sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2b9815d0977ff7df17b1430c931ca25284d70859ec38c7034a68aa88dddea488 | Python | 3,221 | 109 | # slurm-launch.py
# Usage:
# python slurm-launch.py --exp-name test \
# --command "rllib train --run PPO --env CartPole-v0"
import argparse
import subprocess
import sys
import time
from pathlib import Path
template_file = Path(__file__) / "slurm-template.sh"
JOB_NAME = "${JOB_NAME}"
NUM_NODES = "${NUM_NODES}"
NUM... |
d58810f77d5b67aa90b34eb9cd00da39f8b3713fedca9e4440c76692240ad210 | Python | 3,221 | 89 | import torch
from ..utils.module_with_records import ModuleWithRecords
class BaseDistance(ModuleWithRecords):
def __init__(
self, normalize_embeddings=True, p=2, power=1, is_inverted=False, **kwargs
):
super().__init__(**kwargs)
self.normalize_embeddings = normalize_embeddings
... |
e70b08a3a1f715c18767d8318476aaab904473e31c512244e8737700a0ec8738 | Python | 3,221 | 61 | import torch
########################################################################################################################
def create_feature_maps(init_channel_number, number_of_fmaps):
return [init_channel_number * 2 ** k for k in range(number_of_fmaps)]
def FFDrealign4(input):
# batch channel time... |
202ee0fac64e1df68a1d97b419e36e71156896c281cef5d3b1bd205dea1a4202 | Python | 3,222 | 94 | import abc
from enum import Enum
from typing import Dict, List, Optional
from ray.data._internal.execution.interfaces.common import NodeIdStr
ResourceType = str
ResourceDict = Dict[ResourceType, float]
RequesterId = str
LabelKey = str
LabelValue = str
LabelSelector = Dict[LabelKey, LabelValue]
ReservedResources = Dic... |
a6078cad45781806196a4bc306e3258926dd759fc04a51bc2d815957a1cfc744 | Python | 3,222 | 91 | import os
import subprocess
from typing import Dict, List, Tuple
from ray.autoscaler._private.docker import with_docker_exec
from ray.autoscaler.command_runner import CommandRunnerInterface
class FakeDockerCommandRunner(CommandRunnerInterface):
"""Command runner for the fke docker multinode cluster.
This co... |
87c95062249d5e1b69410c64df82005c81dec637467fab989c144d270dce861a | Python | 3,223 | 83 | import cv2
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from models_.detectors.YOLOv3 import YOLOv3
class LiveCameraDataset(Dataset):
def __init__(self, camera_id=0, epoch_length=1, resolution=(384, 288), interpolation=cv2.INTER_CUBIC,
mu... |
d514ddca29cac5af4c017eb7eee3b4a0ff470d2543fc64da9e09c6a6673127d6 | Python | 3,223 | 101 | #!/usr/bin/env python3.7
"""Remove files except those specified from samples directories.
The copyrights of this software are owned by Duke University. Please
refer to the LICENSE and README.md files for licensing instructions. The
source code can be found on the following GitHub repository:
https://github.com/wmglab... |
9e2da47c6d8b6427cf7dd4f283b8b60b686c98482027a6c928688f256224b4d0 | Python | 3,224 | 94 | """Store analysis results of an ensemble."""
import argparse
import logging
import pickle
from flyvis import Ensemble
from flyvis.analysis.clustering import umap_and_clustering_generator
from flyvis.utils.config_utils import HybridArgumentParser
logging.basicConfig(
format="[%(asctime)s] [%(filename)s:%(lineno)d... |
eda627c19bdf9b982544e90da236604d2543c2647b498e0fae772dc8d2eb759a | Python | 3,224 | 108 | # 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, software
# distributed under th... |
8fffc1cdf1a743627f717a1d6ab8ecbe4b9bd0a6a1a2d1129f159642fc36885f | Python | 3,225 | 107 | # 3rd party dependencies
import matplotlib.pyplot as plt
import numpy as np
import cv2
# project dependencies
from deepface import DeepFace
from deepface.modules import verification
from deepface.models.FacialRecognition import FacialRecognition
from deepface.commons.logger import Logger
logger = Logger()
... |
3d787be034bedd5793a52c0d4d0a7ac43489ca55dc8117b019e8b26bd0b59050 | Python | 3,226 | 105 | """
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
import torch
import yaml
def convert_fine_tune_checkpoint(
fine_t... |
6545533eb7ef52375d9e479f5138a8fbfcc3b6585624e2f9868538efc0d618e2 | Python | 3,226 | 112 | from typing import Any, Tuple
def generic_expectations(
name: str, summary: dict, batch: Any, *args
) -> Tuple[str, dict, Any]:
batch.expect_column_to_exist(name)
if summary["n_missing"] == 0:
batch.expect_column_values_to_not_be_null(name)
if summary["p_unique"] == 1.0:
... |
b3014fc925748bf5f970cc03a39b7dcc31dee337bf5c680074509f7f34c5bd05 | Python | 3,226 | 80 | from numba import jit
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from collections import namedtuple
from sklearn.linear_model import LogisticRegression
@jit(nopython=True)
def test_train_sets(population, n_test):
n_cells, n_t, n_reps = population.sha... |
05b946780464561c24d51d5d1f63658ca2767686e636a747e7f896bdfe161c13 | Python | 3,227 | 102 | import vamb
import re
import subprocess
import unittest
def grep(path, regex):
with open(path) as file:
for line in file:
m = regex.search(line)
if m is not None:
g = m.groups()
return (int(g[0]), int(g[1]), int(g[2]))
raise ValueError(f"Could n... |
26d06b874d0052e050a2193d514d638f9df0915410e13b8d98bb9f57eb2dc927 | Python | 3,227 | 91 | """
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.
