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 typing import Optional, Sequence, Union # needed for typehints_formatter hack
from scico.typing import ( # needed for typehints_formatter hack
ArrayIndex,
AxisIndex,
DType,
)
# An explanation for this nasty hack, the primary purpose of which is to avoid
# the very long definition of the scico.typi... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/conf/85-dtype_typehints.py | 0.893527 | 0.225961 | 85-dtype_typehints.py | pypi |
import re
from inspect import getmembers, isfunction
# Rewrite module names for certain functions imported into scico.numpy so that they are
# included in the docs for that module. While a bit messy to do so here rather than in a
# function run via app.connect, it is necessary (for some yet to be identified reason)
# ... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/conf/80-scico_numpy.py | 0.749179 | 0.255187 | 80-scico_numpy.py | pypi |
Operators
=========
An operator is a map from :math:`\mathbb{R}^n` or :math:`\mathbb{C}^n`
to :math:`\mathbb{R}^m` or :math:`\mathbb{C}^m`. In SCICO, operators
are primarily used to represent imaging systems and provide
regularization. SCICO operators are represented by instances of the
:class:`.Operator` class.
SCIC... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/include/operator.rst | 0.955981 | 0.969785 | operator.rst | pypi |
.. _blockarray_class:
BlockArray
==========
.. testsetup::
>>> import scico
>>> import scico.numpy as snp
>>> from scico.numpy import BlockArray
>>> import numpy as np
>>> import jax.numpy
The class :class:`.BlockArray` provides a way to combine arrays of
different shapes into a single object for use... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/include/blockarray.rst | 0.963213 | 0.764232 | blockarray.rst | pypi |
Learned Models
==============
In SCICO, neural network models are used to represent imaging problems and provide different modes of data-driven regularization.
The models are implemented in `Flax <https://flax.readthedocs.io/>`_, and constitute a representative sample of frequently used networks.
FlaxMap
-------
SC... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/include/learning.rst | 0.967163 | 0.983769 | learning.rst | pypi |
.. _optimizer:
Optimization Algorithms
=======================
ADMM
----
The Alternating Direction Method of Multipliers (ADMM)
:cite:`glowinski-1975-approximation` :cite:`gabay-1976-dual` is an
algorithm for minimizing problems of the form
.. math::
:label: eq:admm_prob
\argmin_{\mb{x}, \mb{z}} \; f(\mb{x})... | /scico-0.0.4.tar.gz/scico-0.0.4/docs/source/include/optimizer.rst | 0.929568 | 0.807726 | optimizer.rst | pypi |
# Construct an index README file and a docs example index file from
# source index file "scripts/index.rst".
# Run as
# python makeindex.py
import re
from pathlib import Path
import nbformat as nbf
import py2jn
import pypandoc
src = "scripts/index.rst"
# Make dict mapping script names to docstring header titl... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/makeindex.py | 0.462716 | 0.352536 | makeindex.py | pypi |
import jax
import scico
import scico.numpy as snp
import scico.random
from scico import denoiser, functional, linop, loss, metric, plot
from scico.data import kodim23
from scico.optimize.admm import ADMM, LinearSubproblemSolver
from scico.solver import cg
from scico.util import device_info
"""
Define downsampling fun... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/superres_ppp_dncnn_admm.py | 0.79909 | 0.526525 | superres_ppp_dncnn_admm.py | pypi |
import os
from time import time
import jax
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scico import flax as sflax
from scico import metric, plot
from scico.flax.examples import load_ct_data
"""
Prepare parallel processing. Set an arbitrary processor count (only
applies if GPU is not available).
"""... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_astra_unet_train_foam2.py | 0.723505 | 0.523116 | ct_astra_unet_train_foam2.py | pypi |
r"""
Image Deconvolution with TV Regularization (ADMM Solver)
========================================================
This example demonstrates the solution of an image deconvolution problem
with isotropic total variation (TV) regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - C \mathbf{x}
\... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_tv_admm.py | 0.939789 | 0.955089 | deconv_tv_admm.py | pypi |
import numpy as np
import jax
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import functional, linop, loss, metric, plot, random
from scico.optimize.admm import ADMM, LinearSubproblemSolver
from scico.util import device_info
"""
Create a ground truth image.
