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 Iterable
from sciencebeam_parser.models.data import (
ContextAwareLayoutTokenFeatures,
ContextAwareLayoutTokenModelDataGenerator,
LayoutModelData
)
class HeaderDataGenerator(ContextAwareLayoutTokenModelDataGenerator):
def iter_model_data_for_context_layout_token_features(
s... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/header/data.py | 0.760028 | 0.161155 | data.py | pypi |
import logging
from typing import Iterable, Mapping, Optional, Tuple
from sciencebeam_parser.utils.misc import iter_ids
from sciencebeam_parser.document.semantic_document import (
SemanticAddressLine,
SemanticAffiliationAddress,
SemanticContentFactoryProtocol,
SemanticContentWrapper,
SemanticCountr... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/models/affiliation_address/extract.py | 0.741861 | 0.151467 | extract.py | pypi |
import os
import logging
from time import monotonic
from typing import Dict, Iterable, Mapping, Optional, Sequence, Set
import PIL.Image
from sciencebeam_parser.utils.bounding_box import BoundingBox
from sciencebeam_parser.document.semantic_document import SemanticGraphic
from sciencebeam_parser.document.layout_docum... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/processors/cv_graphic_provider.py | 0.835618 | 0.210685 | cv_graphic_provider.py | pypi |
import functools
import logging
import os
from abc import ABC, abstractmethod
from typing import Counter, Iterable, List, Optional, Sequence
from sciencebeam_parser.utils.bounding_box import BoundingBox
from sciencebeam_parser.document.layout_document import (
LayoutBlock,
LayoutDocument,
LayoutGraphic,
... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/processors/graphic_provider.py | 0.816077 | 0.184859 | graphic_provider.py | pypi |
from typing import NamedTuple, Set
from sciencebeam_parser.config.config import AppConfig
from sciencebeam_parser.processors.document_page_image import (
DEFAULT_PDF_RENDER_DPI
)
from sciencebeam_parser.processors.graphic_matching import DEFAULT_MAX_GRAPHIC_DISTANCE
class RequestFieldNames:
"""
"Abstrac... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/processors/fulltext/config.py | 0.818954 | 0.185929 | config.py | pypi |
import logging
import multiprocessing
from typing import (
Iterable,
Iterator,
List,
Mapping,
NamedTuple,
Optional,
Sequence,
Tuple,
Type,
Union
)
from sciencebeam_parser.models.data import AppFeaturesContext, DEFAULT_APP_FEATURES_CONTEXT
from sciencebeam_parser.models.model imp... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/processors/fulltext/processor.py | 0.82029 | 0.169956 | processor.py | pypi |
import logging
import os
from contextlib import ExitStack
from dataclasses import dataclass
from pathlib import Path
from tempfile import TemporaryDirectory
from time import monotonic
from typing import List, Optional, Set
from zipfile import ZipFile
from lxml import etree
from sciencebeam_trainer_delft.utils.downloa... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/app/parser.py | 0.703142 | 0.154951 | parser.py | pypi |
import mimetypes
from typing import Optional, Sequence
class MediaTypes:
"""
Media Types used by ScienceBeam Parser.
Where possible, these correspond to official media types.
In some instances, no official media type is defined yet.
"""
PDF = 'application/pdf'
DOC = 'application/msword'
... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/utils/media_types.py | 0.699152 | 0.204501 | media_types.py | pypi |
import logging
import re
from itertools import zip_longest
from typing import Mapping, NamedTuple, Optional, Sequence, Tuple, Union
from lxml import etree
from lxml.builder import ElementMaker
LOGGER = logging.getLogger(__name__)
class TagExpression(NamedTuple):
tag: str
attrib: Mapping[str, str]
def ... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/utils/xml_writer.py | 0.72331 | 0.179064 | xml_writer.py | pypi |
import os
import codecs
from contextlib import contextmanager
from typing import Iterable, Sequence
from urllib.parse import urlparse
import fsspec
from sciencebeam_trainer_delft.utils.io import (
auto_uploading_output_file as _auto_uploading_output_file,
is_external_location,
open_file
)
def get_file_s... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/utils/io.py | 0.659076 | 0.151467 | io.py | pypi |
import argparse
import logging
import os
from typing import Iterable, List, Optional, Sequence, Tuple
from lxml import etree
from sciencebeam_trainer_delft.utils.io import (
auto_download_input_file
)
from sciencebeam_trainer_delft.sequence_labelling.reader import (
load_data_crf_lines
)
from sciencebeam_trai... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/training/cli/generate_delft_data.py | 0.68215 | 0.190253 | generate_delft_data.py | pypi |
import logging
from typing import Any, Mapping, Optional, Union
from lxml import etree
LOGGER = logging.getLogger(__name__)
T_XSLT_Input = Union[etree.ElementBase, etree.ElementTree]
class XsltTransformerWrapper:
def __init__(
self,
xslt_template: str,
xslt_template_parameters: Option... | /sciencebeam_parser-0.1.8.tar.gz/sciencebeam_parser-0.1.8/sciencebeam_parser/transformers/xslt.py | 0.848659 | 0.244386 | xslt.py | pypi |
# ScienceBeam Trainer DeLFT
Work in-progress..
