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 |
|---|---|---|---|---|---|---|
MCSE | MCSE-master/SentEval/senteval/tools/__init__.py | 0 | 0 | 0 | py | |
MCSE | MCSE-master/SentEval/senteval/tools/ranking.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Image Annotation/Search for COCO with Pytorch
"""
from __future__ import absolute_import, division, unicode_literals
impor... | 15,275 | 41.433333 | 109 | py |
openqasm | openqasm-main/convert2pdf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import subprocess
CONVERT_COMMAND = 'texi2pdf'
def main(relative_tex_filepath):
if not os.path.exists(relative_tex_filepath):
print(
'File %s does not exist.' % relative_tex_filepath, file=sys.stderr)
return -1
... | 1,138 | 24.886364 | 79 | py |
openqasm | openqasm-main/convert2svg.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import subprocess
CONVERT_COMMAND = 'pdftocairo'
def main(relative_tex_filepath):
if not os.path.exists(relative_tex_filepath):
print(
'File %s does not exist.' % relative_tex_filepath, file=sys.stderr)
return -1
... | 1,200 | 25.688889 | 79 | py |
openqasm | openqasm-main/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 4,580 | 35.943548 | 100 | py |
openqasm | openqasm-main/source/grammar/openqasm_reference_parser/exceptions.py | __all__ = ["Qasm3ParserError"]
class Qasm3ParserError(Exception):
pass
| 77 | 12 | 34 | py |
openqasm | openqasm-main/source/grammar/openqasm_reference_parser/tools.py | import contextlib
import io
import antlr4
from antlr4.tree.Trees import Trees, ParseTree
from . import Qasm3ParserError
from .qasm3Lexer import qasm3Lexer
from .qasm3Parser import qasm3Parser
__all__ = ["pretty_tree"]
def pretty_tree(*, program: str = None, file: str = None) -> str:
"""Get a pretty-printed str... | 2,569 | 35.197183 | 83 | py |
openqasm | openqasm-main/source/grammar/openqasm_reference_parser/__init__.py | from .exceptions import *
from .tools import *
from .qasm3Lexer import qasm3Lexer
from .qasm3Parser import qasm3Parser
| 119 | 23 | 36 | py |
openqasm | openqasm-main/source/grammar/tests/test_grammar.py | import itertools
import os
import pathlib
from typing import List, Union, Sequence
import pytest
import yaml
import openqasm_reference_parser
TEST_DIR = pathlib.Path(__file__).parent
REPO_DIR = TEST_DIR.parents[2]
def find_files(
directory: Union[str, os.PathLike], suffix: str = "", raw: bool = False
) -> List... | 4,529 | 32.80597 | 80 | py |
openqasm | openqasm-main/source/openqasm/tools/update_antlr_version_requirements.py | import sys
import re
def parse_versions():
with open(sys.argv[2], "r") as version_file:
for line in version_file:
comment_start = line.find("#")
if comment_start >= 0:
line = line[: line.find("#")]
line = line.strip()
if not line:
... | 1,687 | 32.76 | 99 | py |
openqasm | openqasm-main/source/openqasm/tests/conftest.py | import collections
import pathlib
import pytest
import openqasm3
TEST_DIR = pathlib.Path(__file__).parent
ROOT_DIR = TEST_DIR.parents[2]
EXAMPLES_DIR = ROOT_DIR / "examples"
EXAMPLES = tuple(EXAMPLES_DIR.glob("**/*.qasm"))
# Session scoped because we want the parsed examples to be session scoped as well.
@pytest.f... | 1,012 | 30.65625 | 98 | py |
openqasm | openqasm-main/source/openqasm/tests/test_qasm_parser.py | import dataclasses
import pytest
from openqasm3.ast import (
AccessControl,
AliasStatement,
AngleType,
Annotation,
ArrayLiteral,
ArrayReferenceType,
ArrayType,
AssignmentOperator,
BinaryExpression,
BinaryOperator,
BitType,
BitstringLiteral,
BoolType,
BooleanLiter... | 60,537 | 31.901087 | 100 | py |
openqasm | openqasm-main/source/openqasm/tests/test_printer.py | import dataclasses
import pytest
import openqasm3
from openqasm3 import ast
def _remove_spans(node):
"""Return a new ``QASMNode`` with all spans recursively set to ``None`` to
reduce noise in test failure messages."""
if isinstance(node, list):
return [_remove_spans(item) for item in node]
i... | 23,718 | 24.232979 | 99 | py |
openqasm | openqasm-main/source/openqasm/tests/test_openqasm_tests.py | import openqasm3
def test_examples(example_file):
"""Loop through all example files, verify that the ast_parser can parse the file.
