sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
84b3bd14fd2fc4849ac4999d4731bc03354734823d86e51bb8fbf6813799167c | Python | 1,696 | 44 | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=1, verbose=False, delta=0):
"""
Args:
patience (int): How long to wait after last time validation loss improved... |
acce3ffd3396688da9380a0bdffe736e4e257cfafdb6956c842121e7a5b9b1ff | Python | 1,697 | 61 | # 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.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py... |
b8314a248b6b39504b06067db4b6550670266bb305967c790058708f09fb7994 | Python | 1,697 | 52 | import os
import sys
import re
import types
import itertools
import PeakcallingReport
import Annotations
from cgatReport.Tracker import *
##########################################################################
##########################################################################
##############################... |
1b35050762186fa1df2716f56147c35f6c2a9d5770505f19673612f26df41ece | Python | 1,699 | 53 | import os
import sys
import socket
import pandas as pd
from configs.configs import get_config_dict
from helpers.kernel_analysis import plot_lengthscale_window_length_relation
if __name__ == "__main__":
model_name = sys.argv[1] # 'VWP_joint' or 'SVWP_joint'
data_split = 'all'
kernel_param = 'kernel_le... |
e19d6cb4a357eb0f466ada589c0569a8a636ae9711025259f68c5390a6cf202c | Python | 1,700 | 73 | from pathlib import Path
import mne
study_name = "age-prediction-benchmark"
# On drago
N_JOBS = 80
bids_root = Path("/storage/store3/data/LEMON_EEG_BIDS")
# deriv_root = Path("/storage/store3/derivatives/LEMON_EEG_BIDS/")
deriv_root = Path("/storage/store3/derivatives/LEMON_EEG_BIDS_2/")
subjects_dir = Path('/storage... |
acb4c848c04b46b2d9553f730f3eb571e00fc6cf2233e2fd9328698e7581293d | Python | 1,701 | 42 | from dataset import CrossDocked2020_SBDD, collate_mols
from torch.utils.data import DataLoader
from rdkit import Chem
dataset = CrossDocked2020_SBDD()
atomic_num_to_type = {}
atomic_element_to_type = {}
for i in range(len(dataset)-1, -1, -1):
if i%1000 == 0:
print(i)
print(atomic_num_to_type)
... |
475fa3c6aa4d8bf5e343b85bd35a4b83fa3c088ba38d1090607dc9215a150283 | Python | 1,703 | 50 | import torch
from torch.optim import SGD
# Additional Scripts
from utils.transunet import TransUNet
from utils.utils import dice_loss
from config import cfg
class TransUNetSeg:
def __init__(self, device):
self.device = device
self.model = TransUNet(img_dim=cfg.transunet.img_dim,
... |
814768f8109881aa761f9df45a5cab7f25a383a8ab05f17b4af71b30bf56ea91 | Python | 1,703 | 62 | import numpy as np
import steps.interface
from steps.geom import *
from steps.model import *
from steps.rng import *
from steps.saving import *
from steps.sim import *
from . import DiffPoint
########################################################################
# Inject species in the whole mesh and let them degra... |
e950775d50abf066396ab00a92ddd467cb196b72af9f2eb143600769398a849b | Python | 1,703 | 61 | # 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.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py... |
0f3b72861ffe451fd1f77c09ddeda5b39ff15e079247ccb0c3f5edff46d346e3 | Python | 1,704 | 58 | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn import LayerNorm, ReLU, Linear
from torch_geometric.nn import GATConv, DeepGCNLayer
from sklearn.metrics import f1_score
class SMNetEncoder(nn.Module):
def __init__(self, in_channels, hidden_channels, num_layers, edge_vec... |
199a84956d90e2b24a2821b5485ac38b31919b5534d55cbe9d6ea604b95811d7 | Python | 1,704 | 58 | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn import LayerNorm, ReLU, Linear
from torch_geometric.nn import GCNConv, DeepGCNLayer
from sklearn.metrics import f1_score
class SMNetEncoder(nn.Module):
def __init__(self, in_channels, hidden_channels, num_layers, edge_vec... |
e103444cecb55250dd79bb5d04bb6b6e8f33d8fd186d175b98a372480fbc401a | Python | 1,704 | 68 | import os
import numpy as np
import scipy
import scipy.spatial
def append_feature(raw, data, flaten=False):
data = np.array(data)
if flaten:
data = data.reshape(-1, 1)
if raw is None:
raw = np.array(data)
else:
raw = np.vstack((raw, data))
return raw
def calculate_map(fts... |
d408d1e8b94abef0d40f9fe5d12fa8f719cce4e2ba95b49de58ef4262c9f1ca8 | Python | 1,705 | 40 | import unittest
from scripts import extract_workflow_connections
from scripts import prepare_data
class TestRecommendationSystemData(unittest.TestCase):
def test_recommendation_system_data(self):
workflow_file_path = "scripts/tests/test_data/worflow-connection-subset-20-04_test.tsv"
tool_usage_f... |
cd140d97e8592a590e8960502a4ddb80f5adeddbe1971f2d314489e1f8d534b7 | Python | 1,706 | 49 | import runpy
import sys
from pathlib import Path
import pytest
from pgmpy.base import PDAG
from pgmpy.utils import _warnings
@pytest.mark.parametrize("category", [UserWarning, FutureWarning])
def test_warn_external_reports_test_caller(category):
with pytest.warns(category, match="Check this result") as recorded... |
