.nn.emb import EmbeddingSpecification, RepresentationModule from pykeen.triples import TriplesFactory __all__ = [ 'CustomRepresentations', 'MockModel', ] class CustomRepresentations(RepresentationModule): """A custom representation module with minimal implementation.""" class MockModel(EntityRelationEmbeddingModel): """A mock model returning fake scores.""" def _generate_fake_scores(self, batch: torch.LongTensor) -> torch.FloatTensor: """Generate fake scores s[b, i] = i of size (batch_size, num_entities).""" batch_size = batch.shape[0] batch_scores = self.scores.view(1, -1).repeat(batch_size, 1) assert batch_scores.shape == (batch_size, self.num_entities) return batch_scores

37.241935

100

0.705067

# -*- coding: utf-8 -*- """Mocks for tests.""" from typing import Optional, Tuple import torch from torch import nn from pykeen.models import EntityRelationEmbeddingModel, Model from pykeen.nn.emb import EmbeddingSpecification, RepresentationModule from pykeen.triples import TriplesFactory __all__ = [ 'CustomRepresentations', 'MockModel', ] class CustomRepresentations(RepresentationModule): """A custom representation module with minimal implementation.""" def __init__(self, num_entities: int, shape: Tuple[int, ...] = (2,)): super().__init__(max_id=num_entities, shape=shape) self.x = nn.Parameter(torch.rand(*shape)) def forward(self, indices: Optional[torch.LongTensor] = None) -> torch.FloatTensor: # noqa:D102 n = self.max_id if indices is None else indices.shape[0] return self.x.unsqueeze(dim=0).repeat(n, *(1 for _ in self.shape)) class MockModel(EntityRelationEmbeddingModel): """A mock model returning fake scores.""" def __init__(self, triples_factory: TriplesFactory): super().__init__( triples_factory=triples_factory, entity_representations=EmbeddingSpecification(embedding_dim=50), relation_representations=EmbeddingSpecification(embedding_dim=50), ) num_entities = self.num_entities self.scores = torch.arange(num_entities, dtype=torch.float) def _generate_fake_scores(self, batch: torch.LongTensor) -> torch.FloatTensor: """Generate fake scores s[b, i] = i of size (batch_size, num_entities).""" batch_size = batch.shape[0] batch_scores = self.scores.view(1, -1).repeat(batch_size, 1) assert batch_scores.shape == (batch_size, self.num_entities) return batch_scores def score_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 return self._generate_fake_scores(batch=hrt_batch) def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 return self._generate_fake_scores(batch=hr_batch) def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 return self._generate_fake_scores(batch=rt_batch) def reset_parameters_(self) -> Model: # noqa: D102 pass # Not needed for unittest

1,177

0

189

82a0f8ff772d456c1a957e6548993cb6ed8fd932

3,968

py

Python

colander_validators/__init__.py

ixmatus/colander-validators

68b43e5d6596383ff604da5f2237d185bd5780c2

[ "BSD-3-Clause" ]

2

2015-06-01T15:17:23.000Z

2015-06-01T18:40:31.000Z

colander_validators/__init__.py

ixmatus/colander-validators

68b43e5d6596383ff604da5f2237d185bd5780c2

[ "BSD-3-Clause" ]

null

colander_validators/__init__.py

ixmatus/colander-validators

68b43e5d6596383ff604da5f2237d185bd5780c2

[ "BSD-3-Clause" ]

null

import re def email(value): """Validate an email address >>> email("barney@purpledino.com") True >>> email("barneydino.com") 'An email address must contain a single @' """ usernameRE = re.compile(r"^[^ \t\n\r@<>()]+$", re.I) domainRE = re.compile(r''' ^(?:[a-z0-9][a-z0-9\-]{0,62}\.)+ # (sub)domain - alpha followed by 62max chars (63 total) [a-z]{2,}$ # TLD ''', re.I | re.VERBOSE) messages = dict( empty='Please enter an email address', noAt='An email address must contain a single @', badUsername='The username portion of the email address is invalid' ' (the portion before the @: {username!s}', socketError='An error occured when trying to connect to the server:' ' {error!s}', badDomain='The domain portion of the email address is invalid' ' (the portion after the @: {domain!s}', domainDoesNotExist='The domain of the email address does not exist' ' (the portion after the @: {domain!s}') if not value: return messages['empty'] value = value.strip() splitted = value.split('@', 1) try: username, domain=splitted except ValueError: return messages['noAt'] if not usernameRE.search(username): return messages['badUsername'].format(username=username) if not domainRE.search(domain): return messages['badDomain'].format(domain=domain) return True def url(value): """Validate a URL completely >>> url("ixmat.us") True >>> url("ixmat") 'You must provide a full domain name (like ixmat.com)' """ messages = dict( noScheme='You must start your URL with http://, https://, etc', badURL='That is not a valid URL', httpError='An error occurred when trying to access the URL: {error!s}', socketError='An error occured when trying to connect to the server: {error!s}', notFound='The server responded that the page could not be found', status='The server responded with a bad status code ({status!s})', noTLD='You must provide a full domain name (like {domain!s}.com)') url_re = re.compile(r''' ^(http|https):// (?:[%:\w]*@)? # authenticator (?: # ip or domain (?P<ip>(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))| (?P<domain>[a-z0-9][a-z0-9\-]{,62}\.)* # subdomain (?P<tld>[a-z]{2,63}|xn--[a-z0-9\-]{2,59}) # top level domain ) (?::[0-9]{1,5})? # port # files/delims/etc (?P<path>/[a-z0-9\-\._~:/\?#\[\]@!%\$&\'\*\+,;=]*)? $ ''', re.I | re.VERBOSE) scheme_re = re.compile(r'^[a-zA-Z]+:') value = value.strip() if not scheme_re.search(value): value = "http://" + value value = encode_idna(value) match = scheme_re.search(value) if not match: return messages['noScheme'] value = match.group(0).lower() + value[len(match.group(0)):] match = url_re.search(value) if not match: return messages['badURL'] if not match.group('domain'): return messages['noTLD'].format(domain=match.group('tld')) return True

32.260163

106

0.521925

import re def email(value): """Validate an email address >>> email("barney@purpledino.com") True >>> email("barneydino.com") 'An email address must contain a single @' """ usernameRE = re.compile(r"^[^ \t\n\r@<>()]+$", re.I) domainRE = re.compile(r''' ^(?:[a-z0-9][a-z0-9\-]{0,62}\.)+ # (sub)domain - alpha followed by 62max chars (63 total) [a-z]{2,}$ # TLD ''', re.I | re.VERBOSE) messages = dict( empty='Please enter an email address', noAt='An email address must contain a single @', badUsername='The username portion of the email address is invalid' ' (the portion before the @: {username!s}', socketError='An error occured when trying to connect to the server:' ' {error!s}', badDomain='The domain portion of the email address is invalid' ' (the portion after the @: {domain!s}', domainDoesNotExist='The domain of the email address does not exist' ' (the portion after the @: {domain!s}') if not value: return messages['empty'] value = value.strip() splitted = value.split('@', 1) try: username, domain=splitted except ValueError: return messages['noAt'] if not usernameRE.search(username): return messages['badUsername'].format(username=username) if not domainRE.search(domain): return messages['badDomain'].format(domain=domain) return True def url(value): """Validate a URL completely >>> url("ixmat.us") True >>> url("ixmat") 'You must provide a full domain name (like ixmat.com)' """ messages = dict( noScheme='You must start your URL with http://, https://, etc', badURL='That is not a valid URL', httpError='An error occurred when trying to access the URL: {error!s}', socketError='An error occured when trying to connect to the server: {error!s}', notFound='The server responded that the page could not be found', status='The server responded with a bad status code ({status!s})', noTLD='You must provide a full domain name (like {domain!s}.com)') url_re = re.compile(r''' ^(http|https):// (?:[%:\w]*@)? # authenticator (?: # ip or domain (?P<ip>(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))| (?P<domain>[a-z0-9][a-z0-9\-]{,62}\.)* # subdomain (?P<tld>[a-z]{2,63}|xn--[a-z0-9\-]{2,59}) # top level domain ) (?::[0-9]{1,5})? # port # files/delims/etc (?P<path>/[a-z0-9\-\._~:/\?#\[\]@!%\$&\'\*\+,;=]*)? $ ''', re.I | re.VERBOSE) scheme_re = re.compile(r'^[a-zA-Z]+:') value = value.strip() if not scheme_re.search(value): value = "http://" + value value = encode_idna(value) match = scheme_re.search(value) if not match: return messages['noScheme'] value = match.group(0).lower() + value[len(match.group(0)):] match = url_re.search(value) if not match: return messages['badURL'] if not match.group('domain'): return messages['noTLD'].format(domain=match.group('tld')) return True def encode_idna(value): from urllib.parse import urlparse, urlunparse scheme, netloc, path, params, query, fragment = urlparse(value) try: netloc = netloc.encode('idna') netloc = netloc.decode('ascii') return str(urlunparse((scheme, netloc, path, params, query, fragment))) except UnicodeError: return value

534

0

23

4db4f3928bb1714fb5828395c74a12d60da6ce54

7,963

py

Python

TwitchApi.py

borgej/BrexBot

2413bd4ec978ed40b8e02b1452dcfac9451ee2c5

[ "Apache-2.0" ]

null

TwitchApi.py

borgej/BrexBot

2413bd4ec978ed40b8e02b1452dcfac9451ee2c5

[ "Apache-2.0" ]

null

TwitchApi.py

borgej/BrexBot

2413bd4ec978ed40b8e02b1452dcfac9451ee2c5

[ "Apache-2.0" ]

null

import json import urllib import requests import Config import logging logging.basicConfig(level=logging.DEBUG) # Class to access the new Twitch 'HELIX' API # The class receives it's authentication from a dictionary (HEADERS) in the Config.py file. # The "Bearer" token must have the required scopes to perform successful API calls. # Function to make a JSON request to the Twitch API # Get the ID of a given username # Gather all available data for a specified user # Get first 100 moderators of a channel. "Pagination" must be used for more than 100 results. # Request requires a valid Bearer (Helix Oauth) token. # Check if a specified user is a moderator in the channel # Check if a specified user is following the channel # Check if a viewer is subscribed to teh channel # Creates a clip from the live stream. Test when live as # when not live it shows a previously created clip. # Check if a user is banned from the channel # Get followed channel since date # The following functions use the Twitch V5 API and require a separate token (OAuth) # The below will retrieve current "Chatters" in a channel. # THESE ARE NOT A TWITCH API FUNCTIONS - UNDOCUMENTED # This has a delayed refresh time (currently unknown). # Note: Due to some viewers/bots being connected anon to the channel # this will only show chatters and not all viewers.

42.811828

131

0.613337

import json import urllib import requests import Config import logging logging.basicConfig(level=logging.DEBUG) # Class to access the new Twitch 'HELIX' API # The class receives it's authentication from a dictionary (HEADERS) in the Config.py file. # The "Bearer" token must have the required scopes to perform successful API calls. class TwitchApi: def __init__(self, channel_name=Config.CHANNEL_NAME): self.channel_id = self.get_user_id(channel_name) # Function to make a JSON request to the Twitch API def json_data(self, url, user=None): try: req = urllib.request.Request(url, headers=Config.HEADERS) resp = urllib.request.urlopen(req) twitch_data = json.loads(json.dumps(json.loads(resp.read()))) return twitch_data except Exception as e: logging.error("Error parsing JSON data.", e) # Get the ID of a given username def get_user_id(self, username=Config.CHANNEL_NAME): try: url = 'https://api.twitch.tv/helix/users?login=' + username user_data = self.json_data(url, username) user_id = user_data['data'][0]['id'] return user_id except Exception as e: logging.error("Unable to retrieve the user ID for " + username, e) # Gather all available data for a specified user def get_user_data(self, username=Config.CHANNEL_NAME): try: url = 'https://api.twitch.tv/helix/users?login=' + username user_data = self.json_data(url, username) user = user_data['data'][0] return user except Exception as e: logging.error("Unable to retrieve the data for " + username, e) def get_channel_id(self, channel_name=Config.CHANNEL_NAME): try: return self.channel_id except Exception as e: logging.error("Could not retrieve channel ID for " + channel_name, e) # Get first 100 moderators of a channel. "Pagination" must be used for more than 100 results. # Request requires a valid Bearer (Helix Oauth) token. def get_moderators(self): try: url = 'https://api.twitch.tv/helix/moderation/moderators?broadcaster_id=' + self.channel_id moderator_data = self.json_data(url) moderator_names = [] for index, item in enumerate(moderator_data['data']): moderator_names.append(item['user_name']) return moderator_names except Exception as e: logging.error("Could not retrieve Moderator list for the channel", e) def get_followers(self): try: url = 'https://api.twitch.tv/helix/users/follows?to_id=' + self.channel_id follower_data = self.json_data(url) follower_names = [] for index, item in enumerate(follower_data['data']): follower_names.append(item['from_name']) return follower_names except Exception as e: logging.error("Could not retrieve Follower list for the channel", e) # Check if a specified user is a moderator in the channel def is_moderator(self, viewer): try: viewer_id = self.get_user_id(viewer) url = 'https://api.twitch.tv/helix/moderation/moderators?broadcaster_id=' + self.channel_id + '&user_id=' + viewer_id moderator_data = self.json_data(url) if moderator_data['data'] is None: return False else: return True except Exception as e: logging.error("Unable to determine if " + viewer + " is a moderator.", e) # Check if a specified user is following the channel def is_follower(self, viewer): try: viewer_id = self.get_user_id(viewer) url = 'https://api.twitch.tv/helix/users/follows?to_id=' + self.channel_id + '&from_id=' + viewer_id follow_data = self.json_data(url) if follow_data['total'] == 0: return False else: return True except Exception as e: logging.error("Unable to determine if " + viewer + " is following the channel.", e) # Check if a viewer is subscribed to teh channel def is_subscriber(self, viewer): try: viewer_id = self.get_user_id(viewer) url = 'https://api.twitch.tv/helix/subscriptions?broadcaster_id=' + self.channel_id + '&user_id=' + viewer_id + '&tier' sub_data = self.json_data(url) if not sub_data['data']: return False else: return True except Exception as e: logging.error("Unable to determin if " + viewer + " is subscribed to the channel.", e) # Creates a clip from the live stream. Test when live as # when not live it shows a previously created clip. def create_clip(self): try: url = 'https://api.twitch.tv/helix/clips?broadcaster_id=' + self.channel_id clip_data = self.json_data(url) return clip_data except Exception as e: logging.error("Couldn't create clip.", e) # Check if a user is banned from the channel def is_banned(self, viewer): try: viewer_id = self.get_user_id(viewer) url = 'https://api.twitch.tv/helix/moderation/banned?broadcaster_id=' + self.channel_id + '&user_id=' + viewer_id banned_data = self.json_data(url) if banned_data['data'] is None: return False else: return True except Exception as e: logging.error("Unable to check if " + viewer + " is banned from the channel.", e) # Get followed channel since date def follower_since(self, viewer): try: viewer_id = self.get_user_id(viewer) url = 'https://api.twitch.tv/helix/users/follows?to_id=' + self.channel_id + '&from_id=' + viewer_id follow_data = self.json_data(url) if follow_data['total'] == 0: return None else: return follow_data['data'][0]['followed_at'] except Exception as e: logging.error("Unable to determine if " + viewer + " is following the channel.", e) # The following functions use the Twitch V5 API and require a separate token (OAuth) def update_channel(self, title, game): try: url = 'https://api.twitch.tv/kraken/channels/' + self.get_channel_id() + '?api_version=5' headers = Config.V5HEADERS data = {'channel[status]': title, 'channel[game]': game, 'channel[channel_feed_enabled]': 'true'} response = requests.put(url=url, headers=headers, params=data) return response except Exception as e: logging.error('Unable to perform V5 API call', e) return None # The below will retrieve current "Chatters" in a channel. # THESE ARE NOT A TWITCH API FUNCTIONS - UNDOCUMENTED # This has a delayed refresh time (currently unknown). # Note: Due to some viewers/bots being connected anon to the channel # this will only show chatters and not all viewers. def get_chatter_data(self, channel): try: url = 'https://tmi.twitch.tv/group/user/' + channel + '/chatters' chatter_data = self.json_data(url) return chatter_data except Exception as e: logging.error('Unable to retrieve chatter data. ', e) return None def all_chatter_names(self, channel): try: chatter_data = self.get_chatter_data(channel)['chatters'] chatters = [item for sublist in chatter_data.values() for item in sublist] return chatters except Exception as e: logging.error('Unable to retrieve chatter names. ', e) return None

6,102

-5

441

c41ff4afe695367041a744827d6210a2ecd34a31

829

py

Python

src/var.py

JayHeng/pzh-knockout-round

c34111da8d69f748e1bc3f7a658426ad59f79af7

[ "BSD-3-Clause" ]

null

src/var.py

JayHeng/pzh-knockout-round

c34111da8d69f748e1bc3f7a658426ad59f79af7

[ "BSD-3-Clause" ]

null

src/var.py

JayHeng/pzh-knockout-round

c34111da8d69f748e1bc3f7a658426ad59f79af7

[ "BSD-3-Clause" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- import sys import os g_playerDict = {'player1':u"nobody", 'player2':u"nobody", 'player3':u"nobody", 'player4':u"nobody", 'player5':u"nobody", 'player6':u"nobody", 'player7':u"nobody", 'player8':u"nobody", 'player9':u"nobody", 'player10':u"nobody", 'player11':u"nobody", 'player12':u"nobody", 'player13':u"nobody", 'player14':u"nobody", 'player15':u"nobody", 'player16':u"nobody", }

26.741935

37

0.463209

#! /usr/bin/env python # -*- coding: utf-8 -*- import sys import os g_playerDict = {'player1':u"nobody", 'player2':u"nobody", 'player3':u"nobody", 'player4':u"nobody", 'player5':u"nobody", 'player6':u"nobody", 'player7':u"nobody", 'player8':u"nobody", 'player9':u"nobody", 'player10':u"nobody", 'player11':u"nobody", 'player12':u"nobody", 'player13':u"nobody", 'player14':u"nobody", 'player15':u"nobody", 'player16':u"nobody", } def getPlayer(): global g_playerDict return g_playerDict def setPlayer( *args ): global g_playerDict g_playerDict = args[0]

96

0

46

5c5ab3597807a949bef2e7b63629871211bc1d50

7,620

py

Python

web/forms/account.py

HongDaMa/sass_project

d971a4307a4057ecb0bea72c34e6c2112cfc2497

[ "Apache-2.0" ]

1

2021-03-01T10:50:05.000Z

web/forms/account.py

HongDaMa/sass_project

d971a4307a4057ecb0bea72c34e6c2112cfc2497

[ "Apache-2.0" ]

5

2021-06-08T21:20:53.000Z

2022-03-12T00:24:58.000Z

web/forms/account.py

HongDaMa/sass_project

d971a4307a4057ecb0bea72c34e6c2112cfc2497

[ "Apache-2.0" ]

null

#!E:\py_virtual_env\saas_project\Scripts\python.exe # -*- coding: utf-8 -*- from django import forms from web import models from django.core.validators import RegexValidator,ValidationError from django.core.exceptions import ValidationError from django.conf import settings from django_redis import get_redis_connection from utils import encrypt from utils.tencent.sms import send_sms_single import random

31.229508

89

0.599213

#!E:\py_virtual_env\saas_project\Scripts\python.exe # -*- coding: utf-8 -*- from django import forms from web import models from django.core.validators import RegexValidator,ValidationError from django.core.exceptions import ValidationError from django.conf import settings from django_redis import get_redis_connection from utils import encrypt from utils.tencent.sms import send_sms_single import random class BootStrapForm(object): BootStrapForm_exclude = [] def __init__(self,*args,**kwargs): super().__init__(*args,**kwargs) for name,field in self.fields.items(): if name in self.BootStrapForm_exclude: continue field.widget.attrs['class'] = 'form-control' field.widget.attrs['placeholder'] = '请输入%s'%(field.label) class RegisterModelForm(BootStrapForm,forms.ModelForm): mobile_phone = forms.CharField( label='手机号', required = True, validators=[RegexValidator(r"^(1[3|4|5|6|7|8|9])\d{9}$")], error_messages={'required':'手机号不能为空。'} ) email = forms.EmailField( label='邮箱', required=True, error_messages={'required':'邮箱不能为空。'} ) password = forms.CharField( label='密码', required = True, widget=forms.PasswordInput(), error_messages={'required':'密码不能为空。'} ) confirm_password = forms.CharField( label='重复密码', required = True, widget=forms.PasswordInput(), error_messages={'required':'请输入重复密码。'} ) code = forms.CharField( label='验证码', required = True, error_messages={'required':'验证码不能为空。'} ) class Meta: model = models.UserInfo fields = ['username','email','password','confirm_password','mobile_phone','code'] def clean_username(self): """ username 的钩子函数 :return: 用户名 """ username = self.cleaned_data.get('username') exists = models.UserInfo.objects.filter(username= username).exists() if exists: raise ValidationError('用户名已存在，请重新输入!') else: return username def clean_email(self): """ email 的钩子函数 :return: 邮箱地址 """ email = self.cleaned_data.get('email') exists = models.UserInfo.objects.filter(email=email).exists() if exists: raise ValidationError('邮箱已存在，请重新输入!') else: return email def clean_password(self): password = self.cleaned_data.get('password') #加密&返回 return encrypt.md5(password) def clean_confirm_password(self): """ confirm_password 的钩子函数 :return: confirm_password重复密码 """ password = self.cleaned_data.get('password') confirm_password = self.cleaned_data.get('confirm_password') if password != encrypt.md5(confirm_password): raise ValidationError('两次输入密码不一致') else: return confirm_password def clean_code(self): """ code 的钩子函数 :return: 正确的验证码 """ mobile_phone = self.cleaned_data.get('mobile_phone') code = self.cleaned_data.get('code') conn = get_redis_connection("default") # 将验证码写入redis value = conn.get(mobile_phone) #将返回的二进制字符串转化成字符串 if value != None: value = str(value,encoding='utf-8') print(value) if value == None: raise ValidationError('验证码不存在或者已过期！') elif value != None and value != code: raise ValidationError('验证码错误，请重新输入！') else: return code class LoginForm(BootStrapForm,forms.Form): username = forms.CharField(label='邮箱或者手机号', required=True) password = forms.CharField( label='密码', required=True, widget=forms.PasswordInput(), error_messages={'required': '密码不能为空。'} ) code = forms.CharField( label='验证码', required=True, error_messages={'required': '验证码不能为空。'} ) #重新__init__将request传入 def __init__(self,request,*args,**kwargs): super().__init__(*args,**kwargs) self.request = request def clean_password(self): password = self.cleaned_data['password'] #加密&返回 return encrypt.md5(password) def clean_code(self): """ code的钩子函数 :return: """ code = self.cleaned_data['code'] image_code = self.request.session.get('image_code') if not image_code: raise ValidationError('验证码不存在，或者已过期') elif code.upper() != image_code.upper(): raise ValidationError('验证码错误') else: return code class Login_SmsForm(BootStrapForm,forms.Form): mobile_phone = forms.CharField( label='手机号', required=True, validators=[RegexValidator(r"^(1[3|4|5|6|7|8|9])\d{9}$")], error_messages={'required': '手机号不能为空'} ) code = forms.CharField( label='验证码', required=True, error_messages={'required': '验证码不能为空。'} ) def __init__(self,*args,**kwargs): super().__init__(*args,**kwargs) for name,field in self.fields.items(): field.widget.attrs['class'] = 'form-control' field.widget.attrs['placeholder'] = '请输入%s'%(field.label) def clean_mobile_phone(self): mobile_phone = self.cleaned_data.get('mobile_phone') exists = models.UserInfo.objects.filter(mobile_phone=mobile_phone).exists() if not exists: raise ValidationError('手机号尚未注册，请先注册！') else: return mobile_phone def clean_code(self): code = self.cleaned_data.get('code') mobile_phone = self.cleaned_data.get('mobile_phone') if not mobile_phone: return code conn = get_redis_connection('default') redis_code = conn.get(mobile_phone) if not redis_code: raise ValidationError('验证码失效或未发送，请重新发送') redis_str_code = redis_code.decode('utf-8') if code != redis_str_code: raise ValidationError('验证码错误，请重新输入') return code class SendsmsForm(forms.Form): mobile_phone = forms.CharField( label='手机号', required=True, validators=[RegexValidator(r"^(1[3|4|5|6|7|8|9])\d{9}$")], error_messages={'required': '手机号不能为空'} ) def __init__(self,request,*args,**kwargs): super().__init__(*args,**kwargs) self.request = request def clean_mobile_phone(self): """手机号验证的钩子函数""" mobile_phone = self.cleaned_data['mobile_phone'] #校验短信模板是否有问题 tpl = self.request.GET.get('tpl') #校验数据库中是否已经有手机号 template_id = settings.TENCET_SMS_TEMPLATES.get(tpl) print(template_id) if not template_id: raise ValidationError('短信模板错误') exists = models.UserInfo.objects.filter(mobile_phone=mobile_phone).exists() if tpl == 'login': if not exists: raise ValidationError('手机号不存在') else: # 校验数据库中是否已有手机号 if exists: raise ValidationError('手机号已存在') code = random.randrange(1000,9999) # 利用腾讯sdk发送短信 sms = send_sms_single(mobile_phone,template_id,[code,]) if sms['result'] != 0: raise ValidationError("短信发送失败，{}".format(sms['errmsg'])) # 去连接池中获取一个连接 conn = get_redis_connection("default") #将验证码写入redis conn.set(mobile_phone, code, ex=180) value = conn.get(mobile_phone) print(value) return mobile_phone

1,797

6,108

115

52a139c5e62ffa00b24af6c10f5d0ac8fb2a1335

431

py

Python

CodingBat/Python/Logic-1/cigar_party.py

Togohogo1/pg

ee3c36acde47769c66ee13a227762ee677591375

[ "MIT" ]

null

CodingBat/Python/Logic-1/cigar_party.py

Togohogo1/pg

ee3c36acde47769c66ee13a227762ee677591375

[ "MIT" ]

1

2021-10-14T18:26:56.000Z

CodingBat/Python/Logic-1/cigar_party.py

Togohogo1/pg

ee3c36acde47769c66ee13a227762ee677591375

[ "MIT" ]

1

2021-08-06T03:39:55.000Z

''' When squirrels get together for a party, they like to have cigars. A squirrel party is successful when the number of cigars is between 40 and 60, inclusive. Unless it is the weekend, in which case there is no upper bound on the number of cigars. Return True if the party with the given values is successful, or False otherwise. '''

39.181818

77

0.758701

''' When squirrels get together for a party, they like to have cigars. A squirrel party is successful when the number of cigars is between 40 and 60, inclusive. Unless it is the weekend, in which case there is no upper bound on the number of cigars. Return True if the party with the given values is successful, or False otherwise. ''' def cigar_party(cigars, is_weekend): return cigars >= 40 and (cigars <= 60 or is_weekend)

