sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
59e876474c3fa70bd071857f460974bc730b7acff3aec8fb6d16bbaa41ee5ecc | Python | 2,337 | 72 | import argparse
import os
import os.path as op
from fsub_extractor.utils.anat_utils import anat_to_gmwmi
# Add input arguments
def get_parser():
parser = argparse.ArgumentParser(
description="CLI tool for converting anatomical file (FreeSurfer output or T1) to a GMWMI. Also produces the 5TT image as an in... |
7005802b2ce65cd961b59ca7b6be404ff78bc62e598963a4375720255a251469 | Python | 2,337 | 60 | import argparse
import numpy as np
from spiking_readout.utils import Logger
from spiking_readout.dataset import SpikingDataset
from spiking_readout.readout import ReadoutParameters, Readout
def fit(input_dir, output_dir, config, seed=0, log_to_file=True, save_model=True, eval_test=False):
# To control randomness
... |
d5487e8eafb858fcc1b34051326ed4a79997369f3efe641aba21a6a01dd7f063 | Python | 2,337 | 80 | import time
from pybuildkite.buildkite import Buildkite
from ci.ray_ci.bisect.validator import Validator
from ci.ray_ci.utils import logger
from ray_release.aws import get_secret_token
from ray_release.configs.global_config import get_global_config
from ray_release.test import Test
BUILDKITE_POSTMERGE_PIPELINE = "p... |
482989d79e215be31e99bce1df7423c04e7aa13466a5874b0a20388146c962f1 | Python | 2,338 | 75 | #!/usr/bin/env python3
"""Functions for module enrichments on dataFrames."""
import numpy as np
import pandas as pd
def calc_df_enrichment(df, col, genes, module_match):
"""
Calculate enrichment of modules in dataframe.
Given a dataframe with genes (df.gene) and some split (df[col]),
calculate the en... |
b2016d0f217b89c84bed2b195c852868185c81626a20bf134aba30b3813c928e | Python | 2,339 | 88 | import asyncio
import time
import ray
from ray import serve
from ray.exceptions import RayActorError
from ray.serve.config import DeploymentActorConfig
# __begin_define_deployment_scoped_actor__
@ray.remote
class SharedCounter:
def __init__(self, start: int = 0):
self._value = start
def increment(se... |
62a61617a5e107d12d60cfa8770dd5d0fee784d3bae22c4f39151751d6c06463 | Python | 2,340 | 55 | import pytest
import numpy as np
from reval.internal_baselines import select_best, evaluate_best
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, davies_bouldin_score
import hdbscan
@pytest.mark.parametrize(
"data, clustering, internal_measure, clust_range, select, expected_type, e... |
8bbd15ed5745a393535007f8cb9497a54e36c6983214e6251ad78b826dbeca5a | Python | 2,340 | 75 | r"""
Input/Output utilities for OmicVerse datasets.
Subpackages:
general: Shared I/O helpers for tabular data and serialization.
bulk: I/O helpers for bulk omics resources.
single: I/O helpers for single-cell data.
spatial: I/O helpers for spatial omics data.
cytometry: I/O helpers for flow / mass ... |
ba25391420bc202cb8919989b19fa9f05ff41a98c176138d134d4a8515908680 | Python | 2,340 | 67 | import cupy as cp
import numpy as np
import matplotlib.pyplot as plt
import time
import torch
from pyJoules.energy_meter import EnergyContext
from pyJoules.device.rapl_device import RaplPackageDomain, RaplDramDomain
from pyJoules.device.nvidia_device import NvidiaGPUDomain
from pyJoules.handler.pandas_handler import ... |
5c7e5e9f20fb39130906a3fa9685a9b95d603f44bca585dd78dd0ebe4a32d106 | Python | 2,342 | 72 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Merge duplicate records and counts files across multiple TSVs.
"""
from typing import List, Text, Optional
from collections import defaultdict
import argparse
import sys
import csv
def merge_duplicates(record_files: List[str], count_files: List[str], fout: str):
... |
579046b51990d406a1199a0125fab8b69e2a4244210da9f86540cd1eecd1ad55 | Python | 2,343 | 68 | """
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 random
import ase
import numpy as np
from fairchem.data.oc.core import Adsorbate
_test_db = {
... |
62df3b6dc0d3c6c11080b461c762eaadd08c58a282c3c9336484ad33d58fd1b5 | Python | 2,343 | 65 | #!/usr/bin/env python3
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# Used to run Google Tests generated during build. Takes in a text file
# containing a list of relative paths to text exectuables to run and path to
# binary output directory of build. The paths are re... |
7fb4d5b51b6c63feba50656b04df5c869ab47f6f9158fb60d0a635bf353e76f3 | Python | 2,344 | 60 | import unittest
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from diffusers.utils.outputs import BaseOutput
@dataclass
class CustomOutput(BaseOutput):
images: Union[List[PIL.Image.Image], np.ndarray]
class ConfigTester(unittest.TestCase):
def test_ou... |
4f0963ea32164d848ea59b8fe863083077b03de590e26c4fa9136e1ccfef9339 | Python | 2,346 | 70 | # __cgraph_overlap_start__
import ray
import time
import torch
from ray.dag import InputNode, MultiOutputNode
@ray.remote(num_cpus=0, num_gpus=1)
class TorchTensorWorker:
def send(self, shape, dtype, value: int, send_tensor=True):
if not send_tensor:
return 1
return torch.ones(shape, d... |
f1bce9716dc53e9ff6b10e8859d7dd2e40c3b0cc5f9fe29ce5a194061208aed5 | Python | 2,346 | 70 | #!/usr/bin/env python
