repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
flair | flair-master/flair/trainers/plugins/functional/__init__.py | 0 | 0 | 0 | py | |
flair | flair-master/flair/trainers/plugins/functional/anneal_on_plateau.py | import logging
import os
from flair.trainers.plugins.base import TrainerPlugin, TrainingInterrupt
from flair.trainers.plugins.metric_records import MetricRecord
from flair.training_utils import AnnealOnPlateau
log = logging.getLogger("flair")
class AnnealingPlugin(TrainerPlugin):
"""Plugin for annealing logic i... | 3,975 | 31.859504 | 132 | py |
flair | flair-master/tests/test_lemmatizer.py | import torch
import flair
from flair.data import Sentence
from flair.models import Lemmatizer
def test_words_to_char_indices():
sentence = Sentence("Hello look what a beautiful day!")
lemmatizer = Lemmatizer() # lemmatizer uses standard char dictionary
d = lemmatizer.dummy_index
e = lemmatizer.end... | 1,907 | 33.690909 | 116 | py |
flair | flair-master/tests/test_labels.py | from typing import List
from flair.data import Label, Relation, Sentence, Span
def test_token_tags():
# example sentence
sentence = Sentence("I love Berlin")
# set 4 labels for 2 tokens ('love' is tagged twice)
sentence[1].add_label("pos", "verb")
sentence[1].add_label("sentiment", "positive")
... | 10,789 | 37.673835 | 80 | py |
flair | flair-master/tests/test_datasets.py | import copy
import shutil
import pytest
import flair
import flair.datasets
from flair.data import MultiCorpus, Sentence
from flair.datasets import ColumnCorpus
from flair.datasets.sequence_labeling import (
ONTONOTES,
JsonlCorpus,
JsonlDataset,
MultiFileJsonlCorpus,
)
def test_load_imdb_data(tasks_b... | 32,776 | 34.168455 | 120 | py |
flair | flair-master/tests/test_tokenize_sentence.py | from typing import List
import pytest
import flair
from flair.data import Sentence, Token
from flair.splitter import (
NewlineSentenceSplitter,
NoSentenceSplitter,
SciSpacySentenceSplitter,
SegtokSentenceSplitter,
SpacySentenceSplitter,
TagSentenceSplitter,
)
from flair.tokenization import (
... | 16,472 | 34.967249 | 115 | py |
flair | flair-master/tests/embedding_test_utils.py | from typing import Any, Dict, List, Optional, Type
import pytest
import torch
from flair.data import Sentence
from flair.embeddings import Embeddings
from flair.embeddings.base import load_embeddings
class BaseEmbeddingsTest:
embedding_cls: Type[Embeddings[Sentence]]
is_token_embedding: bool
is_document... | 7,511 | 39.387097 | 113 | py |
flair | flair-master/tests/test_trainer.py | import pytest
from torch.optim import Adam
import flair
from flair.data import Sentence
from flair.datasets import ClassificationCorpus
from flair.embeddings import DocumentPoolEmbeddings, FlairEmbeddings, WordEmbeddings
from flair.models import SequenceTagger, TextClassifier
from flair.trainers import ModelTrainer
t... | 4,999 | 28.585799 | 118 | py |
flair | flair-master/tests/test_language_model.py | import pytest
from flair.data import Dictionary, Sentence
from flair.embeddings import FlairEmbeddings, TokenEmbeddings
from flair.models import LanguageModel
from flair.trainers.language_model_trainer import LanguageModelTrainer, TextCorpus
@pytest.mark.integration()
def test_train_language_model(results_base_path,... | 4,360 | 36.594828 | 109 | py |
flair | flair-master/tests/test_tars.py | from flair.data import Sentence
from flair.datasets import ClassificationCorpus
from flair.models import TARSClassifier
from flair.trainers import ModelTrainer
def test_init_tars_and_switch(tasks_base_path):
# test corpus
corpus = ClassificationCorpus(tasks_base_path / "imdb")
# create a TARS classifier
... | 2,404 | 31.945205 | 115 | py |
flair | flair-master/tests/conftest.py | from pathlib import Path
import pytest
import torch
import flair
@pytest.fixture(scope="module")
def resources_path():
return Path(__file__).parent / "resources"
@pytest.fixture(scope="module")
def tasks_base_path(resources_path):
return resources_path / "tasks"
@pytest.fixture()
def results_base_path(r... | 1,620 | 23.19403 | 89 | py |
flair | flair-master/tests/test_corpus_dictionary.py | import os
import pytest
import flair
from flair.data import Corpus, Dictionary, Label, Sentence
from flair.datasets import ColumnCorpus, FlairDatapointDataset, SentenceDataset
def test_dictionary_get_items_with_unk():
dictionary: Dictionary = Dictionary(add_unk=True)
dictionary.add_item("class_1")
dict... | 10,036 | 30.662461 | 106 | py |
flair | flair-master/tests/model_test_utils.py | from typing import Any, Dict, List, Optional, Type
import pytest
import flair
from flair.data import Dictionary, Sentence
from flair.embeddings import TransformerEmbeddings
from flair.models import FewshotClassifier
from flair.nn import Model
