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 |
|---|---|---|---|---|---|
from numpy import array, repeat, abs, minimum, floor, float_
from scipy.signal import lfilter_zi, lfilter
from skdh.utility.internal import apply_downsample
from skdh.utility import moving_mean
__all__ = ["get_activity_counts"]
input_coef = array(
[
-0.009341062898525,
-0.025470289659360,
... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/utility/activity_counts.py | 0.931905 | 0.41182 | activity_counts.py | pypi |
from warnings import warn
from numpy import moveaxis, ascontiguousarray, full, nan, isnan
from skdh.utility import _extensions
from skdh.utility.windowing import get_windowed_view
__all__ = [
"moving_mean",
"moving_sd",
"moving_skewness",
"moving_kurtosis",
"moving_median",
"moving_max",
... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/utility/math.py | 0.946818 | 0.657683 | math.py | pypi |
from numpy import require
from numpy.lib.stride_tricks import as_strided
__all__ = ["compute_window_samples", "get_windowed_view"]
class DimensionError(Exception):
"""
Custom error for if the input signal has too many dimensions
"""
pass
class ContiguityError(Exception):
"""
Custom error f... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/utility/windowing.py | 0.951402 | 0.590602 | windowing.py | pypi |
from numpy import (
mean,
asarray,
cumsum,
minimum,
sort,
argsort,
unique,
insert,
sum,
log,
nan,
float_,
)
from skdh.utility.internal import rle
__all__ = [
"average_duration",
"state_transition_probability",
"gini_index",
"average_hazard",
"state_... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/utility/fragmentation_endpoints.py | 0.929432 | 0.592991 | fragmentation_endpoints.py | pypi |
from warnings import warn
from numpy import argmax, abs, mean, cos, arcsin, sign, zeros_like
__all__ = ["correct_accelerometer_orientation"]
def correct_accelerometer_orientation(accel, v_axis=None, ap_axis=None):
r"""
Applies the correction for acceleration from [1]_ to better align acceleration with the ... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/utility/orientation.py | 0.968456 | 0.790692 | orientation.py | pypi |
from skdh.activity import metrics
def get_available_cutpoints(name=None):
"""
Print the available cutpoints for activity level segmentation, or the
thresholds for a specific set of cutpoints.
Parameters
----------
name : {None, str}, optional
The name of the cupoint values to print. I... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/activity/cutpoints.py | 0.767385 | 0.270453 | cutpoints.py | pypi |
from numpy import maximum, abs, repeat, arctan, sqrt, pi
from numpy.linalg import norm
from scipy.signal import butter, sosfiltfilt
from skdh.utility import moving_mean
__all__ = [
"metric_anglez",
"metric_en",
"metric_enmo",
"metric_bfen",
"metric_hfen",
"metric_hfenplus",
"metric_mad",
... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/activity/metrics.py | 0.956166 | 0.623835 | metrics.py | pypi |
from warnings import warn
from numpy import vstack, asarray, int_
from skdh.base import BaseProcess
from skdh.io.base import check_input_file
from skdh.io._extensions import read_geneactiv
class ReadBin(BaseProcess):
"""
Read a binary .bin file from a GeneActiv sensor into memory. Acceleration values are re... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/io/geneactiv.py | 0.920652 | 0.650883 | geneactiv.py | pypi |
from pathlib import Path
import functools
from warnings import warn
from skdh.io.utility import FileSizeError
def check_input_file(
extension,
check_size=True,
ext_message="File extension [{}] does not match expected [{}]",
):
"""
Check the input file for existence and suffix.
