sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
76acbd1a9c1d1e149ffde375a061cac06134e033c6601c47c45760130a7a10b7 | Python | 2,553 | 69 | from pathlib import Path
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.cache import cache_zipped_file
"""
The "Auto" correlation is an interpretable pairwise column metric of the following mapping:
- Variable_type-Variable_type : Method, **Range**
- Categorical-Categorical ... |
e3d146d5298f227f29316ba47d3851f6907288d7e73fdd031ac78465f446f8f3 | Python | 2,553 | 78 | import abc
import numpy as np
class ModelDataset(object):
"""
Private Class. Users need to wrap their datasets as one of its subclasses.
"""
def __init__(self, dataset=None, x=None, y=None, size=0):
"""
A ModelDataset is a wrapper over the model's train/test/validation dataset input ... |
5593fd32d5413a0468e4e765529ac9844a0c9212422119d44a1ccbe84d5f462b | Python | 2,554 | 81 | # %% This will do perturbation of the target data.
# solve for 6 configurations for G + image response and H + non-image response.
# the penalty value is fixed to 10.
import numpy as np
# from SSN_model_funcs import * # tentatively...
import SSN_model_funcs as ssn
import create_new_target_data as cnd
import sys
impo... |
9140a309beef4efbe01f6b4de278900a557ee0fe832fbd242ef06f5ec7eeefe8 | Python | 2,554 | 86 | _base_ = [
'../../_base_/datasets/rtts_coco.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.37... |
0e3a9e0e7793dac7001421c3fbfc94330aa0291599ae2e78645e93f36baef8c6 | Python | 2,556 | 86 | _base_ = [
'../_base_/datasets/ruod_coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.3... |
7c3a496cc05022daeb18b37c6a7aaade5050559a4e66e96d1b2c150b1b55f240 | Python | 2,556 | 71 |
from lisa.core.data_interface import DataInterface
import argparse
import os
import numpy as np
import pandas as pd
from scipy import sparse
TECHNOLOGY = 'Motifs'
def main(species, window_size, motif_metadata, bin_sorted_hits, group_loci = 100000):
motif_metadata = pd.read_csv(motif_metadata, sep = '\t', header... |
8e923b3420e785a77d6cc22771830ab0dd1b76c10bc9d1beb21e1a65aafe5cee | Python | 2,556 | 79 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Python version: 3.10.9
pandas version: 1.5.0
numpy version: 1.23.3
"""
#%% Import Modules
import pandas as pd
import numpy as np
#%% Functions
def dice_coeff(df, thresh=None):
if thresh != None:
ses1 = df[(df["ses"]=="ses-01") & ((df["pval"] < thresh))].r... |
8e8e6d21bfaee9e06c9d47d4315526a30225f73728ed708c826fd490de800d5e | Python | 2,557 | 95 | import pdb
from matplotlib import pyplot as plt
import yaml
from yaml.loader import SafeLoader
import torch
import ray
from latent_time_stepping.AE_models.VAE_encoder import VAEEncoder
from latent_time_stepping.AE_models.autoencoder import Autoencoder
from latent_time_stepping.AE_models.encoder_decoder import (
D... |
0f3aa692d441e41843034b71b9e8845e6d582f8c1a8d772b64eb4f4b0cceebaf | Python | 2,558 | 63 | from utils import *
from sklearn.metrics.pairwise import cosine_similarity
def plot_rcpfld(nrow, ncol, iters, conn, Ix, Iy, title, prjid):
fig, axs = plt.subplots(nrow, ncol, figsize=(ncol/2, nrow/2))
plt.subplots_adjust(left=0.19, right=0.99, bottom=0.1, top=0.9, wspace=0., hspace=0.)
for row in rang... |
33a2515721fbda75572adabdcb7c319e0e87dfdbb0ca449a5570cea4302c967c | Python | 2,558 | 80 | import pytest
from sinabs.backend.dynapcnn.chip_factory import ChipFactory
devices = ChipFactory.supported_devices
def generate_event_list(chip_factory):
Spike = chip_factory.get_config_builder().get_samna_module().event.Spike
# Note that timestamps are in us, chip factory dt in s
return [
Spik... |
7a5f0f1fd5232a563448e8e39f2a828f7781d61402de49067d7303b51da6deb7 | Python | 2,558 | 89 | import pandas as pd
from seaborn import violinplot
import matplotlib.pyplot as plt
from python.utils import load_vitals_from_parquet, custom_logger
from config import TARGETS, TARGET_LABELS, EXTREME_VALUE_LIMITS
DATASETS = ['train', 'validate', 'test', 'eicu']
DATASET_NAMES = {'train': 'Training set',
... |
94498acfc908716132fdadcb5737e9f7e81e1ee1e266ecc5ce3e05ab45cd80cb | Python | 2,558 | 98 | # OmicVerse Analytics Configuration
"""
OmicVerse 统计追踪配置
这个模块提供了对报告使用情况的统计追踪功能。
所有数据都是匿名的，不会收集个人隐私信息。
收集的信息包括：
- 报告生成时间
- 匿名用户ID（基于机器信息的hash）
- 操作系统信息
- 浏览器类型（仅在打开报告时）
- 地理位置（仅国家/地区级别）
"""
# 默认配置
DEFAULT_CONFIG = {
# 是否启用统计
'enable_analytics': True,
# 统计服务器地址
'analytics_endpoint': 'https://analy... |
ee0f31c158750cf10d97741d147594235f1558e6557a001183de0b93b60e37d1 | Python | 2,558 | 81 | import os
import subprocess
bazel_workspace_dir = os.environ.get("BUILD_WORKSPACE_DIRECTORY", "")
MASTER_BRANCH_VERSION = "3.0.0.dev0"
MASTER_BRANCH_JAVA_VERSION = "2.0.0-SNAPSHOT"
def list_java_files(root_dir: str):
"""
Scan the directories and return the sorted list of
pom.xml and pom_template.xml... |
1396e9741a473e1080b44195668bdc985ada506627cffb863831c65a7d2712ad | Python | 2,560 | 78 | from typing import Dict
from torch import load, manual_seed, device
from torch.cuda import is_available
import numpy as np
from common import XAIScenarios, SEED
from utils import load_json_file, dump_as_pickle, load_pickle
from training.models import CNN
from xai.methods import get_attributions, get_baselines
import ... |
3fcabd359ed59d806b78a084a570d958d1fb06c18476e554c5c4c87e2645ef39 | Python | 2,560 | 72 | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from lqit.registry import MODELS
from .base_head import BaseEnhanceHead
@MODELS.register_module()
class EdgeHead(BaseEnhanceHead):
"""[conv+GN+relu]*4+1*1conv."""