"""
np.random.seed(1234)... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_ppp_dncnn_admm.py | 0.834204 | 0.660487 | deconv_ppp_dncnn_admm.py | pypi |
r"""
ℓ1 Total Variation Denoising
============================
This example demonstrates impulse noise removal via ℓ1 total variation
:cite:`alliney-1992-digital` :cite:`esser-2010-primal` (Sec. 2.4.4)
(i.e. total variation regularization with an ℓ1 data fidelity term),
minimizing the functional
$$\mathrm{argmin}_... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_l1tv_admm.py | 0.915067 | 0.931618 | denoise_l1tv_admm.py | pypi |
r"""
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 ... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/sparsecode_admm.py | 0.898908 | 0.890913 | sparsecode_admm.py | pypi |
r"""
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 represe... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/sparsecode_pgm.py | 0.890097 | 0.927034 | sparsecode_pgm.py | pypi |
r"""
TV-Regularized Abel Inversion
=============================
This example demonstrates a TV-regularized Abel inversion by solving the
problem
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|_2^2 + \lambda \| C \mathbf{x} \|_1 \;,$$
where $A$ is the Abel projector (with an implementati... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_abel_tv_admm.py | 0.922426 | 0.939969 | ct_abel_tv_admm.py | pypi |
r"""
Non-negative Poisson Loss Reconstruction (APGM)
===============================================
This example demonstrates the use of class
[pgm.PGMStepSize](../_autosummary/scico.optimize.pgm.rst#scico.optimize.pgm.PGMStepSize)
to solve the non-negative reconstruction problem with Poisson negative
log likelihood... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/sparsecode_poisson_pgm.py | 0.944228 | 0.903847 | sparsecode_poisson_pgm.py | pypi |
r"""
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
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_astra_noreg_pcg.py | 0.920222 | 0.973968 | ct_astra_noreg_pcg.py | pypi |
r"""
Complex Total Variation Denoising with PDHG Solver
==================================================
This example demonstrates solution of a problem of the form
$$\argmin_{\mathbf{x}} \; f(\mathbf{x}) + g(C(\mathbf{x})) \;,$$
where $C$ is a nonlinear operator, via non-linear PDHG
:cite:`valkonen-2014-primal... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_cplx_tv_pdhg.py | 0.930387 | 0.929055 | denoise_cplx_tv_pdhg.py | pypi |
r"""
Complex Total Variation Denoising with NLPADMM Solver
=====================================================
This example demonstrates solution of a problem of the form
$$\argmin_{\mb{x}} \; f(\mb{x}) + g(\mb{z}) \; \text{such that}\;
H(\mb{x}, \mb{z}) = 0 \;,$$
where $H$ is a nonlinear function, via a variant ... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_cplx_tv_nlpadmm.py | 0.944523 | 0.931711 | denoise_cplx_tv_nlpadmm.py | pypi |
r"""
TV-Regularized Sparse-View CT Reconstruction
============================================
This example demonstrates solution of a sparse-view CT reconstruction
problem with isotropic total variation (TV) regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|_2^2 + \lambda \| ... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_astra_tv_admm.py | 0.895323 | 0.928959 | ct_astra_tv_admm.py | pypi |
r"""
Deconvolution Microscopy (All Channels)
=======================================
This example partially replicates a [GlobalBioIm
example](https://biomedical-imaging-group.github.io/GlobalBioIm/examples.html)
using the [microscopy data](http://bigwww.epfl.ch/deconvolution/bio/)
provided by the EPFL Biomedical Ima... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_microscopy_allchn_tv_admm.py | 0.931346 | 0.900573 | deconv_microscopy_allchn_tv_admm.py | pypi |
r"""
Circulant Blur Image Deconvolution with TV Regularization
=========================================================
This example demonstrates the solution of an image deconvolution problem
with isotropic total variation (TV) regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_circ_tv_admm.py | 0.932122 | 0.933613 | deconv_circ_tv_admm.py | pypi |
import numpy as np
import jax
import matplotlib.pyplot as plt
import svmbir
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import functional, linop, metric, plot
from scico.linop import Diagonal
from scico.linop.radon_svmbir import SVMBIRSquaredL2Loss, TomographicProjector
from scico... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_svmbir_tv_multi.py | 0.772144 | 0.544378 | ct_svmbir_tv_multi.py | pypi |
r"""
Deconvolution Microscopy (Single Channel)
=========================================
This example partially replicates a [GlobalBioIm
example](https://biomedical-imaging-group.github.io/GlobalBioIm/examples.html)
using the [microscopy data](http://bigwww.epfl.ch/deconvolution/bio/)
provided by the EPFL Biomedical... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_microscopy_tv_admm.py | 0.917261 | 0.936168 | deconv_microscopy_tv_admm.py | pypi |
import numpy as np
import jax
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import functional, linop, loss, metric, plot, random
from scico.optimize.admm import ADMM, LinearSubproblemSolver
from scico.util import device_info
"""
Create a ground truth image.