from flair.trainers import ModelTrainer
class BaseModelTest:
model_cl... | 8,882 | 36.167364 | 118 | py |
flair | flair-master/tests/test_visual.py | from flair.data import Sentence, Span, Token
from flair.embeddings import FlairEmbeddings
from flair.visual import Highlighter
from flair.visual.ner_html import HTML_PAGE, PARAGRAPH, TAGGED_ENTITY, render_ner_html
from flair.visual.training_curves import Plotter
def test_highlighter(resources_path):
with (resourc... | 2,506 | 31.141026 | 95 | py |
flair | flair-master/tests/test_datasets_biomedical.py | import inspect
import logging
import os
import tempfile
from operator import itemgetter
from pathlib import Path
from typing import Callable, List, Optional, Type
import pytest
from tqdm import tqdm
import flair
from flair.data import Sentence, Token, _iter_dataset
from flair.datasets import ColumnCorpus, biomedical
... | 13,573 | 34.815303 | 118 | py |
flair | flair-master/tests/test_sentence.py | from flair.data import Sentence
def test_sentence_context():
# make a sentence and some right context
sentence = Sentence("George Washington ging nach Washington.")
sentence._next_sentence = Sentence("Das ist eine schöne Stadt.")
assert sentence.right_context(1) == [sentence._next_sentence[0]]
as... | 2,992 | 38.381579 | 120 | py |
flair | flair-master/tests/__init__.py | 0 | 0 | 0 | py | |
flair | flair-master/tests/test_multitask.py | import pytest
import flair
from flair.data import Sentence
from flair.datasets import SENTEVAL_CR, SENTEVAL_SST_GRANULAR
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import MultitaskModel, TextClassifier
from flair.nn.multitask import make_multitask_model_and_corpus
from flair.trainers ... | 2,209 | 31.5 | 117 | py |
flair | flair-master/tests/test_utils.py | from flair.data import Dictionary
from flair.training_utils import convert_labels_to_one_hot
def test_convert_labels_to_one_hot():
label_dict = Dictionary(add_unk=False)
label_dict.add_item("class-1")
label_dict.add_item("class-2")
label_dict.add_item("class-3")
one_hot = convert_labels_to_one_ho... | 440 | 26.5625 | 66 | py |
flair | flair-master/tests/models/test_relation_classifier.py | from operator import itemgetter
from typing import Dict, List, Optional, Set, Tuple
import pytest
from torch.utils.data import Dataset
from flair.data import Relation, Sentence
from flair.datasets import ColumnCorpus, DataLoader
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import Relat... | 9,948 | 40.627615 | 118 | py |
flair | flair-master/tests/models/test_sequence_tagger.py | import pytest
import flair
from flair.embeddings import FlairEmbeddings, WordEmbeddings
from flair.models import SequenceTagger
from flair.trainers import ModelTrainer
from tests.model_test_utils import BaseModelTest
class TestSequenceTagger(BaseModelTest):
model_cls = SequenceTagger
pretrained_model = "ner-... | 4,859 | 38.193548 | 120 | py |
flair | flair-master/tests/models/test_relation_extractor.py | import pytest
from flair.data import Sentence
from flair.datasets import ColumnCorpus
from flair.embeddings import TransformerWordEmbeddings
from flair.models import RelationExtractor
from tests.model_test_utils import BaseModelTest
class TestRelationExtractor(BaseModelTest):
model_cls = RelationExtractor
tr... | 2,183 | 33.666667 | 102 | py |
flair | flair-master/tests/models/test_tars_classifier.py | import pytest
from flair.data import Sentence
from flair.datasets import ClassificationCorpus
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import TARSClassifier
from tests.model_test_utils import BaseModelTest
class TestTarsClassifier(BaseModelTest):
model_cls = TARSClassifier
... | 4,232 | 38.194444 | 119 | py |
flair | flair-master/tests/models/test_text_regressor.py | import pytest
import flair
from flair.embeddings import DocumentRNNEmbeddings, WordEmbeddings
from flair.models.text_regression_model import TextRegressor
from tests.model_test_utils import BaseModelTest
class TestTextRegressor(BaseModelTest):
model_cls = TextRegressor
train_label_type = "regression"
tra... | 1,016 | 31.806452 | 116 | py |
flair | flair-master/tests/models/test_entity_linker.py | import pytest
from flair.data import Sentence
from flair.datasets import NEL_ENGLISH_AIDA
from flair.embeddings import TransformerWordEmbeddings
from flair.models import EntityLinker
from tests.model_test_utils import BaseModelTest
class TestEntityLinker(BaseModelTest):
model_cls = EntityLinker
train_label_t... | 1,132 | 28.815789 | 102 | py |
flair | flair-master/tests/models/__init__.py | 0 | 0 | 0 | py | |
flair | flair-master/tests/models/test_tars_ner.py | import pytest
import flair
from flair.data import Sentence
from flair.embeddings import TransformerWordEmbeddings
from flair.models import TARSTagger