Parameters
... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/io/base.py | 0.78535 | 0.273797 | base.py | pypi |
from numpy import load as np_load
from skdh.base import BaseProcess
from skdh.io.base import check_input_file
class ReadNumpyFile(BaseProcess):
"""
Read a Numpy compressed file into memory. The file should have been
created by `numpy.savez`. The data contained is read in
unprocessed - ie acceleration... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/io/numpy_compressed.py | 0.870542 | 0.500977 | numpy_compressed.py | pypi |
from warnings import warn
from numpy import vstack, asarray, ascontiguousarray, minimum, int_
from skdh.base import BaseProcess
from skdh.io.base import check_input_file
from skdh.io._extensions import read_axivity
class UnexpectedAxesError(Exception):
pass
class ReadCwa(BaseProcess):
"""
Read a binar... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/io/axivity.py | 0.89796 | 0.60288 | axivity.py | pypi |
from sys import version_info
from numpy import isclose, where, diff, insert, append, ascontiguousarray, int_
from numpy.linalg import norm
from scipy.signal import butter, sosfiltfilt
import lightgbm as lgb
from skdh.utility import get_windowed_view
from skdh.utility.internal import rle
from skdh.features import Bank... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/gait/get_gait_classification.py | 0.551574 | 0.30767 | get_gait_classification.py | pypi |
from numpy import fft, argmax, std, abs, argsort, corrcoef, mean, sign
from scipy.signal import detrend, butter, sosfiltfilt, find_peaks
from scipy.integrate import cumtrapz
from pywt import cwt
from skdh.utility import correct_accelerometer_orientation
from skdh.gait.gait_endpoints import gait_endpoints
def get_cwt... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/gait/get_gait_events.py | 0.870625 | 0.564399 | get_gait_events.py | pypi |
from numpy import (
max,
min,
mean,
arccos,
sum,
array,
sin,
cos,
full,
nan,
arctan2,
unwrap,
pi,
sign,
diff,
abs,
zeros,
cross,
)
from numpy.linalg import norm
from skdh.utility.internal import rle
def get_turns(gait, accel, gyro, fs, n_strides... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/gait/get_turns.py | 0.835651 | 0.590455 | get_turns.py | pypi |
import functools
import logging
from numpy import zeros, roll, full, nan, bool_, float_
def basic_asymmetry(f):
@functools.wraps(f)
def run_basic_asymmetry(self, *args, **kwargs):
f(self, *args, **kwargs)
self._predict_asymmetry(*args, **kwargs)
return run_basic_asymmetry
class GaitBou... | /scikit_digital_health-0.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/skdh/gait/gait_endpoints/base.py | 0.899953 | 0.286144 | base.py | pypi |
import numpy as np
from .._commonfuncs import LocalEstimator
from scipy.spatial.distance import pdist, squareform
class TLE(LocalEstimator):
"""Intrinsic dimension estimation using the Tight Local intrinsic dimensionality Estimator algorithm. [Amsaleg2019]_ [IDRadovanović]_
Parameters
----------
epsi... | /scikit-dimension-0.3.3.tar.gz/scikit-dimension-0.3.3/skdim/id/_TLE.py | 0.898555 | 0.875734 | _TLE.py | pypi |
import numpy as np
import warnings
from .._commonfuncs import get_nn, GlobalEstimator
from scipy.optimize import minimize
from sklearn.utils.validation import check_array
class MiND_ML(GlobalEstimator):
# SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
"""Intrinsic dimension estimation using... | /scikit-dimension-0.3.3.tar.gz/scikit-dimension-0.3.3/skdim/id/_MiND_ML.py | 0.824638 | 0.49939 | _MiND_ML.py | pypi |
import numpy as np
from scipy.spatial.distance import pdist, squareform
from sklearn.utils.validation import check_array
from .._commonfuncs import GlobalEstimator
class KNN(GlobalEstimator):
# SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
"""Intrinsic dimension estimation using the kNN al... | /scikit-dimension-0.3.3.tar.gz/scikit-dimension-0.3.3/skdim/id/_KNN.py | 0.923846 | 0.760384 | _KNN.py | pypi |
from sklearn.utils.validation import check_array
import numpy as np
from sklearn.metrics.pairwise import pairwise_distances_chunked
from sklearn.linear_model import LinearRegression
from .._commonfuncs import get_nn, GlobalEstimator
class TwoNN(GlobalEstimator):
# SPDX-License-Identifier: MIT, 2019 Francesco Mo... | /scikit-dimension-0.3.3.tar.gz/scikit-dimension-0.3.3/skdim/id/_TwoNN.py | 0.963857 | 0.798344 | _TwoNN.py | pypi |
import warnings
import numpy as np
from sklearn.metrics import pairwise_distances_chunked
from .._commonfuncs import get_nn, GlobalEstimator
from sklearn.utils.validation import check_array
class CorrInt(GlobalEstimator):
"""Intrinsic dimension estimation using the Correlation Dimension. [Grassberger1983]_ [IDHin... | /scikit-dimension-0.3.3.tar.gz/scikit-dimension-0.3.3/skdim/id/_CorrInt.py | 0.882453 | 0.662309 | _CorrInt.py | pypi |
The MIT License (MIT)<br>
Copyright (c) 2017 Massachusetts Institute of Technology<br>
Author: Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI: Pankratius)<br>
Permission is hereby granted, free of charge, to any person obtaining a copy<br>
of ... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/examples/Amazon_Offload.ipynb | 0.555918 | 0.699036 | Amazon_Offload.ipynb | pypi |
from skdiscovery.data_structure.framework import PipelineItem
import numpy as np
from sklearn.decomposition import PCA
from sklearn.decomposition import FastICA
class General_Component_Analysis(PipelineItem):
'''
Performs either ICA or PCA analysis on series data
'''
def __init__(self, str_descrip... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/series/analysis/gca.py | 0.705278 | 0.32142 | gca.py | pypi |
import collections
import numpy as np
import scipy.optimize as optimize
import skdaccess.utilities.pbo_util as pbo_utils
from skdiscovery.data_structure.framework import PipelineItem
from skdiscovery.utilities.patterns import pbo_tools
from skdiscovery.utilities.patterns.pbo_tools import SourceWrapper, MogiVectors
... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/series/analysis/mogi.py | 0.677154 | 0.442215 | mogi.py | pypi |
from skdiscovery.data_structure.framework import PipelineItem
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
class OffsetDetrend(PipelineItem):
'''
Trend filter that fits a stepwise function to linearly detrended series data
On detrended data this filter fits a st... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/series/filters/offset_detrend.py | 0.736211 | 0.637905 | offset_detrend.py | pypi |
from collections import OrderedDict
from skdiscovery.data_structure.framework.base import PipelineItem
from skdiscovery.utilities.patterns.image_tools import divideIntoSquares
import numpy as np
class TileImage(PipelineItem):
'''
Create several smaller images from a larger image
'''
def __init__(se... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/image/generate/tile_image.py | 0.77223 | 0.371507 | tile_image.py | pypi |
from skdiscovery.data_structure.framework.base import PipelineItem
from skdiscovery.data_structure.framework import DiscoveryPipeline
from skdiscovery.data_structure.generic.accumulators import DataAccumulator
from skdiscovery.data_structure.table.filters import CalibrateGRACE, Resample, CalibrateGRACEMascon
from skd... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/fusion/grace.py | 0.675122 | 0.286063 | grace.py | pypi |
from skdiscovery.data_structure.framework import PipelineItem
import numpy as np
from sklearn.decomposition import PCA
from sklearn.decomposition import FastICA
class General_Component_Analysis(PipelineItem):
'''
Performs a general component analysis on table data.