Tests: FAIRChemCalculator over the AllScAIP-OMol102M conserving
checkpoint — single H2O molecule energies/forces, batched
pred... |
7ed87a7421fc59a443f6955d9cdf86099518b32f6e629beb05a696dff56fefed | Python | 3,228 | 78 | r"""Ambient / contamination-RNA removal for droplet scRNA-seq — ``ov.pp.ambient``.
Ambient ("soup") RNA — cell-free transcripts released by lysed cells —
contaminates every droplet during library prep. Left uncorrected it
inflates marker genes in cell types that never expressed them and biases
downstream DE, annotatio... |
7f0d3b0d349ad8b2447a82491ecf7fa05ba61764404af343c9cb93cd0ffeeea5 | Python | 3,228 | 86 | from typing import Union
import numpy as np
from merlin.core.compat import cupy
from merlin.core.protocols import Transformable
from merlin.dag import BaseOperator
from merlin.dag.selector import ColumnSelector
from merlin.schema import Schema
from merlin.table import Device, TensorTable
class Padding(BaseOperator)... |
a6cc3f902f1d56a4955df541222f11f3fc06c0d44925bc1f9d04cf186371a9ec | Python | 3,229 | 84 | import os
from deprecated import deprecated
def read_token(pred_labels, valid_labels, tokens, offsets):
if valid_labels != None:
d = {'token': [], 'pred': [], 'valid': [], 'offset':[]}
else:
d = {'token': [], 'pred': [], 'offset':[]}
with open(pred_labels) as f:
for li... |
b93f2d0414b66c29c8513431f51c3879e282d2050019eed9c53185317be8c823 | Python | 3,229 | 77 | """Fused CPU kernel for COMMOT's sparse l1-unbalanced Sinkhorn (``unot_sinkhorn_l1_sparse``).
acceleration: playbook §4.4 (numba-jitted inner loop) + §4.1 (fusion), class (E).
Each half-step evaluates exactly COMMOT's update
f <- eps*log(a) - eps*log(sum_j exp((-C_ij + f_i + g_j)/eps) + exp((-m + f_i)/eps)) + f
... |
12e70509aa8e039bb73b0bd3fa3cae7b64935ca3042c0e05b86ec6797d25ec8e | Python | 3,230 | 89 | import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
from skimage.color import rgb2yuv, yuv2rgb
from yuv_frame_io import YUV_Read,YUV_Write
device = ... |
dec881c84e955a866653922fb8799d4eb33ec55720210587d118bba040d73b5b | Python | 3,230 | 86 | # Copyright 2015 The TensorFlow Authors. 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 applica... |
e3a7651e18306033316d0a9a5d0fb7fa1330aa56f697192334bac7d6d97c22f1 | Python | 3,231 | 130 | import argparse
import torch
import random
import os
import numpy as np
import pandas as pd
from chromoformer import ChromoformerDataset
from chromoformer import ChromoformerRegressor
from tqdm import tqdm
from sklearn import metrics
from scipy.stats import pearsonr
def seed_everything(seed=42):
random.seed(see... |
740258f94648be352b022ed5e6c92903dd2f474e2673f46ca0f11c0a2d1adf04 | Python | 3,232 | 76 | from get_Data import *
from model import *
import warnings
from param import *
def train(args):
similarity_feature = similarity_feature_process(args)
edge_idx_dict, g = load_fold_data(args)
print("*************************starting the train*****************************")
metric_result_list... |
9114cb66e7180979604d7d27292c9ad5d392e78a02aacd4255b8e0cd62949930 | Python | 3,233 | 88 | CODE_DIR = '/home/kelarion/github/'
SAVE_DIR = '/mnt/c/Users/mmall/Documents/uni/columbia/assignment_errors/'
import socket
import os
import sys
import numpy as np
import scipy as sp
import scipy.linalg as la
import scipy.io as sio
from sklearn import svm, manifold, linear_model
from sklearn.model_selection import cr... |
08eef6e8dab2a835ccc8e491f7b9ba8e660a581289ae9cfc783953cca798f973 | Python | 3,234 | 109 | import numpy as np
import torch
from torch import nn
class EmpiricalNormalization(nn.Module):
"""Normalize mean and variance of values based on empirical values.
