sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f3e04939b6c0721806fb36b4b65c2b47150af4f1d1acff4148852e8d78666998 | Python | 2,993 | 56 | """
Tests if sum of gaussian curvatures approximates 2*pi as the gaussian curvature of a closed surface should be.
"""
from scanometrics.core import ScanOMetrics_project
import numpy as np
import matplotlib.pyplot as plt
bids_database = '/home/david/Documents/DATA/test_precomputed_dataset'
proc_pipeline = 'freesurfer... |
f9e08247e7112d807d8c81835b6948b71b026a8e219c3bbb5f18d37838c2bc3a | Python | 2,993 | 91 | """PufferLib vectorized environment integration for rl_games.
pufferlib is optional and not a declared extra: releases up to 3.0.0 pin
numpy<2.0 and gymnasium<=0.29.1, which conflict with this project's floors
(the extra returns with pufferlib 4.0). Install it without its pins -- the
core vectorization API works on cu... |
15561b51d1a0b7ab06caf8f6cc95f9a2aa5efb5d23e10188049b72323cf7011b | Python | 2,994 | 88 | # Calculate structure searching performance
import numpy as np
dataset_dir = "../../dataset"
with open("out.m8") as f:
mmseqs_lines = [l.rstrip() for l in f.readlines()]
domid_to_fam = {}
fams_search = []
with open(dataset_dir + "/search_all.txt") as f:
for line in f.readlines():
cols = line.rstrip(... |
e1132668ce984ebbdea997a355244b45ad6f39c7298723364db193b89bb878d5 | Python | 2,994 | 86 | """
What this script does: Combining subwords and generating a new vec representation file for Futrell2018 dataset
Supported Arguments for model variant:
* For GPT-Neo and OPT family, everything before the slash shown in the get_llm_vec.py
file (i.e., EleutherAI/ and facebook/) is not needed in any step after get_l... |
446bf2cb6a618fe7d3db69a16381ecd7fd0b27f8a273b5e0a16871912f682ead | Python | 2,995 | 81 | import jax
import jax.numpy as jnp
import numpy as np
import os
from hyperparameters import SimArgs
from utils_training import run_epoch
import utils
import parameters
import data
def check_save_folder(sim_params: SimArgs):
base_path = '../simulations' # 'check_params'
# building a simulation ID based on th... |
f535cff300af6c2c812931e3f65441842cb36acb7b6cc38b01096149a3301157 | Python | 2,995 | 83 | """Regression test: `uv build` artifacts must not leak research data assets.
Builds both the sdist and wheel into a temporary directory (never the repo's
own `dist/`, to avoid a build-inside-build trap) and asserts the installable
package files are present while non-package historical research assets
(root `.nii`/`.np... |
b2fc50d135b68db476f6c0f90c16ce0dee392e22906b06699989eb03f2daa18e | Python | 2,996 | 96 | import math
import os
import sys
import unittest
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from treeshrink.threshold_lib import threshold_l_kernel, threshold_loglnorm
KERNEL_VALUES = np.array([
1.02, 1.05, 1.08, 1.11, 1.25, 1.32,
1.41, 1.55, 1.76, 2.05, 2.40, 2.90,
]... |
cc8c949964bb91645150fee72195005463356ea3b4708e3b22564c243ceb55f2 | Python | 2,996 | 115 | """Unit test for cooridnate-gathering routine"""
import os
import sys
import numpy as np
from pathlib import Path
import h5py
import pyrosetta
import pytest
import protein_holography
from protein_holography.coordinates.pyrosetta_hdf5_proteins import (
get_structural_info, pad_structural_info)
from protein_hologr... |
4a68d3402956eae39593d39db4d5e6dbee4fff5cee8eedba677f5d38a7c17402 | Python | 2,997 | 87 |
import json
import random
import cine
import tube2
from skimage import io as skio
from matplotlib import pyplot
from matplotlib import patches
#import scipy
import numpy
from sklearn import neighbors, svm, ensemble
import itertools
PAUSE = 1/64
def draw_object(ax, entry):
row = entry[1]
col = entry[2]
ma... |
56beb431e6cea909ccc42cda37b84d74584f2b5964b93dad2b56d4a624daff64 | Python | 2,997 | 80 | from numba import njit, types
from numba.typed import Dict
import numpy as np, sys
from Wavenet.evaluation import evaluation
from configuration import config
from Wavenet.training import training
@njit(cache=True, fastmath=True, nogil=True)
def Nagumo_3D(dic, Iapps, noise, w0, v0, y0):
n_integrations = int(dic['i... |
bc45efd975dae671a4c6a39424f8d00114e57a1798a622e18d71eac48884bff1 | Python | 2,997 | 75 | #Run and save results of sweeps over eps, eta for 5 fields.
