sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bbfded852bae13c6806392b7ad28a87a96e9b67e2f91df5f22e1abcc0a76c452 | Python | 2,847 | 80 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
from typing import TYPE_CHECKING, Optional
import pandas as pd
from monty.dev import requires
from fa... |
2f05ec43571eb695575828f54d3c6e060b66f4332d367fc5ec0da7efa8de1cc1 | Python | 2,848 | 90 | import scdrs
import pandas as pd
import scanpy as sc
from anndata import AnnData
import os, subprocess, tempfile
CORTEX_URL = "https://storage.googleapis.com/linnarsson-lab-www-blobs/blobs/cortex/expression_mRNA_17-Aug-2014.txt"
# save to files
if not os.path.exists("data/"):
os.makedirs("data/")
# download gene... |
57956f15882370427e5fc7cad34403c03e3b2663269f6bb39a629b66e35625da | Python | 2,848 | 70 | import pandas as pd
from warnings import warn
def sc_QC(X: pd.DataFrame,
min_lib_size: float = 1000,
remove_outlier_cells: bool = True,
min_percent: float = 0.05,
max_mito_ratio: float = 0.1,
min_exp_avg: float = 0,
min_exp_sum: float = 0) -> pd.DataFrame:
... |
8cf587b081af4b0218eaa00ef59630c39f462032b7b622c6533146f5447c97fe | Python | 2,848 | 84 | import numpy as np
from abc import ABC, abstractmethod
from ml4h.defines import EPS
from tensorflow.keras.applications import imagenet_utils
class Normalizer(ABC):
@abstractmethod
def normalize(self, tensor: np.ndarray) -> np.ndarray:
"""Shape preserving transformation"""
pass
def normal... |
172467031053e5eca075d893cfa307446b062bd1896e5369b50f1022f82ffac4 | Python | 2,849 | 95 | # !/usr/bin/python
# -----------------------------------------
# Turn the h5ad files into chunked-by-gene hdf5
# TODO: Both raw and normalized
# NOTE: Works at 80G vmem
# -----------------------------------------
import glob
import h5py
from scipy import sparse
import re
import gzip
import pickle
import numpy as np
imp... |
822c7ca07cebe52294199ad031485db3d1a4c7f425c3aaa84e975f3d3cfd8cfa | Python | 2,849 | 87 | """Script to run synthetic recordings for each model of an ensemble."""
import argparse
import logging
from importlib import resources
from typing import List
from flyvis.utils.compute_cloud_utils import launch_range
logging.basicConfig(
format="[%(asctime)s] [%(filename)s:%(lineno)d] %(message)s", level=logging... |
8a50ce93a7440b2d53148560b7285fc3a650ed7ebed15a11f40cc375e13df319 | Python | 2,849 | 86 | # Generated by Django 4.1.6 on 2023-02-06 19:18
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
("infer", "0011_weights_args_weights_log_weights_spm"),
]
operations = [
migrations.CreateModel(
n... |
b88b51937eaf5b1fc053748fd7242d72d45fa634fe81c9f1c91cb25af9fda893 | Python | 2,851 | 86 | # Copyright (c) DP Technology.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pandas as pd
import os
import argparse
import numpy as np
import glob
def cal_metrics(df):
mae = np.abs(df["predict"] - df["target"]).mean()
mse = ... |
b401d5543d7eefba750853e793907495dd2088da20754ac90423c50859bf0ac1 | Python | 2,852 | 87 | """
Canonicalize USPTO-MIT text splits.
USPTO-MIT uses the shared chemistry canonicalization helpers, but keeps a
dataset-specific input flow and removes reactant fragments unrelated to the
product atom maps.
