sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d9210bc3268c4c5fab78d647de564670726272db556506b67555aa9e3415aa11 | Python | 2,915 | 102 | import torch
from torch import nn as nn
from deepsith import iSITH
class Vanilla_iSITH(nn.Module):
def __init__(
self, ntau = 5, tau_min = 1, tau_max = 40, buff_max = 80,
k = 10, dt = 1, g = 0, ttype=torch.FloatTensor
):
super(Vanilla_iSITH, self).__init__()
self.nta... |
3ce6e33e3c6203df154729a212b91c0de66040dba27731ad387ecba2c4976945 | Python | 2,916 | 85 | from typing import Any, Dict, Optional
from ray._private.utils import split_address
from ray.dashboard.modules.dashboard_sdk import SubmissionClient
try:
import aiohttp
import requests
except ImportError:
aiohttp = None
requests = None
DEPLOY_PATH = "/api/serve/applications/"
DELETE_PATH = "/api/ser... |
89dfd9824439d2de1fbbd7ba5b85eaa392e6e21d1eb0d80fc60340abe48df479 | Python | 2,916 | 56 | import tensorflow as tf
from mol_graph import max_nb
from utils.nn import *
def rcnn_wl_last(graph_inputs, hidden_size, depth, training=True):
input_atom, input_bond, atom_graph, bond_graph, num_nbs = graph_inputs
atom_features = tf.nn.relu(linearND(input_atom, hidden_size, "atom_embedding", init_bias=None))
... |
edf63ac158672e65b49e1c83d06354a7b8bcfa59958527fa799e54082d01502f | Python | 2,918 | 76 | # 3rd party dependencies
import pytest
# project dependencies
from deepface.commons import onnx_utils
from deepface.commons.logger import Logger
logger = Logger()
CPU = "CPUExecutionProvider"
CUDA = "CUDAExecutionProvider"
COREML = "CoreMLExecutionProvider"
def test_default_runs_on_cpu(monkeypatch):
monkeypatc... |
62add6d25900306871e691a4953be89811e3a91d6701500b05c625cd905243dd | Python | 2,921 | 86 | # Copyright 2022 The Music Spectrogram Diffusion Authors.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache... |
c62ae400d3acc877e15ee21ad10e45fd126998907854df8914b608181cc99125 | Python | 2,921 | 93 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import argparse
import logging
from pathlib import Path
from fairchem.experimental.legacy.utils import... |
9fc0b05c6f805698d239a85cc98394605f41e9d3c1dbc25bdcc5d0bb671e3dc2 | Python | 2,922 | 90 | import optuna
import pytest
from siapy.optimizers.configs import (
CreateStudyConfig,
OptimizeStudyConfig,
TabularOptimizerConfig,
)
from siapy.optimizers.parameters import TrialParameters
from siapy.optimizers.scorers import Scorer
def test_create_study_config_defaults():
config = CreateStudyConfig(... |
d85db0b460d0cb3728d9246b7877cea5ebb017e2316084f2230b3929ae46ea91 | Python | 2,922 | 92 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig, ax = plt.subplots(figsize=(8, 6), dpi=300)
# Define Software (SW) and Hardware (HW) labels
sw_labels = [f"SW {i}" for i in range(1, 7)]
hw_labels = [f"HW {i}" for i in range(1, 6)]
# Draw the Software (SW) labels on the lef... |
dbe1f4c39d42cc69eb401dbeb8279db0b06c7a1ec48cd340aefdbf350b510606 | Python | 2,922 | 93 | """
This file serves as a documentation example and CI test for autoscaling data parallel attention deployment.