The `example_file` fixture is generated in `conftest.py`. These tests are automatically skipped
if the examples directly cannot be found.
"""
with open(example_file... | 386 | 28.769231 | 100 | py |
openqasm | openqasm-main/source/openqasm/tests/__init__.py | 0 | 0 | 0 | py | |
openqasm | openqasm-main/source/openqasm/docs/conf.py | # In general, we expect that `openqasm3` is installed and available on the path
# without modification.
import openqasm3
project = 'OpenQASM 3 Reference AST'
copyright = '2021, OpenQASM 3 Team and Contributors'
author = 'OpenQASM 3 Team and Contributors'
release = openqasm3.__version__
extensions = [
# Allow aut... | 534 | 25.75 | 79 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/visitor.py | """
=====================================================
AST Visitors and Transformers (``openqasm3.visitor``)
=====================================================
Implementation of an example AST visitor :obj:`~QASMVisitor`, which can be
inherited from to make generic visitors of the reference AST. Deriving from
t... | 3,365 | 36.4 | 95 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/parser.py | """
=============================
Parser (``openqasm3.parser``)
=============================
Tools for parsing OpenQASM 3 programs into the :obj:`reference AST <openqasm3.ast>`.
The quick-start interface is simply to call ``openqasm3.parse``:
.. currentmodule:: openqasm3
.. autofunction:: openqasm3.parse
The rest ... | 34,306 | 38.34289 | 109 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/printer.py | """
==============================================================
Generating OpenQASM 3 from an AST Node (``openqasm3.printer``)
==============================================================
.. currentmodule:: openqasm3
It is often useful to go from the :mod:`AST representation <openqasm3.ast>` of an OpenQASM 3 pro... | 33,232 | 37.688009 | 100 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/properties.py | from . import ast
__all__ = ["precedence"]
_PRECEDENCE_TABLE = {
ast.Concatenation: 0,
# ... the rest of the binary operations come very early ...
ast.UnaryExpression: 11,
# ... power expression ...
# "Call"-like expressions bind very tightly.
ast.IndexExpression: 13,
ast.FunctionCall: 13,... | 2,522 | 32.64 | 89 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/__init__.py | """
===================================
OpenQASM 3 Python reference package
===================================
This package contains the reference abstract syntax tree (AST) for representing
OpenQASM 3 programs, tools to parse text into this AST, and tools to manipulate
the AST.
The AST itself is in the :obj:`.ast` ... | 922 | 27.84375 | 89 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/ast.py | """
========================================
Abstract Syntax Tree (``openqasm3.ast``)
========================================
.. currentmodule:: openqasm3.ast
The reference abstract syntax tree (AST) for OpenQASM 3 programs.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typin... | 19,394 | 16.777269 | 95 | py |
openqasm | openqasm-main/source/openqasm/openqasm3/_antlr/__init__.py | """ANTLR-generated files for parsing OpenQASM 3 files.
This package sets up its import contents to be taken from the generated files
whose ANTLR version matches the installed version of the ANTLR runtime. The
generated files should be placed in directories called ``_<major>_<minor>``,
where `major` is 4, and `minor` ... | 3,203 | 45.434783 | 100 | py |
openqasm | openqasm-main/source/_extensions/multifigure.py | # -*- coding: utf-8 -*-
import itertools
from docutils.parsers.rst import Directive, directives
from docutils import nodes
DEFAULT_ROW_ITEM_COUNT = 4
MULTIFIGURE_HTML_CONTENT_TAG = 'div'
MULTIFIGURE_HTML_ITEM_TAG = 'div'
MULTIFIGURE_HTML_CAPTION_TAG = 'span'
class multifigure_content(nodes.General, nodes.Element):... | 4,293 | 28.210884 | 79 | py |
NeuroKit | NeuroKit-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""The setup script."""