"""
import argparse
import os
import pandas as pd
from rdkit import Chem
from canonicalize_data_csv import ... |
cc32e6f4ca0a51c552d342e5f4722a0b27e4dd5be34e9ffe78c715425b070fe6 | Python | 2,852 | 106 | # -*- coding: utf-8 -*-
"""
Created on Tue Aug 5 15:22:00 2025
@author: ju-bar
Dielectric and related functions
This code is part of the 'emilys' repository
https://github.com/ju-bar/emilys
published under the GNU General Publishing License, version 3
"""
def dielelectric_penn(E, q, Ep, Eg, Gamma):
"""
This... |
cbaf9e5d9ede0d593cf8756f3b35874c9a92bb9af7640b34e2168cd930fd5b88 | Python | 2,853 | 73 | ####################################################################################################
# Copyright (c) 2020 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
7e010c9ca0a8864d289b911ffeb0225bfce3f79f6f3ed8aeeb1da169cc711ed5 | Python | 2,854 | 77 | import numpy as np
import scanpy as sc
import squidpy as sq
import pandas as pd
def get_gaston_input_adata(data_folder, get_rgb=False, spot_umi_threshold=50):
adata=sq.read.visium(data_folder)
adata.obsm['spatial']=adata.obsm['spatial'].astype(float)
sc.pp.filter_cells(adata, min_counts=spot_umi_thres... |
6c91233044f91ea9757ba0286b124072dbbc5bc994e1b3290376ae98a9c86d79 | Python | 2,855 | 77 | # -*- coding: UTF-8 -*-
# © Kohulan Rajan - 2020
import argparse
import os
import pickle
import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument("file", nargs="+")
args = parser.parse_args()
print(args)
# Initial Setup for GPU
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpus = tf.config.experime... |
8de752bcab88a11fc60f03529c16921c03c275c1f47e19a17d204d7f095a7e03 | Python | 2,856 | 97 | import torch
import torch.nn as nn
import numpy as np
from convex_optim_base.prox_optim import BatchParallelProxProblem
from typing import Tuple
class PixelwiseLinearL1Regression(BatchParallelProxProblem):
CELL_COEFFS_IDX_ARGS = 0
# shape (n_pixels, n_trials)
IMAGES_KWARGS = 'images'
# shape (n_... |
fec9387fb3204a53c6312c372138527b271d011722c2289a10533911d40d2770 | Python | 2,856 | 65 | # Ultralytics YOLO 🚀, GPL-3.0 license
import re
from pathlib import Path
import pkg_resources as pkg
from setuptools import find_packages, setup
# Settings
FILE = Path(__file__).resolve()
PARENT = FILE.parent # root directory
README = (PARENT / 'README.md').read_text(encoding='utf-8')
REQUIREMENTS = [f'{x.name}{x.... |
5369d91dda06a7e2265ffcfcf17871fcfa1115dd0843c00d58c335cb5c710cbb | Python | 2,858 | 76 | import logging
import json
from agent.data.entities.config import BACKUPS_PATH
from agent.data.sql.sql_mgmt import execute_query, execute_read_query, get_connection, select_fields_where
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger(__name__)
logger.setLevel(... |
c0982ca47d740fa19f028557644c88fd0790097f3da008448ec959bd4bac3afc | Python | 2,858 | 86 | import numpy as np
from .auto_encoder import AutoEncoder, VariationalAutoEncoder
from .molecule_gnn_model import GNN, GNN_graphpred
from .schnet import SchNet
import torch
import torch.nn as nn
from torch_geometric.nn.inits import uniform
from torch_geometric.nn import global_add_pool, global_max_pool, global_mean_poo... |
444c50cbf6af6a9fa7fd47cde63510d55c8f52216237e19fb3e2428dd6194604 | Python | 2,859 | 84 | # -*- coding: utf-8 -*-
"""
Created on Fri May 10 12:37:58 2024
@author: nlancaster
"""
raw_file = "P:/Projects/NML_TPC_2023_ThermoFisher_Phosphoproteomics_q-OT-Astral/20230412_HEK293T_phospho_rep/20230412_HEK239T_April_phosphobatch_250ng_15min_2Th_500AGC_rep01.raw"
def RT_MS1_MS2(rawfile,ms2_mass):#add rawfile obje... |
147b646619677c71bd5c150bd68599f14e743be6042eabf6e9f8f849c821784a | Python | 2,861 | 67 | import scanpy as sc
import pandas as pd
import flowsig as fs
adata = sc.read('data/chen22_svg_E9.5.h5ad')
# We construct 10 gene expression modules using the raw cell count.
fs.pp._flow_preprocessingconstruct_gems_using_nsf(adata,
n_gems = 20,
layer_key = 'count... |
775f9bac6d6bc67b22c14529bb3548ff9a95be497ad1a5ec70ab62886d9b7c20 | Python | 2,861 | 95 | """MetaCell-2 (tanaylab/metacells) backend.
The upstream ``metacells`` package is large (~50 modules + C extensions)
and is **not** vendored. Users who want this backend should
``pip install metacells`` separately — the backend raises a clear
ImportError otherwise.