Currently, the two built-in types of ... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/analysis/gca.py | 0.847021 | 0.349089 | gca.py | pypi |
# 3rd part imports
import numpy as np
import pandas as pd
from scipy.optimize import brute
from fastdtw import fastdtw
# scikit discovery imports
from skdiscovery.data_structure.framework import PipelineItem
from skdiscovery.utilities.patterns import trend_tools as tt
# Standard library imports
from collections impo... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/analysis/rotate_pca.py | 0.761006 | 0.525125 | rotate_pca.py | pypi |
from skdiscovery.data_structure.framework.base import PipelineItem
import numpy as np
import pandas as pd
from statsmodels.robust import mad
class MIDAS(PipelineItem):
'''
*In Development* A basic MIDAS trend estimator
See http://onlinelibrary.wiley.com/doi/10.1002/2015JB012552/full
'''
... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/analysis/midas.py | 0.841109 | 0.362743 | midas.py | pypi |
from skdiscovery.data_structure.framework import PipelineItem
import pandas as pd
import numpy as np
class Correlate(PipelineItem):
'''
Computes the correlation for table data and stores the result as a matrix.
'''
def __init__(self, str_description, column_names = None, local_match = False, correla... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/analysis/correlate.py | 0.708414 | 0.39158 | correlate.py | pypi |
import collections
import numpy as np
import scipy.optimize as optimize
import skdaccess.utilities.pbo_util as pbo_utils
from skdiscovery.data_structure.framework import PipelineItem
import skdiscovery.utilities.patterns.pbo_tools as pbo_tools
from skdiscovery.utilities.patterns.pbo_tools import SourceWrapper, MogiV... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/analysis/mogi.py | 0.612657 | 0.453927 | mogi.py | pypi |
from skdiscovery.data_structure.framework import PipelineItem
import numpy as np
import matplotlib.pyplot as plt
import math
class Plotter(PipelineItem):
'''
Make a plot of table data
'''
def __init__(self, str_description, column_names=None, error_column_names = None, num_columns = 3, width=13, heigh... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/accumulators/plotter.py | 0.842669 | 0.441312 | plotter.py | pypi |
# Framework import
from skdiscovery.data_structure.framework.base import PipelineItem
# 3rd party libraries import
import pandas as pd
class CombineColumns(PipelineItem):
'''
Create a new column by selecting data from a column
Fills in any missing values using a second column
'''
def __init__... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/filters/combine_columns.py | 0.641198 | 0.498047 | combine_columns.py | pypi |
import numpy as np
import pandas as pd
from skdiscovery.data_structure.framework import PipelineItem
from skdiscovery.utilities.patterns import kalman_smoother
class KalmanFilter(PipelineItem):
'''
Runs a forward and backward Kalman Smoother with a FOGM state on table data
For more information see: Ji,... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/filters/kalman.py | 0.688678 | 0.500244 | kalman.py | pypi |
from skdiscovery.data_structure.framework import PipelineItem
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
class OffsetDetrend(PipelineItem):
'''
Trend filter that fits a stepwise function to linearly detrended table data
On detrended data this filter fits a step... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/filters/offset_detrend.py | 0.742422 | 0.626153 | offset_detrend.py | pypi |
from skdiscovery.data_structure.framework import PipelineItem
from skdiscovery.utilities.patterns import trend_tools
class MedianFilter(PipelineItem):
'''
A Median filter for table data
'''
def __init__(self, str_description, ap_paramList, interpolate=True,
subtract = False,regular... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/filters/median.py | 0.897395 | 0.328812 | median.py | pypi |
from skdiscovery.data_structure.framework.base import PipelineItem
import numpy as np
class WeightedAverage(PipelineItem):
''' This filter performs a rolling weighted average using standard deviations as weight '''
def __init__(self, str_description, ap_paramList, column_names, std_dev_column_names=None, p... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/filters/weighted_average.py | 0.907093 | 0.369941 | weighted_average.py | pypi |
import pandas as pd
from skdiscovery.data_structure.framework import PipelineItem
from skdiscovery.utilities.patterns import trend_tools
class TrendFilter(PipelineItem):
'''
Trend Filter that removes linear and sinusoidal (annual, semi-annual) trends on series data.