def __init__(self,
in_channels=256,
... |
4e1da2a3604d0aa3c238f967db214458e64f9b3cf64b2889fda6d09256479307 | Python | 2,560 | 57 | from pkg_resources import parse_version
from configparser import ConfigParser
import setuptools, shlex
assert parse_version(setuptools.__version__)>=parse_version('36.2')
# note: all settings are in settings.ini; edit there, not here
config = ConfigParser(delimiters=['='])
config.read('settings.ini')
cfg = config['DEF... |
70021e36075f96fcdc58f8f064c59a36abc5e5fdb65033c2210339951bbeeb7c | Python | 2,560 | 58 | import torch.nn as nn
from torch.autograd import Variable
class RNNModel(nn.Module):
"""Container module with an encoder, a recurrent module, and a decoder."""
def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):
super(RNNModel, self).__init__()
self.drop ... |
cd3e2e63080215dcd1a6f14f6362fde27d29bbf7addbdd674d3ee9d571070508 | Python | 2,561 | 64 | 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 lifelines import CoxPHFitter
from lifelines.utils import concordance_index
import pandas as pd
i... |
966bd5b0d20185bcb765209c22cb4f66a3ac32775ee8bdb87eb4f0a360e5b743 | Python | 2,562 | 83 | """Simplified PDB API client using direct REST endpoints."""
import logging
from typing import Any, Dict, List, Optional
from .base import BaseAPIClient
from ..config import settings
logger = logging.getLogger(__name__)
class SimplePDBClient(BaseAPIClient):
"""Simplified client for RCSB PDB REST API."""
... |
d4735df76cb37156c203d5d2bed0666fb2e13bcef6c5df64b77c6262de9dfa83 | Python | 2,563 | 100 | #!/bin/python
#------------------- Description & Notes --------------------#
#------------------- Dependencies ---------------------------#
# Standard library imports
from pathlib import Path
# External imports
import pytest
# Internal imports
from Synsor import make_parser
from Synsor import is_valid_parser
from ... |
8b2adb351283e1601f4e4e7515b199fd4fa8d6da8ec01c05490e2ad428f428eb | Python | 2,564 | 65 | # Adapted from [aiodebug](https://gitlab.com/quantlane/libs/aiodebug)
# Copyright 2016-2022 Quantlane s.r.o.
# 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.apac... |
9e3a316373ea42f450108b0fd9b8be939d5d5c9699aa04df85feff454d9b320f | Python | 2,564 | 65 | from typing import Dict, Optional, Union
import ray
def _get_node_id_from_node_ip(node_ip: str) -> Optional[str]:
"""Returns the node ID for the first alive node with the input IP."""
for node in ray.nodes():
if node["Alive"] and node["NodeManagerAddress"] == node_ip:
return node["NodeID"... |
3c39eac11a8de60881c8c15ebc84bb2e5d3408bd53449b4e70f2b8773a1bec4d | Python | 2,565 | 76 | from __future__ import annotations
import re
from .models import MessageEnvelope, PolicyDecision, RuntimeTaskState
class MessagePolicy:
"""Decide whether an incoming message starts work or joins the queue."""
def decide(self, envelope: MessageEnvelope, state: RuntimeTaskState) -> PolicyDecision:
if... |
52bcfb1ec12fd6cf4281403ce693b460afb4a7eeb6ce56d01e12eb916c6e6304 | Python | 2,565 | 84 | import logging
from pathlib import Path
from pokeformer.preproc.freesasa import calc_sasa
from pokeformer.utils.pdb_utils import (calc_id, calc_id_model,
get_nei_atoms_by_pdbfile,
get_nei_atoms_by_pdbid, get_pdb_path)
logger = logging.get... |
8f5c822c7a56c6de83e4c594fc40795ac25ddbe1ba67953c83006e74ace817b8 | Python | 2,565 | 71 | """Backend Protocol + return container for metacell algorithms."""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Optional, Protocol, runtime_checkable
import numpy as np
from scipy import sparse
class UnsupportedCapability(NotImplementedError):
"""Raised wh... |
17434833e9007471d4a6538254e5dbdc95aff825b0fe66065f2fdd2e5f1c7e0e | Python | 2,567 | 56 | import pandas as pd
import os, logging
import ast
from misc.save_files import read_pickle, write_pickle, read_pandas_from_csv, write_pandas_to_csv
from misc.counter import count_dict
def dict_of_abbreviations(df: pd.DataFrame, config: dict):
"""
Go through each publication and check for abbreviations (longer... |
7133b275013dec92f1a718378939358be7d594099be3a2ac68ca1de5798ca1aa | Python | 2,567 | 64 | """
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.