e11d21969320e6d09bb98b4e6bafc8ff6b1b7d5bf9a678147be6849251936746 | Python | 1,706 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_569998790',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
6bd283ddde458d73792af9e88bd11ee3c63769ef728cadf195b6d30a08fa37aa | Python | 1,707 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_471077857',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
ba9a1b46408d1b4b119080026181a9d36b66fd91d1c6bc9387b220eb92faf8c4 | Python | 1,707 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_471077857',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
bdfa602c2b4da4be6b73f42b7573010eb55344c18a48487e062f56309e73d8aa | Python | 1,707 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_488698341',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
f207cd32ac09b04e5ea1aed02e270d95829d30b79630498555c59439dc60c09c | Python | 1,707 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_354190013',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
0d868a87d8553931784df35644dd8b6fc9d11822dca5aebecdad18d86a6cf9c7 | Python | 1,708 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_354190013',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
48711ec99c76d595b15bbe24bfb7763433dc21dfb60e787a6a495d6f02dfa9c2 | Python | 1,708 | 56 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_488698341',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
5e796aa5a435ca37f3a1a2877783e67a1ba98a289921c779355e3f9a029bbab7 | Python | 1,708 | 57 | from bmtk.builder.networks import NetworkBuilder
import cell_functions
net = NetworkBuilder('single_neuron')
net.add_nodes(cell_name='Cell_569998790',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive_a... |
b130d5e75b6e7023b1552cf8452709015b9ccd3cd0e860ab5c5653c80c6159ed | Python | 1,708 | 41 | r"""A logger related utils file: tqdm style, logger loader.
"""
import os
from datetime import datetime
from cilog import create_logger
from torch.utils.tensorboard import SummaryWriter
pbar_setting = {'colour': '#a48fff', 'bar_format': '{l_bar}{bar:20}{r_bar}',
'dynamic_ncols': True, 'ascii': '░▒█'}
... |
ffb7fbdeb5c9f7311c1dff5080959dcc6ae86e1f953cbf22233d8ddd2cc9cf5c | Python | 1,708 | 49 | """
This module is an example of a barebones function plugin for napari
It implements the ``napari_experimental_provide_function`` hook specification.
see: https://napari.org/docs/dev/plugins/hook_specifications.html
Replace code below according to your needs.
"""
from typing import TYPE_CHECKING
from enum import En... |
58566f14aca14699a54e090d44f774763fd119b616803767aeea4ad9c1ff4d8c | Python | 1,709 | 51 | import argparse
from dival import DataPairs
from dliplib.reconstructors import get_reconstructor
from dliplib.utils.helper import load_standard_dataset
from dliplib.utils.plot import plot_reconstructors_tests
from dliplib.utils.helper import set_use_latex
set_use_latex()
def get_parser():
"""Adds arguments to ... |
f8bbfca6a12bbfead3492ac60d154af79f6519bb85ce36063ae71bdc3a249e52 | Python | 1,709 | 44 | """Test for CombinedMemory class."""
import re
import pytest
from langchain_classic.memory import CombinedMemory, ConversationBufferMemory
@pytest.fixture
def example_memory() -> list[ConversationBufferMemory]:
example_1 = ConversationBufferMemory(memory_key="foo")
example_2 = ConversationBufferMemory(memo... |
3038e5eeb128a6d16ced561a6d324ce039755a6ae1d9927282c80cf73b440dbb | Python | 1,710 | 54 | import os
import sys
import re
import types
import itertools
import matplotlib.pyplot as plt
import numpy
import numpy.ma
from ChipseqReport import *
import Annotations
##########################################################################
##########################################################################... |
cdaaaa13cc4cd167a8c0ff8c56e7699dcc54b962f6253accd00917b0dbf481d8 | Python | 1,710 | 50 | from typing import Any
from langchain_core._api.deprecation import deprecated
from langchain_core.runnables.base import RunnableBindingBase
from langchain_core.runnables.utils import Input, Output
@deprecated(
since="1.0.7",
removal="2.0.0",
message=(
"langchain_classic.hub.pull is deprecated. Us... |
bf089bb8637daa6cb891b1a9008d3a1cbff003e025c42f051f6125f3e9e31b2f | Python | 1,711 | 61 | # 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.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_ru... |
faeb0df07cff1feac431680465474a1c131baded980b52e5f90ff34c24c79e38 | Python | 1,711 | 37 | from django.shortcuts import render
from django.contrib import messages
from django.views.generic.edit import FormView
from django.core.exceptions import ValidationError
from .forms import PsthAnalysisForm
from .models import Analysis
from .psth_analysis import psth
class IndexView(FormView):
model = Analysis
... |
23b46dc227c318481d683722fb5204cf42441e7fe333c1bfb5cb106064f78d1b | Python | 1,712 | 55 | """
To ease the pain of ensuring compatibility with new data structures or datasets,
this file collects key IO functions for data, metadata, and annotations
that may be edited by a user to fit their particular use case.