Works on table data
'''
def ... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/table/filters/trend.py | 0.639286 | 0.458652 | trend.py | pypi |
class PipelineItem(object):
'''
The general class used to create pipeline items.
'''
def __init__(self, str_description, ap_paramList=[]):
'''
Initialize an object
@param str_description: String description of filter
@param ap_paramList: List of AutoParam parameters.
... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/data_structure/framework/base.py | 0.749729 | 0.30243 | base.py | pypi |
import numpy as np
from shapely.geometry import Polygon, Point
from collections import OrderedDict
def shoelaceArea(in_vertices):
"""
Determine the area of a polygon using the shoelace method
https://en.wikipedia.org/wiki/Shoelace_formula
@param in_vertices: The vertices of a polygon. 2d Array where ... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/utilities/patterns/polygon_utils.py | 0.758555 | 0.852445 | polygon_utils.py | pypi |
import statsmodels.api as sm
import numpy as np
import imreg_dft as ird
import shapely
import scipy as sp
def buildMatchedPoints(in_matches, query_kp, train_kp):
'''
Get postions of matched points
@param in_matches: Input matches
@param query_kp: Query key points
@param train_kp: Training key po... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/utilities/patterns/image_tools.py | 0.589598 | 0.684679 | image_tools.py | pypi |
# 3rd party imports
import numpy as np
import pandas as pd
def getPCAComponents(pca_results):
'''
Retrieve PCA components from PCA results
@param pca_results: PCA results from a pipeline run
@return Pandas DataFrame containing the pca components
'''
date_range = pd.date_range(pca_results['... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/utilities/patterns/general_tools.py | 0.882326 | 0.52975 | general_tools.py | pypi |
import skdaccess.utilities.pbo_util as pbo_tools
import skdiscovery.data_structure.series.analysis.mogi as mogi
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
def multiCaPlot(pipeline, mogiFlag=False, offset=.15, direction='H',pca_comp=0,scaleFactor=2.5,map_res='i'):
... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/visualization/multi_ca_plot.py | 0.582372 | 0.563078 | multi_ca_plot.py | pypi |
import numpy as np
import pandas as pd
import matplotlib
from matplotlib.patches import Polygon
from scipy.spatial import SphericalVoronoi
import pyproj
# utility functions for generating the spherical voronoi tesselation.
def sphericalToXYZ(lat,lon,radius=1):
'''
Convert spherical coordinates to x,y,z
... | /scikit-discovery-0.9.18.tar.gz/scikit-discovery-0.9.18/skdiscovery/visualization/spherical_voronoi.py | 0.735831 | 0.734465 | spherical_voronoi.py | pypi |
.. raw:: html
<img alt="scikit-diveMove" src="docs/source/.static/skdiveMove_logo.png"
width=10% align=left>
<h1>scikit-diveMove</h1>
.. image:: https://img.shields.io/pypi/v/scikit-diveMove
:target: https://pypi.python.org/pypi/scikit-diveMove
:alt: PyPI
.. image:: https://github.com/spluque/scikit-... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/README.rst | 0.876172 | 0.744006 | README.rst | pypi |
.. _demo_ellipsoid-label:
==============================================
Ellipsoid modelling for calibration purposes
==============================================
Magnetometers are highly sensitive to local deviations of the magnetic
field, affecting the desired measurement of the Earth geomagnetic field.
