sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
45ae713eafc17b9603da332a37731511bbc2beca65b5dd083f49e1c659d1ac34 | Python | 3,102 | 112 |
"""
@Mariellapanag
Created on Feb 2023
@author: mariella
Functions for computing abnormalities from continuous iEEG data.
Includes
map_NaN_values_channel
TODO: Write down comments on this script
"""
import numpy as np
def map_NaN_values_IMFchar(data, nan_all):
[n_modes, n_roi, time] = data.shape
... |
3ad81d9448c0879ef2dca8c79d658f8e17e8c7a6ddd96a853afd63ce30440287 | Python | 3,103 | 75 | import pandas as pd
import dataset
import datasets_manager
import runner
from base import BaseTrainer
from parse_config import ConfigParser
from utils.logger import get_logger
def multi_train_bootstrap(config: ConfigParser):
logger = get_logger('runner.multi_train_bootstrap')
data = {project_id: config.init... |
37be744b36136dda50ce05f5dab48670258905e522528c5f6fad74bae8a2ccf8 | Python | 3,104 | 74 | """
This script reads computes the percentage of time the eyes were closed during
the MRI sessions. Closure of the eyes is determined by the blink state and only
microsleeps (i.e. eye closure longer than 10s) are counted.
The script returns a dataframe per task with the sleep percentage.
Inputs: path
Outputs: csv... |
630cacc5c1d4f1c2916dfa4d29fc21b9e98201dbe160a6b51158b47a3c1838f2 | Python | 3,104 | 87 | from __future__ import annotations
import re
from typing import Any
from wikibaseintegrator.datatypes.basedatatype import BaseDataType
from wikibaseintegrator.wbi_config import config
from wikibaseintegrator.wbi_enums import WikibaseSnakType
class Property(BaseDataType):
"""
Implements the Wikib... |
cc06a84c2866ba7b0cca7ab7475a690dd1b2f283896e8291f9d0b4a782bd032c | Python | 3,105 | 71 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LSTM(nn.Module):
r'''
A simple but effective long-short term memory (LSTM) network structure from the book of Zhang et al. For more details, please refer to the following information.
- Book: Zhang X, Yao L. Deep Learning for EEG-Ba... |
efab06afd9afb86ac385f6a5aabae1787a91f9a2e6b443f65fb4ed511d5260ab | Python | 3,105 | 104 | # Node-group asset "Contains Geometry" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHEC... |
6d4b019555dfae9f88943fdc92bb78fbb719554dd2ca76ae68c9c5a1bab3dd4b | Python | 3,106 | 92 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
bb47e22ac60bee2b5cce855f26fa842bb60a011da0347531651b493a30645949 | Python | 3,106 | 62 | """RUN_HERE.py 2026-10-15. Runs any script of this archive from the folder where the archive was unpacked.
Every script ran on my computer from the folder D:/eucys and names that folder in its own text. I keep the scripts
byte-for-byte as they ran, because MANIFEST.tsv and the pre-registrations record their SHA-256. Th... |
25a878a82d7fd5d6090f69b3e49171038f645568538d4b16cde81c65694fa04d | Python | 3,107 | 87 | import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
class MultiModalDataset(Dataset):
def __init__(self, datasets, transforms=None):
self.datasets = datasets
self._transforms = transforms
all_mrns = [dataset.uids for dataset in self... |
4d0d8a2de44511c5f2787cdd7ed769c555344ab06370bc8c6e0b9c33c6e82d27 | Python | 3,107 | 85 | from copy import copy
def update_param(name, param):
if name == 'min_sdev': # create deprecated alias for 'min_sdev'
threshold = copy(param)
threshold['name'] = 'threshold'
return [threshold, param]
if name == 'eps_sdev': # create deprecated alias for 'eps_sdev'
eps = copy(pa... |
cda21a47443c09c08b6254ebb6402dea14e35396bae753758b6e11f5d8aff344 | Python | 3,107 | 92 | """
Wrapping classes for using igraph package
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 24.02.15
"""
__author__ = 'martinez'
try:
from globals import *
except:
from pyseg.globals import *
try:
import disperse_io
except:
import pyseg.disperse_io
import pexceptio... |
6e37ae79d6d46d06054061fb359b1e50fe61bafca46461493c4bed906475851f | Python | 3,109 | 106 | import sys
import random
import os
import argparse
import sys
import pwd
import time
import subprocess
import re
import shutil
from sklearn import metrics
import re
from sklearn.metrics import roc_curve,roc_auc_score
import numpy as np
import glob
import logging
import io
import tensorflow as tf
#from callbacks import... |
470295e83e1835233e8056bb957df14619e0ad9a352a90e74a14be3894d70b14 | Python | 3,110 | 84 | # 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
#
# ht... |
ee8b15039139d9b07a4c280a1096bf5c4b4955ce3d3128dd47c86b1bd5194cb6 | Python | 3,110 | 75 | from autograd import numpy as npa
from autograd import value_and_grad
from typing import Callable, List
from scipy.optimize import minimize
def unfilter_design(
target: List[float],
processing: Callable,
maxiter: int = 100,
regularization: float = 0.1,
):
"""Given a processing function, uses opti... |
d82245dc9afe8de6fe9b54b4357e71d61b335dca602919655fa4675fd40f4471 | Python | 3,111 | 59 | """
Query co-registered functional data from the MICrONS dataset
last modified: András Ecker 09.2023
"""