import re
from setuptools import find_packages, setup
# Utilities
with open("README.rst") as readme_file:
readme = readme_file.read()
with open("NEWS.rst") as history_file:
history = history_file.read()
history = history.replace("\n-----... | 2,317 | 25.953488 | 121 | py |
NeuroKit | NeuroKit-master/studies/complexity_eeg/make_data.py | import os
import mne
import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Parameters
# =============================================================================
datasets = [
"../../data/lemon/lemon/", # Path to local ... | 12,454 | 34.585714 | 98 | py |
NeuroKit | NeuroKit-master/studies/ecg_benchmark/make_data.py | import pandas as pd
import neurokit2 as nk
# Load ECGs
ecgs = ["../../data/gudb/ECGs.csv",
"../../data/mit_arrhythmia/ECGs.csv",
"../../data/mit_normal/ECGs.csv",
"../../data/ludb/ECGs.csv",
"../../data/fantasia/ECGs.csv"]
# Load True R-peaks location
rpeaks = [pd.read_csv("../../data... | 5,866 | 32.335227 | 91 | py |
NeuroKit | NeuroKit-master/studies/hrv_frequency/make_data.py | import pandas as pd
import numpy as np
import neurokit2 as nk
# Load True R-peaks location
datafiles = [pd.read_csv("../../data/gudb/Rpeaks.csv"),
pd.read_csv("../../data/mit_arrhythmia/Rpeaks.csv"),
pd.read_csv("../../data/mit_normal/Rpeaks.csv"),
pd.read_csv("../../data/fantasi... | 1,516 | 31.978261 | 89 | py |
NeuroKit | NeuroKit-master/studies/erp_gam/script.py | import numpy as np
import pandas as pd
import neurokit2 as nk
import matplotlib.pyplot as plt
import mne
# Download example dataset
raw = mne.io.read_raw_fif(mne.datasets.sample.data_path() + '/MEG/sample/sample_audvis_filt-0-40_raw.fif')
events = mne.read_events(mne.datasets.sample.data_path() + '/MEG/sample/sample_a... | 3,394 | 32.613861 | 109 | py |
NeuroKit | NeuroKit-master/studies/microstates_howmany/script.py | import os
import mne
import scipy
import numpy as np
import pandas as pd
import neurokit2 as nk
import matplotlib.pyplot as plt
import autoreject
from autoreject.utils import interpolate_bads
import scipy.stats
data_path = "D:/Dropbox/RECHERCHE/N/NeuroKit/data/rs_eeg_texas/data/"
files = os.listdir(data_path)
resul... | 1,816 | 22.294872 | 137 | py |
NeuroKit | NeuroKit-master/tests/tests_microstates.py | # -*- coding: utf-8 -*-
import mne
import numpy as np
import neurokit2 as nk
# =============================================================================
# Peaks
# =============================================================================
def test_microstates_peaks():
# Load eeg data and calculate gfp
... | 951 | 28.75 | 102 | py |
NeuroKit | NeuroKit-master/tests/tests_ecg.py | # -*- coding: utf-8 -*-
import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pytest
import neurokit2 as nk
def test_ecg_simulate():
ecg1 = nk.ecg_simulate(
duration=20, length=5000, method="simple", noise=0, random_state=0
)
assert len(ecg1) == 5000
ecg2 = nk.ecg_simulate... | 12,392 | 32.136364 | 88 | py |
NeuroKit | NeuroKit-master/tests/tests_ecg_delineate.py | import pathlib
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
import neurokit2 as nk
SHOW_DEBUG_PLOTS = False
MAX_SIGNAL_DIFF = 0.03 # seconds
@pytest.fixture(name="test_data")
def setup_load_ecg_data():
"""Load ecg signal and sampling rate."""
def load_si... | 2,795 | 30.772727 | 112 | py |
NeuroKit | NeuroKit-master/tests/tests.py | import doctest
import pytest
if __name__ == "__main__":
doctest.testmod()
pytest.main()
| 99 | 10.111111 | 26 | py |
NeuroKit | NeuroKit-master/tests/tests_eog.py | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import mne
import numpy as np
import pytest
import neurokit2 as nk
def test_eog_clean():
# test with exported csv
eog_signal = nk.data("eog_200hz")["vEOG"]
eog_cleaned = nk.eog_clean(eog_signal, sampling_rate=200)
assert eog_cleaned.size == eog... | 5,372 | 31.96319 | 96 | py |
NeuroKit | NeuroKit-master/tests/tests_eda.py | import platform
import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
import neurokit2 as nk
# =============================================================================
# EDA
# =============================================================================
def test_e... | 8,687 | 29.808511 | 101 | py |
NeuroKit | NeuroKit-master/tests/tests_events.py | import matplotlib.pyplot as plt
import numpy as np
import pytest
import neurokit2 as nk
# =============================================================================
# Events
# =============================================================================
def test_events_find():
signal = np.cos(np.linspace(st... | 1,897 | 26.911765 | 79 | py |
NeuroKit | NeuroKit-master/tests/tests_complexity.py | from collections.abc import Iterable
import antropy
import nolds
import numpy as np
import pandas as pd
from pyentrp import entropy as pyentrp
import sklearn.neighbors
from packaging import version
# import EntropyHub
import neurokit2 as nk
# For the testing of complexity, we test our implementations against existin... | 23,316 | 32.94032 | 127 | py |
NeuroKit | NeuroKit-master/tests/tests_stats.py | import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Stats
# =============================================================================
def test_standardize():
rez = np.sum(nk.standardize([1, 1, 5, 2, 1]))
assert... | 3,101 | 28.542857 | 110 | py |
NeuroKit | NeuroKit-master/tests/tests_epochs.py | import numpy as np
import neurokit2 as nk
def test_epochs_create():
# Get data
data = nk.data("bio_eventrelated_100hz")
# Find events
events = nk.events_find(data["Photosensor"], threshold_keep='below',
event_conditions=["Negative", "Neutral", "Neutral", "Negative"])
... | 1,507 | 33.272727 | 92 | py |
NeuroKit | NeuroKit-master/tests/tests_signal_fixpeaks.py | # -*- coding: utf-8 -*-
import numpy as np
import numpy.random
import pytest
import neurokit2 as nk
from neurokit2.signal.signal_fixpeaks import _correct_artifacts, _find_artifacts, signal_fixpeaks
def compute_rmssd(peaks):
rr = np.ediff1d(peaks, to_begin=0)
rr[0] = np.mean(rr[1:])
rmssd = np.sqrt(np.me... | 9,417 | 35.362934 | 119 | py |
NeuroKit | NeuroKit-master/tests/tests_ecg_findpeaks.py | # -*- coding: utf-8 -*-
