sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
544ea9f6b92fe0610974be187a1c37df1687e7585a46f0442b164ddf093cce3e | Python | 2,451 | 60 | """ """
import json
from galaxy.util.plugin_config import plugins_dict
DEFAULT_SHELL_PLUGIN = "LocalShell"
ERROR_MESSAGE_NO_JOB_PLUGIN = "No job plugin parameter found, cannot create CLI job interface"
ERROR_MESSAGE_NO_SUCH_JOB_PLUGIN = "Failed to find job_plugin of type %s, available types include %s"
class CliI... |
df9fb5be7f2fe5b10c56f1ca45b3b6f18b33dd9b07bc925bfbf37da895f011e5 | Python | 2,452 | 70 | #!/usr/bin/env python3
'''Compute a single row of a distance matrix from a pdbinfo file.
This allows for distributed processing'''
import clustering,argparse,sys
from rdkit.Chem import AllChem as Chem
from rdkit.Chem import AllChem
from rdkit.DataStructs import FingerprintSimilarity as fs
from rdkit.Chem.Fingerprin... |
3b3d7c565623f7737372b28a7c836078f54b63676833747c592089c36fab3ba0 | Python | 2,453 | 96 | import sys;
import os;
import json
import numpy;
my_path=os.path.dirname(os.path.realpath(__file__));
sys.path.append(os.path.abspath(my_path+'/../..'));
import biswebpython.core.bis_objects as bis;
# -------------
names = [
[ 'index_big_combo_left_2.vtk.json' , 'left_small' , 'lobes_left.json' , 5494 , 2985 ],
... |
90a9c8fad562e783af3cc0be974823d9cc536d484c258c0c28a15b2e48285154 | Python | 2,453 | 55 | import yaml
from config_manager import config_field
from config_manager import config_template
from config_manager import base_configuration
from tests import constants
class TestCase:
_person_template = config_template.Template(
fields=[
config_field.Field(name=constants.Constants.NUM_ARMS... |
0b87f0663b71701be1954ff19e64cb18ed75b9c9e8cbe4207dd0ee27cae345ff | Python | 2,454 | 79 | from datetime import datetime
from unittest import mock
import dateutil.relativedelta
from api.mocks import FakeAuthContext
from carbonserver.api.infra.repositories.repository_experiments import (
SqlAlchemyRepository,
)
from carbonserver.api.usecases.experiment.project_sum_by_experiment import (
ProjectSumsB... |
af1ccb852b50ccf6cdd9810085a198fa9efffda802b26fe9a43cb7fcb5f538a1 | Python | 2,454 | 79 | import os
import sys
import glob
import numpy as np
def sort_matrix(swcmatrix):
updated_swcmatrix = swcmatrix
sRe = updated_swcmatrix[:,6]
Li = list(range(1,(len(updated_swcmatrix[:,1])+1)))
Li1 = Li[:-1]
for i in Li1:
if updated_swcmatrix[i,6] != -1:
pids= np.wher... |
cd1d287d44c55bea3afc61eb6287fec5dd9ffbefafe1749fed310f1d2d37b684 | Python | 2,455 | 55 | # -*- coding: utf-8 -*-
"""
Created on Tue Feb 28 16:28:17 2023
@author: Caterina Brighi
"""
import SimpleITK as sitk
import pandas as pd
import os
from ImageAnalysisFunctions import getMaxRoi,getMeanRoi
data_supradir = 'Path to data directory'
subjs_path = [ f.path for f in os.scandir(data_supradir) ... |
e4c3dbe893f9df09b33a7cb0952a1409e1d4b5e2e19aafae415219e62b26fba9 | Python | 2,455 | 81 | # 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 seqdata as sd
# -- Project information -----------------------------------------------------
# https://www.sp... |
4ad27e6616fedd5fa63ade1a92973732bd38e9afae709a1178f8b2b6a08d2bde | Python | 2,456 | 81 | import numpy as np
import matplotlib.patches as mpatches
from .analytics import Analysis
def plot_knockdown_results(
ax, results, *, model_groups, color_for_model_group, analysis_to_plot, alpha=0.5
):
"""
Plot knockdown results.