"""
np.random.seed(1234)... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_ppp_bm3d_admm.py | 0.81372 | 0.526282 | deconv_ppp_bm3d_admm.py | pypi |
r"""
3D TV-Regularized Sparse-View CT Reconstruction
===============================================
This example demonstrates solution of a sparse-view, 3D CT
reconstruction problem with isotropic total variation (TV)
regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|_2^2 + \... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_astra_3d_tv_admm.py | 0.920348 | 0.948775 | ct_astra_3d_tv_admm.py | pypi |
r"""
Video Decomposition via Robust PCA
==================================
This example demonstrates video foreground/background separation via a
variant of the Robust PCA problem
$$\mathrm{argmin}_{\mathbf{x}_0, \mathbf{x}_1} \; (1/2) \| \mathbf{x}_0
+ \mathbf{x}_1 - \mathbf{y} \|_2^2 + \lambda_0 \| \mathbf... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/video_rpca_admm.py | 0.89197 | 0.965803 | video_rpca_admm.py | pypi |
import numpy as np
import jax
from bm3d import bm3d_rgb
from colour_demosaicing import demosaicing_CFA_Bayer_Menon2007
import scico
import scico.numpy as snp
import scico.random
from scico import functional, linop, loss, metric, plot
from scico.data import kodim23
from scico.optimize.admm import ADMM, LinearSubprobl... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/demosaic_ppp_bm3d_admm.py | 0.865835 | 0.614654 | demosaic_ppp_bm3d_admm.py | pypi |
import numpy as np
import jax
import matplotlib.pyplot as plt
import svmbir
from matplotlib.ticker import MaxNLocator
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import metric, plot
from scico.functional import BM3D, NonNegativeIndicator
from scico.linop import Diagonal, Identity
... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_svmbir_ppp_bm3d_admm_prox.py | 0.793466 | 0.509032 | ct_svmbir_ppp_bm3d_admm_prox.py | pypi |
r"""
Convolutional Sparse Coding with Mask Decoupling (ADMM)
=======================================================
This example demonstrates the solution of a convolutional sparse coding
problem
$$\mathrm{argmin}_{\mathbf{x}} \; \frac{1}{2} \Big\| \mathbf{y} -
B \Big( \sum_k \mathbf{h}_k \ast \mathbf{x}_k \Big... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/sparsecode_conv_md_admm.py | 0.954542 | 0.972727 | sparsecode_conv_md_admm.py | pypi |
r"""
TV-Regularized 3D DiffuserCam Reconstruction
============================================
This example demonstrates reconstruction of a 3D DiffuserCam
:cite:`antipa-2018-diffusercam`
[dataset](https://github.com/Waller-Lab/DiffuserCam/tree/master/example_data).
The inverse problem can be written as
$$\mathrm{... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/diffusercam_tv_admm.py | 0.932776 | 0.972779 | diffusercam_tv_admm.py | pypi |
r"""
TV-Regularized Low-Dose CT Reconstruction
=========================================
This example demonstrates solution of a low-dose CT reconstruction problem
with isotropic total variation (TV) regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
\|_W^2 + \lambda \| C \mathbf... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_astra_weighted_tv_admm.py | 0.908544 | 0.914023 | ct_astra_weighted_tv_admm.py | pypi |
import numpy as np
import jax
import matplotlib.pyplot as plt
import svmbir
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import metric, plot
from scico.functional import BM3D, NonNegativeIndicator
from scico.linop import Diagonal, Identity
from scico.linop.radon_svmbir import SVMBI... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_svmbir_ppp_bm3d_admm_cg.py | 0.768125 | 0.616301 | ct_svmbir_ppp_bm3d_admm_cg.py | pypi |
import numpy as np
import jax
import matplotlib.pyplot as plt
import svmbir
from matplotlib.ticker import MaxNLocator
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import metric, plot
from scico.functional import BM3D
from scico.linop import Diagonal, Identity
from scico.linop.radon... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_fan_svmbir_ppp_bm3d_admm_prox.py | 0.806853 | 0.555073 | ct_fan_svmbir_ppp_bm3d_admm_prox.py | pypi |
r"""
Convolutional Sparse Coding (ADMM)
==================================
This example demonstrates the solution of a simple convolutional sparse
coding problem
$$\mathrm{argmin}_{\mathbf{x}} \; \frac{1}{2} \Big\| \mathbf{y} -
\sum_k \mathbf{h}_k \ast \mathbf{x}_k \Big\|_2^2 + \lambda \sum_k
( \| \mathbf{x}_k... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/sparsecode_conv_admm.py | 0.944817 | 0.938181 | sparsecode_conv_admm.py | pypi |
r"""
Total Variation Denoising (ADMM)
================================
This example compares denoising via isotropic and anisotropic total
variation (TV) regularization :cite:`rudin-1992-nonlinear`
:cite:`goldstein-2009-split`. It solves the denoising problem
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} -... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_tv_admm.py | 0.89241 | 0.828176 | denoise_tv_admm.py | pypi |
r"""
Training of DnCNN for Denoising
===============================
This example demonstrates the training and application of the DnCNN model
from :cite:`zhang-2017-dncnn` to denoise images that have been corrupted
with additive Gaussian noise.
"""
import os
from time import time
import numpy as np
import jax
fr... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_dncnn_train_bsds.py | 0.920994 | 0.649051 | denoise_dncnn_train_bsds.py | pypi |
import numpy as np
import jax
from xdesign import Foam, discrete_phantom
import scico.numpy as snp
from scico import functional, linop, loss, metric, plot, random
from scico.optimize import ProximalADMM
from scico.util import device_info
"""
Create a ground truth image.