#Date written and executed: 2022-04-26
#Runs multiple replicates at each set of parameter values
# ran test run with two replicates at one set of parameter values
import numpy as np
from placerg.funcs import *
from placerg.funcsrg import *
from placerg.object... |
61550784608107267123e229ba7382d965c8f3f935c22db54b06df34ffed07fd | Python | 2,998 | 59 | import sys, argparse
import pandas as pd
argparser = argparse.ArgumentParser(description='Does an inner merge of two space-delimited data tables.')
argparser.add_argument('f1', type=str, nargs=1, help='Path to first input data table')
argparser.add_argument('f2', type=str, nargs=1, help='Path to second input data tabl... |
23a96c77c299c1c89d9f2c8cbf3c0217bfce5574ca78b7306c5fcfd68a2fa28d | Python | 2,999 | 86 | import sys
import types
import pytest
import pandas as pd
pytestmark = pytest.mark.single_cpu
@pytest.fixture
def dummy_backend():
db = types.ModuleType("pandas_dummy_backend")
setattr(db, "plot", lambda *args, **kwargs: "used_dummy")
return db
@pytest.fixture
def restore_backend():
"""Restore th... |
8c85c77b4785f3c9ba2cb5c3878455b12761dda730e26542b6401b4e739f1652 | Python | 3,000 | 83 | ''' This program will scrub an upload order file of searchable_neurons using a manifest
'''
__version__ = '1.0.0'
import argparse
import re
import sys
from tqdm import tqdm
import jrc_common.jrc_common as JRC
#pylint: disable=logging-fstring-interpolation
MANIFEST = {}
def terminate_program(msg=None):
''' Term... |
2c30165fc9a24311ad4174374e9ca89b77124c9c29a706da25ca895f17e7fec2 | Python | 3,002 | 67 | import os
import numpy as np
import cv2
import argparse
from multiprocessing import Pool
def image_write(path_A, path_B, path_AB):
im_A = cv2.imread(path_A, 1) # python2: cv2.CV_LOAD_IMAGE_COLOR; python3: cv2.IMREAD_COLOR
im_B = cv2.imread(path_B, 1) # python2: cv2.CV_LOAD_IMAGE_COLOR; python3: cv2.IMREAD_COL... |
2f08d0123decd1c39ff1c2c601a1a68d7f56bb71e3f2f1ea845f5d3a4cdef378 | Python | 3,002 | 113 | import argparse
# Performs predictions using a trained model.
# Predictions are performed the same method we used and are saved to a
# target directory.
def get_args():
parser = argparse.ArgumentParser(
description='Train UNet Model',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
p... |
e97bbd955c8b6f294572da2b337e536c227cfd374494e9518b132070c74ff0a1 | Python | 3,002 | 81 | # -*- coding: utf-8 -*-
import os
from keras.callbacks import *
from config import ModelConfig
from callbacks import SWA
class BaseModel(object):
def __init__(self, config: ModelConfig):
self.config = config
self.callbacks = []
self.model = self.build()
def add_model_checkpoint(self... |
03c3413192571cacef600f15bdd42e89746ed5e3a74ab43a5cb4c6a048274b6c | Python | 3,003 | 89 | # Copyright 2026 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agree... |
81bfaa3284f13528b45f3b5840a39cd673a1610dc492d523182787f0945bf35c | Python | 3,003 | 84 | import pytest
import pandas as pd
import pandas._testing as tm
class TestAssign:
def test_assign(self):
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
original = df.copy()
result = df.assign(C=df.B / df.A)
expected = df.copy()
expected["C"] = [4, 2.5, 2]
tm.as... |
8a6389f0d5e53152fe395e5312d29d9ddec1a17df012040353656a19c414e494 | Python | 3,003 | 64 | """
Module to run FSL processing scripts.
"""
import os
from scanometrics.utils import logging
import subprocess
import nibabel as nib
from shutil import which
def fsl_pve_vols(fsl_dir, subj_id, T1_file):
"""Run FSL's BET and FAST scripts on subject subj_id, and extract Partial Volume Estimates (PVEs) for CSF, G... |
c1d14296d57b07adf4115cc169eea52ccb29abb70a06a8fe3ad6a1734fa6ccd2 | Python | 3,003 | 84 | '''
@Author: your name
@Date: 2019-12-20 19:02:25
@LastEditTime: 2020-05-26 20:58:12
@LastEditors: Please set LastEditors
@Description: In User Settings Edit
@FilePath: /matengfei/KGCN_Keras-master/config.py
'''
# -*- coding: utf-8 -*-
import os
RAW_DATA_DIR = os.getcwd()+'/raw_data'
PROCESSED_DATA_DIR = os.getcwd()+... |
03319f1ddb9e70cf8eafe7f18a6aaacf156f3cef750bad4e0e5f4406887d2159 | Python | 3,004 | 56 | from pathlib import Path
import subprocess
import sys
import os
import pandas as pd
recording_folder_to_process = sys.argv[1] # something like /mnt/datastore/Harry/test_recordings or /mnt/datastore/Harry/Cohort7_october2020/of
N_SHUFFLES = 1000
local_scratch_path = '/exports/eddie/scratch/s1228823/recordings'
ELEANOR... |
6d3dc9a4574aabd3ce8edf2645fbe40384b8e0120def312207b58c854fe7bd0c | Python | 3,004 | 72 | """Tests interpolation."""
import pytest
import torch
from mrpro.utils.interpolate import apply_lowres, interp, interpolate
@pytest.fixture
def data() -> torch.Tensor:
"""Create a simple 5D tensor with a linear ramp of step size 1."""
data = torch.arange(0, 20).repeat(10, 10, 1).unsqueeze(0).unsqueeze(0)
... |
c4880a74af4d10d223f098bba18239ac031194b92bc163e9adb3c06f2ce006db | Python | 3,008 | 116 | import torch
import numpy as np
def convert_numpy2tensor(array: np.ndarray, device: torch.device) -> torch.Tensor:
"""
Performs conversion of Numpy array on the CPU to Torch Tensor
on the GPU.
