sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
77cf99ad5d25af532340934ed305ce91d0609b2fcb4fa19f8c42d378b84eedd6 | Python | 36,571 | 994 | """
Copyright (c) Meta, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
Adapted from:
https://github.com/FAIR-Chem/fairchem/tree/main/src/fairchem/core/models/scn
"""
import logging
import time
from typing import List, ... |
dcd98be5dfe17b248c6e8f598fbfbed003b72af44610d030fb0c0a00fefa6262 | Python | 36,602 | 870 | import pandas as pd
import numpy as np
import wfdb
import ast
import torch
import torch.nn as nn
import torch, queue
from torch.utils.data.sampler import SequentialSampler, RandomSampler, BatchSampler
import numpy as np
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
#from .core import *
import ... |
523db07b069293686fbe0165bc5ae0d974b440d4f668533f7a139e4dcfbcf98d | Python | 36,627 | 924 | from __future__ import annotations
from typing import Literal
from pydantic import BaseModel, Field
class LandmarkPoint(BaseModel):
x: float
y: float
z: float
class ClippingConfig(BaseModel):
# leave as None to get the usual clip for the target (cranial for cranium,
# facial for face). "manual... |
245bc6e7719b57c02273bb99ea728c7680341a62dbb375638b7ceac439330ba7 | Python | 36,633 | 1,169 | ##!/bin/env python
'''farm.py - process data stream on cluster
===========================================
Purpose
-------
This script reads data from stdin and splits it into independent
chunks to be executed on a cluster.
Usage
-----
As a basic, but not very useful example, the following command will
take the in... |
ff7ff623a22c2c9fe9caaa99e482c027dfddc6af07111dfc1478ac01c4a4f6c5 | Python | 36,741 | 748 | """Methods for searching for amplicons and guides within them.
This combines primer_search, to search for amplicons bound by primers,
and guide_search, to search for guides within those amplicons. We
term the combination of these a "target".
These methods score possible targets and search for the top N of them.
"""
... |
b94a11387a977b34e42b51658b7e10dc66005e8bc4645f37b68441d2c09a5d72 | Python | 36,895 | 838 | """
This file contains functions used to copy and compress data in order to prepare it for the upload to Harvard Dataverse.
It also contains functions to decompress and re-arrange the data to a consistent folder structure after downloading it from Harvard Dataverse.
Author: jonas.braun@epfl.ch
"""
import os
import shut... |
f06d29b3e08ea705abcfb56813c486bc3f0ff2c3ff65bbc6695b831bdb6ebcd7 | Python | 36,917 | 1,142 | # VIN-HMI EEG dataset
# each (action/intention) sample/run belongs to 1 scenario and has 3 or 4 event types
#
# events = {
# "Resting",
# "Thinking" (eye closed),
# "Thinking and Acting",
# "Typing",
# }
#
# action run:
# scenarios = {
# "nâng tay trái",
# "nâng tay phải",
# ... |
126026b86ed0a1d06f5945bcd06798ce1608381ee61efb18083329c8793a07b9 | Python | 36,954 | 916 | #!/usr/bin/env python3
# pylint: disable=line-too-long
"""
Various functions for annotating genomes.
"""
# System imports
import pickle
import shutil
import os
import tempfile
import logging
import subprocess
import multiprocessing as mp
from os import path, close, mkdir, listdir
from dataclasses import dataclass
from... |
fbc549abbb6361d4d6245dba68abcc3c987dba0a4f9102c5604782842ce7286e | Python | 36,976 | 667 | """
This script contains the classess of the layers that we have implemented for our model
"""
import tensorflow as tf
class AttGraphConvLayer(tf.keras.layers.Layer):
def __init__(self, outDim,activation=None,alphaLReLU=0.2,dropAtt=None, **kwargs):
# constructor, which just calls super constructor
... |
5fff4ef3a73ed4dba2115cc537158dfca244e499cc5697a15ba5a3c543918b93 | Python | 37,018 | 900 | """PII detection and handling middleware for agents."""
