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 requests
import json
import time
import datetime
import os
def write_json(file, data):
with open(file,'w') as f:
json.dump(data, f, indent=4)
"""
Checks the responses from the server and throws errors accordingly.
"""
def check_response(response):
if response.status_code == 401:
raise ... | /schoolsoft_api-1.0.3-py3-none-any.whl/schoolsoft_api/schoolsoft_api.py | 0.484624 | 0.205356 | schoolsoft_api.py | pypi |
===========
schoolutils
===========
schoolutils provides a simple, efficient way to track and manage
student data. It includes:
* a database for storing information about students, courses,
assignments, and grades
* a command-line interface for interacting with the database
* tools for calculating grades
* tool... | /schoolutils-0.1.7.zip/schoolutils-0.1.7/README.rst | 0.744935 | 0.777617 | README.rst | pypi |
import hashlib
import hmac
from six import text_type
import six.moves.urllib.request, six.moves.urllib.parse, six.moves.urllib.error
from xblock.core import XBlock
from xblock.fields import Scope, String
from xblock.fragment import Fragment
from .schoolyourself import SchoolYourselfXBlock
class SchoolYourselfRevie... | /schoolyourself_xblock-0.2-py3-none-any.whl/schoolyourself/schoolyourself_review.py | 0.58948 | 0.21348 | schoolyourself_review.py | pypi |
import six.moves.urllib.request, six.moves.urllib.parse, six.moves.urllib.error
from xblock.core import XBlock
from xblock.fragment import Fragment
from .schoolyourself import SchoolYourselfXBlock
class SchoolYourselfLessonXBlock(SchoolYourselfXBlock):
"""
This block renders a launcher button for a School ... | /schoolyourself_xblock-0.2-py3-none-any.whl/schoolyourself/schoolyourself_lesson.py | 0.678114 | 0.202778 | schoolyourself_lesson.py | pypi |
import hashlib
class CrawledResource:
"""A resource crawled by the crawler.
This is an adapter bewteen crawler and API.
The id is computed by the originating url and the id it has in the url.
"""
def __init__(self, resource, origin_urls:list, id_in_origin=""):
"""Create a new crawled re... | /schul_cloud_url_crawler-1.0.17.tar.gz/schul_cloud_url_crawler-1.0.17/schul_cloud_url_crawler/crawled_resource.py | 0.728459 | 0.229978 | crawled_resource.py | pypi |
import click
import schul_cloud_url_crawler.resource_client as resource_client
from schul_cloud_resources_api_v1 import ApiClient, ResourceApi
from schul_cloud_resources_api_v1.rest import ApiException
import schul_cloud_resources_api_v1.auth as auth
from urllib3.exceptions import MaxRetryError
import traceback
import ... | /schul_cloud_url_crawler-1.0.17.tar.gz/schul_cloud_url_crawler-1.0.17/schul_cloud_url_crawler/cli.py | 0.450601 | 0.153422 | cli.py | pypi |
class ArgumentError(Exception):
def __init__(self, message=""):
self.message = message
super().__init__(self.message)
class ArgumentTypeError(ArgumentError):
def __init__(self, func_name, argument_name, allowed_types, actual_type, arg):
if type(argument_name) == str and len(argument_n... | /schulich_ignite-0.1.3-py3-none-any.whl/spark/util/Errors.py | 0.506836 | 0.187504 | Errors.py | pypi |
HTMLColors = [
"aliceblue",
"antiquewhite",
"aqua",
"aquamarine",
"azure",
"beige",
"bisque",
"black",
"blanchedalmond",
"blue",
"blueviolet",
"brown",
"burlywood",
"cadetblue",
"chartreuse",
"chocolate",
"coral",
"cornflowerblue",
"cornsilk",
... | /schulich_ignite-0.1.3-py3-none-any.whl/spark/util/HTMLColors.py | 0.585457 | 0.377426 | HTMLColors.py | pypi |
from math import sin, cos
from ..decorators import validate_args, ignite_global
from numbers import Real
@validate_args([Real, Real, Real, Real],
[Real, Real, Real, Real, Real],
[Real, Real, Real, Real, Real, Real],
[Real, Real, Real, Real, Real, Real, str])
@ignite_global... | /schulich_ignite-0.1.3-py3-none-any.whl/spark/util/helper_functions/arc_functions.py | 0.647241 | 0.39097 | arc_functions.py | pypi |
from ..decorators import *
from ..HTMLColors import HTMLColors
import re
from ..Errors import *
from numbers import Real
from math import pi
from math import sqrt
import random
@validate_args([str, str])
def helper_parse_color_string(self, func_name, s):
rws = re.compile(r'\s')
no_ws = rws.sub('', s).lower()
... | /schulich_ignite-0.1.3-py3-none-any.whl/spark/util/helper_functions/misc_functions.py | 0.577614 | 0.356055 | misc_functions.py | pypi |
from __future__ import annotations
from typing import TYPE_CHECKING, Dict
if TYPE_CHECKING:
from ...core import Core
from functools import reduce
from operator import and_
from ..decorators import *
_phys_to_typed = {
"Backquote": ('`', '~'),
"Digit1": ('1', '!'),
"Digit2": ('2', '@'),
"Digit3": (... | /schulich_ignite-0.1.3-py3-none-any.whl/spark/util/helper_functions/keyboard_functions.py | 0.655557 | 0.314392 | keyboard_functions.py | pypi |
import itertools
from gettext import gettext as _
from typing import (
Collection, Container, List, Mapping, Tuple, Sequence
)
from schulze_condorcet.util import as_vote_string, as_vote_tuples
from schulze_condorcet.strength import winning_votes
from schulze_condorcet.types import (
Candidate, DetailedResultLe... | /schulze-condorcet-2.0.0.tar.gz/schulze-condorcet-2.0.0/schulze_condorcet/schulze_condorcet.py | 0.907072 | 0.453625 | schulze_condorcet.py | pypi |
# schupy -- A python package for modeling and analyzing Schumann resonances
schupy is an open-source python package aimed at modeling and analyzing Schumann resonances (SRs), the global electromagnetic resonances of the Earth-ionosphere cavity resonator in the lowest part of the extremely low frequency band (<100 Hz).... | /schupy-1.0.12.tar.gz/schupy-1.0.12/README.md | 0.929136 | 0.955444 | README.md | pypi |
import json
import urllib.parse
from . import urls
from .account_information import Position, Account
from .authentication import SessionManager
class Schwab(SessionManager):
def __init__(self, **kwargs):
"""
The Schwab class. Used to interact with schwab.