:param array: A Numpy array.
:param device: device to run inference on.
"""
return t... |
312f8f7688b2c371f8df055a3c957d89294b0dbd793e3fe82594698876d53b5d | Python | 3,009 | 80 | import sys
import os
import numpy as np
from scipy.optimize import curve_fit
from helper_make_data import make_data
from helper_history import history
from helper_make import make
def savefiles(B11, B12, B21, B22, B, H, string):
np.savetxt("data_processed/Bias11_%s.txt"%string, B11)
np.savetxt("data_processed/Bias12... |
9186fd4c2d4493468453bc22337ac854649c40ad0c6d0074391ce3191ae83afa | Python | 3,012 | 92 | import numpy
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
def updateIndexName(df, dictionary):
allIndex = df.index.values
for i in range(len(allIndex)):
if allIndex[i] in dictionary:
allIndex[i] = dictionary[allIndex[i]]
df.index = allIndex
re... |
dde8a1d5c10ca73f5bb8deae89abeeaa0f32fe6c1348ef13a29de59ddfe3e7fe | Python | 3,013 | 64 | import argparse
import os
import shutil
import logging
import time
from deepddi import DeepDDI
from deepddi import preprocessing
from deepddi import result_processing
if __name__ == '__main__':
start = time.time()
parser = argparse.ArgumentParser()
parser.add_argument('-o', '--output_dir', required=True,... |
20f9424f8cd8b3b2627e4ee0ea829945b271b8eb44684f07d33b05694c693167 | Python | 3,014 | 98 |
import os
import subprocess
import math
import shutil
import numpy as np
from os.path import isfile, join
from PIL import Image
import random
import csv
from datetime import datetime, timedelta
path = 'phase_recognition_annotations/'
video_path = 'dataset_videos/phase_recognition/'
case_list = os.listdir(path)
case... |
eff91f3624da019f36fc053625548a844563cb15585d1c3fd178dfb953f7da03 | Python | 3,014 | 84 | """Tests for `multimodal_ad.data.synthetic`."""
from pathlib import Path
import numpy as np
from multimodal_ad.data.manifest import ScanManifest
from multimodal_ad.data.synthetic import (
SyntheticDatasetConfig,
generate_synthetic_dataset,
load_synthetic_manifest,
)
from multimodal_ad.data.volumes import... |
0c88b3d710a352cdf01ffa7fd9a2e70b4e724ba8a0242dc202590052196d4262 | Python | 3,015 | 87 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("by", ["A", "B", ["A", "B"]])
def test_size(df, by):
grouped = df.groupby(by=by)
result = grouped.size()
for key, group in grouped:
assert result[key] == len(group)
@pytest.mark.parametriz... |
10cbd845e065c4985c12060beb152950b52e2eb05c4a4da330267cd82d43a4af | Python | 3,015 | 91 | import argparse
import numpy as np
import random
import torch
import windseer.evaluation as eval
import windseer.plotting as plotting
import windseer.utils as utils
parser = argparse.ArgumentParser(
description='Evaluate the model performance on real flight data'
)
parser.add_argument('config_yaml', help='Inp... |
12772d1cf52debb3393b6ef7a61f06a3d952a1ecf44150987ff95b2ba824b54a | Python | 3,015 | 102 | import os
import numpy as np
from torch.utils.data import Dataset
class P3B3(Dataset):
"""P3B3 Synthetic Dataset.
Args:
root: str
Root directory of dataset where CANDLE loads P3B3 data.
partition: str
dataset partition to be loaded.
