sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1837fbea52e522fa12ce403924d074cabfdacaeaea4ca05f062a403080a83459 | Python | 3,623 | 107 | targetMean = 140
targetStd = 5
initStep = 0
import pandas as pd
resultsMean = []
resultsStd = []
for i in range(40):
base_name = 'R:/Temp/Threads3/Thread'+str(i)+'/Simulation/output/Data.csv'
df = pd.read_csv(base_name)
if df.iloc[0,0]>1000:
resultsMean.append(0)
else:
resultsMean.appe... |
ef64b47e3851e622f1f219051719faa5519196a0b4c1ec03b74fe5ec14c1e713 | Python | 3,624 | 104 | import pandas as pd
import numpy as np
# read all xlsx files in the folder
list_intensities = []
for batch in ["Batch1_0108 DATA.xlsx", "Batch2_0110 DATA.xlsx", "Batch3_0124 DATA.xlsx", "Batch4_0219 DATA.xlsx", "Batch5_0221 DATA.xlsx", "Batch6_0304 DATA.xlsx", "Batch7_0306 DATA.xlsx"]:
intensities = pd.read_excel(... |
c42dcbb213212149940d8330ab4af6a796355ca1e6cde00180fac2153378c0e1 | Python | 3,626 | 100 | """Callback Handler streams to stdout on new llm token."""
import sys
from typing import Any
from langchain_core.callbacks import StreamingStdOutCallbackHandler
from typing_extensions import override
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
class FinalStreamingStdOutCallbackHandler(StreamingStdOutCa... |
785d504ee79880866844490e4afcc765ce1cb75b8f15599ecfa92ba76c37c800 | Python | 3,627 | 105 | """Test the criteria eval chain."""
import pytest
from langchain_classic.evaluation.criteria.eval_chain import (
_SUPPORTED_CRITERIA,
Criteria,
CriteriaEvalChain,
CriteriaResultOutputParser,
LabeledCriteriaEvalChain,
)
from langchain_classic.evaluation.schema import StringEvaluator
from tests.unit... |
d1e29b6655722fbf5587f7dc3e23680832dc74c010f6e5cdd8e4cd06a010e0f6 | Python | 3,628 | 119 | import asyncio
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class MergerRetriever(BaseRetriever):
"""Retriever that merges the results of mult... |
301e234858510fef9ac033072ae0f1a37aa499830d3615afc89ddbc5cdada57d | Python | 3,631 | 106 | import numpy as np
from pnabind.utils import oneHotEncode
from .get_atom_kdtree import getAtomKDTree
def mapPointFeaturesToStructure(points, atom_list, features, feature_names, kdtree=None, reduce_method='sum', impute=False, impute_value=0.0):
if features.ndim == 1:
features = features.reshape(-1, 1)
... |
595fccf66aff31bcfeae6734f535a9bb804d01c3ec5439f463d7d1b491613858 | Python | 3,631 | 121 | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: animalai/communicator_objects/vector_proto.proto
import sys
_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from g... |
3e99931ed8fef4fab0934a258d3d647de1dc52d761e01516f578e7af2fddc592 | Python | 3,636 | 97 | import pandas as pd
import numpy as np
import scanpy as sc
import time
dt_dir = ""
import cell2location
import matplotlib.pyplot as plt
sc.set_figure_params(dpi_save = 600,figsize=(15,15),fontsize=45)
import datatable as dt
#%% import ref sc data
sc_data_ref = pd.read_csv(dt_dir+"sc_ref_hippo.csv",index_col=0)
sc_data... |
49b57123cf0e2b2ef3eb57afae8739483d437f6ad0b9a8ae89d6094fa36a7875 | Python | 3,636 | 63 | # 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... |
4ec0076dc9df886ffb2846c7b17582084a395f9ec69d77b58abe31195ac12890 | Python | 3,636 | 103 | from multiprocessing import Pool
import numpy as np
import pandas as pd
from sklearn.linear_model import LassoLars
from sklearn.preprocessing import StandardScaler
from sklearn.base import clone
from sklearn.model_selection import ParameterSampler
from sklearn.metrics import roc_curve, auc, accuracy_score, f1_score
... |
a81ab5f9072b9766e69f86d40c5337bdb8cebc439b6c53b7a76eac3024f93ed5 | Python | 3,637 | 116 | # -*- coding: utf-8 -*-
from __future__ import print_function, division
import torch
import torch.nn as nn
class SEBlock(nn.Module):
def __init__(self, in_channels, r):
super(SEBlock, self).__init__()
redu_chns = int(in_channels / r)
self.se_layers = nn.Sequential(
nn.Adapti... |
efebed1fd446c6f71b82dcb44eed064c8ea59ba6b4c33178f569d369608cd272 | Python | 3,637 | 114 | """Test conversation chain and memory."""