#######################
# unused functions
#
# These are not presently used in the Caiman codebase, but are
# being kept for being potentially useful (and either not trivial to
# reimplement, or themselves a nontrivial idea).
#
# Code in this file should:
# 1) Not be considered for purposes of "d... |
bae2ebf41234e6c0128f4094f13a0d2193b5fa114ad6555f18f446f3ab039905 | Python | 2,350 | 88 | import collections
import pytest
from pfrl.collections.random_access_queue import RandomAccessQueue
@pytest.mark.parametrize("maxlen", [1, 10, None])
@pytest.mark.parametrize("init_seq", [None, [], range(5)])
class TestRandomAccessQueue:
@pytest.fixture(autouse=True)
def setUp(self, maxlen, init_seq):
... |
4aa2b22a22de43754ed694c308073b1bf6508008218303427162dd2937d922ad | Python | 2,351 | 49 | """Binary hashing and sparse local histograms, without dense 2**L arrays."""
import torch
def block_geometry(height, width, block_size, overlap):
bh, bw = block_size
sy, sx = (int((1 - overlap) * b + .5) for b in block_size)
if height < bh or width < bw:
raise ValueError(f"Histogram block {block_s... |
627aba95b10b351572f941ad9369bae06d0ebd8030cd7ef9dc48f19a8f9e4c1d | Python | 2,351 | 71 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Convert RdTest de novo format to standard format
"""
import argparse
import numpy as np
import pandas as pd
def reformat_dn_rdtest(dn_metrics):
id_vars = 'chr Start End CNVID Type Family AffectedMember'.split()
metrics = pd.melt(dn_metrics, id_vars=id_vars... |
289b54d282cad35902289bbc0f4c477147f092baad975d06d585630936c8e213 | Python | 2,352 | 62 | import torch
def add_noise(x, noise, snr_db):
"""Add zero-mean noise scaled to a per-lead SNR (dB) relative to the signal power."""
signal = x.var(dim=-1, keepdim=True, unbiased=False)
noise = noise - noise.mean(dim=-1, keepdim=True)
power = noise.pow(2).mean(dim=-1, keepdim=True).clamp_min(1e-12)
... |
63e25a38952fc264313ec441790ba74830c2954379fe4a06265c30c0fe16564d | Python | 2,352 | 56 | import logging
from datetime import datetime
logger = logging.getLogger(__name__)
timezones = [
{"offset": "-12:00", "value": "Etc/+12"},
{"offset": "-11:00", "value": "Pacific/Pago_Pago"},
{"offset": "-10:00", "value": "Pacific/Honolulu"},
{"offset": "-09:00", "value": "America/Anchorage"},
{"off... |
cec37b3340d383a899b373dd18ef226c1bd87cc12361da12bcd8bed1fc467f10 | Python | 2,352 | 58 | import numpy as np
def generate_example_timeseries(num_examples, length):
"""Generates a dataset of three-channel timeseries of desired size,
meant to substitute (but not exactly approximate) wearable sensor data.
First two channels represent heart rate and stepcount, modelled
as cyclical trends with ... |
44725d11fdbbb169364ba4e23d7715430e03a44efaf6b0bdb5b7c9781aafc12b | Python | 2,354 | 61 | #! /usr/bin/env python3
# ReScience yaml to bibtex converter
# Released under the BSD two-clauses licence
def generate_bibtex(filename, article):
content = (
"@Article {{{_.authors[0].lastname}:{_.date_published.year},\n"
" author = {{{_.authors_full}}},\n"
" title = {{{{{_.t... |
95e1ece1f370f0ae5675840e97ac245acb5c9ef643bd09fe8013368284ec73ed | Python | 2,354 | 78 | # -*- 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 classes for the RPUConfig."""