Triaxial... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/docs/source/demo_ellipsoid.rst | 0.842118 | 0.715797 | demo_ellipsoid.rst | pypi |
import logging
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.formula.api as smf
logger = logging.getLogger(__name__)
# Add the null handler if importing as library; whatever using this library
# should set up logging.basicConfig() as needed
logger... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/calibspeed.py | 0.876898 | 0.591222 | calibspeed.py | pypi |
import logging
import numpy as np
import pandas as pd
from skdiveMove.zoc import ZOC
from skdiveMove.core import diveMove, robjs, cv, pandas2ri
from skdiveMove.helpers import (get_var_sampling_interval, _cut_dive,
rle_key, _append_xr_attr)
logger = logging.getLogger(__name__)
# Add the ... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/tdrphases.py | 0.862988 | 0.258595 | tdrphases.py | pypi |
import logging
import pandas as pd
from skdiveMove.tdrsource import TDRSource
from skdiveMove.core import robjs, cv, pandas2ri, diveMove
from skdiveMove.helpers import _append_xr_attr
logger = logging.getLogger(__name__)
# Add the null handler if importing as library; whatever using this library
# should set up loggin... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/zoc.py | 0.844088 | 0.329109 | zoc.py | pypi |
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
def _night(times, sunrise_time, sunset_time):
"""Construct Series with sunset and sunrise times for given dates
Parameters
----------
times : pandas.Series
... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/plotting.py | 0.867092 | 0.571468 | plotting.py | pypi |
import pandas as pd
from skdiveMove.helpers import (get_var_sampling_interval,
_append_xr_attr, _load_dataset)
_SPEED_NAMES = ["velocity", "speed"]
class TDRSource:
"""Define TDR data source
Use xarray.Dataset to ensure pseudo-standard metadata
Attributes
----------
... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/tdrsource.py | 0.884083 | 0.545467 | tdrsource.py | pypi |
import json
__all__ = ["dump_config_template", "assign_xr_attrs"]
_SENSOR_DATA_CONFIG = {
'sampling': "regular",
'sampling_rate': "1",
'sampling_rate_units': "Hz",
'history': "",
'name': "",
'full_name': "",
'description': "",
'units': "",
'units_name': "",
'units_label': "",
... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/metadata.py | 0.586049 | 0.224331 | metadata.py | pypi |
import numpy as np
import pandas as pd
import xarray as xr
from skdiveMove.core import robjs, cv, pandas2ri, diveMove
__all__ = ["_load_dataset", "_get_dive_indices", "_append_xr_attr",
"get_var_sampling_interval", "_cut_dive",
"_one_dive_stats", "_speed_stats", "rle_key"]
def _load_dataset(fil... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/helpers.py | 0.874118 | 0.423547 | helpers.py | pypi |
import logging
import numpy as np
import pandas as pd
from skdiveMove.tdrphases import TDRPhases
import skdiveMove.plotting as plotting
import skdiveMove.calibspeed as speedcal
from skdiveMove.helpers import (get_var_sampling_interval,
_get_dive_indices, _append_xr_attr,
... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/tdr.py | 0.884583 | 0.378947 | tdr.py | pypi |
import numpy as np
from scipy.optimize import curve_fit
# Mapping of error type with corresponding tau and slope
_ERROR_DEFS = {"Q": [np.sqrt(3), -1], "ARW": [1.0, -0.5],
"BI": [np.nan, 0], "RRW": [3.0, 0.5],
"RR": [np.sqrt(2), 1]}
def _armav_nls_fun(x, *args):
coefs = np.array(args... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/imutools/allan.py | 0.786295 | 0.651577 | allan.py | pypi |
import numpy as np
from scipy.spatial.transform import Rotation as R
def normalize(v):
"""Normalize vector
Parameters
----------
v : array_like (N,) or (M,N)
input vector
Returns
-------
numpy.ndarray
Normalized vector having magnitude 1.
"""
return v / np.linalg... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/imutools/vector.py | 0.940463 | 0.70304 | vector.py | pypi |
import numpy as np
import pandas as pd
import allantools as allan
import ahrs.filters as filters
from scipy import constants, signal, integrate
from sklearn import preprocessing
from skdiveMove.tdrsource import _load_dataset
from .allan import allan_coefs
from .vector import rotate_vector
_TIME_NAME = "timestamp"
_DEP... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/imutools/imu.py | 0.91055 | 0.622746 | imu.py | pypi |
import logging
import re
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import scipy.signal as signal
import xarray as xr
from skdiveMove.tdrsource import _load_dataset
from .imu import (IMUBase,
_ACCEL_NAM... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/imutools/imucalibrate.py | 0.785884 | 0.480662 | imucalibrate.py | pypi |
import numpy as np
# Types of ellipsoid accepted fits
_ELLIPSOID_FTYPES = ["rxyz", "xyz", "xy", "xz", "yz", "sxyz"]
def fit_ellipsoid(vectors, f="rxyz"):
"""Fit a (non) rotated ellipsoid or sphere to 3D vector data
Parameters
----------
vectors: (N,3) array