"""
import os
import random
import shutil
import sys
import zipfile
from datetime import datetime
import ase.io
def main():
"""
Rattl... |
a7d18a030100ff56cbc9b248f6f416ed19a02962170f8cd2c2a4e3791d80971c | Python | 2,570 | 86 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Low level implementation of torch-based tile."""
from typing import Union, Tuple
from numpy import ndarray
from torch import Tensor, tensor
from torch impor... |
46ce67754a4f52a81441a18348500692d6f5d5c8293acd8e61b698e3fa9c231c | Python | 2,571 | 99 | # -*- coding: utf-8 -*-
"""
Created on Fri Dec 18 11:17:00 2020
@author: ju-bar
Root finding functions
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
#
def root_poly_2(y, a, b, c):
"""
Retur... |
23bbdf59fcbdfacce0f38980838ef54e5603a8a454bb2a3f43b7b2f06d847c82 | Python | 2,573 | 69 | # built-in dependencies
from typing import Union, List, Any, cast
from abc import ABC, abstractmethod
# 3rd party dependencies
import numpy as np
from numpy.typing import NDArray
# keras is not imported here on purpose, a model of another backend would not need it
# Notice that all facial attribute analysis models m... |
db5d5e789d5f3261c19b92cbcabc7cb7f760c56d564449bd29b82afecdbaacde | Python | 2,574 | 86 | import torch.nn as nn
import torch
import numpy as np
import random
import os
import logging
def set_seed(rndseed, cuda: bool = True, extreme_mode: bool = False):
os.environ["PYTHONHASHSEED"] = str(rndseed)
random.seed(rndseed)
np.random.seed(rndseed)
torch.manual_seed(rndseed)
if cuda:
tor... |
46ddc6bc050432b803048bf3f4482952d1e0f42e57258dde36396efa4a330b1d | Python | 2,575 | 80 | # 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... |
6706c76c217be5927aff1527b80e96c551872a3d901fb04940ef386d42ad4a5e | Python | 2,575 | 56 | import grpc
import time
import projectmetis.python.utils.proto_messages_factory as proto_factory
from projectmetis.python.logging.metis_logger import MetisLogger
from projectmetis.python.utils.grpc_services import GRPCServerClient
from projectmetis.proto import learner_pb2_grpc
class GRPCLearnerClient(GRPCServerCli... |
89e2ce555305a6082d48389514c0e549812626f84045e9f0e6f35dba5701b922 | Python | 2,575 | 86 | """
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 math
import torch
import torch.nn as nn
@torch.jit.script
def gaussian(x: torch.Tensor, mean,... |
09296d3645a70cc85bd0b7e482cb27c943b13d9f8e1c090d3517b6415e4cb69a | Python | 2,576 | 71 | import xarray as xr
import numpy as np
import pandas as pd
class JointPost:
def __init__(self, Z, theta, theta_cv = None, constant = None):
self.Z = Z
self.theta = theta
self.theta_cv = theta_cv
self.constant = constant
def new(bulk_id, gene_id, cell_type, gibbs_list):
... |
5dcf736873bb4c19ea1ab8b3a02037871cc5d7ee05589da0b9c80532f1bfb026 | Python | 2,576 | 67 | import kaggle