72

0

23

537517161f8f98490b06de9ff4e5fad7cffa2079

7,138

py

Python

release/stubs.min/System/Windows/Forms/__init___parts/NotifyIcon.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/System/Windows/Forms/__init___parts/NotifyIcon.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/System/Windows/Forms/__init___parts/NotifyIcon.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

class NotifyIcon(Component, IComponent, IDisposable): """ Specifies a component that creates an icon in the notification area. This class cannot be inherited. NotifyIcon() NotifyIcon(container: IContainer) """ def Dispose(self): """ Dispose(self: NotifyIcon,disposing: bool) """ pass def GetService(self, *args): """ GetService(self: Component,service: Type) -> object Returns an object that represents a service provided by the System.ComponentModel.Component or by its System.ComponentModel.Container. service: A service provided by the System.ComponentModel.Component. Returns: An System.Object that represents a service provided by the System.ComponentModel.Component,or null if the System.ComponentModel.Component does not provide the specified service. """ pass def MemberwiseClone(self, *args): """ MemberwiseClone(self: MarshalByRefObject,cloneIdentity: bool) -> MarshalByRefObject Creates a shallow copy of the current System.MarshalByRefObject object. cloneIdentity: false to delete the current System.MarshalByRefObject object's identity,which will cause the object to be assigned a new identity when it is marshaled across a remoting boundary. A value of false is usually appropriate. true to copy the current System.MarshalByRefObject object's identity to its clone,which will cause remoting client calls to be routed to the remote server object. Returns: A shallow copy of the current System.MarshalByRefObject object. MemberwiseClone(self: object) -> object Creates a shallow copy of the current System.Object. Returns: A shallow copy of the current System.Object. """ pass def ShowBalloonTip(self, timeout, tipTitle=None, tipText=None, tipIcon=None): """ ShowBalloonTip(self: NotifyIcon,timeout: int,tipTitle: str,tipText: str,tipIcon: ToolTipIcon) Displays a balloon tip with the specified title,text,and icon in the taskbar for the specified time period. timeout: The time period,in milliseconds,the balloon tip should display. tipTitle: The title to display on the balloon tip. tipText: The text to display on the balloon tip. tipIcon: One of the System.Windows.Forms.ToolTipIcon values. ShowBalloonTip(self: NotifyIcon,timeout: int) Displays a balloon tip in the taskbar for the specified time period. timeout: The time period,in milliseconds,the balloon tip should display. """ pass def __enter__(self, *args): """ __enter__(self: IDisposable) -> object Provides the implementation of __enter__ for objects which implement IDisposable. """ pass def __exit__(self, *args): """ __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) Provides the implementation of __exit__ for objects which implement IDisposable. """ pass def __init__(self, *args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self, container=None): """ __new__(cls: type) __new__(cls: type,container: IContainer) """ pass BalloonTipIcon = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the icon to display on the balloon tip associated with the System.Windows.Forms.NotifyIcon. Get: BalloonTipIcon(self: NotifyIcon) -> ToolTipIcon Set: BalloonTipIcon(self: NotifyIcon)=value """ BalloonTipText = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the text to display on the balloon tip associated with the System.Windows.Forms.NotifyIcon. Get: BalloonTipText(self: NotifyIcon) -> str Set: BalloonTipText(self: NotifyIcon)=value """ BalloonTipTitle = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the title of the balloon tip displayed on the System.Windows.Forms.NotifyIcon. Get: BalloonTipTitle(self: NotifyIcon) -> str Set: BalloonTipTitle(self: NotifyIcon)=value """ CanRaiseEvents = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets a value indicating whether the component can raise an event. """ ContextMenu = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the shortcut menu for the icon. Get: ContextMenu(self: NotifyIcon) -> ContextMenu Set: ContextMenu(self: NotifyIcon)=value """ ContextMenuStrip = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the shortcut menu associated with the System.Windows.Forms.NotifyIcon. Get: ContextMenuStrip(self: NotifyIcon) -> ContextMenuStrip Set: ContextMenuStrip(self: NotifyIcon)=value """ DesignMode = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets a value that indicates whether the System.ComponentModel.Component is currently in design mode. """ Events = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets the list of event handlers that are attached to this System.ComponentModel.Component. """ Icon = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets the current icon. Get: Icon(self: NotifyIcon) -> Icon Set: Icon(self: NotifyIcon)=value """ Tag = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets an object that contains data about the System.Windows.Forms.NotifyIcon. Get: Tag(self: NotifyIcon) -> object Set: Tag(self: NotifyIcon)=value """ Text = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets the ToolTip text displayed when the mouse pointer rests on a notification area icon. Get: Text(self: NotifyIcon) -> str Set: Text(self: NotifyIcon)=value """ Visible = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets a value indicating whether the icon is visible in the notification area of the taskbar. Get: Visible(self: NotifyIcon) -> bool Set: Visible(self: NotifyIcon)=value """ BalloonTipClicked = None BalloonTipClosed = None BalloonTipShown = None Click = None DoubleClick = None MouseClick = None MouseDoubleClick = None MouseDown = None MouseMove = None MouseUp = None

23.95302

221

0.666293

class NotifyIcon(Component, IComponent, IDisposable): """ Specifies a component that creates an icon in the notification area. This class cannot be inherited. NotifyIcon() NotifyIcon(container: IContainer) """ def Dispose(self): """ Dispose(self: NotifyIcon,disposing: bool) """ pass def GetService(self, *args): """ GetService(self: Component,service: Type) -> object Returns an object that represents a service provided by the System.ComponentModel.Component or by its System.ComponentModel.Container. service: A service provided by the System.ComponentModel.Component. Returns: An System.Object that represents a service provided by the System.ComponentModel.Component,or null if the System.ComponentModel.Component does not provide the specified service. """ pass def MemberwiseClone(self, *args): """ MemberwiseClone(self: MarshalByRefObject,cloneIdentity: bool) -> MarshalByRefObject Creates a shallow copy of the current System.MarshalByRefObject object. cloneIdentity: false to delete the current System.MarshalByRefObject object's identity,which will cause the object to be assigned a new identity when it is marshaled across a remoting boundary. A value of false is usually appropriate. true to copy the current System.MarshalByRefObject object's identity to its clone,which will cause remoting client calls to be routed to the remote server object. Returns: A shallow copy of the current System.MarshalByRefObject object. MemberwiseClone(self: object) -> object Creates a shallow copy of the current System.Object. Returns: A shallow copy of the current System.Object. """ pass def ShowBalloonTip(self, timeout, tipTitle=None, tipText=None, tipIcon=None): """ ShowBalloonTip(self: NotifyIcon,timeout: int,tipTitle: str,tipText: str,tipIcon: ToolTipIcon) Displays a balloon tip with the specified title,text,and icon in the taskbar for the specified time period. timeout: The time period,in milliseconds,the balloon tip should display. tipTitle: The title to display on the balloon tip. tipText: The text to display on the balloon tip. tipIcon: One of the System.Windows.Forms.ToolTipIcon values. ShowBalloonTip(self: NotifyIcon,timeout: int) Displays a balloon tip in the taskbar for the specified time period. timeout: The time period,in milliseconds,the balloon tip should display. """ pass def __enter__(self, *args): """ __enter__(self: IDisposable) -> object Provides the implementation of __enter__ for objects which implement IDisposable. """ pass def __exit__(self, *args): """ __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) Provides the implementation of __exit__ for objects which implement IDisposable. """ pass def __init__(self, *args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self, container=None): """ __new__(cls: type) __new__(cls: type,container: IContainer) """ pass def __str__(self, *args): pass BalloonTipIcon = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the icon to display on the balloon tip associated with the System.Windows.Forms.NotifyIcon. Get: BalloonTipIcon(self: NotifyIcon) -> ToolTipIcon Set: BalloonTipIcon(self: NotifyIcon)=value """ BalloonTipText = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the text to display on the balloon tip associated with the System.Windows.Forms.NotifyIcon. Get: BalloonTipText(self: NotifyIcon) -> str Set: BalloonTipText(self: NotifyIcon)=value """ BalloonTipTitle = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the title of the balloon tip displayed on the System.Windows.Forms.NotifyIcon. Get: BalloonTipTitle(self: NotifyIcon) -> str Set: BalloonTipTitle(self: NotifyIcon)=value """ CanRaiseEvents = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets a value indicating whether the component can raise an event. """ ContextMenu = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the shortcut menu for the icon. Get: ContextMenu(self: NotifyIcon) -> ContextMenu Set: ContextMenu(self: NotifyIcon)=value """ ContextMenuStrip = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets or sets the shortcut menu associated with the System.Windows.Forms.NotifyIcon. Get: ContextMenuStrip(self: NotifyIcon) -> ContextMenuStrip Set: ContextMenuStrip(self: NotifyIcon)=value """ DesignMode = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets a value that indicates whether the System.ComponentModel.Component is currently in design mode. """ Events = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets the list of event handlers that are attached to this System.ComponentModel.Component. """ Icon = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets the current icon. Get: Icon(self: NotifyIcon) -> Icon Set: Icon(self: NotifyIcon)=value """ Tag = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets an object that contains data about the System.Windows.Forms.NotifyIcon. Get: Tag(self: NotifyIcon) -> object Set: Tag(self: NotifyIcon)=value """ Text = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets the ToolTip text displayed when the mouse pointer rests on a notification area icon. Get: Text(self: NotifyIcon) -> str Set: Text(self: NotifyIcon)=value """ Visible = property(lambda self: object(), lambda self, v: None, lambda self: None) """Gets or sets a value indicating whether the icon is visible in the notification area of the taskbar. Get: Visible(self: NotifyIcon) -> bool Set: Visible(self: NotifyIcon)=value """ BalloonTipClicked = None BalloonTipClosed = None BalloonTipShown = None Click = None DoubleClick = None MouseClick = None MouseDoubleClick = None MouseDown = None MouseMove = None MouseUp = None

18

0

29

6219e63f4cbb5f076b00f07b3a8aca7479ab646f

1,052

py

Python

Python/DataCampChallenge-main/problem.py

BechirTr/Some_Projects

ac0d3975f5969903a0fcf67cf72f7d9cae42d272

[ "Apache-2.0" ]

null

Python/DataCampChallenge-main/problem.py

BechirTr/Some_Projects

ac0d3975f5969903a0fcf67cf72f7d9cae42d272

[ "Apache-2.0" ]

null

Python/DataCampChallenge-main/problem.py

BechirTr/Some_Projects

ac0d3975f5969903a0fcf67cf72f7d9cae42d272

[ "Apache-2.0" ]

1

2021-02-02T17:09:04.000Z

import os import pandas as pd import rampwf as rw from sklearn.model_selection import ShuffleSplit import numpy as np problem_title = 'Salary prediction' _target_column_name = 'SalaryUSD' # A value which will be used to create wrapper objects for y_pred Predictions = rw.prediction_types.make_regression() # An object implementing the workflow workflow = rw.workflows.Estimator() score_types = [ rw.score_types.RMSE(name='rmse', precision=3), ] # READ DATA

23.909091

75

0.694867

import os import pandas as pd import rampwf as rw from sklearn.model_selection import ShuffleSplit import numpy as np problem_title = 'Salary prediction' _target_column_name = 'SalaryUSD' # A value which will be used to create wrapper objects for y_pred Predictions = rw.prediction_types.make_regression() # An object implementing the workflow workflow = rw.workflows.Estimator() score_types = [ rw.score_types.RMSE(name='rmse', precision=3), ] def get_cv(X, y): cv = ShuffleSplit(n_splits=3, test_size=0.2, random_state=57) return cv.split(X, y) # READ DATA def _read_data(path, df_filename): df = pd.read_csv(os.path.join(path, 'data', df_filename), index_col=0) y = df[_target_column_name] X = df.drop(_target_column_name, axis=1) return X, np.log(1+y.values) def get_train_data(path='.'): df_filename = 'train.csv' return _read_data(path, df_filename) def get_test_data(path='.'): df_filename = 'test.csv' return _read_data(path, df_filename)

458

0

100

f9f1f120411b015f12b61e2e79f925200ac0f016

6,008

py

Python

kochira/services/core/logger.py

nattofriends/kochira

8c41f9936ed588a8acc365a2829f06521388e55b

[ "MS-PL" ]

null

kochira/services/core/logger.py

nattofriends/kochira

8c41f9936ed588a8acc365a2829f06521388e55b

[ "MS-PL" ]

1

2019-05-13T22:02:18.000Z

kochira/services/core/logger.py

nattofriends/kochira

8c41f9936ed588a8acc365a2829f06521388e55b

[ "MS-PL" ]

1

2019-05-13T21:22:02.000Z

""" IRC message logger. Enables logging of messages to flat files. """ import threading from datetime import datetime from kochira import config from kochira.service import Service, Config from pathlib import Path service = Service(__name__, __doc__) @service.config @service.setup @service.shutdown @service.hook("sighup") @service.hook("own_message", priority=10000) @service.hook("own_notice", priority=10000) @service.hook("invite", priority=10000) @service.hook("join", priority=10000) @service.hook("kill", priority=10000) @service.hook("kick", priority=10000) @service.hook("mode_change", priority=10000) @service.hook("channel_message", priority=10000) @service.hook("private_message", priority=10000) @service.hook("nick_change", priority=10000) @service.hook("channel_notice", priority=10000) @service.hook("private_notice", priority=10000) @service.hook("part", priority=10000) @service.hook("topic_change", priority=10000) @service.hook("quit", priority=10000) @service.hook("ctcp_action", priority=10000)

27.18552

85

0.651298

""" IRC message logger. Enables logging of messages to flat files. """ import threading from datetime import datetime from kochira import config from kochira.service import Service, Config from pathlib import Path service = Service(__name__, __doc__) @service.config class Config(Config): log_dir = config.Field(doc="Path to the log directory.", default="logs") def _is_log_open(ctx, channel): return (ctx.client.name, channel) in ctx.storage.handles def _get_file_handle(ctx, channel): k = (ctx.client.name, channel) if k not in ctx.storage.handles: client_name_path = ctx.storage.path / ctx.client.name if not client_name_path.exists(): client_name_path.mkdir(parents=True) path = client_name_path / (channel + ".log") f = path.open("ab") service.logger.debug("Opened handle for: %s", path) ctx.storage.handles[k] = f return ctx.storage.handles[k] def _hostmask_for(client, nickname): user = client.users.get(nickname, {}) username = user.get("username") or "" hostname = user.get("hostname") or "" if not username and not hostname: return "" return "{username}@{hostname}".format(username=username, hostname=hostname) def log(ctx, channel, what): now = datetime.utcnow() f = _get_file_handle(ctx, channel) with ctx.storage.lock: f.write(("{now} {what}\n".format( now=now.isoformat(), what=what )).encode("utf-8")) f.flush() def log_message(ctx, target, origin, message, format): sigil = " " if target in ctx.client.channels: for sigil2, mode in ctx.client._nickname_prefixes.items(): if origin in ctx.client.channels[target]["modes"].get(mode, []): sigil = sigil2 log(ctx, target, format.format(sigil=sigil, origin=origin, message=message)) def log_global(ctx, origin, what): for channel, info in ctx.client.channels.items(): if origin in info["users"]: log(ctx, channel, what) if _is_log_open(ctx, origin): log(ctx, origin, what) def close_all_handles(storage): with storage.lock: for f in storage.handles.values(): f.close() storage.handles = {} service.logger.debug("Log handles closed") @service.setup def setup_logger(ctx): ctx.storage.handles = {} ctx.storage.path = Path(ctx.config.log_dir) ctx.storage.lock = threading.Lock() @service.shutdown def shutdown_logger(ctx): close_all_handles(ctx.storage) @service.hook("sighup") def flush_log_handles(ctx): close_all_handles(ctx.storage) @service.hook("own_message", priority=10000) def on_own_message(ctx, target, message): on_channel_message(ctx, target, ctx.client.nickname, message) @service.hook("own_notice", priority=10000) def on_own_notice(ctx, target, message): on_channel_notice(ctx, target, ctx.client.nickname, message) @service.hook("invite", priority=10000) def on_invite(ctx, target, origin): log(ctx, origin, "-!- {origin} [{hostmask}] is inviting you to {channel}".format( origin=origin, hostmask=_hostmask_for(ctx.client, origin), channel=target)) @service.hook("join", priority=10000) def on_join(ctx, target, origin): log(ctx, target, "--> {origin} [{hostmask}] joined".format( origin=origin, hostmask=_hostmask_for(ctx.client, origin))) @service.hook("kill", priority=10000) def on_kill(ctx, target, by, message=None): log_global(ctx, target, "<== {target} was killed by {by}: {message}".format( target=target, by=by, message=message or "")) @service.hook("kick", priority=10000) def on_kick(ctx, channel, target, by, message=None): log(ctx, channel, "<-- {target} was kicked by {by}: {message}".format( target=target, by=by, message=message or "")) @service.hook("mode_change", priority=10000) def on_mode_change(ctx, channel, modes, by): log(ctx, channel, "-!- {by} set modes: {modes}".format( by=by, modes=" ".join(modes))) @service.hook("channel_message", priority=10000) def on_channel_message(ctx, target, origin, message): log_message(ctx, target, origin, message, "<{sigil}{origin}> {message}") @service.hook("private_message", priority=10000) def on_private_message(ctx, origin, message): on_channel_message(ctx, origin, origin, message) @service.hook("nick_change", priority=10000) def on_nick_change(ctx, old, new): what = "-!- {old} is now known as {new}".format(old=old, new=new) log_global(ctx, new, what) log_global(ctx, old, what) @service.hook("channel_notice", priority=10000) def on_channel_notice(ctx, target, origin, message): log_message(ctx, target, origin, message, "-{sigil}{origin}- {message}") @service.hook("private_notice", priority=10000) def on_private_notice(ctx, origin, message): on_channel_notice(ctx, origin, origin, message) @service.hook("part", priority=10000) def on_part(ctx, target, origin, message=None): log(ctx, target, "<-- {origin} parted: {message}".format( origin=origin, message=message or "")) @service.hook("topic_change", priority=10000) def on_topic_change(ctx, target, message, by): log(ctx, target, "-!- {by} changed the topic: {message}".format( by=by, message=message)) @service.hook("quit", priority=10000) def on_quit(ctx, origin, message=None): log_global(ctx, origin, "<== {origin} [{hostmask}] quit: {message}".format( origin=origin, hostmask=_hostmask_for(ctx.client, origin), message=message or "")) @service.hook("ctcp_action", priority=10000) def on_ctcp_action(ctx, origin, target, message): if target == ctx.client.nickname: target = origin log_message(ctx, target, origin, message, " * {sigil}{origin} {message}")

4,261

77

601

c1339e18a7f733a5c9225797b9a9145e7b651fbb

4,560

py

Python

tests/test_classifier_api.py

pmatigakis/classifier

b37c89df947f26a8001aa7a51dad86af1921ed19

[ "MIT" ]

1

2020-01-07T00:05:09.000Z

tests/test_classifier_api.py

pmatigakis/classifier

b37c89df947f26a8001aa7a51dad86af1921ed19

[ "MIT" ]

2

2019-12-20T19:02:37.000Z

2019-12-20T19:02:38.000Z

tests/test_classifier_api.py

pmatigakis/classifier

b37c89df947f26a8001aa7a51dad86af1921ed19

[ "MIT" ]

1

2017-05-24T19:56:08.000Z

import json from unittest import main from unittest.mock import patch from common import ClassifierTestCaseWithMockClassifiers from classifier import __VERSION__ from classifier.ml import Classifier if __name__ == "__main__": main()

25.333333

75

0.557237

import json from unittest import main from unittest.mock import patch from common import ClassifierTestCaseWithMockClassifiers from classifier import __VERSION__ from classifier.ml import Classifier class ClassificationEndpointTests(ClassifierTestCaseWithMockClassifiers): def test_probability_classifier(self): client = self.app.test_client() data = { "data": [[5.4, 3.0, 4.5, 1.5]] } headers = { "Content-Type": "application/json", } response = client.post( "/api/v1/predict/iris_probabilities", data=json.dumps(data), headers=headers ) self.assertEqual(response.status_code, 200) data = json.loads(response.data.decode()) self.assertIn("results", data) self.assertEqual(len(data["results"]), 1) self.assertEqual(len(data["results"][0]), 3) self.assertAlmostEqual( data["results"][0]["Iris-setosa"], 0.01031, 5) self.assertAlmostEqual( data["results"][0]["Iris-versicolor"], 0.36503, 5) self.assertAlmostEqual( data["results"][0]["Iris-virginica"], 0.62465, 5) def test_classifier(self): client = self.app.test_client() data = { "data": [[5.4, 3.0, 4.5, 1.5]] } headers = { "Content-Type": "application/json", } response = client.post( "/api/v1/predict/iris", data=json.dumps(data), headers=headers) self.assertEqual(response.status_code, 200) data = json.loads(response.data.decode()) self.assertDictEqual( data, { "results": ["Iris-virginica"] } ) def test_classifier_does_not_exist(self): client = self.app.test_client() data = { "data": [[5.4, 3.0, 4.5, 1.5]] } headers = { "Content-Type": "application/json", } response = client.post( "/api/v1/predict/fail_test", data=json.dumps(data), headers=headers ) self.assertEqual(response.status_code, 404) data = json.loads(response.data.decode()) self.assertDictEqual( data, { "classifier": "fail_test", "error": "unknown classifier" } ) @patch.object(Classifier, "classify") def test_error_while_running_classification(self, classify_mock): classify_mock.side_effect = Exception client = self.app.test_client() data = { "data": [[5.4, 3.0, 4.5, 1.5]] } headers = { "Content-Type": "application/json", } response = client.post( "/api/v1/predict/iris", data=json.dumps(data), headers=headers) self.assertEqual(response.status_code, 500) data = json.loads(response.data.decode()) self.assertDictEqual( data, { "error": "failed to classify object", "classifier": "iris" } ) class ClassifiersResourceTests(ClassifierTestCaseWithMockClassifiers): def test_get_available_classifiers(self): client = self.app.test_client() response = client.get("/api/v1/classifiers") self.assertEqual(response.status_code, 200) data = json.loads(response.data.decode()) self.assertCountEqual( data, [ 'iris', 'iris_probabilities' ] ) class HealthResourceTests(ClassifierTestCaseWithMockClassifiers): def test_get_health(self): client = self.app.test_client() response = client.get("/service/health") self.assertEqual(response.status_code, 200) data = json.loads(response.data.decode()) self.assertDictEqual(data, {"result": "ok"}) class InformationResourceTests(ClassifierTestCaseWithMockClassifiers): def test_get_information(self): client = self.app.test_client() response = client.get("/service/information") self.assertEqual(response.status_code, 200) data = json.loads(response.data.decode()) self.assertDictEqual( data, { "host": "127.0.0.1", "port": 8022, "service": "classifier", "version": __VERSION__ } ) if __name__ == "__main__": main()

3,803

343

170

537e3e050876d7c9d2b8f2ecca7ee3f33f63f223

7,246

py

Python

utils.py

lobachevzky/on-policy-curiosity

1d1e9c05b04d313d15e698fb8a05c3116085381f

[ "MIT" ]

1

2021-11-22T09:43:13.000Z

utils.py

lobachevzky/on-policy-curiosity

1d1e9c05b04d313d15e698fb8a05c3116085381f

[ "MIT" ]

2

2019-12-17T04:05:25.000Z

2020-03-18T18:38:31.000Z

utils.py

lobachevzky/on-policy-curiosity

1d1e9c05b04d313d15e698fb8a05c3116085381f

[ "MIT" ]

1

2020-05-29T15:14:36.000Z

# third party import argparse import csv import random import re import subprocess from dataclasses import fields, is_dataclass from functools import reduce from io import StringIO from typing import List, Optional import numpy as np import torch import torch.jit import torch.nn as nn from gym import spaces import gym # Necessary for my KFAC implementation. # https://github.com/openai/baselines/blob/master/baselines/common/tf_util.py#L87 @torch.jit.script RESET = "\033[0m" def hierarchical_parse_args(parser: argparse.ArgumentParser, include_positional=False): """ :return: { group1: {**kwargs} group2: {**kwargs} ... **kwargs } """ args = parser.parse_args() positional = list(get_positionals(parser._action_groups)) nonpositional = dict(get_nonpositionals(parser._action_groups)) optional = nonpositional.pop("optional arguments") nonpositional = {**nonpositional, **optional} if include_positional: return positional, nonpositional return nonpositional

26.542125

87

0.635109

# third party import argparse import csv import random import re import subprocess from dataclasses import fields, is_dataclass from functools import reduce from io import StringIO from typing import List, Optional import numpy as np import torch import torch.jit import torch.nn as nn from gym import spaces import gym def round(x, dec): return torch.round(x * 10 ** dec) / 10 ** dec def grad(x, y): return torch.autograd.grad( x.mean(), y.parameters() if isinstance(y, nn.Module) else y, retain_graph=True ) def get_render_func(venv): if hasattr(venv, "envs"): return venv.envs[0].render elif hasattr(venv, "venv"): return get_render_func(venv.venv) elif hasattr(venv, "env"): return get_render_func(venv.env) return None # Necessary for my KFAC implementation. class AddBias(nn.Module): def __init__(self, bias): super(AddBias, self).__init__() self._bias = nn.Parameter(bias.unsqueeze(1)) def forward(self, x): if x.dim() == 2: bias = self._bias.t().view(1, -1) else: bias = self._bias.t().view(1, -1, 1, 1) return x + bias def init(module, weight_init, bias_init, gain=1): weight_init(module.weight.data, gain=gain) bias_init(module.bias.data) return module # https://github.com/openai/baselines/blob/master/baselines/common/tf_util.py#L87 def init_normc_(weight, gain=1): weight.normal_(0, 1) weight *= gain / torch.sqrt(weight.pow(2).sum(1, keepdim=True)) def set_index(array, idxs, value): idxs = np.array(idxs) if idxs.size > 0: array[tuple(idxs.T)] = value def get_index(array, idxs): idxs = np.array(idxs) if idxs.size == 0: return np.array([], array.dtype) return array[tuple(idxs.T)] def get_n_gpu(): nvidia_smi = subprocess.check_output( "nvidia-smi --format=csv --query-gpu=memory.free".split(), universal_newlines=True, ) return len(list(csv.reader(StringIO(nvidia_smi)))) - 1 def get_random_gpu(): return random.randrange(0, get_n_gpu()) def get_freer_gpu(): nvidia_smi = subprocess.check_output( "nvidia-smi --format=csv --query-gpu=memory.free".split(), universal_newlines=True, ) free_memory = [ float(x[0].split()[0]) for i, x in enumerate(csv.reader(StringIO(nvidia_smi))) if i > 0 ] return int(np.argmax(free_memory)) def init_(network: nn.Module, non_linearity: nn.Module = nn.ReLU): if non_linearity is None: return init(network, init_normc_, lambda x: nn.init.constant_(x, 0)) # return init(network, nn.init.orthogonal_, lambda x: nn.init.constant_(x, 0)) return init( network, nn.init.orthogonal_, lambda x: nn.init.constant_(x, 0), nn.init.calculate_gain(non_linearity.__name__.lower()), ) def broadcast3d(inputs, shape): return inputs.view(*inputs.shape, 1, 1).expand(*inputs.shape, *shape) def interp(x1, x2, c): return c * x2 + (1 - c) * x1 @torch.jit.script def log_prob(i, probs): return torch.log(torch.gather(probs, -1, i)) def trace(module_fn, in_size): return torch.jit.trace(module_fn(in_size), example_inputs=torch.rand(1, in_size)) RESET = "\033[0m" def k_scalar_pairs(*args, **kwargs): for k, v in dict(*args, **kwargs).items(): mean = np.mean(v) if not np.isnan(mean): yield k, mean def set_seeds(cuda, cuda_deterministic, seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) cuda &= torch.cuda.is_available() if cuda and cuda_deterministic: torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(1) def hierarchical_parse_args(parser: argparse.ArgumentParser, include_positional=False): """ :return: { group1: {**kwargs} group2: {**kwargs} ... **kwargs } """ args = parser.parse_args() def key_value_pairs(group): for action in group._group_actions: if action.dest != "help": yield action.dest, getattr(args, action.dest, None) def get_positionals(groups): for group in groups: if group.title == "positional arguments": for k, v in key_value_pairs(group): yield v def get_nonpositionals(groups: List[argparse._ArgumentGroup]): for group in groups: if group.title != "positional arguments": children = key_value_pairs(group) descendants = get_nonpositionals(group._action_groups) yield group.title, {**dict(children), **dict(descendants)} positional = list(get_positionals(parser._action_groups)) nonpositional = dict(get_nonpositionals(parser._action_groups)) optional = nonpositional.pop("optional arguments") nonpositional = {**nonpositional, **optional} if include_positional: return positional, nonpositional return nonpositional def get_device(name): match = re.search("\d+$", name) if match: device_num = int(match.group()) % get_n_gpu() else: device_num = get_random_gpu() return torch.device("cuda", device_num) def astuple(obj): def gen(): for f in fields(obj): yield astuple(getattr(obj, f.name)) if is_dataclass(obj): return tuple(gen()) return obj def asdict(obj): def gen(): for f in fields(obj): yield f.name, asdict(getattr(obj, f.name)) if hasattr(obj, "_asdict"): # noinspection PyProtectedMember return obj._asdict() if is_dataclass(obj): return dict(gen()) return obj class Discrete(spaces.Discrete): def __init__(self, low: int, high: int): self.low = low self.high = high super().__init__(1 + high - low) def sample(self) -> int: return self.low + super().sample() def contains(self, x) -> bool: return super().contains(x - self.low) def __repr__(self) -> str: return f"Discrete({self.low}, {self.high})" def __eq__(self, other) -> bool: return ( isinstance(other, Discrete) and self.low == other.low and self.high == other.high ) def get_max_shape(*xs) -> np.ndarray: def compare_shape(max_so_far: Optional[np.ndarray], opener: np.ndarray): new = np.array(opener.shape) return new if max_so_far is None else np.maximum(new, max_so_far) return reduce(compare_shape, map(np.array, xs), None) def space_shape(space: gym.Space): if isinstance(space, gym.spaces.Box): return space.low.shape if isinstance(space, gym.spaces.Dict): return {k: space_shape(v) for k, v in space.spaces.items()} if isinstance(space, gym.spaces.Tuple): return tuple(space_shape(s) for s in space.spaces) if isinstance(space, gym.spaces.MultiDiscrete): return space.nvec.shape if isinstance(space, gym.spaces.Discrete): return (1,) if isinstance(space, gym.spaces.MultiBinary): return (space.n,) raise NotImplementedError

5,332

15

817

3e3b84b9bfcb2d82c9de1b00bbb595e034cc5661

28,845

py

Python

flatsat/comms/user_segment/radio/flowgraphs/unit_test/test_flatsat_rx.py

cromulencellc/hackasat-final-2021

d01a1b5d7947b3e41ae2da3ec63d5f43278a5eac

[ "MIT" ]