Must be either 'train' ... |
5371ef53162df455b32014d0561a2eeb08a6d0a81a53de7c38ce61ce85a6663f | Python | 3,016 | 92 | # -------------------------------------------------------------------------------------------------------------------------------------
# Following code curated for GCPNet (https://github.com/BioinfoMachineLearning/GCPNet):
# ----------------------------------------------------------------------------------------------... |
c9b32198e7be7d1c0a20f8448ec7ae3f2e109f87b1553a9c50f796eca29d1e78 | Python | 3,017 | 81 | # Copyright 2026 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agre... |
713b0be8997626a378fe8541805132f0f268203868e83e847519ba6000c3e02b | Python | 3,018 | 46 | from prepare_data.create_cell_feat import *
from prepare_data.create_drug_feat import *
from prepare_data.create_drp_dict import *
from torch.utils.data import Dataset, DataLoader
from torch_geometric.data import Batch
drug_response_dict, drug_name, cell_name = read_dr_dict()
ge_HN_feat, ge_sim_dict, cnv_dict, mut_dic... |
8c8cb9e7d62a7f5310ef218819880295525aa7c4982b792320dada1fef62a51f | Python | 3,018 | 83 | import numpy as np
import timeit
import os
import matplotlib.pyplot as plt
from glm_config import cfg
# https://stackoverflow.com/a/72875625
import numba as nb
@nb.njit(parallel=True) # , fastmath=True
def diag_dot(a, b):
res = np.zeros(a.shape[0])
for i in nb.prange(a.shape[0]):
for j in range(a.shap... |
28b2765ac9b14acef5d105e52932f3776bdbb525e0b4a62109bceff65a309e1c | Python | 3,020 | 76 | import pathlib
import importlib.resources
from PyQt6 import uic, QtWidgets
import dclab
from ... import session
from ...util import get_valid_filename
class ExportFilter(QtWidgets.QDialog):
def __init__(self, parent, pipeline, file_format, *args, **kwargs):
super(ExportFilter, self).__init__(parent=par... |
d357ff7057e26c36202b90353d34302fc41bb3f5ea27146c02e5b69a00550b66 | Python | 3,020 | 90 | # Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or ... |
28754204e0ae7a1f1969727748a52dd9fb14d5668bdd1315d6f197f4b7f967e9 | Python | 3,024 | 80 | import torch
import torch.nn as nn
from darts.api import Model
from darts.modules.conv.mixed_layer import MixedLayer
from darts.modules.operations.conv import ConvBlock, FactorizedReduce
class Cell(Model):
def __init__(self, num_nodes, multiplier, cpp, cp, c, reduction, reduction_prev):
"""
:par... |
0247449c21a229ab3f488d86a5f612c4deaf277e5e128cd5ecc5c24c237e84ff | Python | 3,025 | 83 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GCNConv, GATConv, GINConv, global_add_pool
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
# GCN-CNN based model
class GAT_GCN(torch.nn.Module)... |
e2abff9995ebd179c87e7f35a69cc6c2b134d8fc82a81494690a7e7ea233f154 | Python | 3,025 | 79 | # Copyright 2018-2023 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or... |
b331f4419239d5f92227a50cec819ab39b6a01fd255e5481c2e1b064c3913f6c | Python | 3,027 | 84 | from __future__ import print_function
import sys, os, re, getopt
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
import pdb
def smallestContainingCat ( tr, targbeg, targend, catbeg=0 ):
catmid,lfind = smallestContainingCat ( tr.ch[0], targbeg, targend, cat... |
a2a1023876db99baa510a6951a9ec9348b6a1d605a43e667c243259a1a9a4119 | Python | 3,031 | 78 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
sys.path.insert(0, os.path.abspath('../'))
# -- Project information -------------------------... |
d417f90eb90c461afac7d34dc719247be351bf032d3e410ee721fed6190c729b | Python | 3,031 | 59 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
LstmState = Tuple[torch.Tensor, torch.Tensor]
# region Model architectures
class UnidirectionalVoiceActivityDetector(nn.Module):
"""
A recurrent neural network that uses unidirectional LSTM cells to predict... |
851dde8b536da6cb75b03023fb06bb685d3fc2ad8f63ffbaef8051082b8239ea | Python | 3,033 | 95 | """Exponential Moving Average (EMA) dictionary."""
from collections.abc import ItemsView, KeysView, Mapping, ValuesView
from typing import Any
import torch
class EMADict:
"""
Exponential Moving Average (EMA) dictionary.
Maintains an EMA of values for each key. On update, existing keys are
updated w... |
b0763e5aff5a9d0201517e911a23769b90a2302623a0e600c0066d279c482839 | Python | 3,034 | 102 | """Parsing module for HCNN predictions"""
import logging
import os
from typing import Dict, List
import numpy as np
from tensorflow.data import Dataset
from tensorflow.keras import Model
from protein_holography.utils.protein import (
ol_to_ind_size)
L_MAX = 5
def get_energy(
seq: str,
energies: np.ndar... |
284ad4ac6646eb989ddc0e7af65aeca8d04c95dca17a56cc236f5495f786b723 | Python | 3,037 | 110 | from math import log, sqrt
def minVar_outliers(L,max_out_num,min_gvf=0.2):
# perform minVar bisect multiple times as long as
# the lower half (i.e. L1) can still be bisect with
# gvf at least the specified min_gvf; if the first
# bisection has gvf < min_gvf, then return the max value
L.sort()
n = len(L)
... |
aa4b34aecbc3f8b8d240a55e85de47fe4b17efdf360dae05bf6f10b734af1e3f | Python | 3,037 | 124 | __all__ = [
"MzCalculatorBase",
"MassAnnotationProto",
"MzAnnotationProto",
"MassArray",
"MzArray",
"ChargeArray",
]
import abc
from typing import (
Generic,
Iterable,
Protocol,
Sequence,
Tuple,
TypeVar,
Union,
overload,
runtime_checkable,
)
import numpy as ... |
cf7f696d3bbd99b354e842ff5880743cde376fe9419e6f262264ec6ab485e842 | Python | 3,037 | 96 | # %%
from julearn import run_cross_validation, PipelineCreator
from julearn.utils import configure_logging, logger, raise_error
from sklearn.model_selection import RepeatedStratifiedKFold
import pandas as pd
import numpy as np
from pathlib import Path
configure_logging(level="INFO")
data_dir = Path(__file__).parent... |
33cf492c66bbebd11ff28f9e00eece776f765ffcd3ff39e569898efaba43b0ed | Python | 3,038 | 72 | from kcsd import csd_profile as csd
#from kcsd import MoIKCSD
from kcsd import ValidateKCSD, ValidateKCSD1D, ValidateKCSD2D, ValidateKCSD3D, SpectralStructure, ValidateMoIKCSD
from kcsd import VisibilityMap1D, VisibilityMap2D, VisibilityMap3D
# Function to initialize default parameters
def initialize(value):
if v... |
c4f40658ad093cdc3f546eb8ba7ebefbdfa9aacc0efdcff8dfc587638fbbed75 | Python | 3,040 | 90 | # Import libraries
import pandas as pd
import numpy as np
import nltk
from nltk.stem.isri import ISRIStemmer
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.me... |
f00ff0048ab760fbdefacdd0c4b92ba8f2c56734f614c050f11563aa96d16266 | Python | 3,040 | 95 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
6e7492f331cabcd0637aa298e2492b40bc883304d2398c0ae2ad555c00b115d6 | Python | 3,041 | 90 | import logging
import colorlog
from msmfcode.core.config import *
def addLoggingLevel(levelName, levelNum, methodName=None):
"""
Comprehensively adds a new logging level to the `logging` module and the
currently configured logging class.