"""
import h5py
import json
import numpy as np
import os
import pandas as pd
from pathlib import Pa... |
6eee9855d66a2955f2163b2c21b41e5f234d18146ba82a009b1ee38369430ea7 | Python | 1,712 | 61 | # 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.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_ru... |
f5e4658ff4c280d9300049ba91ba916add26b85e7aaa41226c82160359e0d2ae | Python | 1,712 | 42 | import argparse
from pathlib import Path
from alpaca import ProvenanceGraph
def main(source, output):
source_path = Path(source).expanduser().absolute()
output_path = Path(output).expanduser().absolute()
prov_graph = ProvenanceGraph(source_path,
attributes=('dtype', 'sha... |
38f1ae03b3b7fbc23ec070a505ced856c06969c8741bc141b40048307b0e5788 | Python | 1,713 | 47 | #%% import libs
import pandas as pd
import numpy as np
import tangram as tg
import scanpy as sc
import time
dt_dir = "benchmark/"
#%% import ref sc data
sc_data_ref = pd.read_csv(dt_dir+"hippo_t1_t4/sc_ref/sc_ref_hippo.csv",index_col=0)
sc_data_labels = pd.read_csv(dt_dir+"hippo_t1_t4/sc_ref/sc_label_hippo.csv",index_... |
48c3aec00c34148bf636737d2a8b266563e15a6488ffe79c921bc3a93149637a | Python | 1,713 | 61 | import subprocess
import re
# The name of your test executable
TEST_BINARY = "../build/bin/debug/rayx-core-tst"
def list_tests():
"""Run the test binary with --gtest_list_tests to get a list of all tests."""
result = subprocess.run([TEST_BINARY, '--gtest_list_tests'], stdout=subprocess.PIPE, text=True)
... |
1c9466e9de9e661353fc3876752e118eae540dc2fe6ed1a4285daee3dff0140e | Python | 1,716 | 61 | # 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.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_ru... |
52d1aada56c043d958e127ab4775cc7818f71f7db7b5d35d9119aad0104df688 | Python | 1,716 | 54 | ####################################################################################
#
# 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... |
ba4261f49acb87e5a3e01b81c4b3db05df4026b172663693da88cb542d4be309 | Python | 1,716 | 54 | from copy import deepcopy
import random
import numpy as np
from torch_geometric.data import Batch
class BalancedDataLoader(object):
def __init__(self, data_list, nc, batch_size, shuffle=True):
self.data_list = data_list
self.nc = nc
self.batch_size = max(batch_size, nc-1)
... |
856f434d47fe71d16ba42f5e840b1d3e7f512003272f0487da209b1d1610fb3b | Python | 1,717 | 64 | import MDAnalysis as mda
import pyemma
import numpy as np
import os
import requests
# -- Functions to download from online repositories
def download_from_gpcrmd(filename, folder):
"""
Downloads a file from GPCRmd.
Parameters
----------
filename : str
Name of the file to down... |
983498149b18b99ba1d6137d52e2563cfccfbb3fa94b461448353eb6192cd503 | Python | 1,718 | 58 | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn import LayerNorm, ReLU, Linear
from torch_geometric.nn import SAGEConv, DeepGCNLayer
from sklearn.metrics import f1_score
class SMNetEncoder(nn.Module):
def __init__(self, in_channels, hidden_channels, num_layers, edge_ve... |
facec29810da04024b18b1abaa1f4d8f05936488888b90f1165824e0300e2386 | Python | 1,718 | 32 | """
Script to regenerate all videos related to experimental data analysis.
This script also generates the plots shown in Figure 2a.
Running this script requires raw data that is ~1 TB large and can be requested from the Authors of the manuscript.