Structure:
1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
2. Docs example (between __dp_autoscaling_example_start/end__): Embedded in Sphinx docs... |
726f84a81908226dc45a71eff1b8189cb89735d303266bbb741170749f149d87 | Python | 2,924 | 81 | ####################################################################################################
# Copyright (c) 2020 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
01aa590e95277705e86e77e42338846a41301b79cbe9718de73cba2028e78376 | Python | 2,925 | 83 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_2a = pd.read_excel('heads.xlsx', '2a')
df_2b = pd.read_excel('heads.xlsx', '2b')
# Parameters and their corresponding accuracies for 9 subjects
# Updated parameters for both experiments
labels = [1, 2, 4, 8, 16]
labels = [str(i) for i in labels]... |
7283c5927a8e47637c10ce2bb7bab8cbfc48b1ff3c1286bcdca4a9ffefe577e4 | Python | 2,926 | 92 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
92f0ebfe766d18919adcef537c8acc28cc19b4d3c2be0b88defc7b5c946d8a88 | Python | 2,926 | 85 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import argparse
import pandas as pd
import pysam
import svtk.utils as svu
from collections import namedtuple
def select_mosaic_candidates(vcf, metrics, mode, cutoffs):
candidates = metrics.loc[metrics.svtype.isin('DEL DUP'.split())]
candidates = candi... |
ca61ff15721b16528dfba1b48be4b157fad5201ef11fffb4bfc848b1b3fc3b0c | Python | 2,926 | 78 | # -*- coding: utf-8 -*-
"""
Created on Fri Jul 12 13:03:00 2019
@author: ju-bar
Image shift utilities
This code is part of the 'emilys' repository
https://github.com/ju-bar/emilys
published under the GNU General Publishing License, version 3
"""
import numpy as np
from emilys.image.imagedata import maxpos, com
from ... |
1296abc7e0c7d0e9957017febb20e1fbaf41bcfa1ba96b08aa0d83de47769a92 | Python | 2,927 | 69 | import os, sys, logging, re
sys.path.append(os.getcwd())
import pandas as pd
import numpy as np
from tqdm import tqdm
from collections import Counter
from misc.save_files import read_pickle, read_pandas_from_csv, write_pickle
from misc.counter import get_most_occuring_element
from Data_Managment.citation_analysis impo... |
b0e88c7f1444dcbe1a0ed904a2420b246eb74c6705832601a061f1fc5b7021ab | Python | 2,927 | 94 | from ngs_mapper.reads import sffs_to_fastq
import os, sys
from functools import partial
from glob import glob
import gzip
from Bio import SeqIO
from . import log
logger = log.setup_logger(__name__, log.get_config())
{
'sff' : 'sff',
'ab1' : 'abi', # SeqIO.convert(x, 'abi', y, 'fastq')
}
drop_ext = lambda s:... |
9a277c39226e4511148f90663cb60274835a4bac9fb4461e5d9c5255e7572ca3 | Python | 2,928 | 100 | import glob
import logging
import os
from typing import List, Optional, Tuple
from ray._private.accelerators.accelerator import AcceleratorManager
from ray._private.ray_constants import env_bool
logger = logging.getLogger(__name__)
ASCEND_RT_VISIBLE_DEVICES_ENV_VAR = "ASCEND_RT_VISIBLE_DEVICES"
NOSET_ASCEND_RT_VISIB... |
c50985bafd94ff26ec623d66141417184ed485551d6be04f9a24033d7b273192 | Python | 2,928 | 98 | import sys
from scipy.stats import spearmanr, pearsonr
from loguru import logger
import numpy as np
from sklearn import metrics
def Cors(x, y):
return spearmanr(x, y), pearsonr(x, y)
def loadpred(path):
with open(path, "r") as f:
content = f.readlines()
content = [x.replace("\n", "").split("\t")... |
e553bbc0fb3f177baddda2cd7613c50678bfd10c9534c98c7b2e3d2f47879714 | Python | 2,928 | 102 | import numpy as np
"""
Adapted from UniTVelo (Gao et al. 2022, bioRxiv)
Selects a set of genes to regularize in gene-space
"""
def get_weight(x, y=None, perc=95):
from scipy.sparse import issparse
xy_norm = np.array(x.toarray() if issparse(x) else x)
if y is not None:
if issparse(y):
... |
116f84935a30e70e93c885b1e1a803caf02bc0cfa963a4fc7f9a4f4f3d93c51b | Python | 2,929 | 55 | from Bio import SeqIO
threshold_up = 0.5
threshold_down = 0.01
for threshold in range(2, 101):
junction_scores = f'/ccb/cybertron/khchao/splam-analysis-results/src_tools_evaluation/splam_result/RELEASED/neg_{threshold}/LOG/_junction_score.bed'
fasta_file = f'/ccb/cybertron/khchao/splam-analysis-results/train... |
1d5409af05729c9d546b81194534f99b301eaaf122519af226723345e0661a62 | Python | 2,930 | 98 | # __serve_example_begin__
import requests
import starlette
from transformers import pipeline
from io import BytesIO
from PIL import Image
from ray import serve
from ray.serve.handle import DeploymentHandle
@serve.deployment
def downloader(image_url: str):
image_bytes = requests.get(image_url).content
image ... |
4e7c784d5af3fa8bf4c954cb2f1652205dbb0ac2cc4174c219cd141b1f798b0b | Python | 2,931 | 97 | from collections import Counter
class CodeVocab(object):
END_CODE = '<end>'
PAD_CODE = '<pad>'
UNK_CODE = '<unk>'
def __init__(self, threshold=None, topK=None, code2name=None):
super().__init__()
# special_codes = None # [CodeVocab.END_CODE, CodeVocab.PAD_CODE, CodeVocab.UNK_CODE]
... |
769ae97ef8df672bee80e10cd7a3051e2360d3b8ae4aef3451a209d2cd7615f0 | Python | 2,931 | 103 | """Tests for Ray test utility classes.
This module contains pytest-based tests for SignalActor and Semaphore classes
from ray._common.test_utils. These test utility classes are used for coordination
and synchronization in Ray tests.