"""
from __future__ import annotations
import time... |
54203a2bd19a5cc45daabe3dde27ab20c84aed94b77193cd175019328b12f500 | Python | 2,862 | 86 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import argparse
import sys
import pysam
import pandas as pd
import numpy as np
def rewrite_SR_coords(record, metrics, pval_cutoff, bg_cutoff):
row = metrics.loc[record.id]
# Do not use bg_cutoff for common variants
if row.SRQ >= pval_cutoff and (r... |
a1c1328fd3e053fe83a5eae77e7bbce645fd3da56b403346d8d548f730db0064 | Python | 2,862 | 54 | #-----------------------------------------------------------------------------------------
#Author: Brandon S Coventry
#Date: 12/18/23
#Purpose: Demonstrate the role of sample size on two comparison t-tests and Bayes t-tests
#Requires: Data from DBS_LFP_GenData.py
#------------------------------------------------------... |
fafbf19981b395650c3fac37a5a0c45affccac9f28f33a3f12d847dc455df56b | Python | 2,862 | 69 | import django.db
from django import forms
from infer.models import Organism, Architecture, Weights
class ProteomeSubmitForm(forms.Form):
try:
organism_choices = [(organism.id, f"{organism.scientific_name} ({organism.common_name})") for organism in
Organism.objects.all()]
e... |
52e8c21a3bea47618a29788230a8e80bbc7d350837ef907458ebc2d1b6104f5d | Python | 2,863 | 95 | _base_ = [
'../_base_/datasets/rtts_coco.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(
type='TOOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... |
f88e6703c76e66090339bf4565ee2cc4b9bc2b96be4a3146f6b955e742e12c92 | Python | 2,863 | 72 | import torch
from ..utils import common_functions as c_f
class WeightMixin:
def __init__(self, weight_init_func=None, **kwargs):
super().__init__(**kwargs)
self.weight_init_func = weight_init_func
if self.weight_init_func is None:
self.weight_init_func = self.get_default_weigh... |
fa8760765d73bab890a2abbf183dd6e4e5bfde35a1a5eb7aa1c9ae519ac83111 | Python | 2,863 | 96 | """
Train a diffusion model on images.
"""
import argparse
from .guided_diffusion import dist_util, logger
from .guided_diffusion.cell_datasets_loader import load_data
from .guided_diffusion.resample import create_named_schedule_sampler
from .guided_diffusion.script_util import (
model_and_diffusion_defaults,
... |
897ab28bfe88521bb54177f09fd08aedf657ee842f63755dbf2851a0f5cb621c | Python | 2,864 | 76 | # imports
import torch
import pickle
from ..collator_for_classification import DataCollatorForGeneClassification
from .. import TOKEN_DICTIONARY_FILE
"""Geneformer collator for multi-task cell classification."""
class DataCollatorForMultitaskCellClassification(DataCollatorForGeneClassification):
class_type = "cel... |
ad98d849b7f26a363e6115a4cb5cab9095515fe8d033031fa4e3858351d04066 | Python | 2,865 | 128 | """
OVAgent harness contracts, traces, replay, and cleanup helpers.
"""
from .cleanup import CleanupFinding, CleanupReport, HarnessCleaner
from .contracts import (
HARNESS_EVENT_TYPES,
STREAM_EVENT_TYPES,
ArtifactRef,
ApprovalRequest,
HarnessEvent,
HarnessCapabilities,
HarnessEventType,
... |
b8be5f411936b74b91855da65246a50b92b8ab49bfa08bd32a5ff71ceecd8311 | Python | 2,865 | 102 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 6 17:44:12 2022
Used to create the first figure in the paper
@author: pbolan
"""
from utility_functions import *
from os import path
import glob
import nibabel as nib
import matplotlib.pyplot as plt
#%%
vmin = 0
vmax_T = 4
vmax_S0 = 1
vmax_img... |
b584e1539ad00926997445753a04b647feaf35654d44cd32ffb0af0bbbc52b7b | Python | 2,866 | 79 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Tests for continuing to train."""
from typing import Union
from pytest import mark
from torch import allclose, manual_seed, mean, rand, tensor
from torch.nn... |
e5f36d563fb1d7721e878653355e1fcbd2f3502f4ba093656d5a6d7838226fbf | Python | 2,867 | 71 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_mean
from gvp import GVP, GVPConvLayer, LayerNorm
class MQAModel(nn.Module):
"""
GVP-GNN for Model Quality Assessment. From https://github.com/drorlab/gvp-pytorch
node_in_dim: node dimensions in input grap... |
794c5de0691350c581102b25a10ab57d6e2fb9b95b3d25bca6f5982550642960 | Python | 2,868 | 74 | #visualize
from descartes import PolygonPatch
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
import os
import pandas as pd
import rasterio
from rasterio.plot import show
from src import neon_paths
import tempfile
def index_to_example(index,... |
8c9b4926a9138763d24dfc75b36bb640f5f37cb38d6af896fc5fcd8180fb1466 | Python | 2,868 | 97 | from types import SimpleNamespace
import neo
import numpy
import quantities as pq
import mozaik
from mozaik.tools.recording import _is_supported, _pack, gather_recording
class RootComm:
rank = 0
size = 2
def __init__(self, other_packet=None, supported=True):
self.other_packet = other_packet
... |
5910742f2379837816867ebd466f89fd264a1f7a422c18d4fe104ae7e72f7550 | Python | 2,870 | 74 | import cv2
import numpy as np
import os
import tensorflow.keras as keras
import tensorflow.keras.layers as L
import tensorflow.keras.backend as B
import tensorflow.keras.models as M
import tensorflow.keras.optimizers as O
import net_org_v2 as net
import tensorflow.keras.losses as Ls
route_all = 'D:/VO_ver... |
5c04e2ae0807a3c6025eb0447517445729e9ed4c92c557d5ca70398a7153a708 | Python | 2,870 | 78 | import unittest
import numpy as np
import random
import vamb
class TestAAE(unittest.TestCase):
tnfs = np.random.random((111, 103)).astype(np.float32)
rpkm = np.random.random((111, 14)).astype(np.float32)
lens = np.random.randint(2000, 5000, size=111)
contignames = ["".join(random.choices("abcdefghijkl... |
ba8f4f8c3fc80927d6b51d554a85b38469e1e95bce56fa2d5f797fcf0093115b | Python | 2,870 | 95 | _base_ = [
'../_base_/datasets/duo_coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='TOOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.37... |
eb0c0fb8973fd35b1d1e56e580bbd2e6f020f8a6d4b676e4c0e3ae1f8aa2920b | Python | 2,870 | 84 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
cc29cffe841f961d6fbf23fa6daafa44ac0248553d136888f268481795e8ba77 | Python | 2,871 | 83 | """Query and gallery crops must reach represent in the same color order."""