from typing import Type, Union, Any, TYPE_CHECKING
from dataclasses import dataclass, field
from .runtime import RuntimeP... |
e56763ae1e9a1ede7af3a1865f4a0ae7469963c96c10f61b46bdcf9e8ce1dc82 | Python | 2,354 | 69 | from loguru import logger
import os
import sys
import json
from deltasplice.constant import CL, EL, Fapath
from pyfasta import Fasta
import pandas
SavePath = "data/FAS"
# hgfile["chr10"][90770509:90770572].upper() matches
def main(File, species, SavePath):
mutinfo = pandas.read_csv(File)
fafile = Fasta(os.pa... |
23ed1c50a7532c81aba7cd92d2bcd3f73bd37d1f2d2a318baa26c3c850cffeab | Python | 2,355 | 58 | import numpy
import pandas
def assert_indices_equal(i1: pandas.Index, i2: pandas.Index, context: str):
assert numpy.array_equal(i1.isnull(), i2.isnull()), f"{context}: index null values not equal"
assert numpy.array_equal(i1[~i1.isnull()], i2[~i2.isnull()]), f"{context}: index non-null values not equal"
def... |
74800fd8e617f837bf0d54139070bf3a1417a9d0d605370b4786d1bbc3c51bcb | Python | 2,356 | 79 | # flake8: noqa
# fmt: off
from typing import Iterator, Union, List
import pyarrow
from ray.data.block import Block
# __datasource_constructor_start__
from ray.data.datasource import FileBasedDatasource
class ImageDatasource(FileBasedDatasource):
def __init__(self, paths: Union[str, List[str]], *, mode: str):
... |
b9b117f0abd9d7cb59822958b06cb9a55b5a9a3a851a47d0e82e6b6c4da6d41f | Python | 2,356 | 82 | import os
import torch
import torch.nn as nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn.dynapcnn_visualizer import DynapcnnVisualizer
from sinabs.from_torch import from_model
weights_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "dvs_gesture_params.pt"
)
... |
d9e32b08bba3d65635516ae02f9d1f5f5a993278c4f496ad9555a55140ea5bf9 | Python | 2,356 | 55 | #----------------------------------------------------------------------------------------------------------
# Author: Brandon S Coventry
# Date: 12/16/2023
# Purpose: Generate data to address asymptotics of Bayesian vs Frequentist inference on a DBS-LFP model
#-----------------------------------------------------------... |
d2f3315876545bf23adb092310eccceec5cbd31c73e2ee597f68a45d6dce72fd | Python | 2,357 | 73 | import torch
import sinabs
import sinabs.backend.dynapcnn as sdl
def get_network(has_identity_weights=True, n_layers=9, has_bias=False):
sequential = torch.nn.Sequential()
for i in range(n_layers):
layer = torch.nn.Conv2d(1, 1, (1, 1), bias=has_bias)
if has_identity_weights:
with ... |
19938f6571dff47d61ba2295c437d925437d12a77e6e0444696d7fd7d8304684 | Python | 2,359 | 50 | ####################################################################################################
# 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... |
9d4c34c0b7fe018f15211fb8081c3813e4fb81b211255f97f65fbe28b8b0c35e | Python | 2,359 | 71 | import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
import numpy as np
import torch
from solver import Solver
from optimizer import Optimizer
import matplotlib
import plt_config
import matplotlib.pyplot as plt
matplotlib.use('Agg') # for running on server without GUI
color = plt.rcParams['axes.prop_cycl... |
1f66065eebe52c31278c53e313f7581adec3cfa0a7ec511fad1da15709cdb09a | Python | 2,360 | 98 | # flake8: noqa
import requests
# __echo_class_start__
# File name: echo.py
from starlette.requests import Request
from ray import serve
@serve.deployment
class EchoClass:
def __init__(self, echo_str: str):
self.echo_str = echo_str
def __call__(self, request: Request) -> str:
return self.ec... |
8042cada215dc23dc4d2e45ffe461e46019da24b853e4b0fe99b7015c675ad59 | Python | 2,360 | 65 | #!/usr/bin/env python3
from __future__ import print_function
import argparse
import os
import subprocess
def parse_args():
parser = argparse.ArgumentParser("Extracts FASTA/FASTQ segments from another fasta")
parser.add_argument('--input', '-i', dest='input', required=True, type=str,
he... |
527eb499ad3111196c8ee8c306af39356afbd8e5080abf3d274a317d435313aa | Python | 2,361 | 56 | import importlib
import json
import argparse
import torch
from benchmark.utils import benchmark_forward
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_grad=True)
w = torch.randn(b... |
79c63edb8f9fef709a41d8e230157074c715e389f557a85fe5a030846a6aa182 | Python | 2,361 | 73 | from abc import abstractmethod, ABC
import sqlite3
from google.cloud.bigquery import Client
class DatabaseClient(ABC):
def __init__(self, client):
self.client = client
super(DatabaseClient, self).__init__()
@abstractmethod
def execute(self, query: str):
pass
class BigQueryDatab... |
e1c80a40b7d9b4b4796a3dbab3883968d95fac1a7d968da146f4df0a2dd38aee | Python | 2,361 | 77 | import numpy as np
import torch
import random
from .spaces import num_flat_space_elements
from .spaces import nd_to_flat_space_index
from .spaces import flat_to_nd_space_index