"""
import sys
import time
import pytest
import ray
from ray._common.test_utils imp... |
942a1736403802bf10e43b3f2f323bbd1d2dbc7f3cd7bcea3202570953310150 | Python | 2,931 | 74 | from .._registry import register_function
@register_function(
aliases=["添加P值", "add_palue", "add_pvalue", "p值标注", "统计标注"],
category="pl",
description="Add p-value annotation with connecting line to matplotlib plot",
examples=[
"# Basic p-value annotation",
"fig, ax = ov.pl.boxplot(data... |
afd843aa018348662f7b05a4bf019a2ac678e69689896d68e616318de412efca | Python | 2,931 | 101 | from __future__ import annotations
import math
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
import torch.nn.functional as F
if TYPE_CHECKING:
from fairchem.core.models.allscaip.configs import (
GlobalConfigs,
GraphNeuralNetworksConfigs,
RegularizationConfigs,
)
... |
b61af3370a5ae0b1e68d1210d7fac6c2d2c86e1237cd6140155cec06d02c0c9b | Python | 2,931 | 94 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
7ab17f4d56627976d1899db2cfc3edec53076d3b01790d25e26eef7113dc8a54 | Python | 2,932 | 81 | """Run with pip install 'pymilvus[milvus-lite]'; no model weights are needed."""
from types import SimpleNamespace
import numpy as np
import pytest
from deepface.modules import datastore
from deepface.modules.database import milvus
@pytest.mark.parametrize("normalized", [True, False])
@pytest.mark.parametrize("sim... |
c25d2e2d00041dc6670fa1c14081b389b67c234ba106b4a35e5ce6e7fcb61cde | Python | 2,932 | 89 | import os
import secrets
import shutil
import sys
import tempfile
# RayConfig reads the auth mode once at import, so set the mode before importing
# ray. A per-process random token, inherited by subprocesses (the head and the
# submitted C++ job), lets them agree without a hard-coded secret.
os.environ["RAY_AUTH_MODE"... |
724446b4fd3a1381be73a402775dc4c120494cb123092190827f196a08d3f438 | Python | 2,933 | 99 | """
This file serves as a documentation example and CI test for OpenAI API batch inference.
"""
import os
from ray.data.llm import HttpRequestProcessorConfig, build_processor
def run_openai_example():
# __openai_example_start__
import ray
OPENAI_KEY = os.environ["OPENAI_API_KEY"]
ds = ray.data.from... |
88667f389449dfb3b1d4be3c6aeb40277d8d9cb92f68a5c9ba6ddf2c126ab1f9 | Python | 2,933 | 100 | """
This file serves as a documentation example and CI test.
Structure:
1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
2. Docs example (between __transcription_example_start/end__): Embedded in Sphinx docs via literalinclude.
3. Test validation (deployment ... |
f40c76b94a0ccee7191c0cf1ad0c731ad4d1c4511994b4994526a28d809f3d4f | Python | 2,933 | 122 | # -*- coding: utf-8 -*-
"""
Created on Thu Apr 21 11:48:46 2022
@author: jpv88
"""
from elephant.spike_train_generation import NonStationaryPoissonProcess
from elephant.spike_train_generation import StationaryPoissonProcess
from neo.core import AnalogSignal
import numpy as np
import quantities as pq
# %%
# rate:... |
b5767cd805e6cbd6b064bc65a6845481762c1383127eda477809da9b5ac60e36 | Python | 2,934 | 81 | """
***
built as part of the Russell Ray Lab Breathing And Physiology Analysis Pipeline
***
Breathe Easy - an automated waveform analysis pipeline
Copyright (C) 2022
Savannah Lusk, Andersen Chang,
Avery Twitchell-Heyne, Shaun Fattig,
Christopher Scott Ward, Russell Ray.
This program is free software: you can redis... |
619ffe477ab4ba2631ab0901538a3b04e3e3e1640db45c1701850e362b42cb49 | Python | 2,940 | 70 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
0aa50ee3bfec561f036478b3416f02edd55fac548851450cb6b960022aa65850 | Python | 2,941 | 78 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import pandas as pd
from scipy import stats
def softplus(data):
return np.log(1. + np.exp(data))
def simulate_cell_types(num_clusters, num_cells, num_markers_per_cluster, num_bg_genes, marker_expr=1.0, marker_covar=0.8):
total_gene... |
3fa8e7ff59f8e7ee1def6af3308495f6ff21904b199313196ceef2afc4e0a7ab | Python | 2,941 | 117 | """
implementing "a network with residual connections" example in https://github.com/synsense/sinabs/issues/181
"""
import torch
import torch.nn as nn
from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
from sinabs.layers import IAFSqueeze, Merge
class SNN(nn.Module):
def __init__(self, batc... |
890e7ed5affe83159294f67f9ebfdeb733c1723f8a27b2cb42ce5b95159093e3 | Python | 2,941 | 75 | import os
import shutil
from string import Template
import pandas as pd
from ..eeg import Eeg
class BidsConverter:
def __init__(self, BIDS_DIR, DATASET, root, outDir, loadAnnotationsFromEdf, montage = Eeg.Montage.UNIPOLAR, electrodes = Eeg.ELECTRODES_10_20):
self.BIDS_DIR = BIDS_DIR
self.DATASET... |
ab7a223faacba3601ea747969244f3cbd9c3045beab43c48ef9d88396dd7370a | Python | 2,941 | 81 | import os
import secrets
import sys
# RayConfig reads the auth mode once at import, so set the mode before importing
# ray. A per-process random token, inherited by subprocesses, lets the cluster
# and this process agree without a hard-coded secret.
os.environ["RAY_AUTH_MODE"] = "token"
os.environ.setdefault("RAY_AUTH... |
d0fe16eeffe379f82bc3a0dc42f8f1181a3e62f60d8bbd03f092fcda87bdb1ed | Python | 2,941 | 87 | '''
test_k4atypes.py
Tests for the k4a types and enums.