Args:
shape (int or tuple of int): Shape of input values except batch axis.
batch_axis (int): Batch axis.
eps (float): Smal... |
17b974d9a4747fead8647766c7302d1f448b4e43e44c975b4e68f9ae8e6cf97f | Python | 3,234 | 108 | '''adapet the how-to-use guide from PEKNN, but check if CRP can handle it'''
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from task import CSW
from model import PECRP
from utils import compute_smth_mean, to_sqnp
from scipy.spatial import distance_matrix
sns.set(style='white', palette='colorb... |
95880f0f571e866fbea6c3a9c61cf3937015a31ea93c7b24a6e6ff271a1272e8 | Python | 3,234 | 86 | ####################################################################################################
# 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... |
d70722d91fded6880754d2651aebb5d7834ee1cdca08f9882cc5f3cd76e5d31f | Python | 3,234 | 69 | import argparse
import json
import os.path
import keras
import glob
import numpy as np
import PIL.Image
from tensorflow import keras as k
import time
from utilities import extract_patches
from osgeo import gdal
PIL.Image.MAX_IMAGE_PIXELS = 500000000
def calculate_qs(tile_path, w_path, json_name, mod... |
48c2e4b7c2024e3beeae9ec4d1d47ba97123484345cf0b5fd2696b079a2e675c | Python | 3,236 | 41 | from fr import *
import matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt
plt.rcParams.update({"font.family":"DejaVu Serif","font.size":8.5})
fit0=json.load(open('fr_fit.json')); x=np.array(fit0['const']['x']); al,rh,lk,be,br,_=unpack(x,'const')
g=v.groupby(['age','sp'])
fig,ax=plt.subplots(2,2,figsize... |
c0cfadfbf9c416a591bbe2879985aa53c181447e70969678de1174823e45b75e | Python | 3,236 | 111 | r"""
Bulk omics analysis utilities.
This module provides comprehensive tools for bulk RNA-seq analysis including:
- Differential expression analysis with DESeq2
- Gene co-expression network analysis (WGCNA)
- Pathway enrichment analysis (GSEA)
- Protein-protein interaction networks
- Batch effect correction
- Multi-om... |
399a58cec9002b1a175f6868ea1614285e76285b70c3c976c2aad59be44a730c | Python | 3,238 | 93 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""aihwkit example 8: simple network with one layer using Tiki-taka learning rule.
Simple network that consist of one analog layer. The network aims to learn
to... |
945593c15dc3cc53eacc98c2096b01a3fd193cdb1bf3e70a0ea78f3650efda9c | Python | 3,238 | 125 | import math
import pdb
from typing import Any, Mapping
import torch
import torch.nn as nn
from latent_time_stepping.time_stepping_models.parameter_encoder import ParameterEncoder
#from torchdiffeq import odeint_adjoint as odeint
from torchdiffeq import odeint as odeint
class NODE(nn.Module):
def __init__(
... |
cf091e23db7da29471dd405951fa96e38a204254ce6bd08505e2884fac882f18 | Python | 3,238 | 80 | import torch
import torch.nn as nn
from ..utils import create_norm
import torch.nn.functional as F
class ResMLPDecoder(nn.Module):
def __init__(self, in_dim, hidden_dim, out_dim, num_layers, dropout, norm, batch_num):
super().__init__()
self.layers = nn.ModuleList()
assert num_layers > 1, '... |
196810b71a9f1fbf28cc1249f40cee50d1e719e93abd252ed306a9cee18d7cb6 | Python | 3,240 | 124 | # -*- coding: utf-8 -*-
#
"""
std_depth.py
Standardize a depth record from Dragen CNV.
"""
from svtk.utils import is_smaller_chrom
from .standardize import VCFStandardizer, NULL_GT
@VCFStandardizer.register('dragen_cnv')
class DragenCnvStandardizer(VCFStandardizer):
# Dragen CNV is a depth-based caller; tag ca... |
06a71a3ea47586fae5ca7dd30eabac4b0dc86770b4d92533f3402453e87c4246 | Python | 3,241 | 79 | import os
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
tissues = {'WM':3,'CGM':2,'DGM':4}
titles = {'WM':'White matter','CGM':'Cortical gray matter','DGM':'Deep gray matter'}
pars = ['D','f','vd']
plot_par = {'D':'$D$','f':'$f$','vd':'$v_d$'}
units = {'D':'µm$^2$/ms','f':'%','vd':'mm/s'}
sc... |
3e46af064c613bdd5835cf400215a811f90316e5eff4349f49175af82c3f505c | Python | 3,242 | 95 | import numpy as np
import torch
def to_batched(x):
"""Return x as a (B, C, T) tensor and a function restoring the input format."""
is_numpy = isinstance(x, np.ndarray)
x = torch.as_tensor(x)
if not x.is_floating_point():
raise TypeError(f"expected a floating-point signal, got {x.dtype}")
i... |
edad0850cf576e72629e341a369cf53d552ea733882d02825064beb5d8978b75 | Python | 3,242 | 85 | import numpy as np
import nibabel as nib
from scipy.interpolate import RegularGridInterpolator
from nibabel.orientations import axcodes2ornt, ornt_transform
def find_corners(img, aff, margin):
coord = np.argwhere(img>0)
coord_h=np.concatenate((coord, np.ones((coord.shape[0],1))),axis=1).T
RAS = aff@coord_h... |
16cc9d69db3c821a24b316cb80c28773ef18fcf909a8b68c02bcf93495de4723 | Python | 3,243 | 77 | """CytoSignal ligand-receptor database (exported from the R package data)."""