import os.path
import numpy as np
import pandas as pd
# Trick to directly access internal functions for unit testing.
#
# Using neurokit2.ecg.ecg_findpeaks._ecg_findpeaks_MWA doesn't
# work because of the "from .ecg_findpeaks import ecg_findpeaks"
# statement in neurokit2/ecg/__init.__.py.
fro... | 1,984 | 42.152174 | 118 | py |
NeuroKit | NeuroKit-master/tests/tests_ppg.py | # -*- coding: utf-8 -*-
import itertools
import numpy as np
import pytest
import neurokit2 as nk
durations = (20, 200, 300)
sampling_rates = (25, 50, 500)
heart_rates = (50, 120)
freq_modulations = (0.1, 0.4)
params = [durations, sampling_rates, heart_rates, freq_modulations]
params_combis = list(itertools.produ... | 6,974 | 27.125 | 104 | py |
NeuroKit | NeuroKit-master/tests/tests_signal.py | import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
import scipy.signal
import neurokit2 as nk
# =============================================================================
# Signal
# =============================================================================
d... | 15,358 | 33.748869 | 125 | py |
NeuroKit | NeuroKit-master/tests/__init__.py | 0 | 0 | 0 | py | |
NeuroKit | NeuroKit-master/tests/tests_rsp.py | # -*- coding: utf-8 -*-
import copy
import random
import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pytest
import neurokit2 as nk
random.seed(a=13, version=2)
def test_rsp_simulate():
rsp1 = nk.rsp_simulate(duration=20, length=3000, random_state=42)
assert len(rsp1) == 3000
rsp... | 13,128 | 33.732804 | 106 | py |
NeuroKit | NeuroKit-master/tests/tests_hrv.py | import numpy as np
import pandas as pd
import pytest
import neurokit2 as nk
from neurokit2 import misc
def test_hrv_time():
ecg_slow = nk.ecg_simulate(duration=60, sampling_rate=1000, heart_rate=60, random_state=42)
ecg_fast = nk.ecg_simulate(duration=60, sampling_rate=1000, heart_rate=150, random_state=42)
... | 7,652 | 35.099057 | 100 | py |
NeuroKit | NeuroKit-master/tests/tests_eeg.py | import mne
import numpy as np
import pooch
import neurokit2 as nk
# =============================================================================
# EEG
# =============================================================================
def test_eeg_add_channel():
raw = mne.io.read_raw_fif(
str(mne.datasets... | 3,675 | 29.633333 | 144 | py |
NeuroKit | NeuroKit-master/tests/tests_bio.py | import numpy as np
import neurokit2 as nk
def test_bio_process():
sampling_rate = 1000
# Create data
ecg = nk.ecg_simulate(duration=30, sampling_rate=sampling_rate)
rsp = nk.rsp_simulate(duration=30, sampling_rate=sampling_rate)
eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate, sc... | 2,058 | 35.767857 | 116 | py |
NeuroKit | NeuroKit-master/tests/tests_data.py | import os
import numpy as np
import neurokit2 as nk
path_data = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data")
# =============================================================================
# Data
# =============================================================================
d... | 1,187 | 30.263158 | 118 | py |
NeuroKit | NeuroKit-master/tests/tests_emg.py | import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
import scipy.stats
import neurokit2 as nk
# =============================================================================
# EMG
# =============================================================================
def test... | 6,367 | 31.161616 | 135 | py |
NeuroKit | NeuroKit-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- REQUIREMENTS -----------------------------------------------------