4

2022-02-25T05:45:27.000Z

2022-03-10T01:05:27.000Z

flatsat/comms/user_segment/radio/flowgraphs/unit_test/test_flatsat_rx.py

cromulencellc/hackasat-final-2021

d01a1b5d7947b3e41ae2da3ec63d5f43278a5eac

[ "MIT" ]

null

flatsat/comms/user_segment/radio/flowgraphs/unit_test/test_flatsat_rx.py

cromulencellc/hackasat-final-2021

d01a1b5d7947b3e41ae2da3ec63d5f43278a5eac

[ "MIT" ]

2

2022-03-02T02:14:16.000Z

2022-03-05T07:36:18.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # SPDX-License-Identifier: GPL-3.0 # # GNU Radio Python Flow Graph # Title: Flatsat RX Test # Author: dev # GNU Radio version: 3.9.3.0 from distutils.version import StrictVersion if __name__ == '__main__': import ctypes import sys if sys.platform.startswith('linux'): try: x11 = ctypes.cdll.LoadLibrary('libX11.so') x11.XInitThreads() except: print("Warning: failed to XInitThreads()") import os import sys sys.path.append(os.environ.get('GRC_HIER_PATH', os.path.expanduser('~/.grc_gnuradio'))) from PyQt5 import Qt from gnuradio import eng_notation from gnuradio import qtgui from gnuradio.filter import firdes import sip from flatsat_rx import flatsat_rx # grc-generated hier_block from gnuradio import analog from gnuradio import blocks import pmt from gnuradio import digital from gnuradio import filter from gnuradio import gr from gnuradio.fft import window import signal from argparse import ArgumentParser from gnuradio.eng_arg import eng_float, intx from gnuradio import uhd import time from gnuradio.filter import pfb from gnuradio.qtgui import Range, RangeWidget from PyQt5 import QtCore from usersegment_tx import usersegment_tx # grc-generated hier_block import satellites.hier import test_flatsat_rx_epy_block_0_0_0_0_0_0 as epy_block_0_0_0_0_0_0 # embedded python block from gnuradio import qtgui if __name__ == '__main__': main()

42.988077

154

0.668123

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # SPDX-License-Identifier: GPL-3.0 # # GNU Radio Python Flow Graph # Title: Flatsat RX Test # Author: dev # GNU Radio version: 3.9.3.0 from distutils.version import StrictVersion if __name__ == '__main__': import ctypes import sys if sys.platform.startswith('linux'): try: x11 = ctypes.cdll.LoadLibrary('libX11.so') x11.XInitThreads() except: print("Warning: failed to XInitThreads()") import os import sys sys.path.append(os.environ.get('GRC_HIER_PATH', os.path.expanduser('~/.grc_gnuradio'))) from PyQt5 import Qt from gnuradio import eng_notation from gnuradio import qtgui from gnuradio.filter import firdes import sip from flatsat_rx import flatsat_rx # grc-generated hier_block from gnuradio import analog from gnuradio import blocks import pmt from gnuradio import digital from gnuradio import filter from gnuradio import gr from gnuradio.fft import window import signal from argparse import ArgumentParser from gnuradio.eng_arg import eng_float, intx from gnuradio import uhd import time from gnuradio.filter import pfb from gnuradio.qtgui import Range, RangeWidget from PyQt5 import QtCore from usersegment_tx import usersegment_tx # grc-generated hier_block import satellites.hier import test_flatsat_rx_epy_block_0_0_0_0_0_0 as epy_block_0_0_0_0_0_0 # embedded python block from gnuradio import qtgui class test_flatsat_rx(gr.top_block, Qt.QWidget): def __init__(self): gr.top_block.__init__(self, "Flatsat RX Test", catch_exceptions=True) Qt.QWidget.__init__(self) self.setWindowTitle("Flatsat RX Test") qtgui.util.check_set_qss() try: self.setWindowIcon(Qt.QIcon.fromTheme('gnuradio-grc')) except: pass self.top_scroll_layout = Qt.QVBoxLayout() self.setLayout(self.top_scroll_layout) self.top_scroll = Qt.QScrollArea() self.top_scroll.setFrameStyle(Qt.QFrame.NoFrame) self.top_scroll_layout.addWidget(self.top_scroll) self.top_scroll.setWidgetResizable(True) self.top_widget = Qt.QWidget() self.top_scroll.setWidget(self.top_widget) self.top_layout = Qt.QVBoxLayout(self.top_widget) self.top_grid_layout = Qt.QGridLayout() self.top_layout.addLayout(self.top_grid_layout) self.settings = Qt.QSettings("GNU Radio", "test_flatsat_rx") try: if StrictVersion(Qt.qVersion()) < StrictVersion("5.0.0"): self.restoreGeometry(self.settings.value("geometry").toByteArray()) else: self.restoreGeometry(self.settings.value("geometry")) except: pass ################################################## # Variables ################################################## self.samp_rate = samp_rate = 1e6 self.tx_samp_rate = tx_samp_rate = samp_rate self.num_chan = num_chan = 18 self.freq = freq = 901e6 self.tx_samp_rate_0 = tx_samp_rate_0 = samp_rate self.tx_gain = tx_gain = 70 self.tx_freq = tx_freq = freq self.timing_bw = timing_bw = .045 self.strobe_delay = strobe_delay = 1000 self.squelch = squelch = -70 self.sps = sps = 4 self.rx_switch = rx_switch = 0 self.rx_samp_rate_0 = rx_samp_rate_0 = samp_rate self.rx_samp_rate = rx_samp_rate = samp_rate self.rx_gain = rx_gain = 0 self.rx_freq = rx_freq = freq self.lpf_taps = lpf_taps = firdes.low_pass(1.0, samp_rate, samp_rate/num_chan*0.8,5e3, window.WIN_HAMMING, 6.76) self.eb = eb = .22 self.deviation = deviation = 1.5 self.damping = damping = 1.0 self.constel = constel = digital.constellation_bpsk().base() self.constel.gen_soft_dec_lut(8) self.chan_samp_rate = chan_samp_rate = tx_samp_rate/(num_chan) self.chan_cutoff = chan_cutoff = samp_rate/num_chan*0.8 ################################################## # Blocks ################################################## self._tx_gain_tool_bar = Qt.QToolBar(self) self._tx_gain_tool_bar.addWidget(Qt.QLabel('TX Gain' + ": ")) self._tx_gain_line_edit = Qt.QLineEdit(str(self.tx_gain)) self._tx_gain_tool_bar.addWidget(self._tx_gain_line_edit) self._tx_gain_line_edit.returnPressed.connect( lambda: self.set_tx_gain(int(str(self._tx_gain_line_edit.text())))) self.top_grid_layout.addWidget(self._tx_gain_tool_bar, 10, 0, 1, 1) for r in range(10, 11): self.top_grid_layout.setRowStretch(r, 1) for c in range(0, 1): self.top_grid_layout.setColumnStretch(c, 1) self._timing_bw_range = Range(0.001, 0.1, 0.001, .045, 200) self._timing_bw_win = RangeWidget(self._timing_bw_range, self.set_timing_bw, 'Timing BW', "counter_slider", float, QtCore.Qt.Horizontal) self.top_layout.addWidget(self._timing_bw_win) self.tab1 = Qt.QTabWidget() self.tab1_widget_0 = Qt.QWidget() self.tab1_layout_0 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab1_widget_0) self.tab1_grid_layout_0 = Qt.QGridLayout() self.tab1_layout_0.addLayout(self.tab1_grid_layout_0) self.tab1.addTab(self.tab1_widget_0, 'TX') self.tab1_widget_1 = Qt.QWidget() self.tab1_layout_1 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab1_widget_1) self.tab1_grid_layout_1 = Qt.QGridLayout() self.tab1_layout_1.addLayout(self.tab1_grid_layout_1) self.tab1.addTab(self.tab1_widget_1, 'RX') self.top_grid_layout.addWidget(self.tab1, 0, 0, 6, 6) for r in range(0, 6): self.top_grid_layout.setRowStretch(r, 1) for c in range(0, 6): self.top_grid_layout.setColumnStretch(c, 1) self.tab0 = Qt.QTabWidget() self.tab0_widget_0 = Qt.QWidget() self.tab0_layout_0 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab0_widget_0) self.tab0_grid_layout_0 = Qt.QGridLayout() self.tab0_layout_0.addLayout(self.tab0_grid_layout_0) self.tab0.addTab(self.tab0_widget_0, 'AGC') self.tab0_widget_1 = Qt.QWidget() self.tab0_layout_1 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab0_widget_1) self.tab0_grid_layout_1 = Qt.QGridLayout() self.tab0_layout_1.addLayout(self.tab0_grid_layout_1) self.tab0.addTab(self.tab0_widget_1, 'Correlation') self.tab0_widget_2 = Qt.QWidget() self.tab0_layout_2 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab0_widget_2) self.tab0_grid_layout_2 = Qt.QGridLayout() self.tab0_layout_2.addLayout(self.tab0_grid_layout_2) self.tab0.addTab(self.tab0_widget_2, 'Timing') self.tab0_widget_3 = Qt.QWidget() self.tab0_layout_3 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab0_widget_3) self.tab0_grid_layout_3 = Qt.QGridLayout() self.tab0_layout_3.addLayout(self.tab0_grid_layout_3) self.tab0.addTab(self.tab0_widget_3, 'Equalizer') self.tab0_widget_4 = Qt.QWidget() self.tab0_layout_4 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab0_widget_4) self.tab0_grid_layout_4 = Qt.QGridLayout() self.tab0_layout_4.addLayout(self.tab0_grid_layout_4) self.tab0.addTab(self.tab0_widget_4, 'Costas') self.tab0_widget_5 = Qt.QWidget() self.tab0_layout_5 = Qt.QBoxLayout(Qt.QBoxLayout.TopToBottom, self.tab0_widget_5) self.tab0_grid_layout_5 = Qt.QGridLayout() self.tab0_layout_5.addLayout(self.tab0_grid_layout_5) self.tab0.addTab(self.tab0_widget_5, 'Tab 5') self.top_grid_layout.addWidget(self.tab0, 0, 6, 6, 6) for r in range(0, 6): self.top_grid_layout.setRowStretch(r, 1) for c in range(6, 12): self.top_grid_layout.setColumnStretch(c, 1) self._strobe_delay_range = Range(1, 1000, 1, 1000, 200) self._strobe_delay_win = RangeWidget(self._strobe_delay_range, self.set_strobe_delay, 'Strobe Delay', "counter_slider", int, QtCore.Qt.Horizontal) self.top_layout.addWidget(self._strobe_delay_win) self._squelch_range = Range(-100, 0, 1, -70, 200) self._squelch_win = RangeWidget(self._squelch_range, self.set_squelch, 'Power Squelch', "counter_slider", float, QtCore.Qt.Horizontal) self.top_layout.addWidget(self._squelch_win) self._rx_switch_range = Range(0, 1, 1, 0, 200) self._rx_switch_win = RangeWidget(self._rx_switch_range, self.set_rx_switch, 'RX On/Off', "dial", float, QtCore.Qt.Horizontal) self.top_grid_layout.addWidget(self._rx_switch_win, 14, 0, 1, 1) for r in range(14, 15): self.top_grid_layout.setRowStretch(r, 1) for c in range(0, 1): self.top_grid_layout.setColumnStretch(c, 1) self._rx_gain_tool_bar = Qt.QToolBar(self) self._rx_gain_tool_bar.addWidget(Qt.QLabel('RX Gain' + ": ")) self._rx_gain_line_edit = Qt.QLineEdit(str(self.rx_gain)) self._rx_gain_tool_bar.addWidget(self._rx_gain_line_edit) self._rx_gain_line_edit.returnPressed.connect( lambda: self.set_rx_gain(int(str(self._rx_gain_line_edit.text())))) self.top_grid_layout.addWidget(self._rx_gain_tool_bar, 11, 0, 1, 1) for r in range(11, 12): self.top_grid_layout.setRowStretch(r, 1) for c in range(0, 1): self.top_grid_layout.setColumnStretch(c, 1) self._deviation_range = Range(0.1, 2, 0.1, 1.5, 200) self._deviation_win = RangeWidget(self._deviation_range, self.set_deviation, 'Deviation', "counter_slider", float, QtCore.Qt.Horizontal) self.top_layout.addWidget(self._deviation_win) self._damping_range = Range(0.1, 2, 0.1, 1.0, 200) self._damping_win = RangeWidget(self._damping_range, self.set_damping, 'Damping', "counter_slider", float, QtCore.Qt.Horizontal) self.top_layout.addWidget(self._damping_win) self.usersegment_tx_0 = usersegment_tx() self.usersegment_tx_0.set_max_output_buffer(4096) self.uhd_usrp_source_0 = uhd.usrp_source( ",".join(("", "")), uhd.stream_args( cpu_format="fc32", args='', channels=list(range(0,1)), ), ) self.uhd_usrp_source_0.set_subdev_spec('A:B', 0) self.uhd_usrp_source_0.set_samp_rate(rx_samp_rate) self.uhd_usrp_source_0.set_time_now(uhd.time_spec(time.time()), uhd.ALL_MBOARDS) self.uhd_usrp_source_0.set_center_freq(rx_freq, 0) self.uhd_usrp_source_0.set_antenna('RX2', 0) self.uhd_usrp_source_0.set_bandwidth(rx_samp_rate, 0) self.uhd_usrp_source_0.set_gain(rx_gain, 0) self.uhd_usrp_sink_0 = uhd.usrp_sink( ",".join(("", "")), uhd.stream_args( cpu_format="fc32", args='', channels=list(range(0,1)), ), '', ) self.uhd_usrp_sink_0.set_samp_rate(tx_samp_rate) self.uhd_usrp_sink_0.set_time_now(uhd.time_spec(time.time()), uhd.ALL_MBOARDS) self.uhd_usrp_sink_0.set_center_freq(tx_freq, 0) self.uhd_usrp_sink_0.set_antenna('TX/RX', 0) self.uhd_usrp_sink_0.set_gain(tx_gain, 0) self.satellites_rms_agc_0 = satellites.hier.rms_agc(alpha=1e-2, reference=1.0) self.qtgui_sink_x_1 = qtgui.sink_c( 1024, #fftsize window.WIN_BLACKMAN_hARRIS, #wintype 0, #fc samp_rate, #bw 'TX', #name True, #plotfreq True, #plotwaterfall True, #plottime True, #plotconst None # parent ) self.qtgui_sink_x_1.set_update_time(1.0/10) self._qtgui_sink_x_1_win = sip.wrapinstance(self.qtgui_sink_x_1.qwidget(), Qt.QWidget) self.qtgui_sink_x_1.enable_rf_freq(False) self.tab1_layout_0.addWidget(self._qtgui_sink_x_1_win) self.qtgui_sink_x_0_0_0_0_1 = qtgui.sink_c( 1024*4, #fftsize window.WIN_BLACKMAN_hARRIS, #wintype 0, #fc chan_samp_rate, #bw 'Costas', #name True, #plotfreq False, #plotwaterfall True, #plottime True, #plotconst None # parent ) self.qtgui_sink_x_0_0_0_0_1.set_update_time(1.0/10) self._qtgui_sink_x_0_0_0_0_1_win = sip.wrapinstance(self.qtgui_sink_x_0_0_0_0_1.qwidget(), Qt.QWidget) self.qtgui_sink_x_0_0_0_0_1.enable_rf_freq(False) self.tab0_layout_4.addWidget(self._qtgui_sink_x_0_0_0_0_1_win) self.qtgui_sink_x_0_0_0_0 = qtgui.sink_c( 1024*4, #fftsize window.WIN_BLACKMAN_hARRIS, #wintype 0, #fc chan_samp_rate, #bw 'Equalizer', #name True, #plotfreq False, #plotwaterfall True, #plottime True, #plotconst None # parent ) self.qtgui_sink_x_0_0_0_0.set_update_time(1.0/10) self._qtgui_sink_x_0_0_0_0_win = sip.wrapinstance(self.qtgui_sink_x_0_0_0_0.qwidget(), Qt.QWidget) self.qtgui_sink_x_0_0_0_0.enable_rf_freq(False) self.tab0_layout_3.addWidget(self._qtgui_sink_x_0_0_0_0_win) self.qtgui_sink_x_0_0_0 = qtgui.sink_c( 1024*4, #fftsize window.WIN_BLACKMAN_hARRIS, #wintype 0, #fc chan_samp_rate, #bw 'Timing', #name True, #plotfreq False, #plotwaterfall True, #plottime True, #plotconst None # parent ) self.qtgui_sink_x_0_0_0.set_update_time(1.0/10) self._qtgui_sink_x_0_0_0_win = sip.wrapinstance(self.qtgui_sink_x_0_0_0.qwidget(), Qt.QWidget) self.qtgui_sink_x_0_0_0.enable_rf_freq(False) self.tab0_layout_2.addWidget(self._qtgui_sink_x_0_0_0_win) self.qtgui_sink_x_0_0 = qtgui.sink_c( 1024*4, #fftsize window.WIN_BLACKMAN_hARRIS, #wintype 0, #fc chan_samp_rate, #bw 'AGC', #name True, #plotfreq False, #plotwaterfall True, #plottime True, #plotconst None # parent ) self.qtgui_sink_x_0_0.set_update_time(1.0/10) self._qtgui_sink_x_0_0_win = sip.wrapinstance(self.qtgui_sink_x_0_0.qwidget(), Qt.QWidget) self.qtgui_sink_x_0_0.enable_rf_freq(False) self.tab0_layout_0.addWidget(self._qtgui_sink_x_0_0_win) self.qtgui_sink_x_0 = qtgui.sink_c( 1024*32, #fftsize window.WIN_BLACKMAN_hARRIS, #wintype 0, #fc samp_rate, #bw 'Wideband RX', #name True, #plotfreq True, #plotwaterfall True, #plottime True, #plotconst None # parent ) self.qtgui_sink_x_0.set_update_time(1.0/10) self._qtgui_sink_x_0_win = sip.wrapinstance(self.qtgui_sink_x_0.qwidget(), Qt.QWidget) self.qtgui_sink_x_0.enable_rf_freq(False) self.tab1_layout_1.addWidget(self._qtgui_sink_x_0_win) self.pfb_synthesizer_ccf_0 = filter.pfb_synthesizer_ccf( num_chan, lpf_taps, False) self.pfb_synthesizer_ccf_0.set_channel_map([0,10,12,14,16,1,3,5,7,11,13,15,17,2,4,6,8,9]) self.pfb_synthesizer_ccf_0.declare_sample_delay(0) self.pfb_channelizer_ccf_0 = pfb.channelizer_ccf( num_chan, lpf_taps, 1, 100) self.pfb_channelizer_ccf_0.set_channel_map([0,10,12,14,16,1,3,5,7,11,13,15,17,2,4,6,8,9]) self.pfb_channelizer_ccf_0.declare_sample_delay(0) self.flatsat_rx_0 = flatsat_rx( damping=damping, deviation=deviation, timing_bw=timing_bw, ) self.epy_block_0_0_0_0_0_0 = epy_block_0_0_0_0_0_0.blk(enabled=rx_switch) self.blocks_socket_pdu_0_0_2_2 = blocks.socket_pdu('UDP_SERVER', '', '3008', 10000, False) self.blocks_socket_pdu_0_0_2_1 = blocks.socket_pdu('UDP_SERVER', '', '3007', 10000, False) self.blocks_socket_pdu_0_0_2_0 = blocks.socket_pdu('UDP_SERVER', '', '3006', 10000, False) self.blocks_socket_pdu_0_0_2 = blocks.socket_pdu('UDP_SERVER', '', '3005', 10000, False) self.blocks_socket_pdu_0_0_1 = blocks.socket_pdu('UDP_SERVER', '', '3004', 10000, False) self.blocks_socket_pdu_0_0_0 = blocks.socket_pdu('UDP_SERVER', '', '3003', 10000, False) self.blocks_socket_pdu_0_0 = blocks.socket_pdu('UDP_SERVER', '', '3002', 10000, False) self.blocks_socket_pdu_0 = blocks.socket_pdu('UDP_SERVER', '', '3001', 10000, False) self.blocks_random_pdu_0 = blocks.random_pdu(100, 100, 0xFF, 2) self.blocks_null_source_0_0_0 = blocks.null_source(gr.sizeof_gr_complex*1) self.blocks_null_source_0_0 = blocks.null_source(gr.sizeof_gr_complex*1) self.blocks_null_source_0 = blocks.null_source(gr.sizeof_gr_complex*1) self.blocks_null_sink_0_0 = blocks.null_sink(gr.sizeof_gr_complex*1) self.blocks_null_sink_0 = blocks.null_sink(gr.sizeof_gr_complex*1) self.blocks_multiply_const_vxx_0 = blocks.multiply_const_cc(0.5) self.blocks_message_strobe_0_0 = blocks.message_strobe(pmt.intern("TEST"), strobe_delay) self.blocks_message_debug_0_0 = blocks.message_debug(True) self.analog_pwr_squelch_xx_0 = analog.pwr_squelch_cc(squelch, 1e-4, 0, True) ################################################## # Connections ################################################## self.msg_connect((self.blocks_message_strobe_0_0, 'strobe'), (self.blocks_random_pdu_0, 'generate')) self.msg_connect((self.blocks_random_pdu_0, 'pdus'), (self.epy_block_0_0_0_0_0_0, 'tx_in')) self.msg_connect((self.blocks_random_pdu_0, 'pdus'), (self.usersegment_tx_0, 'pdu')) self.msg_connect((self.blocks_socket_pdu_0, 'pdus'), (self.usersegment_tx_0, 'pdu')) self.msg_connect((self.flatsat_rx_0, 'pdu'), (self.blocks_message_debug_0_0, 'print_pdu')) self.msg_connect((self.flatsat_rx_0, 'pdu'), (self.epy_block_0_0_0_0_0_0, 'rx_in')) self.connect((self.analog_pwr_squelch_xx_0, 0), (self.satellites_rms_agc_0, 0)) self.connect((self.blocks_multiply_const_vxx_0, 0), (self.uhd_usrp_sink_0, 0)) self.connect((self.blocks_null_source_0, 0), (self.pfb_synthesizer_ccf_0, 0)) self.connect((self.blocks_null_source_0_0, 11), (self.pfb_synthesizer_ccf_0, 16)) self.connect((self.blocks_null_source_0_0, 3), (self.pfb_synthesizer_ccf_0, 8)) self.connect((self.blocks_null_source_0_0, 2), (self.pfb_synthesizer_ccf_0, 7)) self.connect((self.blocks_null_source_0_0, 0), (self.pfb_synthesizer_ccf_0, 5)) self.connect((self.blocks_null_source_0_0, 12), (self.pfb_synthesizer_ccf_0, 17)) self.connect((self.blocks_null_source_0_0, 8), (self.pfb_synthesizer_ccf_0, 13)) self.connect((self.blocks_null_source_0_0, 10), (self.pfb_synthesizer_ccf_0, 15)) self.connect((self.blocks_null_source_0_0, 4), (self.pfb_synthesizer_ccf_0, 9)) self.connect((self.blocks_null_source_0_0, 7), (self.pfb_synthesizer_ccf_0, 12)) self.connect((self.blocks_null_source_0_0, 9), (self.pfb_synthesizer_ccf_0, 14)) self.connect((self.blocks_null_source_0_0, 5), (self.pfb_synthesizer_ccf_0, 10)) self.connect((self.blocks_null_source_0_0, 6), (self.pfb_synthesizer_ccf_0, 11)) self.connect((self.blocks_null_source_0_0, 1), (self.pfb_synthesizer_ccf_0, 6)) self.connect((self.blocks_null_source_0_0_0, 1), (self.pfb_synthesizer_ccf_0, 3)) self.connect((self.blocks_null_source_0_0_0, 2), (self.pfb_synthesizer_ccf_0, 4)) self.connect((self.blocks_null_source_0_0_0, 0), (self.pfb_synthesizer_ccf_0, 2)) self.connect((self.flatsat_rx_0, 0), (self.qtgui_sink_x_0_0_0, 0)) self.connect((self.flatsat_rx_0, 1), (self.qtgui_sink_x_0_0_0_0, 0)) self.connect((self.flatsat_rx_0, 2), (self.qtgui_sink_x_0_0_0_0_1, 0)) self.connect((self.pfb_channelizer_ccf_0, 1), (self.analog_pwr_squelch_xx_0, 0)) self.connect((self.pfb_channelizer_ccf_0, 0), (self.blocks_null_sink_0, 0)) self.connect((self.pfb_channelizer_ccf_0, 9), (self.blocks_null_sink_0_0, 7)) self.connect((self.pfb_channelizer_ccf_0, 12), (self.blocks_null_sink_0_0, 10)) self.connect((self.pfb_channelizer_ccf_0, 3), (self.blocks_null_sink_0_0, 1)) self.connect((self.pfb_channelizer_ccf_0, 4), (self.blocks_null_sink_0_0, 2)) self.connect((self.pfb_channelizer_ccf_0, 16), (self.blocks_null_sink_0_0, 14)) self.connect((self.pfb_channelizer_ccf_0, 14), (self.blocks_null_sink_0_0, 12)) self.connect((self.pfb_channelizer_ccf_0, 7), (self.blocks_null_sink_0_0, 5)) self.connect((self.pfb_channelizer_ccf_0, 5), (self.blocks_null_sink_0_0, 3)) self.connect((self.pfb_channelizer_ccf_0, 2), (self.blocks_null_sink_0_0, 0)) self.connect((self.pfb_channelizer_ccf_0, 11), (self.blocks_null_sink_0_0, 9)) self.connect((self.pfb_channelizer_ccf_0, 17), (self.blocks_null_sink_0_0, 15)) self.connect((self.pfb_channelizer_ccf_0, 15), (self.blocks_null_sink_0_0, 13)) self.connect((self.pfb_channelizer_ccf_0, 8), (self.blocks_null_sink_0_0, 6)) self.connect((self.pfb_channelizer_ccf_0, 10), (self.blocks_null_sink_0_0, 8)) self.connect((self.pfb_channelizer_ccf_0, 6), (self.blocks_null_sink_0_0, 4)) self.connect((self.pfb_channelizer_ccf_0, 13), (self.blocks_null_sink_0_0, 11)) self.connect((self.pfb_synthesizer_ccf_0, 0), (self.blocks_multiply_const_vxx_0, 0)) self.connect((self.satellites_rms_agc_0, 0), (self.flatsat_rx_0, 0)) self.connect((self.satellites_rms_agc_0, 0), (self.qtgui_sink_x_0_0, 0)) self.connect((self.uhd_usrp_source_0, 0), (self.pfb_channelizer_ccf_0, 0)) self.connect((self.uhd_usrp_source_0, 0), (self.qtgui_sink_x_0, 0)) self.connect((self.usersegment_tx_0, 0), (self.pfb_synthesizer_ccf_0, 1)) self.connect((self.usersegment_tx_0, 0), (self.qtgui_sink_x_1, 0)) def closeEvent(self, event): self.settings = Qt.QSettings("GNU Radio", "test_flatsat_rx") self.settings.setValue("geometry", self.saveGeometry()) self.stop() self.wait() event.accept() def get_samp_rate(self): return self.samp_rate def set_samp_rate(self, samp_rate): self.samp_rate = samp_rate self.set_chan_cutoff(self.samp_rate/self.num_chan*0.8) self.set_lpf_taps(firdes.low_pass(1.0, self.samp_rate, self.samp_rate/self.num_chan*0.8, 5e3, window.WIN_HAMMING, 6.76)) self.set_rx_samp_rate(self.samp_rate) self.set_rx_samp_rate_0(self.samp_rate) self.set_tx_samp_rate(self.samp_rate) self.set_tx_samp_rate_0(self.samp_rate) self.qtgui_sink_x_0.set_frequency_range(0, self.samp_rate) self.qtgui_sink_x_1.set_frequency_range(0, self.samp_rate) def get_tx_samp_rate(self): return self.tx_samp_rate def set_tx_samp_rate(self, tx_samp_rate): self.tx_samp_rate = tx_samp_rate self.set_chan_samp_rate(self.tx_samp_rate/(self.num_chan)) self.uhd_usrp_sink_0.set_samp_rate(self.tx_samp_rate) def get_num_chan(self): return self.num_chan def set_num_chan(self, num_chan): self.num_chan = num_chan self.set_chan_cutoff(self.samp_rate/self.num_chan*0.8) self.set_chan_samp_rate(self.tx_samp_rate/(self.num_chan)) self.set_lpf_taps(firdes.low_pass(1.0, self.samp_rate, self.samp_rate/self.num_chan*0.8, 5e3, window.WIN_HAMMING, 6.76)) def get_freq(self): return self.freq def set_freq(self, freq): self.freq = freq self.set_rx_freq(self.freq) self.set_tx_freq(self.freq) def get_tx_samp_rate_0(self): return self.tx_samp_rate_0 def set_tx_samp_rate_0(self, tx_samp_rate_0): self.tx_samp_rate_0 = tx_samp_rate_0 def get_tx_gain(self): return self.tx_gain def set_tx_gain(self, tx_gain): self.tx_gain = tx_gain Qt.QMetaObject.invokeMethod(self._tx_gain_line_edit, "setText", Qt.Q_ARG("QString", str(self.tx_gain))) self.uhd_usrp_sink_0.set_gain(self.tx_gain, 0) def get_tx_freq(self): return self.tx_freq def set_tx_freq(self, tx_freq): self.tx_freq = tx_freq self.uhd_usrp_sink_0.set_center_freq(self.tx_freq, 0) def get_timing_bw(self): return self.timing_bw def set_timing_bw(self, timing_bw): self.timing_bw = timing_bw self.flatsat_rx_0.set_timing_bw(self.timing_bw) def get_strobe_delay(self): return self.strobe_delay def set_strobe_delay(self, strobe_delay): self.strobe_delay = strobe_delay self.blocks_message_strobe_0_0.set_period(self.strobe_delay) def get_squelch(self): return self.squelch def set_squelch(self, squelch): self.squelch = squelch self.analog_pwr_squelch_xx_0.set_threshold(self.squelch) def get_sps(self): return self.sps def set_sps(self, sps): self.sps = sps def get_rx_switch(self): return self.rx_switch def set_rx_switch(self, rx_switch): self.rx_switch = rx_switch self.epy_block_0_0_0_0_0_0.enabled = self.rx_switch def get_rx_samp_rate_0(self): return self.rx_samp_rate_0 def set_rx_samp_rate_0(self, rx_samp_rate_0): self.rx_samp_rate_0 = rx_samp_rate_0 def get_rx_samp_rate(self): return self.rx_samp_rate def set_rx_samp_rate(self, rx_samp_rate): self.rx_samp_rate = rx_samp_rate self.uhd_usrp_source_0.set_samp_rate(self.rx_samp_rate) self.uhd_usrp_source_0.set_bandwidth(self.rx_samp_rate, 0) def get_rx_gain(self): return self.rx_gain def set_rx_gain(self, rx_gain): self.rx_gain = rx_gain Qt.QMetaObject.invokeMethod(self._rx_gain_line_edit, "setText", Qt.Q_ARG("QString", str(self.rx_gain))) self.uhd_usrp_source_0.set_gain(self.rx_gain, 0) def get_rx_freq(self): return self.rx_freq def set_rx_freq(self, rx_freq): self.rx_freq = rx_freq self.uhd_usrp_source_0.set_center_freq(self.rx_freq, 0) def get_lpf_taps(self): return self.lpf_taps def set_lpf_taps(self, lpf_taps): self.lpf_taps = lpf_taps self.pfb_channelizer_ccf_0.set_taps(self.lpf_taps) self.pfb_synthesizer_ccf_0.set_taps(self.lpf_taps) def get_eb(self): return self.eb def set_eb(self, eb): self.eb = eb def get_deviation(self): return self.deviation def set_deviation(self, deviation): self.deviation = deviation self.flatsat_rx_0.set_deviation(self.deviation) def get_damping(self): return self.damping def set_damping(self, damping): self.damping = damping self.flatsat_rx_0.set_damping(self.damping) def get_constel(self): return self.constel def set_constel(self, constel): self.constel = constel def get_chan_samp_rate(self): return self.chan_samp_rate def set_chan_samp_rate(self, chan_samp_rate): self.chan_samp_rate = chan_samp_rate self.qtgui_sink_x_0_0.set_frequency_range(0, self.chan_samp_rate) self.qtgui_sink_x_0_0_0.set_frequency_range(0, self.chan_samp_rate) self.qtgui_sink_x_0_0_0_0.set_frequency_range(0, self.chan_samp_rate) self.qtgui_sink_x_0_0_0_0_1.set_frequency_range(0, self.chan_samp_rate) def get_chan_cutoff(self): return self.chan_cutoff def set_chan_cutoff(self, chan_cutoff): self.chan_cutoff = chan_cutoff def main(top_block_cls=test_flatsat_rx, options=None): if StrictVersion("4.5.0") <= StrictVersion(Qt.qVersion()) < StrictVersion("5.0.0"): style = gr.prefs().get_string('qtgui', 'style', 'raster') Qt.QApplication.setGraphicsSystem(style) qapp = Qt.QApplication(sys.argv) tb = top_block_cls() tb.start() tb.show() def sig_handler(sig=None, frame=None): tb.stop() tb.wait() Qt.QApplication.quit() signal.signal(signal.SIGINT, sig_handler) signal.signal(signal.SIGTERM, sig_handler) timer = Qt.QTimer() timer.start(500) timer.timeout.connect(lambda: None) qapp.exec_() if __name__ == '__main__': main()