from .set_values import set_value_by_function
from .set_values import linear_value_adaptation
from .similarity_search import inverse_distance_k... |
17ee7ed4f5efde1228b3927c153ecefff67f85fccb1d0c3c27d681c78c9540f7 | Python | 2,362 | 81 | #!/usr/bin/python
####################################################################################################
# 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>
#
# ... |
3908ada84ef52107ad6242cc9f1f1cd61aa491acb4826d4ab1dc78bf49b98ec1 | Python | 2,363 | 88 | # __example_code_start__
from io import BytesIO
from fastapi import FastAPI
from fastapi.responses import Response
import torch
from ray import serve
from ray.serve.handle import DeploymentHandle
app = FastAPI()
@serve.deployment(num_replicas=1)
@serve.ingress(app)
class APIIngress:
def __init__(self, diffusi... |
8967a4be6ae7f33e3a4fa4e82f3db42cc50799128bf154108d2de554169c58a9 | Python | 2,364 | 76 | import unittest
from neuron_morphology.feature_extractor.data import Data
from neuron_morphology.feature_extractor.mark import Mark
from neuron_morphology.feature_extractor.marked_feature import marked
from neuron_morphology.feature_extractor.feature_extraction_run import \
FeatureExtractionRun
from neuron_morphol... |
ef1f780f56fb036988316f077308b6b6ab8504358e2d545b6d4b390688fe1df5 | Python | 2,364 | 65 | import pickle
import requests
import pysam
import subprocess
import concurrent.futures
file_path = 'files/'
samples_to_file, regions = pickle.load(open(file_path + '/bams/queries.pkl', 'rb'))
##your token
with open(file_path + '/bams/gdc-user-token.2021-12-13T20_06_44.138Z.txt') as f:
token = f.read()
def load_u... |
727687058f00fa0857777a2e1a46f10d866d712ca78328bb2f22dc07e303832d | Python | 2,365 | 100 | from typing import Optional
from jaxtyping import DTypeLike
from functools import partial
import numpy as np
import jax
import jax.numpy as jnp
import jax.random as jr
from jax.sharding import PositionalSharding
jax.config.update("jax_enable_x64", True)
jax.config.update("jax_default_matmul_precision", "float32")
jax... |
c07bb12bfe7850b48c41fc683a1510b9d76a335018fed2072cab3f73d35ef1f0 | Python | 2,366 | 63 | from typing import Optional, Sequence
import torch
from rising.transforms import BaseTransform
from rising.transforms.functional import one_hot_batch
__all__ = ["OneHot", "ArgMax"]
class OneHot(BaseTransform):
"""
Convert to one hot encoding. One hot encoding is applied in first dimension
which results... |
f9ab4bec567193a0fc27048ff42292cf1d8fb0b29456a1b64229dc6676892df9 | Python | 2,366 | 59 | import logging
import json
from agent.data.entities.config import BACKUPS_PATH, ROOT_LOGGER_ID
from agent.data.sql.sql_mgmt import execute_query, get_connection
from agent.data.sql.sql_mgmt import select_fields_where
logger = logging.getLogger(ROOT_LOGGER_ID)
def insert_triple(connection, triple_dict):
insert_t... |
396f13bb586fe444475ac401b2cf456cf6310eba0dff23db1f053ff5e86063bc | Python | 2,367 | 79 | """
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 random
import numpy as np
import pytest
from fairchem.data.oc.core import Adsorbate, Bulk, Inte... |
fed7e68ff6c9e905a31cbcebfe5b6dcbff550aea906ffd987adcce0f8be0ab12 | Python | 2,370 | 67 | from typing import Dict, Optional
from ray._common.observability.internal_event import InternalEventBuilder
from ray.core.generated.events_base_event_pb2 import RayEvent as RayEventProto
from ray.core.generated.platform_event_pb2 import PlatformEvent, Source
from ray.util.annotations import DeveloperAPI
@DeveloperAP... |
957ca282d538f9ade166b1928a67ef32863a5debc137b4bd47bc2e7b026a98f3 | Python | 2,371 | 69 | import torch
import torch.nn as nn
import pytorch_lightning as pl
from torch.nn import functional as F
import sinabs.layers as sl
# import sinabs.exodus.layers as sel
class SNN(pl.LightningModule):
def __init__(self, batch_size, lr=1e-3):
super().__init__()
self.batch_size = batch_size
sel... |
a7707e37a261373f69bc4d5c18e37a3141c10f4e1f13c00a6802be43899f7fbc | Python | 2,371 | 73 | #!/bin/env python
import argparse
import pysam
import sys
from typing import List, Text, Optional
def _parse_arguments(argv: List[Text]) -> argparse.Namespace:
# noinspection PyTypeChecker
parser = argparse.ArgumentParser(
description="Prepare call set for evaluation against PacBio variants, includin... |
d330e063d88e7a0c3ce12e6132df5ea41210f6f38a2ea390c0d6808237a7585f | Python | 2,372 | 72 | from typing import Literal
from scvi.module.base import PyroBaseModuleClass
from ...models.base._pyro_mixin import AutoGuideMixinModule, init_to_value
class RegressionBaseModule(PyroBaseModuleClass, AutoGuideMixinModule):
def __init__(
self,
model,
amortised: bool = False,
encode... |
eb68eedbae9c21c247da73c78ffb8e53abbf710a475af47769d5036a4f4e41dc | Python | 2,372 | 80 | # TODO: [V2] Deprecated doc code to delete.