from __future__ import annotations
from functools import partial
from typing import TYPE_CHECKING, Any, ClassVar, Literal
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage
from langgraph.stream import StreamTransformer
from t... |
4d8ad86f597366a47d8513368342b4bf8e94d49cfd725d33da19172a8b16c49d | Python | 37,114 | 874 | # based on https://github.com/atomicarchitects/equiformer_v2/blob/main/nets/equiformer_v2/equiformer_v2_oc20.py
import logging
import time
import math
import numpy as np
import torch
import torch.nn as nn
from torch_geometric.data import Data, Batch
import torch_scatter
from pyexpat.model import XML_CQUANT_OPT
from .... |
bd8ca7927d785a5b70a0a7a27d1108e65e5ad7c2dc828010cd729c386ebdc8a8 | Python | 37,129 | 897 | import time
import torch.nn.functional as F
from .modules import *
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius_graph
from e3nn import o3
from torch_scatter import scatter
from e3nn.nn import FullyConnectedNet, Gate, Activation
from e3nn.o3 import... |
e7aaf98ecb4c1a7d930c574fe8a3b7053a9553628846ccc219761ba922209efe | Python | 37,202 | 831 | """ This module provides different types of training algorithms for RBMs
running on CPU. The structure is kept modular to simplify the
understanding of the code and the mathematics. In addition the modularity
helps to create other kind of training algorithms by inheritance.
:Implemented:
- CD ... |
dfa55e97b4c45f2a670395782fc652698561318d25567b450ad3128b4a4124e7 | Python | 37,212 | 1,031 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
ddd8b059cdfdbb5bd31f125924ffb776a81aeff5273466f67d4b3b5c16e0bb3e | Python | 37,226 | 1,292 | from pathlib import Path
import math
from rich.console import Console
from rich.table import Table
from rich.pretty import Pretty
import numpy as np
import pandas as pd
import cv2
from sklearn.cluster import MeanShift
from skimage.transform import hough_circle, hough_circle_peaks
import torch
from torch.utils.d... |
d10a3ae4bffa61a7912750c56cf600ff022ec2a1f1a24de2b830264ee26c9e1b | Python | 37,259 | 899 | # fix the issue that invariant feature in Convnet should come from src and dst nodes.
# https://github.com/divelab/AIRS/issues/23
import time
import torch.nn.functional as F
from .modules import *
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius_graph... |
b243cebfe2591c2bf03ae5f89fe055fd2cea3d490c6fe0756cf418021369a5d3 | Python | 37,277 | 1,034 | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 28 23:57:02 2020
@author: Xingyan Liu
"""
from typing import Union, Sequence, Dict, Optional, Tuple
import logging
import numpy as np
import pandas as pd
import scanpy as sc
from scipy import sparse
import torch as th
import dgl
from ..utils import preprocess as pp
from... |
67131f5ef0b65d9a621c3d8cfaa1bd1217f93a2d18c0ffac2ce1830d8737860d | Python | 37,325 | 1,034 | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 28 23:57:02 2020
@author: Xingyan Liu
"""
from typing import Union, Sequence, Dict, Optional, Tuple
import logging
import numpy as np
import pandas as pd
import scanpy as sc
from scipy import sparse
import torch as th
import dgl
from ..utils import preprocess as pp
from... |
238f7efceffe8069ce7ebbe6fb9b7ba98a89fe2ee015d62fd701f03a913364f5 | Python | 37,327 | 1,346 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 17 15:40:11 2022
@author: mariano
"""
import sys, os
import cooler
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import scipy as scy
import time
import pdb
import esprenv as env
from numba imp... |
1989186f94ac3f002c26a587378714335a5e600e5771efe5b0706d7808744d43 | Python | 37,335 | 719 | """
return hypergraph and subgraph
DOSE: C2, CELL C3
GeneSet Hypergraph include
1. c2cp pathway gene sets
2. c3 tf-targeting genes
3. tissue-specific TFs
GeneSet Subgraph include
1. tsp: tissue-specific expressed genes
2. es: essential genes
3. ef: effective genes (gene effect)
4. grn: cell line-specific targeting ... |
c87e61cefaeb5e72978c747c1b7cf6cec4faf1ebe26ebf97b7125539260ba56a | Python | 37,354 | 1,034 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