Copyright (C) Microsoft Corporation. All rights reserved.
'''
import ctypes
from threading import Lock
import k4a
# Save capture to reuse in tests since it takes a while to get a capture from the device.
glb_capture = None
glb_color_format = None
glb_col... |
3ee2aecb641ab1330d01d8ca295f4dad727f904b01fa67dd34ccf918e178ebc1 | Python | 2,942 | 99 | # stdlib dependencies
from typing import Any, List, Union
# 3rd party dependencies
import numpy as np
from numpy.typing import NDArray
import torch
# project dependencies
from deepface.commons import weight_utils
from deepface.models.demography.DemographyUtils import find_apparent_age
from deepface.models.demography.... |
70915f4df6e3ff4835a7683e3ab3446232cab24d352c99077e69d8b505cc35db | Python | 2,942 | 93 | import numpy as np
from siapy.datasets.schemas import TabularDatasetData
from siapy.utils.plots import display_signals
# Create sample spectral signatures dataset
rng = np.random.default_rng(seed=42)
# Generate synthetic spectral data (simulate 4 bands: R, G, B, NIR)
n_samples = 50
n_bands = 4
spectral_data = rng.no... |
914b7cb8a937b425315257d3cdc699cc59b479107abfe37a6f96d7586124f494 | Python | 2,942 | 83 | # -------------------------------------------------------------------------------------------------------------------------------------
# Following code curated for Bio-Diffusion (https://github.com/BioinfoMachineLearning/bio-diffusion):
# --------------------------------------------------------------------------------... |
8e6456b506a2387940c3ce7baa1ad7a7170ac217e3fe5c630600d5a2d79ad068 | Python | 2,944 | 76 | import time
import moabbpatch.datasets
import moabbpatch.baseparadigm
moabbpatch.baseparadigm._store = False
# As the rest event is not really available in the other datasets,
# but it is needed to let moabb calculate the accuracy instead of
# ROC it must be removed from the physionet dataset
# See moabbpatch/basepara... |
9af54af91e9e812d0ce4b8dce8f28f11232260c85eb04c0123bcbb31c794276c | Python | 2,946 | 102 | import pytest
from tblib import pickling_support
import ray
from ray import cloudpickle
from ray.air._internal.util import StartTraceback, exception_cause, skip_exceptions
from ray.tune import Tuner
@pytest.fixture
def ray_start_2_cpus():
address_info = ray.init(num_cpus=2)
yield address_info
# The code ... |
226c28943296761965f81b41fde493cf894df31071d94b1a22a2fa7d6b7dd5bd | Python | 2,947 | 66 | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 1 16:55:11 2024
@author: Administrator
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Adjusting the number of parameters to 8 and adding a second experiment scheme
param_values = np.arange(1, 11) # Parameter values from 1 to 8
... |
7bbf5007951a2b1b4b67e573a85e324eb61b357c1381ecb0a2ab3c5a1a2ae690 | Python | 2,947 | 74 | import sys
import scipy.io as sio
import os
import numpy as np
from scipy import special
import h5py
if __name__ == "__main__":
num_Exp = 10
list_Exp_ID = ['sim_'+str(x) for x in range(0,num_Exp)]
r_bg_ratio = 3
d0 = 0.8
# Load traces and ROIs
lam = 8
name_video = sys.argv[1] # 'lowBG=5e... |
aa3ac67475f0d39df54eba84695095b526796fdaa3c1b409b56fa461e771fe92 | Python | 2,947 | 77 | import cv2
import numpy as np
import os
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as L
import tensorflow.keras.backend as B
import tensorflow.keras.models as M
import tensorflow.keras.optimizers as O
import tensorflow.keras.losses as Ls
import tensorflow_addons.la... |
0f52503d39819bdd473ec997c5e0182161ea44880487b5555afffc800c769fff | Python | 2,948 | 68 | import subprocess
import argparse
import os
parser = argparse.ArgumentParser(description='Run gene connectivity with multi-model multi-edge, a commandline tool')
# can be 'gcn' or 'pna' or 'linear_regression' or 'auto_encoder' or 'mlp'
model_choices=['gcn', 'pna', 'linear_regression', 'auto_encoder', 'mlp']
parser.ad... |
af29aab7b6969145ef923bd1bfa7a477eb5abbb7767916c4f78690da490af5fc | Python | 2,948 | 69 | ####################################################################################################
# Copyright (c) 2019, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is free softw... |
8ff512b8237a4bc5bafb8c4d0d6cf6f45edeb3b29649b7bae077c43556dc383c | Python | 2,951 | 108 | import math
import matplotlib.pyplot as plt
import os
import subprocess
import tempfile