import os
os.environ["RAY_TRAIN_V2_ENABLED"] = "0"
import tempfile
import horovod.torch as hvd
import ray
from ray import train
from ray.train import Checkpoint, ScalingConfig
import ray.train.torch # Need this to use `train.torch.get_device()`
from ray.train.horovod impo... |
b8dbbd83b332f83c220e103afa32bfcdc579350c477953dac23f4dffdd8d4ddb | Python | 2,373 | 92 |
import pandas as pd
from lisa import FromGenes
import requests
import fire
import argparse
import numpy as np
URL = 'http://lisa.cistrome.org/gallery/{id}_{dir}.txt'
TEST = [
('189', ['NFE2']),
('172',['PPARG']),
('187',['YY1']),
('220',['RUNX3']),
('135', ['HNF4A']),
('154',['MYOD1']),
(... |
2885edd9becdaf23424d15a7608843673585dd6c106ba3549496e99f6dd3ab74 | Python | 2,374 | 74 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Noise model info in rpu_config to neural network model"""
# pylint: disable=no-name-in-module,import-error
from aihwkit.cloud.converter.definitions.i_input_f... |
f07d0a08da3d429ade3d51b370969dc4c16a0da224bd1371baf3358fd5b19569 | Python | 2,374 | 83 | from . import unittest
import mock
from ngs_mapper import runsample
class TestPBSJob(unittest.TestCase):
def setUp(self):
self.runsample_args = self.rsa = [
'readsdir', 'foo.fasta', 'prefix',
'-trim_qual', '25', '-head_crop', '0',
'-minth', '0', '--CN', 'bar', '-od', 'o... |
468942bf0f5b8e244fc90c2b29144e3aa0ef7962add0c9811cabe0e46fc7b961 | Python | 2,375 | 98 | """
scLLM: Single-cell Language Models
==================================
A unified interface for single-cell language models including scGPT, scFoundation and others.
Main classes:
- SCLLMManager: High-level interface for model operations (RECOMMENDED)
- ModelFactory: Factory for creating different model types
- ScG... |
78cc7815c65a2f8d7568fbaae5c529dd461a36cfd632841723d69cd500c5b7e1 | Python | 2,376 | 78 |
# -*- coding: utf-8 -*-
"""
The module is used for computing the composition of amino acids, dipetide and
3-mers (tri-peptide) for a given protein sequence.
References
----------
.. [1] Reczko, M. and Bohr, H. (1994) The DEF data base of sequence based protein
fold class predictions. Nucleic Acids Res, 22, ... |
8616c5a9cc9b9470431b78447e95ead141503a33f79b1f888ec0cd88f53210c4 | Python | 2,376 | 56 | # helper utils for reading in data
import numpy as np
import os
# reads in a list
def read_list(path, trans=None):
collection = []
with open(path) as infile:
for line in infile:
newline = line.strip().split()
if len(newline) == 1:
newline = newline[0]
... |
c806d36fbc876238248fd98b9ac039cb4edc84f958e9c34d96b3bd68e1e434f6 | Python | 2,376 | 65 | import os
import cv2
import numpy as np
from utils.tool import get_type_max, read_img, save_img
from omegaconf import OmegaConf
import torch
import json
from einops import rearrange, repeat
from utils.ssim import ssim as ssim_calc
from utils.ssim import ms_ssim as ms_ssim_calc
import copy
def cal_iou_acc_pre(data_gt:n... |
d6435b0798f40658f0e453ea0acde6847ab1852ad9d2e2a53c763238afc25c53 | Python | 2,376 | 58 | from transformers import PreTrainedTokenizer
class scMulanTokenizer(PreTrainedTokenizer):
def encode(self, text, *args, **kwargs):
# scMulan calls ``tokenizer.encode(list_of_token_strings)``
# treating the list as a pre-tokenized sequence — i.e. one id
# per element. transformers <= 4.40 s... |
0ce19cbeb3632e6c6b741701fd42099048a95ead96c638244e60a6bc945a1de8 | Python | 2,378 | 67 | import numpy as np
import pytest
import torch
from pfrl.functions.lower_triangular_matrix import lower_triangular_matrix
@pytest.mark.parametrize("n", [1, 2, 3, 4, 5])
class TestLowerTriangularMatrix:
@pytest.fixture(autouse=True)
def setUp(self, n):
self.n = n
self.batch_size = 5
sel... |
8b4ddf2e287d4bfefa3a0cdc2e48ce25e6545812f9da309d7d21be99241bbc3a | Python | 2,378 | 81 | import os
import numpy as np
import torch
from tqdm import trange
from solver import Solver
from KS_equation import dynamics
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
torch.set_default_tensor_type(torch.cuda.FloatTensor
if torch.cuda.is_ava... |
2d8109ca301aa5d52098823f8b35a4b521e9107c364eb6314ded26c7483543e6 | Python | 2,380 | 75 | from __future__ import annotations
import dataclasses
import torch
@dataclasses.dataclass
class GraphAttentionData:
"""
Custom dataclass for storing graph data for Graph Attention Networks
TODO: complete this
"""
# attributes
atomic_numbers: torch.Tensor
charge: torch.Tensor
spin: t... |
84da7c7064e89f26894de0228812cdcc1dac8b67d4df949fef30250d4c120bd6 | Python | 2,380 | 53 | import pandas as pd
import zipfile
import os
import logging
from logging import handlers
def HLA_Label_Length_Position_AA_peptides(data, hla, label, length, posi, aa):
posi -= 1
peptides = [item for item in data[data.HLA == hla][data.label == label][data.length == length].peptide