# pip install s... | 4,891 | 33.20979 | 293 | py |
NeuroKit | NeuroKit-master/docs/readme/README_examples.py | import matplotlib
import matplotlib.cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
import neurokit2 as nk
# Setup matplotlib with Agg to run on server
matplotlib.use("Agg")
plt.rcParams["figure.figsize"] = (10, 6.5)
plt.rcParams["savefig.facecolor"] =... | 12,141 | 30.70235 | 100 | py |
NeuroKit | NeuroKit-master/data/eeg_1min_200hz.py | import pickle
import mne
raw = mne.io.read_raw_fif(
mne.datasets.sample.data_path() / "MEG/sample/sample_audvis_raw.fif",
preload=True,
verbose=False,
)
raw = raw.pick(["eeg", "eog", "stim"], verbose=False)
raw = raw.crop(0, 60)
raw = raw.resample(200)
# raw.ch_names
# raw.info["sfreq"]
# Store data (s... | 445 | 20.238095 | 73 | py |
NeuroKit | NeuroKit-master/data/eeg_resting_8min.py | import mne
import numpy as np
import TruScanEEGpy
import neurokit2 as nk
# EDF TO FIF
# ==========
# Read original file (too big to be uploaded on github)
raw = mne.io.read_raw_edf("eeg_restingstate_3000hz.edf", preload=True)
# Find event onset and cut
event = nk.events_find(raw.copy().pick_channels(["Foto"]).to_dat... | 1,318 | 27.673913 | 100 | py |
NeuroKit | NeuroKit-master/data/mit_arrhythmia/download_mit_arrhythmia.py | # -*- coding: utf-8 -*-
"""Script for formatting the MIT-Arrhythmia database
Steps:
1. Download the ZIP database from https://alpha.physionet.org/content/mitdb/1.0.0/
2. Open it with a zip-opener (WinZip, 7zip).
3. Extract the folder of the same name (named 'mit-bih-arrhythmia-database-1.0.0') to the same ... | 2,522 | 33.561644 | 153 | py |
NeuroKit | NeuroKit-master/data/fantasia/download_fantasia.py | # -*- coding: utf-8 -*-
"""Script for formatting the Fantasia Database
The database consists of twenty young and twenty elderly healthy subjects. All subjects remained in a resting state in sinus rhythm while watching the movie Fantasia (Disney, 1940) to help maintain wakefulness. The continuous ECG signals were digit... | 1,886 | 34.603774 | 403 | py |
NeuroKit | NeuroKit-master/data/gudb/download_gudb.py | # -*- coding: utf-8 -*-
"""Script for downloading, formatting and saving the GUDB database (https://github.com/berndporr/ECG-GUDB).
It contains ECGs from 25 subjects. Each subject was recorded performing 5 different tasks for two minutes:
- sitting
- a maths test on a tablet
- walking on a treadmill
- running on a tre... | 2,114 | 33.112903 | 107 | py |
NeuroKit | NeuroKit-master/data/ludb/download_ludb.py | # -*- coding: utf-8 -*-
"""Script for formatting the Lobachevsky University Electrocardiography Database
The database consists of 200 10-second 12-lead ECG signal records representing different morphologies of the ECG signal. The ECGs were collected from healthy volunteers and patients, which had various cardiovascula... | 1,862 | 33.5 | 334 | py |
NeuroKit | NeuroKit-master/data/ptb_xl/download_ptbxl.py | # -*- coding: utf-8 -*-
"""Script for formatting the PTB-XL Database
https://physionet.org/content/ptb-xl/1.0.1/
"""
| 118 | 18.833333 | 44 | py |
NeuroKit | NeuroKit-master/data/testretest_restingstate_eeg/download_script.py | """
https://openneuro.org/datasets/ds003685/
"""
import os
import re
import shutil
import mne
import numpy as np
import openneuro as on
import neurokit2 as nk
# Download cleaned data (takes some time)
on.download(
dataset="ds003685",
target_dir="eeg/raw",
include="sub-*/ses-session1/*eyes*",
)
# Convert... | 2,061 | 32.258065 | 86 | py |
NeuroKit | NeuroKit-master/data/mit_long-term/download_mit_long-term.py | # -*- coding: utf-8 -*-
"""Script for formatting the MIT-Long-Term ECG Database
Steps:
1. Download the ZIP database from https://physionet.org/content/ltdb/1.0.0/
2. Open it with a zip-opener (WinZip, 7zip).
3. Extract the folder of the same name (named 'mit-bih-long-term-ecg-database-1.0.0') to the same f... | 1,993 | 29.212121 | 142 | py |
NeuroKit | NeuroKit-master/data/srm_restingstate_eeg/download_script.py | """
https://openneuro.org/datasets/ds003775/versions/1.0.0
"""
import os
import shutil
import mne
import numpy as np
import openneuro as on
import neurokit2 as nk
# Download cleaned data (takes some time)
on.download(
dataset="ds003775",
target_dir="eeg/raw",
include="sub-*",
exclude="derivatives/cle... | 1,101 | 23.488889 | 94 | py |
NeuroKit | NeuroKit-master/data/mit_normal/download_mit_normal.py | # -*- coding: utf-8 -*-
"""Script for formatting the MIT-Normal Sinus Rhythm Database
Steps:
1. Download the ZIP database from https://physionet.org/content/nsrdb/1.0.0/
2. Open it with a zip-opener (WinZip, 7zip).
3. Extract the folder of the same name (named 'mit-bih-normal-sinus-rhythm-database-1.0.0') ... | 2,009 | 29.923077 | 154 | py |
NeuroKit | NeuroKit-master/data/lemon/download_lemon.py | # -*- coding: utf-8 -*-
"""Script for formatting the LEMON EEG dataset
https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/EEG_MPILMBB_LEMON/EEG_Preprocessed_BIDS_ID/EEG_Preprocessed/
Steps:
1. Download the ZIP database from https://physionet.org/content/nstdb/1.0.0/
2. Open it with a zip-opener (W... | 2,996 | 30.21875 | 132 | py |
NeuroKit | NeuroKit-master/data/mit_nst/download_mit_nst.py | # -*- coding: utf-8 -*-
"""Script for formatting the MIT-Noise Stress Test database
Steps:
1. Download the ZIP database from https://physionet.org/content/nstdb/1.0.0/
2. Open it with a zip-opener (WinZip, 7zip).
3. Extract the folder of the same name (named 'mit-bih-noise-stress-test-database-1.0.0') to t... | 2,000 | 32.915254 | 152 | py |
NeuroKit | NeuroKit-master/neurokit2/__init__.py | """Top-level package for NeuroKit."""
import datetime
import platform
import matplotlib
# Dependencies
import numpy as np
import pandas as pd
import scipy
import sklearn
from .benchmark import *
from .bio import *
from .complexity import *
from .data import *
from .ecg import *
from .eda import *
from .eeg import *
... | 3,105 | 23.650794 | 173 | py |
NeuroKit | NeuroKit-master/neurokit2/video/video_blinks.py | # !!!!!!!!!!!!!!!!!!!!!!!!
# ! NEED HELP WITH THAT !
# !!!!!!!!!!!!!!!!!!!!!!!!
# import numpy as np
# from ..misc import progress_bar
# def video_blinks(video, verbose=True):
# """**Extract blinks from video**"""
# # Try loading menpo
# try:
# import cv2
# import menpo.io
# im... | 2,574 | 35.267606 | 112 | py |
NeuroKit | NeuroKit-master/neurokit2/video/video_skin.py | import numpy as np
from ..misc import find_closest
def video_skin(face, show=False):
"""**Skin detection**
This function detects the skin in a face.
.. note::
This function is experimental. If you are interested in helping us improve that aspect of
NeuroKit (e.g., by adding more detect... | 4,237 | 31.6 | 97 | py |
NeuroKit | NeuroKit-master/neurokit2/video/video_ppg.py | import numpy as np
from ..misc import progress_bar
from .video_face import video_face
from .video_skin import video_skin
def video_ppg(video, sampling_rate=30, verbose=True):
"""**Remote Photoplethysmography (rPPG) from Video**
Extracts the photoplethysmogram (PPG) from a webcam video using the Plane-Orthog... | 3,488 | 30.718182 | 96 | py |
NeuroKit | NeuroKit-master/neurokit2/video/__init__.py | """Submodule for NeuroKit."""
from .video_face import video_face
from .video_plot import video_plot
from .video_ppg import video_ppg
from .video_skin import video_skin
__all__ = ["video_plot", "video_face", "video_skin", "video_ppg"]
| 236 | 25.333333 | 65 | py |
NeuroKit | NeuroKit-master/neurokit2/video/video_plot.py | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
from ..signal import signal_resample
def video_plot(video, sampling_rate=30, frames=3, signals=None):
"""**Visualize video**
This function plots a few frames from a video as an image.
Parameters
----------
video : np.nda... | 3,435 | 26.934959 | 91 | py |
NeuroKit | NeuroKit-master/neurokit2/video/video_face.py | import numpy as np
from ..misc import progress_bar
def video_face(video, verbose=True):
"""**Extract face from video**
This function extracts the faces from a video. This function requires the `cv2, `menpo` and
`menpodetect` modules to be installed.