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.paths import get_data_path
# The dataset in this example is obtained using the `kaggle` api.
# If you haven't done so already, you should set up the api credentials:
# https://github.com/Kaggle/kaggle-api#api-... |
b440d9e02a45fe4513d6c00232a4ec1fee25b5bca430300fe8cd8f03e74fdbd4 | Python | 2,576 | 80 | import numpy as np
import pandas as pd
import pytest
from data_profiling.config import Settings
from data_profiling.model.summary_algorithms import (
describe_counts,
describe_generic,
describe_supported,
histogram_compute,
)
def test_count_summary_sorted(config):
s = pd.Series([1] ... |
c810448ba9954de2dce285585fedfe1009f44b5faeee77adf821377db64323a6 | Python | 2,576 | 75 | import random
import numpy as np
import pytest
from pfrl.collections import prioritized
@pytest.mark.parametrize("uniform_ratio", [0, 0.7, 1])
def test_prioritized_buffer_convergence(uniform_ratio):
expected_corr_range = {0: (0.9, 1), 0.7: (0.5, 0.85), 1: (-0.3, 0.3)}[uniform_ratio]
size = 100
buf = pr... |
1d5a64b6e39fc6ec569b540cae5b062e24ecb3569a006c659d85a825b5cd2ca9 | Python | 2,578 | 90 | import os
import setuptools
import sys
class NumpyImport:
def __repr__(self):
import numpy as np
return np.get_include()
__fspath__ = __repr__
def read(fname):
with open(os.path.join(os.path.dirname(__file__), fname), 'rt') as f:
return f.read()
def requirements():
with open(os.path.join(os.p... |
350dea244e27ced591716b326aafad288f5ab08be6cf243f9f327baf096547ba | Python | 2,579 | 94 | import pytest
import torch
from torch import nn
from sinabs import layers as sl
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn.chip_factory import ChipFactory
from sinabs.backend.dynapcnn.exceptions import (
InvalidGraphStructure,
UnsupportedLayerType,
)
from sinabs.from_torch... |
cadee6aacbcc7149d2e6d2802c060aa774577ef8a691dc7fa6a651602c378f51 | Python | 2,579 | 100 | """Make figures to explain jittering"""
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gamma
plt.style.use('dark_background')
# Create figure and axis
fig, ax = plt.subplots(1, 1, figsize=(7.5, 5))
# Define response function and x values
x = np.arange(0, 30, 0.1)
response = gamma.pdf(x, ... |
85784835fa157af4b2d89637b237448b43b54e908d3fbddf09fd38b5efe28e70 | Python | 2,580 | 77 | import os
import numpy as np
import nibabel as nib
import time
import pandas as pd
output_root = '/path/to/results'
sub = 'sub-01'
im_file = os.path.join(output_root,sub,'{}_run-1_IVIM-avg.nii.gz'.format(sub))
bval_file = im_file.replace('nii.gz','bval')
cval_file = im_file.replace('nii.gz','cval')
mask_file = os.path... |
df011486925f7e950ed241113452c4e77070b19e5dba2fac805896529e0eaa58 | Python | 2,580 | 82 | """Utils for LEF applications"""
import logging
from lefshift import constants
def validate_column(input_df, column, data_type, error_message=None):
"""Validate that the input contains all information in valid types
:param input_df: input dataframe
:type input_df: pd.DataFrame
:param column: name of... |
2a73824072833a93e7356ca52694d5d1b23b821e0084e7e5c393fcde4c6c5294 | Python | 2,582 | 73 | #!/usr/bin/env python3
"""Utility scripts for community detection."""
import logging
import numpy as np
def compute_leiden_partition(graph,
resolution=None,
partition_type=None,
use_weights=False,
n_ite... |
520300b2580f272ba28f4490e95bb7c13ec3adfb0817c23d2bd2095e10372c36 | Python | 2,582 | 67 | import numpy as np
import pandas as pd
import patsy
from scipy import stats
from tqdm import tqdm
def lr_tests(ds, alt_model, null_model='~ 1', gene_column='Gene', batch_size=2000, transformation=np.log1p, rcond=-1):
'''
Compare alt_model and null_model by a likelihood ratio test for every gene in ds.
Ar... |
71daa449e8a00c5df671d052c5eedfacf97e1b85bdc51669910b7baa3916ffe4 | Python | 2,583 | 76 | """Fluorine chemical shift prediction"""
import argparse
import logging
from lefshift.predict import add_predict_subparser, predict
from lefshift.split import add_split_subparser, split
from lefshift.train import add_train_subparser, train
DESCRIPTION = """
LEFShift predicts fluorine chemical shifts using the local(L... |
fb8eb2af340fb7a95d93761ab4ecd625232ec0c4fccd6f57988b9ac04827b517 | Python | 2,584 | 83 | from functools import partial
from itertools import combinations
from random import choices as sample_with_replacement
from random import sample as sample_without_replacement
from typing import List, Sequence
from rising.random.abstract import AbstractParameter
__all__ = ["DiscreteParameter", "DiscreteCombinationsPar... |
e1067078b9518a6e85179ee166eacbc11b25951eeb029a4ccbb4b0ed0228dff7 | Python | 2,586 | 58 | ####################################################################################################
# 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... |
58c5e087d4553a534b80131485fb9fbc265b751b67eb67fff4e4e1dcf75f16a3 | Python | 2,587 | 72 | r"""
This file contains various operations over Neo objects. Such as sum over lists
of Neo objects etc.