from __future__ import annotations
from dataclasses import dataclass
from functools import lru_cache
from pathlib import Path
import numpy as np
import pandas as pd
_DATA = Path(__file__).with_name("data")
TYPES = ("diffusion", "contact")
... |
548a3b73450cbad7a6705f93b763158cc64e31d8a415b2dda4fd3f443debe3bc | Python | 3,243 | 99 | import torch
import torch.nn as nn
import torch.nn.functional as nnf
class SpatialTransformer(nn.Module):
"""
N-D Spatial Transformer
"""
def __init__(self, size, mode='bilinear'):
super().__init__()
self.mode = mode
self.size = size
# create sampling grid
ve... |
6d630c9173e0e09bba208be8bf4fc5e02998b07c3077411281aa00f2051ec671 | Python | 3,243 | 97 | import logging
import time
import aiohttp.web
import ray.dashboard.modules.tests.test_consts as test_consts
import ray.dashboard.modules.tests.test_utils as test_utils
import ray.dashboard.optional_utils as dashboard_optional_utils
import ray.dashboard.utils as dashboard_utils
from ray._private.ray_constants import e... |
afb0942801e0ff8a8758b759dc1576e0708319ce3d9fd349dee49b16d9d05f48 | Python | 3,243 | 90 | import argparse
import datetime
import logging
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions, GoogleCloudOptions, StandardOptions
from ml4h.defines import GCS_BUCKET
from ml4h.tensorize.dataflow import bigquery_ukb_queries
def parse_args():
now_string = datetime.dat... |
cc37669003e1d083846e62f576bd7f5424af94c82416921bd40c19e1f065a837 | Python | 3,244 | 76 | from delete_row_expriment import *
from model import *
import warnings
from param import *
def train(args):
similarity_feature = loading_similarity_feature(args)
edge_idx_dict, g = data_preprocess(args)
print("*************************starting the train*****************************")
metri... |
fa1eb1e0c95453c2154663e8a7ec0ac8fb97dce407e98e0cb5830c3f6b248a29 | Python | 3,244 | 69 | #Imports relevant modules.
import os
import nibabel as nb
import numpy as np
#Defines the pipeline that we want to generate the activation count map for. This will need to be updated each time.
pipeline = '0'
#Based on the above variable, finds the directory corresponding to this pipeline which contains FEAT output.
... |
12f25c8c3537bb3db11b6dbd7cd22d73b5f569e1242f378885271c131c04977c | Python | 3,245 | 71 | import os
import pandas as pd
import matplotlib.pyplot as plt
annotations = ["chess", "gencode", "refseq_ucsc"]
colors = ['blue', 'red', 'green', 'cyan', 'magenta', 'olive', 'black', 'gray', 'orange', 'purple']
os.makedirs("intron_sensitivity_precision", exist_ok=True)
for library in ["polyA", "ribozero"]:
for a... |
55052f50f231fe94f5e4d623e565d61da2853f57bb38863ae0d0ca4aad13b279 | Python | 3,245 | 82 | """
Module for managing version naming scheme and supplying version information.
We use setuptools_scm to get version information from git. The version naming
scheme is specified by defining functions ``version_scheme()`` and
``local_scheme()`` in this module. We have written these so that they mimic
versioneer's "pep... |
98fc7655edcb7072d7384f145c17543c165c7f2a9b92c7e9d32cce4ba90f8f8d | Python | 3,245 | 82 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
from model import utils
from matplotlib import pyplot as plt
from lifelines import CoxPHFitter
import numpy as np
import... |
ae5b8569fd24a89e6c40bf3cb6c1567a0576f594a172bc5419b2b6ec26dfb658 | Python | 3,245 | 72 | from utils import *
from sklearn.metrics.pairwise import cosine_similarity
if __name__ == "__main__":
SHOW = 0
# Declare the datadir and paramfile
datadir = "/cfs/klemming/scratch/n/nbrav/logs/sparsespk-full/2024-04-21_14:40:38:083781"
paramfilename = f"{datadir}/net.par"
param = parsepa... |
5ce427f9a7dfe419bdf13684041bd4acde65899dd38880e4f87101a6eead534e | Python | 3,246 | 88 | import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import matplotlib as mpl
from tueplots import bundles
from matplotlib import cm
from tueplots import figsizes, fontsizes, fonts
from matplotlib import rcParams
accs_arr = np.load('data-fig2and3/accs_arr.npy')
loss_arr = np.load('data-fig2a... |
703a1740e13314f6c51979734133f3690cd6c4c1983f31fdcad9f95151b8028f | Python | 3,246 | 121 | #!/usr/bin/env python
import inspect
import ray
from ray.util.annotations import _is_annotated
IGNORE_PATHS = {
".impl.",
".backend.",
".experimental.",
".internal.",
".generated.",
".test_utils.",
".annotations.",
".deprecation.",
".protobuf.",
".cloudpickle.",
}
def _fulln... |
cdfe35e6edf137f3f7f552663f2d5d6b818bf06c5f15fe0232c7488617b30b4d | Python | 3,246 | 105 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 24 09:37:30 2022
@author: pbolan
"""
import numpy as np
import matplotlib.pyplot as plt
from os import path, getcwd, makedirs
import glob
from skimage import io
import cv2 as cv # Note some issues with opencv-python versions. U
import nibabel as ni... |
ebfffde5472ebae30e9e24ea7e3d23882a1fc57b8bce3dded68da35abc06049c | Python | 3,246 | 76 | from delete_col_expriment import *
from model import *
import warnings
from param import *
def train(args):
similarity_feature = loading_similarity_feature(args)
edge_idx_dict, g = data_preprocess(args)
print("*************************starting the train*****************************")
metri... |
053821016f2b8603269bd0984fa4f0fb62e4d313d0bd27ec0eea5dd61af97f8b | Python | 3,248 | 99 | import pandas as pd
import numpy as np
import argparse
from sklearn.model_selection import KFold
import json
import os
def get_parser():
parser = argparse.ArgumentParser(
description="Generate 5-fold CV splits (subject-based, expanded to contrasts). "
"JSON contains train/val only, CSV... |
1a52077b29d0df214bab8f7769398dbe32387f7af72260025f7c85fa64ea7355 | Python | 3,248 | 73 | r"""Vendored PDBFixer — structure repair for molecular-dynamics preparation.
