code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import logging
import tensorflow as tf
from . import utils
from .dataset import TFDataset
class TFDatasetForTokenClassification(TFDataset):
"""Dataset for token classification in TensorFlow"""
def __init__(self, examples=None, **kwargs) -> None:
super().__init__(examples, **kwargs)
self.inp... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/tf/token_classification_dataset.py | 0.860193 | 0.445831 | token_classification_dataset.py | pypi |
import tensorflow as tf
from . import utils
from .dataset import TFDataset
class TFDatasetForMaksedLanguageModel(TFDataset):
"""Dataset for masked lm in TensorFlow"""
def __init__(
self,
examples,
max_predictions=20,
input_ids="input_ids",
token_type_ids="token_type_i... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/tf/masked_lm_dataset.py | 0.89776 | 0.475971 | masked_lm_dataset.py | pypi |
import logging
import tensorflow as tf
from . import utils
from .dataset import TFDataset
class TFDatasetForQuestionAnswering(TFDataset):
"""Dataset for question answering in TensorFlow."""
def __init__(
self,
examples=None,
input_ids="input_ids",
token_type_ids="token_type_... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/tf/question_answering_dataset.py | 0.886457 | 0.395222 | question_answering_dataset.py | pypi |
import torch
class PTDatasetForSequenceClassification(torch.utils.data.Dataset):
"""Dataset for sequence classification in PyTorch"""
def __init__(
self,
examples,
max_sequence_length=512,
input_ids="input_ids",
token_type_ids="token_type_ids",
attention_mask="... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/pt/sequence_classification_dataset.py | 0.908996 | 0.589303 | sequence_classification_dataset.py | pypi |
import torch
class PTDatasetForSimCSE(torch.utils.data.Dataset):
"""Dataset for SimCSE in PyTorch"""
def __init__(
self, examples, max_sequence_length=512, with_positive_sequence=False, with_negative_sequence=False, **kwargs
) -> None:
super().__init__(**kwargs)
self.examples = ex... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/pt/simcse_dataset.py | 0.883261 | 0.5526 | simcse_dataset.py | pypi |
import torch
from .dataset import PTDataset
class PTDatasetForTokenClassification(PTDataset):
"""Dataset for token classification in PyTorch"""
def __init__(self, examples=None, pad_id=0, max_sequence_length=512, **kwargs) -> None:
super().__init__(pad_id=pad_id, max_sequence_length=max_sequence_len... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/pt/token_classification_dataset.py | 0.873539 | 0.417925 | token_classification_dataset.py | pypi |
import torch
from torch.utils.data import Dataset
class PTDatasetForMaskedLanguageModel(Dataset):
"""Dataset for mlm in PyTorch"""
def __init__(
self,
examples,
max_sequence_length=512,
ignore_index=-100,
input_ids="input_ids",
token_type_ids="token_type_ids",
... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/pt/masked_lm_dataset.py | 0.873228 | 0.583025 | masked_lm_dataset.py | pypi |
import torch
from torch.utils.data import Dataset
class PTDatasetForQuestionAnswering(Dataset):
"""Dataset for QA in PyTorch"""
def __init__(
self,
examples,
max_sequence_length=512,
input_ids="input_ids",
token_type_ids="token_type_ids",
attention_mask="attent... | /rapidnlp_datasets-0.2.0-py3-none-any.whl/rapidnlp_datasets/pt/question_answering_dataset.py | 0.892281 | 0.5867 | question_answering_dataset.py | pypi |
from zope.interface import implements
from .interfaces import IAccessControlList
ACCESS_RIGHTS_PERMISSIONS = {
'reader': [
'view',
],
'author': [
'view',
'create',
],
'if-author': [
'edit',
'delete',
],
'editor': [
'view',
'create',
... | /rapido.core-1.0.6.tar.gz/rapido.core-1.0.6/rapido/core/security.py | 0.594316 | 0.167576 | security.py | pypi |
from zope.interface import implements, alsoProvides, Interface
from zope.component import getMultiAdapter, provideUtility, provideAdapter
from souper.interfaces import ICatalogFactory
from souper.soup import get_soup, Record, NodeAttributeIndexer
from repoze.catalog.indexes.field import CatalogFieldIndex
from repoze.ca... | /rapido.souper-1.0.4.tar.gz/rapido.souper-1.0.4/rapido/souper/soup.py | 0.656438 | 0.183722 | soup.py | pypi |
import argparse
import traceback
import warnings
from io import BytesIO
from pathlib import Path
from typing import Dict, List, Union
import cv2
import numpy as np
import yaml
from onnxruntime import (
GraphOptimizationLevel,
InferenceSession,
SessionOptions,
get_available_providers,
get_device,
)
... | /rapidocr_onnxruntime-1.3.1-py3-none-any.whl/rapidocr_onnxruntime/utils.py | 0.651798 | 0.158532 | utils.py | pypi |
import argparse
import copy
import math
import time
from typing import List
import cv2
import numpy as np
from rapidocr_onnxruntime.utils import OrtInferSession, read_yaml
from .utils import ClsPostProcess
class TextClassifier:
def __init__(self, config):