import numpy as np
import pandas as pd
from scipy.spatial.distance import pdist, squareform
# follow https://github.com/cajal/microns-nda-access to be able to run this!
from caveclient import CAVEclient
from micron... |
e140d74a85d47f68f13c5583be2ea89f0ef124f749ed9f8bf371c4f01687a832 | Python | 3,112 | 124 | # Computes the diffraction orders of a 2D binary grating with
# triangular lattice using a rectangular supercell and verifies
# that only the diffraction orders of the actual unit cell
# produce non-zero power (up to discretization error)
import math
import numpy as np
import meep as mp
resolution = 100 # pixels/μm... |
31eb31c6083704bc6178b671f9d49fdf46fa80aa4e81d557c3a5befae1d2b334 | Python | 3,113 | 88 | import os
import sys
from typing import Optional, Tuple
def glibc_version_string() -> Optional[str]:
"Returns glibc version string, or None if not using glibc."
return glibc_version_string_confstr() or glibc_version_string_ctypes()
def glibc_version_string_confstr() -> Optional[str]:
"Primary implementa... |
889a4182f457fd25d9b6cdf83ae4e2b39866591f743140707dbc25a128c65042 | Python | 3,113 | 124 | from time import time
import numpy as np
import scipy.linalg as la
import scipy.stats as st
class GWAS:
r"""
Linear model for univariate association testing
between `P` phenotypes and `S` inputs (`P`x`S` tests)
Parameters
----------
Y : (`N`, `P`) ndarray
outputs
F : (`N`, `K`) n... |
ed7378a2a88a1359a5be67f8f66fd8f4e620b644fdb74b25d9eebbc35d7b2925 | Python | 3,113 | 103 | # Copyright 2022 HYBRID Team INCIA, UMR5287, CNRS, Université de Bordeaux
# 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 b... |
38299f5527abfde6681e1ce69e47b38a2ad6b0ea17f669ee359010799da6051d | Python | 3,114 | 73 | from ..region_1020 import (FRONTAL_REGION_LIST, GENERAL_REGION_LIST,
HEMISPHERE_REGION_LIST)
from ..standard_1020 import STANDARD_1020_CHANNEL_LOCATION_DICT
from ..utils import (format_adj_matrix_from_adj_list,
format_adj_matrix_from_standard,
format_... |
480a3c9eada320178bbf9021087f586f05f935fde3776dbc95df5c9c1f41603e | Python | 3,114 | 92 | from sklearn.model_selection import train_test_split
import csv
import os
import shutil
import numpy as np
RANDOM_STATE = 123
test_size = 0.2
def create_directory(path, empty_dir=False):
""" Creates directory if it doesnt exist yet, optionally deleting all files in there """
if not os.path.exists(path):
... |
d5eff42649175e679f0d1d5b8d0d2400383c0c37c36a59809fd66682bec9601e | Python | 3,114 | 63 | from __future__ import print_function
import argparse
import os
import sys
import numpy as np
from topaz.cuda import set_device
from topaz.stats import normalize_images
from topaz.torch import set_num_threads
name = 'normalize'
help = 'normalize a set of images using the 2-component Gaussian mixture model'
def add_... |
e1fd45ef2f95955f6a0dc9af7481f9fd971a53100744ac4d1bf41844dc99e47a | Python | 3,114 | 66 | """Python function to plot folded and unfolded hippocampus
Modified from jordandekraker/hippunfold_toolbox https://github.com/jordandekraker/hippunfold_toolbox/blob/main/hippunfold_toolbox/plotting.py
-removed dentate gyrus label
-load template point and cell data from github
-view set to 'ventral' for both... |
436af0676ea180de661cb12975d3de92b2bf8e666a9d77b179ff3056c87f6051 | Python | 3,115 | 80 | """Tests for Pose_detect_mmdetect3x.select_detections, APT's post-processing of detector boxes.
python test_select_detections.py (or: pytest test_select_detections.py)
"""
import numpy as np
from Pose_detect_mmdetect3x import select_detections, kept_detections
def box(x0, width=100., y0=0., height=100.):
"... |
4bad95372f27ceb4633d5c981783b2e115ad84023b90a19947fd1f29c2d27df2 | Python | 3,115 | 91 | """
Classes for implementing the visitors used by graph_tool package
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 27.11.14
"""
__author__ = 'martinez'
__version__ = "$Revision: 002 $"
__author__ = 'martinez'
import graph_tool.all as gt
#########################################... |
fe953d5a797214cf8dc92c31802c82fddd09683b2f411b0f9eb437236aa33307 | Python | 3,115 | 98 | #!/usr/bin/env python3
import argparse, os, sys, time
#import warnings, json, gzip
import numpy as np
import sklearn.metrics as metrics
hg19_chromsize = {"chr1": 249250621, "chr2": 243199373,
"chr3": 198022430, "chr4": 191154276,
"chr5": 180915260, "chr6": 171115067,
"chr7": 159138663, "ch... |
70dfdef3f087dff183fbb0580f535f2cdcaca9a8eb11e8b24bc7891280940fa9 | Python | 3,116 | 62 | import argparse
import torch
import prescient.simulate as traj
from prescient.train.model import *
def create_parser():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--data_path", required=True, help="Path to PRESCIENT data file stored as a torch pt.")