from langchain_core.documents import Document
from langchain_core.language_models import FakeListLLM
from langchain_classic.chains.conversational_retrieval.base import (
ConversationalRetrievalChain,
)
from langchain_classic.memory.buffer import ConversationBufferMemory
f... |
14440ccf38960cf7079e82b12b46d0041815fd288cb2057b0f670dc0959d8a3a | Python | 3,638 | 107 | import tensorflow as tf
from keras import backend as K
from dipgnn.utils.register import registers
from dipgnn.trainers.base_trainer import BaseTrainer
def R_squared(y, y_pred):
residual = tf.reduce_sum(tf.square(tf.subtract(y,y_pred)))
total = tf.reduce_sum(tf.square(tf.subtract(y, tf.reduce_mean(y))... |
dfe8f9973972f8ad916f310dd0ec9b62d89c0cff8571961492d70cb8b854f478 | Python | 3,638 | 105 | #!/usr/bin/env python3
import argparse
import numpy as np
import nibabel as nib
from time import time
from dipy.data import get_sphere
from dipy.reconst.csdeconv import odf_sh_to_sharp
from dipy.reconst.shm import calculate_max_order
from shconv import convert_sh_basis
from _sharpen_parallel import odf_sh_to_sharp_p... |
4465d324fe5c0f2b44d4835b06ee78ac18752688f6f4650a71b951ea8b235538 | Python | 3,640 | 80 | import os
import random
import torch
import shutil
"""
This code generates a sub dataset containing around a given number of labels (e.g. 100) and only two
labels randomly (e.g. 0 and 2). This dataset aims to evaluate Svetlana's ability to predict accurately
being trained on a tiny dataset.
"""
# Votre dictionnaire... |
860e64e0836902caa74cdd6ca25610d7286a43ae6b1ac4c90848e3da406aaa60 | Python | 3,640 | 100 |
import torch
from torch import nn
def single_modified_forward(self, x_input: torch.Tensor) -> torch.Tensor:
"""
Modify forward pass of the model
"""
x_input = self.features(x_input)
return x_input
def single_dense_modify(model):
"""
Add linear and dropout layers to final classifier laye... |
b8984d196f756e103f86bb0812b0e4bc164ae4764e5a0cc719c2ec954ba07ce0 | Python | 3,641 | 85 | # 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.
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import joblib
from .data import DataHub
from .models import NNModel
... |
431c2b87bbd7c533e25d1fd9a263d299dbbdf2691f8d632eaa10730a262da61d | Python | 3,644 | 98 | import json
from json import JSONDecodeError
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import ChatGeneration, Generatio... |
ff4872a17c801f7b2fc5585aca25de6759b4ccb9ed50d2231b7d8ef43d7987fd | Python | 3,646 | 116 | """Core language model abstractions.
LangChain has two main classes to work with language models: chat models and
"old-fashioned" LLMs (string-in, string-out).
**Chat models**
Language models that use a sequence of messages as inputs and return chat messages
as outputs (as opposed to using plain text).
Chat models ... |
393240eb3ebf60ec7dfceecff5dae381f4a2438d91a1d82d0a8f7b179b05e41c | Python | 3,647 | 131 | from EPI_MRI.Regularizers import *
from optimization.EPIOptimize import *
class Preconditioner(ABC):
"""
Defines structure of a preconditioner object. All Preconditioner objects have method to build and
evaluate using preconditioner.
"""
def __init__(self):
pass
@abstractmethod
de... |
ff00bbd91a59ecd17bcf200e30a3551b204725772902a2ccd02f97899def768f | Python | 3,647 | 101 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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... |
07885157501dffbdb36056ad389b115e9b0bd1395ab75167d1219e1a2a2236af | Python | 3,649 | 128 | import pickle
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import figure_config # noqa: E402
mpl.rcParams.update(figure_config.mpl_style)
def adjust_rate_scale(r):
return 2.5 * r
if __name__ == "__main__":
with open(f"../data/fig8/res.pkl", "rb") as f:
res = pickl... |
09b120604e05f2b17c4fcfcc7cd7eb7cf97091d8e8752e491bb36c024dc5bd6f | Python | 3,649 | 96 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 6 22:24:58 2021
@author: athanasiadis
"""
import numpy as np
def computer(att_res,global_clusters_indices,subs_ids,meaned_clusters_indices):
"""
computer
______________________
This is a function that computes the gravity c... |
1b7fe7c19a57fa53bef7b7efca665b1f4628d0af55962b7cdd7938c62e2e3665 | Python | 3,650 | 60 | import pandas as pd
import numpy as np
import re
import argparse
def get_data(reference_path,rawdata_path,save_path,cancer):
data_ID=pd.read_csv(rawdata_path+'/'+cancer+'.DNAmethy.csv',sep=',',usecols=[0])
data_ID.columns=['ID']
CPG_id=pd.read_csv(reference_path+'/HM450.hg38.manifest.gencode.v36.tsv',sep=... |
29ae2528534cdd010c57a77dbc0cf8cd11c2bce43c5f753f77d723f27b902b81 | Python | 3,650 | 129 | import numpy as np
import os
os.path.dirname(os.path.abspath(__file__))
current_path = os.path.dirname(os.path.abspath(__file__))
from NeuronModel import integrate_and_fire, izhikevich, integrate_and_fire_MSN
import InitFunctions
params_MSN_Yim = {
'init': InitFunctions.dirac(-80.),
'tau': 200. / 12.5,
'... |
3673ddcf14d9e290ff595bf2fcaebb14e47487472885736fe0a5c79e7a716feb | Python | 3,650 | 99 | """Memory used to save agent output AND intermediate steps."""