# PNAS figure dimensions
# 1 column: 8.7 cm
# 2 column: 17.8 cm
# Modified from https://github.com/Wookai/paper-tips-and-tricks
# https://stackoverflow.com/questions/14708695/specify-figure-size-in-centimeter-in-matplotlib
def cm... |
e396fb6bdff602853f9791c652d851a644dff532faf4e6b931091c57c931006f | Python | 2,952 | 102 | import numpy as np
from openmm import unit
from endstate_correction.neq import perform_switching
from .test_system import setup_ZINC00077329_system
def test_collect_work_values():
"""test if we are able to collect samples as anticipated"""
from endstate_correction.neq import _collect_work_values
nr_of_s... |
cb47a33fd3a2e72e0754d88c616d11ccb15fd953e41352b6871e76c008bd58f3 | Python | 2,953 | 110 | import numpy as np
class gene(object):
def __init__(self, geneID, geneType, binID = -1):
"""
geneType: 'MR' master regulator or 'T' target
bindID is optional
"""
self.ID = geneID
self.Type = geneType
self.binID = binID
self.Conc = []
self.... |
0cb7fb228fbae5199a0df2804c53c961c8ee5a8a9c78aca435654fff8b6e309c | Python | 2,954 | 79 | import logging
from scipy.stats import ttest_rel, jarque_bera, shapiro, wilcoxon
from agent.data.entities.config import ROOT_LOGGER_ID
ALPHA = 0.05
NORMAL = "normal distribution"
NOT_NORMAL = "not normal distribution"
STAT_SIGN = "statistically significant"
NOT_STAT_SIGN = "not statistically significant"
logger = l... |
8d1269455fc42fe8ccd9675573356561334baa7e17865859e57b074075e984e9 | Python | 2,954 | 96 | # stdlib dependencies
from typing import List, Union, Any
# 3rd party dependencies
from numpy.typing import NDArray
# project dependencies
from deepface.models.facial_recognition.tf import VGGFace
from deepface.commons import package_utils, weight_utils
from deepface.models.Demography import Demography
from deepface.... |
4422b3379f28d58216fd976da2b1d17bcd9339495671710216a286a9672e08a9 | Python | 2,955 | 104 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
For the selected k, gather the transition distances across all subjects across
runs and within run.
Output:
transmean.csv Contains transition distances for all subjects, across runs.
crun+'_transmean.csv' Contains transition distances for all subjects, within run.
Us... |
58a83e4f300529bc0469ba693546960d49347112933f1bf48de742b1677b6d63 | Python | 2,955 | 94 | import numpy as np
import torch
log_2pi = np.log(2.0 * np.pi)
def compute_loglik(x, variances):
"""
Assumes a zero-mean mulitivariate normal with a diagonal covariance function
Parameters:
x: array, shape (D,)
observations
variances: array, shape (D,)
Diagonal values... |
9297af714c305f21f6d75e1138a5d9ed43c1f79a05f398f3b4658c64acc2ff0b | Python | 2,955 | 80 | # built-in dependencies
from typing import List, Union, cast
# third-party dependencies
import numpy as np
# project dependencies
from deepface.config.minmax import get_minmax_values
from deepface.commons.embed_utils import is_flat_embedding
def normalize_embedding_minmax(
model_name: str, embeddings: Union[Lis... |
533b52e8d27e0c77ef72ff8234707d6a172bf0897a580f8098b077b3603a3f7e | Python | 2,956 | 93 | from typing import Any, Callable, List, Optional, Tuple
class Barrier:
"""Barrier to collect results and process them in bulk.
A barrier can be used to collect multiple results and process them in bulk once
a certain count or a timeout is reached.
For instance, if ``max_results=N``, the ``on_complet... |
c976af1630e8f9d05e3f3e96b785c404c3c1d2f204ae957ceecd9b665b422db9 | Python | 2,957 | 86 | import numpy as np
from scipy.linalg import solve_continuous_lyapunov
from scipy.linalg import expm
def auto_corr(W, DMatrix, tau, lags):
# Calculates the auto correlation function
# Function inputs
# W: connectivity matrix
# tau: E and I timescale constants
# DMatrix: structure of input nois... |
62622622c61660bdd2e2aaa0f0a7478a3a43912610bf2ad0125898ce83b75aac | Python | 2,958 | 79 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
bccffc6257866c0a4380e2ef0cf21280f8fe94b360e60c1309357d62e6f29251 | Python | 2,958 | 91 | from typing import List, Any
import torch
from monai.inferers import Inferer
from src.networks.vqvae.vqvae import VQVAEBase
class VQVAEExtractionInferer(Inferer):
def __init__(self, d_network: torch.nn.Module = None) -> None:
Inferer.__init__(self)
self.d_network = d_network
def __call__(
... |
3250e0befb1ffa77884f99a22eab8d40d0b4df0187019d2654a14a80b3f6d274 | Python | 2,959 | 84 | """FastTree wrapper for approximately-maximum-likelihood phylogenetic inference.