A thin(ish) wrapper around [DeLFT](https://github.com/kermitt2/delft) to enable training in the cloud.
Some of the main features:
- resources (model, data etc.) can be loaded from remote sources, currently:
- HTTP (`https://`, `http://`)
- Google Storage (`gs://`)
... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/README.md | 0.455199 | 0.902266 | README.md | pypi |
from collections import Counter, defaultdict, OrderedDict
from typing import Dict, Iterable, List
import numpy as np
def iter_flat_batch_tokens(batch_tokens: List[List[str]]):
return (
token
for doc_tokens in batch_tokens
for token in doc_tokens
)
def iter_flat_features(features: np... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/input_info.py | 0.76908 | 0.616676 | input_info.py | pypi |
import logging
import os
import time
from functools import partial
from typing import Callable, Iterable, List, Optional, Tuple, Union, cast
import numpy as np
from delft.sequenceLabelling.models import BaseModel
from delft.sequenceLabelling.preprocess import WordPreprocessor, FeaturesPreprocessor
from delft.sequence... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/wrapper.py | 0.826187 | 0.161783 | wrapper.py | pypi |
import json
import difflib
import logging
from xml.sax.saxutils import escape as xml_escape
from typing import Optional, Union, Iterable, List, Tuple
import numpy as np
from delft.sequenceLabelling.evaluation import get_entities
LOGGER = logging.getLogger(__name__)
class TagOutputFormats:
JSON = 'json'
DA... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/tag_formatter.py | 0.717309 | 0.271206 | tag_formatter.py | pypi |
import logging
import itertools
from functools import partial
from typing import Any, Dict, List, Iterable, Set, Tuple, Union
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import Mi... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/preprocess.py | 0.742795 | 0.309245 | preprocess.py | pypi |
import argparse
import logging
from typing import Dict, List, Optional, NamedTuple
import keras
import numpy as np
from delft.sequenceLabelling.preprocess import WordPreprocessor
from delft.sequenceLabelling.models import BaseModel
from sciencebeam_trainer_delft.utils.misc import (
parse_comma_separated_str,
... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/transfer_learning.py | 0.870721 | 0.190122 | transfer_learning.py | pypi |
import logging
import re
from itertools import islice
from typing import Iterable, List, Tuple
import numpy as np
from delft.sequenceLabelling.reader import _translate_tags_grobid_to_IOB
LOGGER = logging.getLogger(__name__)
# partially copied from delft/sequenceLabelling/reader.py
def iter_load_data_and_labels_c... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/reader.py | 0.598077 | 0.345436 | reader.py | pypi |
import logging
import os
from typing import NamedTuple, Optional
import numpy as np
from delft.sequenceLabelling.evaluation import (
f1_score,
accuracy_score,
precision_score,
recall_score
)
from delft.sequenceLabelling.trainer import Trainer as _Trainer
from delft.sequenceLabelling.trainer import Sc... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/trainer.py | 0.873728 | 0.280898 | trainer.py | pypi |
import logging
import json
from typing import List, Type, Union
from keras.models import Model
from keras.layers.merge import Concatenate
from keras.layers import (
Dense, LSTM, Bidirectional, Embedding, Input, Dropout,
TimeDistributed
)
import delft.sequenceLabelling.wrapper
from delft.utilities.layers impor... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/models.py | 0.912782 | 0.230432 | models.py | pypi |
import logging
from collections import Counter
from itertools import zip_longest
from typing import List, Optional
import numpy as np
from delft.utilities.Tokenizer import tokenizeAndFilterSimple
from sciencebeam_trainer_delft.sequence_labelling.dataset_transform import (
DatasetTransformer
)
from sciencebeam_t... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/dataset_transform/unroll_transform.py | 0.821868 | 0.254257 | unroll_transform.py | pypi |
import logging
import tempfile
import os
from pathlib import Path
from typing import Iterable, IO, List, Optional, Tuple
import numpy as np
from delft.sequenceLabelling.reader import (
_translate_tags_grobid_to_IOB as translate_tags_grobid_to_IOB
)
from sciencebeam_trainer_delft.sequence_labelling.evaluation imp... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/engines/wapiti_adapters.py | 0.690768 | 0.297062 | wapiti_adapters.py | pypi |
import logging
import threading
import os
import sys
from collections import Counter
from itertools import islice
from multiprocessing import cpu_count
from typing import IO, List, Iterable, Optional, cast
import subprocess
LOGGER = logging.getLogger(__name__)
DEFAULT_STOP_EPSILON_VALUE = '0.00001'
DEFAULT_STOP_WI... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/engines/wapiti.py | 0.557845 | 0.169612 | wapiti.py | pypi |
import argparse
import logging
from typing import Optional
import requests
from sciencebeam_trainer_delft.sequence_labelling.evaluation import (
ClassificationResult
)
LOGGER = logging.getLogger(__name__)
DEFAULT_TRAIN_START_MESSAGE_FORMAT = '\n'.join([
'Model training started',
'model_path: `{model_p... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/utils/train_notify.py | 0.804021 | 0.310028 | train_notify.py | pypi |
import logging
from pathlib import Path
from typing import List, Optional, NamedTuple, Union
from sciencebeam_trainer_delft.utils.typing import T
from sciencebeam_trainer_delft.sequence_labelling.tools.checkpoints import (
get_checkpoints_json,
get_checkpoint_meta