83beeb462e4106cb5132d23c6d5e74bf441b34adc341ed82645ccdeb34b0b685 | Python | 37,360 | 1,034 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
ef31cf40ad7519d2cb52a53a5bb56a61c31aaacf445f30744594a666d234fd16 | Python | 37,384 | 978 | from __future__ import annotations
from collections.abc import Hashable, Iterable
from typing import Any
import networkx as nx
import numpy as np
import pandas as pd
from scipy.stats import multivariate_normal
from pgmpy import logger
from pgmpy.base import DAG
from pgmpy.factors.continuous import LinearGaussianCPD
... |
202fff59d427e591bb8209478af7b6bf9dbbb9673d5da1834d2f20f668b1eb34 | Python | 37,407 | 917 | # -*- coding: utf-8 -*-
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~IMPORTS~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# Standard library imports
from collections import OrderedDict, namedtuple, Counter
import hashlib
# Third party imports
from tqdm import tqdm
import numpy as np
import pandas as pd
import jinja2
from pyfaidx imp... |
10d692bbd4cfccd82ad514649682f79306b9cd730235c3838bc3d1a96a455dea | Python | 37,441 | 1,229 | """Base callback handler for LangChain."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from collections.abc import Sequence
from uuid import UUID
from langchain_protocol.protocol import MessagesData
from tenacity import RetryCallState
... |
0d40f04e0e6f5df5f304933ab44c230952b8924104244ee595cafaa0277a4edc | Python | 37,445 | 988 | import json
from collections import namedtuple
from datetime import datetime
from pathlib import Path
from zipfile import ZipFile
import h5py
import numpy as np
from scipy.signal import convolve2d
from torch.utils.data import Dataset
from ..utils import no_transforms, recursively_load_dict_contents_from_group
from .e... |
a06e9584b1220d545f6d0ae5cad68bedaf2d057fd8e69de54513dff4fa38e8a7 | Python | 37,488 | 1,131 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
from torch.nn.modules.loss import _WeightedLoss
from torch.utils.tensorboard import SummaryWriter
from sklearn.model_selection import train_test_split
from sci... |
a011f6ca0424465ee944539e6b4d85a751095d554f027036997aab52382a0c72 | Python | 37,497 | 923 | """Main training entry point for pre-training and downstream fine-tuning.
"""
import json
import os
import random
import time
from functools import wraps
from typing import Callable, List, Sequence
import fsspec
import hydra
import numpy as np
import pytorch_lightning as pl
import torch
import wandb
from omegaconf i... |
4bbabc86e0637743aef50ca2ea1aeff49291d088dc77734aa4a5ed2bbe52d929 | Python | 37,499 | 1,042 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
1448dce265d4a7fc30ea43bdfc10ca657c36cbaf4134a4b067211ce5f302af62 | Python | 37,664 | 905 | '''Fairly basic set of tools for real-time data augmentation on image data.
Can easily be extended to include new transformations,
new process methods, etc...
'''
#Code from https://github.com/oeway/keras/blob/extendImageDataGenerator/keras/preprocessing/image.py
#wfrom __future__ import absolute_import
from __future... |
e078f72a767ac050694ff3fe596efcd9ffd020910625e61bcc89d7e90cb3594a | Python | 37,686 | 793 | # Code from https://github.com/titu1994/DenseNet
'''DenseNet models for Keras.
# Reference
- [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993.pdf)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/pdf/1611.09326.pdf)
'''
from __futu... |
8f55c3c305e7a909366f6958d6890a8811afeef234a461d7d4e836ab3ee52511 | Python | 37,789 | 1,010 | import numpy
from typing import Union
import pandas
import hummuspy.config
import hummuspy.explore_network
import hummuspy
def format_multilayer(
TF_layer: Union[list, pandas.DataFrame],
ATAC_layer: Union[list, pandas.DataFrame],
RNA_layer: Union[list, pandas.DataFrame],
TF_ATAC_bipartite: pandas.Data... |
9506295684bb0c72553a80b9018b9d722ebdd80ff79f293bd60ffbd8b9f9b0cb | Python | 37,886 | 908 | '''Fairly basic set of tools for real-time data augmentation on image data.