Wraps the real ``FastTree`` / ``FastTreeMP`` CLI
(http://www.microbesonline.org/fasttree/). Install via
``conda install -c bioconda fasttree``.
Input : one aligned FASTA (from :func:`omicverse.alignment.mafft`).
Output: one newick tree.
... |
aa9e8d65de982974eba3314d4fa73ee6279aedb8cd46ebcda7aee5331bbcb2a4 | Python | 2,959 | 94 | import os
import csv
import re
import numpy as np
import argparse
from src.models.train_model import train
from src.models.predict_model import predict_horizon
from src.data.load_data import load_data, load_params, make_input_labels
HORIZON = 6
rng = np.random.default_rng(2022)
def main(args):
"""Train a model... |
6c59041623fa331193383ee13a155e2fe7e1204191b60bc871ead353dfda0115 | Python | 2,960 | 83 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import tarfile
import tempfile
from pathlib import Path
import pytest
from fairchem.core.common.utils... |
13cd2c39ae68ed961e93dd13168aedaa59b0e908c1646cea250c8b1ebe61ceae | Python | 2,961 | 82 | import numpy as np
import torch
import torch.fft
import torch.nn.functional as F
from tqdm import trange
import torchvision.transforms.functional as tvf
from wave_optics import *
def optimizer(initial, lossFunc, *args, **kwargs):
''' base optimizer function
initial: initial seed tensor or list of t... |
17c6cc5354ad7c342e30f7b8efbe02aebbe29bcc32b94c154cfcb3ff056eefe5 | Python | 2,963 | 89 | from sklearn.metrics import roc_auc_score, average_precision_score, f1_score,accuracy_score,recall_score,matthews_corrcoef,confusion_matrix,roc_curve, precision_recall_curve
import numpy as np
import pandas as pd
import os
from sklearn.metrics import auc
import keras.backend as k
from scipy import stats
from model impo... |
c1dfa883c9e53bc23e12e4ed3bb18822348da71a8912d326fc37a22be870709b | Python | 2,963 | 104 | # -*- coding: utf-8 -*-
"""Dumping ground for random bits and bobs. I use this for several projects so
there will be a lot of irrelevant functions."""
import numpy as np
import os, sys, io
def set_to_array(s, dtype=float):
return np.fromiter(iter(s), count=len(s), dtype=dtype)
def sep_last_ax(points):
poin... |
1414a9fdfabc511cf9a23d05f1170f5d74ed16b594a92ed446c8936680db6b7a | Python | 2,964 | 89 | # Copyright 2026 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
2c8bb9ca95471e3b92c9af9be3e0d07649b1c618fd78cc089b60f498086f35d2 | Python | 2,965 | 91 | import csv
from os.path import *
import os
import sys
import re
from glob import glob
def main( args ):
# Ensure new RBS is created
if not isdir( args.outrbs ):
os.makedirs( args.outrbs )
index = index_samples( args.samplesheet )
split_samples( index, args.inrbs, args.outrbs )
def split_sa... |
441880dae07518e13aa7d3a8b9db7a6038133238523b70f2049d897a572ab469 | Python | 2,965 | 68 | import torch
import torch.nn as nn
class LSTM_encoder(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers=1):
super(LSTM_encoder, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.num... |
18bc65abd644cff2f4c968e14492cd1c0ba2d7fbecd5e7b9d42cb675c50a9bb5 | Python | 2,966 | 84 | from typing import Sequence
import os
import pandas as pd
class LayeredPointDepths:
DF_COLS = {
"ids", "layer_name", "depth", "local_layer_pia_side_depth",
"local_layer_wm_side_depth", "point_type"
}
def __init__(
self,
ids: Sequence,
layer_name: Sequence[str]... |
80cf22269e4bf8815e98ccb7aaf17b3b1423cb2860f19443f711e8cf0aefe43f | Python | 2,966 | 88 | # doc/source/serve/advanced-guides/multi-app-container.md downloads this
# file in a Dockerfile from its raw GitHub URL on master. Renaming or moving
# the file breaks that page.
# __serve_example_begin__
from ray import serve
import re
import subprocess
from typing import List
import starlette.requests
def hard_nor... |
c147d71535f777ce129b55b92c3ac78071089f198db7d0308cbc1f35178851ab | Python | 2,966 | 87 | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
def plot_expected_vs_obs(
mu, data, gene1="ENSG00000081237", gene1_lab="PTPRC", gene2="ENSG00000167286", gene2_lab="CD3D"
):
r"""Plot expected vs observed values of a pair of genes (2D histogram).
:param mu: ndarray of values expected b... |
ce6bf92cef046fb26c313645d307e825cadcb3921c3635fca2d5746740e0635a | Python | 2,966 | 91 | import os.path as osp
import warnings
from mmengine.config import Config
from mmengine.runner import EpochBasedTrainLoop, IterBasedTrainLoop
from ..engine.hooks.lark_hook import LarkHook
def process_lark_hook(cfg: Config, lark_file: str) -> list:
"""Process LarkHook in custom_hooks.
Here are three cases:
... |
3e637aa9127f1be96955a26ca715f160ee12cb5f13994a55ca76c04ba87a7b79 | Python | 2,968 | 72 | """Compatibility shim over lerobot's torchcodec video decoding.