self.cls_image_shape = config["cls_image_shape"... | /rapidocr_onnxruntime-1.3.1-py3-none-any.whl/rapidocr_onnxruntime/ch_ppocr_v2_cls/text_cls.py | 0.608129 | 0.162712 | text_cls.py | pypi |
import argparse
import math
import time
from typing import List
import cv2
import numpy as np
from rapidocr_onnxruntime.utils import OrtInferSession, read_yaml
from .utils import CTCLabelDecode
class TextRecognizer:
def __init__(self, config):
self.session = OrtInferSession(config)
if self.ses... | /rapidocr_onnxruntime-1.3.1-py3-none-any.whl/rapidocr_onnxruntime/ch_ppocr_v3_rec/text_recognize.py | 0.723993 | 0.172991 | text_recognize.py | pypi |
import argparse
import time
import cv2
import numpy as np
from rapidocr_onnxruntime.utils import OrtInferSession, read_yaml
from .utils import DBPostProcess, create_operators, transform
class TextDetector:
def __init__(self, config):
pre_process_list = {
"DetResizeForTest": {
... | /rapidocr_onnxruntime-1.3.1-py3-none-any.whl/rapidocr_onnxruntime/ch_ppocr_v3_det/text_detect.py | 0.624866 | 0.344609 | text_detect.py | pypi |
import argparse
from io import BytesIO
from pathlib import Path
from typing import Dict, List, Union
import cv2
import numpy as np
import yaml
from openvino.runtime import Core
from PIL import Image, UnidentifiedImageError
root_dir = Path(__file__).resolve().parent
InputType = Union[str, np.ndarray, bytes, Path]
cl... | /rapidocr_openvino-1.3.1-py3-none-any.whl/rapidocr_openvino/utils.py | 0.735452 | 0.20828 | utils.py | pypi |
import argparse
import copy
import math
import time
from typing import List
import cv2
import numpy as np
from rapidocr_openvino.utils import OpenVINOInferSession, read_yaml
from .utils import ClsPostProcess
class TextClassifier:
def __init__(self, config):
self.cls_image_shape = config["cls_image_shap... | /rapidocr_openvino-1.3.1-py3-none-any.whl/rapidocr_openvino/ch_ppocr_v2_cls/text_cls.py | 0.604282 | 0.170577 | text_cls.py | pypi |
import argparse
import math
import time
from pathlib import Path
from typing import List
import cv2
import numpy as np
from rapidocr_openvino.utils import OpenVINOInferSession, read_yaml
from .utils import CTCLabelDecode
class TextRecognizer:
def __init__(self, config):
self.rec_image_shape = config["re... | /rapidocr_openvino-1.3.1-py3-none-any.whl/rapidocr_openvino/ch_ppocr_v3_rec/text_recognize.py | 0.745954 | 0.199094 | text_recognize.py | pypi |
import argparse
import time
import cv2
import numpy as np
from rapidocr_openvino.utils import OpenVINOInferSession, read_yaml
from .utils import DBPostProcess, create_operators, transform
class TextDetector:
def __init__(self, config):
pre_process_list = {
"DetResizeForTest": {
... | /rapidocr_openvino-1.3.1-py3-none-any.whl/rapidocr_openvino/ch_ppocr_v3_det/text_detect.py | 0.66072 | 0.351422 | text_detect.py | pypi |
import argparse
import copy
import math
import time
from typing import List
import cv2
import numpy as np
from rapidocr_openvinogpu.utils import OpenVINOInferSession, read_yaml
from .utils import ClsPostProcess
class TextClassifier():
def __init__(self, config):
self.cls_image_shape = config['cls_image... | /rapidocr_openvinogpu-0.0.9-py3-none-any.whl/rapidocr_openvinogpu/ch_ppocr_v2_cls/text_cls.py | 0.590071 | 0.177864 | text_cls.py | pypi |
import argparse
import math
import time
from pathlib import Path
from typing import List
import cv2
import numpy as np
from rapidocr_openvinogpu.utils import OpenVINOInferSession, read_yaml
from .utils import CTCLabelDecode
class TextRecognizer():
def __init__(self, config):
self.rec_image_shape = conf... | /rapidocr_openvinogpu-0.0.9-py3-none-any.whl/rapidocr_openvinogpu/ch_ppocr_v3_rec/text_recognize.py | 0.567937 | 0.207135 | text_recognize.py | pypi |
import argparse
import time
import cv2
import numpy as np
from rapidocr_openvinogpu.utils import OpenVINOInferSession, read_yaml
from .utils import DBPostProcess, create_operators, transform
class TextDetector():
def __init__(self, config):
self.preprocess_op = create_operators(config['pre_process'])
... | /rapidocr_openvinogpu-0.0.9-py3-none-any.whl/rapidocr_openvinogpu/ch_ppocr_v3_det/text_detect.py | 0.721645 | 0.305905 | text_detect.py | pypi |
import argparse
import warnings
from pathlib import Path
from typing import Dict, List, Tuple, Union
import cv2
import filetype
import fitz
import numpy as np
try:
from rapidocr_onnxruntime import RapidOCR
except:
warnings.warn(
"Can't find the rapidocr_onnxruntime module,"
"try to import the ... | /rapidocr_pdf-0.0.5-py3-none-any.whl/rapidocr_pdf/main.py | 0.597608 | 0.159315 | main.py | pypi |
import base64
import copy
import json
from collections import namedtuple
from functools import reduce
from typing import List, Tuple, Union
import cv2
import numpy as np
from rapidocr_onnxruntime import RapidOCR
class OCRWebUtils():
def __init__(self) -> None:
self.ocr = RapidOCR()
self.WebReturn... | /rapidocr_web-0.1.8-py3-none-any.whl/rapidocr_web/task.py | 0.755727 | 0.200616 | task.py | pypi |