`levelName` becomes an attribute of the `logging` module with... |
4d4d13ce9552e4784dcb9f749b690a77a46041cddd7094215d0f1bc3891380b1 | Python | 3,046 | 86 | # Copyright 2025 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agre... |
e08783b350dad687ef9bd8fe8c8086d3455b6cbfc812cffd8e672dcbed4bf888 | Python | 3,046 | 115 | import argparse
import copy
import numpy as np
from pathlib import Path
import pickle
from sklearn.linear_model import Lasso
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
from model.data import Dataset
parser = argparse.ArgumentParser(description='Predict property using Energy ... |
70dd2ff801e927fb63394ac327c134cf1f66e996d9b1a70eb9ae44e9f9d2ffbe | Python | 3,047 | 81 | import glob
import os
import random
import numpy as np
import pandas as pd
import torch.nn as nn
import torch
from src.model import common as common
from src.model.ae_model import autoencoder
from src.args_parser import get_parser
import src.utils as utils
BARON_DIR = str("./data/scPan/baron/CLASS_WISE/")
MURARO_DIR ... |
7bc0eaab0d715f56c8cb4bb7c4307141fab90283e97b793179d75108b6fe8e0e | Python | 3,048 | 89 | """Tests for inversion recovery signal model."""
from collections.abc import Sequence
import pytest
import torch
from mrpro.operators.models import InversionRecovery
from mrpro.utils import RandomGenerator
from tests import autodiff_test
from tests.operators.models.conftest import SHAPE_VARIATIONS_SIGNAL_MODELS
@py... |
c2d1f96c33ac8b3b85cff77a68771d46fd17b0bbf602d640d5c4a4cc96a067e7 | Python | 3,048 | 100 | 1# -*- coding: utf-8 -*-
"""
Created on Thu Feb 11 09:20:31 2021
This file downloads text-to-speech from google servers
@author: Simon Kern
"""
import os
from google.cloud import texttospeech
from scipy.io import wavfile
# file needs to be present
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '../google-cloud-cred... |
321fbcb3f6e8674897c8881c0b887f8eea97e93bd580e58d97bcc865561a542d | Python | 3,051 | 89 | # Copyright 2018-2023 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or... |
6ace7f69446cf72ba9e50cdcd2ca37d336a6e3e3dab9f73e23ac941c35dd45ef | Python | 3,052 | 99 | from __future__ import print_function, absolute_import, division
import json
import numpy as np
import os
from PIL import Image, ImageDraw
from collections import namedtuple
data_folder = 'semantic_segmentation_images_annotations/Images_and_Supervisely_Annotations/'
folder = 'json'
folder_masks_inst = 'mask_instrument... |
ce67c20c92b3b9200009ff5e2928f69926717447f79874e0d802d178eee225d5 | Python | 3,053 | 102 | #! /usr/bin/env python3
# Integrated tests of esl-shuffle miniapp
#
# Usage: ./esl-shuffle.itest.py <builddir> <srcdir> <tmpfile_prefix>
# Example: ./esl-shuffle.itest.py .. .. tmpfoo
#
# SRE, Sat 07 Jul 2018 [Woodland Lodge, Titusville PA]
# supersedes esl-shuffle.itest.pl
#
import sys
import os
import subprocess
... |
d59fa9802d01e62a467aaf7738b7f761ab32e8d1adaa1886bddaa0c0606b75f8 | Python | 3,054 | 84 | '''
Created on Nov 6, 2012
@author: regger
'''
import numpy as np
import neuron
h = neuron.h
# TODO: this needs to be automated... i.e., should be such that
# section, or better, segment is passed as an argument and all
# ionic/axial/capacitive currents are automatically calculated
# => add automatic rec... |
dd6dcc9960c32efa328074f477d565180d911e5a6a7373465e73b77a2f86c2f2 | Python | 3,054 | 79 | #!/usr/bin/env python
import torch
from torch.utils.data import DataLoader
import pickle
from rdkit import Chem
from tqdm import tqdm
import data_struct as ds
from data_struct import MolData, Vocabulary
from data_struct import Variable, decrease_learning_rate
from model import RNN
# import sys
def pretrain(restore_fr... |
64753774d36594d5256a3f749c0ee598d152d6cce108b1f355cd8a8a1b0aad17 | Python | 3,055 | 103 | #!/usr/bin/env python3
# Copyright 2022 Image Analysis Lab, German Center for Neurodegenerative Diseases (DZNE), Bonn
#
# 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.apach... |
86c1eaf51e9e5596039bfb5b20907f4c67d3e7cf0c44e073372dc0d1be84a415 | Python | 3,055 | 80 | import argparse
import logging
def get_args():
"""
Get arguments from command line with argparse.