"""
self.headless = ... | /schwab_api-0.2.3.tar.gz/schwab_api-0.2.3/schwab_api/schwab.py | 0.575349 | 0.228931 | schwab.py | pypi |
import logging
import sys
__all__ = ['contextfile_logger', 'ForwardingLogger']
class ForwardingLogger(logging.Logger):
"""
This logger forwards messages above a certain level (by default: all messages)
to a configured parent logger. Optionally it can prepend the configured
"forward_prefix" to all *f... | /schwarzlog-0.6.2.tar.gz/schwarzlog-0.6.2/schwarz/log_utils/forwarding_logger.py | 0.562177 | 0.196402 | forwarding_logger.py | pypi |
import json
from typing import Tuple
import pandas as pd
import requests
BRANCH_URL = "https://bank.gov.ua/NBU_BankInfo/get_data_branch?json"
PARENT_URL = "https://bank.gov.ua/NBU_BankInfo/get_data_branch_glbank?json"
def split_names(s) -> Tuple[str, str]:
"""This will split the `NAME_E` line from the API into... | /schwifty-2023.6.0.tar.gz/schwifty-2023.6.0/scripts/get_bank_registry_ua.py | 0.673729 | 0.286263 | get_bank_registry_ua.py | pypi |
import json
import re
from urllib.parse import urljoin
import requests
from bs4 import BeautifulSoup
COUNTRY_CODE_PATTERN = r"[A-Z]{2}"
EMPTY_RANGE = (0, 0)
URL = "https://www.swift.com/standards/data-standards/iban"
def get_raw():
soup = BeautifulSoup(requests.get(URL).content, "html.parser")
link = soup.... | /schwifty-2023.6.0.tar.gz/schwifty-2023.6.0/scripts/get_iban_registry.py | 0.447219 | 0.293664 | get_iban_registry.py | pypi |
from math import sqrt
# Pandas imports
from pandas import DataFrame
# Numpy imports
from numpy import mean, std, median, amin, amax, percentile
# Scipy imports
from scipy.stats import skew, kurtosis, sem
from .base import Analysis, std_output
from .exc import NoDataError, MinimumSizeError
from ..data import Vector,... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/analysis/stats.py | 0.906564 | 0.328341 | stats.py | pypi |
from numpy import float_, int_
class Analysis(object):
"""Generic analysis root class.
Members:
_data - the data used for analysis.
_display - flag for whether to display the analysis output.
_results - A dict of the results of the test.
Methods:
logic - This method needs... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/analysis/base.py | 0.899621 | 0.594845 | base.py | pypi |
from scipy.stats import linregress, pearsonr, spearmanr
from pandas import DataFrame
from ..data import Vector, is_vector
from .base import Analysis, std_output
from .exc import NoDataError, MinimumSizeError
from .hypo_tests import NormTest
class Comparison(Analysis):
"""Perform a test on two independent vectors... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/analysis/comparison.py | 0.80213 | 0.474509 | comparison.py | pypi |
from .hypo_tests import NormTest, KSTest, TwoSampleKSTest, MannWhitney, TTest, Anova, Kruskal, EqualVariance
from .comparison import LinearRegression, Correlation, GroupCorrelation, GroupLinearRegression
from .stats import VectorStatistics, GroupStatistics, GroupStatisticsStacked, CategoricalStatistics
def determine_... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/analysis/__init__.py | 0.898785 | 0.709227 | __init__.py | pypi |
import warnings
import six
from math import sqrt, fabs
# matplotlib imports
from matplotlib.pyplot import (
show, subplot, yticks, xlabel, ylabel, figure, setp, savefig, close, xticks, subplots_adjust
)
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Circle
# Numpy imports
from numpy impor... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/graphs/vector.py | 0.815673 | 0.537163 | vector.py | pypi |
_colors = (
(0.0, 0.3, 0.7), # blue
(1.0, 0.1, 0.1), # red
(0.0, 0.7, 0.3), # green
(1.0, 0.5, 0.0), # orange
(0.1, 1.0, 1.0), # cyan
(1.0, 1.0, 0.0), # yellow
(1.0, 0.0, 1.0), # magenta
(0.5, 0.0, 1.0), # purple
(0.5, 1.0, 0.0), # light green
(0.0, 0... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/graphs/base.py | 0.840619 | 0.585575 | base.py | pypi |
import math
# matplotlib imports
from matplotlib.pyplot import show, xticks, savefig, close, subplots, subplots_adjust
# local imports
from .base import Graph
from ..data import Categorical, is_group, is_categorical
from ..analysis.exc import MinimumSizeError, NoDataError
class CategoricalGraph(Graph):
def __i... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/graphs/categorical.py | 0.532182 | 0.290578 | categorical.py | pypi |
import pandas as pd
import numpy as np
# Import from local
from .data import Data, is_data
from .data_operations import flatten, is_iterable
class EmptyVectorError(Exception):
"""
Exception raised when the length of a Vector object is 0.