__author__ = "Caitlin Rose, Vinaya Valsan"
import os
import sys
import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
from argparse import ArgumentParser
from matplotlib import rcParams
from glob import glob
from ligo.skymap import plot
from ligo.skymap import postprocess
optp = ArgumentParser()
op... | /rapidpe_rift_pipe-0.0.6-py3-none-any.whl/rapidpe_rift_pipe-0.0.6.data/scripts/plot_skymap.py | 0.428951 | 0.342379 | plot_skymap.py | pypi |
import os
import shutil
import importlib.resources
from pstats import f8
from dataclasses import dataclass
from typing import Dict
@dataclass
class FunctionProfile:
ncalls: int
tottime: float
percall_tottime: float
cumtime: float
percall_cumtime: float
file_name: str
line_number: int
... | /rapidpe_rift_pipe-0.0.6-py3-none-any.whl/rapidpe_rift_pipe/profiling.py | 0.754463 | 0.348479 | profiling.py | pypi |
from functools import partial
from smartmin.models import SmartModel
from django.db import models
from django.utils.translation import gettext_lazy as _
from dash.orgs.models import Org
from dash.utils import generate_file_path
class Category(SmartModel):
"""
Every organization can choose to categorize the... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/categories/models.py | 0.531209 | 0.167117 | models.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [("orgs", "0005_orgbackground"), migrations.swappable_dependency(settings.AUTH_USER_MODEL)]
operations = [
migrations.CreateModel(
name="Category",
... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/categories/migrations/0001_initial.py | 0.528777 | 0.154249 | 0001_initial.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
("categories", "0002_auto_20140820_1415"),
]
operations = [
migrations.CreateModel(
... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/categories/migrations/0003_categoryimage.py | 0.562056 | 0.157622 | 0003_categoryimage.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("orgs", "0001_initial")]
operations = [
migrations.CreateModel(
name="Story",
fields=... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/stories/migrations/0001_initial.py | 0.577257 | 0.154376 | 0001_initial.py | pypi |
import inspect
import json
import logging
from functools import wraps
from celery import shared_task, signature
from django_redis import get_redis_connection
from django.apps import apps
from django.utils import timezone
from .models import Invitation, TaskState
ORG_TASK_LOCK_KEY = "org-task-lock:%s:%s"
logger = l... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/orgs/tasks.py | 0.439266 | 0.154727 | tasks.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("orgs", "0004_auto_20140804_1453")]
operations = [
migrations.CreateModel(
name="OrgBackground",
... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/orgs/migrations/0005_orgbackground.py | 0.510008 | 0.157687 | 0005_orgbackground.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [("orgs", "0006_auto_20140919_2056")]
operations = [
migrations.AlterField(
model_name="invitation",
name="created_by",
field=models.F... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/orgs/migrations/0007_auto_20140922_1514.py | 0.506347 | 0.197019 | 0007_auto_20140922_1514.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [migrations.swappable_dependency(settings.AUTH_USER_MODEL)]
operations = [
migrations.CreateModel(
name="Invitation",
fields=[
("i... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/orgs/migrations/0001_initial.py | 0.546254 | 0.164516 | 0001_initial.py | pypi |
import time
from abc import ABCMeta, abstractmethod
from enum import Enum
from typing import Optional, Tuple
"""
Sync support
"""
class SyncOutcome(Enum):
created = 1
updated = 2
deleted = 3
ignored = 4
class BaseSyncer(object, metaclass=ABCMeta):
"""
Base class for classes that describe ho... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/utils/sync.py | 0.878334 | 0.264252 | sync.py | pypi |
import calendar
import datetime
import json
import os
import random
from collections import OrderedDict
from itertools import islice
from uuid import uuid4
from dateutil.relativedelta import relativedelta
from django.core.cache import cache
from django.utils import timezone
def intersection(*args):
"""
Retu... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/utils/__init__.py | 0.715921 | 0.334209 | __init__.py | pypi |
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("orgs", "0001_initial")]
operations = [
migrations.CreateModel(
name="DashBlock",
field... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/dashblocks/migrations/0001_initial.py | 0.623377 | 0.17252 | 0001_initial.py | pypi |
from django import template
from django.conf import settings
from django.db.models import Prefetch
from dash.dashblocks.models import DashBlock, DashBlockType
"""
This module offers one templatetag called ``load_dashblocks``.