from tests.model_test_utils import BaseModelTest
class TestTarsTagger(BaseModelTest):
model_cls = TARSTagger
train_label_type = "ner"
model_args = {"task_n... | 3,630 | 34.950495 | 120 | py |
flair | flair-master/tests/models/test_text_classifier.py | import pytest
import flair.datasets
from flair.data import Sentence
from flair.embeddings import DocumentRNNEmbeddings, FlairEmbeddings, WordEmbeddings
from flair.models import TextClassifier
from flair.samplers import ImbalancedClassificationDatasetSampler
from flair.trainers import ModelTrainer
from tests.model_test... | 4,076 | 39.77 | 118 | py |
flair | flair-master/tests/models/test_word_tagger.py | import pytest
import flair
from flair.embeddings import TransformerWordEmbeddings
from flair.models import TokenClassifier
from tests.model_test_utils import BaseModelTest
class TestWordTagger(BaseModelTest):
model_cls = TokenClassifier
train_label_type = "pos"
training_args = {
"max_epochs": 2,
... | 1,212 | 26.568182 | 67 | py |
flair | flair-master/tests/embeddings/test_word_embeddings.py | from typing import Any, Dict
from flair.embeddings import MuseCrosslingualEmbeddings, NILCEmbeddings, WordEmbeddings
from tests.embedding_test_utils import BaseEmbeddingsTest
class TestWordEmbeddings(BaseEmbeddingsTest):
embedding_cls = WordEmbeddings
is_token_embedding = True
is_document_embedding = Fal... | 1,033 | 30.333333 | 87 | py |
flair | flair-master/tests/embeddings/test_byte_pair_embeddings.py | from flair.embeddings import BytePairEmbeddings
from tests.embedding_test_utils import BaseEmbeddingsTest
class TestBytePairEmbeddings(BaseEmbeddingsTest):
embedding_cls = BytePairEmbeddings
is_token_embedding = True
is_document_embedding = False
default_args = {"language": "en"}
| 299 | 29 | 57 | py |
flair | flair-master/tests/embeddings/test_transformer_word_embeddings.py | import importlib.util
import warnings
import pytest
import torch
from PIL import Image
from transformers.utils import is_detectron2_available
from flair.data import BoundingBox, Dictionary, Sentence
from flair.embeddings import TransformerJitWordEmbeddings, TransformerWordEmbeddings
from flair.models import SequenceT... | 15,110 | 45.352761 | 120 | py |
flair | flair-master/tests/embeddings/test_flair_embeddings.py | from flair.data import Dictionary, Sentence
from flair.embeddings import (
DocumentLMEmbeddings,
DocumentRNNEmbeddings,
FlairEmbeddings,
)
from flair.models import LanguageModel
from tests.embedding_test_utils import BaseEmbeddingsTest
class TestFlairEmbeddings(BaseEmbeddingsTest):
embedding_cls = Fla... | 1,707 | 30.054545 | 119 | py |
flair | flair-master/tests/embeddings/test_simple_token_embeddings.py | from flair.data import Dictionary
from flair.embeddings import CharacterEmbeddings, HashEmbeddings, OneHotEmbeddings
from tests.embedding_test_utils import BaseEmbeddingsTest
vocab_dictionary = Dictionary(add_unk=True)
vocab_dictionary.add_item("I")
vocab_dictionary.add_item("love")
vocab_dictionary.add_item("berlin")... | 923 | 29.8 | 82 | py |
flair | flair-master/tests/embeddings/test_stacked_embeddings.py | from flair.data import Sentence
from flair.embeddings import (
FlairEmbeddings,
StackedEmbeddings,
TokenEmbeddings,
WordEmbeddings,
)
from flair.embeddings.base import load_embeddings
def test_stacked_embeddings():
glove: TokenEmbeddings = WordEmbeddings("turian")
flair_embedding: TokenEmbeddi... | 1,851 | 33.296296 | 95 | py |
flair | flair-master/tests/embeddings/test_transformer_document_embeddings.py | from flair.data import Dictionary
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import TextClassifier
from flair.nn import Classifier
from tests.embedding_test_utils import BaseEmbeddingsTest
class TestTransformerDocumentEmbeddings(BaseEmbeddingsTest):
embedding_cls = TransformerDoc... | 1,549 | 37.75 | 104 | py |
flair | flair-master/tests/embeddings/__init__.py | 0 | 0 | 0 | py | |
flair | flair-master/tests/embeddings/test_tfidf_embeddings.py | from flair.data import Sentence
from flair.embeddings import DocumentTFIDFEmbeddings
from tests.embedding_test_utils import BaseEmbeddingsTest
class TFIDFEmbeddingsTest(BaseEmbeddingsTest):
embedding_cls = DocumentTFIDFEmbeddings
is_document_embedding = True
is_token_embedding = False
default_args = ... | 508 | 27.277778 | 57 | py |
flair | flair-master/tests/embeddings/test_document_transform_word_embeddings.py | from typing import Any, Dict, List
from flair.embeddings import (
DocumentCNNEmbeddings,
DocumentLMEmbeddings,
DocumentPoolEmbeddings,
DocumentRNNEmbeddings,
FlairEmbeddings,
TokenEmbeddings,
WordEmbeddings,
)
from tests.embedding_test_utils import BaseEmbeddingsTest
word: TokenEmbeddings ... | 2,301 | 33.358209 | 91 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/DA_preparation.py | # -*- coding: utf-8 -*-