"""
np.random.seed(1234)
N = 512 # image siz... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_ppp_dncnn_padmm.py | 0.81637 | 0.684989 | deconv_ppp_dncnn_padmm.py | pypi |
r"""
Comparison of Optimization Algorithms for Total Variation Denoising
===================================================================
This example compares the performance of alternating direction method of
multipliers (ADMM), linearized ADMM, proximal ADMM, and primal–dual
hybrid gradient (PDHG) in solving th... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_tv_multi.py | 0.907091 | 0.909385 | denoise_tv_multi.py | pypi |
r"""
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 exam... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/ct_abel_tv_admm_tune.py | 0.925626 | 0.755997 | ct_abel_tv_admm_tune.py | pypi |
r"""
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_adm... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_tv_admm_tune.py | 0.917043 | 0.814828 | deconv_tv_admm_tune.py | pypi |
r"""
Image Deconvolution with TV Regularization (Proximal ADMM Solver)
=================================================================
This example demonstrates the solution of an image deconvolution problem
with isotropic total variation (TV) regularization
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} ... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_tv_padmm.py | 0.927388 | 0.964355 | deconv_tv_padmm.py | pypi |
r"""
Total Variation Denoising with Constraint (APGM)
================================================
This example demonstrates the solution of the isotropic total variation
(TV) denoising problem
$$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - \mathbf{x}
\|_2^2 + \lambda R(\mathbf{x}) + \iota_C(\mathbf... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/denoise_tv_pgm.py | 0.963265 | 0.883739 | denoise_tv_pgm.py | pypi |
import numpy as np
import jax
import scico.numpy as snp
from scico import functional, linop, loss, metric, plot, random
from scico.examples import create_3d_foam_phantom, downsample_volume, tile_volume_slices
from scico.optimize.admm import ADMM, LinearSubproblemSolver
from scico.util import device_info
"""
Create a... | /scico-0.0.4.tar.gz/scico-0.0.4/examples/scripts/deconv_ppp_bm4d_admm.py | 0.803868 | 0.543893 | deconv_ppp_bm4d_admm.py | pypi |
import math
import numbers
from cerberus import Validator
from scidash_api.exceptions import ScidashClientValidatorException
class ValidatorExtended(Validator):
def _validate_isnan(self, isnan, field, value):
"""
Check, is value NaN or not
The rule's arguments are validated against thi... | /scidash-api-1.3.0.tar.gz/scidash-api-1.3.0/scidash_api/validator.py | 0.564098 | 0.182881 | validator.py | pypi |
import boto3
import itertools
import os
import os.path
import pandas
import pyarrow
import scidbpy
from .driver import Driver
from .coord import coord2delta, delta2coord
__version__ = '19.11.6'
type_map_pyarrow = dict(
[(t.__str__(), t) for t in (pyarrow.binary(),
pyarrow.bool_()... | /scidb-bridge-19.11.6.tar.gz/scidb-bridge-19.11.6/scidbbridge/__init__.py | 0.603231 | 0.236913 | __init__.py | pypi |
import os
import json
import abc
import shutil
from zipfile import ZipFile
from click import Path as ClickPath, UsageError
from clint.textui import progress
from typing import Dict, List
from pathlib import Path
import pprint
import requests
from loguru import logger
from .utils import option, command, Cli, setup_log... | /scidra_module_utils-0.2.1-py3-none-any.whl/scidra/module_utils/base_module.py | 0.512205 | 0.164785 | base_module.py | pypi |
# Clea
This project is an XML front matter metadata reader for documents
that *almost* follows the [SciELO Publishing Schema],
extracting and sanitizing the values regarding the affiliations.
## Installation
One can install Clea with either:
```
pip install scielo-clea # Minimal
pip install scielo-clea[cl... | /scielo-clea-0.4.4.tar.gz/scielo-clea-0.4.4/README.md | 0.427038 | 0.92597 | README.md | pypi |
from .misc import get_lev
def aff_contrib_inner_gen(article):
"""Generator of matching <aff> and <contrib> of an article
as pairs of Branch instances,
using a strategy based on SQL's INNER JOIN."""
affs_ids = [get_lev(aff.node, "id") for aff in article.aff]
contrib_rids = [[get_lev(xref, "rid")
... | /scielo-clea-0.4.4.tar.gz/scielo-clea-0.4.4/clea/join.py | 0.637934 | 0.391813 | join.py | pypi |
import json
import re
from accessstats.client import ThriftClient
REGEX_ISSN = re.compile("^[0-9]{4}-[0-9]{3}[0-9xX]$")
REGEX_ISSUE = re.compile("^[0-9]{4}-[0-9]{3}[0-9xX][0-2][0-9]{3}[0-9]{4}$")
REGEX_ARTICLE = re.compile("^S[0-9]{4}-[0-9]{3}[0-9xX][0-2][0-9]{3}[0-9]{4}[0-9]{5}$")
def _code_type(code):
if not... | /scielo_accessstatsapi-1.1.0.tar.gz/scielo_accessstatsapi-1.1.0/accessstats/queries.py | 0.400984 | 0.208642 | queries.py | pypi |
import logging
import sys
import re
import numpy as np
import string
from six import string_types
from unidecode import unidecode
from nltk.stem.porter import PorterStemmer
from nltk.tokenize import WhitespaceTokenizer
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decompositi... | /science_concierge-0.1.tar.gz/science_concierge-0.1/science_concierge/science_concierge.py | 0.649245 | 0.298453 | science_concierge.py | pypi |
import json
from typing import Dict, List
import json
from pathlib import Path
import abc
class JSONObject:
def to_json(self) -> str:
return json.dumps(self, default=lambda o: o.__dict__(),
sort_keys=True,
indent=4)
class Author(JSONObject):
def ... | /science_data_structure-0.0.4.tar.gz/science_data_structure-0.0.4/science_data_structure/descriptions.py | 0.637031 | 0.168309 | descriptions.py | pypi |
from pathlib import Path
from typing import List
from author import Author
from core import JSONObject
from logger import LogEntry
import uuid
import json
from typing import Dict
import abc
from datetime import datetime
class NodeProperty(JSONObject):
@abc.abstractproperty
def name(self):
raise NotIm... | /science_data_structure-0.0.4.tar.gz/science_data_structure-0.0.4/science_data_structure/meta.py | 0.786623 | 0.158597 | meta.py | pypi |
import numpy as np
from typing import List
class Variable:
"""Class for optimization variables.