Array of measured x, y, z vector c... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/imutools/ellipsoid.py | 0.849441 | 0.810066 | ellipsoid.py | pypi |
import logging
from abc import ABCMeta, abstractmethod
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from skdiveMove.helpers import rle_key
logger = logging.getLogger(__name__)
# Add the null handler if importing as lib... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/bouts/bouts.py | 0.929424 | 0.57678 | bouts.py | pypi |
import logging
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.special import logit, expit
from statsmodels.distributions.empirical_distribution import ECDF
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from . import bouts
logger = logging.getLogger... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/bouts/boutsmle.py | 0.92412 | 0.617916 | boutsmle.py | pypi |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from statsmodels.distributions.empirical_distribution import ECDF
from . import bouts
class BoutsNLS(bouts.Bouts):
"""Nonlinear Least Squares fitting for models of Poisson process mixtures
Methods for modelling l... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/bouts/boutsnls.py | 0.936677 | 0.774328 | boutsnls.py | pypi |
r"""Tools and classes for the identification of behavioural bouts
A histogram of log-transformed frequencies of `x` with a chosen bin width
and upper limit forms the basis for models. Histogram bins following empty
ones have their frequencies averaged over the number of previous empty bins
plus one. Models attempt t... | /scikit-diveMove-0.3.0.tar.gz/scikit-diveMove-0.3.0/skdiveMove/bouts/__init__.py | 0.916801 | 0.969584 | __init__.py | pypi |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
import xarray as xr
from skdownscale.pointwise_models import BcsdPrecipitation, BcsdTemperature
# utilities for plotting cdfs
def plot_cdf(ax=None, **kwargs):
if ax:
plt.sca(ax)
else:
ax ... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/examples/bcsd_example.ipynb | 0.525612 | 0.648209 | bcsd_example.ipynb | pypi |
import numpy as np
import pandas as pd
import probscale
import scipy
import seaborn as sns
import xarray as xr
from matplotlib import pyplot as plt
def get_sample_data(kind):
if kind == 'training':
data = xr.open_zarr('../data/downscale_test_data.zarr.zip', group=kind)
# extract 1 point of traini... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/examples/utils.py | 0.519521 | 0.40392 | utils.py | pypi |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
import xarray as xr
from skdownscale.pointwise_models import AnalogRegression, PureAnalog
# open a small dataset for training
training = xr.open_zarr("../data/downscale_test_data.zarr.zip", group="training")
tra... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/examples/gard_example.ipynb | 0.483892 | 0.701432 | gard_example.ipynb | pypi |
import numpy as np
import pandas as pd
from .utils import default_none_kwargs
class GroupedRegressor:
"""Grouped Regressor
Wrapper supporting fitting seperate estimators distinct groups
Parameters
----------
estimator : object
Estimator object such as derived from `BaseEstimator`. This ... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/skdownscale/pointwise_models/grouping.py | 0.868771 | 0.543651 | grouping.py | pypi |
import warnings
import pandas as pd
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils import check_array, check_X_y
from sklearn.utils.validation import check_is_fitted
class TimeSynchronousDownscaler(BaseEstimator):
def _check_X_y(self, X, y, **kwargs):
if isinstance(X, pd.DataFr... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/skdownscale/pointwise_models/base.py | 0.880116 | 0.561996 | base.py | pypi |
import collections
import copy
import numpy as np
from sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin
from sklearn.linear_model import LinearRegression
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from .trend import LinearTrendTransformer
from .utils ... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/skdownscale/pointwise_models/quantile.py | 0.905947 | 0.528168 | quantile.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.linear_model import LinearRegression
from sklearn.utils.validation import check_is_fitted
from .utils import default_none_kwargs
class LinearTrendTransformer(TransformerMixin, BaseEstimator):
"""Transform features by removin... | /scikit-downscale-0.1.5.tar.gz/scikit-downscale-0.1.5/skdownscale/pointwise_models/trend.py | 0.928433 | 0.530236 | trend.py | pypi |
# scikit-dsp-comm
[](https://pypi.python.org/pypi/scikit-dsp-comm)
[](https://anaconda.org/conda-forge/scikit-dsp-comm)
[
class FECHamming(object):
"""
Class responsible for creating hamming block codes and then
encoding and decoding. Methods provided include ... | /scikit-dsp-comm-2.0.3.tar.gz/scikit-dsp-comm-2.0.3/src/sk_dsp_comm/fec_block.py | 0.793306 | 0.450541 | fec_block.py | pypi |
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
from logging import getLogger
log = getLogger(__name__)
def firwin_lpf(n_taps, fc, fs = 1.0):
"""
Design a windowed FIR lowpass filter in terms of passband