"""
pass
class UnequalVectorLengthError(Exception):
"""
... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/data/numeric.py | 0.818592 | 0.724889 | numeric.py | pypi |
def is_data(obj):
"""
Test if the passed array_like argument is a sci_analysis Data object.
Parameters
----------
obj : object
The input object.
Returns
-------
test result : bool
The test result of whether seq is a sci_analysis Data object or not.
"""
return is... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/data/data.py | 0.927961 | 0.827689 | data.py | pypi |
import six
import numpy as np
import pandas as pd
def to_float(seq):
"""
Takes an arguement seq, tries to convert each value to a float and returns the result. If a value cannot be
converted to a float, it is replaced by 'nan'.
Parameters
----------
seq : array-like
The input object.
... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/data/data_operations.py | 0.886439 | 0.695222 | data_operations.py | pypi |
from warnings import warn
# Import packages
import pandas as pd
# Import from local
from .data import Data, is_data
from .data_operations import flatten, is_iterable
class NumberOfCategoriesWarning(Warning):
warn_categories = 50
def __str__(self):
return "The number of categories is greater than {... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/data/categorical.py | 0.883995 | 0.547646 | categorical.py | pypi |
class DefaultPreferences(type):
"""The type for Default Preferences that cannot be modified"""
def __setattr__(cls, key, value):
if key == "defaults":
raise AttributeError("Cannot override defaults")
else:
return type.__setattr__(cls, key, value)
def __delattr__(cls... | /sci_analysis-2.2.1rc0.tar.gz/sci_analysis-2.2.1rc0/sci_analysis/preferences/preferences.py | 0.727395 | 0.156427 | preferences.py | pypi |
import pandas as pd
import os
from sci_annot_eval.common.bounding_box import AbsoluteBoundingBox, RelativeBoundingBox
from . parsers.parserInterface import Parser
from sci_annot_eval import evaluation
def build_id_file_dict(path: str):
result = {}
for file in os.listdir(path):
no_extension = file.spli... | /sci_annot_eval-0.0.9-py3-none-any.whl/sci_annot_eval/benchmarking.py | 0.557845 | 0.216094 | benchmarking.py | pypi |
import argparse
from sci_annot_eval.common.bounding_box import AbsoluteBoundingBox, RelativeBoundingBox
from sci_annot_eval.exporters.sci_annot_exporter import SciAnnotExporter
from . helpers import rasterize_pdfs, pdffigures2_page_splitter, deepfigures_prediction
import coloredlogs
import logging
from enum import Enu... | /sci_annot_eval-0.0.9-py3-none-any.whl/sci_annot_eval/cli_entrypoint.py | 0.446977 | 0.207235 | cli_entrypoint.py | pypi |
import cv2 as cv
import numpy as np
from ..common.bounding_box import AbsoluteBoundingBox, RelativeBoundingBox
def delete_multiple_elements(list_object, indices):
indices = sorted(indices, reverse=True)
for idx in indices:
list_object.pop(idx)
def make_absolute(
bbox_list: list[RelativeBoundin... | /sci_annot_eval-0.0.9-py3-none-any.whl/sci_annot_eval/helpers/helpers.py | 0.639624 | 0.354629 | helpers.py | pypi |
from sci_annot_eval.common.sci_annot_annotation import Annotation, SciAnnotOutput
from ..common.bounding_box import AbsoluteBoundingBox, RelativeBoundingBox
from . exporterInterface import Exporter
import json
from typing import TypedDict, Any
class SciAnnotExporter(Exporter):
def export_to_dict(self, input: list... | /sci_annot_eval-0.0.9-py3-none-any.whl/sci_annot_eval/exporters/sci_annot_exporter.py | 0.767777 | 0.270817 | sci_annot_exporter.py | pypi |
from . parserInterface import Parser
from sci_annot_eval.common.bounding_box import AbsoluteBoundingBox, BoundingBox, RelativeBoundingBox, TargetType
from sci_annot_eval.common.prediction_field_mapper import PredictionFieldMapper
from .. helpers import helpers
import json
from typing import Any, Type
class PdfFigures2... | /sci_annot_eval-0.0.9-py3-none-any.whl/sci_annot_eval/parsers/pdffigures2_parser.py | 0.714927 | 0.430447 | pdffigures2_parser.py | pypi |
from sci_annot_eval.common.bounding_box import RelativeBoundingBox
from . parserInterface import Parser
from .. common.bounding_box import AbsoluteBoundingBox, BoundingBox, RelativeBoundingBox, TargetType
from ..common.sci_annot_annotation import Annotation, SciAnnotOutput
from .. helpers import helpers
import re
impor... | /sci_annot_eval-0.0.9-py3-none-any.whl/sci_annot_eval/parsers/sci_annot_parser.py | 0.81721 | 0.308359 | sci_annot_parser.py | pypi |
import datetime
from sci_api_req import config
from ..api_provider import ApiProvider
class DONKIProvider(ApiProvider):
"""
The Space Weather Database Of Notifications, Knowledge, Information (DONKI) is
a comprehensive on-line tool for space weather forecasters, scientists, and the
general space scie... | /sci_api_req-0.1.1-py3-none-any.whl/sci_api_req/providers/NASA/donki_provider.py | 0.659405 | 0.338651 | donki_provider.py | pypi |
from ..api_provider import ApiProvider
from sci_api_req import config
import datetime
class NeoWsProvider(ApiProvider):
"""
You can use NeoWs(Near Earth Object Web Service) to search for Asteroids based on
their closest approach date to Earth, lookup a specific Asteroid with its NASA JPL
small body id... | /sci_api_req-0.1.1-py3-none-any.whl/sci_api_req/providers/NASA/neows_provider.py | 0.846578 | 0.35855 | neows_provider.py | pypi |
import logging
from pathlib import Path
import subprocess
import warnings
from typing import Dict, List, Optional, Tuple, Union
from fab.util import string_checksum
logger = logging.getLogger(__name__)
class Compiler(object):
"""
A command-line compiler whose flags we wish to manage.