parser.add_argument("--model_path", re... |
7901a1c67c2eeb838b674b7bcb66bf6760cd8de0f3e9d45c09adf84af708be35 | Python | 3,116 | 94 | """Elastic constants recipes for MLIPs."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc import flow
from quacc.recipes.common.elastic import elastic_tensor_flow as elastic_tensor_flow_
from quacc.recipes.mlip.core import relax_job, static_job
from quacc.wflow_tools.customizers impor... |
b47768dcbc7d74cd9b3647a08f90aaa902cda0b435a7d78118bc73d58c327158 | Python | 3,117 | 73 | from ..region_1020 import (FRONTAL_REGION_LIST, GENERAL_REGION_LIST,
HEMISPHERE_REGION_LIST)
from ..standard_1020 import STANDARD_1020_CHANNEL_LOCATION_DICT
from ..utils import (format_adj_matrix_from_adj_list,
format_adj_matrix_from_standard,
format_... |
fb910b6d4e6d6148cf501defeae08db6c65e113c901104c060228a5256aa56c6 | Python | 3,117 | 89 | """Smoke test for the CCF boundary editor pyqtgraph viewer."""
import numpy as np
import pytest
@pytest.fixture
def qapp():
from PySide6.QtWidgets import QApplication
app = QApplication.instance() or QApplication([])
yield app
def test_viewer_constructs_and_loads_volumes(qapp, tmp_path):... |
3aba5ee85e15a223b9ecc53475140fdf3ca29719f07de296c6c3941c8fbb4380 | Python | 3,119 | 105 | import numpy as np
from numpy.random import default_rng
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResBlock(nn.Module):
def __init__(self, conv1_filters, conv2_filters):
super().__init__()
self.conv1 = nn.Conv2d(conv1_filters, conv2_filters, 3, padding=1... |
50e4f5645c4ebb3a9274076e5bbd78c3ac37769c03ffb731529aa4e2a3269b7e | Python | 3,119 | 85 | import warnings
from os.path import exists as pexists
from os.path import join as pjoin
from bids import BIDSLayout
class ThingsMRIdataset:
def __init__(self, root_path: str, validate: bool = True):
# path attributes
assert isinstance(root_path, str)
self.root_path = root_path
sel... |
ee29f9d4eb6b0fc3b87078a84763c0070e3731bd088c86150cd705a40aece6a3 | Python | 3,119 | 100 | import torch
from torch import nn
class VAE_vae(nn.Module):
def __init__(self, opt):
super().__init__()
self.opt = opt
input_dim = opt.input_dim
hidden_dim = opt.hidden_dim
latent_dim = opt.latent_dim
self.encoder1 = nn.Sequential(
nn.Linear(in... |
b62fc34fc82c956666e0c9fc1b0242bbd4e060600f61640c365eb9303b110da9 | Python | 3,121 | 74 | import torch
import torch.nn as nn
class Score(nn.Module):
def __init__(self, input_size, hidden_size):
super(Score, self).__init__()
self.attn = nn.Linear(hidden_size + input_size, hidden_size)
self.score = nn.Linear(hidden_size, 1, bias=False)
def forward(self, hidden, candi_embeddin... |
5b354293c1da5def66e47a564e920b7097e8002bb2c7302fd9ea10af13e987f8 | Python | 3,122 | 98 | """
Optuna example that optimizes a neural network classifier configuration for the
MNIST dataset using tf.keras.
In this example, we optimize the validation accuracy of MNIST classification using
tf.keras. We optimize the filter and kernel size, kernel stride and layer activation.