Can easily be extended to include new transformations,
new process methods, etc...
'''
#Code from https://github.com/oeway/keras/blob/extendImageDataGenerator/keras/preprocessing/image.py
# Used for Random Crop function currently
#wfrom __fut... |
6289a6aa0544521ed478a9d936434927799f3f720f69bc4a980559864c394e29 | Python | 37,906 | 1,001 | import argparse
import torch
from torch import nn
import numpy as np
from data import get_dataset
import pandas as pd
import pickle as pk
from pymatgen.io.jarvis import JarvisAtomsAdaptor
from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Structure
from jarvis.core.atoms import Atoms
from tor... |
d78702f022da3c9a48a0bf361b94f2ff5e913e93b13ba694a2d62286eecb9fdf | Python | 37,929 | 1,122 | """
This module is copied over from scikit-learn with some tweaks to
prevent centering datasets.
The :mod:`sklearn.pls` module implements Partial Least Squares (PLS).
"""
# Author: Edouard Duchesnay <edouard.duchesnay@cea.fr>
# License: BSD 3 clause
import numbers
import warnings
from abc import ABCMeta, abstractme... |
1d02c176c0e8e311ca2df62321f406a6f3316c30f31cec19c8c20157f395ce05 | Python | 37,954 | 1,006 | import copy
import warnings
from collections.abc import Mapping, Sequence
from dataclasses import dataclass
from itertools import chain
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
NamedTuple,
Optional,
Tuple,
Union,
)
import numpy as np
import torch
from torch import T... |
b1239ad46f5167c31828a5820d11b9e1af6d41489de1d24d19af67ab2f248d67 | Python | 38,100 | 1,054 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
2b771068eda19b6f783e12ff483a02ab6ef8264108f3af5e486d3381fb1e7fb6 | Python | 38,178 | 914 | import math
from argparse import Namespace
from typing import Optional
from time import time
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import wandb
from torch_scatter import scatter_add, scatter_mean
f... |
58b688d909ce03ccd6be0f7219cccb05d30b4909589eac4015eacbf469ed2f45 | Python | 38,232 | 714 | '''
--------------------------------
MODULUS MOTION TRACKING
--------------------------------
History:
Date Programmer Description
---------- ---------- ----------------------------
29/06/2023 S MU v1.0.0
'''
_version='1.0.0'
import os
import sys
sys.path.inse... |
c8f0c7ba303a8b83db90c789715dff7c0ce92047088e79bcaf86f632a4cbb78f | Python | 38,269 | 1,059 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
bb850a417a6ee434dac80d40a3aaab398336aeb78a816cec32d4843b4606c219 | Python | 38,297 | 1,066 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
c4bb51331d4e21a6db815cf869de93aaa2a530b577f95391296d25aef463e503 | Python | 38,335 | 843 | """Methods for analyzing coverage of a design against a collection of sequences.
"""
from collections import defaultdict
import logging
import math
import numpy as np
from easyDesign.utils import guide
logger = logging.getLogger(__name__)
class Design:
"""Immutable object representing a design.
This can ... |
13d257fb42e5bca02db9a8ae72c392d94d54b027c0d61454461e57e70c031232 | Python | 38,336 | 1,191 | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... |
e9145d7a586607e20af455b5053be0fae6f66564c0a710b89b075525465ec3dd | Python | 38,388 | 1,209 | """
module for behavioural processing with functions for behaviour classification and behaviour onset detection.