Args:
ax: matplotlib axis
results: dict[model name -> d... |
53aed25a05b2fde72d2ccf69c9d26881c046663a6d15782bce5ee5a7f6be72df | Python | 2,456 | 63 | import numpy as np
import pandas as pd
import os
import glob
import math
import json
import scipy
import scipy.stats
import pickle as pckl
from PIL import Image
import argparse
import sys
sys.path.append('/om4/group/mcdermott/user/jfeather/projects/robust_audio_networks/robustness/metamers_paper_model_analysis_folder... |
5a3be34cb180f39d9eb2cdeb943a0e95abfa888176ffff803baddfbb2417b811 | Python | 2,456 | 69 | ## Read write encoder
import sys,os
from shutil import copyfile
from datetime import datetime,timezone,timedelta
import serial
import csv
import configparser
port = 'COM5' # NOTE! This will change between computers, and may change when a computer is restarted
baudrate = 9600
# initialize output csvfile
root_folder ... |
cc62b7d59c65203283988d3c00fae3db0a0c89c55ad143164a5cb385fe740298 | Python | 2,456 | 95 | import pandas as pd
import numpy as np
import sys
from base import configs
import torch
from base.model import MBSClassifier
#from base.CBP_dataloader import MetalBindingSite
from base.metal_binding_site import MetalBindingSite
def df_to_pdb(pdb_df):
columns = pdb_df.columns.tolist()
pdb_df['character'] = pdb... |
7b47405acf199eb576ee05e8d726fef9a150c576b9ae6c8145e1f7af29632d03 | Python | 2,457 | 80 | import numpy as np
from PyQt5 import QtWidgets, QtCore
class InfDoubleSlider(QtWidgets.QSlider):
valueChangedFloat = QtCore.pyqtSignal("double")
sliderReleasedFloat = QtCore.pyqtSignal("double")
def __init__(self, *args, **kwargs):
super(InfDoubleSlider, self).__init__(*args, **kwargs)
s... |
e640da434e41ec9c889301ef7bd2c3039441ba9c057ae8865737b0ec2024598d | Python | 2,457 | 80 | import numpy as np
from PyQt6 import QtWidgets, QtCore
class InfDoubleSlider(QtWidgets.QSlider):
valueChangedFloat = QtCore.pyqtSignal("double")
sliderReleasedFloat = QtCore.pyqtSignal("double")
def __init__(self, *args, **kwargs):
super(InfDoubleSlider, self).__init__(*args, **kwargs)
s... |
f85b5f9c96dedf7f4846772a46c8d86d009fd539c770f1a990e1903920136d45 | Python | 2,457 | 69 | '''
Obtain data matrices suitable for the zero-shot learning algorithm.
'''
import sys
sys.path.append('..')
import argparse
from config import fname, events_id, triggers, categories
import mne
import numpy as np
import scipy.io
from pathlib import Path
# Be verbose
mne.set_log_level('INFO')
# Handle command line ar... |
1ddeea492c78a8f2e7ce0761f1ac74e8311e359515bd160b472f29367f0d2228 | Python | 2,458 | 80 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Increasing(_BaseTraceHierarchyType):
_parent_path_str = "waterfall"
_path... |
4c7173ef0636e4bc1e8cbf3791d6e8d18fceff77603f91027e79e564cffcac28 | Python | 2,458 | 69 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from typing import Sequence, Tuple
from .._data_obj import Dataset, IndexArg, VarArg
from .. import fmtxt, plot
def scatter_table(
xs: Sequence[VarArg],
color: VarArg = None,
sub: IndexArg = None,
ds: Dataset = None,
dia... |
77a4f7e9c77a5174f313edf13428556b9a6ac17bd48a1e0733b127e31948188c | Python | 2,458 | 64 | import unittest
import logging
import pandas as pd
from pmsurv.models.weibull_linear import WeibullModelLinear
from pmsurv.models.exponential_model import ExponentialModel
import arviz as az
logger = logging.getLogger(__name__)
class TestVeteranDataset(unittest.TestCase):
def test_weibull_linear_veteran(self):
... |
7df4d6da62335369647aa8106d77348d13248ace2310ed7fd4ca5b2ccd327839 | Python | 2,458 | 82 | import pytest