)
LOGGER = logging.getLogger(__name__)
cl... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/utils/checkpoints.py | 0.898133 | 0.27282 | checkpoints.py | pypi |
import argparse
import concurrent.futures
import logging
import json
import os
from collections import OrderedDict
from typing import Dict, List, Optional
from tqdm.auto import tqdm
from sciencebeam_trainer_delft.utils.io import open_file
from sciencebeam_trainer_delft.utils.cli import (
add_default_arguments,
... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/tools/checkpoints.py | 0.718496 | 0.162413 | checkpoints.py | pypi |
import logging
import argparse
from argparse import _ActionsContainer as ArgParseActionsContainer
from typing import List
from sciencebeam_trainer_delft.utils.misc import parse_number_ranges
from sciencebeam_trainer_delft.sequence_labelling.utils.train_notify import (
add_train_notification_arguments
)
from sci... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/tools/grobid_trainer/cli_args.py | 0.803097 | 0.150496 | cli_args.py | pypi |
import logging
import time
import tempfile
import os
from collections import Counter
from datetime import datetime, timezone
from itertools import islice
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
from sc... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/sequence_labelling/tools/grobid_trainer/utils.py | 0.646572 | 0.268309 | utils.py | pypi |
import logging
import time
from functools import partial
from typing import List, Tuple
import pandas as pd
import delft.textClassification.models
import delft.textClassification.wrapper
from sciencebeam_trainer_delft.text_classification.wrapper import Classifier
from sciencebeam_trainer_delft.utils.download_manager... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/text_classification/cli_utils.py | 0.703549 | 0.267387 | cli_utils.py | pypi |
import logging
from collections import OrderedDict
from typing import List
import numpy as np
from sklearn.metrics import (
log_loss,
roc_auc_score,
f1_score,
precision_score,
recall_score
)
LOGGER = logging.getLogger(__name__)
class ClassificationResult:
def __init__(
self,
... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/text_classification/evaluation.py | 0.86898 | 0.383237 | evaluation.py | pypi |
from typing import Tuple, List
import pandas as pd
import numpy as np
from sciencebeam_trainer_delft.utils.io import auto_uploading_output_file
# mostly copied from:
# https://github.com/kermitt2/delft/blob/v0.2.3/delft/textClassification/reader.py
def get_filepath_csv_separator(filepath: str):
if filepath.en... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/text_classification/reader.py | 0.785925 | 0.337913 | reader.py | pypi |
import logging
import math
import os
from typing import List
import numpy as np
from sklearn.metrics import log_loss, roc_auc_score
from keras.models import Model
from keras.callbacks import Callback
from sciencebeam_trainer_delft.text_classification.saving import (
ModelSaver
)
from sciencebeam_trainer_delft.t... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/text_classification/models.py | 0.699357 | 0.183942 | models.py | pypi |
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints
# mostly copied from:
# https://github.com/kermitt2/delft/blob/v0.2.3/delft/utilities/Attention.py
# - updated to be compatible with newer Keras version
class Attention(Layer):
def __i... | /sciencebeam_trainer_delft-0.0.31.tar.gz/sciencebeam_trainer_delft-0.0.31/sciencebeam_trainer_delft/utils/models/Attention.py | 0.922023 | 0.362095 | Attention.py | pypi |
from __future__ import absolute_import
import logging
from io import StringIO
from backports import csv # pylint: disable=no-name-in-module
from six import text_type
import apache_beam as beam
from apache_beam.io.textio import WriteToText
from apache_beam.io.filesystem import CompressionTypes
from apache_beam.io.f... | /sciencebeam_utils-0.1.5.tar.gz/sciencebeam_utils-0.1.5/sciencebeam_utils/beam_utils/csv.py | 0.630799 | 0.184217 | csv.py | pypi |
import logging
from random import getrandbits
import apache_beam as beam
from apache_beam.metrics.metric import Metrics
def get_logger():
return logging.getLogger(__name__)
def Spy(f):
def spy_wrapper(x):
f(x)
return x
return spy_wrapper
def MapSpy(f):
return beam.Map(Spy(f))
de... | /sciencebeam_utils-0.1.5.tar.gz/sciencebeam_utils-0.1.5/sciencebeam_utils/beam_utils/utils.py | 0.639398 | 0.198763 | utils.py | pypi |
import logging
import os
from functools import reduce # pylint: disable=redefined-builtin
from typing import Iterable, List, Tuple
from apache_beam.io.filesystems import FileSystems
from sciencebeam_utils.utils.collection import (
groupby_to_dict,
sort_and_groupby_to_dict
)
from .file_path import strip_ext
... | /sciencebeam_utils-0.1.5.tar.gz/sciencebeam_utils-0.1.5/sciencebeam_utils/utils/file_pairs.py | 0.514888 | 0.170854 | file_pairs.py | pypi |
import argparse
import logging
import errno
from math import trunc
from random import shuffle
from datetime import datetime
from itertools import chain
from typing import List
from backports import csv # pylint: disable=no-name-in-module
from six import text_type
from sciencebeam_utils.beam_utils.io import open_fil... | /sciencebeam_utils-0.1.5.tar.gz/sciencebeam_utils-0.1.5/sciencebeam_utils/tools/split_csv_dataset.py | 0.499512 | 0.259914 | split_csv_dataset.py | pypi |
# In[1]:
from datetime import datetime, timedelta
# In[2]:
def create_date_from_str(date_str, date_format='%Y%m%d'):
'''
Create a Datetime object from a string with specific date_format.
date_str: a date string (required).
date_format: the date format of date_str. Default is %Y%m%d.