"""
# attributes
_x_min = None # variables
_x_max = None # variables
_x_type = None # variables' type
def __init__(self, x_min: np.ndarray, x_max: np.ndarray, x_type: List[str]=None):
... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/builder/variable.py | 0.912089 | 0.510802 | variable.py | pypi |
from science_optimization.solvers.pareto_samplers import BaseParetoSamplers
from science_optimization.solvers import OptimizationResults
from science_optimization.builder import OptimizationProblem
from science_optimization.function import GenericFunction, LinearFunction
from typing import Any
import numpy as np
from c... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/solvers/pareto_samplers/lambda_sampler.py | 0.942593 | 0.587174 | lambda_sampler.py | pypi |
import numpy as np
from science_optimization.builder import OptimizationProblem
from science_optimization.function import GenericFunction
from science_optimization.solvers import Optimizer
from science_optimization.problems import SeparableResourceAllocation
from science_optimization.algorithms.decomposition import Dua... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/examples/decomposition_example.py | 0.779196 | 0.557243 | decomposition_example.py | pypi |
import numpy as np
from science_optimization.builder import OptimizationProblem
from science_optimization.function import QuadraticFunction
from science_optimization.solvers.pareto_samplers import NonDominatedSampler, EpsilonSampler, LambdaSampler, MuSampler
from science_optimization.problems import GenericProblem
impo... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/examples/pareto_sampling_cs0.py | 0.847858 | 0.60542 | pareto_sampling_cs0.py | pypi |
import numpy as np
from science_optimization.solvers import Optimizer
from science_optimization.builder import OptimizationProblem
from science_optimization.function import GenericFunction
from science_optimization.problems import Quadratic, GenericProblem
from science_optimization.algorithms.derivative_free import Ne... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/examples/neldermead_example.py | 0.687945 | 0.544922 | neldermead_example.py | pypi |
import numpy as np
from science_optimization.solvers import Optimizer
from science_optimization.builder import OptimizationProblem
from science_optimization.function import GenericFunction
from science_optimization.problems import Quadratic, GenericProblem
from science_optimization.algorithms.derivative_free import Ne... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/examples/neldermead_article_example.py | 0.486819 | 0.462473 | neldermead_article_example.py | pypi |
import numpy as np
from science_optimization.builder import OptimizationProblem
from science_optimization.function import QuadraticFunction
from science_optimization.function import GenericFunction
from science_optimization.solvers.pareto_samplers import NonDominatedSampler, EpsilonSampler, LambdaSampler, MuSampler
fro... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/examples/pareto_sampling_cs1.py | 0.832169 | 0.574335 | pareto_sampling_cs1.py | pypi |
import numpy as np
from science_optimization.builder import OptimizationProblem
from science_optimization.function import QuadraticFunction
from science_optimization.solvers import Optimizer
from science_optimization.problems import GenericProblem
from science_optimization.algorithms.cutting_plane import EllipsoidMetho... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/examples/multiobjective_example.py | 0.770206 | 0.634656 | multiobjective_example.py | pypi |
import numpy as np
from science_optimization.algorithms import BaseAlgorithms
from science_optimization.builder import OptimizationProblem
from science_optimization.problems import GenericProblem
from science_optimization.function import GenericFunction, FunctionsComposite
from science_optimization.solvers import Optim... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/decomposition/dual_decomposition.py | 0.889042 | 0.45641 | dual_decomposition.py | pypi |
import numpy as np
from science_optimization.algorithms.derivative_free import NelderMead
from science_optimization.algorithms import BaseAlgorithms
from science_optimization.algorithms.search_direction import QuasiNewton, GradientAlgorithm, NewtonAlgorithm
from science_optimization.builder import OptimizationProblem
... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/lagrange/augmented_lagrangian.py | 0.910468 | 0.551151 | augmented_lagrangian.py | pypi |
import copy
import numpy as np
from science_optimization.algorithms.utils import box_constraints
from science_optimization.solvers import OptimizationResults
from science_optimization.builder import OptimizationProblem
from science_optimization.function import BaseFunction
from science_optimization.algorithms import Ba... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/derivative_free/nelder_mead.py | 0.787646 | 0.533944 | nelder_mead.py | pypi |
import nlpalg
import numpy as np
from science_optimization.algorithms import BaseAlgorithms
from science_optimization.solvers import OptimizationResults
from science_optimization.builder import OptimizationProblem
class EllipsoidMethod(BaseAlgorithms):
"""Ellipsoid algorithm method.