`PDBFixer <https://github.com/openmm/pdbfixer>`_ (Peter Eastman, Stanford /
Simbios) repairs experimental and predicted structures so they can actually be
simulated: it builds missing residues and heavy atoms from residue templates,
replaces ... |
a15ce861edbd35f6ddf2b7257522f47caea2d943fb098dacb84f2e4db5713652 | Python | 3,248 | 93 | import warnings
from typing import Any, Iterable
import numpy as np
import pandas as pd
from siapy.core.exceptions import InvalidInputError
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
category=DeprecationWarning,
message="pkg_resources is deprecated as an API",
... |
b26de9326351fd6987edbc112ad8ae5d200964b058b0b4c659b9968686e2f700 | Python | 3,248 | 99 | """
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 pytest
from fairchem.applications.cattsunami.core.autoframe import (
AutoFrameTransfer,
... |
b43049a3312d63747f079f5ad5e8fea2761a843553545b6e5ec4614d9e94c366 | Python | 3,248 | 91 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class BlockV2(nn.Module):
expansion = 1
def __init__(self, input_channels, output_channels, stride = 1, downsample = None):
super(BlockV2, self).__init__()
... |
52bbe2b76517f5df8ac9a25fff3763c4236c535d06663e379f4c7f460bf9d57d | Python | 3,250 | 97 | # -*- coding: utf-8 -*-
"""
Created on Thu Nov 4 14:04:15 2021
@author: Nikhil
"""
startTime = datetime.now()
filename_raw = input("Enter filename without extention(.tif):")
filename_full = str(filename_raw) + str(file_extention)
sampling_interval_mins = input("Enter sampling interval (time in MINS bet... |
87d66e510f43dc3a9ad3736c3ace910ea589c445a97790b5d3787b177fab01a7 | Python | 3,250 | 76 | import math
import torch
import torch.nn.functional as F
from mmengine.model.utils import stack_batch as mmengine_stack_batch
from mmengine.utils import is_list_of
def stack_batch(tensor_list,
pad_size_divisor=1,
pad_value=0,
channel_conversion=False,
m... |
552f3ee725a6e31024eb15c6ff27aaf4c3aab0b6c741b49e6e124787f3333891 | Python | 3,251 | 102 |
from typing import Callable, Iterable
from PyQt5.QtWidgets import QMessageBox, QFileDialog
def notify_error(msg: str, title: str = "Error"):
"""Notify user of some error"""
QMessageBox.critical(None, title, msg)
def notify_warning(msg: str, title: str = "Warning"):
"""Notify user of some warning"""
Q... |
3ae66710d3fd6d59f16f6207558914ef1e02ac8bdda1d75dc75e7e2ad23ef154 | Python | 3,252 | 56 | def identify_constitutive_alternative_exon(gene):
'''
Aim: Define the exons as either constitutive (in majority/all of the isoforms) or alternative
Constitutive here is defined as exons that are in the majority of the isoforms
unless also specified in the UCSC genome browser basic annotation (i.e.... |
6cc3db01abc713cfb1427c8f4aa3052a6619fae565e9c01bfe8bb54c9607a0f4 | Python | 3,252 | 70 | import os
import pandas as pd
import matplotlib.pyplot as plt
annotations = ["refseq_ucsc"]#["chess", "gencode", "refseq_ucsc"]
colors = ['blue', 'red', 'green', 'cyan', 'magenta', 'olive', 'black', 'gray', 'orange', 'purple']
os.makedirs("intron_sensitivity_precision", exist_ok=True)
for library in ["polyA_STAR", "... |
7252d1839bbc513c7e476f745b2e12a397e7c2a228579c5e396042bbd5a05d9e | Python | 3,252 | 73 | from get_Data import *
from model import *
import warnings
from param import *
def train(args):
similarity_feature = similarity_feature_process(args)
edge_idx_dict, g = load_fold_data(args)
print("*************************starting the train*****************************")
print("***************... |
aafabcbe282f9b595b2f1c879182a6c6a7adbe35a15acf146f58bbbb51dd56f0 | Python | 3,252 | 41 | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: user_defined_protos.proto
# Protobuf Python Version: 4.25.1
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google... |
cabd1ab8c8da43e9f547fe4755cd5d678becd5e44231bfbcedf1a22ddaf9a415 | Python | 3,252 | 101 | import mdtraj
import pytest
from importlib_resources import files
import endstate_correction
from endstate_correction.protocol import BSSProtocol, AllResults
from endstate_correction.protocol import (
perform_endstate_correction,
NEQProtocol,
)
from endstate_correction.simulation import (
EndstateCorrectio... |
b9aeb31e63e1f17f5a429b60d1fea4cae125e2a7a406664773503e446d215c8e | Python | 3,253 | 100 | #!/usr/bin/env python3
"""Utility functions for calculating / estimating correlation."""