# assimilation shallow water
import numpy as np
def VAR_3D(xb,Y,H,B,R): #booleen=0 garde la trace
dim_x = xb.size
#dim_y = Y.size
Y.shape = (Y.size,1)
xb1=np.copy(xb)
xb1.shape=(xb1.size,1)
K=np.dot(B,np.dot(np.transpose(H),np.linalg.pinv(np.dot(H,np.dot(B,np.transpose(... | 1,041 | 25.05 | 129 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/prediction_plotting.py | # %%
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
import matplotlib.pyplot as plt
#
data=np.load('data2/trainset_withx_repeat_shwater3_uniform0011_test6_1114.npy').astype(np.float32)
data1=np.load('data2/trainset_withx_repeat_shwater... | 12,529 | 31.973684 | 109 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/data_generation_no_v_20.py | #shallow water propagation
"""
Solution of Shallow-water equations using a Python class.
Adapted for Python training course at CNRS from https://github.com/mrocklin/ShallowWater/
Dmitry Khvorostyanov, 2015
CNRS/LMD/IPSL, dmitry.khvorostyanov @ lmd.polytechnique.fr
"""
import time
from pylab import *
import matplotli... | 7,057 | 25.335821 | 176 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/shallowwater.py | """
Solution of Shallow-water equations using a Python class.
Adapted for Python training course at CNRS from https://github.com/mrocklin/ShallowWater/
Dmitry Khvorostyanov, 2015
CNRS/LMD/IPSL, dmitry.khvorostyanov @ lmd.polytechnique.fr
"""
import time
from pylab import *
import matplotlib.gridspec as gridspec
impor... | 9,745 | 24.989333 | 169 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/simulated_data_generation.py | #shallow water propagation
"""
Solution of Shallow-water equations using a Python class.
Adapted for Python training course at CNRS from https://github.com/mrocklin/ShallowWater/
Dmitry Khvorostyanov, 2015
CNRS/LMD/IPSL, dmitry.khvorostyanov @ lmd.polytechnique.fr
"""
import time
from pylab import *
import matplotli... | 7,041 | 25.276119 | 176 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/constructB.py | # coding: utf8
#construction of matrix B and special H with measure on the boarder
import numpy as np
import math
from scipy.linalg import sqrtm
from shallowwater import *
##def B_Balgovind(n,Sigma,L):
## Gamma = np.identity(n)
## for i in xrange(n):
## for j in xrange(n):
## Gamma[i,j] = ( 1. ... | 4,880 | 31.324503 | 84 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/shallowwater_lstm1000_model.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import tensorflow.keras.backend as K
#... | 6,880 | 30.135747 | 157 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/shallowwater_lstm200_model.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import tensorflow.keras.backend as K
#... | 6,889 | 29.622222 | 157 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lstmR_d05R_plotting.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import matplotlib.pyplot as plt
# check scikit-learn version
# check scikit-learn version
import pandas as pd
# def data_set_order(file):
# train_data = np.array(pd.read_csv(file))
# r0=train_data[:,:1... | 9,710 | 29.731013 | 101 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lorenz_lstm1000.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import keras.backend as K
imp... | 6,328 | 25.817797 | 121 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/simulated_data_generation.py | # -*- coding: utf-8 -*-
# generate the trainning set for keras regression
import numpy as np
from scipy.optimize import fmin
from scipy.optimize import fmin_l_bfgs_b
#from scipy.optimize import fmin_ncg
from scipy.linalg import sqrtm
import math
from constructB import *
from lorentz_attractor import *
import matp... | 5,902 | 34.993902 | 192 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/constructB.py | # coding: utf8
#construction of matrix B and special H with measure on the boarder
import numpy as np
import math
from scipy.linalg import sqrtm
##def B_Balgovind(n,Sigma,L):
## Gamma = np.identity(n)
## for i in xrange(n):
## for j in xrange(n):
## Gamma[i,j] = ( 1. + abs(i-j)/L)*np.exp(-abs(i... | 3,935 | 32.355932 | 130 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lorentz_attractor.py | # -*- coding: utf-8 -*-
# lorentz system
import numpy as np
import time
import random
import matplotlib.pyplot as plt
import itertools
import math
from constructB import *
# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
def... | 5,328 | 30.720238 | 119 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lorenz_lstm200.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import keras.backend as K
# c... | 6,201 | 25.618026 | 121 | py |
PMEmo | PMEmo-master/features.py | #! usr/bin/env python3
# -*- coding: utf-8 -*-
'''
This features.py is used to extract audio features based on openSIMLE.