'... | /scienceindata_dates-0.0.3.tar.gz/scienceindata_dates-0.0.3/src/scienceindata_dates/scienceindata_dates.py | 0.789356 | 0.374076 | scienceindata_dates.py | pypi |
# ScienceIO API Demo
In this demo, we'll:
- Log in with our user account
- Make our first request
- Put the request in a pandas dataframe and analyze
```
import pandas as pd
import yaml
from IPython.display import display, JSON
from analytics import *
from scienceio import ScienceIO
```
## Initialize client
```
s... | /scienceio-2.2.0.tar.gz/scienceio-2.2.0/examples/example-analytics-2.ipynb | 0.479747 | 0.902395 | example-analytics-2.ipynb | pypi |
import argparse
import pandas as pd
from convert_data_model import convert_data_model
def count_text(df) -> int:
"""len(df) = # of text spans"""
return len(df)
def count_text_unique(df) -> int:
"""unique text spans (no correction for caps/lower/etc.)"""
return df.text.nunique()
def count_concept... | /scienceio-2.2.0.tar.gz/scienceio-2.2.0/examples/analytics.py | 0.758242 | 0.430506 | analytics.py | pypi |
# ScienceIO API Analytics
In this demo, we'll:
- Log in with our user account
- Make our first request
- Put the request in a pandas dataframe and analyze
```
import pandas as pd
import yaml
from IPython.display import display, JSON
from analytics import *
from scienceio import ScienceIO
```
## Initialize client
... | /scienceio-2.2.0.tar.gz/scienceio-2.2.0/examples/example-analytics-1.ipynb | 0.537041 | 0.886273 | example-analytics-1.ipynb | pypi |
import argparse
import pandas as pd
def beta_mapper():
"""For converting previous data models to beta model"""
return {
"text": "text",
"start": "pos_start",
"end": "pos_end",
"text_norm": "concept_id",
"entity": "concept_id",
"canonical_name": "concept_name",
... | /scienceio-2.2.0.tar.gz/scienceio-2.2.0/examples/convert_data_model.py | 0.580471 | 0.417568 | convert_data_model.py | pypi |
<div id="top"></div>
<h1 align="center">
<br>
Sciencer Toolkit
</h1>
<h4 align="center">A smarter way to find articles.</h4>
<p align="center">
<a href="https://pypi.org/project/sciencer/">
<img src="https://img.shields.io/pypi/status/sciencer.svg?style=flat-square"
alt="PyPi Package Version"></a... | /sciencer-0.1.3.tar.gz/sciencer-0.1.3/README.md | 0.528777 | 0.817538 | README.md | pypi |
<h1 align="center">
ScienceWorld
</h1>
<p align="center">
<!-- Version badge using shields.io -->
<a href="https://github.com/allenai/ScienceWorld/releases">
<img src="https://img.shields.io/github/v/release/allenai/ScienceWorld">
</a>
<!-- Link to tutorials badge using shields.io -->
<a href="https://huggin... | /scienceworld-1.1.3.tar.gz/scienceworld-1.1.3/README.md | 0.512449 | 0.93744 | README.md | pypi |
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of... | /scienlib-1.1.tar.gz/scienlib-1.1/.github/CODE_OF_CONDUCT.md | 0.58439 | 0.685038 | CODE_OF_CONDUCT.md | pypi |
from __future__ import annotations
import re
from pathlib import Path
from typing import Any, Dict, List, Optional
import pandas as pd
from sem.structure_parsing import recursive_folder_parsing
class ResultManager:
"""A manager for experimental results.