The datasource decodes frames by calling lerobot's ``decode_video_frames_torchcodec``.
The only gap in ``lerobot >= 0.5.0`` is that the function's decoder cache opens
files without credentials, so explicit ``storage_options`` never reach
``fsspec.open``.... |
3ac305dd375991c4a5c4dd3cad289e6a01564a6e7faa7b9d601b96223161eafb | Python | 2,969 | 45 | import os
import itertools
import pandas as pd
from scipy.cluster import hierarchy
from scipy.spatial.distance import squareform
#######################################################################
# predefined parameters
#######################################################################
def calculate_identi... |
b59510bcb9ae26856917f7a6a8d9097de8c83e55a79236c99d90458a21674888 | Python | 2,969 | 92 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
7a6e9b2a9b6c09c31666c69e1185351f6562a5c96b334f35411084115bf5d82f | Python | 2,971 | 128 | import os
import sys
import time
import errno
import time
import timeit
import numpy as np
from numba import jit
import warnings
warnings.simplefilter("ignore")
@jit(nopython=False)
def VEP2Dmodel(params, constants, init_conditions, SC, dt, ts):
nt=ts.shape[0]
nn=SC.shape[0]
#parameters
eta... |
6bacc847e57ab4204ece0bbe788a0a21ea8174f77e06cf1cded98f403aada59e | Python | 2,972 | 102 | # flake8: noqa
# __compile_neuron_code_start__
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch, torch_neuronx
hf_model = "j-hartmann/emotion-english-distilroberta-base"
neuron_model = "./sentiment_neuron.pt"
model = AutoModelForSequenceClassification.from_pretrained(hf_model)
t... |
6d111e3eb3305c2eac1afd1c2be45ea13ceb6c1a4fa81a4540bdc653ad4f1aa8 | Python | 2,972 | 116 | #!/usr/bin/env python2
import sys
import argparse
import json
import bam
import bqd
import samtools
def main():
args = parse_args()
print_json( args )
def print_json( args ):
pileup = samtools.nogap_mpileup(args.bamfile)
pileup = bqd.parse_pileup( pileup )
set_unmapped_mapped_reads( args.bamfile... |
abb7addcf68736566f00aac62329b0c9011da41831be540444d6ee5e77786474 | Python | 2,972 | 134 | import itertools
from PIL import Image
import pickle
import os
import numpy as np
import pandas as pd
import yaml
Image.MAX_IMAGE_PIXELS = None
def mkdir(path):
dirname = os.path.dirname(path)
if dirname != '':
os.makedirs(dirname, exist_ok=True)
def load_image(filename, verbose=True):
img = ... |
1ff075271231ffebbe39b233cb222e68655e5519b23bd34a260854bd2d9822aa | Python | 2,973 | 103 | """
Minimal working example for shotgun VAE using time-warped birdsong.
0) Define directories and parameters.
1) Tune preprocessing parameters.
2) Warp song renditions & train a generative model.
3) Plot.
4) The world is your oyster.
"""
from itertools import repeat
from joblib import Parallel, delayed
import numpy ... |
ef17de5f54d4d1f01517501eb06ac8dabbdd6af1cf976e9949e30460a8a3591b | Python | 2,975 | 70 | from Bio import SeqIO
import sys, os
import random
type = sys.argv[1]
target = "pos_MANE"
project_root = "../../train/src/INPUTS/800bp/"
output_dir = f"../../results/mutation/{target}/seq/"
def mutate_sequence(sequence, position, new_nucleotide):
"""Mutate the sequence at the specified position with the new nucle... |
7118cdc5baf65e9d0925a20bbb00c507c422d31aab36e749ba5a5aaaee0a58e7 | Python | 2,976 | 123 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
from pathlib import Path
import ase
from ase.calculators.vasp import Vasp
def setup_vasp_calc_mof(at... |
f9e090fc1c85c50493e5d825c39b67d910e8383a8cad373d688ac4ad35506794 | Python | 2,976 | 62 | from scipy.stats import pearsonr
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
from utils import oneHot
from train_model import train_evaluate_model
import pandas as pd
import numpy as np
from tensorflow.keras.layers import *
kwargs_ala = {'dense_si... |
95cac77575e1c97f3fb069d3721f6c16182e8f0f29265de10bc8306fb2f751d7 | Python | 2,978 | 110 | import numpy as np
MIN_T = 1e-4
MAX_UPDATE = None
class Schedule:
"""
Cooling Schedule for the SA optimization
Methods
-------
init()
Initalization.
update()
Update inner states with a new entropy.
has_next()
True if not terminate.