import logging
import numpy as np
def calculate_margin_genes(X):
from scipy.sparse import issparse
margin = np.mean(X > 0, axis=0).copy()
if issparse(X):
margin = np.array(margin)[0]
return(margin)
def t... |
68ee8f64903ba7552fbac8ee5b7aff5c1f85da45088381cde8e31a0dab18877a | Python | 3,254 | 88 | import numpy as np
import pickle
import argparse
import tqdm
from fastconv.corr1d import single_filter_multiple_data_correlate1D
from jitter_stimulus_variance_fit_quality import construct_PSTH_gaussian_kernel
def compute_flashed_repeats_psth(spike_trains: np.ndarray,
psth_kernel: ... |
d4a39b2535abeb52d55d6351c2d9a2c256514caee433853b3d612e979fe90f20 | Python | 3,254 | 55 | """Tensor-free, pickle-free inference for the bundled binary ExtraTrees model."""
from pathlib import Path
import numpy as np
class ToxicityPredictor:
def __init__(self, path=None):
path = Path(path) if path is not None else Path(__file__).with_name('etoxpred_model.npz')
with np.load(path, allow_p... |
7e51be438ba07cfc21693529e1f6147cf1e95f33e14d0ec0f8958ab846924436 | Python | 3,255 | 72 | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 20 11:37:52 2023
@author: xzhou
"""
import torch
import torch.nn.functional as F
def multi_contrastive_loss(embP, embM, embF,PP_adj,MM_adj,PM_adj,PF_adj,MF_adj,tau):
loss = torch.tensor(0, dtype=float, requires_grad=True)
loss = loss + P_con_loss(e... |
444ca94973491f338cf434a12b2b84a4d5a76bfa5c5fc8acc10c04ac67af1605 | Python | 3,258 | 113 | # ruff: noqa
import packaging.version
# Pydantic is a dependency of `ray["default"]` but not the minimal installation,
# so handle the case where it isn't installed.
try:
import pydantic
PYDANTIC_INSTALLED = True
except ImportError:
pydantic = None
PYDANTIC_INSTALLED = False
if not PYDANTIC_INSTALLE... |
98b3f2c403d1f38de72f5aa242d2186106ec32b76edffb5a3c0f818798e1c45d | Python | 3,258 | 106 | import os
import urllib.parse
from pathlib import Path
from typing import Union
class URI:
"""Represents a URI, supporting path appending and retrieving parent URIs.
Example Usage:
>>> s3_uri = URI("s3://bucket/a?scheme=http¶m=1")
>>> s3_uri
URI<s3://bucket/a?scheme=http¶m=1>... |
879ec131a8a83fadf3b8327b6e8643512c1f3aaf7c4a994da357c9d1bc3ee52b | Python | 3,259 | 48 | import numpy
import mozaik
import pylab
from mozaik.visualization.plotting import *
from mozaik.analysis.technical import NeuronAnnotationsToPerNeuronValues
from mozaik.analysis.analysis import *
from mozaik.analysis.vision import *
from mozaik.storage.queries import *
from mozaik.storage.datastore import PickledDataSt... |
67ecf1db935cbe9390b79b62be49322b3401e30aa1cc3a5bae1508f5ff3e3a30 | Python | 3,260 | 115 | import sys
import re
import numpy as np
from utils import save_pickle, read_lines, write_lines, load_tsv
from visual import plot_matrix, plot_labels, plot_label_masks
from marker_score import get_marker_score
def adjust_temperature(probs, temperature):
logits = np.log(probs)
logits /= temperature
probs ... |
53dfa4dd6feb06c33552565f36b2bfc8c4ae7416f0a70f94f4ca98fdb0e68eb5 | Python | 3,261 | 80 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
import numpy as np
from model.model import Encoders, NN
from model.layers import Losses
import tensorflow as tf
from skl... |
c110f7818f3ddc0efb00daf263fc54413cfe39b2fabca189e8cc3e0aa6f9acc1 | Python | 3,261 | 80 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
import numpy as np
from model.model import Encoders, NN
from model.layers import Losses
import tensorflow as tf
from skl... |
e657360c58dcd735e0b4d2580ef17f9aaa8a89a820476b6550f462ee0ec954e4 | Python | 3,264 | 105 | """Tools for manipulating dense matrices."""