It takes care of collecting results organized in diff... | /scientific-experiment-manager-0.1.0.tar.gz/scientific-experiment-manager-0.1.0/sem/manager.py | 0.942122 | 0.608216 | manager.py | pypi |
import torch
import random
import numpy as np
import argparse
from sentence_transformers import SentenceTransformer, util
from transformers import DataCollatorWithPadding
from collections import defaultdict
import json
import wandb
import ipdb
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/predict_similarity_scoring_unlabelled_sbert.py | 0.682785 | 0.261312 | predict_similarity_scoring_unlabelled_sbert.py | pypi |
import torch
import random
import numpy as np
import argparse
import pandas as pd
from functools import partial
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification, AutoModel
from transformers import AutoConfig
from transformers import Trainer
from transformers import Trai... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/predict_similarity_scoring_unlabelled.py | 0.785144 | 0.242295 | predict_similarity_scoring_unlabelled.py | pypi |
import torch
import random
import numpy as np
import argparse
import json
import os
from functools import partial
from datasets import load_metric
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification, AutoModel
from transformers import AutoConfig
from transformers import Tr... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/train_supervised.py | 0.662687 | 0.22378 | train_supervised.py | pypi |
import argparse
import random
from functools import partial
import wandb
import json
import os
import numpy as np
import torch
import torch.nn.functional as F
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
from transformers import DataCollatorWithPadding
from transfo... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/eval_unsupervised_paraphrase_detection.py | 0.625209 | 0.257479 | eval_unsupervised_paraphrase_detection.py | pypi |
import torch
import random
import numpy as np
import argparse
import wandb
import torch.nn.functional as F
import torch
import json
import os
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from transformers import Trainer
from transformers import TrainingArguments
fro... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/eval_unsupervised_nli.py | 0.646014 | 0.269736 | eval_unsupervised_nli.py | pypi |
import torch
from torch import nn
import random
import numpy as np
import argparse
import json
import os
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer, losses, evaluation, models
from torch.utils.data import DataLoader
import wandb
import pandas as pd
import ipdb
from utils.data... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/train_supervised_sentence_transformers.py | 0.744749 | 0.218899 | train_supervised_sentence_transformers.py | pypi |
import torch
import random
import numpy as np
import argparse
from sentence_transformers import SentenceTransformer
import wandb
import json
import os
import ipdb
import torch.nn.functional as F
from utils.data_processor import read_dataset_raw
from utils.metrics import compute_regression_metrics
from utils.data_proc... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/eval_unsupervised_sts.py | 0.671363 | 0.266406 | eval_unsupervised_sts.py | pypi |
import torch
import random
import numpy as np
import argparse
from sentence_transformers import SentenceTransformer, util
from transformers import AutoModelForSequenceClassification
from transformers import AutoConfig
from transformers import AutoTokenizer
from transformers import Trainer
from transformers import Train... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/eval_evidence_retrieval.py | 0.700588 | 0.269416 | eval_evidence_retrieval.py | pypi |
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
from typing import List, AnyStr, Tuple, Dict
from sklearn.metrics import mean_squared_error
from scipy.stats import pearsonr, spearmanr
import ipdb
def accuracy(preds: np.ndarray, labels: np.ndarray) -> float:
return np.sum(preds == la... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/utils/metrics.py | 0.825343 | 0.645357 | metrics.py | pypi |
import torch
from torch import nn
from torch.optim import SGD
from transformers import AutoModel
from tqdm import tqdm
import torch.nn.functional as F
import ipdb
class GradientReversal(torch.autograd.Function):
"""
Basic layer for doing gradient reversal
"""
lambd = 1.0
@staticmethod
def forw... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/utils/model.py | 0.93528 | 0.508971 | model.py | pypi |
import numpy as np
def mean_reciprocal_rank(rs):
"""Score is reciprocal of the rank of the first relevant item
First element is 'rank 1'. Relevance is binary (nonzero is relevant).
Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank
>>> rs = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]
>>> mean... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/matching_experiments/utils/rank_metrics.py | 0.923842 | 0.822688 | rank_metrics.py | pypi |
from sentence_transformers import SentenceTransformer, util
from typing import Optional, AnyStr, List
import numpy as np
import torch
import torch.nn.functional as F
class SimilarityEstimator(object):
"""
Estimator of information matching score (IMS) between two scientific sentences
"""
def __init__(
... | /scientific-information-change-1.0.0.tar.gz/scientific-information-change-1.0.0/scientific_information_change/estimate_similarity.py | 0.949494 | 0.651036 | estimate_similarity.py | pypi |
from __future__ import annotations
from pathlib import Path
from functools import wraps
from typing import TypeVar, List, Tuple, Union, Callable, Optional
from warnings import warn, filterwarnings, catch_warnings
from textwrap import dedent
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticke... | /scientific_plots-1.7.2-py3-none-any.whl/scientific_plots/default_plots.py | 0.945951 | 0.587795 | default_plots.py | pypi |