Author: jonas.braun@epfl.ch
"""
from vid_functional import make_function... |
4c4e9a95c0a036ed9a317d63feb1d530cb72418c175855c5551e71d918d329d1 | Python | 1,719 | 44 | #!/usr/bin/python2.7
#dbsnp_hash_table_maker.py
#uses pandas commands to take in downloaded BED files and make hash tables with rs numbers as keys and GRCh38 chromosome positions as values
#designed to take bed_chr_#.bed.gz files and output json hash table files in same directory
import pandas as pd
import json
def ... |
86a6085aa06d508a3a42653489498592e5ce6a02974be615d582b71a5510202b | Python | 1,719 | 90 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import numpy as np
from numpy.random import randn
import pandas as pd
import math
import pingouin as pg
from scipy.cluster.hierarchy import linkage, dendrogram, leaves_list
from scipy.spatial.distance import pdist
from sklearn.dec... |
18affaecfb7f8d7acf9e6917e1d46044b4e74c7dc06ecd395411ff6c6258b91b | Python | 1,720 | 56 | import numpy as np
import os
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.utils.data as data
from config import get_test_config
from data import ModelNet40
from models import MeshNet
from utils.retrival import append_feature, calculate_map
cfg = get_test_config()
os.environ['CUD... |
22e0ef2381e50928301f5d1c2f67a0f622b22ff2fc88ef6ea1fa6be2ba459aa4 | Python | 1,720 | 90 | from langchain_classic import llms
EXPECT_ALL = [
"AI21",
"AlephAlpha",
"AmazonAPIGateway",
"Anthropic",
"Anyscale",
"Arcee",
"Aviary",
"AzureMLOnlineEndpoint",
"AzureOpenAI",
"Banana",
"Baseten",
"Beam",
"Bedrock",
"CTransformers",
"CTranslate2",
"Cerebr... |
40047b1927c9404109bf51035f6454ca361b47ff5a6f4e26119a4f4d5fe64ea8 | Python | 1,720 | 50 | import numpy as np
import torch
from torch_geometric.data import Batch
from . import dataset_register
from .utils.load_data import process_graph
from .utils.collate_fn import BatchDenseMatrix
def process_data(smiles, model_name='gin'):
graphs = process_graph(smiles)
if model_name == 'gin':
loader = Ba... |
4d181582d75593b3ecf699ebb2408a9642ba0065a0e8faf1379af6539d0f9afc | Python | 1,720 | 59 | import torch
import numpy as np
def distance(p, eps=1e-10):
# [*, 2, 3]
return (eps + torch.sum((p[..., 0, :] - p[..., 1, :]) ** 2, dim=-1)) ** 0.5
def dihedral(p, eps=1e-10):
# p: [*, 4, 3]
# [*, 3]
u1 = p[..., 1, :] - p[..., 0, :]
u2 = p[..., 2, :] - p[..., 1, :]
u3 = p[..., 3, :] - p[... |
7e6954372e68acc3d04cdd9f0089fc73a5f3d2009f924323a64a566646637d5d | Python | 1,720 | 28 | from misc.positions import PositionList
"""This example demonstrates how to generate new imaging positions from a set of positions after the sample has been picked up and likely shifted or rotated.
This method relies on measuring a set of reference positions (at least 3) before and after moving the dish. You can then ... |
5f401b62bcb401c568ae7b861c498a35dce91f286471a832b9435d950af0b9f1 | Python | 1,724 | 59 | from __future__ import annotations
import numpy as np
from libs.common.graph import Graph
from libs.common.metrics import Metrics
from libs.common.position import Position
from libs.common.weight import Weight
class Connection(Graph):
def __init__(self, client: int, weight: Weight, position: Position, strongly:... |
73a6adec9664e1e21c0edf0d2910ad4243a6ca51fe7ee0730a6e4219aeafd340 | Python | 1,724 | 67 | import logging
class Register:
"""
This code is extracted from delta (https://github.com/Delta-ML/delta).
"""
def __init__(self, registry_name):
self._dict = {}
self._name = registry_name
def __setitem__(self, key, value):
if not callable(value):
r... |
226520a883f4632b57ffe525fb46b7cc94bcf5b9d286d380b549900ec03eb65d | Python | 1,725 | 52 | """Joint birefringence + phase reconstruction using the waveorder API.
NOTE: Work in progress. The simulate/reconstruct pipeline runs end-to-end
but the simulation physics and reconstruction quality are still being validated.
"""
from waveorder.api import birefringence, birefringence_and_phase, phase
biref_settings ... |
80ac8a8c4b167cc47ee8cb4e1942b4ae0459704acc2a6d2f756ab33d071a97ee | Python | 1,725 | 51 | #Code from https://github.com/aravindsiv/dan_qa
from keras.layers import Layer
from keras import backend as K
import tensorflow as tf
class AverageWords(Layer):
def __init__(self):
super(AverageWords,self).__init__()
self.supports_masking = True
def call(self, x, mask=None):
axis = K.... |
4a79d4226c541fc869c8b0c2ef478bc7e4745149ebaeda8d0dc3786bee71377b | Python | 1,726 | 44 | """Augmentation policy for pseudo-images, deliberately minimal.
Pseudo-images are simpler and far less variable than natural photos, and the
feature->pixel layout is *fixed and meaningful* (pixel (i,j) is always feature g).