"""
# attributes
... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/cutting_plane/ellipsoid_method.py | 0.82151 | 0.428592 | ellipsoid_method.py | pypi |
import abc
import numpy as np
from science_optimization.algorithms import BaseAlgorithms
from science_optimization.algorithms.unidimensional import GoldenSection, MultimodalGoldenSection
from science_optimization.solvers import OptimizationResults
from science_optimization.algorithms.utils import hypercube_intersection... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/search_direction/base_search_direction.py | 0.809803 | 0.443902 | base_search_direction.py | pypi |
from science_optimization.algorithms import BaseAlgorithms
from science_optimization.solvers import OptimizationResults
from science_optimization.function import LinearFunction
from science_optimization.builder import OptimizationProblem
from scipy.optimize import linprog
import numpy as np
class ScipyBaseLinear(Base... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/linear_programming/scipy_base_linear.py | 0.944382 | 0.388241 | scipy_base_linear.py | pypi |
from science_optimization.algorithms import BaseAlgorithms
from science_optimization.solvers import OptimizationResults
from science_optimization.function import LinearFunction
from science_optimization.builder import OptimizationProblem
from ortools.linear_solver import pywraplp
import numpy as np
class Glop(BaseAlg... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/algorithms/linear_programming/glop.py | 0.932176 | 0.500854 | glop.py | pypi |
import numpy as np
from .base_function import BaseFunction
class PolynomialFunction(BaseFunction):
"""
Class that implements a polynomial function
"""
_flag_num_g = False # this function uses analytical gradient
def __init__(self, exponents, coefficients):
"""The constructor for the ... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/function/polynomial_function.py | 0.818845 | 0.755997 | polynomial_function.py | pypi |
from .base_function import BaseFunction
class GenericFunction(BaseFunction):
"""Class to convert a python function to a BaseFunction instance."""
def __init__(self, func, n, grad_func=None):
"""Constructor of a generic function.
Args:
func : (callable) instance of a python fu... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/function/generic_function.py | 0.91501 | 0.437944 | generic_function.py | pypi |
import numpy as np
import numpy.matlib
from .base_function import BaseFunction
class QuadraticFunction(BaseFunction):
"""
Class that implements a quadratic function
"""
_flag_num_g = False # this function uses analytical gradient
def __init__(self, Q, c, d=0):
""" Set parameters for ... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/function/quadratic_function.py | 0.880964 | 0.632588 | quadratic_function.py | pypi |
import numpy as np
from science_optimization.function import BaseFunction, LinearFunction, FunctionsComposite
class AugmentedLagrangeFunction(BaseFunction):
"""
Class that deals with the function used in the Augmented Lagrangian method
"""
eq_aux_func = None
ineq_aux_func = None
aux_rho = Non... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/function/lagrange_function.py | 0.758421 | 0.443721 | lagrange_function.py | pypi |
import numpy as np
import numpy.matlib
from .base_function import BaseFunction
class LinearFunction(BaseFunction):
"""
Class that implements a linear function
"""
_flag_num_g = False # this function uses analytical gradient
def parameter_check(self, c: np.ndarray, d):
# checking c p... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/function/linear_function.py | 0.861188 | 0.573081 | linear_function.py | pypi |
import numpy as np
from science_optimization.builder import BuilderOptimizationProblem, Objective, Variable, Constraint
from science_optimization.function import BaseFunction, FunctionsComposite
class RosenSuzukiProblem(BuilderOptimizationProblem):
"""Concrete builder implementation.