Require: openSMILE-2.2rc1
OpenSMILE only support audios in WAV format,
so before using this script you could
transform MP3s into WAVs by transformat.sh.
'''
__author__ = 'huizhang'
import csv
im... | 14,123 | 81.116279 | 8,566 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/setup.py | import pathlib
from setuptools import setup
# The directory containing this file
HERE = pathlib.Path(__file__).parent
# The text of the README file
README = (HERE / "README.md").read_text()
# This call to setup() does all the work
setup(
name="explainable_ai_image_measures",
version="1.0.1",
description=... | 989 | 32 | 91 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/scoring_metric.py | import numpy as np
import torch
from sklearn.metrics import auc
import torch.nn.functional as F
from explainable_ai_image_measures.irof import IrofDataset
from explainable_ai_image_measures.pixel_relevancy import PixelRelevancyDataset
class Measures:
def __init__(self,
model,
ba... | 7,637 | 37.771574 | 120 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/pixel_manipulation.py | import torch
from torch.utils.data import Dataset
import abc
class PixelManipulationBase(Dataset):
"""
Requires that self._pixel_batches is defined in the constructor
"""
def __init__(self, image, attribution, insert, batch_size, device, baseline_color):
self._image = image
self._batc... | 5,848 | 35.55625 | 118 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/irof.py | import torch
import numpy as np
from skimage.segmentation import slic
from explainable_ai_image_measures.pixel_manipulation import PixelManipulationBase
class IrofDataset(PixelManipulationBase):
def __init__(
self, image, attribution, batch_size, irof_segments, irof_sigma, device, baseline_color
):
... | 3,308 | 35.766667 | 95 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/pixel_relevancy.py | import torch
from explainable_ai_image_measures.pixel_manipulation import PixelManipulationBase
class PixelRelevancyDataset(PixelManipulationBase):
def __init__(self, image, attribution, insert, batch_size, package_size, device, baseline_color):
PixelManipulationBase.__init__(
self, image, at... | 2,552 | 41.55 | 101 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/__init__.py | from explainable_ai_image_measures.scoring_metric import Measures
__version__ = "1.0.1"
__all__ = ["Measures"]
| 112 | 21.6 | 65 | py |
ellip-corr | ellip-corr-master/PyEllipCorr.py | import os
from numpy import f2py
import numpy as np
from collections import defaultdict
from pyellip import select_phase, coeffs, ellip
class PyEllipCorr:
def __init__(self):
self._tbl_fn = os.path.join(os.path.dirname(__file__), 'ellip/elcordir.tbl')
self._coeffs = defaultdict(list)
# end func... | 1,696 | 32.94 | 95 | py |
ellip-corr | ellip-corr-master/__init__.py | 0 | 0 | 0 | py | |
ellip-corr | ellip-corr-master/tests/test_ellipticity_corr.py | from __future__ import print_function
import pytest
from PyEllipCorr import PyEllipCorr
"""
The values below were extracted from ttimel
"""
"""
Source latitude: -30
Source depth (km): 124
Azimuth from source: 39
delta: 65
# code time time(el) dT/dD dT/dh d2T/dD2
"""
data= """ 1 ... | 3,076 | 42.957143 | 80 | py |
ellip-corr | ellip-corr-master/ellip/__init__.py | 0 | 0 | 0 | py | |
ellip-corr | ellip-corr-master/tau/__init__.py | 0 | 0 | 0 | py | |
zpdgen | zpdgen-master/itg_ai_loc.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
from scipy.optimize import root
etai=2.5
LnbyR=0.2
rbyR=0.18
kpar=0.1
tau=1.0
def epsfun(v):
om=v[0]+1j*v[1]
omsi=-ky
omdi=2*omsi*LnbyR
za=-om/omdi
zb=-np.sqrt(2)*kpar/omdi
b=ky**2
i10=gp.Inm(za,zb,b,1,0)
i12=gp.Inm(z... | 1,029 | 21.888889 | 69 | py |
zpdgen | zpdgen-master/itg_ai_loc_mat.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