``load_dashblocks`` does a query for all active DashBlock objects
for the passed in DashBlo... | /rapidpro_dash-1.14.0-py3-none-any.whl/dash/dashblocks/templatetags/dashblocks.py | 0.70202 | 0.229622 | dashblocks.py | pypi |
import inspect
class FunctionManager(object):
def __init__(self):
self._functions = {}
def add_library(self, library):
"""
Adds functions from a library module
:param library: the library module
:return:
"""
for fn in library.__dict__.copy().values():
... | /rapidpro-expressions-1.8.tar.gz/rapidpro-expressions-1.8/temba_expressions/functions/__init__.py | 0.64232 | 0.246482 | __init__.py | pypi |
import random
from datetime import date as _date, time as _time
from dateutil.relativedelta import relativedelta
from decimal import Decimal, ROUND_FLOOR, ROUND_HALF_UP, ROUND_DOWN, ROUND_UP
from temba_expressions import conversions
from temba_expressions.utils import decimal_pow, decimal_round
E = Decimal('2.718281... | /rapidpro-expressions-1.8.tar.gz/rapidpro-expressions-1.8/temba_expressions/functions/excel.py | 0.689201 | 0.403185 | excel.py | pypi |
import json
import argparse
from parsers.creation.contentindexparser import ContentIndexParser
from parsers.sheets.csv_sheet_reader import CSVSheetReader
from parsers.sheets.xlsx_sheet_reader import XLSXSheetReader
from parsers.sheets.google_sheet_reader import GoogleSheetReader
from rapidpro.models.containers import ... | /rapidpro_flow_toolkit-0.0.1.tar.gz/rapidpro_flow_toolkit-0.0.1/main.py | 0.462716 | 0.217005 | main.py | pypi |
import copy
from rapidpro_flow_tools.parsers.common.rowdatasheet import RowDataSheet
from rapidpro_flow_tools.logger.logger import get_logger, logging_context
LOGGER = get_logger()
class SheetParser:
def __init__(self, row_parser, table, context={}):
'''
Args:
row_parser: parser to c... | /rapidpro_flow_tools-2.0.7.tar.gz/rapidpro_flow_tools-2.0.7/src/rapidpro_flow_tools/parsers/common/sheetparser.py | 0.566258 | 0.258484 | sheetparser.py | pypi |
from __future__ import absolute_import, unicode_literals
from abc import ABCMeta
from ..exceptions import FlowParseException
class TranslatableText(object):
"""
Text that may be a single untranslated value or a translation map
"""
def __init__(self, value):
self.value = value
@classmetho... | /rapidpro-flows-1.2.9.tar.gz/rapidpro-flows-1.2.9/temba_flows/definition/__init__.py | 0.786295 | 0.215103 | __init__.py | pypi |
import json
import sqlalchemy
import sqlalchemy.types
import temba_client.v1.types
__author__ = 'Tomasz J. Kotarba <tomasz@kotarba.net>'
__copyright__ = 'Copyright (c) 2016, Tomasz J. Kotarba. All rights reserved.'
__maintainer__ = 'Tomasz J. Kotarba'
__email__ = 'tomasz@kotarba.net'
class RapidProCache(object):
... | /rapidpro-pull-1.0.3.tar.gz/rapidpro-pull-1.0.3/rapidpropull/cache.py | 0.71889 | 0.156781 | cache.py | pypi |
import temba_client.v1
import rapidpropull.cache
__author__ = 'Tomasz J. Kotarba <tomasz@kotarba.net>'
__copyright__ = 'Copyright (c) 2016, Tomasz J. Kotarba. All rights reserved.'
__maintainer__ = 'Tomasz J. Kotarba'
__email__ = 'tomasz@kotarba.net'
class DownloadTask(object):
"""
Provides a mechanism for ... | /rapidpro-pull-1.0.3.tar.gz/rapidpro-pull-1.0.3/rapidpropull/download.py | 0.674158 | 0.195671 | download.py | pypi |
from __future__ import print_function
import sys
import json
import temba_client.v1
import temba_client.exceptions
import docopt
import rapidpropull.download
__author__ = 'Tomasz J. Kotarba <tomasz@kotarba.net>'
__copyright__ = 'Copyright (c) 2016, Tomasz J. Kotarba. All rights reserved.'
__maintainer__ = 'Tomasz J.... | /rapidpro-pull-1.0.3.tar.gz/rapidpro-pull-1.0.3/rapidpropull/cli.py | 0.568536 | 0.155719 | cli.py | pypi |
from ..serialization import (
BooleanField,
DatetimeField,
IntegerField,
ListField,
ObjectDictField,
ObjectField,
ObjectListField,
SimpleField,
TembaObject,
)
class ObjectRef(TembaObject):
"""
Used for references to objects in other objects
"""
uuid = SimpleField()... | /rapidpro_python-2.12.0.tar.gz/rapidpro_python-2.12.0/temba_client/v2/types.py | 0.608478 | 0.312318 | types.py | pypi |
import copy
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
def scenarios_similarity(R):
"""Determines similarity in robustness from multiple scenario sets
Robustness is a function of scenarios, de... | /rapidrobustness-0.0.6.tar.gz/rapidrobustness-0.0.6/rapid/robustness/analysis/comparisons.py | 0.902527 | 0.876951 | comparisons.py | pypi |
import numpy as np
from .transforms import t1, t2, t3
def maximin(f, maximise=True):
"""Maximin metric (worst-case scenario)
The maximin (minimax) metric was first used by Wald (1950).
It is a very risk averse metric that assumes that the scenario
that will occur is the scenario under which the perf... | /rapidrobustness-0.0.6.tar.gz/rapidrobustness-0.0.6/rapid/robustness/metrics/common_metrics.py | 0.947217 | 0.894237 | common_metrics.py | pypi |
from .transforms import t1, t2, t3
class custom_R_metric:
"""Create a custom robustness metric
"""
def __init__(self, t1_func, t2_func, t3_func):
"""Initialize the custom Robustness metric
"""
self.t1_func = t1_func
self.t2_func = t2_func
self.t3_func = t3_func
... | /rapidrobustness-0.0.6.tar.gz/rapidrobustness-0.0.6/rapid/robustness/metrics/custom_metrics.py | 0.932423 | 0.533458 | custom_metrics.py | pypi |
import numpy as np
def f_identity(f):
"""Identity transform included for completeness.
Parameters
----------
f : np.ndarray, shape=(m, 1)
Transformed performance values to be maximised.
m decision alternatives and n scenarios
Returns
-------
np.ndarray, shape=(m, )
... | /rapidrobustness-0.0.6.tar.gz/rapidrobustness-0.0.6/rapid/robustness/metrics/transforms/t3.py | 0.953654 | 0.913484 | t3.py | pypi |
import numpy as np
def all_scenarios(f):
"""Use all scenarios. Provided for completeness.