This class builds the R... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/problems/rosen_suzuki.py | 0.760517 | 0.523177 | rosen_suzuki.py | pypi |
from science_optimization.builder import BuilderOptimizationProblem
from science_optimization.builder import Objective
from science_optimization.builder import Variable
from science_optimization.builder import Constraint
from science_optimization.function import FunctionsComposite, LinearFunction
import numpy as np
fro... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/problems/mip.py | 0.95202 | 0.64058 | mip.py | pypi |
from science_optimization.builder import BuilderOptimizationProblem
from science_optimization.builder import Objective
from science_optimization.builder import Variable
from science_optimization.builder import Constraint
from science_optimization.function import FunctionsComposite
class SeparableResourceAllocation(Bu... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/problems/separable_resource_allocation.py | 0.953416 | 0.49823 | separable_resource_allocation.py | pypi |
from science_optimization.builder import BuilderOptimizationProblem
from science_optimization.builder import Objective
from science_optimization.builder import Variable
from science_optimization.builder import Constraint
from science_optimization.function import FunctionsComposite
class GenericProblem(BuilderOptimiza... | /science_optimization-9.0.2-cp310-cp310-manylinux_2_35_x86_64.whl/science_optimization/problems/generic.py | 0.947076 | 0.479686 | generic.py | pypi |
__all__ = ['parse_pdf', 'logger']
# Cell
import logging
from pathlib import Path
from typing import Optional, Dict, Any
import requests
logger = logging.getLogger(__name__)
def parse_pdf(server_address: str, file_path: Path, port: str = '', timeout: int = 60
) -> Optional[Dict[str, Any]]:
'''
... | /science_parse_api-1.0.1-py3-none-any.whl/science_parse_api/api.py | 0.890205 | 0.575946 | api.py | pypi |
import logging
import json
import re
import os
import time
import datetime
import feedparser
import dateutil.parser
from os.path import expanduser
from scibot.telebot import telegram_bot_sendtext
from scibot.streamer import listen_stream_and_rt
from schedule import Scheduler
# logging parameters
logger = logging.getLo... | /scienceBot-0.1.1.1.tar.gz/scienceBot-0.1.1.1/scibot/tools.py | 0.499268 | 0.164852 | tools.py | pypi |
## Creative Commons Attribution 4.0 International
Creative Commons Attribution 4.0 International (CC BY 4.0) URL:
<http://creativecommons.org/licenses/by/4.0/>
Creative Commons Corporation (“Creative Commons”) is not a law firm and does not
provide legal services or legal advice. Distribution of Creative Commons publ... | /sciencebasepy-2.0.13-py3-none-any.whl/sciencebasepy-2.0.13.dist-info/LICENSE.md | 0.692018 | 0.78611 | LICENSE.md | pypi |
# ScienceBeam Alignment
[](LICENSE)
ScienceBeam Alignment provides generic low-level sequence alignment utility functions, similar to Python's [SequenceMatcher](https://docs.python.org/3/library/difflib.html).
This project is currently mainly used f... | /sciencebeam_alignment-0.0.5.tar.gz/sciencebeam_alignment-0.0.5/README.md | 0.635222 | 0.988256 | README.md | pypi |
from __future__ import absolute_import, print_function
import logging
import timeit
import numpy as np
from sciencebeam_alignment.align import (
SimpleScoring,
CustomScoring,
LocalSequenceMatcher,
require_native
)
DEFAULT_MATCH_SCORE = 2
DEFAULT_MISMATCH_SCORE = -1
DEFAULT_GAP_SCORE = -3
DEFAULT_SC... | /sciencebeam_alignment-0.0.5.tar.gz/sciencebeam_alignment-0.0.5/sciencebeam_alignment/align_performance.py | 0.577376 | 0.210644 | align_performance.py | pypi |
import logging
import warnings
from collections import deque
from itertools import islice
from abc import ABCMeta, abstractmethod
from contextlib import contextmanager
import numpy as np
from six import (
with_metaclass,
string_types,
binary_type
)
try:
from sciencebeam_alignment.align_fast_utils imp... | /sciencebeam_alignment-0.0.5.tar.gz/sciencebeam_alignment-0.0.5/sciencebeam_alignment/align.py | 0.647464 | 0.344085 | align.py | pypi |
import dataclasses
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import (
Callable,
Iterable,
Iterator,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
cast
)
from typing_extensions import Protocol
from sciencebeam_parser.doc... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/document/semantic_document.py | 0.851907 | 0.244611 | semantic_document.py | pypi |
import dataclasses
import logging
import itertools
import operator
from dataclasses import dataclass, field
from functools import partial
from typing import Callable, List, Iterable, NamedTuple, Optional, Sequence, Tuple
from sciencebeam_parser.utils.bounding_box import BoundingBox
from sciencebeam_parser.utils.tokeni... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/document/layout_document.py | 0.870776 | 0.279988 | layout_document.py | pypi |
import logging
from typing import Dict, Iterable, List, Optional, Union
from lxml import etree
from lxml.builder import ElementMaker
from sciencebeam_parser.utils.xml import get_text_content
from sciencebeam_parser.utils.xml_writer import parse_tag_expression
from sciencebeam_parser.document.layout_document import (
... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/document/tei/common.py | 0.709523 | 0.154185 | common.py | pypi |
import logging
from typing import (
Dict,
List,
Mapping,
Optional,
Sequence,
Set,
Union
)
from lxml import etree
from sciencebeam_parser.document.semantic_document import (
SemanticAddressField,
SemanticAffiliationAddress,
SemanticAuthor,
SemanticMarker
)