"""
import urllib
import optuna
... |
ca8dc4015bdf28904ef6301d1db0620e5bec11116911da2dd97ad92ec3f0efe1 | Python | 3,122 | 82 | #!/usr/bin/env python
from __future__ import print_function
import pybullet as p
from pybullet_tools.utils import add_data_path, connect, dump_body, disconnect, wait_for_user, \
get_movable_joints, get_sample_fn, set_joint_positions, get_joint_name, LockRenderer, link_from_name, get_link_pose, \
multiply, Po... |
01e806a122782f1630f5b278f4a7017e091a1c675d181369ba0a8775a486860f | Python | 3,123 | 71 | import os.path
from collections import defaultdict
from ckg.graphdb_builder import builder_utils
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="signorConfig.yml", data_type='databases')
directory = os.path.join(databases_directory, "SIGNOR")
builder_utils.c... |
8dbaf858f256ea41afcb8f2ed8281a72fe73ae49f5c0162b41fa154a6f4d7071 | Python | 3,123 | 92 | import autograd.numpy.random as npr
from autograd.test_util import combo_check
def stat_check(fun, test_complex=True, **kwargs):
# Tests functions that compute statistics, like sum, mean, etc
x = 3.5
A = npr.randn()
B = npr.randn(3)
C = npr.randn(2, 3)
D = npr.randn(1, 3)
check = combo_che... |
09faa5bfec59fe19c12c314536ec9ea7a0de3181750c02507ec1a73cb3af5615 | Python | 3,124 | 98 | import numpy as np
import pandas as pd
from tqdm import tqdm
import json
from collections import OrderedDict
def onehot_encoding(
sequence: str,
length: int,
alphabet: str = "ACGT",
neutral_alphabet: str = "N",
neutral_value=0.25,
dtype=np.float32,
) -> np.ndarray:
"""One-hot encode sequen... |
215ef28a2c40c1c75db0cacadfcc845fafc214fb00130f160202c252f67a3f5b | Python | 3,124 | 36 | """posthoc_th_candidates_2026-10-07.py (POST HOC, descriptive; not pre-registered).
Avishai & Parnas (2025, Commun. Biol. 8:1555) found about three tyrosine-hydroxylase-positive (dopamine) cells per lobe among
the ~65 glutamatergic ventral local neurons labelled by OK371-Gal4, and could not say which cell types they ar... |
622ee5f6853a8179e077fa8092184106f042e089e057c5089ec012f7874144cd | Python | 3,124 | 82 | #!/usr/bin/env python
from __future__ import print_function
import pybullet as p
from pybullet_tools.utils import add_data_path, connect, dump_body, disconnect, wait_for_user, \
get_movable_joints, get_sample_fn, set_joint_positions, get_joint_name, LockRenderer, link_from_name, get_link_pose, \
multiply, Po... |
72045479b08bc459a7fb483b0707d687d77b0fe4523241357511c9da114e88b4 | Python | 3,124 | 90 | from typing import Optional
from functools import partial
import torch
from torch.nn.modules.loss import _Loss
from ._functional import focal_loss_with_logits
from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
__all__ = ["FocalLoss"]
class FocalLoss(_Loss):
def __init__(
self,
... |
44d3ca2e4572a9c102f2a8287b4c810cf2a3e7e202552024f7b2f74a7fb30341 | Python | 3,125 | 76 | import numpy as np
import pickle as pkl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib as mpl
from matplotlib import rc
#rc('font',**{'family':'serif','serif':['Computer Modern Roman']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
font... |
42a40fcaf50fd3ab202e8c46f9ca2e1d4cd10691a4c735d547e9e39ea4cfec21 | Python | 3,126 | 67 | #!/usr/bin/env python
# Run this file from lis_ltamp/control_tools
# That way it has the right file path for the .dae file
# This specific file depends on openrave.
# But once the C++ files are generated, openrave is not needed.
from openravepy import *
from openravepy import ikfast
from openravepy.misc import InitOp... |
4ccdf0e450648f18a0b59b2799efccf855d2259fc00ab835d7467ad8882152a7 | Python | 3,126 | 89 | from __future__ import annotations
import pytest
torch = pytest.importorskip("torch")
from importlib.util import find_spec
import numpy as np
from ase.build import bulk
from quacc.recipes.mlip.core import relax_job, static_job
libraries = []
if has_matcalc := find_spec("matcalc") and find_spec("matgl"):
libra... |
85921103e1941e84c06221532097d2796bd0e36f00c594f1ede67ec416a7fc79 | Python | 3,126 | 101 | import os
import toml
import glob
import shutil
import logging
from ase.io import read
from ase.atoms import Atoms
from quacc import get_settings
from quacc import strip_decorator
from typing import List, Dict, Any
from quacc.recipes.mace.ts import ts_job, irc_job, geodesic_job
def geodesic_ts_hess_irc_mace(
... |
9f1661257125afa2874002cae3bcb8d0eb11b4bf68c47d67ad7a2ca28d9dce2a | Python | 3,126 | 114 | # Node-group asset "Set Phi Psi Angle" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHEC... |
c696dba5a69e5f5ac516a0fa606a252d7b7a74eacf50a66dddb1402817770f85 | Python | 3,126 | 73 | """
OXASL - BASIL: Quantification using Bayesian model fitting
The BASIL module is a little more complex than the other Workspace based
modules because of the number of options available and the need for flexibility
in how the modelling steps are run.