if item[... |
0b12e5780dbfcf608fc584a9df90b1fc0991551596dff9c572107fb22ede390d | Python | 2,381 | 75 | # coding: utf-8
import os
import sys
import pytest
import torch
import ray
import ray.cluster_utils
from ray.dag import InputNode, MultiOutputNode
from ray.tests.conftest import * # noqa
if sys.platform != "linux" and sys.platform != "darwin":
pytest.skip("Skipping, requires Linux or Mac.", allow_module_level=T... |
2c117ef9d1070dd01439b506a79f24d6e9cab38ba0d98b4bb8bb8be01e463f18 | Python | 2,381 | 76 | '''study mean/max pairwise corr as the number of random vector grows'''
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from utils.stats import compute_stats
from scipy.stats import pearsonr
sns.set(style='white', palette='colorblind', context='talk')
n_sims = 30
more = 0
f, axes = plt.subpl... |
cdba2abc868c89a9f4966a4fc2c8139e36f22ed6e8e8e60eadf19a9132763285 | Python | 2,382 | 78 |
import os
import pdb
import numpy as np
from tqdm import tqdm
import torch
import matplotlib.pyplot as plt
import yaml
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 Decoder,... |
fd05e000ccb00829fad1625a5ddfd0a838d89bfd055d8e78f36025bd470e8da6 | Python | 2,384 | 95 | 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... |
51686991adb8c3409eb250a801b00dadf6e51b692070dd9d6cb06f32ed06d266 | Python | 2,385 | 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... |
aa4e58abc218b813ef99a04d41b00298f9b08fe10607d819ec469ceb52ab0b9a | Python | 2,385 | 73 | # 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, ... |
c99499bf2a5d36bb18daf286c6e780299502a83edf29bb3e27958117a805f02a | Python | 2,385 | 63 | """Facenet prewhitening uses the original model's adjusted standard deviation."""
from types import SimpleNamespace
import numpy as np
import pytest
from deepface import DeepFace
from deepface.modules import modeling, preprocessing
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametriz... |
d4e4e4bd626acf998c097161577f54bfe56d583616104779f2667838964dc196 | Python | 2,385 | 95 | 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... |
939ef31646df9722fd2f6b66ab04719d7ee237854b93a9ca93243512248d1aa1 | Python | 2,386 | 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... |
dd19bd5701568a824bb3603882dc22a98ac218f9a9e312b941db4bbe798166fa | Python | 2,386 | 74 | # built-in dependencies
from typing import List, Any
import warnings
# 3rd party dependencies
from numpy.typing import NDArray
# project dependencies
from deepface.models.Detector import Detector, FacialAreaRegion
# mtcnn is an optional dependency requiring keras, it is imported within __init__ on
# purpose - deepfa... |
05c34a6ed02062fffd5a61564b2ff228b1fcd09b78c5b61c211790231fbecbb7 | Python | 2,387 | 97 | import zipfile
import pytest
from stl import mesh
_STL_FILE = b"""
solid test.stl
facet normal -0.014565 0.073223 -0.002897
outer loop
vertex 0.399344 0.461940 1.044090
vertex 0.500000 0.500000 1.500000
vertex 0.576120 0.500000 1.117320
endloop
endfacet
endsolid test.stl
"""
def test_single_stl(tmp... |
1e2cefc3cce29d9e9557d383e82bba8f1acbbe99d63416b033c12147f84fd6f4 | Python | 2,387 | 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... |
e2bdb125079183db676d2b2c626b19413911cb63fd2efd8919a653204af0f357 | Python | 2,387 | 65 | import numpy as np
import matplotlib.pyplot as plt
import time
import torch
from pyJoules.energy_meter import EnergyContext
from pyJoules.device.rapl_device import RaplPackageDomain, RaplDramDomain
from pyJoules.device.nvidia_device import NvidiaGPUDomain
from pyJoules.handler.pandas_handler import PandasHandler
de... |
fde569be716ad07621b5275e7e405114cf37c800f9bec09983654c440eb1cb57 | Python | 2,387 | 48 | ####################################################################################################
# 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... |
3f112732f9f0ddc4458262e1eba99e3a4e2904ac60e5ded6c1a365037ad3ff0b | Python | 2,388 | 91 | from itertools import product
import pytest
import torch
import torch.nn as nn
def test_init_defaults():
from sinabs.backend.dynapcnn.dvs_layer import DVSLayer
dvs_layer = DVSLayer(input_shape=(128, 128))
print(dvs_layer.get_config_dict())
data = torch.rand((1, 2, 128, 128))
out = dvs_layer(d... |
bced901cf4597533f002961cdf85c08fe9d80c9c8f203acf479864f0d6664d50 | Python | 2,389 | 99 | import logging
import pandas as pd
from datetime import date, datetime
from agent.data.entities.config import ROOT_LOGGER_ID
from agent.data.sql.sql_mgmt import get_connection
from agent.data.entities.item import insert_item, item_tuple_to_dict
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging... |
a7dac532d156de068c7e10bc74c084d35c86ed1fed1a504fb0f51e6815316afc | Python | 2,390 | 88 | from typing import Any