from __future__ import annotations
import csv
import locale
from contextlib import contextmanager
from copy import copy, deepcopy
from functools import wraps
from typing import (
Generator, Optional, Union, Callable, Any, overload)
from pathlib import Path
from warnings import warn, catch_warnings, simplefilter
fr... | /scientific_plots-1.7.2-py3-none-any.whl/scientific_plots/plot_settings.py | 0.744656 | 0.243817 | plot_settings.py | pypi |
from __future__ import print_function
import re
from functools import wraps
from subprocess import Popen, PIPE
from sys import __stdout__
from os import mkdir
from os.path import dirname, exists
from typing import Iterable, Optional, List, Callable, TypeVar, Union, Any
from pathlib import Path
from collections import ... | /scientific_plots-1.7.2-py3-none-any.whl/scientific_plots/utilities.py | 0.551815 | 0.288379 | utilities.py | pypi |
from __future__ import annotations
from os.path import join
from math import pi
from queue import Queue
from threading import Thread
from subprocess import check_output
from typing import (
List, Tuple, TypeVar, Union, Iterable, Any, Optional)
from pathlib import Path
import matplotlib as mpl
import matplotlib.py... | /scientific_plots-1.7.2-py3-none-any.whl/scientific_plots/two_d_plot.py | 0.901004 | 0.486392 | two_d_plot.py | pypi |
import re
from typing import Any, Callable, Iterable, List, Optional
# ========================================= What can be exported? =========================================
__all__ = ['strings_to_', 'strings_to_integers', 'strings_to_floats', 'string_to_float', 'match_one_string',
'match_one_pattern', ... | /scientific_string-0.1.0-py3-none-any.whl/scientific_string/__init__.py | 0.86852 | 0.540621 | __init__.py | pypi |
import matplotlib.pyplot as plt
import numpy as np
from scientific_tools.graphics.function_graphs import plot_2Dfunction
import scientific_tools.physics.uncertainty as uncertainty
def plot_uncertainty_function(f, u_f, min_x, max_x, values_number, args_before_x=[], args_after_x=[], title="", xlabel="", ylabel="", fu... | /scientific_tools-0.0.0a17-py3-none-any.whl/scientific_tools/graphics/uncertainty_graphs.py | 0.777469 | 0.784484 | uncertainty_graphs.py | pypi |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
def plot_2Dfunction(function, min_x, max_x, values_number, args_before_x=[], args_after_x=[], title="", xlabel="", ylabel="", function_label="", color="blue", linestyle ="-", **kwargs) :
"""Trace the 2D graphic of the function "function"... | /scientific_tools-0.0.0a17-py3-none-any.whl/scientific_tools/graphics/function_graphs.py | 0.562537 | 0.73053 | function_graphs.py | pypi |
"""Calculate standard uncertainty (standart uncertainty mainly)"""
from warnings import WarningMessage
import numpy as np
def standard_uncertainty(u_x, u_y, dz_dx, dz_dy) :
"""Calculate the standard uncertainty of z with the general formule."""
return np.sqrt((u_x*dz_dx)**2+(u_y*dz_dy)**2)
def standard_uncer... | /scientific_tools-0.0.0a17-py3-none-any.whl/scientific_tools/physics/uncertainty.py | 0.888414 | 0.783947 | uncertainty.py | pypi |
import sys
from argparse import ArgumentParser, RawTextHelpFormatter
from scientisst.constants import *
class ArgParser:
class MyParser(ArgumentParser):
def error(self, message):
sys.stderr.write("error: %s\n\n" % message)
self.print_help()
sys.exit(2)
def __init__... | /scientisst_sense-1.1.0-py3-none-any.whl/sense_src/arg_parser.py | 0.463444 | 0.206834 | arg_parser.py | pypi |
class InvalidAddressError(Exception):
"""
The specified address is invalid.
"""
def __init__(self):
super().__init__("The specified address is invalid.")
class BTAdapterNotFoundError(Exception):
"""
No Bluetooth adapter was found.
"""
def __init__(self):
super().__ini... | /scientisst_sense-1.1.0-py3-none-any.whl/scientisst/exceptions.py | 0.793346 | 0.191933 | exceptions.py | pypi |
# Discriminant Analysis with categorical variables (DISQUAL)
```
# Chargement des librairies
import numpy as np
import pandas as pd
#changement de dossier
import os
os.chdir("d:/Bureau/PythonProject/packages/scientisttools/data/")
DTrain = pd.read_excel("CongressVotePipeline.xlsx",sheet_name="train",header=0)
displa... | /scientisttools-0.0.8.tar.gz/scientisttools-0.0.8/notebooks/disqual_example.ipynb | 0.408277 | 0.755592 | disqual_example.ipynb | pypi |
# Additionnal functions
```
from scientisttools.utils import *
import numpy as np
from scipy.spatial.distance import pdist,squareform
# Match arg
lst = ["gaussian", "epanechnikov", "rectangular", "triangular"]
print(match_arg("gauss", lst))
print(match_arg("pauss", lst))
# is_euclidean
np.random.seed(123)
w = np.ar... | /scientisttools-0.0.8.tar.gz/scientisttools-0.0.8/notebooks/utils.ipynb | 0.407687 | 0.816736 | utils.ipynb | pypi |
# Canonical Discriminant Analysis on Iris dataset
```
from seaborn import load_dataset
import numpy as np
import pandas as pd
iris = load_dataset("iris")
print(iris.head())
# Chargement de la
from scientisttools.discriminant_analysis import CANDISC
candisc = CANDISC(n_components=2,
target=["spec... | /scientisttools-0.0.8.tar.gz/scientisttools-0.0.8/notebooks/candisc_iris.ipynb | 0.492432 | 0.766731 | candisc_iris.ipynb | pypi |
# Canonical Discriminant Analysis (CANDISC)
```
# Chargement des librairies
import numpy as np
import pandas as pd
import os
os.chdir("d:/Bureau/PythonProject/packages/scientisttools/data/")
# Chargement de la base
DTrain = pd.read_excel("Data_Illustration_Livre_ADL.xlsx",sheet_name="WINE",header=0)
DTrain.head()
... | /scientisttools-0.0.8.tar.gz/scientisttools-0.0.8/notebooks/candisc_wine.ipynb | 0.457137 | 0.660172 | candisc_wine.ipynb | pypi |
# Scierra
Scierra [_see-eh-rah_] is a **S**imulated **C**++ **I**nt**er**preter with **R**ecurrent **A**daptation.