The main function is ``basil`` which performs model fitting on ASL... |
231b814d36aa0c5cd8d5ee936a580bf9905b8b538b44dcbdc02dfa58e24f554b | Python | 3,127 | 113 | # -*- coding: utf-8 -*-
"""
Created on Sat May 28 22:55:11 2022
@author: ftead
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from numpy import trapz
from scipy.signal import find_peaks
import seaborn as sns
#%%
#testing fofr calculation of a single nucleus
df1 = pd.re... |
ae584a26c49d63dd01861aeda2113f85ed9bdb8d45b1e69db38ec813a6445c6c | Python | 3,127 | 86 | # Importing core packages
import numpy as np
import matplotlib.pyplot as plt
import os
global slm_pitch
slm_pitch = 36e-6
def CreateBlank(img_size):
# Input sizes
img_dim = [np.int(np.round(img_size[0]/slm_pitch)), np.int(np.round(img_size[1]/slm_pitch))]
# Creating the array
img_array = np.zeros(img... |
89b17f7ee094d8fc9d2b87f9e578bc8f5261a2c66804afa5451cc54fe0eab077 | Python | 3,128 | 96 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 4 14:54:33 2023
@author: emma
data generator for passing sample weights based on bias group
"""
from numpy.random import seed
seed(1)
import tensorflow as tf
tf.random.set_seed(1)
import random
random.seed(1)
import numpy as np
import pandas as... |
b2346387cb8fa686f58d019b55317ea304688c55a9b90afddaf8bf36196327db | Python | 3,128 | 83 | """WarningCollector / collect_run_notes: WARNING+ capture for the QC panel."""
import logging
from regtools.registration.run_notes import WarningCollector, collect_run_notes
def _logger(name):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG) # let INFO reach handlers so the level fi... |
d206a3e6a82da5f10de24762d733cae59280fee489760191ab74760d4dc02c9a | Python | 3,128 | 89 | import torch
import scipy
import numpy as np
import pandas as pd
from typing import ClassVar
from torch.utils.data import Dataset
from .utils import Seq2Tensor
class SeqDatasetProb(Dataset):
POINTS: ClassVar[np.ndarray] = np.array([-np.inf, *range(1, 18, 1), np.inf])
""" Sequence dataset. """
... |
2cfb3ed472ce9a9d0837d0663fca60ebce81ad4d77b3bc2e8d25b6424e987ca0 | Python | 3,129 | 76 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from fig_utils import *
def fig7(root, save_figure=True):
env_ids = ["PongNoFrameskip-v4", "SpaceInvadersNoFrameskip-v4",
"Endu... |
bcc50774a58a16c0374e715e8ec1b7c4a737ebe574e549bf68cdac26f4b6eb53 | Python | 3,129 | 92 | import os
import torch
import numpy as np
import importlib
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
from torch.nn.parallel import DataParallel, DistributedDataParallel
from torch.nn.init import xavier_normal_, kaiming_normal_
import functools
from functools import partial
import s... |
e946be4bf12234498f35daebf3560e90690149ac3cf6b408afa92c3049d1ab55 | Python | 3,129 | 88 | #!/bin/env python3
"""
Plot wave front, wave back and phase defect line
"""
import ithildin as ith
import numpy as np
import matplotlib.pyplot as plt
import sys
from typing import List
from scipy.interpolate import RectBivariateSpline
Line = np.ndarray # shape (N, D), N number of points, D spatial dimensions
sd = ith.... |
840c25a9422ac2ccbd3c86230c608bc67ad4467e02787cba2bbe5b01ea352bdf | Python | 3,130 | 84 | """ Preprocessing: This script imports the behavioral data """
import numpy as np
import pandas as pd
from en_utilities import safe_save_dataframe
# Preprocessing of empirical data from O'Leary et al.
# --------------------------------------------------
def preprocess_data(df_raw):
""" Preprocessing of the raw ... |
879be9996d74f100a211f055cb2b4fd744dcd4caf870e2f2a279c2796236115c | Python | 3,130 | 75 | import argparse
import json
import os
import sys
from constants import DATA_DIR
from config_train import config as train_config
from src.training.train_utils import Logger
from src.models import get_regressor_v2
from src.dataloaders.semantic_transforms_dataset import TF_BRIGHTNESS, TF_PERSPECTIVE, TF_CONTRAST, TF_ROTA... |
4507deac6122f53f3dce280e2bc05cc1dd32dd39fef6b228d37ba3d325b5bdbb | Python | 3,131 | 60 | #!/usr/bin/env python
import os
import pandas as pd
import numpy as np
from nltools.data import Brain_Data, Adjacency
from nltools.mask import expand_mask
from sklearn.metrics import pairwise_distances
## location of main project directory on HPC
projpath = '/project/3011157.03/Simon/proj_2022_CABB_movie/'
print('Th... |
8bf6577d6dd1b6e05321a93602dd39974be0d051fa1b18d685571d29ffd5e4d9 | Python | 3,131 | 76 | import numpy as np
import pickle as pkl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib as mpl
from matplotlib import rc
#rc('font',**{'family':'serif','serif':['Computer Modern Roman']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
font... |
859f2ee6d8ce06a7c416e1e7407feb340b6e8dc77f97be3cb92db80e28c8a503 | Python | 3,133 | 85 | """Unit tests for the NGF (edge-alignment) and FSC (spectral) metrics."""