from typing import Any
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from typing_extensions import override
from langchain_classic.agents.format_scratchpad import (
for... |
b659e808f481cce691c0ccea5690981bfa4c4ded081b3326a484b099b0ba1102 | Python | 3,650 | 83 | import numpy as np
def voxelize(x, x_data, n=20, c=13, dist=10, plot=False, bb_only=0):
"""Function to voxelize atom coordinate data ahead of training. Could be sped up on GPU
Args:
x_coords (np.array): num_res x num_return x 1 x 3 -- canonicalized coordinates about each residue with center resid... |
c9958e2968416f7fcbea0504eb2265eab76ccae580af4ab3b9a75bc4d19b803d | Python | 3,650 | 89 | """ Automatically discard noise clusters conservatively (by amplitude, spatial spread, ISI violation ratio) and viz"""
#%%
import json
import os
from time import time
from copy import deepcopy
import gc
from collections import OrderedDict
import multiprocessing
import shutil # rmtree
import numpy as np
# from scipy.io... |
6e04736635fce1cc77b7f3de862263b50c69b466e75b3436e0c659a33185e688 | Python | 3,651 | 101 | import torch
from tqdm import tqdm
import numpy as np
import random
from models import *
from utils import *
from diffusion import NoiseScheduleVP
from sampling import get_sampling_fn
from datasets import utils as du
from torch_geometric.data import Batch
import logging
import pickle
import functools
import tree
import... |
764b0a1ff4fa29f62baf22f74fa67c0e7b2b60fb23d41b094905a231214afa19 | Python | 3,654 | 108 | """Tag and filter middleware-internal model calls.
Tag internal calls with `internal_call_metadata()` and declare
`transformers = (InternalCallTransformer,)` on the middleware class to keep
them out of `run.messages`. Both APIs are public for third-party middleware.
"""
from __future__ import annotations
import secr... |
865ad96af84d31cc60a0787fb125e4eee23480446bd67876d99bd7f5edf0058e | Python | 3,654 | 129 | #!/usr/bin/env python
# coding: utf-8
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
from __future__ import print_function
from glob import glob
from os.path import join as pjoin
import sys
sys.path.insert(0, '.')
from setupbase import (
create_cmdclass, ins... |
d14409a693dcee5f9da504a40a25b1462ec26010776cd9e3a7521d56580599d5 | Python | 3,654 | 114 | #!/usr/bin/env python3
"""
Download the entire Allen Reference Human Brain Atlas (34 years, cortex-gyral) as a JPG stack
- use the brain-map.org API
Usage
----
allen_download.py
Authors
----
Mike Tyszka, Caltech, Division of Humaninities and Social Sciences
Dates
----
2017-05-24 JMT From scratch
License
----
This f... |
a524c43d76e66bcd9f6cb7a0905792cfe0105c1c83494b118cb4c055372024f9 | Python | 3,655 | 134 | #!/usr/bin/env python3
"""
Map the Allen developmental human brain ontology (16) to CIT
simple label numbering. Output master conversion table as a CSV file.
Usage
----
allen2cit.py -o <conversion table CSV file>
allen2cit.py -h
Example
----
>>> allen2cit.py -o Allen2CIT.csv
Authors
----
Mike Tyszka, Caltech, Divisi... |
4dc5d39a160d0363a524a70a326276b68e4242f9bfd5b1373e4ce254f68a70f5 | Python | 3,658 | 145 | """A simple progress bar for the console."""