"""
import quantities as qt
from neo.core.analogsignal import AnalogSignal as NeoAnalogSignal
import numpy
def neo_sum(l):
r"""
This function gets a list of Neo objects and it
adds them up. Importantly u... |
998f0787b051bcce32a82cc688691fcc729bac5e905b0689613a46f059130c3b | Python | 2,587 | 81 | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io.wavfile
import torch
import utils
font = {"size": 10, "family": "Arial"}
plt.rc("font", **font)
def rms_normalise_audio(x):
x = x - torch.mean(x)
rms = torch.sqrt(torch.mean(torch.pow(x, 2)))
return x / (10 * rms)
def ... |
a7cee2271e8e1ffb22c12f9b50b69da9679bd56ed580ba82b690dabd5e12765d | Python | 2,587 | 79 | from typing import Any, Type, Optional, Set, Dict
def _name(type_: Type) -> str:
return type_.__name__ if hasattr(type_, "__name__") else str(type_)
class DaciteError(Exception):
pass
class DaciteFieldError(DaciteError):
def __init__(self, field_path: Optional[str] = None):
super().__init__()
... |
ca94132602489a1bd91e986eb62bd8a62af705a7767c61e7200e8b28325b2786 | Python | 2,587 | 74 | import torch
import torch.nn.functional as F
from tqdm import tqdm
from dice_loss import *
def miou(logits, targets, eps=1e-6):
"""
logits: (torch.float32) shape (N, C, H, W)
targets: (torch.float32) shape (N, H, W), value {0,1,...,C-1}
"""
outputs = torch.argmax(logits, dim=1, keepdim=True).typ... |
ffcd1f0f022b1edd3801eae491865a109fcae7c6349bcc62d8a3e6561e27799b | Python | 2,587 | 67 | import json
import re
import subprocess
import unittest
from importlib import resources as impresources
from shutil import rmtree
from termcolor import cprint
from epilepsy2bids.bids.chbmit.convert2bids import convert as convertChbmit
from epilepsy2bids.bids.seizeit.convert2bids import convert as convertSeizeit
from ... |
f63ca0d6763546bbb3d7ec849254a6d4652d6c1a4063794b9c690be86f0a124c | Python | 2,588 | 109 | import pdb,sys,os
import argparse
import anndata
import scanpy as sc
import numpy as np
from scipy import sparse
def get_eg_representatives(name:str) -> None:
"""
Used for acquiring representatives' single-cell data in the example. Automatically check the latest representatives and store their single-cell da... |
0c385150f50101a891150c228fbf349b206a20d741fd9648936c689dc581562d | Python | 2,591 | 81 | 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__)
RBLN_RT_VISIBLE_DEVICES_ENV_VAR = "RBLN_DEVICES"
NOSET_RBLN_RT_VISIBLE_DEVICES_ENV_VAR = "RAY_EXP... |
bc1ee5f0d4c6e61bdab8850b04af0dbf87f897fa397d0675e3763b3da707662c | Python | 2,594 | 91 | import os
import sys
from os.path import *
import re
from glob import glob
def parse_flagstats( flagstatfile ):
'''
535812 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 duplicates
521761 + 0 mapped (97.38%:-nan%)
535812 + 0 paired in sequencing
267930 + 0 read1
267882 + 0 read2
... |
29b41babd992db6d11cf62511958d0231f29b2dc7c4ffb3e8ac6c7db7edf3ee9 | Python | 2,595 | 96 | # -*- coding: utf-8 -*-
"""Test most of the contents of the vtkplotlib.plots module."""
import numpy as np
import vtkplotlib as vpl
import pytest
from tests._common import numpy_stl
pytestmark = pytest.mark.order(8)
def test_arrow():
points = np.random.uniform(-10, 10, (2, 3))
vpl.scatter(points)
vpl.... |
2b592e55ae7373eed349ba9b98d0561368d0c0bb3ad0c59ad8865eeb8efa922c | Python | 2,596 | 108 | # flake8: noqa
# __start_my_first_deployment__
from ray import serve
from ray.serve.handle import DeploymentHandle
@serve.deployment
class MyFirstDeployment:
# Take the message to return as an argument to the constructor.
def __init__(self, msg):
self.msg = msg
def __call__(self):
return... |
ac35e659346c32d2094a1ff37ea9927c12ab28c835af40282806f101e9a46f6d | Python | 2,596 | 81 | # stdlib dependencies
from typing import Any, List, Union
# 3rd party dependencies
import numpy as np
from numpy.typing import NDArray
import cv2
# project dependencies
from deepface.models.demography.DemographyUtils import EMOTION_LABELS
from deepface.models.demography.onnx.OnnxDemography import OnnxDemography
from ... |
11397e979ff5f7c416c91a96065b36f00f8593d79c612fae067a2920c108a113 | Python | 2,597 | 73 | from typing import Dict, Optional, Union
import numpy as np
import tensorflow as tf
from ray.air.util.data_batch_conversion import _unwrap_ndarray_object_type_if_needed
def convert_ndarray_to_tf_tensor(
ndarray: np.ndarray,
dtype: Optional[tf.dtypes.DType] = None,
type_spec: Optional[tf.TypeSpec] = None... |
84218c4f4eca722961a83a68195aba9cdb4ca62486083719dff57bffec777ba1 | Python | 2,598 | 100 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Wrapper for breakpoints stored in VCF records
"""
import numpy as np
import svtk.utils as svu