import cv2
import numpy as np
import pytest
from deepface.models.Detector import DetectedFace, FacialAreaRegion
from deepface.models.FacialRecognition import FacialRecognition
from deepface.modules import detection, modeling, recognition
cl... |
09c4d3c6165341ed7f15fc683a4dd835edf1838480f042d879741c3d945c4684 | Python | 2,872 | 29 | import json,numpy as np
import matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt
plt.rcParams.update({"font.family":"DejaVu Serif","font.size":8.5})
exec(open('zh.py').read().split('# ---------------- Tables S3/S4')[0].replace("print(","(lambda *a,**k:None)("))
o=json.load(open('zh.json'))
fig,ax=plt.s... |
2644819ba16505c8df8af1d81bba7ad6b5aa1d379432da187e9d3bcfe06d64c6 | Python | 2,872 | 98 | """
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 io
import logging
import pickle
import warnings
logger = logging.getLogger(__name__)
# Allowli... |
a366ec98f383b281c2ba7d01cc491859c48f145da615d28059763bc60f0fd82b | Python | 2,872 | 95 | _base_ = [
'../../_base_/datasets/rtts_coco.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='TOOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.37... |
489e9a84a62becd3d424096faba4674c7b792d19d4c7ec2123c789fd7c84689f | Python | 2,873 | 66 | ####################################################################################################
# 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... |
2a221e379174bd36e6267bc433d655fe26b7e5d4b65b5b4c938cff6a9a28b2f5 | Python | 2,874 | 95 | _base_ = [
'../_base_/datasets/ruod_coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='TOOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.3... |
b0d31271be75e71c6316391b24df52998d4e5822f0f2abb810eb3bfb912e00d4 | Python | 2,874 | 91 | # Copyright 2024 The bayesnf Authors.
#
# 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 ... |
a1a87c708b3f18c2cb5fed11d12a9e2ea255acbb9f15757dc4281ca802987225 | Python | 2,875 | 72 | import copy
from torch.utils.data import Dataset
import numpy as np
class Sim_Dataset(Dataset):
def __init__(self, X, y, device = 'cpu'):
self.X = X
self.y = y
self.device = device
def __len__(self):
return len(self.y)
def __getitem__(self, idx):
return sel... |
5af86da6b6e1e57149f3176b29eac10f4d35419f34da7bb7b3be531168de88f0 | Python | 2,876 | 101 | import torch
import os
import DUnet
from torch.utils.data import DataLoader, Dataset
import numpy as np
from torch import nn, optim
import argparse
dataset_path = 'dataset/'
torch.manual_seed(42)
bz = 1
class DiceLoss(nn.Module):
def init(self):
super(DiceLoss, self).init()
def forward(self, pred,... |
16b673794ae2cf4b66ef6b25474f2abb1b0669417741a15f9ed3f75b76388761 | Python | 2,877 | 70 | from keras.layers import Dense, Dropout, Flatten, Input
from keras.layers.convolutional import Conv3D, MaxPooling3D
from keras.layers.advanced_activations import PReLU as prelu
from keras.layers.normalization import BatchNormalization as BN
from keras import backend as K
from keras.models import Model
K.set_image_dim_o... |
cdbcd4d72cc277c11a2170fe9eff89b304c468874a21bd0101823b615cd22aea | Python | 2,877 | 71 | """
The code for the research presented in the paper titled "Enabling inverse design of plasmon-based nonlinear square resonators with deep neural networks."
This code corresponds to the article's Forward Deep Neural Network (DNN) section.
This code regenerates the Fig. 4b and c of the paper.
Please cite the pape... |
0a2f2ac14ce88a84eeda8a560362155d76ffad44e54ec156c4bb4023d01ee8db | Python | 2,878 | 84 | from numpy.lib.function_base import re
import pyvisa
from pyvisa.constants import *
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import struct
from time import sleep
import socket
import serial
import os
import scipy.io as scio
import math
import cv2
import pdb
import time
... |
6959ed709d8ae28e71be2527c1acab305b7d37e013aac2970ac636a56c20b55b | Python | 2,878 | 100 | """
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.