"""
def __init... | /sci_fab-1.0-py3-none-any.whl/fab/tools.py | 0.582254 | 0.257199 | tools.py | pypi |
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Iterable, Union, Dict, List
from fab.constants import BUILD_TREES
from fab.dep_tree import filter_source_tree, AnalysedDependent
from fab.util import suffix_filter
class ArtefactsGetter(ABC):
"""
Abstract base class for artefact ... | /sci_fab-1.0-py3-none-any.whl/fab/artefacts.py | 0.793706 | 0.346403 | artefacts.py | pypi |
from argparse import ArgumentParser
from pathlib import Path
from typing import Dict, Optional
from fab.steps.analyse import analyse
from fab.steps.c_pragma_injector import c_pragma_injector
from fab.steps.compile_c import compile_c
from fab.steps.link import link_exe
from fab.steps.root_inc_files import root_inc_file... | /sci_fab-1.0-py3-none-any.whl/fab/cli.py | 0.540196 | 0.239905 | cli.py | pypi |
import logging
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Union, Tuple
from fparser.common.readfortran import FortranFileReader # type: ignore
from fparser.two.parser import ParserFactory # type: ignore
from fparser.two.utils import FortranSyntaxError # type: ignore
from fab im... | /sci_fab-1.0-py3-none-any.whl/fab/parse/fortran_common.py | 0.708011 | 0.220804 | fortran_common.py | pypi |
import json
import logging
from abc import ABC
from pathlib import Path
from typing import Union, Optional, Dict, Any, Set
from fab.util import file_checksum
logger = logging.getLogger(__name__)
class ParseException(Exception):
pass
class AnalysedFile(ABC):
"""
Analysis results for a single file. Abs... | /sci_fab-1.0-py3-none-any.whl/fab/parse/__init__.py | 0.696578 | 0.257552 | __init__.py | pypi |
import logging
from typing import Optional, Iterable
from fab.steps import step
from fab.util import file_walk
logger = logging.getLogger(__name__)
class _PathFilter(object):
# Simple pattern matching using string containment check.
# Deems an incoming path as included or excluded.
def __init__(self, *... | /sci_fab-1.0-py3-none-any.whl/fab/steps/find_source_files.py | 0.812049 | 0.40486 | find_source_files.py | pypi |
import logging
from string import Template
from typing import Optional
from fab.build_config import BuildConfig
from fab.constants import OBJECT_FILES, OBJECT_ARCHIVES
from fab.steps import step
from fab.util import log_or_dot
from fab.tools import run_command
from fab.artefacts import ArtefactsGetter, CollectionGette... | /sci_fab-1.0-py3-none-any.whl/fab/steps/archive_objects.py | 0.572723 | 0.47457 | archive_objects.py | pypi |
import logging
import os
from string import Template
from typing import Optional
from fab.constants import OBJECT_FILES, OBJECT_ARCHIVES, EXECUTABLES
from fab.steps import step
from fab.util import log_or_dot
from fab.tools import run_command
from fab.artefacts import ArtefactsGetter, CollectionGetter
logger = loggin... | /sci_fab-1.0-py3-none-any.whl/fab/steps/link.py | 0.623606 | 0.227308 | link.py | pypi |
import logging
import os
import shutil
from dataclasses import dataclass
from pathlib import Path
from typing import Collection, List, Optional, Tuple
from fab.build_config import BuildConfig, FlagsConfig
from fab.constants import PRAGMAD_C
from fab.metrics import send_metric
from fab.util import log_or_dot_finish, i... | /sci_fab-1.0-py3-none-any.whl/fab/steps/preprocess.py | 0.727104 | 0.153549 | preprocess.py | pypi |
import re
from pathlib import Path
from typing import Pattern, Optional, Match
from fab import FabException
from fab.constants import PRAGMAD_C
from fab.steps import run_mp, step
from fab.artefacts import ArtefactsGetter, SuffixFilter
DEFAULT_SOURCE_GETTER = SuffixFilter('all_source', '.c')
# todo: test
@step
def c... | /sci_fab-1.0-py3-none-any.whl/fab/steps/c_pragma_injector.py | 0.468791 | 0.173831 | c_pragma_injector.py | pypi |
import multiprocessing
from fab.metrics import send_metric
from fab.util import by_type, TimerLogger
from functools import wraps
def step(func):
"""Function decorator for steps."""