Author: jonas.braun@epfl.ch
"""
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import pickle
from scipy.ndimage import gaussian_filter1d, gaussia... |
9aa70e05a095339603a12681309525bd01a48451532b948b092d9813a1e62c8f | Python | 38,438 | 1,061 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
0174e276f6d33ef4bfbcf1e019f0a829299e16e6fc431a4325bcc3eedcffe170 | Python | 38,613 | 1,214 | import os
import numpy as np
import pandas as pd
from hbayesdm.base import PATH_COMMON
def alt_preprocess_func(self, raw_data, general_info, additional_args):
# Iterate through grouped_data
subj_group = iter(general_info['grouped_data'])
# Use general_info(s) about raw_data
# subjs = general_info['... |
5851b1c3b5a84925a0d2b170453b06e4a8ecd2dc8fa31ced27577abfb0804782 | Python | 38,774 | 945 | import os.path
import sys
sys.path.append('../')
import came_origin as came
from came_origin import pipeline, pp, pl
import warnings
warnings.filterwarnings("ignore")
import logging
import os
#import sys
#sys.path.append('../')
from pathlib import Path
import numpy as np
import pandas as pd
from matplotlib import pypl... |
752b331c12fc175ba24dfe0e747caba277b796a707f75e6083ca7aa625b457e2 | Python | 38,791 | 1,032 | """ Test suite for pymaid.
For testing we are by default using the publicly hosted CATMAID instance of
Virtual Fly Brain (https://fafb.catmaid.virtualflybrain.org/).
You can switch the CATMAID instance and the neurons, volumes and annotations
used for testing by setting environment variables. For example:
PYMAID_TES... |
ecd98f241ebddbb026ab525270d82b050bded3ff3dc6547e6b029df1507ef80a | Python | 38,812 | 1,212 | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... |
8d4dfd172cfd877f53765f2f64ef211487cc605488eb90d8d1f88a0a3159b487 | Python | 38,846 | 875 | '''
Survival topic models experiment script
Authors: Lexie Li, George H. Chen
'''
import gc
import itertools
import sys, os, argparse, json, pickle
os.environ['OPENBLAS_MAIN_FREE'] = '1'
import numpy as np
import torch
from lifelines.utils import concordance_index
from progressbar import ProgressBar
from pycox.evaluat... |
9dfc2b5279bf1183ec81a9ecbf31317b6eb55648cf10f08e7ba03498781b4e00 | Python | 38,889 | 1,078 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
ef40c35102a51a664262949ee08a4d725973295e41f9761f20fa003e1ca78812 | Python | 38,907 | 947 | import os.path
import sys
sys.path.append('../')
import came
from came import pipeline, pp, pl
import warnings
warnings.filterwarnings("ignore")
import logging
import os
#import sys
#sys.path.append('../')
from pathlib import Path
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt # optional
... |
85566ef74a41d794ecd6a808adcd6123ced13e2c7d14c079ace2016190994f4f | Python | 39,011 | 726 | import torch
from torch.nn import functional as F
import numpy as np
import matplotlib.pyplot as plt
import psutil
from KineticAssembly_AD import VecSim
from KineticAssembly_AD import VectorizedRxnNet
from torch.optim.lr_scheduler import StepLR
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.optim.lr_... |
f007c9a4969ca481706871c9d7426b8ded49b1c004955ae7e433bce93cc13870 | Python | 39,019 | 1,072 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
75c53989b58e70e171723c293203895fee119ffd4323bb6763baed2d3fc691dc | Python | 39,078 | 1,042 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
26105248fcb2dcd846632d4f3815daf661120ccbcb4d2ffbf20b5ef4ba1537f0 | Python | 39,083 | 726 | import torch
from torch.nn import functional as F
import numpy as np
import matplotlib.pyplot as plt
import psutil
from KineticAssembly_AD import VecSim
from KineticAssembly_AD import VectorizedRxnNet
from torch.optim.lr_scheduler import StepLR
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.optim.lr_... |
5a53cc963c9de60c259f29375d96871b46a8a173e0450d22ba04cd4e7382facd | Python | 39,088 | 1,074 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
cd68c8d203fc2d6d7a4e70fd4dd668e5e030c2109663e1adca3497d43b4e9a84 | Python | 39,113 | 1,074 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
6114e16c9b74686e441bffd2576637fa8efb27cae2ca8d30fd97958636a3f2e1 | Python | 39,150 | 1,074 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
6ec181dca45ee7449040150b574e46931cd402930ecc106db28a79fabccc4a16 | Python | 39,170 | 570 | import torch
import numpy as np
num2aa=[
'ALA','ARG','ASN','ASP','CYS',
'GLN','GLU','GLY','HIS','ILE',
'LEU','LYS','MET','PHE','PRO',
'SER','THR','TRP','TYR','VAL',
'UNK','MAS',
]
aa2num= {x:i for i,x in enumerate(num2aa)}
# full sc atom representation (Nx14)
aa2long=[
(" N "," CA "," C ... |
c8e62ee279272d11437f0b9f0da8c8d30fd132984005b9f27c4feeca05bfee61 | Python | 39,240 | 1,080 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
4f6b25112cf4155c8df4f057b095a1924ca384363e0c7c1a41fb5983f41ed122 | Python | 39,246 | 954 | """Module contains logic for indexing documents into vector stores."""