"""
parser = argparse.ArgumentParser(
prog='sam-merger.py',
description="""Merge a series of SAM format files from a chunked alignment pipeline.""")
parser.add_argument("-i", "--infile... |
5979fbb645ded9c2038b1fe34a8460d47bc361efc894ec58f6b93bac7485296c | Python | 3,056 | 82 | """MSE-Functional."""
from collections.abc import Sequence
import torch
from mrpro.operators.functionals.L2NormSquared import L2NormSquared
class MSE(L2NormSquared):
r"""Functional class for the mean squared error."""
def __init__(
self,
target: torch.Tensor | complex | None = None,
... |
59ea429d5c6d4b763150a86e00e78852f632b58c41fbcbe59460ee7c21995307 | Python | 3,056 | 98 | """Test fetching data from OSF."""
import numpy as np
import pytest
from nimare.annotate import GCLDAModel
from gradec.fetcher import (
_fetch_features,
_fetch_gradients,
_fetch_metamaps,
_fetch_model,
_fetch_neuroquery_counts,
_fetch_spinsamples,
)
@pytest.mark.parametrize(
"dset_nm,mod... |
62ecaaecbe52a465385f07f6e59025679bd5f3e69ffecc9c28f1ae6ba1568a11 | Python | 3,056 | 81 | """Load vision encoder from plain ViT models using HuggingFace."""
import logging
import math
import safetensors.torch
import torch
import torch.nn as nn
logger = logging.getLogger(__name__)
# to ignore imports for sphinx-autoapidoc
__all__: list[str] = []
class VisionEncoder(nn.Module):
"""Wrapper around a g... |
723204f6365bb1c4fe11ebad61c706e23e097f7467a8e1b128ebfd27d2848c44 | Python | 3,060 | 55 | #!/usr/bin/env python
"""
Helper utilities for cleaning up intermediate BASALT files.
"""
import os
def cleanup(assembly_list):
"""
Remove intermediate index/coverage files and archive key matrices.
Parameters
----------
assembly_list : list
List of assemblies. Currently unused but kept... |
88459b460c6f311b42282e4a34e9ab3deeb70ecf8dcc82b2b8a51430af35e34f | Python | 3,061 | 98 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import set_module
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.inference im... |
a711259da2f96e6cb31834025f609fabf6562743fe0a4d1cd7f99eb3eceda9f3 | Python | 3,063 | 89 | """Tests for `multimodal_ad.data.oasis`."""
from pathlib import Path
import polars as pl
import pytest
from multimodal_ad.data.oasis import (
OasisLayout,
OasisLayoutError,
SessionIdError,
load_clinical_table,
load_scan_index,
parse_session_id,
validate_layout,
)
def test_parse_session_... |
a75932ad13f82ab4f036275cfb786418bb217d194caadd38397c5de35ea72ffb | Python | 3,064 | 211 | """
Fixed constants (units, atom types, etc.)
"""
import qcelemental as qcel
au2ang = qcel.constants.conversion_factor("bohr", "angstrom")
elem_to_z = {
'H' : 1,
'LI' : 3,
'Li' : 3,
'BE' : 4,
'Be' : 4,
'B' : 3,
'C' : 6,
'N' : 7,
'O' : 8,
'F' : 9,
'NA' : 11,
'Na'... |
c562f99c05876b566e99755a9134620f9edef06b989a44fa0ff162291a539985 | Python | 3,064 | 87 | import argparse
import pandas as pd
from time import perf_counter
import multiprocessing as mp
import os
class controller:
def __init__(self, namespace):
self.args = namespace
self.args.data_dir = self.args.script_dir + '/' + self.args.data_dir
def run(self):
args_str = ''
for... |
21b55daefa10b75e765a41bb5d2a3c4332f4430217a86e9087f050a65b9dcf1e | Python | 3,065 | 104 | # Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or... |
47423590d1eaa68e0d4649d80686c0321bcc069b47ba3df8e56a3dd1395077c0 | Python | 3,065 | 106 | import torch.nn as nn
from roost.cgcnn.model import DescriptorNetwork
from roost.core import BaseModelClass
from roost.segments import MeanPooling
class CrystalGraphPreNet(BaseModelClass):
"""
Create a crystal graph convolutional neural network for predicting total
material properties.