In human words, it's a interactive interpreter for C++, which allows you to run and debug your program immediately as you type. Well, basically. But the implementation is slightly trickier.
To get a qui... | /scierra-0.6.1.tar.gz/scierra-0.6.1/README.md | 0.649023 | 0.851953 | README.md | pypi |
# sciex
Framework for "scientific" experiments (Result organization; Experiment and Trial setup; Baseline Comparisons)
This tool helps strip out the repetitive parts of setting up and running experiments, and lets you focus on writing the logic of trial running and result types. This reduces the stupid errors one may ... | /sciex-0.3.tar.gz/sciex-0.3/README.md | 0.874533 | 0.897874 | README.md | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019, Lluís Vilanova"
__license__ = "GPL version 3 or later"
# pylint: disable=no-name-in-module,import-error
from sciexp2.common import utils
from sciexp2.common.filter import Filter
# pylint: disable=redefined-builtin
def extract(template, function, filter=... | /sciexp2-expdata-0.1.7.tar.gz/sciexp2-expdata-0.1.7/sciexp2/expdata/pandas.py | 0.785144 | 0.382718 | pandas.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2009-2023, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import glob
import os
import six
import pydoc
from sciexp2.common import text
import sciexp2.expdef.system
#: Paths to search for available templates.
#:
#: The order of the list establishes w... | /sciexp2-expdef-2.0.13.tar.gz/sciexp2-expdef-2.0.13/sciexp2/expdef/templates.py | 0.645232 | 0.162579 | templates.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2009-2023, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import abc
import glob
import imp
import os
import shutil
import six
import weakref
import sciexp2.common.instance
from sciexp2.common.filter import *
from sciexp2.common import text
from sciexp... | /sciexp2-expdef-2.0.13.tar.gz/sciexp2-expdef-2.0.13/sciexp2/expdef/system/__init__.py | 0.741955 | 0.205416 | __init__.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2013-2023, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import collections
import contextlib
import multiprocessing
import multiprocessing.pool
from . import utils
#: Default amount of parallelism.
PARALLELISM = True
def get_parallelism(paralleli... | /sciexp2-expdef-2.0.13.tar.gz/sciexp2-expdef-2.0.13/sciexp2/common/parallel.py | 0.849285 | 0.354266 | parallel.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2008-2023, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import collections.abc
import re
import linecache
def _re_match(value, pattern):
cre = re.compile(pattern)
return cre.match(value) is not None
class Filter:
"""Boolean expression ... | /sciexp2-expdef-2.0.13.tar.gz/sciexp2-expdef-2.0.13/sciexp2/common/filter.py | 0.811116 | 0.495178 | filter.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2008-2023, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import os
import shutil
import signal
import subprocess
import tempfile
import functools
import collections
import weakref
import numpy as np
import six
from . import pp
from . import progress
... | /sciexp2-expdef-2.0.13.tar.gz/sciexp2-expdef-2.0.13/sciexp2/common/utils.py | 0.624064 | 0.166167 | utils.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2023, Lluís Vilanova"
__license__ = "GPL version 3 or later"
from collections import OrderedDict
try:
from collections.abc import Mapping
except:
pass
import pystache
import re
from .utils import OrderedSet
import six
import sys
class ParseError(... | /sciexp2-expdef-2.0.13.tar.gz/sciexp2-expdef-2.0.13/sciexp2/common/text.py | 0.569613 | 0.231788 | text.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import collections
import re
from . import kernel
def set_freq(shell, path="cpupower", ld_library_path="", freq="max"):
"""Set frequency scaling.
Parameters
----------
shell
... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/cpu.py | 0.815416 | 0.357343 | cpu.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
from contextlib import contextmanager
import joblib
@contextmanager
def step(message, logger=print):
"""Show simple progress messages around a piece of code.
Parameters
----------... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/util.py | 0.775095 | 0.168446 | util.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import logging
from . import wait
logger = logging.getLogger(__name__)
def check_version(shell, version, fail=True):
"""Check that a specific linux kernel version is installed.
Param... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/kernel.py | 0.780244 | 0.165593 | kernel.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
from . import spur
def install(shell, package):
"""Install given `package` using `shell`."""
if spur.is_ssh_shell(shell):
hostname = shell.hostname
else:
hostname =... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/files.py | 0.560012 | 0.215846 | files.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import re
import io
import time
from . import spur
def run(shell, *args, **kwargs):
"""Run command with a timeout.
Parameters
----------
shell
Shell used to run given co... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/wait.py | 0.738292 | 0.18352 | wait.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import collections
import os
import re
import six
# pylint: disable=redefined-builtin
def get_tids(shell, pid, filter=None):
"""Get ids of all threads in a given process.