import gym
from gym.spaces import Discrete
from gym.wrappers import StepAPICompatibility
class OldStepEnv(gym.Env):
def __init__(self):
self.action_space = Discrete(2)
self.observation_space = Discrete(2)
def step(self, action):
obs = self.observation_space.sample()
... |
a154eb56a73f94dc9a843b22bd9a4976ad5aea02bf4aa2ce8efa662255a1c705 | Python | 2,458 | 80 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Decreasing(_BaseTraceHierarchyType):
_parent_path_str = "waterfall"
_path... |
d70b097c81b922a41b7902d881c2013cdadc5bce676c0d5f6d56eb5033ebeb9c | Python | 2,458 | 70 | import json
import logging
import os
from pathlib import Path
import pytest
from ome_zarr.data import astronaut, create_zarr
from ome_zarr.format import FormatV05
from ome_zarr.utils import download, info
def log_strings(idx, c, y, x, cc, cy, cx, dtype):
yield f"resolution: s{idx}"
yield f" - shape ('c', 'y... |
ddf467029ed5be776d0c929074f27001fe4153c7f2c73ed6feee696d08513762 | Python | 2,458 | 81 | from typing import Any, Callable, Dict, List, TypeVar, cast
import pytest
from torch.nn import Linear
from torch.optim import SGD
from lightly.utils import optim
TestFunction = TypeVar("TestFunction", bound=Callable[..., None])
parametrize = cast(
Callable[..., Callable[[TestFunction], TestFunction]], pytest.mar... |
f9c2b80241c220f52937de43e013bf490ecf64f3a13080dca2a92614955b51cb | Python | 2,458 | 80 | '''
Generate one-hot embeddings and save as one
file per sequence. Use md5 hash of sequence as file name.
We do it this way so that we can just reuse the whole ESM-based setup without
any changes aside from the input dimension.
'''
from hashlib import md5
from esm import Alphabet, FastaBatchedDataset, ProteinBertMode... |
0af6ade97da79c00a96a41dc0c99ded38cd2ffeb16e4d58bd021b5ae1277e79f | Python | 2,459 | 73 | """W051-STOCH / AUD-CODE 4.5: ``resample_rows_from`` audit counts mean what they say."""
from __future__ import annotations
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens.intervention import PerRowDatums, reference, resample_rows_from, sampling_records
class _OneBlock(nn.Modu... |
23940b5b9ca529a6df4350c5d0512469da63185121d57c91def6e1e573f4fca8 | Python | 2,459 | 75 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import pytorch_lightning as p... |
46498af5fc98f95248e2cc7801e8a94479d503797286acbd13ea3244f4b918ee | Python | 2,459 | 49 | """Module to be imported in the Jupyter Notebook"""
# License: GNU General Public License v3.0
import ipywidgets as widgets
import gui.selectionWindow as sw
from gui.plotWindow import PlotOut
from gui.widgets import run_button_widget, plots_show_button
from gui.methods import calculate_results, make_plot
from gui.termi... |
5a93a1610983be281bbb2656c6a665cf84d0116af990bcaf7abb579ad087baf4 | Python | 2,459 | 67 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
fontsize = 16
plt.style.use('seaborn-colorblind')
plt.rcParams.update({'font.size': fontsize,
'font.family': 'sans-serif',
'font.sans-serif': ['Arial'],
#'axes.labelsize': 'x... |
605b117b1dfc31edbafd9bc5e6bf6b6f84adb4ba60b8b6f98a3941e8182c20f4 | Python | 2,459 | 74 | import numpy as np
from ..preprocess._preprocess import dinuc_shuffle_seqs
def generate_profile_set(base_sequence, num_sample):
"""
create a subset of sequences as background by matching nucleotide profiles
:param base_sequence: sequences to use for matching
:param num_sample: sample size
:return:... |
ba81896267fe07b04f853ba94eaa5c4c7e09eb7fe6336eb4bfcdc1c1556c15bf | Python | 2,459 | 72 | """
Generic methods and classes used throughout.