This model is... |
4471a24175390b6a37f20f0376063ca0dd8bbabc7c0bcf9fda40db9858d49333 | Python | 3,066 | 102 | # Copyright 2026 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agre... |
a266b33b381e057d3be34b0856b7bf693ca1960644a3c56a6df1e4490f437a13 | Python | 3,066 | 88 | """Tests for the compact merge-proposition queue."""
from phylib.utils import connect, unconnect
from phy.gui.qt import Qt
from phy.gui.tests.test_widgets import _wait_until_table_ready
from .._proposition_view import MergePropositionsView
def test_merge_propositions_compact_projection_and_activation(qtbot):
v... |
389b3a82eeb6b231807bfea9f74521e57cc954f666005becb2bcc2199cdab10f | Python | 3,067 | 86 | #! /usr/bin/env python3
# rmanprocess.py
# Massages output of PolyGlotMan's `rman -f latex2e` to fit Tufteian userguide style.
# Example:
# rman -f latex2e hmmbuild.man | rmanprocess.py > manpage.tex
#
import sys
import re
in_synopsis = False
if len(sys.argv) == 1:
f = sys.stdin
else:
f = open(sys.argv[... |
e2344a0932a82315aff140bfcd8a73d2082fad22a4bbfa585cb82228063a35ca | Python | 3,067 | 107 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.14.5
# kernelspec:
# display_name: ol-connectome
# language: python
# name: python3
# ---
# %%
# %load_ext autoreload
"""
This cell does ... |
561c56b89933b246fa932d21627100d7916284a8ed69f3e825472502d6ff1d05 | Python | 3,070 | 106 | import sys
import os
import pysam
import pybedtools
from pybedtools import BedTool
#print len(sys.argv)
if len(sys.argv) != 6:
sys.exit('Usage: python sc_atac_readcounter.py [Input Bam file] [Input Index table (NA if no table)] [Human Cell BED] [Mouse Cell BED] [Output file]')
inputbam = sys.argv[1]
indextable = sys... |
e2bc449fbc26688e3b10ae7836e1f9fd6eda2ee9701cb2ac7f4dec8f515cc80b | Python | 3,070 | 53 |
#TODO: split into multiple tests that share tmpdir and run in order
import os
def test_all(tmpdir, capsys):
data_dir = str(tmpdir.realpath())
input_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'input_files/'))
cache_dir = os.path.join(input_dir, 'fake-cache')
conf = ['conf', 'data_di... |
91a1bfe65b8abba7f5a7458272ce31cd067b5358025aa6dff0a4149325b6b68f | Python | 3,071 | 110 | import numpy as np
import pytest
from pandas.core.dtypes import dtypes
from pandas.core.dtypes.common import is_extension_array_dtype
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import ExtensionArray
class DummyDtype(dtypes.ExtensionDtype):
pass
class DummyArray(ExtensionArray):
... |
7a45382dddea218e0be55441004fcd15efd8d821f35e8603b4dbe6df2a227182 | Python | 3,073 | 65 | from scipy.ndimage import *
from base.diffeo import *
from base.kernelFunctions import *
from gaussianDiffeonsImageMatching import *
def compute(createImages=True):
if createImages:
[x,y] = np.mgrid[0:100, 0:100]/50.
x = x-1
y = y-1
I1 = filters.gaussian_filter(255*np.array(.06 -... |
719db9a835460309115f3674869c0ccd50e57068abb49cf5e01212eaafb5876a | Python | 3,074 | 89 | import numpy as np
import pandas as pd
import datetime
import time
def parse_input(data: pd.DataFrame, annotation, verbose: bool=False) -> None:
"""Parse input data and annotation
Checks that data and annotation / metadata match.
Checks for duplicates.
Appends _dp{n} to duplicated cell id's.
... |
1f9ef90c0abc02e416aaef46db59c2b62fcdf7c455a870dfef2f43cc337c0042 | Python | 3,075 | 92 | import json
finalsample = []
filename0 = r"E:\experiment\mal\super\super3.json"
filename1 = r"E:\experiment\mal\trash3.json"
filename2 = r"E:\experiment\mal\super\super3final.json"
with open(filename0, 'r') as f0:
data0 = json.load(f0)
with open(filename1, 'r') as f1:
data1 = json.load(f1)
for i in d... |
3d1c309210cf4c1e052ac8bb9e2047580beadcfa9815cdcef512121179f76402 | Python | 3,075 | 98 | # Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or... |
0a51145d306abca048d5f6774bf62c9390e0b5f088f06cc4368b563030239914 | Python | 3,076 | 99 | import torch
import bionetwork
import numpy
import time
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import pandas
def makeNetwork(curSize):
parameters = bionetwork.trainingParameters(iterations=100, clipping=1)
interactionsPerNode = 10
sparsity = interactionsPerNode/cu... |
1b860697c64d1c3c5c8f4976d1281433d6009de5f36c9fe8391acd3fd3575076 | Python | 3,077 | 78 | import pytest
from pandas.compat import is_platform_arm
import pandas as pd
import pandas._testing as tm
from pandas.util.version import Version
pytestmark = [pytest.mark.single_cpu]
numba = pytest.importorskip("numba")
pytestmark.append(
pytest.mark.skipif(
Version(numba.__version__) == Version("0.61")... |
d4ceacde5ff47088c09604e567520c202817730535f3104f77ad6e55858ff5fe | Python | 3,077 | 96 | from __future__ import print_function, absolute_import, division
import json
import numpy as np
import os
from PIL import Image, ImageDraw
from collections import namedtuple
data_folder = 'semantic_segmentation_images_annotations/Images_and_Supervisely_Annotations/'
folder = 'json'
folder_masks_anatomy_inst = 'mask_in... |
4a35eae391ef1dfe492fc659a811fc0f6d174dda202cc14f6a9a6a9d5b85adbb | Python | 3,078 | 76 | """Modified Look-Locker inversion recovery (MOLLI) signal model for T1 mapping."""