critical frequencies f1 < f2 in Hz relative to sampling rate
fs i... | /scikit-dsp-comm-2.0.3.tar.gz/scikit-dsp-comm-2.0.3/src/sk_dsp_comm/fir_design_helper.py | 0.749912 | 0.37088 | fir_design_helper.py | pypi |
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
from logging import getLogger
log = getLogger(__name__)
def IIR_lpf(f_pass, f_stop, Ripple_pass, Atten_stop,
fs = 1.00, ftype = 'butter', status = True):
"""
Design an IIR lowpass filter using scipy.signal.iirdesign.... | /scikit-dsp-comm-2.0.3.tar.gz/scikit-dsp-comm-2.0.3/src/sk_dsp_comm/iir_design_helper.py | 0.801431 | 0.4856 | iir_design_helper.py | pypi |
from matplotlib import pylab
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal
from . import sigsys as ssd
from . import fir_design_helper as fir_d
from . import iir_design_helper as iir_d
from logging import getLogger
log = getLogger(__name__)
import warnings
class rate_change(object)... | /scikit-dsp-comm-2.0.3.tar.gz/scikit-dsp-comm-2.0.3/src/sk_dsp_comm/multirate_helper.py | 0.634317 | 0.493958 | multirate_helper.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator, RegressorMixin, clone, is_regressor, is_classifier
from sklearn.utils.validation import check_is_fitted, check_X_y, check_array
from sklearn.exceptions import NotFittedError
from sklearn.model_selection import train_test_split
class QuantileStackRegressor(Base... | /scikit-duplo-0.1.7.tar.gz/scikit-duplo-0.1.7/skduplo/meta/quantile_stack_regressor.py | 0.926183 | 0.812012 | quantile_stack_regressor.py | pypi |
from sklearn.base import BaseEstimator, RegressorMixin, clone, is_regressor
from sklearn.utils.validation import check_is_fitted, check_X_y, check_array
from sklearn.exceptions import NotFittedError
import pandas as pd
import numpy as np
class BaselineProportionalRegressor(BaseEstimator, RegressorMixin):
"""
A... | /scikit-duplo-0.1.7.tar.gz/scikit-duplo-0.1.7/skduplo/meta/baseline_proportional_regressor.py | 0.942275 | 0.498901 | baseline_proportional_regressor.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator, RegressorMixin, clone, is_regressor, is_classifier
from sklearn.utils.validation import check_is_fitted, check_X_y, check_array
from sklearn.exceptions import NotFittedError
from sklearn.model_selection import train_test_split
class RegressorStack(BaseEstimato... | /scikit-duplo-0.1.7.tar.gz/scikit-duplo-0.1.7/skduplo/meta/regressor_stack.py | 0.933073 | 0.737962 | regressor_stack.py | pypi |
import time
# --------------------------------------
class Timer:
def __init__(self):
# Global Time objects
self.globalStartRef = time.time()
self.globalTime = 0.0
self.globalAdd = 0
# Match Time Variables
self.startRefMatching = 0.0
self.globalMatching = ... | /scikit-eLCS-1.2.4.tar.gz/scikit-eLCS-1.2.4/skeLCS/Timer.py | 0.520253 | 0.173743 | Timer.py | pypi |
import random
import copy
import math
class Classifier:
def __init__(self,elcs,a=None,b=None,c=None,d=None):
#Major Parameters
self.specifiedAttList = []
self.condition = []
self.phenotype = None #arbitrary
self.fitness = elcs.init_fit
self.accuracy = 0.0
se... | /scikit-eLCS-1.2.4.tar.gz/scikit-eLCS-1.2.4/skeLCS/Classifier.py | 0.615088 | 0.229018 | Classifier.py | pypi |
import numpy as np
import scipy as sp
import warnings
from scipy.linalg import LinAlgWarning
from sklearn.exceptions import DataConversionWarning
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.ut... | /scikit_elm-0.21a0-py3-none-any.whl/skelm/solver_batch.py | 0.89875 | 0.61086 | solver_batch.py | pypi |
import numpy as np
import warnings
from scipy.special import expit
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels, type_of_target
from sklearn.preprocessing i... | /scikit_elm-0.21a0-py3-none-any.whl/skelm/elm.py | 0.877844 | 0.352425 | elm.py | pypi |
import scipy as sp
from enum import Enum
from sklearn.metrics import pairwise_distances
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_array, check_is_fitted, check_random_state
class HiddenLayerType(Enum):
RANDOM = 1 # Gaussian random projection
SPARSE ... | /scikit_elm-0.21a0-py3-none-any.whl/skelm/utils.py | 0.932522 | 0.450359 | utils.py | pypi |
import numpy as np
import scipy as sp
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_random_state
from sklearn.utils.validation import check_array, check_is_fitted
from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection
from .utils import Pairwi... | /scikit_elm-0.21a0-py3-none-any.whl/skelm/hidden_layer.py | 0.875282 | 0.437343 | hidden_layer.py | pypi |
import numpy as np
import scipy as sp
import warnings
from sklearn.exceptions import DataConversionWarning
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.utils import check_X_y, check_array
from... | /scikit_elm-0.21a0-py3-none-any.whl/skelm/solver_dask.py | 0.84075 | 0.609292 | solver_dask.py | pypi |
# scikit-embeddings
Utilites for training word, document and sentence embeddings in scikit-learn pipelines.
## Features
- Train Word, Paragraph or Sentence embeddings in scikit-learn compatible pipelines.
- Stream texts easily from disk and chunk them so you can use large datasets for training embeddings.