Parameters
... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/process.py | 0.808748 | 0.251947 | process.py | pypi |
__author__ = "Lluís Vilanova"
__copyright__ = "Copyright 2019-2020, Lluís Vilanova"
__license__ = "GPL version 3 or later"
import atexit
import collections
import logging
import os
import signal
import sys
import threading
import time
import traceback
import six
import spur
import spur.ssh
_LOGGER = logging.getLog... | /sciexp2-exprun-0.3.3.tar.gz/sciexp2-exprun-0.3.3/sciexp2/exprun/spur.py | 0.561696 | 0.15746 | spur.py | pypi |
<p align="center">
<img src="https://raw.githubusercontent.com/SciFin-Team/SciFin/master/docs/logos/logo_scifin_github.jpg" width=400 title="hover text">
</p>
# SciFin
SciFin is a python package for Science and Finance.
## Summary
The SciFin package is a Python package designed to gather and develop methods fo... | /SciFin-0.1.0.tar.gz/SciFin-0.1.0/README.md | 0.700383 | 0.895751 | README.md | pypi |
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='AspectActlog',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serial... | /sciflow-0.2.tar.gz/sciflow-0.2/datafiles/migrations/0001_initial.py | 0.589598 | 0.17575 | 0001_initial.py | pypi |
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Descriptors',
fields=[
('id', models.AutoField(auto_create... | /sciflow-0.2.tar.gz/sciflow-0.2/substances/migrations/0001_initial.py | 0.573559 | 0.160135 | 0001_initial.py | pypi |
from datafiles.df_functions import *
from pathlib import Path
from sciflow.settings import *
def testimport():
""" import test data from static/files in the DB"""
folder = Path(BASE_DIR + "/static/files/")
for file in folder.iterdir():
if str(file).endswith('.jsonld'):
filename = str(f... | /sciflow-0.2.tar.gz/sciflow-0.2/datasets/ds_functions.py | 0.450359 | 0.306611 | ds_functions.py | pypi |
import json
from typing import Literal
import re
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from tabulate import tabulate
from sciform import Formatter, ExpMode, RoundMode, SignMode, FormatOptions
def get_scale_and_offset_from_offset_str(
ax: plt.Axes, axis: Lite... | /sciform-0.28.2.tar.gz/sciform-0.28.2/examples/fit_plot_with_sciform.py | 0.718989 | 0.425247 | fit_plot_with_sciform.py | pypi |
# scify-file-reader
The scify-file-reader package provides a convenient class for handling multiple files with the same structure in a directory. It offers functionality to read and process data from various file types, including CSV, XLSX, Parquet, and JSON.
## Installation
You can install scify-file-reader using pi... | /scify-file-reader-0.0.2.tar.gz/scify-file-reader-0.0.2/README.md | 0.892829 | 0.888324 | README.md | pypi |
import os
import re
import zipfile
from io import BytesIO
from pathlib import Path
from typing import Union, IO, Tuple
import pandas as pd
import pyarrow.parquet as pq
class FileReader:
"""
A class to handle and process multiple files with identical structures within a directory or a zip archive.
Args:
... | /scify-file-reader-0.0.2.tar.gz/scify-file-reader-0.0.2/scify_file_reader/file_reader.py | 0.738858 | 0.297011 | file_reader.py | pypi |
from scipy.spatial import cKDTree as KDTree
import numpy as np
class IDW(object):
"""
# https://mail.python.org/pipermail/scipy-user/2010-June/025920.html
# https://github.com/soonyenju/pysy/blob/master/pysy/scigeo.py
inverse-distance-weighted interpolation using KDTree:
invdisttree = Invdisttree(... | /scigeo-0.0.10.tar.gz/scigeo-0.0.10/scigeo-history-versions/scigeo-0.0.2/geobox.py | 0.884601 | 0.489503 | geobox.py | pypi |
import math
import datetime
class Sunriseset:
def __init__(self, timestamp = None, format = r"%Y-%m-%d"):
if isinstance(timestamp, str):
timestamp = datetime.datetime.strptime(timestamp, format)
self.timestamp = timestamp
def __call__(self, lon, lat):
coords = {'longitude' ... | /scigeo-0.0.10.tar.gz/scigeo-0.0.10/scigeo-history-versions/scigeo-0.0.2/sun.py | 0.596551 | 0.399812 | sun.py | pypi |
from pathlib import Path
from shapely.geometry import Polygon
import rasterio as rio
from rasterio.mask import mask
from rasterio.enums import Resampling
import geopandas as gpd
import warnings
import numpy as np
class Raster(object):
"""
the wrapper of rasterio
"""
def __init__(self, path):
s... | /scigeo-0.0.10.tar.gz/scigeo-0.0.10/scigeo-history-versions/scigeo-0.0.2/geoface.py | 0.643553 | 0.396594 | geoface.py | pypi |
import hashlib
import json
import random
import re
import time
from typing import Optional
import requests
from scihub_cn.models import PaperDetailDescription
def translate(content: str, proxy=None) -> str:
"""对文本content进行翻译"""
lts = str(int(time.time() * 1000))
salt = lts + str(random.randint(0, 9))
... | /scihub-cn-0.1.1.tar.gz/scihub-cn-0.1.1/scihub_cn/utils.py | 0.545528 | 0.162945 | utils.py | pypi |
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