from collections.abc import Sequence
import torch
from mrpro.operators.SignalModel import SignalModel
from mrpro.utils.reshape import unsqueeze_right
class MOLLI(SignalModel[torch.Tensor, torch.Tensor, torch.Tensor]):
r"""Signal... |
20f5620b9ae9254a997e81aa6b505acca76e0ab3f0aa315fb417a3638381a721 | Python | 3,080 | 85 | import os
from typing import List, Optional
import numpy as np
import torch
import logging
import multiprocessing
import importlib.util
from .architectures import model_architecture, INPUT_SHAPE
from .....image import Stack
from ..... import CHECKPOINT_DIR, IQA3D_URL
def get_iqa3d_checkpoint() -> str:
model_dir =... |
c824794b30b1d5dece81e61cd9c2384864317a63ea44f5f3e9dc95a56fae9188 | Python | 3,081 | 96 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.ticker as ticker
import matplotlib.transforms as transforms
import numpy as np
from pathlib import Path
from statistics import mean, stdev
plt.rcParams['font.family'] = 'Gargi'
p = Path('../../data/grouped_bars')
#dataSheet = p ... |
03950ae81d41e2f2c8e15fe40e2bd1ee7f8d0f3c9dd87838bd795444498807f3 | Python | 3,083 | 87 | import numpy as np
import pandas as pd
import time
import datetime
from scipy import stats
def anova(df: pd.DataFrame, annotation: np.ndarray, threshold: float=0.00001, verbose: bool=False):
"""Apply ANOVA and filter data
Applies ANOVA to identify lowly / sporadically expressed genes and remove them.
Value... |
6d73aa43b7c10b187ba8daf23d0175772231aeccf6dcd8ba531d6a2d47a814ad | Python | 3,083 | 105 | """
Check that the test suite reaches the top-level pandas namespace through ``pd``.
Objects in the top-level pandas namespace are used as ``pd.Series``, ``pd.concat``
and so on, rather than imported directly with ``from pandas import Series``.
Anything else is imported from the module which defines it.
This is meant... |
1a2710e90f476088a28b7913b5ff7da86a4031499fb3bf8c30392b23150e2601 | Python | 3,084 | 105 | import logging
import argparse
from datetime import datetime
import time
from sbrnet_core.config_loader import load_config
from sbrnet_core.synthetic_data.generate_synthetic_data_v1 import make_synthetic_dataset
# Get the current timestamp as a string
current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
# Def... |
f22c2146084aac75aaeab33f666a0f7fdee23366e5ab3359ef649be63ba81179 | Python | 3,084 | 63 | import os
import re
from sphinx.transforms import SphinxTransform
from sphinx import addnodes
from docutils import nodes
class AutoIndexSectionTransform(SphinxTransform):
# Priority 100 runs early during the parsing phase
default_priority = 100
def apply(self, **kwargs):
for section in self.docum... |
24278c59ad88adad0fc5da697e471dfb04e641b8008f574e1d4871309d89b489 | Python | 3,087 | 119 | # -*- coding: utf-8 -*-
# @File name: model_Q10_comparation.py
# @Author: Alicia Garrido-Peña
# @Date: 2023-08-11 02:07:05
# @Contact: alicia.garrido@uam.es
# "Copyright (c) 2023 Alicia Garrido-Peña. All Rights Reserved."
# Use of this source code is govern by GPL-3.0 license that
# can be found in the LICENSE file... |
be85d67a4a4d615e3ac9a3fed29720a9accb370cb9d9b4c937e9c11eba5002be | Python | 3,087 | 87 | """Contains a lazy-loaded interface to the HDF5 module. For internal use only."""
import importlib
from collections.abc import Callable
from types import ModuleType
from typing import Any
_MISSING_MODULES_EXC = ImportError(
"This feature requires the 'aiohttp', 'h5py' and 'fsspec' packages. "
"They can be ins... |
17ea9af4a440abfb73ef867b91bb1e8abfbff6b7c0c04f532da5e114e1821542 | Python | 3,088 | 94 | # MOFTransformer version 2.0.0
import argparse
import textwrap
from importlib import import_module
from argparse import RawTextHelpFormatter
from moftransformer import __version__
commands = [
# ('info' , 'moftransformer.cli.info'),
("install-griday", "moftransformer.cli.install_griday"),
("unins... |
3f721924fe0bcc37f4fd8541b2951f58b4f62f1b87ee4c7cec6e7825a03f19a4 | Python | 3,090 | 85 | # License: see [LICENSE, LICENSES/legged_gym/LICENSE]
from legged_gym import LEGGED_GYM_ROOT_DIR
import os
import isaacgym
from legged_gym.envs import *
from legged_gym.utils import get_args, export_policy_as_jit, task_registry, Logger
import numpy as np
import torch
import time
def play(args):
env_cfg, train_... |
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