from scipy.optimize import root
etai=2.5
LnbyR=0.2
rbyR=0.18
kpar=0.1
tau=1.0
def epsfun(v):
om=v[0]+1j*v[1]
omsi=-ky
omdi=2*omsi*LnbyR
za=-om/omdi
zb=-np.sqrt(2)*kpar/omdi
b=ky**2
anm=np.zeros((4,3),dtype=np.complex128)
... | 1,084 | 22.085106 | 68 | py |
zpdgen | zpdgen-master/plot_eps.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
etai=2.5
LnbyR=0.2
rbyR=0.18
kpar=0.1
tau=1.0
ky=0.06
def epsfun(v):
om=v
omsi=-ky
omdi=2*omsi*LnbyR
za=-om/omdi
zb=-kpar/omdi*np.sqrt(2)
b=ky**2
i10=gp.Inm(za,zb,b,1,0)
i12=gp.Inm(za,zb,b,1,2)
i30=gp.Inm(za,zb,b,... | 908 | 21.170732 | 86 | py |
zpdgen | zpdgen-master/py_time.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
import time
nlist=[[1,0],[1,2],[3,0]]
ii=0;
xx,yy=np.meshgrid(np.arange(-6,6,0.1),np.arange(-6,6,0.1))
za=xx+1j*yy;
zb=0.0
b=0.09
for ns in nlist:
ii=ii+1;
[n,m]=ns;
print('computing I'+str(n)+str(m)+' ...')
t0 = time.clock()
inm=g... | 376 | 19.944444 | 58 | py |
zpdgen | zpdgen-master/gpdf.py | from numpy import reshape,shape,rank,transpose
from inmzpd import inmweid,gmweid
from epszpd import epsweid,sigweid
def Inm(za,zb,b,n,m):
res=inmweid(za,zb,b,n,m)
if (shape(za) ==()):
res=res[0]
return res
res=reshape(res,shape(transpose(za)))
res=transpose(res)
return res
def epsi... | 1,280 | 22.290909 | 54 | py |
zpdgen | zpdgen-master/plot_inm_re.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
nlist=[[1,0],[2,0],[3,0],[1,2],[2,2],[3,2],[1,4],[2,4],[3,4]]
cnts=np.arange(-2,2.2,0.2)
wdts=np.ones(cnts.shape);
wdts[0]=2.0
wdts[5]=2.0
wdts[10]=4.0
wdts[15]=2.0
wdts[20]=2.0
vcb = np.arange(-12,14,2)
ii=0;
xx,yy=np.meshgrid(np.arange(-6,6,0.1),np.... | 1,970 | 35.5 | 97 | py |
zpdgen | zpdgen-master/plot_itg_jykim94.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
from scipy.optimize import root
etai=2.5
LnbyR=0.2
rbyR=0.18
kpar=0.1
tau=1.0
def epsfun(v):
om=v[0]+1j*v[1]
omsi=-ky
omdi=2*omsi*LnbyR
za=-om/omdi
zb=-np.sqrt(2)*kpar/omdi
b=ky**2
i10=gp.Inm(za,zb,b,1,0)
i12=gp.Inm(z... | 1,715 | 24.61194 | 69 | py |
zpdgen | zpdgen-master/itg_ai_loc_comb.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
from scipy.optimize import root
etai=2.5
LnbyR=0.2
rbyR=0.18
kpar=0.1
tau=1.0
def epsfun(v):
om=v[0]+1j*v[1]
omsi=-ky
omdi=2*omsi*LnbyR
za=-om/omdi
zb=-np.sqrt(2)*kpar/omdi
b=ky**2
pars=(omdi,etai,tau,ky,kpar)
eps=gp.... | 935 | 20.767442 | 68 | py |
zpdgen | zpdgen-master/plot_inm_im.py | import numpy as np
import matplotlib.pyplot as plt
import gpdf as gp
nlist=[[1,0],[2,0],[3,0],[1,2],[2,2],[3,2],[1,4],[2,4],[3,4]]
cnts=np.arange(-2,2.2,0.2)
wdts=np.ones(cnts.shape);
wdts[0]=2.0
wdts[5]=2.0
wdts[10]=4.0
wdts[15]=2.0
wdts[20]=2.0
vcb = np.arange(-12,14,2)
ii=0;
xx,yy=np.meshgrid(np.arange(-6,6,0.1),np.... | 1,970 | 35.5 | 97 | py |
GAStimator | GAStimator-master/setup.py | from setuptools import setup
with open("README.md", "r") as fh:
long_description = fh.read()
setup(name='gastimator',
version='0.4.7',
description='Implementation of a Python MCMC gibbs-sampler with adaptive stepping',
url='https://github.com/TimothyADavis/GAStimator',
author='Timoth... | 962 | 29.09375 | 90 | py |
GAStimator | GAStimator-master/gastimator/priors.py | #!/usr/bin/env python3
# coding: utf-8
import numpy as np
class priors:
def __init__():
pass
class gaussian:
def __init__(self,mu,sigma):
self.mu=mu
self.sigma=sigma
def eval(self,x,**kwargs):
xs = (x - self.mu) / self.sigma
... | 422 | 22.5 | 92 | py |
GAStimator | GAStimator-master/gastimator/__init__.py | #!/usr/bin/env python3
# coding: utf-8
from gastimator.gastimator import gastimator
from gastimator.priors import priors | 120 | 29.25 | 44 | py |
GAStimator | GAStimator-master/gastimator/corner_plot.py | ##############################################################################