import os
import sys
PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if PROJECT_ROOT not in sys.path:
sys.path.insert(0, PROJECT_ROOT)
import numpy as np
import pytest
from scipy.ndimage import gaus... |
f8b64a44fe638e55173346544c7b4207e36de1f9eeebf1cc6acb07442d07ca90 | Python | 3,134 | 97 | from __future__ import print_function
from pddlstream.algorithms import instantiate_task
instantiate_task.FD_INSTANTIATE = True
from pddlstream.algorithms import instantiation
instantiation.USE_RELATION = True
from pddlstream.algorithms import refinement
refinement.CONSTRAIN_PLANS = False
from pddlstream.language.con... |
2d6ac0eab7f8dcb85b4e1e2af72cfb08f565bd5aa4429aae3927123a9134949d | Python | 3,135 | 88 | from __future__ import annotations
import numpy as np
import pytest
from ase.build import bulk, molecule
from maggma.stores import MemoryStore
from quacc import change_settings
from quacc.recipes.lj.core import freq_job, relax_job, static_job
def test_static_job(tmp_path, monkeypatch):
monkeypatch.chdir(tmp_pat... |
7c4178a9d37251514a133a07471b7951c7d3bf957d8467848a2f50eec0127a02 | Python | 3,136 | 133 | #!/usr/bin/env python
# coding: utf-8
# ## Compare model size/performance correlations using different measures
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# In[2]:
corr_methods = ['pearson', 'spearman', 'ccc']
quantile = 0.01
corr_methods_df = []
for c... |
f3e02c94cddce7234d624f7ec578087dcb526bfa3a468294f7ee0daa0222edac | Python | 3,136 | 83 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
775a7bc874a287622366d9bfbe1ae837143980d9c6818cfda4d1acc164a1cac6 | Python | 3,137 | 73 | #!/usr/bin/env python
from __future__ import print_function
import pybullet as p
from pybullet_tools.pr2_utils import HEAD_LINK_NAME, PR2_GROUPS, get_viewcone, get_detections, \
REST_LEFT_ARM, rightarm_from_leftarm, inverse_visibility, get_detection_cone, visible_base_generator, \
DRAKE_PR2_URDF
from pybulle... |
2f016da78b770b3b5dae7e31ea519bc3d9245895736590749ef332a9f894d577 | Python | 3,138 | 78 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
c1c1c5807cdf7f3d23a225e03485a6ab1c6aef757735f34b6fd0e3c345e08243 | Python | 3,138 | 100 | import pytest
import torch
from elasticai.creator.base_modules.linear import Linear
from elasticai.creator.nn.binary.math_operations import MathOperations
operations = MathOperations()
class TestBinaryOperations:
def test_quantization_returns_1_for_0_8(self) -> None:
x = torch.tensor([0.8])
x = ... |
9c1bd0e819538a752a31a98d07965ca1f8720088194fb4d955cd850ae26c1f55 | Python | 3,139 | 95 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
e97a02941b8f8f0980383459fd287611e5e1d1837fed649eb1c89017128d46c3 | Python | 3,139 | 90 | import unittest
import herdingspikes as hs
import matplotlib.pyplot as plt
import os
from shutil import rmtree
import urllib.request
# raw data location
DATADIR = "data/"
DATA = os.path.join(DATADIR, "visapy_data.npy")
URL = "https://www.dropbox.com/s/za017ro7ff88a5e/visapy_data.npy?dl=1"
# detection parameters
FILE... |
4278abcfffc7b678198126db3237a13337362c9c9dc6d5eced173091c6d70674 | Python | 3,140 | 109 | import os, sys
import time
import numpy as np
import torch
from torch.utils.data import DataLoader
from rdkit.Chem import QED
from rdkit.Chem import RDConfig
from rdkit.Chem.QED import qed
from rdkit.Chem.Descriptors import *
from rdkit.Chem.rdMolDescriptors import *
from .libs.models import MyModel
from .libs.io_infe... |
4831d2c60d4936f0ebfe9b6c28c1d26a4e5c1f9bdc76ff59c0f7aff068bd8e35 | Python | 3,140 | 94 | import copy
from abc import ABC
from typing import Sequence
from elasticai.creator_plugins.lutron_filter.nn.binarize import Binarize
from torch import Tensor
from torch.nn import Module
from elasticai.creator_plugins.grouped_filter import FilterParameters
from .lutron_module_protocol import LutronFilter as _LutronFi... |
382db965e5a92baa45853eca12e46d2adac1ac5ade7113d096354b2c1bf2ded8 | Python | 3,141 | 93 | """Per-layer rotate/transpose for the napari viewer: changing one layer's
transform must not touch the others. The core helpers set a single layer's
.rotate / .affine and are exercised here with a fake layer; the widget wiring
is checked with a mock viewer under a QApplication.