So the photo-vision augmentation toolbox is actively harmful here:
* flips / rotations / cro... |
080f5e09c3eeabf336df9db5a53a505be8fbfa7b7dc37bbaedac253c269e2d8d | Python | 1,727 | 47 | #! /usr/local/bin/python
#'#################################################################################
#'#################################################################################
#' Run random search on tcga data
#'#################################################################################
#'######... |
3fb8f89ccdd3fbd81d17116daa08a86595dedaf0b5034ce7248e5e05af2dd1fd | Python | 1,727 | 67 | """Small utility functions"""
import logging
import os
import platform
import re
from typing import Tuple
import psutil
import torch
logger = logging.getLogger("casanovo")
def n_workers() -> int:
"""
Get the number of workers to use for data loading.
This is the maximum number of CPUs allowed for the ... |
86f33a738149a56bb6ff313888b03b8eada5cf2311987994943c43c8c26d34c6 | Python | 1,727 | 63 | from __future__ import annotations
import numpy as np
from libs.function.function import Tanh
from libs.model.neurons import (
LI,
LinearSelectDecoder,
LinearSelectEncoder,
RandomNeurons,
)
class Network:
def __init__(
self,
input_dim: int,
hidden_dim: int,
output... |
05c27f51d6a31fce7e878e436a2068843d79d192fe27b298d73e9a57064f6f77 | Python | 1,731 | 46 | from fcest.helpers.filtering import _compute_lower_frequency_cutoff
import numpy as np
from scipy.signal import butter, filtfilt, lfilter
def bandpass_filter_data(
y_observed: np.array, window_length: int, repetition_time: float,
freq_high: float = 0.08
):
"""
We want to remove frequencies bet... |
1d304b8367ac5ea757c795293a9c646e9318e127eb3517a9df7b9034e4a8082a | Python | 1,731 | 53 | import jax.numpy as jnp
import flax.linen as nn
from typing import Sequence, Any
from projectlib.models.layerwise import LayerwiseModule
from projectlib.utils import flatten
Dtype = Any
class MLP(LayerwiseModule):
"""A simple MLP model."""
features: Sequence[int]
nclasses: int
dtype: Dtype = jnp.floa... |
4c6c08d684c089fd034adbbdd54da54d166b1f2fd064ebda0bf1a3f4255380f7 | Python | 1,732 | 54 | ####################################################################################
#
# 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... |
a8bf5a07c62cc8d8cb1bd26342ed82de6c2226bc230eb2a3660e5e3476f621b3 | Python | 1,732 | 55 | """ Implementation of the command line interface.
"""
from argparse import ArgumentParser
from t5chem.__version__ import __version__
from t5chem.run_prediction import add_args as pred_args
from t5chem.run_prediction import predict
from t5chem.run_trainer import add_args as train_args
from t5chem.run_trainer import tra... |
756db231fcf61f4c53e29e50c56a58f50ba0b33b1cbf731950b56130af8a7072 | Python | 1,733 | 50 | import pickle
import csv
import numpy as np
import glob
path_files = "pickle"
path_output = "derived_files/CSV/Data"
# find all pickle files:
pickle_files = glob.glob(path_files + "/*.pickle")
for file in pickle_files:
with open(file, 'rb') as f:
data = pickle.load(f, encoding='latin1')
# Create csv... |
0ffd4a0382f0bb828658d05df02153ffed2dfdc82ecdf66cc1c3c9bdefe36627 | Python | 1,734 | 52 | try:
from napari.qt import thread_worker
except:
raise Exception('Napari must be installed to use these features')
import numpy as np
import time
def start_napari_signalling(viewer, dataset):
"""
Start up a threadworker, which will check for new images arrived in the dataset
and then signal to nap... |
7342b1f8875e272378bdbaddd3655027f060b0993bc881a2d92947a2b68d6bcf | Python | 1,735 | 58 | import pandas as pd
import seaborn as sns
def get_color_col(meta=None, color=None, verbose=False):
"""
Determine color column
"""
color = 'milestone_id' if color is None else color
if color not in meta.columns:
raise ValueError(f'Color column {color} is not in metadata.')
return color
... |
38383f0265f67126e49b9facd74e4924130d94ded5852e2b76a6d7b713c0f9e7 | Python | 1,737 | 47 | import torch
from src.logic import logic_utils, train_logic, test_logic, lesion
from src.models.feedforward import FFNetwork
from src.PID import runner
from src import utils
from src.parsers import logic_parser
if __name__ == "__main__":
"""
*****************************************************************
... |
9ecbaa4d4b8bac36d1e715b06eb1e72a0588a5cab66418333deb72691283ff46 | Python | 1,737 | 46 | #%% import
import scanpy as sc
import numpy as np
import pandas as pd
from GraphST import GraphST
import polars as pl
from sklearn.metrics import mean_squared_error,roc_auc_score,normalized_mutual_info_score,adjusted_rand_score
import time
#%% reading st data
dataset_dir = ""
libd_data_dir = dataset_dir+"benchmark/spa... |
d90ed79fdb947fe25afe03d912cf463cf24f20469ebc4609a65fe10e5818a9b4 | Python | 1,737 | 65 | """
2023.08.30
author: femke.hurtak@epfl.ch
Comparison of the clustering results with a shuffled control.