25,997

27

1,342

650df8b817d43a116b8c59edc813e4e70e09997b

715

py

Python

py/sc.py

Utsho/Car-Number-Plate-Reading

5c8c92bb320d9b19a0d54015d80a75dfc4cbf60a

[ "CNRI-Python" ]

1

2019-08-24T04:06:35.000Z

py/sc.py

Utsho/Car-Number-Plate-Reading

5c8c92bb320d9b19a0d54015d80a75dfc4cbf60a

[ "CNRI-Python" ]

null

py/sc.py

Utsho/Car-Number-Plate-Reading

5c8c92bb320d9b19a0d54015d80a75dfc4cbf60a

[ "CNRI-Python" ]

null

from random import randint import os from PIL import Image path='final' classnames=os.listdir(path) images=[] f1=open('input_index.txt','w') for temp in classnames: images.append(Image.open('final/'+temp)) f1.write(temp+"\n") f1.close() k=len(images) im2=[] for i in range(k): img=images[i] im2.append(img) h=0 for i in range(k): h=h+40*im2[i].size[1]/im2[i].size[0]+5 result = Image.new('L', (40,h),'white') lp=0 for i in range(k): img=im2[i] img=img.convert('L') print 'before ', print img.size h=40*img.size[1]/img.size[0] if h<1: h=1 img=img.resize((40,h),Image.ANTIALIAS) print 'after ', print img.size result.paste(img, box=(0, lp)) lp=lp+5+img.size[1] result.save('res.png')

16.627907

41

0.653147

from random import randint import os from PIL import Image path='final' classnames=os.listdir(path) images=[] f1=open('input_index.txt','w') for temp in classnames: images.append(Image.open('final/'+temp)) f1.write(temp+"\n") f1.close() k=len(images) im2=[] for i in range(k): img=images[i] im2.append(img) h=0 for i in range(k): h=h+40*im2[i].size[1]/im2[i].size[0]+5 result = Image.new('L', (40,h),'white') lp=0 for i in range(k): img=im2[i] img=img.convert('L') print 'before ', print img.size h=40*img.size[1]/img.size[0] if h<1: h=1 img=img.resize((40,h),Image.ANTIALIAS) print 'after ', print img.size result.paste(img, box=(0, lp)) lp=lp+5+img.size[1] result.save('res.png')

0

e855e1e160879d94bc5165d60b4668e5bdedecc3

10,836

py

Python

ansible/venv/lib/python2.7/site-packages/ansible/modules/storage/netapp/na_elementsw_network_interfaces.py

gvashchenkolineate/gvashchenkolineate_infra_trytravis

0fb18850afe0d8609693ba4b23f29c7cda17d97f

[ "MIT" ]

17

2017-06-07T23:15:01.000Z

2021-08-30T14:32:36.000Z

ansible/ansible/modules/storage/netapp/na_elementsw_network_interfaces.py

SergeyCherepanov/ansible

875711cd2fd6b783c812241c2ed7a954bf6f670f

[ "MIT" ]

9

2017-06-25T03:31:52.000Z

2021-05-17T23:43:12.000Z

ansible/ansible/modules/storage/netapp/na_elementsw_network_interfaces.py

SergeyCherepanov/ansible

875711cd2fd6b783c812241c2ed7a954bf6f670f

[ "MIT" ]

3

2018-05-26T21:31:22.000Z

2019-09-28T17:00:45.000Z

#!/usr/bin/python # (c) 2018, NetApp, Inc # GNU General Public License v3.0+ (see COPYING or # https://www.gnu.org/licenses/gpl-3.0.txt) ''' Element Software Node Network Interfaces - Bond 1G and 10G configuration ''' from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'certified'} DOCUMENTATION = ''' module: na_elementsw_network_interfaces short_description: NetApp Element Software Configure Node Network Interfaces extends_documentation_fragment: - netapp.solidfire version_added: '2.7' author: NetApp Ansible Team (@carchi8py) <ng-ansibleteam@netapp.com> description: - Configure Element SW Node Network Interfaces for Bond 1G and 10G IP address. options: method: description: - Type of Method used to configure the interface. - method depends on other settings such as the use of a static IP address, which will change the method to static. - loopback - Used to define the IPv4 loopback interface. - manual - Used to define interfaces for which no configuration is done by default. - dhcp - May be used to obtain an IP address via DHCP. - static - Used to define Ethernet interfaces with statically allocated IPv4 addresses. choices: ['loopback', 'manual', 'dhcp', 'static'] required: true ip_address_1g: description: - IP address for the 1G network. required: true ip_address_10g: description: - IP address for the 10G network. required: true subnet_1g: description: - 1GbE Subnet Mask. required: true subnet_10g: description: - 10GbE Subnet Mask. required: true gateway_address_1g: description: - Router network address to send packets out of the local network. required: true gateway_address_10g: description: - Router network address to send packets out of the local network. required: true mtu_1g: description: - Maximum Transmission Unit for 1GbE, Largest packet size that a network protocol can transmit. - Must be greater than or equal to 1500 bytes. default: '1500' mtu_10g: description: - Maximum Transmission Unit for 10GbE, Largest packet size that a network protocol can transmit. - Must be greater than or equal to 1500 bytes. default: '1500' dns_nameservers: description: - List of addresses for domain name servers. dns_search_domains: description: - List of DNS search domains. bond_mode_1g: description: - Bond mode for 1GbE configuration. choices: ['ActivePassive', 'ALB', 'LACP'] default: 'ActivePassive' bond_mode_10g: description: - Bond mode for 10GbE configuration. choices: ['ActivePassive', 'ALB', 'LACP'] default: 'ActivePassive' lacp_1g: description: - Link Aggregation Control Protocol useful only if LACP is selected as the Bond Mode. - Slow - Packets are transmitted at 30 second intervals. - Fast - Packets are transmitted in 1 second intervals. choices: ['Fast', 'Slow'] default: 'Slow' lacp_10g: description: - Link Aggregation Control Protocol useful only if LACP is selected as the Bond Mode. - Slow - Packets are transmitted at 30 second intervals. - Fast - Packets are transmitted in 1 second intervals. choices: ['Fast', 'Slow'] default: 'Slow' virtual_network_tag: description: - This is the primary network tag. All nodes in a cluster have the same VLAN tag. ''' EXAMPLES = """ - name: Set Node network interfaces configuration for Bond 1G and 10G properties tags: - elementsw_network_interfaces na_elementsw_network_interfaces: hostname: "{{ elementsw_hostname }}" username: "{{ elementsw_username }}" password: "{{ elementsw_password }}" method: static ip_address_1g: 10.226.109.68 ip_address_10g: 10.226.201.72 subnet_1g: 255.255.255.0 subnet_10g: 255.255.255.0 gateway_address_1g: 10.193.139.1 gateway_address_10g: 10.193.140.1 mtu_1g: 1500 mtu_10g: 9000 bond_mode_1g: ActivePassive bond_mode_10g: LACP lacp_10g: Fast """ RETURN = """ msg: description: Success message returned: success type: str """ import traceback from ansible.module_utils.basic import AnsibleModule from ansible.module_utils._text import to_native import ansible.module_utils.netapp as netapp_utils HAS_SF_SDK = netapp_utils.has_sf_sdk() try: from solidfire.models import Network, NetworkConfig HAS_SF_SDK = True except Exception: HAS_SF_SDK = False class ElementSWNetworkInterfaces(object): """ Element Software Network Interfaces - Bond 1G and 10G Network configuration """ def set_network_config(self): """ set network configuration """ try: self.sfe.set_network_config(network=self.network_object) except Exception as exception_object: self.module.fail_json(msg='Error network setting for node %s' % (to_native(exception_object)), exception=traceback.format_exc()) def get_network_params_object(self): """ Get Element SW Network object :description: get Network object :return: NetworkConfig object :rtype: object(NetworkConfig object) """ try: bond_1g_network = NetworkConfig(method=self.method, address=self.ip_address_1g, netmask=self.subnet_1g, gateway=self.gateway_address_1g, mtu=self.mtu_1g, dns_nameservers=self.dns_nameservers, dns_search=self.dns_search_domains, bond_mode=self.bond_mode_1g, bond_lacp_rate=self.lacp_1g, virtual_network_tag=self.virtual_network_tag) bond_10g_network = NetworkConfig(method=self.method, address=self.ip_address_10g, netmask=self.subnet_10g, gateway=self.gateway_address_10g, mtu=self.mtu_10g, dns_nameservers=self.dns_nameservers, dns_search=self.dns_search_domains, bond_mode=self.bond_mode_10g, bond_lacp_rate=self.lacp_10g, virtual_network_tag=self.virtual_network_tag) network_object = Network(bond1_g=bond_1g_network, bond10_g=bond_10g_network) return network_object except Exception as e: self.module.fail_json(msg='Error with setting up network object for node 1G and 10G configuration : %s' % to_native(e), exception=to_native(e)) def apply(self): """ Check connection and initialize node with cluster ownership """ changed = False result_message = None self.network_object = self.get_network_params_object() if self.network_object is not None: self.set_network_config() changed = True else: result_message = "Skipping changes, No change requested" self.module.exit_json(changed=changed, msg=result_message) def main(): """ Main function """ elementsw_network_interfaces = ElementSWNetworkInterfaces() elementsw_network_interfaces.apply() if __name__ == '__main__': main()

36.362416

131

0.612126

#!/usr/bin/python # (c) 2018, NetApp, Inc # GNU General Public License v3.0+ (see COPYING or # https://www.gnu.org/licenses/gpl-3.0.txt) ''' Element Software Node Network Interfaces - Bond 1G and 10G configuration ''' from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'certified'} DOCUMENTATION = ''' module: na_elementsw_network_interfaces short_description: NetApp Element Software Configure Node Network Interfaces extends_documentation_fragment: - netapp.solidfire version_added: '2.7' author: NetApp Ansible Team (@carchi8py) <ng-ansibleteam@netapp.com> description: - Configure Element SW Node Network Interfaces for Bond 1G and 10G IP address. options: method: description: - Type of Method used to configure the interface. - method depends on other settings such as the use of a static IP address, which will change the method to static. - loopback - Used to define the IPv4 loopback interface. - manual - Used to define interfaces for which no configuration is done by default. - dhcp - May be used to obtain an IP address via DHCP. - static - Used to define Ethernet interfaces with statically allocated IPv4 addresses. choices: ['loopback', 'manual', 'dhcp', 'static'] required: true ip_address_1g: description: - IP address for the 1G network. required: true ip_address_10g: description: - IP address for the 10G network. required: true subnet_1g: description: - 1GbE Subnet Mask. required: true subnet_10g: description: - 10GbE Subnet Mask. required: true gateway_address_1g: description: - Router network address to send packets out of the local network. required: true gateway_address_10g: description: - Router network address to send packets out of the local network. required: true mtu_1g: description: - Maximum Transmission Unit for 1GbE, Largest packet size that a network protocol can transmit. - Must be greater than or equal to 1500 bytes. default: '1500' mtu_10g: description: - Maximum Transmission Unit for 10GbE, Largest packet size that a network protocol can transmit. - Must be greater than or equal to 1500 bytes. default: '1500' dns_nameservers: description: - List of addresses for domain name servers. dns_search_domains: description: - List of DNS search domains. bond_mode_1g: description: - Bond mode for 1GbE configuration. choices: ['ActivePassive', 'ALB', 'LACP'] default: 'ActivePassive' bond_mode_10g: description: - Bond mode for 10GbE configuration. choices: ['ActivePassive', 'ALB', 'LACP'] default: 'ActivePassive' lacp_1g: description: - Link Aggregation Control Protocol useful only if LACP is selected as the Bond Mode. - Slow - Packets are transmitted at 30 second intervals. - Fast - Packets are transmitted in 1 second intervals. choices: ['Fast', 'Slow'] default: 'Slow' lacp_10g: description: - Link Aggregation Control Protocol useful only if LACP is selected as the Bond Mode. - Slow - Packets are transmitted at 30 second intervals. - Fast - Packets are transmitted in 1 second intervals. choices: ['Fast', 'Slow'] default: 'Slow' virtual_network_tag: description: - This is the primary network tag. All nodes in a cluster have the same VLAN tag. ''' EXAMPLES = """ - name: Set Node network interfaces configuration for Bond 1G and 10G properties tags: - elementsw_network_interfaces na_elementsw_network_interfaces: hostname: "{{ elementsw_hostname }}" username: "{{ elementsw_username }}" password: "{{ elementsw_password }}" method: static ip_address_1g: 10.226.109.68 ip_address_10g: 10.226.201.72 subnet_1g: 255.255.255.0 subnet_10g: 255.255.255.0 gateway_address_1g: 10.193.139.1 gateway_address_10g: 10.193.140.1 mtu_1g: 1500 mtu_10g: 9000 bond_mode_1g: ActivePassive bond_mode_10g: LACP lacp_10g: Fast """ RETURN = """ msg: description: Success message returned: success type: str """ import traceback from ansible.module_utils.basic import AnsibleModule from ansible.module_utils._text import to_native import ansible.module_utils.netapp as netapp_utils HAS_SF_SDK = netapp_utils.has_sf_sdk() try: from solidfire.models import Network, NetworkConfig HAS_SF_SDK = True except Exception: HAS_SF_SDK = False class ElementSWNetworkInterfaces(object): """ Element Software Network Interfaces - Bond 1G and 10G Network configuration """ def __init__(self): self.argument_spec = netapp_utils.ontap_sf_host_argument_spec() self.argument_spec.update( method=dict(type='str', required=True, choices=['loopback', 'manual', 'dhcp', 'static']), ip_address_1g=dict(type='str', required=True), ip_address_10g=dict(type='str', required=True), subnet_1g=dict(type='str', required=True), subnet_10g=dict(type='str', required=True), gateway_address_1g=dict(type='str', required=True), gateway_address_10g=dict(type='str', required=True), mtu_1g=dict(type='str', default='1500'), mtu_10g=dict(type='str', default='1500'), dns_nameservers=dict(type='list'), dns_search_domains=dict(type='list'), bond_mode_1g=dict(type='str', default='ActivePassive', choices=['ActivePassive', 'ALB', 'LACP']), bond_mode_10g=dict(type='str', default='ActivePassive', choices=['ActivePassive', 'ALB', 'LACP']), lacp_1g=dict(type='str', default='Slow', choices=['Fast', 'Slow']), lacp_10g=dict(type='str', default='Slow', choices=['Fast', 'Slow']), virtual_network_tag=dict(type='str'), ) self.module = AnsibleModule( argument_spec=self.argument_spec, supports_check_mode=True, ) input_params = self.module.params self.method = input_params['method'] self.ip_address_1g = input_params['ip_address_1g'] self.ip_address_10g = input_params['ip_address_10g'] self.subnet_1g = input_params['subnet_1g'] self.subnet_10g = input_params['subnet_10g'] self.gateway_address_1g = input_params['gateway_address_1g'] self.gateway_address_10g = input_params['gateway_address_10g'] self.mtu_1g = input_params['mtu_1g'] self.mtu_10g = input_params['mtu_10g'] self.dns_nameservers = input_params['dns_nameservers'] self.dns_search_domains = input_params['dns_search_domains'] self.bond_mode_1g = input_params['bond_mode_1g'] self.bond_mode_10g = input_params['bond_mode_10g'] self.lacp_1g = input_params['lacp_1g'] self.lacp_10g = input_params['lacp_10g'] self.virtual_network_tag = input_params['virtual_network_tag'] if HAS_SF_SDK is False: self.module.fail_json(msg="Unable to import the SolidFire Python SDK") else: self.sfe = netapp_utils.create_sf_connection(module=self.module, port=442) def set_network_config(self): """ set network configuration """ try: self.sfe.set_network_config(network=self.network_object) except Exception as exception_object: self.module.fail_json(msg='Error network setting for node %s' % (to_native(exception_object)), exception=traceback.format_exc()) def get_network_params_object(self): """ Get Element SW Network object :description: get Network object :return: NetworkConfig object :rtype: object(NetworkConfig object) """ try: bond_1g_network = NetworkConfig(method=self.method, address=self.ip_address_1g, netmask=self.subnet_1g, gateway=self.gateway_address_1g, mtu=self.mtu_1g, dns_nameservers=self.dns_nameservers, dns_search=self.dns_search_domains, bond_mode=self.bond_mode_1g, bond_lacp_rate=self.lacp_1g, virtual_network_tag=self.virtual_network_tag) bond_10g_network = NetworkConfig(method=self.method, address=self.ip_address_10g, netmask=self.subnet_10g, gateway=self.gateway_address_10g, mtu=self.mtu_10g, dns_nameservers=self.dns_nameservers, dns_search=self.dns_search_domains, bond_mode=self.bond_mode_10g, bond_lacp_rate=self.lacp_10g, virtual_network_tag=self.virtual_network_tag) network_object = Network(bond1_g=bond_1g_network, bond10_g=bond_10g_network) return network_object except Exception as e: self.module.fail_json(msg='Error with setting up network object for node 1G and 10G configuration : %s' % to_native(e), exception=to_native(e)) def apply(self): """ Check connection and initialize node with cluster ownership """ changed = False result_message = None self.network_object = self.get_network_params_object() if self.network_object is not None: self.set_network_config() changed = True else: result_message = "Skipping changes, No change requested" self.module.exit_json(changed=changed, msg=result_message) def main(): """ Main function """ elementsw_network_interfaces = ElementSWNetworkInterfaces() elementsw_network_interfaces.apply() if __name__ == '__main__': main()

2,521

0

27

327abded97602fd35ed2f1723e9822ff6005624c

6,589

py

Python

LoRaSensorMonitoringServer.py

TomNaiser/LoRa-Sensor-Monitoring

090edce9a14147b9eb62a2e065e02e248c41be84

[ "MIT" ]

null

LoRaSensorMonitoringServer.py

TomNaiser/LoRa-Sensor-Monitoring

090edce9a14147b9eb62a2e065e02e248c41be84

[ "MIT" ]

null

LoRaSensorMonitoringServer.py

TomNaiser/LoRa-Sensor-Monitoring

090edce9a14147b9eb62a2e065e02e248c41be84

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Jan 28 10:14:42 2020 @author: Thomas """ #!/usr/bin/env python #Lora Base Server #Reads Data from the ESP32 Lora Receiver via serial Port and generates a CSV file for each LoRa sensor client import time import serial import re import datetime import os.path myDataFileName="/home/pi/BienenWaageMessdaten" ser = serial.Serial( port='/dev/ttyUSB0', #port='COM15', baudrate = 115200, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS, timeout=1 ) measurements=[] currentSensorData=sensorData(sensorID=[-1]) datasetReady=False while 1: x=ser.readline() #print(x) parsedInput=str(x).split('\\t',) SensorIDVal=parseSerialInput("Sensor ID",parsedInput[0]) if SensorIDVal is not None: currentSensorData=sensorData(sensorID=SensorIDVal) if "Sensor ID" in parsedInput[0]: d = datetime.datetime.now() timeNow=time.mktime(d.timetuple()) #get posix time currentSensorData.receiveTime=convertDateTime(d) currentSensorData.receiveTimePosix=timeNow for paramString in parsedInput: SensorIDVal=parseSerialInput("Sensor ID",paramString) if SensorIDVal is not None: currentSensorData.sensorID=SensorIDVal weightVal=parseSerialInput("Weight",paramString) if weightVal is not None: currentSensorData.weight=weightVal voltageVal=parseSerialInput("Voltage",paramString) if voltageVal is not None: currentSensorData.voltage=voltageVal rssidVal=parseSerialInput("RSSID",paramString) if rssidVal is not None: currentSensorData.RSSID=rssidVal swarmAlarmVal=parseSerialInput("Swarm Alarm",paramString) if swarmAlarmVal is not None: currentSensorData.swarmAlarm=swarmAlarmVal else: continue outputDataLine=getOutputDataLine(currentSensorData) print(outputDataLine) appendLineToFile(myDataFileName+'Sensor'+str(int(currentSensorData.sensorID[0]))+'.txt',outputDataLine) # ============================================================================= # SensorIDVal=parseSerialInput("Sensor ID",str(x)) # # if SensorIDVal is not None: # datasetReady=True # #timeNow=str(datetime.datetime.now()) # # d = datetime.datetime.now() # # timeNow=time.mktime(d.timetuple()) # #timeNow=convertDateTime(datetime.datetime.now()) # print("Create new SensorDataSet with sensorID: ",SensorIDVal) # newSensorData=sensorData(sensorID=SensorIDVal,receiveTime=timeNow) # print("Created new SensorDataSet with sensorID: ",SensorIDVal) # #append currentSensorData after the first Dataset has been read completely # #Thats when SensorIDVal is call for the second time # print ("CurrentSensorData: ",currentSensorData.sensorID) # if currentSensorData.sensorID[0]>-1: # if the currentSensorData is not completely new... # measurements.append(currentSensorData) # outputDataLine=getOutputDataLine(currentSensorData) # csd=currentSensorData.sensorID # print ("Output: ",outputDataLine) # appendLineToFile(myDataFileName+'Sensor'+str(int(SensorIDVal[0]))+'.txt',outputDataLine) # # # # currentSensorData=newSensorData # # if datasetReady: # weightVal=parseSerialInput("Weight",str(x)) # if weightVal is not None: # currentSensorData.weight=weightVal # # voltageVal=parseSerialInput("Voltage",str(x)) # if voltageVal is not None: # currentSensorData.voltage=voltageVal # # rssidVal=parseSerialInput("RSSID",str(x)) # if rssidVal is not None: # currentSensorData.RSSID=rssidVal # # swarmAlarmVal=parseSerialInput("Swarm Alarm",str(x)) # if swarmAlarmVal is not None: # currentSensorData.swarmAlarm=swarmAlarmVal # # =============================================================================