.. note::
This function is experimental.... | 2,665 | 26.484536 | 97 | py |
NeuroKit | NeuroKit-master/neurokit2/events/events_find.py | # -*- coding: utf-8 -*-
import itertools
from warnings import warn
import numpy as np
from ..misc import NeuroKitWarning
from ..signal import signal_binarize
def events_find(
event_channel,
threshold="auto",
threshold_keep="above",
start_at=0,
end_at=None,
duration_min=1,
duration_max=No... | 7,961 | 31.365854 | 99 | py |
NeuroKit | NeuroKit-master/neurokit2/events/events_create.py | import numpy as np
from .events_find import _events_find_label
def events_create(event_onsets, event_durations=None, event_labels=None, event_conditions=None):
"""**Create events dictionnary from list of onsets**
Parameters
----------
event_onsets : array or list
A list of events onset.
... | 1,821 | 29.881356 | 97 | py |
NeuroKit | NeuroKit-master/neurokit2/events/events_plot.py | # -*- coding: utf-8 -*-
import matplotlib.cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def events_plot(events, signal=None, color="red", linestyle="--"):
"""**Visualize Events**
Plot events in signal.
Parameters
----------
events : list or ndarray or dict
Eve... | 3,998 | 26.02027 | 97 | py |
NeuroKit | NeuroKit-master/neurokit2/events/__init__.py | """Submodule for NeuroKit."""
from .events_find import events_find
from .events_create import events_create
from .events_plot import events_plot
from .events_to_mne import events_to_mne
__all__ = ["events_find", "events_create", "events_plot", "events_to_mne"]
| 263 | 28.333333 | 74 | py |
NeuroKit | NeuroKit-master/neurokit2/events/events_to_mne.py | # -*- coding: utf-8 -*-
import numpy as np
def events_to_mne(events, event_conditions=None):
"""**Create MNE-compatible events**
Create `MNE <https://mne.tools/stable/index.html>`_ compatible events for integration with
M/EEG.
Parameters
----------
events : list or ndarray or dict
Ev... | 2,061 | 26.864865 | 113 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_noise.py | import numpy as np
from ..misc import check_random_state
def signal_noise(duration=10, sampling_rate=1000, beta=1, random_state=None):
"""**Simulate noise**
This function generates pure Gaussian ``(1/f)**beta`` noise. The power-spectrum of the generated
noise is proportional to ``S(f) = (1 / f)**beta``.... | 4,052 | 31.95122 | 106 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_timefrequency.py | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal
from ..signal.signal_detrend import signal_detrend
def signal_timefrequency(
signal,
sampling_rate=1000,
min_frequency=0.04,
max_frequency=None,
method="stft",
window=None,
window_type="hann",
... | 21,035 | 35.20654 | 99 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_plot.py | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ..events import events_plot
from ..stats import standardize as nk_standardize
def signal_plot(
signal, sampling_rate=None, subplots=False, standardize=False, labels=None, **kwargs
):
"""**Plot signal with even... | 6,532 | 31.665 | 107 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_period.py | # -*- coding: utf-8 -*-
from warnings import warn
import numpy as np
from ..misc import NeuroKitWarning
from .signal_formatpeaks import _signal_formatpeaks_sanitize
from .signal_interpolate import signal_interpolate
def signal_period(
peaks,
sampling_rate=1000,
desired_length=None,
interpolation_met... | 4,293 | 38.394495 | 105 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_flatline.py | # -*- coding: utf-8 -*-
import numpy as np
def signal_flatline(signal, threshold=0.01):
"""**Return the Flatline Percentage of the Signal**
Parameters
----------
signal : Union[list, np.array, pd.Series]
The signal (i.e., a time series) in the form of a vector of values.
threshold : float... | 978 | 24.102564 | 100 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_distort.py | # -*- coding: utf-8 -*-
from warnings import warn
import numpy as np
from ..misc import NeuroKitWarning, check_random_state, listify
from .signal_resample import signal_resample
from .signal_simulate import signal_simulate
def signal_distort(
signal,
sampling_rate=1000,
noise_shape="laplace",
noise_... | 10,658 | 30.35 | 106 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_surrogate.py | import numpy as np
from ..misc import check_random_state
def signal_surrogate(signal, method="IAAFT", random_state=None, **kwargs):
"""**Create Signal Surrogates**
Generate a surrogate version of a signal. Different methods are available, such as:
* **random**: Performs a random permutation of the sign... | 5,520 | 34.619355 | 106 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_autocor.py | import numpy as np
import scipy.signal
import scipy.stats
from matplotlib import pyplot as plt
def signal_autocor(signal, lag=None, demean=True, method="auto", show=False):
"""**Autocorrelation (ACF)**
Compute the autocorrelation of a signal.