from __future__ import annotations
import hashlib
import json
import uuid
import warnings
from itertools import islice
from typing import (
TYPE_CHECKING,
Any,
Literal,
TypedDict,
TypeVar,
cast,
)
from langchain_core.docum... |
33fb48d7c62841b6c5bdaa654d310a160504bf8e92cf0ca2a524f4c0c75ea865 | Python | 39,265 | 948 | import pdb
import torch.nn.functional as F
from .modules import *
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius_graph
from e3nn import o3
from torch_scatter import scatter
from e3nn.nn import FullyConnectedNet, Gate, Activation
from e3nn.o3 import ... |
3f288673d96493a9bcdc1a4db1defb7a3820b1163fefe424d2cd1f2d30d7cc03 | Python | 39,355 | 920 | # TODO update all 2p stim related attr's to naparm submodule
import glob
import os
import time
from typing import Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from imagingplus import TwoPhotonImaging
from imagingplus.main.subcore import MicroscopeMetadata, CellAnnotations
from imagingp... |
f8593a80dc0b0e35edb04f7f315517265947808ba8d1ebcc0bf7a5fa5bfdc89a | Python | 39,356 | 1,068 | import argparse
import torch
from torch import nn
import numpy as np
from data import get_dataset
import pandas as pd
import pickle as pk
from pymatgen.io.jarvis import JarvisAtomsAdaptor
from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Structure
from jarvis.core.atoms import Atoms
from tor... |
1b6e2fe3e26cb00a9e565187eb49d344eae98a8a1693da36807edb1cf06173e1 | Python | 39,451 | 1,048 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
9fdf31302d50c7781c2dc0e9a0431e3e1b0a3ed030f6c7e54fa6e63a71e409e0 | Python | 39,459 | 1,062 | import warnings
import numpy as np
import torch
import matplotlib.pylab as plt
import ast
from os.path import join as opj
from PIL import Image
from imageio import imwrite
from matplotlib.colors import ListedColormap
from pandas import DataFrame, concat
from skimage.morphology import binary_dilation
from dataclasses im... |
c2f794d2ed6a61458339f049d167fc5a2edec758736c66344073788121f4f52a | Python | 39,474 | 952 | import time
import torch.nn.functional as F
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius_graph
from e3nn import o3
from torch_scatter import scatter
from e3nn.nn import FullyConnectedNet, Gate, Activation
from e3nn.o3 import Linear, TensorProduct,... |
979f445734c460eb8240ec80a53518c0cd4acf82cba81322dd526733daaf73fa | Python | 39,489 | 922 | import torch
torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import copy
import math
import re
import torch.distributions as dist
from collections import namedtuple
from transformers.modeling_outputs import MaskedLMOutput
from mamba_ssm.models.mixer_seq_... |
ef4f31dc9d397fca79330b6b881b81f0e204f758ddecfbee8893810afff8b386 | Python | 39,531 | 950 | import time
import torch.nn.functional as F
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius_graph
from e3nn import o3
from torch_scatter import scatter
from e3nn.nn import FullyConnectedNet, Gate, Activation
from e3nn.o3 import Linear, TensorProduct,... |
0dd2fd157d4d4696c96a824c9a8da8e17fe8e93784fb1a3d574e7cacf18566dd | Python | 39,532 | 1,047 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
1a24de5b08768e9719eadd961be71f3163c194ba36411e88a8db20ebc2d2e4c8 | Python | 39,532 | 1,047 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
2bdb7ef301715f0d45b531799207b3cf2f659d0c5f5d01b36ed3e68f91b54218 | Python | 39,541 | 1,047 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
f71215104a1e60f7448907d35c4bc0ddd130433f7d25f7ab66d7c08e3227ec82 | Python | 39,562 | 1,310 | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... |
a6590a03c7c4b557d44085515492fbb7618f58232621f5a489c18806fbac167a | Python | 39,645 | 585 | import torch
import numpy as np
num2aa=[
'ALA','ARG','ASN','ASP','CYS',
'GLN','GLU','GLY','HIS','ILE',
'LEU','LYS','MET','PHE','PRO',
'SER','THR','TRP','TYR','VAL',
'UNK','MAS',
]