from sciencebeam_p... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/document/tei/author.py | 0.640523 | 0.189484 | author.py | pypi |
import logging
from typing import (
Iterable,
List,
)
from lxml import etree
from sciencebeam_parser.document.semantic_document import (
SemanticContentWrapper,
SemanticFigure,
SemanticHeading,
SemanticLabel,
SemanticParagraph,
SemanticRawEquation,
SemanticSection,
SemanticSect... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/document/tei/section.py | 0.601711 | 0.165627 | section.py | pypi |
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Callable, Sequence
import PIL.Image
from sciencebeam_parser.utils.bounding_box import BoundingBox
from sciencebeam_parser.utils.lazy import LazyLoaded, Preloadable
class ComputerVisionModelInstance(ABC):
@abstractmethod
... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/cv_models/cv_model.py | 0.917185 | 0.199152 | cv_model.py | pypi |
import logging
from typing import List, Sequence, Tuple
import PIL.Image
from layoutparser.elements.layout import Layout
from layoutparser.models.auto_layoutmodel import AutoLayoutModel
from layoutparser.models.base_layoutmodel import BaseLayoutModel
from sciencebeam_parser.utils.bounding_box import BoundingBox
from... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/cv_models/layout_parser_cv_model.py | 0.854354 | 0.19787 | layout_parser_cv_model.py | pypi |
from abc import ABC, abstractmethod
from dataclasses import dataclass
import logging
from typing import Iterable, List, Mapping, NamedTuple, Optional, Sequence, Tuple, TypeVar, Union
from lxml import etree
from lxml.builder import ElementMaker
from sciencebeam_parser.utils.xml_writer import XmlTreeWriter
from science... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/training_data.py | 0.843444 | 0.222605 | training_data.py | pypi |
import os
import logging
import threading
from typing import Iterable, Optional, List, Tuple
import numpy as np
from sciencebeam_trainer_delft.sequence_labelling.engines.wapiti import WapitiWrapper
from sciencebeam_trainer_delft.utils.io import copy_file
from sciencebeam_trainer_delft.utils.download_manager import Do... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/wapiti_model_impl.py | 0.719482 | 0.154887 | wapiti_model_impl.py | pypi |
from typing import Iterable
from sciencebeam_parser.models.data import (
ContextAwareLayoutTokenFeatures,
ContextAwareLayoutTokenModelDataGenerator,
LayoutModelData
)
class CitationDataGenerator(ContextAwareLayoutTokenModelDataGenerator):
def iter_model_data_for_context_layout_token_features(
... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/citation/data.py | 0.763836 | 0.180467 | data.py | pypi |
import logging
import re
from typing import Iterable, Mapping, Optional, Set, Tuple, Type, Union
from sciencebeam_parser.utils.misc import iter_ids
from sciencebeam_parser.document.semantic_document import (
SemanticContentFactoryProtocol,
SemanticContentWrapper,
SemanticDate,
SemanticExternalIdentifie... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/citation/extract.py | 0.799521 | 0.208259 | extract.py | pypi |
import logging
import re
from typing import Iterable, List, Mapping, Optional, Tuple, Type, Union, cast
from sciencebeam_parser.document.semantic_document import (
SemanticAuthor,
SemanticContentFactoryProtocol,
SemanticContentWrapper,
SemanticMarker,
SemanticMiddleName,
SemanticMixedContentWra... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/name/extract.py | 0.7324 | 0.162912 | extract.py | pypi |
import logging
from typing import Iterable, Set, Union
from sciencebeam_parser.document.semantic_document import SemanticAuthor
from sciencebeam_parser.models.data import LayoutModelData
from sciencebeam_parser.models.model import (
LabeledLayoutToken,
iter_entity_layout_blocks_for_labeled_layout_tokens
)
from... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/name/training_data.py | 0.74826 | 0.187058 | training_data.py | pypi |
from typing import Iterable
from sciencebeam_parser.models.data import (
ContextAwareLayoutTokenFeatures,
ContextAwareLayoutTokenModelDataGenerator,
LayoutModelData
)
class ReferenceSegmenterDataGenerator(ContextAwareLayoutTokenModelDataGenerator):
def iter_model_data_for_context_layout_token_feature... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/reference_segmenter/data.py | 0.774626 | 0.155976 | data.py | pypi |
import logging
from typing import Iterable, Optional, Tuple
from sciencebeam_parser.utils.misc import iter_ids
from sciencebeam_parser.document.semantic_document import (
SemanticContentWrapper,
SemanticHeading,
SemanticLabel,
SemanticNote,
SemanticRawReference,
SemanticRawReferenceText
)
from ... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/reference_segmenter/extract.py | 0.797399 | 0.186299 | extract.py | pypi |
import logging
import re
from typing import Iterable, Mapping, Optional, Tuple
from sciencebeam_parser.document.semantic_document import (
SemanticContentFactoryProtocol,
SemanticContentWrapper,
SemanticFigureCitation,
SemanticHeading,
SemanticLabel,
SemanticNote,
SemanticParagraph,
Sem... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/fulltext/extract.py | 0.727298 | 0.152095 | extract.py | pypi |
import logging
from typing import Iterable, Tuple
from sciencebeam_parser.document.layout_document import (
LayoutBlock
)
from sciencebeam_parser.document.semantic_document import (
SemanticSection,
SemanticSectionTypes
)
from sciencebeam_parser.models.fulltext.training_data import (
FullTextTeiTrainin... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/fulltext/model.py | 0.671686 | 0.161056 | model.py | pypi |
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