import itertools
import numpy as np
import numpy.typing as npt
#import utils_find_1st as utf1st
from skimage import morphology
def _get_pad(shift: int) -> tuple[int, int]:
return (0, -shift) if shift < 0 else (shift, 0)
def _get_slice(shift: int) -> slice:
if sh... |
0f551f024912510744875857dbc7c40b45558ac9cd6e6e273353f139e5f61083 | Python | 3,265 | 99 | import numpy as np
import matplotlib.pyplot as plt
import os
import struct
import gzip
def download():
'''
Download files from url
'''
os.makedirs(f"{DownloadDir}", exist_ok=True)
os.system(f"wget -nc -P {DownloadDir} {url}")
os.system(f"unzip {DownloadDir}/gzip.zip")
os.system(f"mv gzip/* ... |
15c49c6960d2e5c7a8518d2844fce9dbc153be5955565978cf1bf41607633979 | Python | 3,265 | 99 | # Copyright (c) 2017-present, Facebook, 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 agreed... |
1aa777ff89a519abcbf68c28cc6fae011198604cfb68c3881421aacc902f9be1 | Python | 3,265 | 109 | import numpy as np
from torch.utils.data import DataLoader, Dataset
import scanpy as sc
import pandas as pd
import torch
import sys
sys.path.append('..')
from ..VAE.VAE_model import VAE
from sklearn.preprocessing import LabelEncoder
def load_VAE(vae_path, num_gene, hidden_dim):
autoencoder = VAE(
num_gen... |
2cfcf8ca339297f28af32b0ba043ddaea5bb0978ffa72abf3c3732d3cb85a889 | Python | 3,265 | 73 | from delete_row_expriment import *
from model import *
import warnings
from param import *
def train(args):
similarity_feature = loading_similarity_feature(args)
edge_idx_dict, g = data_preprocess(args)
print("*************************starting the train*****************************")
print("**... |
732ca499f2a3a0ae6d3657779082df4285a84c2dddbfbe052ab11ef0b9568f15 | Python | 3,265 | 116 | import numpy as np
import tonic
import torch
import torch.nn as nn
from sinabs import layers, set_batch_size
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn.chip_factory import ChipFactory
from sinabs.nir import from_nir
import nir
def xytp_collate(batch):
samples = []
target... |
99c51e7da10e0a87d601852654079f62c4acd2a6ba2ef4a08e61541db4a86f9d | Python | 3,266 | 71 | #!/usr/bin/python
import sys
sys.path.insert(0,'../')
import nibabel as nib
from mdgru.helper import argget
import shutil
import os.path
import numpy as np
def create_example_nifti_data(**kw):
shape = argget(kw, "shape", (100, 100, 40))
border_edges = argget(kw, "border_edges", [[30, 70], [30, 70], [10, 30]])... |
dab6f8d1afc4d045105359863fb53438499fd3ad491d2580f17ce28b0de93213 | Python | 3,266 | 73 | from delete_col_expriment import *
from model import *
import warnings
from param import *
def train(args):
similarity_feature = loading_similarity_feature(args)
edge_idx_dict, g = data_preprocess(args)
print("*************************starting the train*****************************")
print("**... |
6debb863af96458ac26659f53cd881c10265a236721a812efea7826055c3969e | Python | 3,267 | 99 | # -*- coding: utf-8 -*-
import copy
import numpy as np
import pytest
from nilearn.image import new_img_like
from nilearn.maskers import NiftiMasker
from fmralign.pairwise_alignment import PairwiseAlignment
from fmralign.tests.utils import (
assert_algo_transform_almost_exactly,
random_niimg,
zero_mean_coe... |
75c5cdbec4592a6812f3f373f7eda91813c67b042d3e798e271784ce8dd7c9e6 | Python | 3,267 | 41 | import numpy
import mozaik
from mozaik.visualization.plotting import *
from mozaik.analysis.technical import NeuronAnnotationsToPerNeuronValues
from mozaik.analysis.analysis import *
from mozaik.storage.queries import *
from mozaik.storage.datastore import PickledDataStore
def perform_analysis_and_visualization(data_... |
a90ad3357a7a93d9f5d1224bfad0dbd1bbdd130938d5c50b3959e4c3b5ad22de | Python | 3,267 | 105 | """
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 numpy as np
import pytest
import torch
from fairchem.core.datasets import data_list_collater
fr... |
cbf165850c0650fb1c3ad065ea63a1e9776279c2929dc31d5b64ab624c0e4e41 | Python | 3,268 | 104 | # 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 --------------------------------------------------------------
import o... |
e7ede91e7b33b5b830103f3bcda0874092cc405a3081334f1d71ad113e89db86 | Python | 3,268 | 46 | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: i_common.proto
"""Generated protocol buffer code."""
from google.protobuf.internal import builder as _builder
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool... |
ba13916fcbd07247225d62edf2cb3efc35b80646017d3ac0daa4c65cec620283 | Python | 3,270 | 105 | import time
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from .forceatlas2 import ForceAtlas2
def test_multithreading():
"""Test and benchmark the multithreaded implementation of ForceAtlas2"""
# Create a random graph
n_nodes = 500
n_edges = 2000
G = nx.gnm_random_graph(... |
1f3034b374637f34d8f87efd930fc0e8d54f3f6038dfbf74a8464faadbeae6fe | Python | 3,272 | 95 | from functools import wraps
from typing import Any, Callable, TypeVar
T = TypeVar("T")
def context_aware_cache(
func: Callable[..., T] = None, context: Callable[[Any], Any] = lambda self: None
) -> Callable[..., T]:
"""
Decorator to cache the result of a method based on its arguments and context.