#
# This is the history of Michele Cappellari modifications
# to Daniel Foreman-Mackey corner_plot routine.
#
# V1.0.0: Included "like" and "xstry" optional inputs
# to show individual points coloured by their likelihood
# and to show al... | 19,149 | 35.406844 | 97 | py |
GAStimator | GAStimator-master/gastimator/gastimator.py | #!/usr/bin/env python3
# coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from joblib import Parallel, delayed,cpu_count
from joblib.externals.loky import get_reusable_executor
def lnlike(data,model,error):
# default log-likelihood function
chi2=np.nansum((data - model)**2... | 16,793 | 38.146853 | 312 | py |
python-pesq | python-pesq-master/setup.py | from setuptools import setup, Extension, find_packages
from setuptools.command.build_ext import build_ext as _build_ext
import os
includes = ['pypesq']
try:
import numpy as np
includes += [os.path.join(np.get_include(), 'numpy')]
except:
pass
extension = Extension("pesq_core",
source... | 1,498 | 30.893617 | 120 | py |
python-pesq | python-pesq-master/pypesq/__init__.py | import warnings
import numpy as np
from pesq_core import _pesq
from math import fabs
EPSILON = 1e-6
def pesq(ref, deg, fs=16000, normalize=False):
'''
params:
ref: ref signal,
deg: deg signal,
fs: sample rate,
'''
ref = np.array(ref, copy=True)
deg = np.array(deg, copy=True... | 1,501 | 25.350877 | 73 | py |
scaper | scaper-master/setup.py | from setuptools import setup
import imp
with open('README.md') as file:
long_description = file.read()
version = imp.load_source('scaper.version', 'scaper/version.py')
setup(
name='scaper',
version=version.version,
description='A library for soundscape synthesis and augmentation',
author='Justin... | 1,903 | 33.618182 | 71 | py |
scaper | scaper-master/tests/test_core.py |
import scaper
from scaper.scaper_exceptions import ScaperError
from scaper.scaper_warnings import ScaperWarning
from scaper.util import _close_temp_files
import pytest
from scaper.core import EventSpec
import tempfile
import backports.tempfile
import os
import numpy as np
import soundfile
import jams
import numbers
fr... | 99,295 | 39.661753 | 119 | py |
scaper | scaper-master/tests/profile_speed.py | """
This is a profiling script to check the performance of
Scaper. It generates 100 soundscapes in sequence
(no parallelization). Running it on 2019 Macbook Pro
currently takes 158.68 seconds (02:38).
"""
import scaper
import numpy as np
import tempfile
import os
import tqdm
import zipfile
import subprocess
import ti... | 5,749 | 30.944444 | 122 | py |
scaper | scaper-master/tests/test_util.py | # CREATED: 10/15/16 7:52 PM by Justin Salamon <justin.salamon@nyu.edu>
'''
Tests for functions in util.py
'''
from scaper.util import _close_temp_files
from scaper.util import _set_temp_logging_level
from scaper.util import _validate_folder_path
from scaper.util import _get_sorted_files
from scaper.util import _popul... | 11,535 | 30.605479 | 102 | py |
scaper | scaper-master/tests/create_regression_data.py | import os
import scaper
import jams
os.chdir('..')