class Breakpoint:
def __init__(self, chrA, posA, chrB, posB, name, samples, strands):
"""
Attributes
----------
chrA : str
pos... |
3d77e7f5c5a9b7676de0cda1e48b354923f8b793336b1eb76937982de94ad809 | Python | 2,600 | 63 | import os
import numpy as np
import pandas as pd
import nibabel as nib
import ivim
tissues = {'WM':3,'CGM':2,'DGM':4}
titles = {'WM':'White matter','CGM':'Cortical gray matter','DGM':'Deep gray matter'}
output_root = '/path/to/results'
subs = [sub for sub in os.listdir(output_root) if sub.startswith('sub')]
pars = ['... |
52d48edd9e1694106918655df6b176f4cab583c6532d415efb2229031b42c1d6 | Python | 2,602 | 69 | import os
import pandas as pd
import subprocess
from multiprocessing import Pool, Manager
metadata_file = "../01_metadata_prep/TSMR_metadata_for_analysis.tsv"
df_metadata = pd.read_csv(metadata_file, sep="\t", index_col=False)
ukb_gwas_dir = "./GWASsumstat/UKB"
savedir = "./reformat_outcome_gwas/"
def read_variant... |
590b85e210991a191d7c9531fc8b946a504c38596adff7f86358e8a5780acafa | Python | 2,604 | 58 | """
Container class definition
"""
from data_profiling.report.presentation.core.container import Container
from data_profiling.report.presentation.flavours.html import templates
class HTMLContainer(Container):
def render(self) -> str:
if self.sequence_type in ["list", "accordion"]:
return ... |
bcaee07186e99c239f166b0ac2982d2326cbdfcdc7e0a9cc799038bd431333a5 | Python | 2,604 | 97 | import os
import sys
import time
import errno
import time
import timeit
import warnings
import numpy as np
import numba
from numba import jit
warnings.simplefilter("ignore")
@jit(nopython=False)
def VEP2Dmodel(params, constants, init_conditions, SC, dt, ts):
nt=ts.shape[0]
nn=SC.shape[0]
#parame... |
cfa93918f1a12d2dc497345641138d6d629d54c65bf1dddadb6c315527053ea2 | Python | 2,604 | 73 | import numpy as np
factorDict ={
"A":[-0.59,-1.30,-0.73,1.57,-0.15],
"C":[-1.34,0.47,-0.86,-1.02,-0.26],
"D":[1.05,0.30,-3.66,-0.26,-3.24],
"E":[1.36,-1.45,1.48,0.11,-0.84],
"F":[-1.01,-0.59,1.89,-0.40,0.41],
"G":[-0.38,1.65,1.33,1.05,2.06],
"H":[0.34,-0.42,-1.67,-1.47,-0.08],
"I":[-1.24,-0.55,2.13,0.39,0.82],
"K":[1.... |
2e0469f078eb5cbd9b9372d397000054065a29cf7a9c60621bb35930af07a699 | Python | 2,606 | 67 | """Apply registration."""
import os
import subprocess
import numpy as np
import nibabel as nb
import glob
# =============================================================================
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti'
# Set subs to work on
SUBS = ['sub-07']
DIRECTIONS = ['PA'... |
f9f5d9af3264bbe448d8016b9a22fc430f1aeb6c261f6b1af8e0d29d2bca3d11 | Python | 2,607 | 55 | import os
import numpy as np
import matplotlib.pyplot as plt
import ivim
# protocol files
examcard_files = {enc:os.path.join('protocol','IVIM_EPI_{}_1.txt'.format(enc)) for enc in ['FC','NC']}
gfile1 = os.path.join('protocol','FWF_CUSTOM005.txt')
gfile2 = os.path.join('protocol','FWF_CUSTOM006.txt')
gfiles = {'FC': [g... |
0911b358afae2c0e3b71a9685faa5124bd26d0ee1dbcfe26ab345236e1dcea08 | Python | 2,609 | 80 | # !/usr/bin/env python
# coding: utf-8
# @Time : 2022/10/28 20:45
# @Author : Z.-H. G.
# @Email_0 : guozhenhao17@mails.ucas.ac.cn
# @Email_1 : guozhenhao@tongji.edu.cn
# @File : integration_metircs.py
# @IDE : PyCharm
import scanpy as sc
import pandas as pd
import os
# NOTE: ``scib`` and ``scanpy.external``... |
b0539c0eb5c8a29a83a947e8e279b16cda0d5d308cd52f7e29ae52d55c0e4076 | Python | 2,609 | 68 | import numpy as np
import torch
import faiss
import faiss.contrib.torch_utils # allows utilizing torch tensors as input to faiss indexes and functions
def inverse_distance_kernel(sq_distances, delta = 1e-3):
"""
Kernel used in Pritzel et. al, 2017
"""
# https://discuss.pytorch.org/t/runtimeerror-functi... |
de92e56c835f59b8b45719da0723df573174cea3c895254411344ef404d12240 | Python | 2,609 | 85 | from typing import Optional, Sequence, Union
import torch
from rising.utils import check_scalar
__all__ = ["mirror", "rot90", "resize_native"]
def mirror(data: torch.Tensor, dims: Union[int, Sequence[int]]) -> torch.Tensor:
"""
Mirror data at dims
Args:
data: input data
dims: dimension... |
834a4c0ae8f05acc04c823a4a7e89c0312ce1de44e482ca34746f9cd049a9701 | Python | 2,610 | 56 | import numpy as np
import numpy.random as random
from numpy import genfromtxt
from scipy import stats
from scipy import spatial
from scipy.spatial import distance
from scipy.spatial import KDTree
from scipy.spatial.distance import cdist
from scipy.stats import percentileofscore
def comparedistances(csv1,csv2,sumofdis... |
c60533c503da0306c4dff4b746bf9409faa5c119447c87c95c51bbc76a3ed443 | Python | 2,610 | 97 | #*