"""
import os
import pandas as pd
from scipy import stats
import plot_params
def get_cluster_stats(working_folder):
# Open clustering_stats.csv as pd.DataFrame
df_data = pd.read_csv(
os.path.joi... |
9a13c43ca87626a89c82c014fc1f0e52ec2737835c2a1867508fbfdfebed6eac | Python | 1,738 | 49 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.chains.ernie_functions.base import (
convert_python_function_to_ernie_function,
convert_to_ernie_function,
create_ernie_fn_chain,
create_ernie_fn_runn... |
17a393bca5fb4b73dea0ed17af7751b94ce80a01bdb9683e99f9ae30f5498424 | Python | 1,740 | 37 | from builtins import range
from src.data import get_sim_data
import numpy as np
import os
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
seeds = [15] # 37, 98, 137, 245]
results = {'mse_train': list(), 'mae_train': list(), 'r2_train': list()}
for ... |
2e86e18493d93782f567f2589610bbd65817bc8609e4ca9d8d17bba5956c9b66 | Python | 1,740 | 72 | """BERNN: Batch Effect Removal Neural Networks for Tandem Mass Spectrometry.
This package provides tools for removing batch effects from mass spectrometry data
using deep learning approaches.
"""
try:
from importlib.metadata import version as _meta_version
__version__ = _meta_version("bernn")
except Exception... |
5d7b3916cd0fa068180e0a0839978ee4cb81d0bc66aa52daa5ceea80d6e7b89a | Python | 1,741 | 53 | import struct
def imagej_metadata_tags(metadata, byteorder):
"""Return IJMetadata and IJMetadataByteCounts tags from metadata dict.
The tags can be passed to the TiffWriter.save function as extratags.
"""
header = [{'>': b'IJIJ', '<': b'JIJI'}[byteorder]]
bytecounts = [0]
body = []
def w... |
ba5db33f1af1331d30b19ac6bf2f7767f123b7ef984b30acae510f688d65b75a | Python | 1,741 | 47 | import os
import numpy as np
import pandas as pd
import shutil
def read_npy_files_and_csv(npy_folder_path, csv_file_path, output_folder_path):
# 创建输出文件夹
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
# 获取所有npy文件
npy_files = [file for file in os.listdir(np... |
4fc3ca289ab165c18ea4fad23cf2643c9085c817e865282d78ced9770e421629 | Python | 1,742 | 47 | import hashlib
from collections import OrderedDict
from collections.abc import Iterable
from collections.abc import Mapping
from typing import Any
def make_hash(obj: Any) -> str:
"""
Given a Python object, returns a 32 character hash string to uniquely identify
the content of the object. The object can be... |
bec058d424e11bdce2ddb04871d8a67acf3539ab8de3b43424edff6e04ecc8a4 | Python | 1,742 | 48 |
from torch.optim.lr_scheduler import MultiStepLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau
from core.scheduler.schedulers import get_warm_up_lr, ConstantLR, PolynomialLR
from utils.loggers import LoggerManager
key2scheduler = {
"constant_lr": ConstantLR,
"poly_lr": PolynomialLR,
"multi_step": M... |
5e8a8715c6f5ad190efb18b3371d66aace65bb46ac33c708a9ef52bade9804da | Python | 1,743 | 59 | from dliplib.utils.data import generate_dataset_cache
from dliplib.utils.helper import load_standard_dataset
ellipses_cache_file_names = {
'train': ['cache_train_ellipses_gts.npy',
'cache_train_ellipses_obs.npy',
'cache_train_ellipses_fbp.npy'],
'validation': ['cache_validation_ell... |
8b0f33c8ccbfacc2960cd715eae18422d6b7664fce1c6ab16f3c8a5a8151145e | Python | 1,743 | 54 | #!/usr/bin/env python3
"""
Extracts PDB files from zip archives for all entries in provided index file
"""
import csv
from pathlib import Path
from zipfile import ZipFile
import click
# for info
CS_ZIP_DIR = '/SAN/bioinf/afdb_domain/zipfiles'
@click.command()
@click.option('--index_file', '-i',
requir... |
e2bb25110aa8e792a78387dc7c862d599e87c9cb170074aee6ca83434e0c7466 | Python | 1,743 | 44 | import numpy as np
def oversample_shortterm(train_dict:None, n_dupl: None):
"""specify the number of short-term survivors to oversample/duplicate in training dictionary"""
count_samples = len(train_dict)
count_long = 0
count_short = 0
short_samples = []
# count the number of short-term and lo... |
5fa99225a4b1e54d5f842ddb1b7546d7c2e8da046cf3486b5d6a11f069cfb35f | Python | 1,745 | 38 | from nowcasting.config import cfg
import os
import numpy as np
import matplotlib.pyplot as plt
base_dir = "flows"
prefixs = ["ebrnn1_0", "ebrnn2_0", "ebrnn3_0", "fbrnn1_0", "fbrnn2_0", "fbrnn3_0"]
forecaster_block_prefix = []
flow_num = 13
lengths = [10, 10, 10, 10, 10, 10]
heights = [64, 32, 16, 64, 32, 16]
widths = [... |
147df21204df2d8cf1116cf0aebaecd7269b5f30d0aae3e92c79f9c87ec02b78 | Python | 1,749 | 61 | # 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.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py... |
91108b89c7a4ff582cc1b75b2e11a8107c1e6d0c4fd8c86c8b2998d334ee3b13 | Python | 1,750 | 60 | """Interface for selecting examples to include in prompts."""