@wraps(func)
def wrapper(*args, **kwargs):
name = func.__name__
# call the function
with TimerLogg... | /sci_fab-1.0-py3-none-any.whl/fab/steps/__init__.py | 0.809238 | 0.251441 | __init__.py | pypi |
import warnings
from pathlib import Path
from typing import Union
from fab.steps import step
from fab.tools import run_command
def current_commit(folder=None):
folder = folder or '.'
output = run_command(['git', 'log', '--oneline', '-n', '1'], cwd=folder)
commit = output.split()[0]
return commit
de... | /sci_fab-1.0-py3-none-any.whl/fab/steps/grab/git.py | 0.465873 | 0.198181 | git.py | pypi |
import mimetypes
import os
import shutil
from typing import Optional
try:
import rarfile
except ImportError: # pragma: no cover
rarfile = None
class RARExtractionNotSupported(Exception):
pass
def _rar_extract(filename, extract_dir):
if rarfile is None:
raise RARExtractionNotSupported('RAR ... | /sci_igm-0.0.2-py3-none-any.whl/igm/utils/archive.py | 0.601477 | 0.182826 | archive.py | pypi |
import hashlib
import sys
import logging
"""
``logging_filters``
-------------------
Python uses `filters`_ to add contextural information to its
:mod:`~python:logging` facility.
Filters defined below are attached to :data:`settings.LOGGING` and
also :class:`~.middleware.LogSetupMiddleware`.
.. _filters:
http://... | /sci-logging-0.2.tar.gz/sci-logging-0.2/scilogging/logging.py | 0.709724 | 0.268462 | logging.py | pypi |
"Utility functions for handling buffers"
import sys as _sys
import numpy as _numpy
def _ord(byte):
r"""Convert a byte to an integer.
>>> buffer = b'\x00\x01\x02'
>>> [_ord(b) for b in buffer]
[0, 1, 2]
"""
if _sys.version_info >= (3,):
return byte
else:
return ord(byte)... | /sci_memex-0.0.3-py3-none-any.whl/memex/translators/igor/util.py | 0.494629 | 0.427337 | util.py | pypi |
"Read IGOR Binary Wave files into Numpy arrays."
# Based on WaveMetric's Technical Note 003, "Igor Binary Format"
# ftp://ftp.wavemetrics.net/IgorPro/Technical_Notes/TN003.zip
# From ftp://ftp.wavemetrics.net/IgorPro/Technical_Notes/TN000.txt
# We place no restrictions on copying Technical Notes, with the
# exc... | /sci_memex-0.0.3-py3-none-any.whl/memex/translators/igor/binarywave.py | 0.576065 | 0.265714 | binarywave.py | pypi |
"Read IGOR Packed Experiment files files into records."
from . import LOG as _LOG
from .struct import Structure as _Structure
from .struct import Field as _Field
from .util import byte_order as _byte_order
from .util import need_to_reorder_bytes as _need_to_reorder_bytes
from .util import _bytes
from .record import R... | /sci_memex-0.0.3-py3-none-any.whl/memex/translators/igor/packed.py | 0.421433 | 0.260648 | packed.py | pypi |
import io as _io
from .. import LOG as _LOG
from ..binarywave import TYPE_TABLE as _TYPE_TABLE
from ..binarywave import NullStaticStringField as _NullStaticStringField
from ..binarywave import DynamicStringField as _DynamicStringField
from ..struct import Structure as _Structure
from ..struct import DynamicStructure ... | /sci_memex-0.0.3-py3-none-any.whl/memex/translators/igor/record/variables.py | 0.602646 | 0.30005 | variables.py | pypi |
from typing import Container
import docker
import logging
import os
import typer
import subprocess
import re
from collections.abc import Mapping
import sys
_LOGGER = logging.getLogger(__name__)
def port_mapping(mapping: str, public: bool) -> Mapping:
m = re.fullmatch("^(([0-9]{1,5})(?:/(?:tcp|udp))?):([0-9]{1,5}... | /sci_oer-1.3.0-py3-none-any.whl/scioer/docker.py | 0.540924 | 0.181191 | docker.py | pypi |
import typer
from collections.abc import Mapping
import click
import scioer.config.load as load
import scioer.config.parse as parser
import os
import re
from typing import Optional
from pathlib import Path
import logging
try:
import readline
except:
import sys
if sys.platform == "win32" or sys.platform ... | /sci_oer-1.3.0-py3-none-any.whl/scioer/cli.py | 0.496582 | 0.158435 | cli.py | pypi |
PALETTES = {
"npg_nrc": {
"Cinnabar": "#E64B35",
"Shakespeare": "#4DBBD5",
"PersianGreen": "#00A087",
"Chambray": "#3C5488",
"Apricot": "#F39B7F",
"WildBlueYonder": "#8491B4",
"MonteCarlo": "#91D1C2",
"Monza": "#DC0000",
"RomanCoffee": "#7E6148... | /sci-palettes-1.0.1.tar.gz/sci-palettes-1.0.1/sci_palettes/palettes.py | 0.432902 | 0.561696 | palettes.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
st... | /sci_stats_dist-0.0.2.tar.gz/sci_stats_dist-0.0.2/sci_stats_dist/Gaussiandistribution.py | 0.807916 | 0.804598 | Gaussiandistribution.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Binomial(Distribution):
""" Binomial distribution class for calculating and
visualizing a Binomial distribution.
Attributes:
mean (float) representing the mean value of the distribution
std... | /sci_stats_dist-0.0.2.tar.gz/sci_stats_dist-0.0.2/sci_stats_dist/Binomialdistribution.py | 0.830044 | 0.804598 | Binomialdistribution.py | pypi |
import traceback
from typing import Union
import pandas as pd
import numpy as np
def combine_csv_files(from_files: list, to_file: str, wanted_cols: Union[list, str, None] = None, *args, **kwargs) -> pd.DataFrame:
"""
Covert several csv files to ONE csv file with specified columns.