- spaCy t... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/README.md | 0.762954 | 0.936576 | README.md | pypi |
import tempfile
from pathlib import Path
from typing import Union
from confection import Config, registry
from huggingface_hub import HfApi, snapshot_download
from sklearn.pipeline import Pipeline
# THIS IS IMPORTANT DO NOT REMOVE
from skembeddings import models, tokenizers
from skembeddings._hub import DEFAULT_READM... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/pipeline.py | 0.817829 | 0.197212 | pipeline.py | pypi |
from abc import ABC, abstractmethod
from typing import Iterable
from confection import Config, registry
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.exceptions import NotFittedError
from tokenizers import Tokenizer
from tokenizers.models import BPE, Unigram, WordLevel, WordPiece
from tokenizer... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/tokenizers/_huggingface.py | 0.893655 | 0.183832 | _huggingface.py | pypi |
from typing import Any, Iterable, Optional, Union
import spacy
from sklearn.base import BaseEstimator, TransformerMixin
from spacy.language import Language
from spacy.matcher import Matcher
from spacy.tokens import Doc, Token
from skembeddings.base import Serializable
# We create a new extension on tokens.
if not To... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/tokenizers/spacy.py | 0.905659 | 0.204025 | spacy.py | pypi |
import tempfile
from typing import Iterable, Literal
import numpy as np
from confection import Config, registry
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.exceptions import NotFittedError
from sklearn.utils import murmurhash3_32
from... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/models/doc2vec.py | 0.863147 | 0.212784 | doc2vec.py | pypi |
import tempfile
from typing import Iterable, Literal
import numpy as np
from confection import Config, registry
from gensim.models import KeyedVectors, Word2Vec
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.exceptions import NotFittedError
from skembeddings.base import Serializable
from skembe... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/models/word2vec.py | 0.828973 | 0.182753 | word2vec.py | pypi |
import collections
from itertools import islice
from typing import Iterable
import mmh3
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.exceptions import NotFittedError
from thinc.api import Adam, CategoricalCrossentropy, Relu, Softmax, chain
from thinc.types import Floats2d
fr... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/models/bloom.py | 0.855791 | 0.228028 | bloom.py | pypi |
from typing import Literal
from confection import registry
from skembeddings.error import NotInstalled
try:
from skembeddings.models.word2vec import Word2VecEmbedding
except ModuleNotFoundError:
Word2VecEmbedding = NotInstalled("Word2VecEmbedding", "gensim")
try:
from skembeddings.models.doc2vec import ... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/models/__init__.py | 0.791781 | 0.164315 | __init__.py | pypi |
from typing import Iterable, Literal, Union
import numpy as np
from gensim.models import KeyedVectors
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.cluster import MiniBatchKMeans
from sklearn.exceptions import NotFittedError
from tqdm import tqdm
from skembeddings.streams.utils import deeplist... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/models/vlawe.py | 0.883808 | 0.287893 | vlawe.py | pypi |
import functools
import random
from itertools import islice
from typing import Callable, Iterable, List, Literal, Optional, TypeVar
from sklearn.base import BaseEstimator
def filter_batches(
chunks: Iterable[list], estimator: BaseEstimator, prefit: bool
) -> Iterable[list]:
for chunk in chunks:
if pr... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/streams/utils.py | 0.868381 | 0.326352 | utils.py | pypi |
import functools
import json
from dataclasses import dataclass
from itertools import islice
from typing import Callable, Iterable, Literal
from sklearn.base import BaseEstimator
from skembeddings.streams.utils import (chunk, deeplist, filter_batches,
flatten_stream, reusable, s... | /scikit_embeddings-0.2.0.tar.gz/scikit_embeddings-0.2.0/skembeddings/streams/_stream.py | 0.906366 | 0.326258 | _stream.py | pypi |
.. figure:: https://github.com/Ibotta/pure-predict/blob/master/doc/images/pure-predict.png
:alt: pure-predict
pure-predict: Machine learning prediction in pure Python
========================================================
|License| |Build Status| |PyPI Package| |Downloads| |Python Versions|
``pure-predict`` spe... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/README.rst | 0.968738 | 0.825027 | README.rst | pypi |
MAPPING = {
"LogisticRegression": "scikit_endpoint.linear_model.LogisticRegressionPure",
"RidgeClassifier": "scikit_endpoint.linear_model.RidgeClassifierPure",
"SGDClassifier": "scikit_endpoint.linear_model.SGDClassifierPure",
"Perceptron": "scikit_endpoint.linear_model.PerceptronPure",
"PassiveAggr... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/map.py | 0.580828 | 0.511717 | map.py | pypi |
from math import exp, log
from operator import mul
from .utils import shape, sparse_list, issparse
def dot(A, B):
"""
Dot product between two arrays.
A -> n_dim = 1
B -> n_dim = 2
"""
arr = []
for i in range(len(B)):
if isinstance(A, dict):
val = sum([v * B[i][k] for k... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/base.py | 0.723798 | 0.71867 | base.py | pypi |
import pickle
import time
from warnings import warn
from distutils.version import LooseVersion
CONTAINERS = (list, dict, tuple)
TYPES = (int, float, str, bool, type)
MIN_VERSION = "0.20"
def check_types(obj, containers=CONTAINERS, types=TYPES):
"""
Checks if input object is an allowed type. Objects can be
... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/utils.py | 0.785267 | 0.296508 | utils.py | pypi |
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