Parameters
-----------
signal : Union[list, np.array,... | 3,754 | 32.230088 | 101 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_merge.py | # -*- coding: utf-8 -*-
import numpy as np
from .signal_resample import signal_resample
def signal_merge(signal1, signal2, time1=[0, 10], time2=[0, 10]):
"""**Arbitrary addition of two signals with different time ranges**
Parameters
----------
signal1 : Union[list, np.array, pd.Series]
The f... | 2,770 | 32.792683 | 98 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_findpeaks.py | # -*- coding: utf-8 -*-
import numpy as np
import scipy.misc
import scipy.signal
from ..misc import as_vector, find_closest
from ..stats import standardize
def signal_findpeaks(
signal,
height_min=None,
height_max=None,
relative_height_min=None,
relative_height_max=None,
relative_mean=True,
... | 7,447 | 29.276423 | 99 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_filter.py | # -*- coding: utf-8 -*-
from warnings import warn
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal
from ..misc import NeuroKitWarning
from .signal_interpolate import signal_interpolate
def signal_filter(
signal,
sampling_rate=1000,
lowcut=None,
highcut=None,
method="butterw... | 15,403 | 40.632432 | 116 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_recompose.py | import matplotlib.pyplot as plt
import numpy as np
import scipy.cluster
from .signal_zerocrossings import signal_zerocrossings
def signal_recompose(components, method="wcorr", threshold=0.5, keep_sd=None, **kwargs):
"""**Combine signal sources after decomposition**
Combine and reconstruct meaningful signal ... | 6,336 | 33.818681 | 95 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_psd.py | # -*- coding: utf-8 -*-
from warnings import warn
import numpy as np
import pandas as pd
import scipy.signal
from ..misc import NeuroKitWarning
def signal_psd(
signal,
sampling_rate=1000,
method="welch",
show=False,
normalize=True,
min_frequency="default",
max_frequency=np.inf,
windo... | 19,000 | 33.6102 | 129 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_power.py | # -*- coding: utf-8 -*-
import matplotlib.cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from .signal_psd import signal_psd
def signal_power(
signal,
frequency_band,
sampling_rate=1000,
continuous=False,
show=False,
normalize=True,
**kwargs,
):
"""**Compute ... | 7,879 | 28.961977 | 121 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_detrend.py | # -*- coding: utf-8 -*-
import numpy as np
import scipy.sparse
from ..stats import fit_loess, fit_polynomial
from .signal_decompose import signal_decompose
def signal_detrend(
signal,
method="polynomial",
order=1,
regularization=500,
alpha=0.75,
window=1.5,
stepsize=0.02,
components=[... | 10,122 | 41.894068 | 113 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_resample.py | # -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import scipy.ndimage
import scipy.signal
def signal_resample(
signal,
desired_length=None,
sampling_rate=None,
desired_sampling_rate=None,
method="interpolation",
):
"""**Resample a continuous signal to a different length or sampli... | 6,386 | 32.615789 | 101 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_fixpeaks.py | # - * - coding: utf-8 - * -
from warnings import warn
import matplotlib.patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ..misc import NeuroKitWarning
from ..stats import standardize
from .signal_formatpeaks import _signal_formatpeaks_sanitize
from .signal_period import signal_perio... | 23,099 | 36.745098 | 116 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_synchrony.py | # -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import scipy.signal
def signal_synchrony(signal1, signal2, method="hilbert", window_size=50):
"""**Synchrony (coupling) between two signals**
Signal coherence refers to the strength of the mutual relationship (i.e., the amount of shared
infor... | 4,293 | 35.084034 | 107 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_simulate.py | # -*- coding: utf-8 -*-
from warnings import warn
import numpy as np
from ..misc import NeuroKitWarning, check_random_state, listify
def signal_simulate(
duration=10,
sampling_rate=1000,
frequency=1,
amplitude=0.5,
noise=0,
silent=False,
random_state=None,
):
"""**Simulate a continuo... | 3,880 | 32.456897 | 106 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_binarize.py | # -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import sklearn.mixture
def signal_binarize(signal, method="threshold", threshold="auto"):
"""**Binarize a continuous signal**
Convert a continuous signal into zeros and ones depending on a given threshold.
Parameters
----------
signa... | 3,686 | 33.138889 | 100 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/signal_sanitize.py | # -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
def signal_sanitize(signal):
"""**Signal input sanitization**
Reset indexing for Pandas Series.
Parameters
----------
signal : Series
The indexed input signal (``pandas Dataframe.set_index()``)
Returns
-------
Se... | 942 | 21.452381 | 85 | py |
NeuroKit | NeuroKit-master/neurokit2/signal/__init__.py | """Submodule for NeuroKit."""
from .signal_autocor import signal_autocor
from .signal_binarize import signal_binarize
from .signal_changepoints import signal_changepoints
from .signal_decompose import signal_decompose
from .signal_detrend import signal_detrend
from .signal_distort import signal_distort
from .signal_fil... | 2,067 | 30.815385 | 54 | py |
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