M→L permutation + Wigner inverse transform operation.
This operation is the final step in the edge message passing pipeline:
1. Permute from M... |
ce3019115215d725aa2c5330fcd91d8f9444a60b84559fc4a71c044c0ad987be | Python | 2,878 | 100 | r"""
Miscellaneous utilities
"""
import random
#from pynvml import *
from anndata import AnnData
from typing import List, Optional, Union
import numpy as np
import torch
def norm_to_raw(
adata: AnnData,
library_size: Optional[Union[str,np.ndarray]] = 'total_counts',
check_size: Optional[int] = 100
) ->... |
eb9f93024ad8216c4ef2940823cb00524264d55dfc7c5fd311c1175d3e6e57d8 | Python | 2,878 | 89 | from itertools import product
import subprocess
import os
import sys
import argparse
TEST_PATH = os.path.join('blah', 'test_files')
common_options = dict(
use_motifs = ['','--use_motifs'],
save_metadata = ['', '--save_metadata'],
assays = ['--assays ' + a for a in ['DNase H3K27ac Direct', 'Dir... |
c5db210758819b5f7a6e7196d3606c5030a561712652e53adb09c02b44f4c70e | Python | 2,881 | 75 | import time
from threading import RLock
from typing import Any, Callable, Dict, List
class EventSummarizer:
"""Utility that aggregates related log messages to reduce log spam."""
def __init__(self):
self.events_by_key: Dict[str, int] = {}
# Messages to send in next summary batch.
self... |
6cdde2d98768fa822c929746fdd3074871d7e5ef6f49ac611a14f3a9d2fa8342 | Python | 2,882 | 67 | import numpy as np
import networkx as nx
import os.path
import matplotlib.pyplot as plt
import time
import math
import gc
gc.collect()
import warnings
warnings.filterwarnings('ignore')
import os
from matplotlib.ticker import FormatStrFormatter, MaxNLocator
from matplotlib.ticker import FixedLocator,FuncForm... |
e8b4127f2c57f5913826aea52d6067cf50b6769f3b4ae7c0ce009dc7b6d696cb | Python | 2,882 | 98 | import argparse
import torch
import os
from copy import deepcopy
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", required=True)
parser.add_argument("-o", "--output", required=True)
args = parser.parse_args()
ckpt = torch.load(args.input, map_location="cpu")
... |
b895d1c63360884bbb7b490e0be00ecbe8de65faf831fbf76c01b27caa809ac4 | Python | 2,883 | 96 | # -*- coding: utf-8 -*-
"""
Created on Sun Mar 5 16:18:59 2023
@author: LeyangXue
"""
import sys
import os
import numpy as np
from multiprocessing import Pool
# Add package path
packagepath = '../kcorePercolation' # Manually specify the path to your script
sys.path.append(packagepath)
from utils import kcorePercol... |
5b3f9243fdd61a5054f7e963fefa32c6f037c14f81f7e89b87287a93dabc7179 | Python | 2,884 | 81 | #!/usr/bin/env python3.7
"""Installs ASCENT.
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-duke/ascent
"""
import os
import subprocess... |
5b1fdf90997d75f2d9d24eade50cdbe4656575edabea6ed21fe87a79e3c3cb91 | Python | 2,885 | 104 | 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... |
8195bbbb05c914ed30b63df56be65a3fa6c469471c173fa9aa20a11a9804c2f4 | Python | 2,885 | 103 | # -*- coding: utf-8 -*-
from vtkplotlib._get_vtk import vtk
import numpy as np
from vtkplotlib.plots.BasePlot import Base2DPlot
class Text(Base2DPlot):
"""2D text at a fixed point on the window (independent of camera
position / orientation).
:param text_str: The text, converts to string if not one alre... |
736056767d59177c9366be2085f905f8d9933caa1dcd70cb8f6f8a66b4d9a14e | Python | 2,886 | 97 | import traceback
import os
from typing import Dict
import boto3
s3 = boto3.client("s3")
dynamo = boto3.client("dynamodb")
class Outputter:
def __init__(self, event: Dict, environ: Dict):
"""Responsible for moving outputs to persistent storage and updating
databases.
"""
self.... |
7f167611972a024a91eafaf5eefb650f5a3a8b8a7cd56ca54cc2b9e63d1b9a37 | Python | 2,886 | 102 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
For the selected k, gather the idiosyncrasies across all subjects across runs
and within run.
Output:
meansim.csv Contains idiosyncrasies for all subjects, across runs.
crun+'_meansim.csv' Contains idiosyncrasies for all subjects, within run.