"""
import os
import sys
impor... |
9ffe9c802529b28832bc1b940cf163c24531c95776d1f44977a4079ba62231a8 | Python | 3,141 | 117 | import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.collections import LineCollection
from sklearn.metrics import roc_auc_score, roc_curve
def plot_auroc_with_uncertainty(
score_ls,
label_ls,
color="green",
label="",
linestyle="-",
alpha_traces=0.0,
alpha_uncert... |
196d53b2e347e36632ed2d69d72cc02d733ae923db9c76af0beac4c290a65b54 | Python | 3,142 | 76 | import argparse
from matplotlib import pyplot as plt
import pytorch_lightning as L
import seaborn as sns
import sys
from statsmodels.stats.descriptivestats import sign_test
# local imports
sys.path.append("..")
from shapelearningtheory.datasets import make_dataset
from helpers import format_table, train_and_validate, u... |
41176b3880fc4a3a7038670ddd6bf28511a065315a54b8d1eab938a05978627c | Python | 3,142 | 86 | # code link: https://neuralnet-pytorch.readthedocs.io/en/latest/_modules/neuralnet_pytorch/optim/nadam.html
import torch as T
from torch import optim
class NAdam(optim.Adam):
"""
Adaptive moment with Nesterov gradients.
http://cs229.stanford.edu/proj2015/054_report.pdf
Parameters
----------
p... |
07b357798c0031b81fa97339d830bdce295b49e9abe8b69859453a7ff550d5fd | Python | 3,143 | 72 | import os.path as path
import numpy as np
from eworm.utils import *
from eworm.network import *
from eworm.network.process import *
import pickle
import time
def add_iclamp(circuit, input_cell_names):
circuit.vclamp = []
for cell_name in input_cell_names:
for sid in range(len(circuit.cell(cell_name=cel... |
547d34ab7ed9ed34f34990e5b09b1b6671ed0aede72166647afbda9947405e38 | Python | 3,144 | 89 |
import argparse
import matplotlib
matplotlib.use('Agg')
import numpy as np
import random
import sys
sys.path.append("/media/benelot/SPACE/loci/IDSC/Physiology/projects/lagrangian-learning-the-final-nla/")
random.seed(1238349532)
from model.network_params import NetworkParams
from model.network_params import Comput... |
72d3eda11d70d3d2ec9bbc42064e1defd270a458c9a49c15dc336762c43ad6f8 | Python | 3,144 | 102 | """
Copies column(s) values from a STAR file to another STAR file, if targe file already contained a set column old values
are overwritten
Input: - Source STAR file
- Target STAR file
- List with te column keys to copy
Output: - A copy of target STAR file with column values i... |
74f7222f018875fabea03ea4cf5a48d0ad2aeac60774b286551b2b2e473a5605 | Python | 3,144 | 91 | import torch
import torch.nn as nn
import math
from diff_frech_mean.frechetmean.manifolds import Poincare, Lorentz
from diff_frech_mean.frechetmean.frechet import frechet_mean
class RiemannianBatchNorm(nn.Module):
def __init__(self, dim, manifold,args):
# def __init__(self, dim, manifold):
super(Riem... |
43eabb642140661690ea80f33c5587d5282514ff2c45dfcd202318568896d221 | Python | 3,145 | 107 | #!/usr/bin/env python
from setuptools import find_packages, setup
import os
import subprocess
import time
version_file = 'realesrgan/version.py'
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
def get_git_hash():
def _minimal_ext_cmd(cmd):
... |
66da7da990fb0758e584141307c8b0b14caac1f1751c4c9084346f52956ebe5d | Python | 3,145 | 93 | """Constructor for spin-echo-based frequency calibration sequence."""
from math import pi
import numpy as np
import pypulseq as pp
from console.utilities.sequences.system_settings import raster
from console.utilities.sequences.system_settings import system as default_system
def constructor(
n_steps: int = 10,
... |
9615fdc7fd0903bd693b0858715fecfddbda242ebe67ac288ff238877662c240 | Python | 3,145 | 93 | """Randomly split the datasets in half
"""
# Set up dependencies
import os
import numpy as np
from utils.array_ops import split_half
from utils.data import load_data, load_meta_data, save_data
from utils.visualize import plot_age_distributions
if __name__ == "__main__":
# Set random seed
n_seed = 2023
n... |
bf82b407e790fb11d1116a2b6ebeb3d1922431f8b40c53296805e3fc24c17f61 | Python | 3,145 | 111 | """Base jobs for MRCC."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc import get_settings
from quacc.calculators.mrcc.mrcc import MRCC, MrccProfile
from quacc.runners.ase import Runner
from quacc.schemas.ase import Summarize
from quacc.utils.dicts import recursive_dict_merge
if TY... |
403bb93f3f67cbf2db4e055084ec071a8482935a65f36cad630b28bdcd3b43cb | Python | 3,146 | 79 | # coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# 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 r... |
e2df7835cafc0a5a3c74c78f7664f891111d93e580b673aef9551e97a58b7f63 | Python | 3,146 | 190 | from __future__ import annotations
from dataclasses import dataclass
import numpy as np
import pandas as pd
MAD_SCALE = 1.4826
@dataclass
class SizeQCResult:
table: pd.DataFrame
median_log_area: float
mad_log_area: float
robust_sigma: float
z_threshold: float
small_count: int
large_c... |
4121105402cb2751f1b553c8ad46d278700d29e4d9ffc32cc4d58f911bb3e7ff | Python | 3,147 | 92 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
74c6a68b9e39b7ab88f2e0978d46b1434cb5ba12cfffead0df6cfb160424c74b | Python | 3,147 | 104 | """Prepare PASCAL Context dataset"""
import click
import shutil
import tarfile
import torch
from tqdm import tqdm
from pathlib import Path
from segm.utils.download import download
def download_pcontext(path, overwrite=False):
_AUG_DOWNLOAD_URLS = [
(
"https://www.dropbox.com/s/wtdibo9lb2fur7... |
054036d297df2ba582a63a48cbc5937436ae6ccb2acb87ef6dd550ba54887d7d | Python | 3,148 | 92 | from functools import lru_cache
from codex.configuration import MIN_SYN_THRESHOLD
from codex.data.neuron_data_factory import NeuronDataFactory
from codex.utils.formatting import percentage, display
from codex.utils.graph_algos import reachable_node_counts
from codex.utils import stats as stats_utils
from codex import... |
3f35c87bd4ca3af6bb1b81873e0d6bdea268b8b1a91cbc696ae73bca13d256f5 | Python | 3,148 | 92 | """String constants and section identifiers for the MediaWiki HED schema format."""