:param na_vals:
:p... | /sci-util-1.2.7.tar.gz/sci-util-1.2.7/sci_util/pd/csv.py | 0.702632 | 0.382459 | csv.py | pypi |
def cnt_split(tar_list, cnt_per_slice):
"""
Yield successive n-sized(cnt_per_slice) chunks from l(tar_list).
>>> x = list(range(34))
>>> for i in cnt_split(x, 5):
>>> print(i)
<<< print result ...
<<< [0, 1, 2, 3, 4]
<<< [5, 6, 7, 8, 9]
<<< [10, 11,... | /sci-util-1.2.7.tar.gz/sci-util-1.2.7/sci_util/list_util/split_util.py | 0.459561 | 0.480052 | split_util.py | pypi |
from sklearn.metrics import (
accuracy_score,
confusion_matrix,
classification_report,
roc_curve,
roc_auc_score,
)
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
def show_classification(y_test, y_pred):
r"""
Confusion matrix
- Binary:
... | /sci_ztools-0.1.4-py3-none-any.whl/z/metrics.py | 0.805096 | 0.590012 | metrics.py | pypi |
from pathlib import Path
import shutil
from typing import Optional, Union, List
try:
import gzip
import tarfile
except:
raise ImportError
def get_path(path: Union[Path, str]) -> Path:
"""Transform to `Path`.
Args:
path (str): The path to be transformed.
Returns:
Path: the `p... | /sci_ztools-0.1.4-py3-none-any.whl/z/sh.py | 0.900157 | 0.292709 | sh.py | pypi |
import os
import random
from itertools import takewhile, repeat
from pathlib import Path
from typing import Union, List, Optional
import numpy as np
import pandas as pd
import torch
from rich import console
from rich.table import Table
from sklearn.model_selection import KFold # Kfold cross validation
import logging
f... | /sci_ztools-0.1.4-py3-none-any.whl/z/utils.py | 0.826116 | 0.332581 | utils.py | pypi |
import pandas as pd
from sklearn.utils import shuffle
from sklearn.model_selection import (
StratifiedShuffleSplit,
StratifiedKFold,
KFold,
train_test_split,
)
from typing import Optional, Union, List, Tuple
from pathlib import Path
import copy
class DataFrame():
def __init__(
self, df: pd... | /sci_ztools-0.1.4-py3-none-any.whl/z/pandas.py | 0.813979 | 0.419648 | pandas.py | pypi |
from paraview.simple import *
import paraview as pv
#### disable automatic camera reset on 'Show'
paraview.simple._DisableFirstRenderCameraReset()
# get active source.
resultfoam = GetActiveSource()
# resultfoam.SkipZeroTime = 0
# check whether T exist
convert_T=False
alldata = pv.servermanager.Fetch(resultfoam)
if(a... | /sciPyFoam-0.4.1.tar.gz/sciPyFoam-0.4.1/example/cases/blockMesh/showTimeYear.py | 0.559049 | 0.399812 | showTimeYear.py | pypi |
# SCIAMACHY data tools
[](https://github.com/st-bender/sciapy/actions/workflows/ci_build_and_test.yml)
[](https://sciapy.rtfd.io/en/latest/?badge=... | /sciapy-0.0.8.tar.gz/sciapy-0.0.8/README.md | 0.532668 | 0.957477 | README.md | pypi |
# SCIAMACHY data tools
[](https://github.com/st-bender/sciapy/actions/workflows/ci_build_and_test.yml)
[](https://sciapy.rtfd.io/en/latest/?badge=... | /sciapy-0.0.8.tar.gz/sciapy-0.0.8/docs/README.md | 0.532668 | 0.957477 | README.md | pypi |
# Regression model intro
## Standard imports
First, setup some standard modules and matplotlib.
```
%matplotlib inline
%config InlineBackend.figure_format = 'png'
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
```
Load the main `sciapy` module and its wrappers for easy access to the used pr... | /sciapy-0.0.8.tar.gz/sciapy-0.0.8/docs/tutorials/regress_intro.ipynb | 0.629775 | 0.940463 | regress_intro.ipynb | pypi |
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from scib_metrics.utils import compute_simpson_index, convert_knn_graph_to_idx
def lisi_knn(X: csr_matrix, labels: np.ndarray, perplexity: float = None) -> np.ndarray:
"""Compute the local inverse simpson index (LISI) for each cell :cite:... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/_lisi.py | 0.928498 | 0.759002 | _lisi.py | pypi |
import logging
from typing import Optional, Union
import numpy as np
import pandas as pd
from ._silhouette import silhouette_label
logger = logging.getLogger(__name__)
def isolated_labels(
X: np.ndarray,
labels: np.ndarray,
batch: np.ndarray,
iso_threshold: Optional[int] = None,
) -> float:
"""... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/_isolated_labels.py | 0.935125 | 0.570271 | _isolated_labels.py | pypi |
import logging
import warnings
from typing import Dict, Tuple
import numpy as np
import scanpy as sc
from scipy.sparse import spmatrix
from sklearn.metrics.cluster import adjusted_rand_score, normalized_mutual_info_score
from sklearn.utils import check_array
from .utils import KMeans, check_square
logger = logging.g... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/_nmi_ari.py | 0.920016 | 0.636155 | _nmi_ari.py | pypi |
import numpy as np
import pandas as pd
from scib_metrics.utils import silhouette_samples
def silhouette_label(X: np.ndarray, labels: np.ndarray, rescale: bool = True, chunk_size: int = 256) -> float:
"""Average silhouette width (ASW) :cite:p:`luecken2022benchmarking`.