# @file Different utility functions
# Copyright (c) Zhewei Yao, Amir Gholami
# All rights reserved.
# This file is part of PyHessian library.
#
# PyHessian is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, ei... |
7055166b5b912ba84589c241495fc24be8e41a9a1075c9eb013d28aebdaaf810 | Python | 2,611 | 82 | # Copyright 2023 BioMap (Beijing) Intelligence Technology Limited
import torch
import torch.nn as nn
from .performer import Gene2VecPositionalEmbedding, RandomPositionalEmbedding
from torch.nn import functional as F
class pytorchTransformerLM_mse(nn.Module):
def __init__(self,
num_tokens,
... |
1b6176e374079668fbd1ef64cc5afd350f98ed9786bda1a1c9bc5a0102a479e3 | Python | 2,612 | 90 | """
Test for issue 1722:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/1722
"""
from datetime import date, datetime
from pyspark.sql import SparkSession
from pyspark.sql import types as T
from data_profiling import ProfileReport
def make_non_numeric_df(spark: SparkSession):
# Intentionally... |
e1474b100c12c0cc6dd87f1853504a752909df3eede49c815863cc5ee8fd8d48 | Python | 2,612 | 74 | import os
from typing import Optional
from functools import cached_property
from pyarrow.parquet import ParquetDataset
import pandas as pd
from torch.utils.data import Dataset
from python.data.data_utils import build_x_y_single_case
from python.utils import custom_logger
from config import TARGETS, FORECAST_HORIZON
... |
c482ece419a34b360e25ae9e7f4d8461aca24e43536e711456dfd3302f24004e | Python | 2,613 | 93 | # stdlib dependencies
from typing import Any, List, Union
# 3rd party dependencies
import torch
from numpy.typing import NDArray
# project dependencies
from deepface.commons import weight_utils
from deepface.models.demography.DemographyUtils import GENDER_LABELS
from deepface.models.demography.pytorch.TorchDemography... |
5e70a9dda32b065ba3f39e2b6765fa3e491130cce2443eb8ba4c6b489ba037f0 | Python | 2,614 | 101 | import torch
import collections
import pdb
import torch.utils.data
import csv
import json
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import math
from pdb import set_trace as stop
import numpy as np
def loadData(filename,windows):
with open(filename) as fi:
csv_reade... |
7e0ab0ce0a044e4632bb5aef2b547928e65568277b971da34abd99f9a8729519 | Python | 2,614 | 83 | #!/usr/bin/env python3
# Converts single sample SV/CNV vcf.gz (e.g., DRAGEN) to .del.bed and .dup.bed files
import argparse
from pysam import VariantFile
DEFAULT_QUAL_CUTOFF = 30
# Writes record in bed format
def write_bed_entry(file, record, score, sv_type, sample):
bed_entries = (
record.chrom,
... |
e97e75e5682a7f9a5b6f9cc259569e240e7e8c3dda8a216ad6a2fe5c2716f5b2 | Python | 2,614 | 83 | # -*- 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.
#... |
262962f79d044a83b55ae93820e7f9fd630a6c129988c27ec5dcc0485265ef0f | Python | 2,615 | 61 | import scanpy as sc
import pandas as pd
import flowsig as fs
adata = sc.read('data/burkhardt21_merged.h5ad')
condition_key = 'Condition'
# We construct 10 gene expression modules using the raw cell count.
fs.pp.construct_gems_using_pyliger(adata,
n_gems = 10,
... |
709ea79ca4caf11c4759efaaac49e06be0ac960b2881c3294898a27eeaa9ed89 | Python | 2,618 | 59 | """
calculate AUC (above threshold) for a given stimulus for all ROIs.
Also generate a distribution of events for each stimuli (AUC-time),
and calculate kurtosis, skewness and the median from the distribution
INPUT: smoothed data, stimulus.csv
OUTPUT: a dataframe with 5 rows (header AUC, kurtosis, skewness and m... |
d41c8bffa1d3070409153df476f1b600b5b3a81d5458f96879b2246f30fe80b7 | Python | 2,618 | 73 | import torch
from ..reducers import AvgNonZeroReducer
from ..utils import loss_and_miner_utils as lmu
from ..losses.base_metric_loss_function import BaseMetricLossFunction
class TripletMarginMMDLoss(BaseMetricLossFunction):
"""
Args:
margin: The desired difference between the anchor-positive distance... |
0937b8d5cb8d7576b874e92a9697a7899016acaa2c5a14aef96aa1a608afa733 | Python | 2,619 | 81 | # 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... |
7c0ba67ed5f57f1147d3615530d80f96951870ac81fbced0250b97dac842a249 | Python | 2,619 | 68 | # This work is licensed under Attribution-NonCommercial 4.0 International License. # noqa
from typing import List
from lqit.edit.models.base_models import BaseGenerator
from lqit.edit.structures import BatchPixelData
from lqit.registry import MODELS
from lqit.utils import ConfigType, OptMultiConfig
@MODELS.register... |
af44040117a9f07990c3311a2487195c79ba4c1a4a072ff75df06a52758c6f23 | Python | 2,619 | 73 | import nibabel as nib
import numpy as np
from lib.qc_surface import (
boundary_chords,
compute_topology,
face_geometry,
self_intersecting_faces,
)
from lib.surface import write_points_faces_to_gifti
from lib.surface_quality import improve_quality
from lib.utils import setup_logger
log_file = snakemake.... |
c84c863a41e015630b09c721e23fdf16484046e605b2f37aab5b3318b9899137 | Python | 2,619 | 72 | import pdb
import torch
import torch.nn as nn
class Preprocessor(nn.Module):
def __init__(self, num_states, num_pars):
self.state_min = [1e12 for _ in range(num_states)]
self.state_max = [-1e12 for _ in range(num_states)]
self.pars_min = [1e12 for _ in range(num_pars)]
self.pars_m... |
e1057802b8c89b550a1c7fa8b2ef01992304ac4667df35df929de62a8d577192 | Python | 2,619 | 82 | import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import math
import time
import torch
from torch import nn
from torch.autogra... |
197682ad6ae2ffcec0452eaae813e66c84b43b065364ac7b905710cf1e8e1027 | Python | 2,620 | 105 | import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
plt.rc('font',size=15)
dt = 0.01
# Protein data
realdata = np.load('datafordistribution.npy')
protein = realdata[0:7500]
protein_size = len(protein)
protein_fft = np.fft.rfft(protein) / protein_size
plt.subplot(2, 1,... |
f718b72e1e186e8060ae14be6117f979d5e0d1dab3d83474403f7bf7773c9ecf | Python | 2,620 | 69 | r"""``ov.tl.bonsai`` — public entry point for the Bonsai tree reconstruction.