Parameters
----------
f : np.ndarray, shape=(m, n)
Transformed performance values to be maximised.
m decision alternatives and n scenarios
Returns
-------
np.ndarray, shape=(m, n')
... | /rapidrobustness-0.0.6.tar.gz/rapidrobustness-0.0.6/rapid/robustness/metrics/transforms/t2.py | 0.928214 | 0.877372 | t2.py | pypi |
import numpy as np
def f_to_R(f_df, R_dict):
"""Calculates robustness from performance values.
Uses a set of performance values, `f`, determined from simulations
across multiple decision alternatives, `l`, different scenarios,
`s`, and calculates robustness, `R`, using a variety of given
robustn... | /rapidrobustness-0.0.6.tar.gz/rapidrobustness-0.0.6/rapid/robustness/evaluator/calc.py | 0.908973 | 0.738259 | calc.py | pypi |
import argparse
import os
import yaml
from ._version import __version__ as version
from .constants import OutputTypes, default_dependency_file_path
from .rapids_dependency_file_generator import (
delete_existing_files,
make_dependency_files,
)
from .rapids_dependency_file_validator import validate_dependencie... | /rapids-dependency-file-generator-1.5.2.tar.gz/rapids-dependency-file-generator-1.5.2/src/rapids_dependency_file_generator/cli.py | 0.550607 | 0.227705 | cli.py | pypi |
import itertools
import yaml
from collections import defaultdict
from os.path import join
def dedupe(dependencies):
deduped = [dep for dep in dependencies if not isinstance(dep, dict)]
deduped = sorted(list(set(deduped)))
dict_like_deps = [dep for dep in dependencies if isinstance(dep, dict)]
dict_dep... | /rapids_env_generator-0.0.5-py3-none-any.whl/rapids_env_generator/rapids_env_generator.py | 0.630799 | 0.264326 | rapids_env_generator.py | pypi |
import math
from typing import Literal, Optional, Union
import cupy as cp
import cupyx as cpx
from cuml.linear_model import LinearRegression
from rapids_singlecell.cunnData import cunnData
def regress_out(
cudata: cunnData,
keys: Union[str, list],
layer: Optional[str] = None,
inplace: bool = True,
... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/cunnData_funcs/_regress_out.py | 0.900793 | 0.658517 | _regress_out.py | pypi |
import math
import warnings
from typing import Optional
import cupy as cp
import cupyx as cpx
from rapids_singlecell.cunnData import cunnData
from ._utils import _check_nonnegative_integers
def normalize_total(
cudata: cunnData, target_sum: int, layer: Optional[str] = None, inplace: bool = True
) -> Optional[c... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/cunnData_funcs/_normalize.py | 0.787523 | 0.482856 | _normalize.py | pypi |
import math
from typing import Optional, Union
import cupy as cp
import numpy as np
from anndata import AnnData
from cuml.common.kernel_utils import cuda_kernel_factory
from cuml.decomposition import PCA, TruncatedSVD
from cuml.internals.input_utils import sparse_scipy_to_cp
from cupyx.scipy.sparse import csr_matrix, ... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/cunnData_funcs/_pca.py | 0.917976 | 0.521532 | _pca.py | pypi |
import math
from typing import Union
import cupy as cp
import cupyx as cpx
import numpy as np
from rapids_singlecell.cunnData import cunnData
_sparse_qc_kernel_csc = cp.RawKernel(
r"""
extern "C" __global__
void caluclate_qc_csc(const int *indptr,const int *index,const float *data,
... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/cunnData_funcs/_simple.py | 0.619011 | 0.437103 | _simple.py | pypi |
import os
import matplotlib.pyplot as plt
import seaborn as sns
from rapids_singlecell.cunnData import cunnData
def scatter(
cudata: cunnData,
x: str,
y: str,
color: str = None,
save: str = None,
show: bool = True,
dpi: int = 300,
) -> None:
"""
Violin plot.
Wraps :func:`seab... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/cunnData_funcs/_plotting.py | 0.693473 | 0.47859 | _plotting.py | pypi |
from typing import Optional, Union
import cupy as cp
import numpy as np
import pandas as pd
from anndata import AnnData
from decoupler.pre import extract, filt_min_n, get_net_mat, match, rename_net
from scipy import stats
from tqdm import tqdm
def fit_mlm(X, y, inv, df):
X = cp.ascontiguousarray(X)
y.shape[1... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/decoupler_gpu/_method_mlm.py | 0.954732 | 0.549157 | _method_mlm.py | pypi |
from typing import Optional, Union
import cupy as cp
import numpy as np
import pandas as pd
from anndata import AnnData
from decoupler.pre import extract, filt_min_n, get_net_mat, match, rename_net
from tqdm import tqdm
def run_perm(estimate, mat, net, idxs, times, seed):
mat = cp.ascontiguousarray(mat)
cp.r... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/decoupler_gpu/_method_wsum.py | 0.882352 | 0.456168 | _method_wsum.py | pypi |
from typing import Literal, Optional
import pandas as pd
from anndata import AnnData
def mde(
adata: AnnData,
device: Optional[Literal["cpu", "cuda"]] = None,
n_neighbors: int = 15,
n_pcs: int = None,
use_rep: str = None,
**kwargs,
) -> None:
"""
Util to run :func:`pymde.preserve_neig... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_pymde.py | 0.921838 | 0.573917 | _pymde.py | pypi |
import math
from types import MappingProxyType
from typing import Any, Literal, Mapping, Optional, Union
import cupy as cp
import numpy as np
from anndata import AnnData
from cuml.manifold.simpl_set import fuzzy_simplicial_set
from cuml.neighbors import NearestNeighbors
from cupyx.scipy.sparse import coo_matrix
from ... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_neighbors.py | 0.91962 | 0.497192 | _neighbors.py | pypi |
from typing import Union
import cudf
import cugraph
import cupy as cp
import numpy as np
from anndata import AnnData
def draw_graph(
adata: AnnData, init_pos: Union[str, bool, None] = None, max_iter: int = 500
) -> None:
"""
Force-directed graph drawing with cugraph's implementation of Force Atlas 2.
... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_draw_graph.py | 0.896495 | 0.543954 | _draw_graph.py | pypi |
from typing import Optional
from anndata import AnnData
from cuml.manifold import TSNE
from ._utils import _choose_representation
def tsne(
adata: AnnData,
n_pcs: int = None,
use_rep: str = None,
perplexity: int = 30,
early_exaggeration: int = 12,
learning_rate: int = 200,
method: str = ... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_tsne.py | 0.962374 | 0.655405 | _tsne.py | pypi |
import cupy as cp
import cupyx as cpx
import cupyx.scipy.sparse
import cupyx.scipy.sparse.linalg
from anndata import AnnData
from scipy.sparse import issparse
def diffmap(
adata: AnnData,
n_comps: int = 15,
neighbors_key: str = None,
sort: str = "decrease",
density_normalize: bool = True,
) -> Non... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_diffmap.py | 0.818011 | 0.694919 | _diffmap.py | pypi |
from typing import Literal, Optional
import numpy as np
from anndata import AnnData
from cuml import UMAP
from cuml.manifold.umap_utils import find_ab_params
from scanpy._utils import NeighborsView
from sklearn.utils import check_random_state
from ._utils import _choose_representation
_InitPos = Literal["spectral", ... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_umap.py | 0.950835 | 0.742702 | _umap.py | pypi |
from typing import Optional
import cudf
import numpy as np
import pandas as pd
from anndata import AnnData
from cugraph import Graph
from cugraph import leiden as culeiden
from cugraph import louvain as culouvain
from cuml.cluster import KMeans
from natsort import natsorted
def leiden(
adata: AnnData,
resolu... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/scanpy_gpu/_clustering.py | 0.949248 | 0.531757 | _clustering.py | pypi |
import math
import cupy as cp
import cupyx as cpx
kernel_gearys_C_num_dense = r"""
extern "C" __global__ void gearys_C_num_dense(const float* data,
const int* adj_matrix_row_ptr, const int* adj_matrix_col_ind, const float* adj_matrix_data,
float* num, int n_samples, int n_features) {
int f = blockIdx.x * blockDim... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/squidpy_gpu/_gearysc.py | 0.669205 | 0.431944 | _gearysc.py | pypi |
from typing import (
Literal, # < 3.8
Optional,
Sequence,
Union,
)
import cupy as cp
import cupyx as cpx
import numpy as np
import pandas as pd
from anndata import AnnData
from scipy import sparse
from statsmodels.stats.multitest import multipletests
from ._gearysc import _gearys_C_cupy
from ._morans... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/squidpy_gpu/_autocorr.py | 0.904424 | 0.61057 | _autocorr.py | pypi |
import math
import cupy as cp
import cupyx as cpx
kernel_morans_I_num_dense = r"""
extern "C" __global__
void morans_I_num_dense(const float* data_centered, const int* adj_matrix_row_ptr, const int* adj_matrix_col_ind,
const float* adj_matrix_data, float* num, int n_samples, int n_features) {
int f = blockIdx.x *... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/squidpy_gpu/_moransi.py | 0.613931 | 0.349144 | _moransi.py | pypi |
import math
from itertools import product
from typing import (
Iterable,
Literal,
Mapping,
Optional,
Sequence,
Union,
)
import cupy as cp
import cupyx as cpx
import numpy as np
import pandas as pd
from anndata import AnnData
from cupyx.scipy.sparse import issparse as cpissparse
from scipy.spars... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/squidpy_gpu/_ligrec.py | 0.844505 | 0.388444 | _ligrec.py | pypi |
from typing import (
TYPE_CHECKING,
Any,
Dict,
Union,
)
import numpy as np
import pandas as pd
from pandas.api.types import infer_dtype, is_categorical_dtype
from scipy import stats
from scipy.sparse import issparse, spmatrix
### Taken from squidpy: https://github.com/scverse/squidpy/blob/main/squidp... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/squidpy_gpu/_utils.py | 0.913607 | 0.538073 | _utils.py | pypi |
import warnings
from collections import OrderedDict
from itertools import repeat
from typing import Any, List, Mapping, MutableMapping, Optional, Union
import anndata
import cupy as cp
import cupyx as cpx
import numpy as np
import pandas as pd
from anndata import AnnData
from anndata._core.index import _normalize_indi... | /rapids_singlecell-0.8.1.tar.gz/rapids_singlecell-0.8.1/src/rapids_singlecell/cunnData/__init__.py | 0.813127 | 0.301658 | __init__.py | pypi |
from __future__ import unicode_literals
import datetime
from django.db import models
from django.utils.translation import ugettext_lazy as _
try:
from django.utils.timezone import now
except ImportError: # Django < 1.4
now = datetime.datetime.now
class Timeline(models.Model):
"A series of milestones wh... | /rapidsms-appointments-0.1.0.tar.gz/rapidsms-appointments-0.1.0/appointments/models.py | 0.666497 | 0.155719 | models.py | pypi |
from __future__ import absolute_import
import operator
import uuid
from django.utils.timezone import now
from . import comparisons
from .base import HealthcareStorage
class DummyStorage(HealthcareStorage):
"In-memory storage. This should only be used for testing."