# Mapping 3 letter AA to 1 letter AA (e.g. ALA to A)
one_letter = ["A", "R", "N", "D", "C", \
"Q", "E... |
371b781fa556a9ddd535eeb80e336962b81a8eec5c8a2949e74768984f0db3c9 | Python | 39,672 | 971 | #@title Huggingface Pretrained Wrapper
# for Huggingface integration, we use a wrapper class around the model
# to load weights
import json
import os
import subprocess
import transformers
from transformers import PreTrainedModel, AutoModelForCausalLM, PretrainedConfig
import re
import torch
import torch.nn as nn
import... |
812a8036ad32fe235bc93a8653ac010528d50015f6ba156d4c2215dc4eaac5e6 | Python | 39,707 | 899 | import torch
import numpy as np
from typing import Any
class Muscle(torch.nn.Module):
"""Base class for `Muscle` objects. If a effector contains several muscles, this object will contain all of
those in a vectorized format, meaning for any given effector there will always be only `one` muscle object,
regardless... |
72b22c21bf42d4dcec8aa94d34867015da1493aee5bdbe77316cf183cffd2849 | Python | 39,713 | 733 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
20cb733817920ad2d7af43dedc2a543f3dd42c8bf5c6b07fcbe45c7f6bf12fb0 | Python | 39,750 | 1,157 | # -*- coding: utf-8 -*-
"""Defines the basenetwork class."""
# Core imports
import abc
# Vulcan imports
from .layers import *
from .utils import set_tensor_device
from .metrics import Metrics
from ..plotters.visualization import display_record, get_save_path
# Generic imports
import pydash as pdash
from tqdm import ... |
8beb112174cb25ec2f6af320156edc10f11613b7ed0e6b05703868316de0b36e | Python | 39,938 | 1,097 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
dabcb1b29da06c387c47f8d1fdb18425896289cc2ceab048f715fc0cb0007f60 | Python | 40,018 | 840 | #!/usr/bin/env python
# coding: utf-8
# Copyright (c) venkatesh-sivaraman.
# Distributed under the terms of the Modified BSD License.
"""
Defines the main Emblaze visualization class, `emblaze.Viewer`.
"""
from ipywidgets import DOMWidget
from numpy.core.fromnumeric import sort
from traitlets import Integer, Unicode... |
e60874dedc4479e526d84d7178c9f671a16f1f95c769b8963709c89128a5f608 | Python | 40,021 | 1,312 | """
================================================
bamstats.py - Tasks for QC'ing Bam Files
================================================
:Author: Adam Cribbs
:Release: $Id$
:Date: |today|
:Tags: Python
Perfroming stats on a bam file is an important task in quality control
of `.bam` files. The majority of tools ... |
ed531d00ed88698652d4ede95f6a97a504304919c57904fd66a779c570c60e09 | Python | 40,024 | 843 | import argparse
import logging
import sys
from copy import deepcopy
sys.path.append('./') # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__)
import torch
from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general ... |
93e49cb99299d6b13bb9d17e62879354526066c29f7d672c49c4c2a158068b7b | Python | 40,028 | 784 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
a4dc8e01904ebb4dc22fead215acd94ddeed3d6459553a2692c345633040e9e0 | Python | 40,078 | 1,033 | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.autograd import Function
from bernn.dl.models.pytorch.utils.stochastic import GaussianSample
from bernn.dl.models.pytorch.utils.distributions import log_normal_standard, log_normal_diag, log_gaussian
from be... |
84abe206133fe6bc0ae8c42275d61d4eb1fc51ea24f0e6f1ac410b18c83aff36 | Python | 40,114 | 1,076 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
74468ece5135745bb6fa2fd4cfa06c2a9f495802b59c730863cd8839746e8cb6 | Python | 40,178 | 1,092 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
MultiplicationArc... |
7785af0ae6ff2fa441eeb7f9cbf6fe62d7e4c8495987ec89ca149a6e07d03771 | Python | 40,232 | 1,052 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
56fc4f6ec2b46937f7c2cd124456386f2c46f9012af841a2c46e58538f79e83e | Python | 40,275 | 775 | """tests for pipeline.py.