from abc import ABC, abstractmethod
from typing import Any
from langchain_core.runnables import run_in_executor
class BaseExampleSelector(ABC):
"""Interface for selecting examples to include in prompts."""
@abstractmethod
def add_example(sel... |
175d674c5f6d06dafa43cd9a6338e0f5b66c49fbbeea3a871a7a5eabf911115c | Python | 1,751 | 42 | """Regression checks for stage-independent file utilities."""
import hashlib
import json
from pathlib import Path
import tempfile
import unittest
from preprocessing.inputs import fingerprint
from utils.files import atomic_json, sha256_file
class FileTests(unittest.TestCase):
def test_checksum_matches_binary_payl... |
85fff80d345ec6bc5cd72b01d21f554cda1054b6ec35968c5d057574f9c4962f | Python | 1,751 | 62 | import os
import sys
import re
import types
import itertools
import math
import numpy
import iotools
from cgatReport.Tracker import *
from cpgReport import *
##########################################################################
##########################################################################
##########... |
07ec59c9c7de6cb966cb4295d3e73c9abdd5c97dadba92d89b36529b30af8ae5 | Python | 1,752 | 51 | # coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from alg.modelopera import get_fea
from network import Adver_network, common_network
from alg.algs.base import Algorithm
class DANN(Algorithm):
def __init__(self, args):
super(DANN, self).__init__(args)
self.feat... |
9cfaf19fbacb21776d8601e9c78bd33fac0e1b64c8ad98317a30ad012b8b265b | Python | 1,752 | 50 | import pandas as pd
import numpy as np
from numba import jit
def get_feature(input_path,save_path):
data=pd.read_csv(input_path+'/promoter_rowdata_TPM.txt',sep='\t')
data=data.fillna(0)
sample=list(data.columns[1:])
data['gene_id']=data['promoter_id'].map(lambda x:x.split('|')[0])
data[data==0]=np... |
0bb55937f7f73f444611521e192b2dca39769a3de402da90e073e631d4663871 | Python | 1,753 | 54 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores.redis.schema import (
FlatVectorField,
HNSWVectorField,
NumericFieldSchema,
RedisDistanceMetric,
RedisField,
RedisModel,
... |
7188884ab36d81fd63252a0f6d7aeea40167615d965f9d4e840280d697eeb8ef | Python | 1,753 | 66 | import numpy as np
from pensa import *
import random
import math
def get_txt_features_ala2(filename, num_frames, cossin=False):
"""
Parses features for ala2 from a text file.
The text file must be formatted with "phi", followed by all phi angles, a blank line,
followed by "psi" and all psi angles, ... |
e87ca9daca8b84a8243e9cd166cc60fae833ddbb5813efe9b5fab5e665c3b57b | Python | 1,754 | 39 | import cv2
from miga_emblaze.emblaze import EmbeddingSet, Embedding, Field
import os
import numpy as np
def to_EmbeddingSet(embeddings, colors):
emb_set = EmbeddingSet([Embedding({Field.POSITION: embeddings, Field.COLOR: colors})])
return emb_set
def to_Embedding(embeddings, colors):
return Embedding({... |
5ec83d3250ce819e6f7725481a266b1ee7f6721ab7d29516c5ba6bbe554891e9 | Python | 1,755 | 50 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
from pointnet_utils import PointNetEncoder, feature_transform_reguliarzer
class get_model(nn.Module):
def __init__(self, num_class):
super(get_model, self).__init__()
self.k = num_cl... |
0742e1a2d8828c21f6dab925cbcf25fc64aa6afc0589149ad65c9749074475c2 | Python | 1,759 | 52 | import torch.nn as nn
import torch.nn.functional as F
from .mp_encoder import Mp_encoder
from .sc_encoder import Sc_encoder
from .contrast import Contrast
class HeCo(nn.Module):
def __init__(self, hidden_dim, feats_dim_list, feat_drop, attn_drop, P, sample_rate,
nei_num, tau, lam):
super(... |
2201a227bd95fdada3112cee30945bd41bb4df10d3bd0718e58bbc3a31258b1b | Python | 1,760 | 75 | r"""A kernel module that contains a global register for unified model, dataset, and OOD algorithms access.
"""
class Register(object):
r"""
Global register for unified model, dataset, and OOD algorithms access.