_patients = {}
_patient_ids = {}
... | /rapidsms-healthcare-0.1.0.tar.gz/rapidsms-healthcare-0.1.0/healthcare/backends/dummy.py | 0.655115 | 0.224013 | dummy.py | pypi |
from __future__ import absolute_import, unicode_literals
import operator
from django.db.models import Q
from django.forms.models import model_to_dict
from django.utils.timezone import now
from .. import comparisons
from ..base import HealthcareStorage
from .models import Patient, Provider, PatientID
class DjangoSt... | /rapidsms-healthcare-0.1.0.tar.gz/rapidsms-healthcare-0.1.0/healthcare/backends/djhealth/storage.py | 0.41834 | 0.213439 | storage.py | pypi |
import urllib
def unicode_to_ismsformat(unicode_str):
"""
Example:
text_to_ismsformat('The quick brown fox settled his disputes out of court.')
-> "0054;0068;0065;0020;0071;0075;0069;0063;006B;0020;0062;0072;006F;0077;
006E;0020;0066;006F;0078;0020;0073;0065;0074;0074;006C;0065;0064;0020;
... | /rapidsms-multimodem-0.1.0.tar.gz/rapidsms-multimodem-0.1.0/rapidsms_multimodem/utils.py | 0.442877 | 0.219463 | utils.py | pypi |
from string import ascii_lowercase
from django import template
register = template.Library()
letters = [l.upper() for l in ascii_lowercase]
@register.filter
def alphabet(index):
return letters[index]
@register.filter
def cell_style(prct):
if prct:
prct = float(prct)
color = (" color: hsla... | /rapidsms-xray-0.5.9b0.tar.gz/rapidsms-xray-0.5.9b0/xray/templatetags/xray_tags.py | 0.565299 | 0.213664 | xray_tags.py | pypi |
The rapidtide package
=====================
Rapidtide is a suite of Python programs used to model, characterize,
visualize, and remove time varying, physiological blood signals from fMRI and fNIRS
datasets. The primary workhorses of the package are the rapidtide program,
which characterizes bulk blood flow, and ha... | /rapidtide-2.6.2.tar.gz/rapidtide-2.6.2/README.rst | 0.693992 | 0.887156 | README.rst | pypi |
Bare metal installation
-----------------------
This gives you the maximum flexibility if you want to look at the code and/or modify things. It may seem a little daunting at first,
but it's not that bad. And if you want a simpler path, skip down to the Docker installation instructions
Required dependencies
`````````... | /rapidtide-2.6.2.tar.gz/rapidtide-2.6.2/INSTALL.rst | 0.580947 | 0.809464 | INSTALL.rst | pypi |
from difflib import SequenceMatcher
from collections import OrderedDict
from collections import Counter
import sys, string
from yaml.composer import Composer
from yaml.reader import Reader
from yaml.scanner import Scanner
from yaml.composer import Composer
from yaml.resolver import Resolver
from yaml.parser import Par... | /rapier-0.0.4.tar.gz/rapier-0.0.4/util/validate_rapier.py | 0.533641 | 0.176423 | validate_rapier.py | pypi |
from typing import Optional
from contextlib import closing
from airflow.providers.snowflake.hooks.snowflake import SnowflakeHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
class MultiStatementSnowflakeOperator(BaseOperator):
"""Executes multiple sql statements.
... | /rappiflow_providers-0.3.6.tar.gz/rappiflow_providers-0.3.6/rappiflow_providers/operators/snowflake_operators.py | 0.909683 | 0.325052 | snowflake_operators.py | pypi |
from contextlib import closing
import logging
import pandas as pd
from airflow.providers.postgres.hooks.postgres import PostgresHook
from airflow.providers.snowflake.hooks.snowflake import SnowflakeHook
from snowflake.connector.pandas_tools import write_pandas
WAREHOUSE = "CPGS"
DATABASE = "FIVETRAN"
def get_run_ti... | /rappiflow_providers-0.3.6.tar.gz/rappiflow_providers-0.3.6/rappiflow_providers/operators/utils/time_execution.py | 0.645455 | 0.200891 | time_execution.py | pypi |
import logging
from binascii import hexlify
from collections import namedtuple
from struct import unpack
from bluetooth_data_tools import short_address
from bluetooth_sensor_state_data import BluetoothData
from home_assistant_bluetooth import BluetoothServiceInfo
from sensor_state_data import DeviceClass, SensorLibrar... | /rapt_ble-0.1.2.tar.gz/rapt_ble-0.1.2/src/rapt_ble/parser.py | 0.664214 | 0.259538 | parser.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
import os
from rapthor.lib.screen import KLScreen, VoronoiScreen
from losoto.h5parm import h5parm
def main(h5parmfile, soltabname='phase000', screen_type='tessellated', outroot='',
bounds_deg=None, bounds_mid_deg=None, skymodel=None,
solsetna... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/make_aterm_images.py | 0.751557 | 0.353679 | make_aterm_images.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
from rapthor.lib.fitsimage import FITSImage
from rapthor.lib import miscellaneous as misc
from astropy.io import fits as pyfits
from astropy.wcs import WCS as pywcs
import numpy as np
def main(input_image_list, vertices_file_list, output_image, skip=False, pad... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/make_mosaic_template.py | 0.672654 | 0.467757 | make_mosaic_template.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
from losoto.h5parm import h5parm
import os
import shutil
import numpy as np
from rapthor.lib import miscellaneous as misc
def main(inh5parm, outh5parms, soltabname='phase000', insolset='sol000'):
"""
Combines two h5parms
Parameters
----------
... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/split_h5parms.py | 0.541894 | 0.261484 | split_h5parms.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
from rapthor.lib import facet
from rapthor.lib import miscellaneous as misc
import lsmtool
def main(skymodel, ra_mid, dec_mid, width_ra, width_dec, region_file):
"""
Make a ds9 region file
Parameters
----------
skymodel : str
Filen... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/make_region_file.py | 0.817975 | 0.394493 | make_region_file.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
import casacore.tables as pt
import logging
import numpy as np
import sys
import os
import subprocess
from astropy.time import Time
import dateutil.parser
from rapthor.lib import miscellaneous as misc
def get_nchunks(msin, nsectors, fraction=1.0, reweight=Fals... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/subtract_sector_models.py | 0.641535 | 0.327158 | subtract_sector_models.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
import lsmtool
import numpy as np
import bdsf
from rapthor.lib import miscellaneous as misc
import casacore.tables as pt
import astropy.io.ascii
from astropy.io import fits as pyfits
from astropy import wcs
from astropy.utils import iers
import os
import json
fr... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/filter_skymodel.py | 0.676834 | 0.421492 | filter_skymodel.py | pypi |
import argparse
import sys
import subprocess
import casacore.tables as pt
import numpy as np
import os
from rapthor.lib import miscellaneous as misc
def concat_ms(msfiles, output_file, concat_property="frequency", overwrite=False):
"""
Concatenate a number of Measurement Set files into one
Parameters
... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/concat_ms.py | 0.689828 | 0.479077 | concat_ms.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
from rapthor.lib import miscellaneous as misc
from astropy.io import fits as pyfits
import numpy as np
import shutil
import os
def main(input_image_list, template_image, output_image, skip=False):
"""
Make a mosaic image
Parameters
----------
... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/make_mosaic.py | 0.622 | 0.390302 | make_mosaic.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
from rapthor.lib import miscellaneous as misc
import logging
import numpy as np
import sys
from astropy.io import fits as pyfits
from astropy import wcs
import os
def main(output_image, input_image=None, vertices_file=None, reference_ra_deg=None,
refe... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/blank_image.py | 0.559049 | 0.436562 | blank_image.py | pypi |
import argparse
from argparse import RawTextHelpFormatter
from rapthor.lib import miscellaneous as misc
from rapthor.lib.fitsimage import FITSImage
from reproject import reproject_interp
from astropy.io import fits as pyfits
from astropy.wcs import WCS as pywcs
import numpy as np
import os
import shutil
def main(inpu... | /rapthor-1.1-py3-none-any.whl/rapthor-1.1.data/scripts/regrid_image.py | 0.749087 | 0.379522 | regrid_image.py | pypi |
SSVC Ore Miner
========================
**Stakeholder-specific Vulnerability Categorization(SSVC) Ore Miner**
The Stakeholder-specific Vulnerability Categorization (SSVC) is a system for prioritizing actions during vulnerability management. SSVC aims to avoid one-size-fits-all solutions in favor of a modular decisio... | /rapticoressvc-0.0.15.tar.gz/rapticoressvc-0.0.15/README.md | 0.561696 | 0.803752 | README.md | pypi |
import numpy as np
import pandas as pd
import xgboost as xgb
from boruta import BorutaPy
from tsfresh.utilities.dataframe_functions import impute
from tsfresh.feature_extraction import (
ComprehensiveFCParameters,
extract_features,
)
from tsfresh import extract_features
from .preprocess import offset_batch_samp... | /raptor_functions-0.4.16-py3-none-any.whl/raptor_functions/supervised/feature_extraction.py | 0.694717 | 0.41484 | feature_extraction.py | pypi |
import pandas as pd
from pathlib import Path
import os
import json
def structured_json_bosch_sensor(path_to_json,saved_folder_path,result):
"""_summary_
Args:
path_to_json (str):
saved_folder_path (str): folder path to save transformed data
result (str): label of sensor data in path_t... | /raptor_functions-0.4.16-py3-none-any.whl/raptor_functions/supervised/data_cleaning_bosch.py | 0.488283 | 0.445047 | data_cleaning_bosch.py | pypi |
import joblib
from io import BytesIO
import boto3
import mlflow
from mlflow.tracking import MlflowClient
from tsfresh.feature_extraction import settings, extract_features
from .feature_extraction import add_offset_gradient
import ast
# REMOTE_TRACKING_URI = 'http://ec2-3-10-175-206.eu-west-2.compute.amazonaws.com:500... | /raptor_functions-0.4.16-py3-none-any.whl/raptor_functions/supervised/prediction.py | 0.624637 | 0.223631 | prediction.py | pypi |
<a href="https://colab.research.google.com/github/Bryant-Dental/raptor_functions/blob/main/raptor_functions/examples/Neural_Network.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install raptor-functions
from raptor_functions.datasets im... | /raptor_functions-0.4.16-py3-none-any.whl/raptor_functions/examples/Neural_Network.ipynb | 0.865594 | 0.895202 | Neural_Network.ipynb | pypi |
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