36.403315

141

0.595386

# -*- coding: utf-8 -*- """ Created on Tue Jan 28 10:14:42 2020 @author: Thomas """ #!/usr/bin/env python #Lora Base Server #Reads Data from the ESP32 Lora Receiver via serial Port and generates a CSV file for each LoRa sensor client import time import serial import re import datetime import os.path class sensorData(object): def __init__(self, sensorID=[0],weight=[0],voltage=[0],RSSID=[0],swarmAlarm=[0],receiveTimePosix=[0],receiveTime=[0] ): self.sensorID = sensorID self.weight = weight self.voltage=voltage self.RSSID=RSSID self.swarmAlarm=swarmAlarm self.receiveTimePosix=receiveTimePosix self.receiveTime=receiveTime def appendLineToFile(filename,content): if os.path.isfile(filename)==False: with open(filename,"a") as myfile: myfile.write("Posix-Zeit\tSensorID\tGewicht\tSpannung\tRSSID\tZeit\n") else: with open(filename, "a") as myfile: myfile.write(content) def getOutputDataLine(mySensorData): outputStr="" outputStr=outputStr+str(mySensorData.receiveTimePosix)+"\t" outputStr=outputStr+str(int(mySensorData.sensorID[0]))+"\t" outputStr=outputStr+str(mySensorData.weight[0])+"\t" outputStr=outputStr+str(mySensorData.voltage[0])+"\t" outputStr=outputStr+str(int(mySensorData.RSSID[0]))+"\t" outputStr=outputStr+str(mySensorData.receiveTime)+"\n" #put datetime to the last column, otherwise kst2 plot seems to confuse the columns return outputStr def parseSerialInput(IDString,serialInputString): value=None if IDString in serialInputString: value=[float(s) for s in re.findall(r'-?\d+\.?\d*', serialInputString)] return value def convertDateTime(dt): timestring="" timestring=timestring+str(dt.year)+'-' timestring=timestring+str(dt.month).zfill(2)+'-' timestring=timestring+str(dt.day).zfill(2)+" " timestring=timestring+str(dt.hour).zfill(2)+":" timestring=timestring+str(dt.minute).zfill(2)+":" timestring=timestring+str(dt.second).zfill(2) print (timestring) return timestring myDataFileName="/home/pi/BienenWaageMessdaten" ser = serial.Serial( port='/dev/ttyUSB0', #port='COM15', baudrate = 115200, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS, timeout=1 ) measurements=[] currentSensorData=sensorData(sensorID=[-1]) datasetReady=False while 1: x=ser.readline() #print(x) parsedInput=str(x).split('\\t',) SensorIDVal=parseSerialInput("Sensor ID",parsedInput[0]) if SensorIDVal is not None: currentSensorData=sensorData(sensorID=SensorIDVal) if "Sensor ID" in parsedInput[0]: d = datetime.datetime.now() timeNow=time.mktime(d.timetuple()) #get posix time currentSensorData.receiveTime=convertDateTime(d) currentSensorData.receiveTimePosix=timeNow for paramString in parsedInput: SensorIDVal=parseSerialInput("Sensor ID",paramString) if SensorIDVal is not None: currentSensorData.sensorID=SensorIDVal weightVal=parseSerialInput("Weight",paramString) if weightVal is not None: currentSensorData.weight=weightVal voltageVal=parseSerialInput("Voltage",paramString) if voltageVal is not None: currentSensorData.voltage=voltageVal rssidVal=parseSerialInput("RSSID",paramString) if rssidVal is not None: currentSensorData.RSSID=rssidVal swarmAlarmVal=parseSerialInput("Swarm Alarm",paramString) if swarmAlarmVal is not None: currentSensorData.swarmAlarm=swarmAlarmVal else: continue outputDataLine=getOutputDataLine(currentSensorData) print(outputDataLine) appendLineToFile(myDataFileName+'Sensor'+str(int(currentSensorData.sensorID[0]))+'.txt',outputDataLine) # ============================================================================= # SensorIDVal=parseSerialInput("Sensor ID",str(x)) # # if SensorIDVal is not None: # datasetReady=True # #timeNow=str(datetime.datetime.now()) # # d = datetime.datetime.now() # # timeNow=time.mktime(d.timetuple()) # #timeNow=convertDateTime(datetime.datetime.now()) # print("Create new SensorDataSet with sensorID: ",SensorIDVal) # newSensorData=sensorData(sensorID=SensorIDVal,receiveTime=timeNow) # print("Created new SensorDataSet with sensorID: ",SensorIDVal) # #append currentSensorData after the first Dataset has been read completely # #Thats when SensorIDVal is call for the second time # print ("CurrentSensorData: ",currentSensorData.sensorID) # if currentSensorData.sensorID[0]>-1: # if the currentSensorData is not completely new... # measurements.append(currentSensorData) # outputDataLine=getOutputDataLine(currentSensorData) # csd=currentSensorData.sensorID # print ("Output: ",outputDataLine) # appendLineToFile(myDataFileName+'Sensor'+str(int(SensorIDVal[0]))+'.txt',outputDataLine) # # # # currentSensorData=newSensorData # # if datasetReady: # weightVal=parseSerialInput("Weight",str(x)) # if weightVal is not None: # currentSensorData.weight=weightVal # # voltageVal=parseSerialInput("Voltage",str(x)) # if voltageVal is not None: # currentSensorData.voltage=voltageVal # # rssidVal=parseSerialInput("RSSID",str(x)) # if rssidVal is not None: # currentSensorData.RSSID=rssidVal # # swarmAlarmVal=parseSerialInput("Swarm Alarm",str(x)) # if swarmAlarmVal is not None: # currentSensorData.swarmAlarm=swarmAlarmVal # # =============================================================================

1,653

4

163

225465fb383bfcf90d72035fd4286c3072f0a432

750

py

Python

sapextractor/utils/tstct/extract_tstct.py

aarkue/sap-meta-explorer

613bf657bbaa72a3781a84664e5de7626516532f

[ "Apache-2.0" ]

2

2021-02-10T08:09:35.000Z

2021-05-21T06:25:34.000Z

sapextractor/utils/tstct/extract_tstct.py

aarkue/sap-meta-explorer

613bf657bbaa72a3781a84664e5de7626516532f

[ "Apache-2.0" ]

null

sapextractor/utils/tstct/extract_tstct.py

aarkue/sap-meta-explorer

613bf657bbaa72a3781a84664e5de7626516532f

[ "Apache-2.0" ]

3

2021-11-22T13:27:00.000Z

2022-03-16T22:08:51.000Z

from copy import copy

27.777778

116

0.644

from copy import copy class Shared: transactions_dictio = {} def apply(con, target_language="E"): df = con.prepare_and_execute_query("TSTCT", ["SPRSL", "TCODE", "TTEXT"], " WHERE SPRSL = '"+target_language+"'") df = df[df["SPRSL"] == target_language] stream = df.to_dict('records') dictio = {} for el in stream: dictio[el["TCODE"]] = el["TTEXT"] return dictio def apply_static(con, transactions=None): if not Shared.transactions_dictio: Shared.transactions_dictio = apply(con) ret = copy(Shared.transactions_dictio) if transactions is not None: transactions = set(transactions).difference(set(ret.keys())) for t in transactions: ret[t] = str(t) return ret

635

21

69

a606e61515f9330200bf5c9be613695f8e5414ce

7,873

py

Python

ogs5py/tools/download.py

GeoStat-Framework/ogs5py

2bc4428c4c485d094e02c129ba5051745df58391

[ "MIT" ]

12

2018-12-11T15:44:58.000Z

2022-03-30T19:04:42.000Z

ogs5py/tools/download.py

GeoStat-Framework/ogs5py

2bc4428c4c485d094e02c129ba5051745df58391

[ "MIT" ]

4

2019-07-09T17:47:05.000Z

2021-12-27T07:34:07.000Z

ogs5py/tools/download.py

GeoStat-Framework/ogs5py

2bc4428c4c485d094e02c129ba5051745df58391

[ "MIT" ]

5

2019-04-04T19:47:56.000Z

2021-04-28T21:56:39.000Z

# -*- coding: utf-8 -*- """ Downloader for ogs5. .. currentmodule:: ogs5py.tools.download Downloader ^^^^^^^^^^ A downloading routine to get the OSG5 executable. .. autosummary:: download_ogs add_exe reset_download OGS5PY_CONFIG ---- """ import os import shutil import tarfile import zipfile from urllib.request import urlretrieve, urlopen import tempfile import platform import lxml.html # TemporaryDirectory not avialable in python2 # from: https://gist.github.com/cpelley/10e2eeaf60dacc7956bb TemporaryDirectory = getattr( tempfile, "TemporaryDirectory", _TemporaryDirectory ) # https://stackoverflow.com/a/34615446/6696397 def get_links(url, ext, build=None): """Get links from url ending with ext and containing build.""" sublinks = [] connection = urlopen(url) dom = lxml.html.fromstring(connection.read()) for link in dom.xpath("//a/@href"): if not link or not link.endswith(ext): continue # skip unwanted if build is None or "build_" + build + "/" in link: sublinks.append( url + link if not link.startswith("http") else link ) return sublinks RELEASE = "https://ogsstorage.blob.core.windows.net/binaries/ogs5/" BUILD = "https://jenkins.opengeosys.org/job/ufz/job/ogs5/job/master/" STABLE = BUILD + "lastStableBuild/" SUCCESS = BUILD + "lastSuccessfulBuild/" # https://stackoverflow.com/a/53222876/6696397 OGS5PY_CONFIG = os.path.join( os.environ.get("APPDATA") or os.environ.get("XDG_CONFIG_HOME") or os.path.join(os.environ["HOME"], ".config"), "ogs5py", ) """str: Standard config path for ogs5py.""" URLS = { "5.7": { "Linux": ( RELEASE + "ogs-5.7.0-Linux-2.6.32-573.8.1.el6.x86_64-x64.tar.gz" ), "Windows": RELEASE + "ogs-5.7.0-Windows-6.1.7601-x64.zip", "Darwin": RELEASE + "ogs-5.7.0-Darwin-15.2.0-x64.tar.gz", }, "5.7.1": { "Windows": ( "https://github.com/ufz/ogs5/releases/download/" + "5.7.1/ogs-5.7.1-Windows-x64.zip" ) }, "5.8": { "Linux": ( RELEASE + "ogs-5.8-Linux-2.6.32-754.3.5.el6.x86_64-x64.tar.gz" ), "Windows": RELEASE + "ogs-5.8-Windows-x64.zip", }, } def download_ogs( version="5.7", system=None, path=OGS5PY_CONFIG, name=None, build=None ): """ Download the OGS5 executable. Parameters ---------- version : :class:`str`, optional Version to download ("5.7", "5.8", "latest" or "stable"). Default: "5.7" system : :class:`str`, optional Target system (Linux, Windows, Darwin). Default: platform.system() path : :class:`str`, optional Destination path. Default: :any:`OGS5PY_CONFIG` name : :class:`str`, optional Destination file name. Default "ogs[.exe]" build : :class:`str`, optional If system is "Linux" and version is "latest" or "stable", you can select a certain build from the ogs 5 builds: * "BRNS": Biogeochemical Reaction Network Simulator * "FEM": Finite Element Method * "GEMS": Gibbs Energy Minimization Solver * "IPQC": IPhreeqc * "LIS": Library of Iterative Solvers * "MKL": Intel Math Kernel Library * "MPI": Message Passing Interface * "PETSC": Portable, Extensible Toolkit for Scientific Computation * "PETSC_GEMS": PETSC and GEMS * "PQC": PHREEQC * "SP": Sparse solver Returns ------- dest : :class:`str` If an OGS5 executable was successfully downloaded, the file-path is returned. Notes ----- There is only an executable on "Darwin" for version "5.7". Taken from: * https://www.opengeosys.org/ogs-5/ * https://jenkins.opengeosys.org/job/ufz/job/ogs5/job/master/ """ URLS["latest"] = { "Linux": get_links(SUCCESS, "tar.gz", build="FEM")[0], "Windows": get_links(SUCCESS, "zip", build=None)[0], } URLS["stable"] = { "Linux": get_links(STABLE, "tar.gz", build="FEM")[0], "Windows": get_links(STABLE, "zip", build=None)[0], } system = platform.system() if system is None else system path = os.path.abspath(path) if not os.path.exists(path): os.makedirs(path) if version not in URLS: raise ValueError( "'{}': unknown version. Use: {}".format(version, list(URLS)) ) urls_version = URLS[version] if system not in urls_version: raise ValueError( "'{}': unsupported system for version '{}'. Use: {}".format( system, version, list(urls_version) ) ) if system == "Linux" and build is not None: if version not in ["stable", "latest"]: raise ValueError( "Use version 'stable' or 'latest' for specific build." ) base_url = STABLE if version == "stable" else SUCCESS links = get_links(base_url, ".tar.gz", build) if len(links) != 1: raise ValueError( "Can't find unique version for build '{}'. Found: {}".format( build, links ) ) ogs_url = links[0] elif build is None or build == "FEM": ogs_url = urls_version[system] else: raise ValueError( "system='{}', build='{}': Could not find matching exe.".format( system, build ) ) print("Downloading: ", ogs_url) ext = ".tar.gz" if ogs_url.endswith(".tar.gz") else ".zip" if name is None: name = "ogs.exe" if system == "Windows" else "ogs" dest = os.path.join(path, name) with TemporaryDirectory() as tmpdirname: data_filename = os.path.join(tmpdirname, "data" + ext) urlretrieve(ogs_url, data_filename) # extract the data if ext == ".tar.gz": z_file = tarfile.open(data_filename, "r:gz") names = z_file.getnames() else: z_file = zipfile.ZipFile(data_filename) names = z_file.namelist() found = "" for file in names: if os.path.basename(file).startswith("ogs"): found = file break if found: z_file.extract(member=found, path=tmpdirname) shutil.copy(os.path.join(tmpdirname, found), dest) z_file.close() return dest if found else None def add_exe(ogs_exe, dest_name=None): """ Add an OGS5 exe to :any:`OGS5PY_CONFIG`. Parameters ---------- ogs_exe : :class:`str` Path to the ogs executable to be copied. dest_name : :class:`str`, optional Destination file name. Default: basename of ogs_exe Returns ------- dest : :class:`str` If an OGS5 executable was successfully copied, the file-path is returned. """ if platform.system() == "Windows" and ogs_exe[-4:] == ".lnk": print("Don't use file links under windows...") return None if os.path.islink(ogs_exe): ogs_exe = os.path.realpath(ogs_exe) if os.path.exists(ogs_exe) and os.path.isfile(ogs_exe): dest_name = ( os.path.basename(ogs_exe) if dest_name is None else dest_name ) dest = os.path.join(OGS5PY_CONFIG, dest_name) shutil.copy(ogs_exe, dest) return dest print("The given ogs_exe does not exist...") return None def reset_download(): """Reset all downloads in :any:`OGS5PY_CONFIG`.""" shutil.rmtree(OGS5PY_CONFIG, ignore_errors=True)

30.87451

78

0.587578

# -*- coding: utf-8 -*- """ Downloader for ogs5. .. currentmodule:: ogs5py.tools.download Downloader ^^^^^^^^^^ A downloading routine to get the OSG5 executable. .. autosummary:: download_ogs add_exe reset_download OGS5PY_CONFIG ---- """ import os import shutil import tarfile import zipfile from urllib.request import urlretrieve, urlopen import tempfile import platform import lxml.html # TemporaryDirectory not avialable in python2 # from: https://gist.github.com/cpelley/10e2eeaf60dacc7956bb class _TemporaryDirectory(object): def __enter__(self): self.dir_name = tempfile.mkdtemp() return self.dir_name def __exit__(self, exc_type, exc_value, traceback): shutil.rmtree(self.dir_name) TemporaryDirectory = getattr( tempfile, "TemporaryDirectory", _TemporaryDirectory ) # https://stackoverflow.com/a/34615446/6696397 def get_links(url, ext, build=None): """Get links from url ending with ext and containing build.""" sublinks = [] connection = urlopen(url) dom = lxml.html.fromstring(connection.read()) for link in dom.xpath("//a/@href"): if not link or not link.endswith(ext): continue # skip unwanted if build is None or "build_" + build + "/" in link: sublinks.append( url + link if not link.startswith("http") else link ) return sublinks RELEASE = "https://ogsstorage.blob.core.windows.net/binaries/ogs5/" BUILD = "https://jenkins.opengeosys.org/job/ufz/job/ogs5/job/master/" STABLE = BUILD + "lastStableBuild/" SUCCESS = BUILD + "lastSuccessfulBuild/" # https://stackoverflow.com/a/53222876/6696397 OGS5PY_CONFIG = os.path.join( os.environ.get("APPDATA") or os.environ.get("XDG_CONFIG_HOME") or os.path.join(os.environ["HOME"], ".config"), "ogs5py", ) """str: Standard config path for ogs5py.""" URLS = { "5.7": { "Linux": ( RELEASE + "ogs-5.7.0-Linux-2.6.32-573.8.1.el6.x86_64-x64.tar.gz" ), "Windows": RELEASE + "ogs-5.7.0-Windows-6.1.7601-x64.zip", "Darwin": RELEASE + "ogs-5.7.0-Darwin-15.2.0-x64.tar.gz", }, "5.7.1": { "Windows": ( "https://github.com/ufz/ogs5/releases/download/" + "5.7.1/ogs-5.7.1-Windows-x64.zip" ) }, "5.8": { "Linux": ( RELEASE + "ogs-5.8-Linux-2.6.32-754.3.5.el6.x86_64-x64.tar.gz" ), "Windows": RELEASE + "ogs-5.8-Windows-x64.zip", }, } def download_ogs( version="5.7", system=None, path=OGS5PY_CONFIG, name=None, build=None ): """ Download the OGS5 executable. Parameters ---------- version : :class:`str`, optional Version to download ("5.7", "5.8", "latest" or "stable"). Default: "5.7" system : :class:`str`, optional Target system (Linux, Windows, Darwin). Default: platform.system() path : :class:`str`, optional Destination path. Default: :any:`OGS5PY_CONFIG` name : :class:`str`, optional Destination file name. Default "ogs[.exe]" build : :class:`str`, optional If system is "Linux" and version is "latest" or "stable", you can select a certain build from the ogs 5 builds: * "BRNS": Biogeochemical Reaction Network Simulator * "FEM": Finite Element Method * "GEMS": Gibbs Energy Minimization Solver * "IPQC": IPhreeqc * "LIS": Library of Iterative Solvers * "MKL": Intel Math Kernel Library * "MPI": Message Passing Interface * "PETSC": Portable, Extensible Toolkit for Scientific Computation * "PETSC_GEMS": PETSC and GEMS * "PQC": PHREEQC * "SP": Sparse solver Returns ------- dest : :class:`str` If an OGS5 executable was successfully downloaded, the file-path is returned. Notes ----- There is only an executable on "Darwin" for version "5.7". Taken from: * https://www.opengeosys.org/ogs-5/ * https://jenkins.opengeosys.org/job/ufz/job/ogs5/job/master/ """ URLS["latest"] = { "Linux": get_links(SUCCESS, "tar.gz", build="FEM")[0], "Windows": get_links(SUCCESS, "zip", build=None)[0], } URLS["stable"] = { "Linux": get_links(STABLE, "tar.gz", build="FEM")[0], "Windows": get_links(STABLE, "zip", build=None)[0], } system = platform.system() if system is None else system path = os.path.abspath(path) if not os.path.exists(path): os.makedirs(path) if version not in URLS: raise ValueError( "'{}': unknown version. Use: {}".format(version, list(URLS)) ) urls_version = URLS[version] if system not in urls_version: raise ValueError( "'{}': unsupported system for version '{}'. Use: {}".format( system, version, list(urls_version) ) ) if system == "Linux" and build is not None: if version not in ["stable", "latest"]: raise ValueError( "Use version 'stable' or 'latest' for specific build." ) base_url = STABLE if version == "stable" else SUCCESS links = get_links(base_url, ".tar.gz", build) if len(links) != 1: raise ValueError( "Can't find unique version for build '{}'. Found: {}".format( build, links ) ) ogs_url = links[0] elif build is None or build == "FEM": ogs_url = urls_version[system] else: raise ValueError( "system='{}', build='{}': Could not find matching exe.".format( system, build ) ) print("Downloading: ", ogs_url) ext = ".tar.gz" if ogs_url.endswith(".tar.gz") else ".zip" if name is None: name = "ogs.exe" if system == "Windows" else "ogs" dest = os.path.join(path, name) with TemporaryDirectory() as tmpdirname: data_filename = os.path.join(tmpdirname, "data" + ext) urlretrieve(ogs_url, data_filename) # extract the data if ext == ".tar.gz": z_file = tarfile.open(data_filename, "r:gz") names = z_file.getnames() else: z_file = zipfile.ZipFile(data_filename) names = z_file.namelist() found = "" for file in names: if os.path.basename(file).startswith("ogs"): found = file break if found: z_file.extract(member=found, path=tmpdirname) shutil.copy(os.path.join(tmpdirname, found), dest) z_file.close() return dest if found else None def add_exe(ogs_exe, dest_name=None): """ Add an OGS5 exe to :any:`OGS5PY_CONFIG`. Parameters ---------- ogs_exe : :class:`str` Path to the ogs executable to be copied. dest_name : :class:`str`, optional Destination file name. Default: basename of ogs_exe Returns ------- dest : :class:`str` If an OGS5 executable was successfully copied, the file-path is returned. """ if platform.system() == "Windows" and ogs_exe[-4:] == ".lnk": print("Don't use file links under windows...") return None if os.path.islink(ogs_exe): ogs_exe = os.path.realpath(ogs_exe) if os.path.exists(ogs_exe) and os.path.isfile(ogs_exe): dest_name = ( os.path.basename(ogs_exe) if dest_name is None else dest_name ) dest = os.path.join(OGS5PY_CONFIG, dest_name) shutil.copy(ogs_exe, dest) return dest print("The given ogs_exe does not exist...") return None def reset_download(): """Reset all downloads in :any:`OGS5PY_CONFIG`.""" shutil.rmtree(OGS5PY_CONFIG, ignore_errors=True)

138

13

75

2fc9e9e0b73e020f3c72b32bf69a10544d35b251

9,397

py

Python

release/stubs.min/Autodesk/Revit/DB/__init___parts/ViewCropRegionShapeManager.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/Autodesk/Revit/DB/__init___parts/ViewCropRegionShapeManager.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/Autodesk/Revit/DB/__init___parts/ViewCropRegionShapeManager.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

class ViewCropRegionShapeManager(object, IDisposable): """ A class that provides access to settings related to the crop assigned to a view or a reference callout. """ def Dispose(self): """ Dispose(self: ViewCropRegionShapeManager) """ pass def GetAnnotationCropShape(self): """ GetAnnotationCropShape(self: ViewCropRegionShapeManager) -> CurveLoop Gets the annotation crop box assigned to the view. Returns: The annotation crop boundary. """ pass def GetCropShape(self): """ GetCropShape(self: ViewCropRegionShapeManager) -> IList[CurveLoop] Gets the crop boundaries that are curently active. Returns: The crop boundaries. """ pass def GetSplitRegionMaximum(self, regionIndex): """ GetSplitRegionMaximum(self: ViewCropRegionShapeManager,regionIndex: int) -> float Returns the proportional location of the maximum boundary of the specified split crop region. regionIndex: Index of region to be split horizontally (numbering starts with 0). Returns: A value from 0 to 1 representing the maximum location for the regions split boundary. This number represents the location as a ratio along the non-split rectangular crop. """ pass def GetSplitRegionMinimum(self, regionIndex): """ GetSplitRegionMinimum(self: ViewCropRegionShapeManager,regionIndex: int) -> float Returns the proportional location of the minimum boundary of the specified split crop region. regionIndex: Index of region to be split horizontally (numbering starts with 0). Returns: A value from 0 to 1 representing the minimum location for the regions split boundary. This number represents the location as a ratio along the non-split rectangular crop. """ pass def IsCropRegionShapeValid(self, boundary): """ IsCropRegionShapeValid(self: ViewCropRegionShapeManager,boundary: CurveLoop) -> bool Verifies that boundary represents one closed curve loop without self-intersections, consisting of non-zero length straight lines in a plane parallel to the view plane. boundary: The crop boundary. Returns: True if the passed crop boundary represents one closed curve loop without self-intersections, consisting of non-zero length straight lines in a plane parallel to the view plane. """ pass def ReleaseUnmanagedResources(self, *args): """ ReleaseUnmanagedResources(self: ViewCropRegionShapeManager,disposing: bool) """ pass def RemoveCropRegionShape(self): """ RemoveCropRegionShape(self: ViewCropRegionShapeManager) Removes any non-rectangular boundary of the view's crop. """ pass def RemoveSplit(self): """ RemoveSplit(self: ViewCropRegionShapeManager) Removes any split applied to the view's crop. """ pass def RemoveSplitRegion(self, regionIndex): """ RemoveSplitRegion(self: ViewCropRegionShapeManager,regionIndex: int) Removes one region in split crop. regionIndex: Index of region to be deleted (numbering starts with 0). """ pass def SetCropShape(self, boundary): """ SetCropShape(self: ViewCropRegionShapeManager,boundary: CurveLoop) Sets the boundary of the view's crop to the specified shape. boundary: The crop boundary. """ pass def SplitRegionHorizontally(self, regionIndex, leftPart, rightPart): """ SplitRegionHorizontally(self: ViewCropRegionShapeManager,regionIndex: int,leftPart: float,rightPart: float) Splits horizontally one region in split crop. regionIndex: Index of region to be split horizontally (numbering starts with 0). leftPart: Relative portion of the original region to become the new left region (0 to 1). rightPart: Relative portion of the original region to become the new right region (0 to 1). """ pass def SplitRegionVertically(self, regionIndex, topPart, bottomPart): """ SplitRegionVertically(self: ViewCropRegionShapeManager,regionIndex: int,topPart: float,bottomPart: float) Splits vertically one region in split crop. regionIndex: Index of region to be split vertically (numbering starts with 0). topPart: Relative portion of the original region to become the new top region (0 to 1). bottomPart: Relative portion of the original region to become the new bottom region (0 to 1). """ pass def __enter__(self, *args): """ __enter__(self: IDisposable) -> object """ pass def __exit__(self, *args): """ __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) """ pass def __init__(self, *args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __repr__(self, *args): """ __repr__(self: object) -> str """ pass BottomAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the bottom of the view crop that determines the location of the annotation crop bottom boundary. Get: BottomAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: BottomAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """ CanBeSplit = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Verifies that the crop of the associated view is permitted to have multiple regions. Get: CanBeSplit(self: ViewCropRegionShapeManager) -> bool """ CanHaveAnnotationCrop = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Verifies that the view is allowed to have an annotation crop. Get: CanHaveAnnotationCrop(self: ViewCropRegionShapeManager) -> bool """ CanHaveShape = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Verifies that the crop of the associated view is permitted to have a non-rectangular shape. Get: CanHaveShape(self: ViewCropRegionShapeManager) -> bool """ IsSplitHorizontally = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Whether or not the view's crop is split (and the split is horizontal). Get: IsSplitHorizontally(self: ViewCropRegionShapeManager) -> bool """ IsSplitVertically = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Whether or not the view's crop is split (and the split is vertical). Get: IsSplitVertically(self: ViewCropRegionShapeManager) -> bool """ IsValidObject = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Specifies whether the .NET object represents a valid Revit entity. Get: IsValidObject(self: ViewCropRegionShapeManager) -> bool """ LeftAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the left of the view crop that determines the location of the annotation crop left boundary. Get: LeftAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: LeftAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """ NumberOfSplitRegions = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The number of split crop regions (1 if the crop is not currently split). Get: NumberOfSplitRegions(self: ViewCropRegionShapeManager) -> int """ RightAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the right of the view crop that determines the location of the annotation crop right boundary. Get: RightAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: RightAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """ ShapeSet = property(lambda self: object(), lambda self, v: None, lambda self: None) """Whether or not the view crop has a non-rectangular shape set. Get: ShapeSet(self: ViewCropRegionShapeManager) -> bool """ Split = property(lambda self: object(), lambda self, v: None, lambda self: None) """Whether or not the view crop is split. Get: Split(self: ViewCropRegionShapeManager) -> bool """ TopAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the top of the view crop that determines the location of the annotation crop top boundary. Get: TopAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: TopAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """

24.728947

221

0.670427

class ViewCropRegionShapeManager(object, IDisposable): """ A class that provides access to settings related to the crop assigned to a view or a reference callout. """ def Dispose(self): """ Dispose(self: ViewCropRegionShapeManager) """ pass def GetAnnotationCropShape(self): """ GetAnnotationCropShape(self: ViewCropRegionShapeManager) -> CurveLoop Gets the annotation crop box assigned to the view. Returns: The annotation crop boundary. """ pass def GetCropShape(self): """ GetCropShape(self: ViewCropRegionShapeManager) -> IList[CurveLoop] Gets the crop boundaries that are curently active. Returns: The crop boundaries. """ pass def GetSplitRegionMaximum(self, regionIndex): """ GetSplitRegionMaximum(self: ViewCropRegionShapeManager,regionIndex: int) -> float Returns the proportional location of the maximum boundary of the specified split crop region. regionIndex: Index of region to be split horizontally (numbering starts with 0). Returns: A value from 0 to 1 representing the maximum location for the regions split boundary. This number represents the location as a ratio along the non-split rectangular crop. """ pass def GetSplitRegionMinimum(self, regionIndex): """ GetSplitRegionMinimum(self: ViewCropRegionShapeManager,regionIndex: int) -> float Returns the proportional location of the minimum boundary of the specified split crop region. regionIndex: Index of region to be split horizontally (numbering starts with 0). Returns: A value from 0 to 1 representing the minimum location for the regions split boundary. This number represents the location as a ratio along the non-split rectangular crop. """ pass def IsCropRegionShapeValid(self, boundary): """ IsCropRegionShapeValid(self: ViewCropRegionShapeManager,boundary: CurveLoop) -> bool Verifies that boundary represents one closed curve loop without self-intersections, consisting of non-zero length straight lines in a plane parallel to the view plane. boundary: The crop boundary. Returns: True if the passed crop boundary represents one closed curve loop without self-intersections, consisting of non-zero length straight lines in a plane parallel to the view plane. """ pass def ReleaseUnmanagedResources(self, *args): """ ReleaseUnmanagedResources(self: ViewCropRegionShapeManager,disposing: bool) """ pass def RemoveCropRegionShape(self): """ RemoveCropRegionShape(self: ViewCropRegionShapeManager) Removes any non-rectangular boundary of the view's crop. """ pass def RemoveSplit(self): """ RemoveSplit(self: ViewCropRegionShapeManager) Removes any split applied to the view's crop. """ pass def RemoveSplitRegion(self, regionIndex): """ RemoveSplitRegion(self: ViewCropRegionShapeManager,regionIndex: int) Removes one region in split crop. regionIndex: Index of region to be deleted (numbering starts with 0). """ pass def SetCropShape(self, boundary): """ SetCropShape(self: ViewCropRegionShapeManager,boundary: CurveLoop) Sets the boundary of the view's crop to the specified shape. boundary: The crop boundary. """ pass def SplitRegionHorizontally(self, regionIndex, leftPart, rightPart): """ SplitRegionHorizontally(self: ViewCropRegionShapeManager,regionIndex: int,leftPart: float,rightPart: float) Splits horizontally one region in split crop. regionIndex: Index of region to be split horizontally (numbering starts with 0). leftPart: Relative portion of the original region to become the new left region (0 to 1). rightPart: Relative portion of the original region to become the new right region (0 to 1). """ pass def SplitRegionVertically(self, regionIndex, topPart, bottomPart): """ SplitRegionVertically(self: ViewCropRegionShapeManager,regionIndex: int,topPart: float,bottomPart: float) Splits vertically one region in split crop. regionIndex: Index of region to be split vertically (numbering starts with 0). topPart: Relative portion of the original region to become the new top region (0 to 1). bottomPart: Relative portion of the original region to become the new bottom region (0 to 1). """ pass def __enter__(self, *args): """ __enter__(self: IDisposable) -> object """ pass def __exit__(self, *args): """ __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) """ pass def __init__(self, *args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __repr__(self, *args): """ __repr__(self: object) -> str """ pass BottomAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the bottom of the view crop that determines the location of the annotation crop bottom boundary. Get: BottomAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: BottomAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """ CanBeSplit = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Verifies that the crop of the associated view is permitted to have multiple regions. Get: CanBeSplit(self: ViewCropRegionShapeManager) -> bool """ CanHaveAnnotationCrop = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Verifies that the view is allowed to have an annotation crop. Get: CanHaveAnnotationCrop(self: ViewCropRegionShapeManager) -> bool """ CanHaveShape = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Verifies that the crop of the associated view is permitted to have a non-rectangular shape. Get: CanHaveShape(self: ViewCropRegionShapeManager) -> bool """ IsSplitHorizontally = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Whether or not the view's crop is split (and the split is horizontal). Get: IsSplitHorizontally(self: ViewCropRegionShapeManager) -> bool """ IsSplitVertically = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Whether or not the view's crop is split (and the split is vertical). Get: IsSplitVertically(self: ViewCropRegionShapeManager) -> bool """ IsValidObject = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Specifies whether the .NET object represents a valid Revit entity. Get: IsValidObject(self: ViewCropRegionShapeManager) -> bool """ LeftAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the left of the view crop that determines the location of the annotation crop left boundary. Get: LeftAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: LeftAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """ NumberOfSplitRegions = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The number of split crop regions (1 if the crop is not currently split). Get: NumberOfSplitRegions(self: ViewCropRegionShapeManager) -> int """ RightAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the right of the view crop that determines the location of the annotation crop right boundary. Get: RightAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: RightAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """ ShapeSet = property(lambda self: object(), lambda self, v: None, lambda self: None) """Whether or not the view crop has a non-rectangular shape set. Get: ShapeSet(self: ViewCropRegionShapeManager) -> bool """ Split = property(lambda self: object(), lambda self, v: None, lambda self: None) """Whether or not the view crop is split. Get: Split(self: ViewCropRegionShapeManager) -> bool """ TopAnnotationCropOffset = property( lambda self: object(), lambda self, v: None, lambda self: None ) """The offset from the top of the view crop that determines the location of the annotation crop top boundary. Get: TopAnnotationCropOffset(self: ViewCropRegionShapeManager) -> float Set: TopAnnotationCropOffset(self: ViewCropRegionShapeManager)=value """

0

181263c57ee32e69218aebc26fb0790bf52e4b8e

1,788

py

Python

large-repo-master/test-3/nb14.py

victorsun123/numpy

1fb036758b4035ab2c132d26f420e5bc3ffc917e

[ "BSD-3-Clause" ]

null

large-repo-master/test-3/nb14.py

victorsun123/numpy

1fb036758b4035ab2c132d26f420e5bc3ffc917e

[ "BSD-3-Clause" ]

null

large-repo-master/test-3/nb14.py

victorsun123/numpy

1fb036758b4035ab2c132d26f420e5bc3ffc917e

[ "BSD-3-Clause" ]

null

# Databricks notebook source INSTGUYIKQEFVDAVPNQI DFXKJDJFAIGTQDFPNZXIHFBFJBXNSCAQMMDSAPVIFVULKEPYGJRUNILQPXYXVGWBNVBHBWSUEGQTKHMFFROKHGJKPJ OCRRFSYKRPOYFVBAHSCCPHGHUA ORMCLMKXZAKDXRSGAXPHGQDBBOGQKFQYRLQQ AKVWPNWJGXQTKSYPSLNOUXSWVYRQ YVOFGDXZWZHFASZQCMZFKHUUBCFUILVBZPHAAMMPWQPOCWRGVAHLTM NCUOQLVTIVWIMABZGYRFYFLMOIUDXEPZTMNSDTHDYWKAINMG UJKXTIQSHVGBGMWYWHLVZJL RDVCHBNRWRCSEUFZCIRJYEECZIIYXHARBS OASOKPNORKQTXEWQTQCWFBCZJRAFHVHRFKMUSOWSDDJNLP HQACLBDITTEMCSLKEUOLWVAFECHWDEWSMPJJLADNOORIOWFFQBXUHBP KCOEBZYAQCFAFLRNZTJGGNXZLRUGEDYMKBMBAIDKKLFAZUCARVQCPUTQPKMKXWPVTSGRMOLJNYGNMYGTJASOUBCMX YXOVQMGQLPZNAQSKHUIKOJHCGKJKLZJWJZKRNSNGQCRMCHEWNNHMSEWWGJZKHSWTOYXOGXSO JSHVIDOARPSYHMPG VDTAPXXSHWBWLVPOOLFJEQBVZRDYQWSBGPTSLSKPXVTUOZZFXXVIFEWQJJKTYLHCEPPOOEYB YGYUMVULDSZBEWELXBTASPDVYUKZORKFQZMLRQKJCPYRRINVLAUSTTEGGPQJVBAMHAYIHFY RXTBRRWTGLSRQPPYWZFNNXUABDGXDPYGYHEVNPLHWVOADDQJWQEWMONTUADOMV YKOYLHAREBGQERUXDMJBSZHYPIIIRZTVMKXVWDCNOFHIAJZZSRJMMFQHCGCWYJYVBYQDBZLXLKMBOE NDLBRNSZCHXOZCSDTBJEC BURTEUHPAWQUUWDBQQWDIVWHGRBAKKBATTBNDLNRZTIYLCPPIPOMVGLMJVXZCNASMHYPWSHTDDOLOQLBQORYWLYZETQ PUMXIXFBWKXVIFAB FEWWASRUZCEZVRRPNWCJNASCAKNVHDSRAMAQGTGRHSFCJPXZIXDJWBEDXZRRKLUEECYMK RQPBYDZGSMFQZXGVUMXLEZFHJFXAACTOQHIUQQMHLXFTZGBPPEYSAXXGDZIZOQCYBWIQYKEBTHBGMEKNWMGGR RPOTNUMNVYNIM ASFTCBFRGKQXMJXFHMXUHUVBIEEQURNIIPGZQBTZUCMRTF FHFDAFRLXKVNJDUTSJKZODTSXTFENNSBEDLWCUICYSZTMQNQYAOJYRL BRXHCYYPLPYENXXYYBQBSEGAKNAGFMIMHWOATNDTJSUWOFJEJMSBGMNGMKKHQLSJGNGKB ZKUDOFKJSSQTWZXOOLTHLGBWWRTUWVAUZIIEGSHPFDYSORQDIXESGZLL MDREQOCVTVBHHDOBQGIOHPWKTYYXCURTDKWANRYHZTXMWWKUEZXWNQFMXSEWWYGL MWUOYEOJYZUBKZDSMYGCNAVFZVXCDOETRKTSMJDZMNQFQTFJZMUHVQCHDMCRZIFRKVCVC ROQHBIPMNTEBHEBKXSFJEDNLKICRSCPULYUYEJZGDUCLDDFUXWWBAEFROFACUQKRDUJFVHRHVVMNLYVNWXVKI HOYJXDQGQSPNBVISRWIJUZFMUZGLOKFWPZKXGJNYELDOKFXKCNIPKDSJVUFTLSL

45.846154

91

0.97651

# Databricks notebook source INSTGUYIKQEFVDAVPNQI DFXKJDJFAIGTQDFPNZXIHFBFJBXNSCAQMMDSAPVIFVULKEPYGJRUNILQPXYXVGWBNVBHBWSUEGQTKHMFFROKHGJKPJ OCRRFSYKRPOYFVBAHSCCPHGHUA ORMCLMKXZAKDXRSGAXPHGQDBBOGQKFQYRLQQ AKVWPNWJGXQTKSYPSLNOUXSWVYRQ YVOFGDXZWZHFASZQCMZFKHUUBCFUILVBZPHAAMMPWQPOCWRGVAHLTM NCUOQLVTIVWIMABZGYRFYFLMOIUDXEPZTMNSDTHDYWKAINMG UJKXTIQSHVGBGMWYWHLVZJL RDVCHBNRWRCSEUFZCIRJYEECZIIYXHARBS OASOKPNORKQTXEWQTQCWFBCZJRAFHVHRFKMUSOWSDDJNLP HQACLBDITTEMCSLKEUOLWVAFECHWDEWSMPJJLADNOORIOWFFQBXUHBP KCOEBZYAQCFAFLRNZTJGGNXZLRUGEDYMKBMBAIDKKLFAZUCARVQCPUTQPKMKXWPVTSGRMOLJNYGNMYGTJASOUBCMX YXOVQMGQLPZNAQSKHUIKOJHCGKJKLZJWJZKRNSNGQCRMCHEWNNHMSEWWGJZKHSWTOYXOGXSO JSHVIDOARPSYHMPG VDTAPXXSHWBWLVPOOLFJEQBVZRDYQWSBGPTSLSKPXVTUOZZFXXVIFEWQJJKTYLHCEPPOOEYB YGYUMVULDSZBEWELXBTASPDVYUKZORKFQZMLRQKJCPYRRINVLAUSTTEGGPQJVBAMHAYIHFY RXTBRRWTGLSRQPPYWZFNNXUABDGXDPYGYHEVNPLHWVOADDQJWQEWMONTUADOMV YKOYLHAREBGQERUXDMJBSZHYPIIIRZTVMKXVWDCNOFHIAJZZSRJMMFQHCGCWYJYVBYQDBZLXLKMBOE NDLBRNSZCHXOZCSDTBJEC BURTEUHPAWQUUWDBQQWDIVWHGRBAKKBATTBNDLNRZTIYLCPPIPOMVGLMJVXZCNASMHYPWSHTDDOLOQLBQORYWLYZETQ PUMXIXFBWKXVIFAB FEWWASRUZCEZVRRPNWCJNASCAKNVHDSRAMAQGTGRHSFCJPXZIXDJWBEDXZRRKLUEECYMK RQPBYDZGSMFQZXGVUMXLEZFHJFXAACTOQHIUQQMHLXFTZGBPPEYSAXXGDZIZOQCYBWIQYKEBTHBGMEKNWMGGR RPOTNUMNVYNIM ASFTCBFRGKQXMJXFHMXUHUVBIEEQURNIIPGZQBTZUCMRTF FHFDAFRLXKVNJDUTSJKZODTSXTFENNSBEDLWCUICYSZTMQNQYAOJYRL BRXHCYYPLPYENXXYYBQBSEGAKNAGFMIMHWOATNDTJSUWOFJEJMSBGMNGMKKHQLSJGNGKB ZKUDOFKJSSQTWZXOOLTHLGBWWRTUWVAUZIIEGSHPFDYSORQDIXESGZLL MDREQOCVTVBHHDOBQGIOHPWKTYYXCURTDKWANRYHZTXMWWKUEZXWNQFMXSEWWYGL MWUOYEOJYZUBKZDSMYGCNAVFZVXCDOETRKTSMJDZMNQFQTFJZMUHVQCHDMCRZIFRKVCVC ROQHBIPMNTEBHEBKXSFJEDNLKICRSCPULYUYEJZGDUCLDDFUXWWBAEFROFACUQKRDUJFVHRHVVMNLYVNWXVKI HOYJXDQGQSPNBVISRWIJUZFMUZGLOKFWPZKXGJNYELDOKFXKCNIPKDSJVUFTLSL

0

87008688f730d39f20393ab216ba561f7bc783bc

2,446

py

Python

hyperbox/networks/bnnas/bn_blocks.py

marsggbo/hyperbox

91dcd04ad30164bcb12209d818df18961fa3f347

[ "MIT" ]

1

2022-01-17T00:34:14.000Z

hyperbox/networks/bnnas/bn_blocks.py

marsggbo/hyperbox

91dcd04ad30164bcb12209d818df18961fa3f347

[ "MIT" ]

null

hyperbox/networks/bnnas/bn_blocks.py

marsggbo/hyperbox

91dcd04ad30164bcb12209d818df18961fa3f347

[ "MIT" ]

null

import torch import torch.nn as nn __all__ = [ 'blocks_dict', 'InvertedResidual', 'conv_1x1_bn', 'conv_bn' ] blocks_dict = { 'k3r3':lambda inp, oup, stride : InvertedResidual(inp, oup, 3, 1, stride, 3), 'k3r6':lambda inp, oup, stride : InvertedResidual(inp, oup, 3, 1, stride, 6), 'k5r3':lambda inp, oup, stride : InvertedResidual(inp, oup, 5, 2, stride, 3), 'k5r6':lambda inp, oup, stride : InvertedResidual(inp, oup, 5, 2, stride, 6), 'k7r3':lambda inp, oup, stride : InvertedResidual(inp, oup, 7, 3, stride, 3), 'k7r6':lambda inp, oup, stride : InvertedResidual(inp, oup, 7, 3, stride, 6), }

32.613333

129

0.554374

import torch import torch.nn as nn __all__ = [ 'blocks_dict', 'InvertedResidual', 'conv_1x1_bn', 'conv_bn' ] blocks_dict = { 'k3r3':lambda inp, oup, stride : InvertedResidual(inp, oup, 3, 1, stride, 3), 'k3r6':lambda inp, oup, stride : InvertedResidual(inp, oup, 3, 1, stride, 6), 'k5r3':lambda inp, oup, stride : InvertedResidual(inp, oup, 5, 2, stride, 3), 'k5r6':lambda inp, oup, stride : InvertedResidual(inp, oup, 5, 2, stride, 6), 'k7r3':lambda inp, oup, stride : InvertedResidual(inp, oup, 7, 3, stride, 3), 'k7r6':lambda inp, oup, stride : InvertedResidual(inp, oup, 7, 3, stride, 6), } class InvertedResidual(nn.Module): def __init__(self, inp, oup, ksize, padding, stride, expand_ratio): super(InvertedResidual, self).__init__() self.stride = stride self.use_res_connect = self.stride == 1 and inp == oup self.expand_ratio = expand_ratio if expand_ratio == 1: self.conv = nn.Sequential( # dw nn.Conv2d(inp, inp, ksize, stride, padding, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.ReLU6(inplace=True), # pw-linear nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ) else: self.conv = nn.Sequential( # pw nn.Conv2d(inp, inp * expand_ratio, 1, 1, 0, bias=False), nn.BatchNorm2d(inp * expand_ratio), nn.ReLU6(inplace=True), # dw nn.Conv2d(inp * expand_ratio, inp * expand_ratio, ksize, stride, padding, groups=inp * expand_ratio, bias=False), nn.BatchNorm2d(inp * expand_ratio), nn.ReLU6(inplace=True), # pw-linear nn.Conv2d(inp * expand_ratio, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ) def forward(self, x): if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) def conv_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True) ) def conv_1x1_bn(inp, oup): return nn.Sequential( nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True) )

1,670

13

122

3c3d8da7b19f669775422aaf440d8177d4613006

5,682

py

Python

experiments/scripts/csv2hdf5_allinone.py

cvelten/DeepSurv

de9b365be6f994cb30eb0a50197930d24e48dee4

[ "MIT" ]

null

experiments/scripts/csv2hdf5_allinone.py

cvelten/DeepSurv

de9b365be6f994cb30eb0a50197930d24e48dee4

[ "MIT" ]

null

experiments/scripts/csv2hdf5_allinone.py

cvelten/DeepSurv

de9b365be6f994cb30eb0a50197930d24e48dee4

[ "MIT" ]

null

import h5py import numpy as np import pandas as pd import os from argparse import ArgumentParser if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('ifile_os') parser.add_argument('ifile_pfs') # parser.add_argument('-e', '--event_col', default='OSEvent') # parser.add_argument('-t', '--time_col', default='TTDy') # parser.add_argument('--txcol', type=str, default='SBRT') # parser.add_argument('--drop', help='drop columns', nargs='+', type=str) # parser.add_argument('--droprows', help='drop rows where [cols] have value --droprowsval', nargs='+', type=str) # parser.add_argument( # '--droprowsval', help='value at which to drop the rows from --droprows, default 1', type=int, default=1) # parser.add_argument('--droprows2', help='drop rows where [cols] have value --droprowsval2', nargs='+', type=str) # parser.add_argument( # '--droprowsval2', help='value at which to drop the rows from --droprows2, default 0', type=int, default=0) args = parser.parse_args() print(args) df = pd.read_csv(args.ifile_os) # print(df) drop_sbrtVS = ['Treatment', 'RFA', 'SBRT_OR_RFA'] drop_rfaVS = ['Treatment', 'SBRT', 'SBRT_OR_RFA'] drop_sbrtORrfa = ['Treatment', 'SBRT', 'RFA'] # # THIS IS FOR OS FIRST frac = 0.5 ds = { 'SBRT_train': df[df.SBRT == 1].sample(frac=frac), 'RFA_train': df[df.RFA == 1].sample(frac=frac), 'NONE_train': df[df.SBRT_OR_RFA == 0].sample(frac=frac) } ds |= { 'SBRT_test': df.loc[df[df.SBRT == 1].index.symmetric_difference(ds['SBRT_train'].index)], 'RFA_test': df.loc[df[df.RFA == 1].index.symmetric_difference(ds['RFA_train'].index)], 'NONE_test': df.loc[df[df.SBRT_OR_RFA == 0].index.symmetric_difference(ds['NONE_train'].index)], } df_sbrtVSnone = { 'train': pd.concat([ds['SBRT_train'], ds['NONE_train']]).drop(columns=drop_sbrtVS), 'test': pd.concat([ds['SBRT_test'], ds['NONE_test']]).drop(columns=drop_sbrtVS) } df_rfaVSnone = { 'train': pd.concat([ds['RFA_train'], ds['NONE_train']]).drop(columns=drop_rfaVS), 'test': pd.concat([ds['RFA_test'], ds['NONE_test']]).drop(columns=drop_rfaVS) } df_sbrtVSrfa = { 'train': pd.concat([ds['SBRT_train'], ds['RFA_train']]).drop(columns=drop_sbrtVS), 'test': pd.concat([ds['SBRT_test'], ds['RFA_test']]).drop(columns=drop_sbrtVS) } df_sbrtORrfa = { 'train': pd.concat([ds['SBRT_train'], ds['RFA_train'], ds['NONE_train']]).drop(columns=drop_sbrtORrfa), 'test': pd.concat([ds['SBRT_test'], ds['RFA_test'], ds['NONE_test']]).drop(columns=drop_sbrtORrfa) } ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_sbrtVSnone.hd5') dataframes_to_hd5(df_sbrtVSnone, ofile_os, 'OSEvent', 'TTDy') ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_rfaVSnone.hd5') dataframes_to_hd5(df_rfaVSnone, ofile_os, 'OSEvent', 'TTDy') ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_sbrtVSrfa.hd5') dataframes_to_hd5(df_sbrtVSrfa, ofile_os, 'OSEvent', 'TTDy') ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_sbrtORrfa.hd5') dataframes_to_hd5(df_sbrtORrfa, ofile_os, 'OSEvent', 'TTDy') # # USE INDICES FROM OS FOR PFS df_PFS = pd.read_csv(args.ifile_pfs) df_sbrtVSnone_pfs = { 'train': df_PFS.loc[df_sbrtVSnone['train'].index].drop(columns=drop_sbrtVS), 'test': df_PFS.loc[df_sbrtVSnone['test'].index].drop(columns=drop_sbrtVS) } df_rfaVSnone_pfs = { 'train': df_PFS.loc[df_rfaVSnone['train'].index].drop(columns=drop_rfaVS), 'test': df_PFS.loc[df_rfaVSnone['test'].index].drop(columns=drop_rfaVS) } df_sbrtVSrfa_pfs = { 'train': df_PFS.loc[df_sbrtVSrfa['train'].index].drop(columns=drop_sbrtVS), 'test': df_PFS.loc[df_sbrtVSrfa['test'].index].drop(columns=drop_sbrtVS) } df_sbrtORrfa_pfs = { 'train': df_PFS.loc[df_sbrtORrfa['train'].index].drop(columns=drop_sbrtORrfa), 'test': df_PFS.loc[df_sbrtORrfa['test'].index].drop(columns=drop_sbrtORrfa) } ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_sbrtVSnone.hd5') dataframes_to_hd5(df_sbrtVSnone_pfs, ofile_pfs, 'PFSEvent', 'TTPy') ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_rfaVSnone.hd5') dataframes_to_hd5(df_rfaVSnone_pfs, ofile_pfs, 'PFSEvent', 'TTPy') ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_sbrtVSrfa.hd5') dataframes_to_hd5(df_sbrtVSrfa_pfs, ofile_pfs, 'PFSEvent', 'TTPy') ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_sbrtORrfa.hd5') dataframes_to_hd5(df_sbrtORrfa_pfs, ofile_pfs, 'PFSEvent', 'TTPy')

41.474453

118

0.656987

import h5py import numpy as np import pandas as pd import os from argparse import ArgumentParser def dataframe_to_deepsurv_ds(df, event_col='Event', time_col='Time'): # Extract the event and time columns as numpy arrays e = df[event_col].values.astype(np.int32) t = df[time_col].values.astype(np.float32) # Extract the patient's covariates as a numpy array x_df = df.drop([event_col, time_col], axis=1) x = x_df.values.astype(np.float32) # Return the deep surv dataframe return { 'x': x, 'e': e, 't': t } def dataframes_to_hd5(df, ofile, event_col, time_col): with h5py.File(ofile, 'w') as h: for k in df: ds = dataframe_to_deepsurv_ds(df[k], event_col, time_col) group = h.create_group(k) group.create_dataset('x', data=ds['x']) group.create_dataset('e', data=ds['e']) group.create_dataset('t', data=ds['t']) if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('ifile_os') parser.add_argument('ifile_pfs') # parser.add_argument('-e', '--event_col', default='OSEvent') # parser.add_argument('-t', '--time_col', default='TTDy') # parser.add_argument('--txcol', type=str, default='SBRT') # parser.add_argument('--drop', help='drop columns', nargs='+', type=str) # parser.add_argument('--droprows', help='drop rows where [cols] have value --droprowsval', nargs='+', type=str) # parser.add_argument( # '--droprowsval', help='value at which to drop the rows from --droprows, default 1', type=int, default=1) # parser.add_argument('--droprows2', help='drop rows where [cols] have value --droprowsval2', nargs='+', type=str) # parser.add_argument( # '--droprowsval2', help='value at which to drop the rows from --droprows2, default 0', type=int, default=0) args = parser.parse_args() print(args) df = pd.read_csv(args.ifile_os) # print(df) drop_sbrtVS = ['Treatment', 'RFA', 'SBRT_OR_RFA'] drop_rfaVS = ['Treatment', 'SBRT', 'SBRT_OR_RFA'] drop_sbrtORrfa = ['Treatment', 'SBRT', 'RFA'] # # THIS IS FOR OS FIRST frac = 0.5 ds = { 'SBRT_train': df[df.SBRT == 1].sample(frac=frac), 'RFA_train': df[df.RFA == 1].sample(frac=frac), 'NONE_train': df[df.SBRT_OR_RFA == 0].sample(frac=frac) } ds |= { 'SBRT_test': df.loc[df[df.SBRT == 1].index.symmetric_difference(ds['SBRT_train'].index)], 'RFA_test': df.loc[df[df.RFA == 1].index.symmetric_difference(ds['RFA_train'].index)], 'NONE_test': df.loc[df[df.SBRT_OR_RFA == 0].index.symmetric_difference(ds['NONE_train'].index)], } df_sbrtVSnone = { 'train': pd.concat([ds['SBRT_train'], ds['NONE_train']]).drop(columns=drop_sbrtVS), 'test': pd.concat([ds['SBRT_test'], ds['NONE_test']]).drop(columns=drop_sbrtVS) } df_rfaVSnone = { 'train': pd.concat([ds['RFA_train'], ds['NONE_train']]).drop(columns=drop_rfaVS), 'test': pd.concat([ds['RFA_test'], ds['NONE_test']]).drop(columns=drop_rfaVS) } df_sbrtVSrfa = { 'train': pd.concat([ds['SBRT_train'], ds['RFA_train']]).drop(columns=drop_sbrtVS), 'test': pd.concat([ds['SBRT_test'], ds['RFA_test']]).drop(columns=drop_sbrtVS) } df_sbrtORrfa = { 'train': pd.concat([ds['SBRT_train'], ds['RFA_train'], ds['NONE_train']]).drop(columns=drop_sbrtORrfa), 'test': pd.concat([ds['SBRT_test'], ds['RFA_test'], ds['NONE_test']]).drop(columns=drop_sbrtORrfa) } ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_sbrtVSnone.hd5') dataframes_to_hd5(df_sbrtVSnone, ofile_os, 'OSEvent', 'TTDy') ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_rfaVSnone.hd5') dataframes_to_hd5(df_rfaVSnone, ofile_os, 'OSEvent', 'TTDy') ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_sbrtVSrfa.hd5') dataframes_to_hd5(df_sbrtVSrfa, ofile_os, 'OSEvent', 'TTDy') ofile_os = os.path.join(os.path.dirname(args.ifile_os), 'liver_os_sbrtORrfa.hd5') dataframes_to_hd5(df_sbrtORrfa, ofile_os, 'OSEvent', 'TTDy') # # USE INDICES FROM OS FOR PFS df_PFS = pd.read_csv(args.ifile_pfs) df_sbrtVSnone_pfs = { 'train': df_PFS.loc[df_sbrtVSnone['train'].index].drop(columns=drop_sbrtVS), 'test': df_PFS.loc[df_sbrtVSnone['test'].index].drop(columns=drop_sbrtVS) } df_rfaVSnone_pfs = { 'train': df_PFS.loc[df_rfaVSnone['train'].index].drop(columns=drop_rfaVS), 'test': df_PFS.loc[df_rfaVSnone['test'].index].drop(columns=drop_rfaVS) } df_sbrtVSrfa_pfs = { 'train': df_PFS.loc[df_sbrtVSrfa['train'].index].drop(columns=drop_sbrtVS), 'test': df_PFS.loc[df_sbrtVSrfa['test'].index].drop(columns=drop_sbrtVS) } df_sbrtORrfa_pfs = { 'train': df_PFS.loc[df_sbrtORrfa['train'].index].drop(columns=drop_sbrtORrfa), 'test': df_PFS.loc[df_sbrtORrfa['test'].index].drop(columns=drop_sbrtORrfa) } ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_sbrtVSnone.hd5') dataframes_to_hd5(df_sbrtVSnone_pfs, ofile_pfs, 'PFSEvent', 'TTPy') ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_rfaVSnone.hd5') dataframes_to_hd5(df_rfaVSnone_pfs, ofile_pfs, 'PFSEvent', 'TTPy') ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_sbrtVSrfa.hd5') dataframes_to_hd5(df_sbrtVSrfa_pfs, ofile_pfs, 'PFSEvent', 'TTPy') ofile_pfs = os.path.join(os.path.dirname(args.ifile_os), 'liver_pfs_sbrtORrfa.hd5') dataframes_to_hd5(df_sbrtORrfa_pfs, ofile_pfs, 'PFSEvent', 'TTPy')

803

0

46

5802aafb5a9a958a3bfd48a3c740eee759d724b5

1,517

py

Python

francisco-topo/lit101.py

Cyphysecurity/ICS-SDN-1

c04d9e7bb7ad945166e969e071a2f82fb5bd18bf

[ "MIT" ]

4

2019-12-17T08:59:57.000Z

2022-01-09T19:52:27.000Z

francisco-topo/lit101.py

Cyphysecurity/ICS-SDN-1

c04d9e7bb7ad945166e969e071a2f82fb5bd18bf

[ "MIT" ]

3

2020-08-13T16:05:46.000Z

2021-10-17T07:49:33.000Z

francisco-topo/lit101.py

Cyphysecurity/ICS-SDN-1

c04d9e7bb7ad945166e969e071a2f82fb5bd18bf

[ "MIT" ]

4

2017-06-14T23:41:50.000Z

2021-03-01T18:54:03.000Z

from minicps.devices import PLC from utils import * import random import logging import time import socket import json import select import signal import sys SENSOR_ADDR = IP['lit101'] LIT101 = ('LIT101', 1) LIT102 = ('LIT102', 1) if __name__ == '__main__': lit101 = Lit101(name='lit101',state=STATE,protocol=LIT101_PROTOCOL,memory=GENERIC_DATA,disk=GENERIC_DATA)