import threading
from collections.abc import Sequence
from typing import Any
from uuid import UUID
from langchain_core.callbacks import base as base_callbacks
from langchain_core.documents import Document
from langchain_core.outputs import LLMResult
from typing_extensions ... |
a7dc88500534243fac16094039394376f097b4b27af1220a1c2c7ec30d90362f | Python | 3,659 | 80 | import os
import pandas as pd
import pickle as pkl
from types import SimpleNamespace
import yaml
from yaml.loader import SafeLoader
import numpy as np
from scipy.ndimage import gaussian_filter
from utils import *
from tqdm import tqdm
np.seterr(all='raise')
def plot_shaded(ax, t, y, color, label):
N... |
168f00ee1d9ed3a177aa5fb6bd44e7e39d668233635c85e2b28e7f36660d1ba6 | Python | 3,661 | 120 | ###########################################
# IMPORTS
###########################################
import numpy as np
import os
from nose.tools import nottest
from unittest import skipIf
###
from perses.dispersed import parallel
###
running_on_github_actions = os.environ.get('GITHUB_ACTIONS', None) == 'true'
def te... |
984157f86db90897e4b4a8a44ea8079faf7e4a09d8308025f0b0a1cb5e28b13b | Python | 3,661 | 94 | from keras.models import Model
from keras.layers import Input, Dense, LSTM, Masking, Dropout
from keras.layers.wrappers import TimeDistributed
from mimic3models.keras_utils import ExtendMask, GetTimestep, LastTimestep
from keras.layers.merge import Multiply
class Network(Model):
def __init__(self, dim, batch_norm... |
ca979975273343f988f809612b9619196190cef63ac2a91118b4cdb25315e9a3 | Python | 3,661 | 121 | #-------------------------------------------------------
# MobileNet in PyTorch.
# See the paper "MobileNets: Efficient Convolutional Neural
# Networks for Mobile Vision Applications" for more details.
#-----------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.func... |
3de0b7ca4ee02b3cf657bfa17f826f4b59e689c98b6cff6b05a463360a8fd6ff | Python | 3,662 | 117 | # builtin modules
import os
import subprocess
# pnabind modules
from pnabind.utils import moveFile
from .mesh import Mesh
def runNanoShaper(atoms, file_prefix, basedir,
clean=True, quiet=True, hydrogens=True, pockets_only=False, mesh_kwargs={}, **kwargs
):
# generate coordinate file
if(not isinst... |
7f7f8c6f31b5a336587a34987997f19ccca1e6b3d86f264b8075bb3b77bc10ed | Python | 3,662 | 102 | from __future__ import annotations
from typing import Any
from langchain_core.outputs import LLMResult
from typing_extensions import override
from langchain_classic.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
class AsyncFinalIteratorCallbac... |
8aa181161472f26a666ca96915c52e626cefc04be3d120d1071959776f6bc4c5 | Python | 3,663 | 102 | import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
class DropDataProcessor:
def __init__(self, df):
self.df = df
# These will be populated after extract_data_label() is called
self.train_key = None
self.test_key = None
self.train_key_set =... |
b5ffeb452b02d4c9bf63e50b2741bbdfd4e731e4330f02c51cb18d9b48c19ba7 | Python | 3,664 | 93 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
import warnings
from typing import List
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
class LinearWarmupCosineAnnealingLR(_LRScheduler):
"""Sets the learning rate of each parameter group to ... |
18e94181d8d2864782247c2d756e0aa9fa54adad36ac4f7466a8fabe9081e0ed | Python | 3,665 | 83 | # Needed for some densenets (however not used atm)
# Code from https://github.com/titu1994/DenseNet
from __future__ import absolute_import
from keras import backend as K_BACKEND
from keras.engine import Layer
from keras.utils.generic_utils import get_custom_objects
from keras.utils.conv_utils import normalize_data_fo... |
c1311800f91efef70c1a4468c8763ab0636016682853016448ce890dff32ea4b | Python | 3,666 | 96 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
9c6d7e0ecdc71654927454c8f2bfb229bc51ffa289c7d0cf495ba44bda183a5f | Python | 3,668 | 96 | '''
Created on Oct 12, 2011
@author: Ying Jin
'''
WIN_SIZE = 1000
GAP = 1000
PRJ_NAME = 'TEpeaks_out'
STEP = 100 # default step/bin size.
FRAG_SIZE = 200 # default fragment size.
#$range = 100;
CUTOFF = 1
epsilon = 0.00000001
NN = 0.99
SHIFTSIZE = 100
MAX_PAIRNUM = 1000
HS_CHROM = 'data/hg19_chromsize'
RN_C... |
f451bb5bfe54066acdd0099de82c048cb1f93b29691fe68d9484ddcbbb2cf30e | Python | 3,668 | 111 | # This extension template provides instructions to add new Conditional Independence (CI) tests to pgmpy.
# Please follow the following steps:
# 1. Copy this file to `pgmpy/ci_tests` and rename it as `your_ci_test.py`.
# 2. Go through this file and address all the TODOs.
# 3. Add an import in `pgmpy/ci_tests/__init__.p... |
dac63554a3306360e4f06df3597b953323d64e4e9271b1b963585c33434c6b69 | Python | 3,669 | 58 | """Export the split definitions used in the paper to splits/ (no model is fitted; the generators are the ones the experiments call).
splits/corpus_a_p3_folds.csv cell_id, dataset, seed, fold (P3 cell-disjoint folds, src/protocols.py, seeds 0 to 4)
splits/corpus_b_p3_folds.csv same for corpus B (twe... |
0ca7d159884314464132a21487e664c137156b66f7c31415b3216e6b59c8cf4d | Python | 3,670 | 92 | """
Analyze subject's performance over the course of the task sessions at the group level.