from hed.schema.hed_schema_constants import HedSectionKey
from hed.schema.schema_io import df_constants
START_HED_STRING = "!# start schema"
END_SCHEMA_STRING = "!# end schema"
END_HED_STRING = "!# end hed"
ROOT_TAG = "'''"
END_TAG =... |
480988c3e82fc811f6087b1f1409b90ccceebafea44b45d407d5531ad60cb51b | Python | 3,148 | 88 | import matplotlib.pyplot as plt
import autograd.numpy as np
import autograd.numpy.random as npr
import autograd.scipy.stats.multivariate_normal as mvn
from autograd import grad
from autograd.misc.optimizers import adam
from autograd.scipy.stats import norm
def black_box_variational_inference(logprob, D, num_samples)... |
ca44c27c6f47760390433604e9ef8c8a78ae30506b16405db6a826ceb7d0a94f | Python | 3,148 | 113 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
8334c43feae58abbd93706921d1d2efa2dcf3575b2d23032651183d6d1d54f54 | Python | 3,150 | 115 | # -*- coding: utf-8 -*-
"""
Created on Tue Oct 24 11:42:24 2023
@author: Dustin Garner
"""
import os
import navis
from cloudvolume import CloudVolume
import flycode.mapping as mapping
link = "graphene://https://prodv1.flywire-daf.com/segmentation/1.0/fly_v31"
cv = CloudVolume(link, progress=True, use_https=True)
p... |
f2d3321989de6e334b20805c738efd5120f1dae718113c448cfdda028588ac59 | Python | 3,150 | 61 | """A spreadsheet of HED tags."""
from hed.models.base_input import BaseInput
from hed.models.column_mapper import ColumnMapper
class SpreadsheetInput(BaseInput):
"""A spreadsheet of HED tags."""
def __init__(
self,
file=None,
file_type=None,
worksheet_name=None,
tag_c... |
a84d2992753011ac1ef70af4fc57f68706325a93e47972422b48615f9a9290b2 | Python | 3,151 | 105 | import sys
import random
import os
import argparse
import sys
import pwd
import time
import subprocess
import re
import shutil
from sklearn import metrics
import re
from sklearn.metrics import roc_curve,roc_auc_score
import numpy as np
import glob
import logging
import io
import tensorflow as tf
#from callbacks import... |
bc35e24c0258f7e10a106ba8e337f91ae7248f44e94c5695fba9427ae6cb061b | Python | 3,151 | 103 | import numpy as np
import torch
from pathlib import Path
def mse(a, b):
return np.linalg.norm(a.copy().flatten() - b.copy().flatten())
class Tokenizer:
def __init__(self, observations):
self.codebook = np.zeros((0, *observations[0].shape), dtype=np.int16)
for o in observations:
... |
0020875b24f058590c0d93392c13b4e8ceffecf46e41dfbc237d1fe16fcb179d | Python | 3,152 | 105 | # Node-group asset "Color Atomic Number" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CH... |
00c7fdae6fb5ce6c84a31a89b5fe3e8bd7bc58f0700f63a513cc46e6b6c6829e | Python | 3,152 | 91 | #!/usr/bin/env python
"""
Creates a PDF figure from hexbin histogram data calculated by ex_Hexbin app (BioShell example)
USAGE:
./make_plots.py data_hex [x_min x_max [y_min y_max]]
"""
#python make_plots.py outputs_from_test/LEU_rotamers.dat -180 180 -180 180 3.6
import math, re, sys, matplotlib
import matpl... |
48a175f4c07a31f00e28958c3bce9340de41593ce957afb3a9c7e4332cf530c3 | Python | 3,152 | 97 | import subprocess
# import pprint
import re
import platform
# import distutils.spawn
import os
import shutil
import sys
ISPY3 = sys.version_info >= (3, 0)
PrettyOutput = False
def get_gpu_memory_free():
nvidiasmi = 'nvidia-smi'
memtotal = []
memfree = []
# check if nvidia-smi exists
# if distuti... |
985bbd993e7b7de2e3c4437a7994e2773b9ffa82630297e6f0f54788a4145d3e | Python | 3,153 | 105 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
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