Usage:
LE_group_stat... |
78c5e91f3ac78e4bc9d399ba367b75f03ecb86da2c484f29d6057702ae125bf0 | Python | 2,887 | 71 | ####################################################################################################
# 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... |
888301e0d2f6d5624a8f409de970c4094ad5b9e6d8591d4a186a20852c1248f3 | Python | 2,887 | 99 | import nibabel as nib
import numpy as np
from sklearn.mixture import GaussianMixture
def assign_gaussian(data, n_components, covariance_type,
init_params='kmeans',
n_init=50,
):
classifier = GaussianMixture(
n_components=n_components,
covariance_type=covariance_type,
init_params=init_params,
n_init=n_init,... |
03ebe50e5bb87deb62e52fc00d5358a7ada8b4e101046f80601c846693818dd1 | Python | 2,890 | 88 |
"""
Polish manually edited MRI white & gray matter segmentations.
"""
import os
import nibabel as nb
import numpy as np
from scipy.ndimage import morphology, generate_binary_structure
from scipy.ndimage import gaussian_filter
# Set data path
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti/... |
1320ac81fc5bca61b675306a97d538508f7374abc29bf96aaea44f8a33f69d8f | Python | 2,890 | 95 | # 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... |
14a202305dc1d122a34979754d2e62933e07899e058e06f99d0ae47cc961910f | Python | 2,890 | 84 | #!/usr/bin/env python
#usage:python RNAelem_fa2fq.py <positive.fa> <negative.fa>
import sys,numpy as np,itertools,random,re
from scipy.stats import fisher_exact as fish
fanameP=sys.argv[1]
fanameN=sys.argv[2] if 2<len(sys.argv) else None
kmin,kmax,thresh,rich,poor,base=3,10,5e-2,[],[],10
def gen_kmer(k):
for kmer i... |
54698702d1631ee1c1fa35846d71d6e186440ec93b40eb34caf87818f6be0f93 | Python | 2,890 | 67 | from __future__ import annotations
import argparse
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Python port of the MetaboLiteLearner MATLAB workflow.")
subparsers = parser.add_subparsers(dest="command", required=True)
convert_parser = subparsers.add_parser... |
282f238c7c7679cf2e1429204e7c68df41a73ede8bf9515d2cf31930c40ec68f | Python | 2,891 | 80 | # -*- 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.
#... |
34bef20cf29291f483aa4e8eee9a8fabf098386a06613f24a804f89e8ae48941 | Python | 2,892 | 72 | #!/usr/bin/env python3
import argparse
import nbformat
def convert_notebook(input_path: str, output_path: str) -> None:
"""
Read a Jupyter notebook and write a Python script, converting all %%bash
cells and IPython "!" commands into subprocess.run calls that raise on error.
Cells that load or autorelo... |
ad614e8e3c03be15e8a21352eb4fc4c8fcaf6ca59367b2411701706f57f2e0d9 | Python | 2,892 | 73 | import numpy as np
import torch
from ..distances import LpDistance
from ..utils import common_functions as c_f
from ..utils import loss_and_miner_utils as lmu
from ..miners.base_miner import BaseTupleMiner
class AngularMiner(BaseTupleMiner):
"""
Returns triplets that form an angle greater than some threshold... |
3b1d35ff397e564113bd4249d490acbcccfe8b1233579482ecb7df10a976aeef | Python | 2,893 | 66 | import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
annotations = ["chess", "gencode", "refseq_ucsc"]
colors = ['blue', 'red', 'green', 'cyan', 'magenta', 'olive', 'black', 'gray', 'orange', 'purple']
os.makedirs("intron_s_p_diff", exist_ok=True)
ind = np.array(list(range(10)))
width = 0... |
43b4591a7c81ea82085baa5c49705118c4a1f72db41424fcdee39f62cdfb6688 | Python | 2,893 | 99 | from typing import Optional
import jax
import jax.numpy as jnp
import jax.random as jr
from .status import SamplerStatus, Samples
from ..state import State
from ..symmetry import Symmetry
from ..utils import ints_to_array, rand_spins
from ..global_defs import get_subkeys
class Sampler:
"""Abstract class for samp... |
0be3c58ce3bc43cfb182bae13983cec1d9fdf11764837c2491e51db997d02bae | Python | 2,894 | 55 | import tensorflow as tf
from mol_graph import max_nb
from utils.nn import *
def rcnn_wl_last(graph_inputs, hidden_size, depth, training=True):
input_atom, input_bond, atom_graph, bond_graph, num_nbs = graph_inputs
atom_features = tf.nn.relu(linearND(input_atom, hidden_size, "atom_embedding", init_bias=None))
... |
2e44f0d0ac999272ce8b87c716871db25d18a8909c33f299104b3c29c9b7a075 | Python | 2,894 | 65 | import scanpy as sc
import pandas as pd
import flowsig as fs
adata = sc.read('burkhardt21_merged.h5ad')
condition_key = 'Condition'
# We construct 10 gene expression modules using the raw cell count.
fs.construct_gems_using_pyliger(adata,
n_gems = 10,
la... |
ac5503861379417dd73140a6fcf12b4932731c12dc65d62eedd8bc35d452ad4f | Python | 2,895 | 106 | # Copyright 2026 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
05dece3e9c73cc65018325fb96452678210244c53d5a0b9dc43aafd363560df6 | Python | 2,898 | 66 | # compare SOTA algorithms
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifie... |
0e0fb595956d4b481e2581fcc8e7f68b76f26662a62111b9c4408c9285b9c189 | Python | 2,899 | 81 | # -*- 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.