27.089286

109

0.633487

from minicps.devices import PLC from utils import * import random import logging import time import socket import json import select import signal import sys SENSOR_ADDR = IP['lit101'] LIT101 = ('LIT101', 1) LIT102 = ('LIT102', 1) class Lit101(PLC): def sigint_handler(self, sig, frame): print "I received a SIGINT!" sys.exit(0) def pre_loop(self, sleep=0.1): signal.signal(signal.SIGINT, self.sigint_handler) signal.signal(signal.SIGTERM, self.sigint_handler) logging.basicConfig(filename=LOG_LIT101_FILE, level=logging.DEBUG) def send_message(self, ipaddr, port, message): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((ipaddr, port)) msg_dict = dict.fromkeys(['Type', 'Variable']) msg_dict['Type'] = "Report" msg_dict['Variable'] = message message = json.dumps(str(msg_dict)) try: ready_to_read, ready_to_write, in_error = select.select([sock, ], [sock, ], [], 5) except: print "Socket error" return if(ready_to_write > 0): sock.send(message) sock.close() def main_loop(self): count = 0 while True: self.level = float(self.get(LIT101)) self.send_message(IP['plc101'], 8754,self.level) time.sleep(0.0005) if __name__ == '__main__': lit101 = Lit101(name='lit101',state=STATE,protocol=LIT101_PROTOCOL,memory=GENERIC_DATA,disk=GENERIC_DATA)

1,015

-3

130

25d702540d9307d9ec2607e46d357ccf5efcd861

268

py

Python

workbench/logbook/migrations/0015_remove_loggedcost_project.py

yoshson/workbench

701558cac3357cd82e4dc99f0fefed12ee81ddc5

[ "MIT" ]

15

2020-09-02T22:17:34.000Z

2022-02-01T20:09:10.000Z

workbench/logbook/migrations/0015_remove_loggedcost_project.py

yoshson/workbench

701558cac3357cd82e4dc99f0fefed12ee81ddc5

[ "MIT" ]

18

2020-01-08T15:28:26.000Z

2022-02-28T02:46:41.000Z

workbench/logbook/migrations/0015_remove_loggedcost_project.py

yoshson/workbench

701558cac3357cd82e4dc99f0fefed12ee81ddc5

[ "MIT" ]

8

2020-09-29T08:00:24.000Z

2022-01-16T11:58:19.000Z

# Generated by Django 2.2.5 on 2019-09-15 10:58 from django.db import migrations

24.363636

82

0.742537

# Generated by Django 2.2.5 on 2019-09-15 10:58 from django.db import migrations class Migration(migrations.Migration): dependencies = [("logbook", "0014_auto_20190915_1250")] operations = [migrations.RemoveField(model_name="loggedcost", name="project")]

0

162

23

e2a9aa8f84f161a41350b5252f7f5caf4c6451b5

10,445

py

Python

test.py

Michal-Hubert/backup_dropbox

6c9c622ce389e789f0eb7aaad16d1ac0b2f688c5

[ "Apache-2.0" ]

null

test.py

Michal-Hubert/backup_dropbox

6c9c622ce389e789f0eb7aaad16d1ac0b2f688c5

[ "Apache-2.0" ]

null

test.py

Michal-Hubert/backup_dropbox

6c9c622ce389e789f0eb7aaad16d1ac0b2f688c5

[ "Apache-2.0" ]

null

''' End to end testing ''' import shutil import os from ConfigParser import SafeConfigParser import textwrap import hashlib import pexpect import sys import leveldb import backup import tempfile from backup import dump_database from myutils import path_leaf TEST_CONFIG_NAME = "config" TEST_DROPBOX_NAME = "dropbox" TEST_TO_BACKUP_DIR = "to_backup" TEST_DATABASE_DIR = "db" TEST_PATH = os.path.dirname(os.path.abspath(__file__)) + '/test/' #Path wehre db will be recovered DB_RECOVERY_PATH = os.path.dirname(os.path.abspath(__file__)) + '/foo/' PASSWORD = "alamakota" BACKUP_TOOL = "python ./ds.py " def reset_dropbox_dir(config): ''' Remove dropbox dropbox directory ''' dropbox_dir = config.get('DropBox', 'drop_box_dir') shutil.rmtree(dropbox_dir) os.makedirs(dropbox_dir) def reset_db(config): ''' Remove databse ''' db_dir = config.get('DB', 'db_path') shutil.rmtree(db_dir) os.makedirs(db_dir) def reset_to_backup_dir(config): ''' Remove directory to backed up ''' to_backup_dir = config.get('Backup', 'to_backup') shutil.rmtree(to_backup_dir) os.makedirs(to_backup_dir) def create_test_config(): ''' Creates config file for testing purposes ''' aa = """\ [Credentials] password=alamakota [DropBox] drop_box_dir = {drop_box_dir} [DB] #Database MUST not be in backed up folder as it's being changed #during backup process. Program backs up databse itself by #gzipping, encryptying and putting in drobox folder as "dbmapping" db_path = {db_path} [Backup] to_backup = {to_backup} to_exclude = """ context = { "drop_box_dir":TEST_PATH+TEST_DROPBOX_NAME+"/", "db_path":TEST_PATH+TEST_DATABASE_DIR+"/", "to_backup":TEST_PATH+TEST_TO_BACKUP_DIR+"/", "passord":PASSWORD } if not os.path.exists(TEST_PATH): os.makedirs(TEST_PATH) with open(TEST_PATH+'/'+TEST_CONFIG_NAME, 'w') as cfgfile: cfgfile.write(textwrap.dedent(aa.format(**context))) if not os.path.exists(context["drop_box_dir"]): os.makedirs(context["drop_box_dir"]) if not os.path.exists(context["db_path"]): os.makedirs(context["db_path"]) if not os.path.exists(context["to_backup"]): os.makedirs(context["to_backup"]) if not os.path.exists(DB_RECOVERY_PATH): os.makedirs(DB_RECOVERY_PATH) def clear(config): ''' Clears all created files, directories and exits ''' db_dir = config.get('DB', 'db_path') if os.path.exists(db_dir): shutil.rmtree(db_dir) dropbox_dir = config.get('DropBox', 'drop_box_dir') if os.path.exists(dropbox_dir): shutil.rmtree(dropbox_dir) to_backup_dir = config.get('Backup', 'to_backup') if os.path.exists(to_backup_dir): shutil.rmtree(to_backup_dir) os.remove(TEST_PATH+TEST_CONFIG_NAME) def clear_all(): ''' Remove all in one shot ''' if os.path.exists(TEST_PATH): shutil.rmtree(TEST_PATH) if os.path.exists(DB_RECOVERY_PATH): shutil.rmtree(DB_RECOVERY_PATH) def test1(): ''' Initially dropbox folder is empty and databse is empty. Check if file is backed up and restored correctly ''' print '-'*50 print "Test 1" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) file_to_backup = config.get('Backup', 'to_backup')+'/some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') checksum_before = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Restore os.remove(file_to_backup) child = pexpect.spawn(BACKUP_TOOL + "restore -d" + TEST_PATH+ TEST_DROPBOX_NAME + " -r" + DB_RECOVERY_PATH) child.expect('Password: ') child.sendline(PASSWORD) print child.read() checksum_after = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() if checksum_before != checksum_after: print "Test 1 failed!" sys.exit(1) else: print "Test 1 ok" def test2(): ''' Initally dropbox folder is empty and database is empty. Check if modified file is backed up and restored correctly. ''' print '-'*50 print "Test 2" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) file_to_backup = config.get('Backup', 'to_backup')+'/some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Modification with open(file_to_backup, 'a') as some_file: some_file.write('The second line') checksum_before = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Restore os.remove(file_to_backup) child = pexpect.spawn(BACKUP_TOOL + "restore -d " + TEST_PATH+ TEST_DROPBOX_NAME + " -r " + DB_RECOVERY_PATH) child.expect('Password: ') child.sendline(PASSWORD) print child.read() checksum_after = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() if checksum_before != checksum_after: print "Test 2 failed!" sys.exit(1) db = leveldb.LevelDB(config.get('DB', 'db_path')) dump_file = os.path.join(tempfile.gettempdir(), '') + 'foo' entries = dump_database(db, dump_file) if entries != 1: print "Test 2 failed!" sys.exit(1) print "Test 2 ok" def test3(): ''' Initally dropbox folder is empty and database is empty. Check if deleted files in source folder is deleted from dropbox as well ''' print '-'*50 print "Test 3" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) file_to_backup = config.get('Backup', 'to_backup')+'/some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Delete os.remove(file_to_backup) #Backup again os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Check if file doesn't exist for subdir, dirs, files in os.walk(config.get('DropBox','drop_box_dir')): for f in files: #Meta entry is not in database if path_leaf(f) == backup.META_DB_NAME + ".xyz": continue else: print "Test 3 failed - deleted file exists in dropbox folder" sys.exit(1) #Database shall not contain any entry entries = 0 db = leveldb.LevelDB(config.get('DB', 'db_path')) dump_file = os.path.join(tempfile.gettempdir(), '') + 'foo' entries = dump_database(db, dump_file) if entries != 0: print "Test 3 failed - deleted file has entry in database" sys.exit(1) print "Test 3 ok" def test4(): ''' Initally dropbox folder is empty and database is empty. Check if deleted source folder (which contains files) causes deletion of this files from dropbox folder. ''' print '-'*50 print "Test 4" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) directory_to_backup = config.get('Backup', 'to_backup')+'/some_dir' os.makedirs(directory_to_backup) file_to_backup = config.get('Backup', 'to_backup')+'/some_dir/some_file1' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') file_to_backup = config.get('Backup', 'to_backup')+'/some_dir/some_file2' with open(file_to_backup, 'w') as some_file: some_file.write('The other very first line') #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Delete shutil.rmtree(directory_to_backup) #Backup again os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Check if file doesn't exist for subdir, dirs, files in os.walk(config.get('DropBox','drop_box_dir')): for f in files: #Meta entry is not in database if path_leaf(f) == backup.META_DB_NAME + ".xyz": continue else: print "Test 4 failed - deleted file exists in dropbox folder" sys.exit(1) #Database shall not contain any entry entries = 0 db = leveldb.LevelDB(config.get('DB', 'db_path')) dump_file = os.path.join(tempfile.gettempdir(), '') + 'foo' entries = dump_database(db, dump_file) if entries != 0: print "Test 4 failed - deleted file has entry in database" sys.exit(1) print "Test 4 ok" def test5(): ''' Initially dropbox folder is empty and databse is empty. Check if file is backed + directory up and restored correctly. It's very much alike test1 but here file is in subdirectory of backed up directory ''' print '-'*50 print "Test 5" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) subdir = config.get('Backup', 'to_backup')+'/dir/' os.makedirs(subdir) file_to_backup = subdir + 'some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') checksum_before = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Restore os.remove(file_to_backup) child = pexpect.spawn(BACKUP_TOOL + "restore -d" + TEST_PATH+ TEST_DROPBOX_NAME + " -r" + DB_RECOVERY_PATH) child.expect('Password: ') child.sendline(PASSWORD) print child.read() checksum_after = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() if checksum_before != checksum_after: print "Test 5 failed!" sys.exit(1) else: print "Test 5 ok" def run_tests(): ''' Run test cases ''' test1() test2() test3() test4() test5() clear_all() if __name__ == "__main__": #TEST_PATH = os.path.dirname(os.path.abspath(__file__)) + '/test/' run_tests()

29.094708

80

0.650742

''' End to end testing ''' import shutil import os from ConfigParser import SafeConfigParser import textwrap import hashlib import pexpect import sys import leveldb import backup import tempfile from backup import dump_database from myutils import path_leaf TEST_CONFIG_NAME = "config" TEST_DROPBOX_NAME = "dropbox" TEST_TO_BACKUP_DIR = "to_backup" TEST_DATABASE_DIR = "db" TEST_PATH = os.path.dirname(os.path.abspath(__file__)) + '/test/' #Path wehre db will be recovered DB_RECOVERY_PATH = os.path.dirname(os.path.abspath(__file__)) + '/foo/' PASSWORD = "alamakota" BACKUP_TOOL = "python ./ds.py " def reset_dropbox_dir(config): ''' Remove dropbox dropbox directory ''' dropbox_dir = config.get('DropBox', 'drop_box_dir') shutil.rmtree(dropbox_dir) os.makedirs(dropbox_dir) def reset_db(config): ''' Remove databse ''' db_dir = config.get('DB', 'db_path') shutil.rmtree(db_dir) os.makedirs(db_dir) def reset_to_backup_dir(config): ''' Remove directory to backed up ''' to_backup_dir = config.get('Backup', 'to_backup') shutil.rmtree(to_backup_dir) os.makedirs(to_backup_dir) def create_test_config(): ''' Creates config file for testing purposes ''' aa = """\ [Credentials] password=alamakota [DropBox] drop_box_dir = {drop_box_dir} [DB] #Database MUST not be in backed up folder as it's being changed #during backup process. Program backs up databse itself by #gzipping, encryptying and putting in drobox folder as "dbmapping" db_path = {db_path} [Backup] to_backup = {to_backup} to_exclude = """ context = { "drop_box_dir":TEST_PATH+TEST_DROPBOX_NAME+"/", "db_path":TEST_PATH+TEST_DATABASE_DIR+"/", "to_backup":TEST_PATH+TEST_TO_BACKUP_DIR+"/", "passord":PASSWORD } if not os.path.exists(TEST_PATH): os.makedirs(TEST_PATH) with open(TEST_PATH+'/'+TEST_CONFIG_NAME, 'w') as cfgfile: cfgfile.write(textwrap.dedent(aa.format(**context))) if not os.path.exists(context["drop_box_dir"]): os.makedirs(context["drop_box_dir"]) if not os.path.exists(context["db_path"]): os.makedirs(context["db_path"]) if not os.path.exists(context["to_backup"]): os.makedirs(context["to_backup"]) if not os.path.exists(DB_RECOVERY_PATH): os.makedirs(DB_RECOVERY_PATH) def clear(config): ''' Clears all created files, directories and exits ''' db_dir = config.get('DB', 'db_path') if os.path.exists(db_dir): shutil.rmtree(db_dir) dropbox_dir = config.get('DropBox', 'drop_box_dir') if os.path.exists(dropbox_dir): shutil.rmtree(dropbox_dir) to_backup_dir = config.get('Backup', 'to_backup') if os.path.exists(to_backup_dir): shutil.rmtree(to_backup_dir) os.remove(TEST_PATH+TEST_CONFIG_NAME) def clear_all(): ''' Remove all in one shot ''' if os.path.exists(TEST_PATH): shutil.rmtree(TEST_PATH) if os.path.exists(DB_RECOVERY_PATH): shutil.rmtree(DB_RECOVERY_PATH) def test1(): ''' Initially dropbox folder is empty and databse is empty. Check if file is backed up and restored correctly ''' print '-'*50 print "Test 1" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) file_to_backup = config.get('Backup', 'to_backup')+'/some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') checksum_before = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Restore os.remove(file_to_backup) child = pexpect.spawn(BACKUP_TOOL + "restore -d" + TEST_PATH+ TEST_DROPBOX_NAME + " -r" + DB_RECOVERY_PATH) child.expect('Password: ') child.sendline(PASSWORD) print child.read() checksum_after = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() if checksum_before != checksum_after: print "Test 1 failed!" sys.exit(1) else: print "Test 1 ok" def test2(): ''' Initally dropbox folder is empty and database is empty. Check if modified file is backed up and restored correctly. ''' print '-'*50 print "Test 2" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) file_to_backup = config.get('Backup', 'to_backup')+'/some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Modification with open(file_to_backup, 'a') as some_file: some_file.write('The second line') checksum_before = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Restore os.remove(file_to_backup) child = pexpect.spawn(BACKUP_TOOL + "restore -d " + TEST_PATH+ TEST_DROPBOX_NAME + " -r " + DB_RECOVERY_PATH) child.expect('Password: ') child.sendline(PASSWORD) print child.read() checksum_after = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() if checksum_before != checksum_after: print "Test 2 failed!" sys.exit(1) db = leveldb.LevelDB(config.get('DB', 'db_path')) dump_file = os.path.join(tempfile.gettempdir(), '') + 'foo' entries = dump_database(db, dump_file) if entries != 1: print "Test 2 failed!" sys.exit(1) print "Test 2 ok" def test3(): ''' Initally dropbox folder is empty and database is empty. Check if deleted files in source folder is deleted from dropbox as well ''' print '-'*50 print "Test 3" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) file_to_backup = config.get('Backup', 'to_backup')+'/some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Delete os.remove(file_to_backup) #Backup again os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Check if file doesn't exist for subdir, dirs, files in os.walk(config.get('DropBox','drop_box_dir')): for f in files: #Meta entry is not in database if path_leaf(f) == backup.META_DB_NAME + ".xyz": continue else: print "Test 3 failed - deleted file exists in dropbox folder" sys.exit(1) #Database shall not contain any entry entries = 0 db = leveldb.LevelDB(config.get('DB', 'db_path')) dump_file = os.path.join(tempfile.gettempdir(), '') + 'foo' entries = dump_database(db, dump_file) if entries != 0: print "Test 3 failed - deleted file has entry in database" sys.exit(1) print "Test 3 ok" def test4(): ''' Initally dropbox folder is empty and database is empty. Check if deleted source folder (which contains files) causes deletion of this files from dropbox folder. ''' print '-'*50 print "Test 4" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) directory_to_backup = config.get('Backup', 'to_backup')+'/some_dir' os.makedirs(directory_to_backup) file_to_backup = config.get('Backup', 'to_backup')+'/some_dir/some_file1' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') file_to_backup = config.get('Backup', 'to_backup')+'/some_dir/some_file2' with open(file_to_backup, 'w') as some_file: some_file.write('The other very first line') #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Delete shutil.rmtree(directory_to_backup) #Backup again os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Check if file doesn't exist for subdir, dirs, files in os.walk(config.get('DropBox','drop_box_dir')): for f in files: #Meta entry is not in database if path_leaf(f) == backup.META_DB_NAME + ".xyz": continue else: print "Test 4 failed - deleted file exists in dropbox folder" sys.exit(1) #Database shall not contain any entry entries = 0 db = leveldb.LevelDB(config.get('DB', 'db_path')) dump_file = os.path.join(tempfile.gettempdir(), '') + 'foo' entries = dump_database(db, dump_file) if entries != 0: print "Test 4 failed - deleted file has entry in database" sys.exit(1) print "Test 4 ok" def test5(): ''' Initially dropbox folder is empty and databse is empty. Check if file is backed + directory up and restored correctly. It's very much alike test1 but here file is in subdirectory of backed up directory ''' print '-'*50 print "Test 5" clear_all() create_test_config() config = SafeConfigParser() config.read(TEST_PATH+TEST_CONFIG_NAME) subdir = config.get('Backup', 'to_backup')+'/dir/' os.makedirs(subdir) file_to_backup = subdir + 'some_file' with open(file_to_backup, 'w') as some_file: some_file.write('Very first line') checksum_before = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() #Backup os.system(BACKUP_TOOL + "backup -c" + TEST_PATH + "/" + TEST_CONFIG_NAME) #Restore os.remove(file_to_backup) child = pexpect.spawn(BACKUP_TOOL + "restore -d" + TEST_PATH+ TEST_DROPBOX_NAME + " -r" + DB_RECOVERY_PATH) child.expect('Password: ') child.sendline(PASSWORD) print child.read() checksum_after = hashlib.md5(open(file_to_backup, 'rb').read()).hexdigest() if checksum_before != checksum_after: print "Test 5 failed!" sys.exit(1) else: print "Test 5 ok" def run_tests(): ''' Run test cases ''' test1() test2() test3() test4() test5() clear_all() if __name__ == "__main__": #TEST_PATH = os.path.dirname(os.path.abspath(__file__)) + '/test/' run_tests()

0

8dd0fb258cc91c20a8c501666b3b19c5e498c313

5,170

py

Python

lookup.py

vlee489/Turnip-Bot

2571846607d6ca57171325211c5eb6572013c767

[ "MIT" ]

5

2020-04-19T22:47:28.000Z

2020-06-01T04:37:12.000Z

lookup.py

vlee489/Turnip-Bot

2571846607d6ca57171325211c5eb6572013c767

[ "MIT" ]

7

2020-04-21T23:25:20.000Z

2021-04-20T07:50:11.000Z

lookup.py

vlee489/Turnip-Bot

2571846607d6ca57171325211c5eb6572013c767

[ "MIT" ]

null

""" This cog deals all the lookup commands """ import asyncio from discord.ext import commands, tasks import discord from lookupAPI import nookipediaAPI, localLookup import os from dotenv import load_dotenv load_dotenv(".env")

44.956522

105

0.547002

""" This cog deals all the lookup commands """ import asyncio from discord.ext import commands, tasks import discord from lookupAPI import nookipediaAPI, localLookup import os from dotenv import load_dotenv load_dotenv(".env") class Lookup(commands.Cog): nookAPI = nookipediaAPI.NookipediaAPI(os.environ.get("nookipedia_API_key")) localAPI = localLookup.LocalLookup() def __init__(self, bot): self.bot = bot @tasks.loop(hours=6) async def updateLists(self): await self.localAPI.updateData() await self.nookAPI.clearOutdatedCache() @commands.command(name='villager', help="Get an overview of a villager and their trites.\n" "<villager>: The villager you want to search for", aliases=['Villager', 'Villagers', 'villagers']) async def villagerOverview(self, ctx, villager): with ctx.typing(): villager = villager.title() response = await self.nookAPI.getVillager(villager) if response is None: await ctx.send("Couldn't find villager\n" "If the villager's name is in 2 part, use \" to enclose the name.\n" "E.G. \"Agent S\"") return embeded = discord.Embed.from_dict(response.response) await ctx.send(embed=embeded) @commands.command(name='critter', help="Get an overview of a critter and their trites.\n" "<critter>: The critter you want to search for", aliases=['bug', 'fish', 'Critter', 'Bug', 'Fish', 'critters', 'Critters']) async def critterOverview(self, ctx, critter): with ctx.typing(): critter = critter.title() response = await self.nookAPI.getCritter(critter) if response is None: await ctx.send("Couldn't find critter\n" "If the critter's name is in 2 part, use \" to enclose the name.\n" "E.G. \"Banded Dragonfly\"") return embeded = discord.Embed.from_dict(response.response) await ctx.send(embed=embeded) @commands.command(name='fossil', help="Get an overview of a fossil and their trites.\n" "<fossil>: The fossil you want to search for", aliases=['fossils', 'Fossil', 'Fossils']) async def fossilOverview(self, ctx, fossil): with ctx.typing(): fossil = fossil.title() response = await self.nookAPI.getFossil(fossil) if response is None: await ctx.send("Couldn't find fossil\n" "If the fossil's name is in 2 part, use \" to enclose the name.\n" "E.G. \"Tyrannosaurus Rex\"") return embeded = discord.Embed.from_dict(response.response) await ctx.send(embed=embeded) @commands.command(name='eventsToday', help="Get an overview of all the events on today in AC\n", aliases=['eventstoday']) async def todayOverview(self, ctx): with ctx.typing(): events = await self.nookAPI.getToday() if events is None: await ctx.send("Unable to get events today >.<") return if len(events['events']) < 1: # We Check that there are events today first await ctx.send("No events are on today in Animal Crossing\n") return embedded = discord.Embed(title='Events Today', description=events['message'], color=0xCF70D3) embedded.set_author(name="Turnip Bot", url="https://github.com/vlee489/Turnip-Bot/", icon_url="https://cdn.vlee.me.uk/TurnipBot/icon.png") for x in range(len(events['events'])): # For each event we add a field and the event info embedded.add_field(name="Events {}:".format(x + 1), value=events['events'][x], inline=False) embedded.set_footer(text="Info from nookipedia.com", icon_url="https://cdn.vlee.me.uk/TurnipBot/Nookipedia.png") await ctx.send(embed=embedded) @commands.command(name='DIY', help="Get an overview of a DIY recipe\n" "<diy>: The item you want to lookup", aliases=['diy']) async def DIYOverview(self, ctx, diy): with ctx.typing(): response = await self.localAPI.getDiy(diy) if response is None: await ctx.send("Couldn't find DIY Recipe\n" "If the recipe's name is in 2 part, use \" to enclose the name.\n" "E.G. \"gold helmet\"") return embedded = discord.Embed.from_dict(response.response) await ctx.send(embed=embedded) def setup(bot): bot.add_cog(Lookup(bot))

3,375

1,519

46

eda16405c98e51c8b837071049470bbd9e53cff0

1,068

py

Python

tests/extractors/test_azlyrics.py

marian-code/LyricsFinder

6a0c7fd3cf3fec6cc410b5d6d8a0e6ff9f48144a

[ "MIT" ]

3

2019-03-06T18:20:36.000Z

2019-12-05T06:23:16.000Z

tests/extractors/test_azlyrics.py

marian-code/LyricsFinder

6a0c7fd3cf3fec6cc410b5d6d8a0e6ff9f48144a

[ "MIT" ]

11

2018-05-04T10:49:39.000Z

2018-10-05T20:36:15.000Z

tests/extractors/test_azlyrics.py

GieselaDev/LyricsFinder

21afa8a6daf194ab0a77a4c0f5834c17eaca49f8

[ "MIT" ]

2

2019-03-05T14:08:25.000Z

2019-08-29T08:02:38.000Z

import hashlib import os import pytest from aiohttp import ClientSession from lyricsfinder.extractors.azlyrics import AZLyrics from lyricsfinder.utils import Request

38.142857

132

0.730337

import hashlib import os import pytest from aiohttp import ClientSession from lyricsfinder.extractors.azlyrics import AZLyrics from lyricsfinder.utils import Request class TestAZLyrics: @pytest.mark.asyncio async def test_can_handle(self): async with ClientSession() as session: assert await AZLyrics.can_handle(Request(session, "https://www.azlyrics.com/lyrics/edsheeran/theateam.html")) is True @pytest.mark.skipif(os.environ.get("TRAVIS") == "true", reason="AZLyrics doesn't respond to Travis' servers. Don't ask me why!") @pytest.mark.asyncio async def test_extraction(self): async with ClientSession() as session: lyrics = await AZLyrics.extract_lyrics(Request(session, "https://www.azlyrics.com/lyrics/edsheeran/theateam.html")) lyrics_hash = hashlib.sha256(lyrics.lyrics.encode("utf-8")).hexdigest() assert lyrics_hash == "e77a63fb93b1d0f373b859963532e41a2dbf2d68d290bf3f919b93b174fe26e3" assert lyrics.title == "The A Team" assert lyrics.artist == "Ed Sheeran"

642

234

23

40ea3f70f9ff9d6bc077279cbd34054808da8844

2,000

py

Python

polling_stations/settings/constants/councils.py

smsmith97/UK-Polling-Stations

ecbd98cb99e89e97354da3960b0063aa36181b11

[ "BSD-3-Clause" ]

null

polling_stations/settings/constants/councils.py

smsmith97/UK-Polling-Stations

ecbd98cb99e89e97354da3960b0063aa36181b11

[ "BSD-3-Clause" ]

null

polling_stations/settings/constants/councils.py

smsmith97/UK-Polling-Stations

ecbd98cb99e89e97354da3960b0063aa36181b11

[ "BSD-3-Clause" ]

null

# settings for councils scraper BOUNDARIES_URL = "https://ons-cache.s3.amazonaws.com/Local_Authority_Districts_April_2019_Boundaries_UK_BFE.geojson" EC_COUNCIL_CONTACT_DETAILS_API_URL = ( "https://electoralcommission.org.uk/api/v1/data/local-authorities.json" ) OLD_TO_NEW_MAP = {} NEW_COUNCILS = [] WELSH_COUNCIL_NAMES = { "AGY": "Cyngor Sir Ynys Môn", # Isle of Anglesey County Council "BGE": "Cyngor Bwrdeistref Sirol Pen-y-bont ar Ogwr", # Bridgend County Borough Council "BGW": "Cyngor Bwrdeistref Sirol Blaenau Gwent", # Blaenau Gwent County Borough Council "CAY": "Cyngor Bwrdeistref Sirol Caerffili", # Caerphilly County Borough Council "CGN": "Cyngor Sir Ceredigion", # Ceredigion County Council "CMN": "Cyngor Sir Gaerfyrddin", # Carmarthenshire County Council "CRF": "Cyngor Caerdydd", # Cardiff Council "CWY": "Cyngor Bwrdeistref Sirol Conwy", # Conwy County Borough Council "DEN": "Cyngor Sir Ddinbych", # Denbighshire County Council "FLN": "Cyngor Sir y Fflint", # Flintshire County Council "GWN": "Cyngor Sir Gwynedd", # Gwynedd Council "MON": "Cyngor Sir Fynwy", # Monmouthshire County Council "MTY": "Cyngor Bwrdeistref Sirol Merthyr Tudful", # Merthyr Tydfil County Borough Council "NTL": "Cyngor Bwrdeistref Sirol Castell-nedd Port Talbot", # Neath Port Talbot County Borough Council "NWP": "Cyngor Dinas Casnewydd", # Newport City Council "PEM": "Cyngor Sir Penfro", # Pembrokeshire County Council "POW": "Cyngor Sir Powys", # Powys County Council "RCT": "Cyngor Bwrdeistref Sirol Rhondda Cynon Taf", # Rhondda Cynon Taf County Borough Council "SWA": "Cyngor Sir a Dinas Abertawe", # City and County of Swansea "TOF": "Cyngor Bwrdeistref Sirol Torfaen", # Torfaen County Borough Council "VGL": "Cyngor Bwrdeistref Sirol Bro Morgannwg", # The Vale of Glamorgan County Borough Council "WRX": "Cyngor Bwrdeistref Sirol Wrecsam", # Wrexham County Borough Council }