"""
import argparse
import data_analysis_utils as dana
import matplotlib.pyplot as plt
import numpy as np
import os.path as op
import pandas as pd
import performance_over_sessions_subject
import plot_utils as plut
import scipy.s... |
c36569c4c7f2b64c867da9a7913b4a03d309cb02e27e626e251e0a671b9d5bcc | Python | 3,671 | 121 | import csv
import json
from pathlib import Path
import numpy as np
import pytest
from PIL import Image
from neurovisfit.common.config import TomlConfig
from neurovisfit.data.cache import ImageCache
@pytest.fixture
def mock_image_cache(tmpdir) -> ImageCache:
# Create a temporary directory for testing
temp_di... |
c0bef184f6fdaaaec0ce4ded40782d2b985d9d184f946519b3645ca28cc43bba | Python | 3,672 | 143 | """
2023.08.30
author: femke.hurtak@epfl.ch
Helping script to load the data and interact with it
"""
import pickle
import os
import pandas as pd
import numpy as np
import params
def load_graph_and_matrices(subset="dn"):
"""
Load the graph from the data directory.
"""
accepted_subsets = ["dn", "dn_gn... |
7dc505011e7a8bb79536f8addca645235f0af18311eca75384bac423ab044b7a | Python | 3,676 | 139 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the Production-Degradation model
# (see validation/masteq.... |
f76673f0862acad3376961fdc4b4d1506b803a9ef260d0305dc19f618acaf011 | Python | 3,683 | 111 | def cycle(iterable):
# see https://github.com/pytorch/pytorch/issues/23900
iterator = iter(iterable)
while True:
try:
yield next(iterator)
except StopIteration:
iterator = iter(iterable)
def alternate(*args):
"""
Given multiple iterators, returns a generator... |
049e07d2ce27a6f77e175c7f9af650910322c1c395cab12a692e3cdd910370f6 | Python | 3,685 | 113 | import logging
import os
import socket
import sys
import matplotlib.pyplot as plt
import pandas as pd
import ptitprince as pt
import seaborn as sns
from configs.configs import get_config_dict
from helpers.figures import get_palette
def plot_brain_state_switch_count(
config_dict: dict,
num_basis_states: int,... |
c1eddebaa0575516a68db345f8e16d2b6d64b87b909e69c36b13a7c416f11e40 | Python | 3,685 | 76 | import argparse
import os
import sys
from tqdm import tqdm
from mimic3benchmark.subject import read_stays, read_diagnoses, read_events, get_events_for_stay,\
add_hours_elpased_to_events
from mimic3benchmark.subject import convert_events_to_timeseries, get_first_valid_from_timeseries
from mimic3benchmark.preprocess... |
fb808f319cda2f264cb84aa9df00be9670cf1faf59b8e8590133f511d395f15a | Python | 3,685 | 101 | """
Creates the ARF/MOSF, COPD/CHF/Cirrhosis, Cancer, and Coma subsets of the
SUPPORT dataset using the 14 features used in the DeepSurv paper; in our
paper we refer to these 4 subsets as SUPPORT1, SUPPORT2, SUPPORT3, and
SUPPORT4 respectively
The original SUPPORT dataset can be obtained from https://hbiostat.org/data... |
becb7468385a900ef39ce31bfa9c0f93f2c0cf928c876d5702204d6e53fe7b3e | Python | 3,689 | 129 | ########################################################################
# Stochastic first-order irreversible reaction.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the First-Order Irreversible
# Reaction model (see validation/first_orde... |
37af88ab08c98d1b92d36de7d2a12301a56797c647e2cf98a5f050b3ce007c68 | Python | 3,692 | 87 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... |
7bcdf973fe5347d9b89b831cb9c77b89846bfd54d3ae71fcd62705b8b1ff9f7a | Python | 3,692 | 95 | # Copyright 2019 DeepMind Technologies Limited. 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
599c7d35da377f6b32ce91d99e3b9c5e7833504339f6182778281510731ad4ae | Python | 3,693 | 95 | """Purpose of this script: try sorting those single sessions separately
that failed when batch run together with other sessions
!!! NOTE !!! untested
"""
import os
import sys
from time import time
import multiprocessing
from queue import Empty as qEmptyException
import traceback
import json
import numpy as np
sys.... |
faaf666800c811e629d3453849cb97ba1b03e49c5c233a9e4f8cf5f5afb6e934 | Python | 3,693 | 139 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the Production-Degradation model