#... |
9472a417b507dcf99019d0db2af78202a60e8dcd61305c1edb8258edb78ad187 | Python | 2,899 | 105 | """Configuration management for the bioinformatics data collector."""
import os
from pathlib import Path
from typing import Optional
from pydantic import Field, field_validator
from pydantic_settings import BaseSettings
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
class APIConfig(BaseSe... |
a5fca0a152088cefa357e09379c5bec87397b1f1afb74b2140194bc50c63b795 | Python | 2,899 | 88 | # parse_statics_log.py
# Copyright (c) 2010 Jes Frellsen
#
# This file is part of Muninn.
#
# Muninn is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# Muninn is distributed in the hope that it will... |
c2d5c6514b73cac07f8b9c004213485c07582d5d42d3c9549f6bd4e31f2e9e83 | Python | 2,901 | 88 | """
Append-only JSONL session history for OmicVerse Agent (P2-2).
Records each ``run()`` call (request, generated code, result summary,
token usage) so that future turns can inject prior context into the
system prompt.
Inspired by Codex ``message_history.rs``.
"""
from __future__ import annotations
import json
impo... |
d2be586e51eb53f289bbe8335afbe34de7e1a4add740bef8907134c8fc2e2ce1 | Python | 2,901 | 117 | import inspect
import logging
import sys
import numpy as np
from ray._private.ray_microbenchmark_helpers import timeit
from ray.util.client.ray_client_helpers import ray_start_client_server
def benchmark_get_calls(ray, results):
value = ray.put(0)
def get_small():
ray.get(value)
results += tim... |
8720f13c3305ffcfb4b8d9784337005a31362c510c232a54b168582f513e9131 | Python | 2,902 | 90 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
When joining VCFs after genotyping, some batches will have different maximum
copy states at multiallelic sites. This script ensures that the maximum
allele count is chosen so all genotype indices are present in the record alts.
"""
import argparse
import pysam
def... |
83a7cc27d8f232af0092e7523895ef6829fd8cd46841606712293caaf61121c8 | Python | 2,903 | 97 | import sys
import pandas as pd
import numpy as np
import pickle
from rdkit import Chem
from mordred import Calculator, descriptors
from tensorflow.keras import models
Factor = float(1/1000.0)
def calc_descriptors(mols, name):
len1 = len(mols)
features = []
for i in range(len1):
des1 = calc.pandas([mols[i]]... |
62f39cbac682bcc4067b070fc6dde04a5a0057918c3d0b5fd6858fa751578d9f | Python | 2,904 | 78 | # Copyright 2026 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
ac0f1bb04626ca6c54dacf8cb6669c9a48ed498856896db155eb6c352736bf9f | Python | 2,904 | 111 | """
Module for simplifying Docker image building, tagging, and pushing.
Our Dockerfile expects a ``VERSION`` build-arg, and by convention we tag our
Docker images using the output of ``git describe``. Running
::
$ python _docker.py build
builds both the normal and slim Docker images, making sure that the correct... |
fe73248d2cc430e21405b5ccfc9018835940893943b28e253ad919d4cbf07a55 | Python | 2,906 | 99 | import pdb
import numpy as np
from tqdm import tqdm
import torch
import matplotlib.pyplot as plt
import yaml
from latent_time_stepping.datasets.AE_dataset import AEDataset
from latent_time_stepping.oracle import ObjectStorageClientWrapper
from latent_time_stepping.utils import (
load_trained_AE_model,
load_tr... |
f43d34ac332117c569e68bdb36f349529362f4f75696bf790656217815d73050 | Python | 2,907 | 82 | import numpy as np
import os
import logging
logger = logging.getLogger('telemanom')
class Channel:
def __init__(self, config, chan_id):
"""
Load and reshape channel values (predicted and actual).