import pytest
from ray.dag.py_obj_scanner import _instances, _PyObjScanner
class Source:
pass
def test_simple_replace():
scanner = _PyObjScanner(source_type=Source)
my_objs = [Source(), [Source(), {"key": Source()}]]
found = scanner.find_nodes(my_objs)
assert len(found)... |
c88146159bbcb9acb45f142eaa9db2b9e796c519de98af47d9a8b11792a91bd0 | Python | 2,390 | 67 | import torch
from networks.attention_gan import Generator, Discriminator
from modules.module_unet import GAN as UnetGan
# Define the custom GAN class inheriting from UnetGan
class GAN(UnetGan):
def __init__(self, config):
super().__init__(config) # Initialize the superclass with the given configuration
... |
8c2a676ddc3ed0ced3fcab8f708f90db759f02a2c8e87950ebcf73aa03d0d5ef | Python | 2,391 | 62 | """Automative patch flattening of all participants"""
import subprocess
import os
import glob
subs = ['sub-06']
for sub in subs:
subFolder = f'/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti/derivatives/{sub}'
anatFolder = f'{subFolder}/ses-01/anat/upsample'
# Make output directory
out... |
b02229e54a9d9863380022409de7245f1fd83e9e7e7c300340291ce51053091a | Python | 2,391 | 65 | from skimage import color
import numpy as np
def overimpose_shade(img, mask, alpha=0.6, col=(1, 0, 0)):
# Construct RGB version of grey-level image
img_color = np.dstack((img, )*3)
img_mask = (np.dstack((mask, )*3)*col).astype(img_color.dtype)
# Convert the input image and color mask to Hue Saturatio... |
45bd28dfeab7dc7dbd08e575855af17297a63ca846ecd7cbfd5867e4d77e1a01 | Python | 2,396 | 83 | # flake8: noqa
# __start_translation_model__
# File name: model.py
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
class Translator:
def __init__(self):
# Load model
self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
self.model = AutoModelForSeq2SeqLM.from_pretrained... |
0a2b67f09334076203713f068ecb5cb40fe24a18f37a53a9627e704109029bc1 | Python | 2,397 | 87 | """
Test for issue 51:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/51
"""
import numpy as np
import pandas as pd
from data_profiling import ProfileReport
def test_issue51(get_data_file):
# Categorical has empty ('') value
file_name = get_data_file(
"buggy1.pkl",
"https:... |
45dbb071b95a8740b52d2bd5035f2844faf5d137b3d9a6d63f8702ec6671dd27 | Python | 2,397 | 76 | import numpy as np
import matplotlib.pyplot as plt
from lib5c.util.plotting import plotter
try:
from sklearn.metrics import auc
sklearn_avail = True
except ImportError:
sklearn_avail = False
auc = None
@plotter
def plot_roc(eval_results, labels, colors=None, **kwargs):
"""
Plots an ROC curv... |
701c5d317c80efc4851ba4fc85ced791e9d262a7f012b630bb7947c1236e9357 | Python | 2,397 | 71 | __author__ = "Simon Andermatt"
__copyright__ = "Copyright (C) 2017 Simon Andermatt"
import logging
from copy import deepcopy
import numpy as np
import torch as th
from mdgru.helper import argget, compile_arguments, generate_defaults_info
class CRNNCell(th.nn.Module):
"""Base convolutional RNN method, implement... |
8afbe6e4622264c9603bfe8bfe26e31fec046546f1cab23de40cc23b7e43237c | Python | 2,397 | 90 | # __starting_ray_start__
import ray
import math
import time
import random
ray.init()
# __starting_ray_end__
# fmt: off
# __defining_actor_start__
@ray.remote
class ProgressActor:
def __init__(self, total_num_samples: int):
self.total_num_samples = total_num_samples
self.num_samples_completed_per_... |
e6ebe89629b166bef98aa111b4a3b80f188f6f6f83d708ebbc21cc00dcdfeb14 | Python | 2,398 | 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 numpy as np
import pytest
from ase import Atoms
from ase.constraints import FixAtoms
from fairc... |
a68f593241e796c068ae8085c314b68a43b55f603a4fa3fc593615c4e8008cfd | Python | 2,399 | 67 | from typing import List, Optional, Union, Any
import torch
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops
def sym_norm(edge_index:torch.Tensor,
num_nodes:int,
edge_weight:Optional[Union[Any,torch.Tensor]]=None,
impro... |
e302e6f018f9f7873048ed88c76e42a4b32f6ad456936b26ac0c5737ad1f0109 | Python | 2,399 | 73 | # 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, ... |
18d8ac12d3831dca919b3e944c69f8dfbe4c2a00534111493bdeaff1fc8b1c53 | Python | 2,400 | 33 | from importlib import resources
from typing import Dict, List
predefined_signatures = dict(
cell_cycle_human=resources.files("omicverse.datasets").joinpath("data_files/cell_cycle_human.gmt").__fspath__(),
cell_cycle_mouse=resources.files("omicverse.datasets").joinpath("data_files/cell_cycle_mouse.gmt").__fspa... |
2c26ad3f9a5e6272fcb2c54c842fe01e153021836be626de28dbc120f6423b9a | Python | 2,400 | 63 | r"""Real Doench-2016 CFD (Cutting Frequency Determination) off-target scoring.