54.054054

116

0.722

# settings for councils scraper BOUNDARIES_URL = "https://ons-cache.s3.amazonaws.com/Local_Authority_Districts_April_2019_Boundaries_UK_BFE.geojson" EC_COUNCIL_CONTACT_DETAILS_API_URL = ( "https://electoralcommission.org.uk/api/v1/data/local-authorities.json" ) OLD_TO_NEW_MAP = {} NEW_COUNCILS = [] WELSH_COUNCIL_NAMES = { "AGY": "Cyngor Sir Ynys Môn", # Isle of Anglesey County Council "BGE": "Cyngor Bwrdeistref Sirol Pen-y-bont ar Ogwr", # Bridgend County Borough Council "BGW": "Cyngor Bwrdeistref Sirol Blaenau Gwent", # Blaenau Gwent County Borough Council "CAY": "Cyngor Bwrdeistref Sirol Caerffili", # Caerphilly County Borough Council "CGN": "Cyngor Sir Ceredigion", # Ceredigion County Council "CMN": "Cyngor Sir Gaerfyrddin", # Carmarthenshire County Council "CRF": "Cyngor Caerdydd", # Cardiff Council "CWY": "Cyngor Bwrdeistref Sirol Conwy", # Conwy County Borough Council "DEN": "Cyngor Sir Ddinbych", # Denbighshire County Council "FLN": "Cyngor Sir y Fflint", # Flintshire County Council "GWN": "Cyngor Sir Gwynedd", # Gwynedd Council "MON": "Cyngor Sir Fynwy", # Monmouthshire County Council "MTY": "Cyngor Bwrdeistref Sirol Merthyr Tudful", # Merthyr Tydfil County Borough Council "NTL": "Cyngor Bwrdeistref Sirol Castell-nedd Port Talbot", # Neath Port Talbot County Borough Council "NWP": "Cyngor Dinas Casnewydd", # Newport City Council "PEM": "Cyngor Sir Penfro", # Pembrokeshire County Council "POW": "Cyngor Sir Powys", # Powys County Council "RCT": "Cyngor Bwrdeistref Sirol Rhondda Cynon Taf", # Rhondda Cynon Taf County Borough Council "SWA": "Cyngor Sir a Dinas Abertawe", # City and County of Swansea "TOF": "Cyngor Bwrdeistref Sirol Torfaen", # Torfaen County Borough Council "VGL": "Cyngor Bwrdeistref Sirol Bro Morgannwg", # The Vale of Glamorgan County Borough Council "WRX": "Cyngor Bwrdeistref Sirol Wrecsam", # Wrexham County Borough Council }

0

f7a75459af31c39231b2c99baeb705677f67cbf6

8,665

py

Python

.scripts/make_docs/event_tuples.py

daakru/X2WOTCCommunityHighlander

de544c30d98fd372b8798dd06adb6d3126fac66f

[ "MIT" ]

null

.scripts/make_docs/event_tuples.py

daakru/X2WOTCCommunityHighlander

de544c30d98fd372b8798dd06adb6d3126fac66f

[ "MIT" ]

null

.scripts/make_docs/event_tuples.py

daakru/X2WOTCCommunityHighlander

de544c30d98fd372b8798dd06adb6d3126fac66f

[ "MIT" ]

null

from enum import Enum from typing import Iterator, List, Optional, Tuple import re _PUNCTUATION = { "(": _Token(_TokenType.LPAREN), ")": _Token(_TokenType.RPAREN), "{": _Token(_TokenType.LBRACE), "}": _Token(_TokenType.RBRACE), "[": _Token(_TokenType.LBRACK), "]": _Token(_TokenType.RBRACK), ",": _Token(_TokenType.COMMA), ":": _Token(_TokenType.COLON), } _KWS = { "in": _Keyword.IN, "out": _Keyword.OUT, "inout": _Keyword.INOUT, } _STARTIDENTCHAR = re.compile(r"[A-Za-z]") _IDENTCHAR = re.compile(r"[A-Za-z0-9\-_<>]") class _peekable(): "Wrap iterator with lookahead to both peek and test exhausted" # Code by Terry Jan Reedy, intentionally submitted to the python-ideas # mailing list for inclusion into itertools. # https://mail.python.org/pipermail//python-ideas/2013-February/019633.html _NONE = object() def _parse_type_sig(lex) -> (InOutness, str, str, Optional[str]): """ "inout bool bShow" -> (InOutness.INOUT, "bool", "bShow", None) "in enum[EInventorySlot] Slot" -> (InOutness.IN, "enum", "Slot", "EInventorySlot") """ param_kind = _kw_to_inout( _expect(lex, _TokenType.KW, "inoutness", "tuple param")) tup_type = _expect(lex, _TokenType.IDENT, "type", "tuple param").ident local_type = None if _try_eat(lex, _TokenType.LBRACK): local_type = _expect(lex, _TokenType.IDENT, "local type", "tuple param").ident _expect(lex, _TokenType.RBRACK, "inoutness", "tuple param") name = _expect(lex, _TokenType.IDENT, "param name", "tuple param") if name.ident.lower() == "self": raise ParseError(f"{name} not a valid local variable name") return param_kind, tup_type, name.ident, local_type

27.507937

89

0.560415

from enum import Enum from typing import Iterator, List, Optional, Tuple import re class _TokenType(Enum): LPAREN = 1 RPAREN = 2 LBRACE = 3 RBRACE = 4 LBRACK = 5 RBRACK = 6 KW = 7 IDENT = 8 COMMA = 9 COLON = 10 class _Keyword(Enum): IN = 1 OUT = 2 INOUT = 3 class InOutness(Enum): IN = 1 OUT = 2 INOUT = 3 def is_in(self) -> bool: return self in [InOutness.IN, InOutness.INOUT] def is_out(self) -> bool: return self in [InOutness.OUT, InOutness.INOUT] def __str__(self) -> str: if self == InOutness.IN: return "in" elif self == InOutness.OUT: return "out" elif self == InOutness.INOUT: return "inout" else: assert False, "unreachable" class NewGameState(Enum): YES = 1 NONE = 2 MAYBE = 3 def __str__(self) -> str: if self == NewGameState.YES: return "yes" elif self == NewGameState.NONE: return "none" elif self == NewGameState.MAYBE: return "maybe" else: assert False, "unreachable" class EventSpec: def __init__(self): pass class EventArg: def __init__(self, ty): self.type = ty self.name = None class _Token: def __init__(self, type: _TokenType): self.type = type def __str__(self): buf = str(self.type) if self.type == _TokenType.KW: buf += f" ({self.kw})" elif self.type == _TokenType.IDENT: buf += f" ({self.ident})" return buf _PUNCTUATION = { "(": _Token(_TokenType.LPAREN), ")": _Token(_TokenType.RPAREN), "{": _Token(_TokenType.LBRACE), "}": _Token(_TokenType.RBRACE), "[": _Token(_TokenType.LBRACK), "]": _Token(_TokenType.RBRACK), ",": _Token(_TokenType.COMMA), ":": _Token(_TokenType.COLON), } _KWS = { "in": _Keyword.IN, "out": _Keyword.OUT, "inout": _Keyword.INOUT, } _STARTIDENTCHAR = re.compile(r"[A-Za-z]") _IDENTCHAR = re.compile(r"[A-Za-z0-9\-_<>]") class ParseError(Exception): def __init__(self, msg: str): self.msg = msg def _ident(pos: int, text: str, first: str) -> (int, str): while pos < len(text) and _IDENTCHAR.match(text[pos]): first += text[pos] pos += 1 return (pos, first) def _lex_event_spec(text: str) -> Iterator[_Token]: pos = 0 while pos < len(text): c = text[pos] pos += 1 if c.isspace(): continue if c in _PUNCTUATION: yield _PUNCTUATION[c] continue if _STARTIDENTCHAR.match(c): (pos, id) = _ident(pos, text, c) if id in _KWS: tok = _Token(_TokenType.KW) tok.kw = _KWS[id] else: tok = _Token(_TokenType.IDENT) tok.ident = id yield tok continue raise ParseError(f"unknown start of token {c}") def _expect(it, t: _TokenType, thing=None, ctx=None) -> _Token: n = next(it) ctx = f" while parsing {ctx}" if ctx is not None else "" thing = f" ({thing})" if thing is not None else "" if n is None or n.type != t: err = " but code block ended" if n is None else f", found {str(n)}" raise ParseError(f"expected {str(t)}{thing}{ctx}{err}") return n def _try_eat(it, t: _TokenType) -> _Token: if it and it.peek.type == t: return next(it) return None def _kw_to_inout(t: _Token) -> InOutness: if t.kw == _Keyword.IN: return InOutness.IN elif t.kw == _Keyword.OUT: return InOutness.OUT elif t.kw == _Keyword.INOUT: return InOutness.INOUT class _peekable(): "Wrap iterator with lookahead to both peek and test exhausted" # Code by Terry Jan Reedy, intentionally submitted to the python-ideas # mailing list for inclusion into itertools. # https://mail.python.org/pipermail//python-ideas/2013-February/019633.html _NONE = object() def __init__(self, iterable): self._it = iter(iterable) self._set_peek() def __iter__(self): return self def __next__(self): if self: ret = self.peek self._set_peek() return ret else: raise StopIteration() def _set_peek(self): try: self.peek = next(self._it) except StopIteration: self.peek = self._NONE def __bool__(self): return self.peek is not self._NONE def _parse_type_sig(lex) -> (InOutness, str, str, Optional[str]): """ "inout bool bShow" -> (InOutness.INOUT, "bool", "bShow", None) "in enum[EInventorySlot] Slot" -> (InOutness.IN, "enum", "Slot", "EInventorySlot") """ param_kind = _kw_to_inout( _expect(lex, _TokenType.KW, "inoutness", "tuple param")) tup_type = _expect(lex, _TokenType.IDENT, "type", "tuple param").ident local_type = None if _try_eat(lex, _TokenType.LBRACK): local_type = _expect(lex, _TokenType.IDENT, "local type", "tuple param").ident _expect(lex, _TokenType.RBRACK, "inoutness", "tuple param") name = _expect(lex, _TokenType.IDENT, "param name", "tuple param") if name.ident.lower() == "self": raise ParseError(f"{name} not a valid local variable name") return param_kind, tup_type, name.ident, local_type def _parse_tuple_data(lex) -> List[Tuple]: _expect(lex, _TokenType.LBRACK, "[", "tuple data") tup = [] comma = False while True: if _try_eat(lex, _TokenType.RBRACK): break if comma: _expect(lex, _TokenType.COMMA, ",", "tuple params") comma = True if _try_eat(lex, _TokenType.RBRACK): break tup.append(_parse_type_sig(lex)) return tup def parse_event_spec(text: str) -> dict: lex = _peekable(_lex_event_spec(text)) spec = EventSpec() comma = False prev_key = None ctx = "event specification" while True: if not lex: break if comma: spec_ctx = ctx + f" after parsing {prev_key}" if prev_key else ctx _expect(lex, _TokenType.COMMA, ",", spec_ctx) if not lex: break comma = True key = _expect(lex, _TokenType.IDENT, "key", ctx).ident prev_key = key _expect(lex, _TokenType.COLON, ":", f"key {key}") if hasattr(spec, key): raise ParseError(f"error, duplicate key {key}") elif key == "EventID": name = _expect(lex, _TokenType.IDENT, "event name", key) spec.id = name.ident elif key == "EventSource": type = _expect(lex, _TokenType.IDENT, "source type", key) spec.source = EventArg(type.ident) if _try_eat(lex, _TokenType.LPAREN): name = _expect(lex, _TokenType.IDENT) spec.source.name = name.ident _expect(lex, _TokenType.RPAREN) elif key == "EventData": if lex and lex.peek.type == _TokenType.LBRACK: tup = _parse_tuple_data(lex) spec.data = EventArg("XComLWTuple") spec.data.tuple = tup else: type = _expect(lex, _TokenType.IDENT, "data type", key) spec.data = EventArg(type.ident) if _try_eat(lex, _TokenType.LPAREN): name = _expect(lex, _TokenType.IDENT, "local name", key) spec.data.name = name.ident _expect(lex, _TokenType.RPAREN, ")", key) elif key == "NewGameState": b = _expect(lex, _TokenType.IDENT, "NewGameStateness (yes, none, maybe)", key).ident if b == "yes": spec.newgs = NewGameState.YES elif b == "none" or b == "None": spec.newgs = NewGameState.NONE elif b == "maybe": spec.newgs = NewGameState.MAYBE else: raise ParseError("expected yes, none, or maybe") else: raise ParseError( "unexpected key (expected EventID, EventSource, EventData, NewGameState)" ) if not hasattr(spec, 'id'): raise ParseError("missing EventID") if not hasattr(spec, 'data'): raise ParseError("missing EventData") if not hasattr(spec, 'source'): raise ParseError("missing EventSource") if not hasattr(spec, 'newgs'): raise ParseError("missing NewGameState") return spec

5,911

360

611

2224eec49cd0fe31d6ede8185df188a65ec015a2

5,380

py

Python

Assignments/hw4/build_kNN.py

spacemanidol/CLMS572

f0380de9912c984ec21607cdb3b1f190853c5ca8

[ "MIT" ]

null

Assignments/hw4/build_kNN.py

spacemanidol/CLMS572

f0380de9912c984ec21607cdb3b1f190853c5ca8

[ "MIT" ]

null

Assignments/hw4/build_kNN.py

spacemanidol/CLMS572

f0380de9912c984ec21607cdb3b1f190853c5ca8

[ "MIT" ]

1

2020-12-26T01:28:41.000Z

import sys import numpy as np import math from collections import Counter if __name__ == "__main__": if len(sys.argv) != 6: print("Usage:build_kNN.py <training_data> <test_data> <k_val> <similarity_func> <sys_output>") exit(-1) else: trainingData, features = readData(sys.argv[1]) testData, _ = readData(sys.argv[2]) candidate, truth = knn(trainingData,testData, int(sys.argv[3]), int(sys.argv[4]),sys.argv[5]) writeMatrix(candidate, truth)

43.387097

193

0.573048

import sys import numpy as np import math from collections import Counter def readData(filename): labels, features, all_features = [], [], [] with open(filename, 'r') as f: for l in f: l = l.strip().split(' ') featureCount = {} labels.append(l[0]) for feature in l[1:]: word, count = feature.split(':') all_features.append(word) featureCount[word] = int(count) features.append(featureCount) all_features = set(all_features) return np.array([labels, features]), all_features def euclidean(a,b): dist = 0 for feature in set(a).union(set(b)): valueA, valueB = 0,0 if feature in a: valueA = a[feature] if feature in b: valueB = b[feature] dist += ((valueA - valueB)*(valueA - valueB)) return math.sqrt(dist) def cosine(a,b): sumAB,normA,normB = 0,0,0 for feature in set(a).union(set(b)): if feature in a and feature in b: sumAB += a[feature]*b[feature] if feature in a: normA += (a[feature]*a[feature]) if feature in b: normB += (b[feature]*b[feature]) return sumAB/ (math.sqrt(normA)*math.sqrt(normB)) def writeMatrix(candidates, truth): lookup = {0:'training', 1:'test'} for i in range(2): print("Confusion matrix for the {} data:\nrow is the truth, column is the system output\n".format(lookup[i])) labels = set(truth[i]).union(set(candidate[i])) d = len(labels) candidateLength = len(candidate[i]) m = np.zeros([d,d]) label2idx, idx2label = {}, {} index, count = 0, 0 for label in labels: label2idx[label] = index idx2label[index] = label index += 1 for j in range(candidateLength): m[label2idx[candidate[i][j]]][label2idx[truth[i][j]]] += 1 if candidate[i][j] == truth[i][j]: count += 1 out = '' for j in range(d): out += ' {}'.format(idx2label[j]) out += '\n' for j in range(d): out += idx2label[j] for k in range(d): out += ' {}'.format(str(int(m[j][k]))) out += '\n' print(" {}\n {} accuracy={:.5f}\n".format(out, str(lookup[i].capitalize()), count/candidateLength)) def vote(neighbors, data): votes, output = {}, '' for neighbor in neighbors: label = data[int(neighbor[1])] if label not in votes: votes[label] = 0 votes[label] += 1 total = sum(votes.values()) for label in sorted(votes.items(), key = lambda x:-x[1]): output += ' {} {}'.format(label[0], votes[label[0]]/total) return sorted(votes.items(), key = lambda x:-x[1])[0][0], output def search(candidateVector, dimensions, data, i, similarityFunc, k, distance): for j in range(dimensions): neighborVector = data[1][j] if similarityFunc == 1: distance[j] = [euclidean(candidateVector,neighborVector),j] elif similarityFunc == 2: distance[j] = [cosine(candidateVector,neighborVector),j] else: print('incorrect similarity function please use cosine or euclidian(2 or 1)') exit(-1) if similarityFunc == 1: if i != -1: distance[i] = float("inf") #if training we set the distance to itself to infinity neighbors = distance[distance[:,0].argsort(kind='mergesort')][:k] else: if i != -1: distance[i] = 0 #if training we set the distance to itself zero neighbors = distance[distance[:,0].argsort(kind='mergesort')][-k:] best, output = vote(neighbors, data[0]) return best, output def knn(trainingData, testData, k, similarityFunc, systemOutputFilename): candidate, truth = [[],[]], [[],[]] dimensions, testDimensions = len(trainingData[1]), len(testData[1]) distance = np.empty(shape=(dimensions,2)) with open(systemOutputFilename, 'w') as w: w.write('%%%%% training data:\n') for i in range(dimensions): truth[0].append(trainingData[0][i]) best, to_write = search(trainingData[1][i], dimensions, trainingData, i, similarityFunc, k, distance) #for each vector we search for knn neighbors and get a result and output string w.write('array:{} {} {}\n'.format(i, best, to_write)) candidate[0].append(best) w.write('\n\n%%%%% test data:\n') for i in range(testDimensions): truth[1].append(testData[0][i]) best, to_write = search(testData[1][i], dimensions, trainingData, -1, similarityFunc, k, distance) #for each vector we search for knn neighbors and get a result and output string w.write('array:{} {} {}\n'.format(i, best, to_write)) candidate[1].append(best) return candidate, truth if __name__ == "__main__": if len(sys.argv) != 6: print("Usage:build_kNN.py <training_data> <test_data> <k_val> <similarity_func> <sys_output>") exit(-1) else: trainingData, features = readData(sys.argv[1]) testData, _ = readData(sys.argv[2]) candidate, truth = knn(trainingData,testData, int(sys.argv[3]), int(sys.argv[4]),sys.argv[5]) writeMatrix(candidate, truth)

4,725

0

159

db5a483b7179a52757b1e303fa5cebe303f5944b

1,622

py

Python

pizzeria/workshop/forms.py

Zachary-Jackson/Django-Pizzeria

5948b10e9e99e7f2e8ffdbe6c0efbb87000808a9

[ "BSD-2-Clause" ]

1

2021-05-20T04:48:25.000Z

pizzeria/workshop/forms.py

Zachary-Jackson/Django-Pizzeria

5948b10e9e99e7f2e8ffdbe6c0efbb87000808a9

[ "BSD-2-Clause" ]

null

pizzeria/workshop/forms.py

Zachary-Jackson/Django-Pizzeria

5948b10e9e99e7f2e8ffdbe6c0efbb87000808a9

[ "BSD-2-Clause" ]

null

from django import forms from . import models STATES = [ ('AL', 'AL'), ('AK', 'AK'), ('AZ', 'AZ'), ('AR', 'AR'), ('CA', 'CA'), ('CO', 'CO'), ('CT', 'CT'), ('DC', 'DC'), ('DE', 'DE'), ('FL', 'FL'), ('GA', 'GA'), ('HI', 'HI'), ('ID', 'ID'), ('IL', 'IL'), ('IN', 'IN'), ('IA', 'IA'), ('KS', 'KS'), ('KY', 'KY'), ('LA', 'LA'), ('ME', 'ME'), ('MD', 'MD'), ('MA', 'MA'), ('MI', 'MI'), ('MN', 'MN'), ('MS', 'MS'), ('MO', 'MO'), ('MT', 'MT'), ('NE', 'NE'), ('NV', 'NV'), ('NH', 'NH'), ('NJ', 'NJ'), ('NM', 'NM'), ('NY', 'NY'), ('NC', 'NC'), ('ND', 'ND'), ('OH', 'OH'), ('OK', 'OK'), ('OR', 'OR'), ('PA', 'PA'), ('RI', 'RI'), ('SC', 'SC'), ('SD', 'SD'), ('TN', 'TN'), ('TX', 'TX'), ('UT', 'UT'), ('VT', 'VT'), ('VA', 'VA'), ('WA', 'WA'), ('WV', 'WV'), ('WI', 'WI'), ('WY', 'WY') ] class IngredientForm(forms.ModelForm): """Form allowing a new Ingredient to be created""" class Meta: """Defines the model the form uses and what fields to use""" model = models.Ingredient fields = ['name'] def clean_name(self): """Over-ride the value for name and title case it""" name = self.cleaned_data['name'] return name.title() class PizzaForm(forms.ModelForm): """Form allowing a new Pizza object to be created""" class Meta: """Defines the model the form uses and what fields to use""" model = models.Pizza fields = ['city', 'state', 'crust', 'ingredients', 'name', 'summary'] # Over-ride the State field to be a state selector widgets = {'state': forms.Select(choices=STATES)}

36.044444

77

0.459926

from django import forms from . import models STATES = [ ('AL', 'AL'), ('AK', 'AK'), ('AZ', 'AZ'), ('AR', 'AR'), ('CA', 'CA'), ('CO', 'CO'), ('CT', 'CT'), ('DC', 'DC'), ('DE', 'DE'), ('FL', 'FL'), ('GA', 'GA'), ('HI', 'HI'), ('ID', 'ID'), ('IL', 'IL'), ('IN', 'IN'), ('IA', 'IA'), ('KS', 'KS'), ('KY', 'KY'), ('LA', 'LA'), ('ME', 'ME'), ('MD', 'MD'), ('MA', 'MA'), ('MI', 'MI'), ('MN', 'MN'), ('MS', 'MS'), ('MO', 'MO'), ('MT', 'MT'), ('NE', 'NE'), ('NV', 'NV'), ('NH', 'NH'), ('NJ', 'NJ'), ('NM', 'NM'), ('NY', 'NY'), ('NC', 'NC'), ('ND', 'ND'), ('OH', 'OH'), ('OK', 'OK'), ('OR', 'OR'), ('PA', 'PA'), ('RI', 'RI'), ('SC', 'SC'), ('SD', 'SD'), ('TN', 'TN'), ('TX', 'TX'), ('UT', 'UT'), ('VT', 'VT'), ('VA', 'VA'), ('WA', 'WA'), ('WV', 'WV'), ('WI', 'WI'), ('WY', 'WY') ] class IngredientForm(forms.ModelForm): """Form allowing a new Ingredient to be created""" class Meta: """Defines the model the form uses and what fields to use""" model = models.Ingredient fields = ['name'] def clean_name(self): """Over-ride the value for name and title case it""" name = self.cleaned_data['name'] return name.title() class PizzaForm(forms.ModelForm): """Form allowing a new Pizza object to be created""" class Meta: """Defines the model the form uses and what fields to use""" model = models.Pizza fields = ['city', 'state', 'crust', 'ingredients', 'name', 'summary'] # Over-ride the State field to be a state selector widgets = {'state': forms.Select(choices=STATES)}

0

69c717b6efcc64167d465109f16e86f9f2d5efd3

530

py

Python

server/src/tests/samples/classes2.py

jhutchings1/pyright

2b8593a58a2aecc95dac49cce92fc16678cd4e14

[ "MIT" ]

null

server/src/tests/samples/classes2.py

jhutchings1/pyright

2b8593a58a2aecc95dac49cce92fc16678cd4e14

[ "MIT" ]

1

2021-08-31T20:37:43.000Z

server/src/tests/samples/classes2.py

jhutchings1/pyright

2b8593a58a2aecc95dac49cce92fc16678cd4e14

[ "MIT" ]

null

# This sample tests the reportIncompatibleMethodOverride # configuration option. # This should generate an error if reportIncompatibleMethodOverride # is enabled. # This should generate an error if reportIncompatibleMethodOverride # is enabled.

24.090909

71

0.684906

# This sample tests the reportIncompatibleMethodOverride # configuration option. class ParentClass(): def my_method1(self, a: int): return 1 def my_method2(self, a: int, b: int): return 1 class ChildClass(ParentClass): # This should generate an error if reportIncompatibleMethodOverride # is enabled. def my_method1(self, a: str): return 1 # This should generate an error if reportIncompatibleMethodOverride # is enabled. def my_method2(self, a: int): return 1

108

8

151

fc04a536fd4df8abf991154f3648f8761ca61cd0

777

py

Python

ixnetwork_restpy/testplatform/sessions/ixnetwork/traffic/trafficitem/configelement/stack/l2VPNFrameRelay_template.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

20

2019-05-07T01:59:14.000Z

2022-02-11T05:24:47.000Z

ixnetwork_restpy/testplatform/sessions/ixnetwork/traffic/trafficitem/configelement/stack/l2VPNFrameRelay_template.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

60

2019-04-03T18:59:35.000Z

2022-02-22T12:05:05.000Z

ixnetwork_restpy/testplatform/sessions/ixnetwork/traffic/trafficitem/configelement/stack/l2VPNFrameRelay_template.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

13

2019-05-20T10:48:31.000Z

2021-10-06T07:45:44.000Z

from ixnetwork_restpy.base import Base from ixnetwork_restpy.files import Files

28.777778

92

0.680824

from ixnetwork_restpy.base import Base from ixnetwork_restpy.files import Files class L2VPNFrameRelay(Base): __slots__ = () _SDM_NAME = 'l2VPNFrameRelay' _SDM_ATT_MAP = { 'HeaderControlWord': 'l2VPNFrameRelay.header.controlWord-1', } def __init__(self, parent, list_op=False): super(L2VPNFrameRelay, self).__init__(parent, list_op) @property def HeaderControlWord(self): """ Display Name: FR IP CW Default Value: 0x03cc Value Format: hex """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['HeaderControlWord'])) def add(self): return self._create(self._map_locals(self._SDM_ATT_MAP, locals()))

152

521

23

adcf45cbcc17af82fe53acf28aef59d0c6beea07

1,105

py

Python

firstapp/rancher_api.py

mudong1991/DevOpsApi

8cc880c7c628f04492427fe73a1a684eadb94e84

[ "Apache-2.0" ]

1

2017-12-22T04:12:10.000Z

firstapp/rancher_api.py

mudong1991/DevOpsApi

8cc880c7c628f04492427fe73a1a684eadb94e84

[ "Apache-2.0" ]

1

2020-01-08T01:49:03.000Z

firstapp/rancher_api.py

mudong1991/DevOpsApi

8cc880c7c628f04492427fe73a1a684eadb94e84

[ "Apache-2.0" ]

null

# -*- coding:utf-8 -*- # file: rancher_api # author: Mundy # date: 2017/9/1 0001 """ rancher api调用 """ import json import requests from django.conf import settings # --------requests设置-------- time_out = 8 headers = { 'content-type': 'application/json', 'Accept': 'application/json', } # 调用receiver_hooks api

23.020833

78

0.697738

# -*- coding:utf-8 -*- # file: rancher_api # author: Mundy # date: 2017/9/1 0001 """ rancher api调用 """ import json import requests from django.conf import settings # --------requests设置-------- time_out = 8 headers = { 'content-type': 'application/json', 'Accept': 'application/json', } def get_response_json(response): return response.json() # 调用receiver_hooks api def request_receiver_hooks(request_url, data): request_s = requests.Session() request_s.headers.update(headers) request_s.verify = False response = request_s.post(request_url, json.dumps(data), timeout=time_out) if response.status_code == 200: return True else: return False def get_rancher_data(request_url, access_key, secret_key): request_session = requests.Session() request_session.auth = (access_key, secret_key) request_session.headers.update(headers) request_session.verify = False response = request_session.get(request_url, timeout=time_out) if response.status_code == 200: return get_response_json(response) else: return ''

715

0

68