"""
def __init__(self):
self.launchers = dict()
self.models = dict()
... |
78d565edc686846283fc738476069b68de59c4d0fcc3312645a65e74fb05adae | Python | 1,760 | 59 | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn import LayerNorm, Linear
from torch_geometric.nn import MLP, GINEConv
from sklearn.metrics import f1_score
class SMNetEncoder(nn.Module):
def __init__(self, in_channels, hidden_channels, num_layers, edge_vec_dim):
... |
10a481e5a885b5d2767aa6d3a3238f110835f925bae2ae81d87389b593c61706 | Python | 1,762 | 71 | import numpy as np
from voxcell import VoxelData
from sys import argv, exit, stderr
from os import cpu_count
from tqdm import tqdm
from collections import defaultdict
import scipy.ndimage as nd
import flatmap_util as fmutil
argc = len(argv)
if argc < 6:
print('Usage: {} <flatmap> <flat xlen> <data> <bkgval> <ou... |
867a142799bc0e8b3a54732b52092246b2b4fbdebdf08047cedac21ca34a70ab | Python | 1,762 | 48 | import numpy as np
from scipy.stats import entropy
from scipy.spatial.distance import jensenshannon
def validate_input(a, b):
if a.shape[0] > a.shape[1] or b.shape[0] > b.shape[1]:
raise AssertionError("0-axis must be the wavelength and 1-axis must be the samples")
if len(a) != len(b):
raise ... |
9425e4e425a518d694b333922b137d961a5aec10d61d4db671f1d90dff7cecb8 | Python | 1,762 | 40 | import matplotlib.pyplot as plt
import os
import sys
from glob import glob
from perses.analysis import utils
if __name__ == '__main__':
directory = sys.argv[1]
files = sorted(glob(os.path.join(os.getcwd(), directory, '*.nc')))
files = [x for x in files if 'checkpoint' not in x]
f, axarr = plt.subplots(... |
0dcb4abf0de8455505bb645e4598f7762517abce0e5eed8c4397c88fe323edd5 | Python | 1,763 | 71 | import torch
from torch import nn
from genie.model.single_feature_net import SingleFeatureNet
from genie.model.pair_feature_net import PairFeatureNet
from genie.model.pair_transform_net import PairTransformNet
from genie.model.structure_net import StructureNet
class Denoiser(nn.Module):
def __init__(self,
c_s, c... |
3bf6432406caa0a95ed3b0aa9a77483b142cd256da69d0c40b69301b9a94db6d | Python | 1,763 | 48 | import shlex
import subprocess
import importlib.resources as pkg_resources
from typing import Union, List
def get_executable_path(executable_name: str) -> str:
# Use pkg_resources to find the binary within the package
with pkg_resources.path('rhe', executable_name) as p:
return str(p)
... |
894945fa08dc4b1f3744c0eff301f927b14bde1ade16d34bee093d648c502b29 | Python | 1,763 | 57 | #!/usr/bin/env python
"""Developer notes
- Prepare for pypi:
python setup.py sdist
- Upload to pypi test server to check:
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
- Use testpypi with pip:
pip install --index-url https://test.pypi.org/simple/ pydeface
- Upload to pypi
twine upload dist/*
""... |
8cf54d396d692daad09323472128a16707797e0de47b5d86b74c762f704a47d1 | Python | 1,763 | 70 | import pandas as pd
import numpy as np
def write_csv_features(feature_names, feature_data, csv_file):
"""
Write features to a CSV file.
Parameters
----------
feature_names : list of str
Names of the features.
features_data : numpy array
Data for the features. Format: [frames, ... |
a79eb0ff1665e889c6332c745b97ae99dff0aaa1d4ea6c01c398a6f3480e363a | Python | 1,763 | 65 | """Test pydantic SerDe.
A set of tests that verifies that Union discrimination works correctly with
the various pydantic base models.
These tests can uncover issues that will also arise during regular instantiation
of the models (i.e., not necessarily from loading or dumping JSON).
"""
import pytest
from pydantic im... |
f0554d0634de53e37e32aac7f18d949e9a0290e18983709fa88ef62ba5f3bc81 | Python | 1,763 | 54 | import re
from langchain_core.output_parsers import BaseOutputParser
class BooleanOutputParser(BaseOutputParser[bool]):
"""Parse the output of an LLM call to a boolean."""
true_val: str = "YES"
"""The string value that should be parsed as True."""
false_val: str = "NO"
"""The string value that s... |
5be4df3ab4427f36a01e20961de14d95fc68df09a38478ebff7b10a5b6a18b8b | Python | 1,766 | 56 | from langchain_core.agents import AgentAction, AgentFinish
from langchain_classic.agents.output_parsers.self_ask import SelfAskOutputParser
def test_follow_up() -> None:
"""Test follow up parsing."""
parser = SelfAskOutputParser()
_input = "Follow up: what is two + 2"
output = parser.invoke(_input)
... |
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