The CFD score (Doench *et al.* 2016, Nat Biotechnol) is the field-standard
CRISPR off-target metric: a product of empirically-measured per-position,
per-mismatch-type retention factors times a PAM factor. The scoring tables
(``mismatch_scor... |
4bdb38e00ced8883927638380cce2d9929bf84ded9410e70e27309b815e74911 | Python | 2,400 | 77 | import numpy as np
from math import sqrt
import matplotlib.pyplot as plt
def generate_online_performance_plot(
performances=None,
colors=None,
xticks=[],
xticks_labels=None,
yticks=[],
yticks_labels=None,
m=20000,
xlabel='',
ylabel='',
la... |
2f753dccfcf281b2c22725c3e3fb0122475250e04c1a91ed52c3c9c6a8e02c1a | Python | 2,401 | 60 | ####################################################################################################
# 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... |
d6d7bddea358ad1776271c58d3fc970cceb26a562b8a188a4ca203e148b827be | Python | 2,401 | 95 | # built-in dependencies
import os
from unittest.mock import patch
# 3rd party dependencies
import pytest
import psycopg
from deepface import DeepFace
from tqdm import tqdm
import pandas as pd
# project dependencies
from deepface.commons.logger import Logger
logger = Logger()
connection_details_dict = {
"host": ... |
22f536d537ac6e72c0039b6e7fdaf335599e5623e354e1ae2eb46dcada519a5b | Python | 2,402 | 69 | import numpy as np
import pytest
from stl import mesh
from .test_mesh_mass_subdivision import _boundary
def moderate_appendage():
bounds = np.array(
[[[0, 0, 0], [2, 2, 2]], [[-1000, 0, 0], [0, 1e-4, 1e-4]]],
dtype=np.float32,
).astype(float)
axes = [np.unique(bounds[:, :, i]) for i in r... |
ee3cbd3a0d02816266b8808673690298a5cbe20b55ee715e1d0869fc8635b14b | Python | 2,403 | 72 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
pandas version: 1.5.0
numpy version: 1.23.3
"""
#%% Import Modules
import pandas as pd
import numpy as np
import glob
import json
from pathlib import Path
#%% Functions
def calculate_deviation_angle(row):
# Extract row and column direction cosines
x_direction... |
621f857e750215e9a5ab78e921494384e700e1eb3fe7451637fbd260a7520deb | Python | 2,404 | 66 | # -*- 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.
#... |
8c75c37a9b54758183c60a782ae97d8e99978dc9ae9e7dbf67485f4708dbc575 | Python | 2,404 | 70 | # Modified from https://github.com/open-mmlab/mmediting/blob/main/mmedit/utils/logger.py # noqa: E501
# Modified from https://github.com/open-mmlab/mmdetection/blob/main/mmdet/utils/logger.py # noqa: E501
import inspect
import logging
from mmengine.logging import print_log
from termcolor import colored
def print_c... |
3135539939cb60c883419a337e01fb5213026103e93ada34bc36b490c8c00e20 | Python | 2,406 | 74 | from typing import Tuple
def conv_output_size(image_length: int, kernel_length: int, stride: int) -> int:
"""Computes output dimension given input dimension, kernel size and stride, assumign no
padding, *per* dimension given.
:param image_length: int image size on one dimension
:param kernel_length: ... |
68a50fef0c7a4c640e8cd25102c7ca65c89036c1f853fc7fdf933d4da1a31703 | Python | 2,406 | 78 | # 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
import setuptools
# -- Project information -----------------------------------------------------
# https://www.sphinx... |
180eac0ecc6a1313d9144ecdb6c8d466723887280af9d614bd77d774a971b176 | Python | 2,408 | 70 | from typing import List
from data_profiling.config import Settings
from data_profiling.report.presentation.core import Container, FrequencyTable
from data_profiling.report.presentation.core.renderable import Renderable
from data_profiling.report.presentation.frequency_table_utils import freq_table
from data_prof... |
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