landmarks are always manual now (see pipeline.py for why automatic detection
got dropped). fast tests use a synthetic ellipsoid with hand-placed
landmarks, and/or template_xy_com.ply (already in the frame the clip
constants assume, since it's literally the mesh the app displays as the
registr... |
f9d2787635e557c138530e8612d589f402ec5a585498bf7f63276c51cd783528 | Python | 40,313 | 1,139 | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 20 20:11:08 2021
@author: Xingyan Liu
"""
import os
from pathlib import Path
from typing import Sequence, Union, Mapping, List, Optional # , Callable
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import scanpy as sc
fr... |
693e760284093682e99a4f9136d69e74619c6b27eb53994b84f5c7e342287f01 | Python | 40,336 | 743 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
53abf1bf007ba776fd91ab0a13084e86aeaf3e6e0b0ee2c0c0496abb2ded75de | Python | 40,472 | 1,100 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
MultiplicationArc... |
7e62f5d0b31d18b31125dd4065f2f20e6fac3c1ba3f8e6386ae00fdcb3447740 | Python | 40,568 | 819 | # Author: Sarthak Jain (Supervisor: Professor Sandra Safo)
import numpy as np
import numpy
import matplotlib.pyplot as plt
import torch
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report
import skfda
from skfda import FDataGrid
fr... |
9e6455af058aca0c7b75006f24f61d173e1de137b62c6f50f10fc48612f0c045 | Python | 40,570 | 1,042 | import tensorflow as tf
import numpy as np
from utils.ProcessData import get_maxDims, gen_smiles2graph
from sklearn.utils import gen_batches
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
def perform_validation(model,validation,val_metric,batchsize,nfeat):
"""
Test the model o... |
02927bd701816775afdfabbf6272cedb68d73c79cd04cb2085417f805f397d5a | Python | 40,614 | 1,090 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
3b40b00fb9135d431bba53abcf5c54d60668b189863d6cefa5c1eb35dd8b2823 | Python | 40,627 | 1,119 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
b35505610a2fbbcbb7764b571027a61f786ced63c605dd7db042a31f31f79c45 | Python | 40,628 | 1,119 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
778318d97cc923745d0f71bc4be58550c2619782b12444ddac115073d3b91c60 | Python | 40,739 | 1,327 | '''windows - Tasks for window based read distribution analysis
======================================================================
Requirements:
* bedtools >= 2.21.0
* picardtools >= 1.106
* samtools >= 1.1
* MEDIPS >= 1.15.0
Reference
---------
'''
import os
import re
import collections
import pandas
import ma... |
f4e91f6860ad9f518e41bfae903dce8cfad3f19ee678a989cc6deda2451ff26e | Python | 40,763 | 1,068 | from __future__ import annotations
import warnings
from collections.abc import AsyncIterator, Callable, Iterator, Sequence
from importlib import util
from typing import Any, Literal, TypeAlias, cast, overload
from langchain_core._api import deprecated
from langchain_core.language_models import (
BaseChatModel,
... |
d8d4ca4ac1110b3e9cf57b7174bb9052125b1badb647a13255f0cdf1ee3f7c45 | Python | 40,800 | 1,213 | import logging
import shutil
import tempfile
import traceback
from pathlib import Path
import h5py as h5
import numpy as np
import pytest
import tifffile
from click.testing import CliRunner
from astrocast.analysis import Events
from astrocast.cli_interfaces import climage, convert_input, delete_h5_dataset, denoise, d... |
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