# (see validation/masteq.... |
09d78350d03b152e8fdf8dfaa894c3e302a831d18eede509ecd06e28037585bc | Python | 3,694 | 78 | import argparse
from src.configs import NG_curriculums
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boo... |
7ddafa97f73297a0e5168a2db6e01871d08783649a0f343701cd57c75124d578 | Python | 3,695 | 123 | """Callback handler that prints to std out."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from typing_extensions import override
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.utils import print_text
if TYPE_CHECKING:
from langchain_core.agents i... |
a07a47e980153c9856693560f8d8e92cb1bccf5d3a65bdfc05ac12c7e97f7be5 | Python | 3,695 | 121 | import hydra
import torch
import torch.nn as nn
import torch.optim as optim
import wandb
from torch_geometric.datasets import QM9
from torch_geometric.loader import DataLoader
from omegaconf import DictConfig
import os
from pathlib import Path
from typing import List
from pytorch_lightning import (
Callback,
... |
79ad05c2142f446f9074ac6194c974a7c51e993046b052e110fcdb844a885c3e | Python | 3,698 | 116 | import os
import pandas as pd
import torch
import numpy as np
from torch_geometric.data import Data
import warnings
warnings.filterwarnings("ignore")
import pickle
from tqdm import tqdm
from torch_geometric.data import DataLoader
import random
import torch.nn as nn
import torch.optim as optim
from torch_geometric.nn im... |
fa989d701e5e70ce553434eb044b44c6629f7f44c651e578ea8cc4e89ea09039 | Python | 3,698 | 109 | """
The Sinc Keras model presented in the paper.
To get the model call 'net' function.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import initializers
from tensorflow.keras.models import Model
from tensorflow.keras.layers import *
from tensorflow.keras.regularizers import *
from brainagemodel... |
179f57d0c7e992da1ac294cff813d43d7e838ff1808da766d9146ad64853c050 | Python | 3,700 | 113 | # Training procedure for CIFAR-100 using DenseNet 40 with growth rate 12.
from __future__ import print_function
import os.path
import densenet
import numpy as np
import sklearn.metrics as metrics
from keras.datasets import cifar100
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGen... |
1cf7d7d0b2da26af5736d03ef2439a10b4f127bdc8d12cd8ba199b03d227d138 | Python | 3,700 | 77 | import os
import pathlib
import numpy as np
import pandas as pd
from brainrender import Scene, Animation, settings
from brainrender.actors import Points
from brainrender._colors import get_random_colors
from vedo import embedWindow, Plotter, show
from brainrender.video import VideoMaker
from scipy.spatial.distance impo... |
e050942d2a5fc979dfc6fcd5ea8dfe9b778c940b448096b150154eee44fa0b00 | Python | 3,700 | 91 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers... |
65b8983e4ea906f140550d58fa5d60c904ccec98727058a109c27d5845f8bd04 | Python | 3,704 | 109 | import hydra
from hydra.core.config_store import ConfigStore
from config import Scenario
from model import Net
import os
from utils import *
from matplotlib.ticker import FormatStrFormatter
from tqdm import tqdm
import matplotlib as mpl
from matplotlib import font_manager
from scipy.stats import pearsonr
import yaml
fr... |
6616e40380d7be07932f68667646f2c36455a7f040179a37ba78ee6bb2465106 | Python | 3,704 | 118 | ''' Example using a big BB-RBMs on the MNIST handwritten digit database.
:Version:
1.1.0
:Date:
24.04.2017
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2017 Jan Melchior
This file is part of the Python library PyDee... |
d8d8c6508098eff7141d255d0a9fe60714d5ef3f1ffb7e43814623a5ca9c4b97 | Python | 3,704 | 123 |
import sys
sys.path.append('../classes')
import class_model
import class_stimuli
import numpy as np
import pickle
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
### MAIN SCRIPT
stimulus_tasks = ['varyposition', 'varysize', 'varyrotation']
stimulus_folder = '../stimuli/simple_seqs/'
save... |
ba8fde1711ebefdb5d8d9f1e6133f8d6923e34ae6a0f21d753a66a6fcede545c | Python | 3,705 | 119 | '''
Author: Alexandra Fischbach