The work lives in :mod:`omicverse.external.bonsai`; this module only registers
the function and records the citation, mirroring how ``ov.single.samap_integrate``
fronts the vendored SAMap.
"""
from __future__ import annotations
from typing ... |
0ce4c9ef4fb2cfb8abe65304a29e5c4801a6929d41764dc09f9df23bbc9aa7cd | Python | 2,621 | 82 | """Generate stimulation protocol."""
import numpy as np
import pandas as pd
import time
# Output folder
FOLDER = '/Users/sebastiandresbach/github/neurovascularCouplingVASO/code/stimulation'
# Pair TR (repetition time)
TR = 3.141
# Number of jitters
NR_JITTERS = 4
# Stimulus durations (in seconds)
STIM_DURS = [1, 2,... |
8238b436eb83d6ce48b4276feb5edd19aed97490f7917db9440ffbac40ce8a3e | Python | 2,621 | 67 | import os
import numpy as np
import torch
from torch.utils.data import Dataset
class ProcessedLigandPocketDataset(Dataset):
def __init__(self, npz_path, center=True):
with np.load(npz_path, allow_pickle=True) as f:
data = {key: val for key, val in f.items()}
# split data based on mas... |
b331841390ce0d41b06c1f8b854508fd0b084beb93e43b87fa95d54f2c7a96e0 | Python | 2,622 | 70 | import requests
from ray import serve
# __doc_import_begin__
from ray.serve.gradio_integrations import GradioServer
import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# __doc_import_end__
# __doc_gradio_app_begin__
example_input = (
"HOUSTON -- Men have landed and walked on the ... |
2f98c3ac99a219869673fa2d740c844f09d4ebfde0fcd29cc44f6c6967897cd4 | Python | 2,623 | 75 | from oddt.fingerprints import PLEC
import sys
import os
import os.path as osp
import pandas as pd
import oddt
import oddt.pandas as opd
from tqdm import tqdm
def generate_plec_pdbbind(raw_path, label_file, output_dir, dim=1024*16):
directories = os.listdir(raw_path)
energy = pd.read_csv(label_file,
... |
5ec807380413ed348ee9ee2de704a012eaf534fbae9fee6d610160fdbd4f314f | Python | 2,623 | 86 | import numpy as np
import json
from spinnaker2 import snn, hardware
import helper
np.random.seed(46)
timesteps = 14
# parameters of convolution layer
in_width = 32
in_height = 32
in_channels = 1
out_channels = 4
filter_height = 3
filter_width = 3
stride_y = 1
stride_x = 1
test_images = np.random.randint(0, 2**8-1, ... |
94bc5c678f2fa6797856ca8eddeac78ed1c4186609a31c4873878e8c239ac365 | Python | 2,623 | 86 | """
This file serves as a documentation example and CI test for basic 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_basic_example_start/end__): Embedded in Sphinx docs via literal... |
cf05511dc505a6aff20b8f732dd7385150234ddd4c175bb92b16a0399e750343 | Python | 2,623 | 80 | from typing import Optional, Union, Sequence
import numpy as np
import jax
from jaxtyping import Key
import equinox as eqx
from .initializers import he_normal
from .modules import Conv, Sequential
from .activation import SinhShift, Exp, Scale, pair_cpl
from .nqs_layers import ReshapeConv, ConvSymmetrize
from ..symmetry... |
34c7a1017d65d414aeb6930d74d6401b7e2f552134919c95632ab94166480a3d | Python | 2,625 | 85 | from torch import optim
from lop.algos.gnt import GnT
from lop.utils.AdamGnT import AdamGnT
import torch.nn.functional as F
class ContinualBackprop(object):
"""
The Continual Backprop algorithm, used in https://arxiv.org/abs/2108.06325v3
"""
def __init__(
self,
net,
... |
7b946b351102725912e0b12b2d579f6a6d1c048c27034e1d5856f50e457c70c5 | Python | 2,625 | 63 | import Utility_working as Utility #custom-made utility file, contains lengthy functions
from constant import *
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import sys
import os
#enter parameters
try:
template_name = sys.argv[1]
print("using constants from termin... |
cc36305ef6586afbae24da80162283c58ed4963954ea05f8685f5338dd2e2118 | Python | 2,625 | 89 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
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