Parameters
----------
X
... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/_silhouette.py | 0.924108 | 0.721449 | _silhouette.py | pypi |
import os
import warnings
from dataclasses import asdict, dataclass
from enum import Enum
from functools import partial
from typing import Any, Callable, Dict, List, Optional, Union
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import AnnData
... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/benchmark/_core.py | 0.913857 | 0.370595 | _core.py | pypi |
from typing import Optional
import jax
import jax.numpy as jnp
import numpy as np
import pandas as pd
from jax import jit
from scib_metrics._types import NdArray
from ._pca import pca
from ._utils import one_hot
def principal_component_regression(
X: NdArray,
covariate: NdArray,
categorical: bool = Fal... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/utils/_pcr.py | 0.958876 | 0.484441 | _pcr.py | pypi |
from functools import partial
from typing import Tuple, Union
import chex
import jax
import jax.numpy as jnp
import numpy as np
from ._utils import get_ndarray
NdArray = Union[np.ndarray, jnp.ndarray]
@chex.dataclass
class _NeighborProbabilityState:
H: float
P: chex.ArrayDevice
Hdiff: float
beta: f... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/utils/_lisi.py | 0.935626 | 0.644267 | _lisi.py | pypi |
from functools import partial
from typing import Literal
import jax
import jax.numpy as jnp
import numpy as np
from sklearn.utils import check_array
from scib_metrics._types import IntOrKey
from ._dist import cdist
from ._utils import get_ndarray, validate_seed
def _initialize_random(X: jnp.ndarray, n_clusters: in... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/utils/_kmeans.py | 0.880026 | 0.44071 | _kmeans.py | pypi |
from typing import Optional, Tuple
import jax.numpy as jnp
from chex import dataclass
from jax import jit
from scib_metrics._types import NdArray
from ._utils import get_ndarray
@dataclass
class _SVDResult:
"""SVD result.
Attributes
----------
u
Array of shape (n_cells, n_components) conta... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/utils/_pca.py | 0.975012 | 0.662223 | _pca.py | pypi |
import logging
from typing import Literal
import numpy as np
import pynndescent
import scipy
from scipy.sparse import csr_matrix, issparse
logger = logging.getLogger(__name__)
def _compute_transitions(X: csr_matrix, density_normalize: bool = True):
"""Code from scanpy.
https://github.com/scverse/scanpy/blo... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/utils/_diffusion_nn.py | 0.674587 | 0.588416 | _diffusion_nn.py | pypi |
import warnings
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from chex import ArrayDevice
from jax import nn
from scipy.sparse import csr_matrix
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import check_array
from scib_metrics._types import ArrayLike, I... | /scib_metrics-0.3.3-py3-none-any.whl/scib_metrics/utils/_utils.py | 0.918441 | 0.585012 | _utils.py | pypi |
from pint.quantity import _Quantity
from sci import units
from pint.errors import UndefinedUnitError
def check_units(value, dimension: str):
""" Check if units are of a certain dimension
Parameters
----------
value: `pint.quantity._Quantity`
The pint :class:`pint.quantity._Quantity` to ch... | /scici-0.1.0.tar.gz/scici-0.1.0/sci/utils.py | 0.895451 | 0.620507 | utils.py | pypi |
from sci import units
from sci.utils import check_units, filter_dict_values, stringify, check_kwargs, pintify
from pint.quantity import _Quantity
from interface import implements, Interface
from typing import Type, Union, List
class _Ref:
''' Base Class for Refs
Refs are physical containers (e.g., syringes... | /scici-0.1.0.tar.gz/scici-0.1.0/sci/refs.py | 0.881538 | 0.32118 | refs.py | pypi |
Copyright (c) 2014 `Science Automation Inc. <http://www.scivm.com>`_. All rights reserved.
email: support@scivm.com
Copyright (c) 2009 `PiCloud, Inc. <http://www.picloud.com>`_. All rights reserved.
email: contact@picloud.com
The cloud package is free software; you can redistribute it and/or
modify it under the t... | /scicloud-3.0.4.tar.gz/scicloud-3.0.4/src/pool_interface.py | 0.803212 | 0.152001 | pool_interface.py | pypi |
from ..cloud import CloudException
class CloudConnection(object):
"""Abstract connection class to deal with low-level communication of cloud adapter"""
_isopen = False
_adapter = None
@property
def opened(self):
"""Returns whether the connection is open"""
return self._is... | /scicloud-3.0.4.tar.gz/scicloud-3.0.4/src/transport/connection.py | 0.743447 | 0.153042 | connection.py | pypi |
Why SCICO?
==========
Advantages of JAX-based Design
------------------------------
The vast majority of scientific computing packages in Python are based
on `NumPy <https://numpy.org/>`__ and `SciPy <https://scipy.org/>`__.
SCICO, in contrast, is based on `JAX
<https://jax.readthedocs.io/en/latest/>`__, which provid... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/advantages.rst | 0.940463 | 0.909947 | advantages.rst | pypi |
import importlib
import inspect
import os
import pkgutil
import sys
from glob import glob
from runpy import run_path
def run_conf_files(vardict=None, path=None):
"""Execute Python files in conf directory.
Args:
vardict: Dictionary into which variable names should be inserted.