Github repo: https://github.com/AlexFischbach/local_csf_pipeline/tree/main
Date: 04-18-2025
Contact: fischbach.a@northeastern.edu
'''
import os
# ----------------------------------------------------------------
# USER-DEFINED PATHS AND PARAMETERS
# ------------------------------------... |
ff027cd0ac7571d473e2aaa1db8fa3ad1ecc2745b2c644f7fd12ea4fe3e5d4a0 | Python | 3,705 | 94 | import os
import torch
import json
import numpy as np
from skimage.io import imread, imsave
from skimage.measure import regionprops
from torch.utils.data import DataLoader
from PredictionDataset import PredictionDataset
from joblib import Parallel, delayed
def draw_predicted_contour(compteur, prop, imagette_contour... |
1d7708d2d81ac7527f305972710a8a61668eb2ed20c1c2c1c3ea7b4b10f33b6c | Python | 3,706 | 139 | """
:AUTHORS: Martin M. Müller, Olivier J. N. Bertrand
:ORGANIZATION: Department of Neurobiology, Bielefeld University
:CONTACT: martin.mueller@uni-bielefeld.de
:SINCE: Wed Nov 24 16:13:28 2021
"""
import numpy as np
from scipy.optimize import minimize_scalar
from scipy.special import i0
from scipy.special import i1
fr... |
3d988006d3c9c642d4184e4d0dcda26fc2c9b1e7458b99cc8e4ca3d8f19bb763 | Python | 3,706 | 129 | ########################################################################
# Stochastic first-order irreversible reaction.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the First-Order Irreversible
# Reaction model (see validation/first_orde... |
c63594b7fa6655d3082d4e706741c5d96ddd6934aaa7e408c6054739bff0ab52 | Python | 3,706 | 117 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmdirect'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process... |
0a960f864f6989b4a679b12c2d205654b132412dfffdff8781b687f39ad6d324 | Python | 3,707 | 77 | import gc
import os
import numpy as np
import pandas as pd
from keras import backend as K
import keras
import random
import tensorflow as tf
from keras.models import Sequential,load_model
from keras.layers import Dense, Dropout, Activation, Conv1D, Input, Flatten
from keras.optimizers import SGD
from keras import regu... |
49f0a0f1f7cb99604c5ba857f349941c8f7315d6785c80d832d8b7863a71f101 | Python | 3,709 | 103 | # Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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... |
302e654934335afbc643f6c3f9a153f6e13893afec564fd0f6611443a510d9ac | Python | 3,711 | 129 | import threading
# threading.Eventの複数ch対応
class MultiEvent():
def __init__(self, chNum = 1):
self._cond = threading.Condition(threading.Lock())
self._chNum = chNum
self._flag = [False for _ in range(chNum)]
def _at_fork_reinit(self):
self._cond._atfork_reinit()
... |
67232a519becc571a00909fb8992b8ea9c0224b5a0d3a699cf8e6858a8351fd5 | Python | 3,711 | 132 | """
:AUTHORS: Martin M. Müller
:ORGANIZATION: Department of Neurobiology, Bielefeld University
:CONTACT: martin.mueller@uni-bielefeld.de
:SINCE: Thu Jan 6 11:04:49 2022
"""
import re
import pandas as pd
import os
import numpy as np
from IPython import get_ipython
from utilities import mle_utils as mlutil
from utiliti... |
7192515c7f788b9166bc81b0d09252a8658c173aa24bfd79fb2f45deb4a70877 | Python | 3,713 | 117 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmrssa'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process.
... |
73fb77729f20eb1089246b93e4506ba9c0f7f6d2ac65e686ab38dee11d4d5781 | Python | 3,714 | 93 | import os
import argparse
import numpy as np
import pandas as pd
import random
random.seed(49297)
from tqdm import tqdm
def process_partition(args, partition, sample_rate=1.0, shortest_length=4.0, eps=1e-6):
output_dir = os.path.join(args.output_path, partition)
if not os.path.exists(output_dir):
os.m... |
6f977e60bf5dfc425a6e1a7d458c61c7d34d71b27379c8ec57ec5f1286855555 | Python | 3,717 | 109 | """Functions for rearranging matrices/data files"""
from ..utils import path as pth
import csv
import numpy as np
def order_dict(data, orderfile):
"""order_dict. Reorders a dictionary according to the order of keys passed in the
list in orderfile. Returns a reordered dictionary.