# FIXTURES
# Paths to files for testing
FG_PATH = 'tests/data/audio/foreground'
BG_PATH = 'tests/data/audio/background'
ALT_FG_PATH = 'tests/data/audio_alt_path/foreground'
ALT_BG_PATH = 'tests/data/audio_alt_path/background'
REG_NAME = 'soundscape_20200501'
# REG_... | 4,773 | 34.626866 | 103 | py |
scaper | scaper-master/tests/__init__.py | 0 | 0 | 0 | py | |
scaper | scaper-master/tests/test_audio.py | # CREATED: 5/5/17 14:36 by Justin Salamon <justin.salamon@nyu.edu>
from scaper.audio import get_integrated_lufs, match_sample_length
from scaper.audio import peak_normalize
from scaper.util import _close_temp_files
import numpy as np
import scipy.signal as sg
import os
import pytest
from scaper.scaper_exceptions impor... | 6,611 | 37.219653 | 86 | py |
scaper | scaper-master/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# scaper documentation build configuration file, created by
# sphinx-quickstart on Thu May 4 17:32:22 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# aut... | 11,254 | 27.20802 | 80 | py |
scaper | scaper-master/scaper/scaper_warnings.py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
# CREATED: 10/13/16 7:08 PM by Justin Salamon <justin.salamon@nyu.edu>
'''Warning classes for Scaper'''
class ScaperWarning(Warning):
'''The root Scaper warning class'''
pass
| 235 | 18.666667 | 70 | py |
scaper | scaper-master/scaper/core.py | try:
import soxbindings as sox
except: # pragma: no cover
import sox # pragma: no cover
import soundfile
import os
import warnings
import jams
from collections import namedtuple
import logging
import tempfile
import numpy as np
import shutil
import csv
from copy import deepcopy
from .scaper_exceptions import S... | 106,280 | 43.712242 | 111 | py |
scaper | scaper-master/scaper/scaper_exceptions.py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
# CREATED: 10/11/16 6:07 PM by Justin Salamon <justin.salamon@nyu.edu>
'''Exception classes for Scaper'''
class ScaperError(Exception):
'''The root Scaper exception class'''
pass
| 239 | 19 | 70 | py |
scaper | scaper-master/scaper/audio.py | # CREATED: 4/23/17 15:37 by Justin Salamon <justin.salamon@nyu.edu>
import numpy as np
import pyloudnorm
import soundfile
from .scaper_exceptions import ScaperError
def get_integrated_lufs(audio_array, samplerate, min_duration=0.5,
filter_class='K-weighting', block_size=0.400):
"""
Re... | 4,982 | 35.108696 | 85 | py |
scaper | scaper-master/scaper/version.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Version info"""
short_version = '1.6'
version = '1.6.5'
| 106 | 14.285714 | 23 | py |
scaper | scaper-master/scaper/util.py | # CREATED: 10/14/16 12:35 PM by Justin Salamon <justin.salamon@nyu.edu>
'''
Utility functions
=================
'''
from contextlib import contextmanager
import logging
import os
import glob
from .scaper_exceptions import ScaperError
import warnings
from .scaper_warnings import ScaperWarning
import scipy
import numpy ... | 15,371 | 28.964912 | 100 | py |
scaper | scaper-master/scaper/__init__.py | #!/usr/bin/env python
"""Top-level module for scaper"""
from .core import Scaper
from .core import generate_from_jams
from .core import trim
from .version import version as __version__
| 186 | 22.375 | 43 | py |
rpg_svo | rpg_svo-master/svo_analysis/setup.py | #!/usr/bin/env python
from distutils.core import setup
from catkin_pkg.python_setup import generate_distutils_setup
d = generate_distutils_setup(
packages=['svo_analysis'],
package_dir={'': 'src'},
install_requires=['rospkg', 'yaml'],
)
setup(**d) | 266 | 21.25 | 60 | py |
rpg_svo | rpg_svo-master/svo_analysis/src/svo_analysis/filter_groundtruth_smooth.py | #!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import transformations
from scipy import signal
save = True
data_filename = '/home/cforster/Datasets/SlamBenchmark/asl_vicon_d2/groundtruth.txt'
filtered_data_filename = '/home/cforster/Datasets/SlamBenchmark/asl_vicon_d2/groundtruth_filtered.txt'
... | 1,875 | 29.754098 | 137 | py |
rpg_svo | rpg_svo-master/svo_analysis/src/svo_analysis/analyse_logs.py | #!/usr/bin/python
import os
import yaml
import numpy as np
import matplotlib.pyplot as plt
def analyse_logs(D, trace_dir):
# identify measurements which result from normal frames and which from keyframes
is_kf = np.argwhere( (D['dropout'] == 1) & (D['repr_n_mps'] >= 0))
is_frame = np.argwhere(D['repr_n_mps']... | 3,497 | 45.64 | 96 | py |
rpg_svo | rpg_svo-master/svo_analysis/src/svo_analysis/analyse_timing.py | #!/usr/bin/python
import os
import numpy as np
import matplotlib.pyplot as plt
def analyse_timing(D, trace_dir):
# identify measurements which result from normal frames and which from keyframes
is_frame = np.argwhere(D['repr_n_mps'] >= 0)
n_frames = len(is_frame)
# set initial time to zero
D['timestamp'... | 3,476 | 50.132353 | 128 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.