Ar... |
a07ee91e6b57c1b13f3a57c081a150501d3eca61afedcdd63b62fb35f009645c | Python | 3,273 | 80 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
import numpy as np
from model.model import Encoders, NN
from model.layers import Losses
import tensorflow as tf
from skl... |
ba629a0f6e57cf2cd224063dc8a281f1c43616f29216b676d788e0fb8fc1eb61 | Python | 3,273 | 80 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
import numpy as np
from model.model import Encoders, NN
from model.layers import Losses
import tensorflow as tf
from skl... |
71c884e837acb96cf7105a7857ec01f78ac64b23c7af5304dabfeb2e4940d3ce | Python | 3,274 | 106 | """K-means backend — trivial baseline.
Each k-means cluster is treated as one metacell; the cluster centroid is
the codebook entry, which gives free out-of-sample assignment via
``predict()``.
"""
from __future__ import annotations
import time
from typing import Optional
import numpy as np
from .base import FitRes... |
34708f39ca462b9f8f8461e369672b2b98cf5d50f2213cbe58e05d116f40c7c6 | Python | 3,275 | 74 | import argparse
import os
import torch
import numpy as np
import torchvision.transforms.functional as tvf
import matplotlib.pyplot as plt
import kornia
import cv2
import imageio
from wave_optics import *
from holograms import *
import warnings
import pandas as pd
warnings.filterwarnings("ignore")
def apply_clip_and_no... |
546cea1f55c48725ebb7f6ab704cf2f388eb579ef87723da06956f2dbf639b53 | Python | 3,275 | 92 | import torch
from ..distances import CosineSimilarity
from ..reducers import DivisorReducer
from ..utils import common_functions as c_f
from ..utils import loss_and_miner_utils as lmu
from ..losses.base_metric_loss_function import BaseMetricLossFunction
from ..losses.mixins import WeightRegularizerMixin
# adapted fr... |
70c3779bf80cd777f6fed7ee5c5f018bf2c678f2f44c40294235119866a9135f | Python | 3,275 | 113 | """
Minimal working example for shotgun VAE using unwarped birdsong.
0) Define directories and parameters.
1) Tune preprocessing parameters.
2) Train the VAE.
3) Plot.
4) The world is your oyster.
"""
from itertools import repeat
from joblib import Parallel, delayed
import numpy as np
import os
from ava.data.data_c... |
729136f018c8da79e805f75e6be72720df0257f08274c83bc585bbe1c8d3700e | Python | 3,275 | 110 | import numpy as np
from torch.utils.data import DataLoader, Dataset
import scanpy as sc
import torch
import sys
sys.path.append('..')
from ..VAE.VAE_model import VAE
from sklearn.preprocessing import LabelEncoder
def load_VAE(vae_path, num_gene, hidden_dim):
autoencoder = VAE(
num_genes=num_gene,
... |
9768adca8a8e048e4f031a114a5c556b596b3b6a948e350d0bfca45ac679ddce | Python | 3,275 | 98 | # Copyright 2023 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
9a3b1ad9e5c0046519388f1215899611560b90fbd2b250feb255569b4a381524 | Python | 3,278 | 80 | import sys
import scipy.io as sio
import os
import numpy as np
from scipy import special
import h5py
if __name__ == "__main__":
data_ind = int(sys.argv[1]) # 3 #
list_data_names = ['blood_vessel_10Hz', 'PFC4_15Hz', 'bma22_epm', 'CaMKII_120_TMT Exposure_5fps']
list_ID_part = ['_part11', '_part12', '_part... |
fd5a2d776d753904ce20d39fb14c0438eebf636b0e6e4894dc526ff891487204 | Python | 3,278 | 102 | # built-in dependencies
from typing import Any, Union, List, Tuple
# 3rd party dependencies
import cv2
from numpy.typing import NDArray
# project dependencies
from deepface.models.Detector import Detector, FacialAreaRegion
class FastMtCnnClient(Detector):
"""
Fast MtCnn Detector from github.com/timesler/fac... |
5784dea1ce1ac7efcdf19e230b0b52490299ded41bcc099507a1f694cf6d9fc3 | Python | 3,279 | 86 | import torch
from ..utils import common_functions as c_f
from ..utils.module_with_records_and_reducer import ModuleWithRecordsAndDistance
class BaseMiner(ModuleWithRecordsAndDistance):
def mine(self, embeddings, labels, ref_emb, ref_labels, species, mnn, setting):
raise NotImplementedError
def outpu... |
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