Args:
data (type)... |
18ce5abeac5947db5272007b05d4de9defef1e615d8ee0b3cfb47a9e717b61cf | Python | 3,720 | 138 | import os
import setuptools
import sys
def read(fname):
with open(os.path.join(os.path.dirname(__file__), fname), 'rt') as f:
return f.read()
def requirements():
with open(os.path.join(os.path.dirname(__file__), 'requirements.txt'), 'rt') as f:
return f.readlines()
EM_CODECS = [
"imagecodecs",
"simpl... |
5b225424db35969c7933a00428ad24ef5e559bfffa5f9ae97220158fb57b6cf2 | Python | 3,722 | 103 | from pathlib import Path
from typing import Any
from typing import Dict
from typing import Optional
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from tqdm import tqdm
from .params import ImageTransform
class ImageCache:
"""A class to cache images from a directory"""
def __... |
c1788973742de80e30fe4904871716b926a5a17cf89cd359fbcdc88a4268c634 | Python | 3,723 | 117 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmdirect'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process... |
05a398ed909324efd6ad31b1714c53b4298e2a525d27581cbfb0dfcaa80215b9 | Python | 3,724 | 124 | import pandas as pd
from jinja2 import Environment, FileSystemLoader
import os
import sys
import numpy as np
def read_csv(file):
data = pd.read_csv(
file, header=0, names=["test_name", "operation", "mean", "std_dev"]
)
hardware_info = data.loc[
data["std_dev"].isna() & ~data["test_name"].i... |
461833c64fb82823860cb38db0a9a4dfae3d8186fde48d1e6129d4def44e8b39 | Python | 3,725 | 109 | import os
import sys
import re
import types
import itertools
import matplotlib.pyplot as plt
import numpy
import numpy.ma
import Stats
import Histogram
from cgatReport.Tracker import *
from cpgReport import *
from cgatReport.odict import OrderedDict as odict
###########################################################... |
55a5107a13008a6cb8423833e61202e335032dc82538cecf16d2616494a92360 | Python | 3,726 | 90 | import argparse
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.utils import Params
from dliplib.utils.data.datasets import CachedDataset
from dliplib.utils.helper import select_hyper_best_parameters, load_standard_dataset
from dliplib.utils.reports import save_results_table
from dliplib... |
69053c9499fe6fdaac4f292bef64d392d15432a46fe838f2ac071f143d9b8774 | Python | 3,728 | 119 | from torch.utils.data import DataLoader
import torch
import numpy as np
from torch import nn,optim
from bestmodel import MyModule
from Dataset import MyDataset
import sklearn.metrics as m
from numba import njit
import pandas as pd
import os
import warnings
warnings.filterwarnings('ignore')
def RMSE(y_true... |
35032721a49feee6ccc0a1212c07bdb5736ef349b5272f29e693f4422526d667 | Python | 3,730 | 84 | # Copyright (c) MONAI Consortium
# 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, so... |
520b4200e6bc47ea1361353d542351f85067acd273b8806560d726e8d30fe998 | Python | 3,730 | 117 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmrssa'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process.
... |
6cbc171840a0ceabef3943a8acacfd9643157f6c42866b41ddae8f77519e640a | Python | 3,731 | 112 | import logging
import numpy as np
import pandas as pd
from helpers.array_operations import map_array, reorder_symmetric_matrix, reorder_lower_triangular_matrix
def load_data(data_file: str, verbose: bool = True) -> (np.array, np.array):
"""
:param data_file:
:param verbose:
:return:
"""
df =... |
85bdb2537b2760f4ee799889fb4288d0c287601366eb3754c3bc5ef16aa0b846 | Python | 3,732 | 137 | """Fake LLMs for testing purposes."""
import asyncio
import time
from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Any
from typing_extensions import override
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.lan... |
0683b6ec4fceda4f536f311764e7acad55b0e8213a2d3b630f2309886240e32d | Python | 3,735 | 115 | """
=====================
Mappability pipeline
=====================
Pipeline to count mappable bases in a given genome
"""
import sys
import os
from ruffus import *
import cgatcore.experiment as E
from cgatcore import pipeline as P
###################################################
# Pipeline configuration
#####... |
ccefd9e5a7bc97c11ec1b5c14f67f1e2e098520259ba33c0c4874c38de378d68 | Python | 3,735 | 108 | """Derivations of standard content blocks from Bedrock content."""
from langchain_core.messages import AIMessage, AIMessageChunk
from langchain_core.messages import content as types
from langchain_core.messages.block_translators.anthropic import (
_convert_to_v1_from_anthropic,
)
def _convert_to_v1_from_bedrock(... |
e48511e30db99a9a8b8d90a43aff36c439fb4641ed0778be56de1b79981b5387 | Python | 3,735 | 110 | from ccg.derivation import Node, DerivationLoader
from ccg.categories import Category
from ccg import combinators as comb
from ccg.penn_treebank_tokenizer import penn_tokenize
from os import path
import os
import jieba
import logging
logging.getLogger("jieba").setLevel(logging.CRITICAL)
_original_open = open
open = ... |
ea9c6e0fbc52db0f819205a29d15742856a25401b75a4393ba744cc1fc400322 | Python | 3,738 | 64 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... |
103d0a3df048bb37473b2fac2b574380fb3e8e05c0a4e60d6e836fa3bbaf74d6 | Python | 3,740 | 113 | import cv2
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm
from typing import Tuple
def obtain_bb(threshed_image : np.ndarray) -> Tuple[int, int, int, int]:
"""
Obtains the bounding box of the lesion
Inputs:
----------------
Outputs:
----------------
x : int :... |
957136bdcabbd4f5c3ee854d0ba19c159f926a90b579d4248ed4b43baa47fe0f | Python | 3,740 | 123 | from omegaconf import OmegaConf
from pathlib import Path
import torch
import torch.optim as optim
from monai.config import print_config
from monai.utils import set_determinism
from tensorboardX import SummaryWriter
from models.vqvae import VQVAE
from training_functions import train_vqvae
from util import get_training... |
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