Usage
---... |
f562c6f0501c1d5881184c880be4497a5244338330ec96aacc880c7a0729a936 | Python | 2,979 | 94 | # coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
064b9551ea9b86af1b6a27325ff702ae7ecbf91d747a9aeda93c55a08aa2676d | Python | 2,980 | 91 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Base class for an Experiment."""
from enum import Enum
from typing import Any, Callable, Dict, Optional
class Signals(Enum):
"""Signals emitted by an E... |
0b10af14c4e689681892f754c717e858aad0bc953f15308a18d33868fffae232 | Python | 2,981 | 77 | import collections
from pfrl.collections.prioritized import PrioritizedBuffer
from pfrl.collections.random_access_queue import RandomAccessQueue
from pfrl.replay_buffer import random_subseq
from pfrl.replay_buffers import EpisodicReplayBuffer, PriorityWeightError
class PrioritizedEpisodicReplayBuffer(EpisodicReplayB... |
81080a406374dfb7b738e9aa9027caea410fde8bd70690c26b0617abf12f24e0 | Python | 2,981 | 79 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 2 16:02:27 2017
@author: agiovann
"""
% % LOAD DATA
params_display = {
'downsample_ratio': .2,
'thr_plot': 0.8
}
analysis_file = '/mnt/ceph/neuro/jeremie_analysis/neurofinder.03.00.test/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_._results... |
0cd13812de0bbd4bc61b24634f0508f111524541e35b029bb8cbc3e385b9a278 | Python | 2,982 | 76 | """
This file generates plots used in Extended Data Fig. 9.
"""
import os
import pickle
from matplotlib import pyplot as plt
from searchParams import *
from defaultParams import *
processing_perturbed_pop = 'exc'
# path to the file where `diff_data` exists. This file is generated after running `figures_hpc.py`
PATH... |
99bc33807245c54b07c8a69b2f9de326310a1c895ec0b9139bdc2e37dc33b030 | Python | 2,982 | 94 | import numpy as np
import pytest, torch
from pathlib import Path
import ecg_augmentations as ea
from tests.common import TWELVE_LEAD
@pytest.mark.parametrize("shape", [(1, -1), (12, -1), (3, 12, -1)])
def test_shape_preserved(transform, ecg, shape):
if shape == (1, -1) and isinstance(transform, TWELVE_LEAD):
... |
a8a2dee698866cd00d42b2c7e8665d87d61078185d93ac5c2e40926fc0f5d1cd | Python | 2,982 | 76 | """
This file generates plots used in Extended Data Fig. 9.
"""
import os
import pickle
from matplotlib import pyplot as plt
from searchParams import *
from defaultParams import *
processing_perturbed_pop = 'inh'
# path to the file where `diff_data` exists. This file is generated after running `figures_hpc.py`
PATH... |
49f776a794a1bb396a82b032324698c61994f94c3583c42d65d240044116caff | Python | 2,983 | 78 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
ace6ba968e2caaa3025467f24c41c88cf130d5b4064e14edaf36a3791dde14cc | Python | 2,984 | 69 | from pybind.fhe import fhe
import numpy as np
if __name__ == "__main__":
batchsize, scalingfactorbits = 4096, 52
ckks = fhe.CKKS(4096, 52, "resources/fheparams/cryptoparams/")
# Generate the necessary CryptoParameters
ckks.gen_crypto_context_and_key_gen()
ckks.load_crypto_params()
data_dimes... |
aeb1b2bfbc0cdbde003cfcdfd0c605463843c917b97252782d6a5431c4e387f1 | Python | 2,984 | 99 | # __serve_deployment_example_begin__
from ray import serve
import aioboto3
import torch
import starlette
@serve.deployment
class ModelInferencer:
def __init__(self):
self.bucket_name = "my_bucket"
@serve.multiplexed(max_num_models_per_replica=3)
async def get_model(self, model_id: str):
... |
481b403e71b74c658d167ffe2c45fb92b53de91f83548735c5bc56f0d163790f | Python | 2,985 | 97 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
96b9378abaf9cb9cef91fe79f574627c245dfafb1fae617a6013c37ffa86655d | Python | 2,985 | 68 | import torch
from ..utils import loss_and_miner_utils as lmu
from .base_miner import BaseTupleMiner
class TripletMarginMiner(BaseTupleMiner):
"""
Returns triplets that violate the margin
Args:
margin
type_of_triplets: options are "all", "hard", or "semihard".
"all" means a... |
026440d0e6a99191ba78aa001fefabc930594ffc8279e20f669c16cbae78b000 | Python | 2,988 | 72 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
22f5318eac614de5b7a4b3f179a5010ebaae281934ec83ef50b253067c0b5e5f | Python | 2,989 | 133 | # Modified from https://github.com/dbolya/tide
# This work is licensed under MIT license.
import os
import sys
from typing import Union
import numpy as np
def mean(arr: list):
if len(arr) == 0:
return 0
return sum(arr) / len(arr)
def find_first(arr: np.array) -> Union[int, None]:
"""Finds the i... |
ab0207364e6ea90d31ce29f6e89643dbbbae6699de63a4e0e24d76c8ac2a2645 | Python | 2,990 | 82 | import csv
import gzip
from pathlib import Path
from os.path import isfile
from typing import Optional
from .dscript import parse_dscript
from .string import parse_string
def to_common(processed_folder: Path, source: str, taxon: Optional[int], identifier: str, version: Optional[str] = None) -> None:
"""
Conv... |
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