Defaults to ... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/docsutil.py | 0.567457 | 0.2709 | docsutil.py | pypi |
# Usage Examples
## Organized by Application
### Computed Tomography
> - [TV-Regularized Abel Inversion](ct_abel_tv_admm.ipynb)
> - [Parameter Tuning for TV-Regularized Abel
> Inversion](ct_abel_tv_admm_tune.ipynb)
> - [CT Reconstruction with CG and PCG](ct_astra_noreg_pcg.ipynb)
> - [3D TV-Regularized S... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/index.ipynb | 0.708818 | 0.633694 | index.ipynb | pypi |
Noisy Data Generation for NN Training
=====================================
This example demonstrates how to generate noisy image data for
training neural network models for denoising. The original images are
part of the
[BSDS500 dataset](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/)
provided... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/denoise_datagen_bsds.ipynb | 0.696268 | 0.97066 | denoise_datagen_bsds.ipynb | pypi |
Non-Negative Basis Pursuit DeNoising (ADMM)
===========================================
This example demonstrates the solution of a non-negative sparse coding
problem
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - D \mathbf{x} \|_2^2
+ \lambda \| \mathbf{x} \|_1 + I(\mathbf{x} \geq 0) \;,$$
where $D$ th... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/sparsecode_admm.ipynb | 0.626467 | 0.944944 | sparsecode_admm.ipynb | pypi |
Parameter Tuning for Image Deconvolution with TV Regularization (ADMM Solver)
=============================================================================
This example demonstrates the use of
[scico.ray.tune](../_autosummary/scico.ray.tune.rst) to tune parameters
for the companion [example script](deconv_tv_admm.rst)... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/deconv_tv_admm_tune.ipynb | 0.734786 | 0.953579 | deconv_tv_admm_tune.ipynb | pypi |
PPP (with BM4D) Volume Deconvolution
====================================
This example demonstrates the solution of a 3D image deconvolution problem
(involving recovering a 3D volume that has been convolved with a 3D kernel
and corrupted by noise) using the ADMM Plug-and-Play Priors (PPP)
algorithm <cite data-cite="ve... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/deconv_ppp_bm4d_admm.ipynb | 0.723016 | 0.965996 | deconv_ppp_bm4d_admm.ipynb | pypi |
Parameter Tuning for TV-Regularized Abel Inversion
==================================================
This example demonstrates the use of
[scico.ray.tune](../_autosummary/scico.ray.tune.rst) to tune
parameters for the companion [example script](ct_abel_tv_admm.rst). The
`ray.tune` class API is used in this example.
... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/ct_abel_tv_admm_tune.ipynb | 0.720467 | 0.944842 | ct_abel_tv_admm_tune.ipynb | pypi |
ℓ1 Total Variation Denoising
============================
This example demonstrates impulse noise removal via ℓ1 total variation
<cite data-cite="alliney-1992-digital"/> <cite data-cite="esser-2010-primal"/> (Sec. 2.4.4)
(i.e. total variation regularization with an ℓ1 data fidelity term),
minimizing the functional
... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/denoise_l1tv_admm.ipynb | 0.806662 | 0.96856 | denoise_l1tv_admm.ipynb | pypi |
3D TV-Regularized Sparse-View CT Reconstruction
===============================================
This example demonstrates solution of a sparse-view, 3D CT
reconstruction problem with isotropic total variation (TV)
regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|_2^2 + \lambda... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/ct_astra_3d_tv_admm.ipynb | 0.778649 | 0.973844 | ct_astra_3d_tv_admm.ipynb | pypi |
CT Reconstruction with CG and PCG
=================================
This example demonstrates a simple iterative CT reconstruction using
conjugate gradient (CG) and preconditioned conjugate gradient (PCG)
algorithms to solve the problem
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|_2^2 \... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/ct_astra_noreg_pcg.ipynb | 0.793946 | 0.9838 | ct_astra_noreg_pcg.ipynb | pypi |
Convolutional Sparse Coding with Mask Decoupling (ADMM)
=======================================================
This example demonstrates the solution of a convolutional sparse coding
problem
$$\mathrm{argmin}_{\mathbf{x}} \; \frac{1}{2} \Big\| \mathbf{y} -
B \Big( \sum_k \mathbf{h}_k \ast \mathbf{x}_k \Big) \Big... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/sparsecode_conv_md_admm.ipynb | 0.689096 | 0.975785 | sparsecode_conv_md_admm.ipynb | pypi |
Convolutional Sparse Coding (ADMM)
==================================
This example demonstrates the solution of a simple convolutional sparse
coding problem
$$\mathrm{argmin}_{\mathbf{x}} \; \frac{1}{2} \Big\| \mathbf{y} -
\sum_k \mathbf{h}_k \ast \mathbf{x}_k \Big\|_2^2 + \lambda \sum_k
( \| \mathbf{x}_k \|_1 ... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/sparsecode_conv_admm.ipynb | 0.719581 | 0.970882 | sparsecode_conv_admm.ipynb | pypi |
Basis Pursuit DeNoising (APGM)
==============================
This example demonstrates the solution of the the sparse coding problem
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - D \mathbf{x}
\|_2^2 + \lambda \| \mathbf{x} \|_1\;,$$
where $D$ the dictionary, $\mathbf{y}$ the signal to be represented,
... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/sparsecode_pgm.ipynb | 0.659734 | 0.971402 | sparsecode_pgm.ipynb | pypi |
Training of DnCNN for Denoising
===============================
This example demonstrates the training and application of the DnCNN model
from <cite data-cite="zhang-2017-dncnn"/> to denoise images that have been corrupted
plot.config_notebook_plotting()
with additive Gaussian noise.
```
import os
from time import t... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/examples/denoise_dncnn_train_bsds.ipynb | 0.657428 | 0.894329 | denoise_dncnn_train_bsds.ipynb | pypi |
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