Args:
config (obj): Config object containing parameters for processing
... |
1ca030d66e29d50edcc9742441ece0840f084b694867607e4b30d440b559f27f | Python | 2,908 | 61 | #!/usr/bin/env python
# coding: utf-8
# author: Maruthi Mutnuri (maruthi2008@gmail.com)
# loading libraries
import pandas as pd
import numpy as np
# loading database files
adm = pd.read_pickle('/project/M-ABeICU176709/ABeICU/data/ADMISSIONS.pickle', compression = 'zip')
pat = pd.read_pickle('/project/M-ABeICU176709/... |
2ecebe15c71733c80fe9eac7688e74fe156675ed350a6d60cc73167269977c57 | Python | 2,908 | 116 | '''fitting two periodic functions using an crp-rnn,
so this is a context dependent task
this is the basis of a bunch of learning hierachical seqs with crp-rnn
'''
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import seaborn as sns
from utils import to_np, to_pth
from model import... |
8f3cb342ba947ff0d31c7c588e0fc4824e99b3076d9d34d27de2d0d013b66763 | Python | 2,908 | 91 | # flake8: noqa
"""
This file serves as a documentation example and CI test for bundle_per_worker placement group config.
Structure:
1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
2. Docs example (between __bundle_per_worker_example_start/end__): Embedded in... |
9c30cb0d497fed08c6e58a42f5a4206ee5c010df2e6e2093074899a172e47d95 | Python | 2,908 | 82 | """SAMPLING ONLY."""
import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
... |
b54ef3bba112c9cf442cfed4bfb45c46e82661b1cbb92787b01fd665ecf7bee2 | Python | 2,910 | 109 | import ray
# __max_replicas_per_node_start__
from ray import serve
@serve.deployment(num_replicas=6, max_replicas_per_node=2, ray_actor_options={"num_cpus": 0.1})
class MyDeployment:
def __call__(self, request):
return "Hello!"
app = MyDeployment.bind()
# __max_replicas_per_node_end__
# __placement_gr... |
12b0a9369c1950c90262bbf41593064f374c69c6cde21756c0d03e376c0639af | Python | 2,911 | 72 | import numpy as np
import torch
from ..distances import LpDistance
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
class AngularLoss(BaseMetricLossFunction):
"""
Implementation of https://arxiv.o... |
8a34790424ac9df6478dda09f254de21267e217b5ada7a2d9da8bcfc5766d7f8 | Python | 2,912 | 83 | import numpy as np
import pandas as pd
import pytest
from data_profiling import ProfileReport
from data_profiling.report.presentation.core import CorrelationTable, Image
from data_profiling.report.structure.correlations import get_correlation_items
@pytest.mark.skip
def generate_df(n: int):
return pd.DataFrame(
... |
810fbb75b4bf268bafaf13b1e088acdf6ae5d4c3bd85595985a436376c3a19cb | Python | 2,913 | 86 | import bpy
import bmesh
import nmv.mesh
import nmv.bmeshi
####################################################################################################
# @create_bmesh_copy_from_mesh_object
####################################################################################################
def create_bmesh_... |
0d4ee2acf4bf48c5551a293f082236fd413c3f73f62c3c8e77236e6f4cc15e0c | Python | 2,914 | 85 | # In R:
# load("your_dir/tutorial.gbm.rdata")
# write.csv(bk.dat, file = "./data/bk_dat.csv")
# write.csv(sc.dat, file = "./data/sc_dat.csv")
# write(cell.type.labels, file = "./data/cell_type_labels.csv")
# write(cell.state.labels, file = "./data/cell_state_labels.csv")
import pandas as pd
import numpy as np
import... |
ad96b03391574de21fa943f754da3fdc7d2c5c847dfb587b0a2394a5e9a8862b | Python | 2,914 | 86 | '''
Defining ROI masks. This is done in several steps:
- Select voxels from activation map based on threshold. Here, we chose half the
maximum z-score as cut-off
- Propagate active voxels across cortical depth using LayNii's UVD filter
- Remove floating bits that might occur in the process
'''
import numpy as np
imp... |
3259eeb63833e3d302d863d05b6efe53fe345461e9aa2b9a43ef2446dd5b8b7e | Python | 2,915 | 92 | '''
compare 1) len(X), 2) human boundaries data and 3) event labels
the time data of len(X) and humam boundaries need to match
- if range(human boundaries) > range(X), hb need to be removed
- if range(human boundaries) < range(X),
'''
import os
import pickle
import numpy as np
import pandas as pd
import seaborn as s... |
54d363685e8806cf9f2ee91bf9aa9d7cd0ce3ef702dc724499a9722090797874 | Python | 2,915 | 85 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 30 09:01:00 2025
@author: ju-bar
Functions handling input of structure data
via the Vesta file format.
The Vesta file format is a text format defining a volume in space
which is filled by atoms. There is additional drawing information
for the Vesta program in the file, w... |
cd4b0e2bee054535f560a1799ddf87c66c899f78732e7d26a55d35a5bb58649b | Python | 2,915 | 65 | """Versioned Mothra model runtime and explicit historical compatibility mode."""
import argparse
import json
import platform
import importlib.metadata
import keras
from .activations import legacy_tanh, legacy_sigmoid
from .optimizers import DeduplicatingAdam
@keras.saving.register_keras_serializable(package="Mothra")
... |
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