"""
import os
import datetime
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
from jaratoolbox import settings
from jaratoolbox import behavioranalysis
from jaratoolbox import extrastats
def get_slope(curveParams, x... |
c2ea7b173653cab4e434aac769d89ada4c21eecd282d01f0210afd47f635ab7f | Python | 2,459 | 66 | from __future__ import annotations
from pybind11_tests import docstring_options as m
def test_docstring_options():
# options.disable_function_signatures()
assert not m.test_function1.__doc__
assert m.test_function2.__doc__ == "A custom docstring"
# docstring specified on just the first overload def... |
ebfbe00de9adbd0bfb1edc801cbc221d0f292e9a4c8057f0110807aec4c647dd | Python | 2,459 | 73 | import numpy as np
import scipy.stats as st
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import roc_curve, roc_auc_score
def bootstrap_metric(metric_fxn, x, y, N=100):
metrics = []
size = x.shape[0]
for _ in tqdm(range(N)):
try:
... |
2e19e54cf93ac877abec70c75656a27bc0bccf6516340e8ac74900e08c33ec67 | Python | 2,460 | 80 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import pytorch_lightning as p... |
426455dec6aeead699e21f392d5663bf89dc4467f8a835f409882657f00ac01d | Python | 2,460 | 64 | import tempfile
import unittest
from Loader.src.utils import *
class TestLoadFunctions(unittest.TestCase):
def setUp(self):
# Create a temporary directory for the test data
self.temp_dir = tempfile.TemporaryDirectory()
# Create a dummy NIfTI file for testing
self.nii_file = Path(s... |
7d7623ac683d9595d7be65d2d79478154a3a6552312a2c4fec1b4ae144ae69f7 | Python | 2,460 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 22 15:14:45 2022
@author: mikkel
For information about NatMEG-PD please refer to the data descriptor:
Vinding, M. C., Eriksson, A., Comarovschii, I., Waldthaler, J., Manting, C. L.,
Oostenveld, R., Ingvar, M., Svenningsson, P., & Lundqvist, D. ... |
a8af9e266d04917a77f22e3397941e2289eee9580bf28fa1258749a6c96296a6 | Python | 2,460 | 69 | from imodelsx import submit_utils
from os.path import dirname
import os.path
repo_dir = dirname(dirname(os.path.abspath(__file__)))
# python /home/chansingh/llm-tree/experiments/01_train_model.py --use_cache 0 --subsample_frac 1 --model_name llm_tree --max_depth 3 --use_llm_refine 1
# python /home/chansingh/llm-tree/... |
f20428cc2022ad512e8774b3f1f3d8a0f7e74f855d2871955d95adca8bfeeab3 | Python | 2,461 | 70 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
import absl.app
import optuna
import sys
import tensorflow.compat.v1 as tfv1
from deepspeech_training.evaluate import evaluate
from deepspeech_training.train import create_model
from deepspeech_training.util.config im... |
74050ba75a7cdf7ed36421755cb900aaaca540b7ef7b0ee409fb15490760e3a1 | Python | 2,463 | 79 | from permacache import permacache, stable_hash
import torch
from modular_splicing.utils.construct import construct
from .kayla_gene_model import all_transformed_hsmm
from .transformed_models import unwrap_state
@permacache(
"notebooks/hsmm/direct_inference_experiment/directly_infer_output_5",
key_function=... |
d64f4d84eed504b1ce84559952140d1257ff245cdd40b81547c5aeabc0598f78 | Python | 2,463 | 100 | # -*- coding: utf-8 -*-
# @Author : Yue Liu
# @Email : yueliu19990731@163.com
# @Time : 2022/7/28 14:07
import copy
import torch
import numpy as np
def diffusion_adj(adj, self_loop=True, mode="ppr", transport_rate=0.2):
"""
graph diffusion
:param adj: input adj matrix
:param self_loop: if add t... |
67094779aa6ce626c9447f88d4e43ec3b9c3f0056817c8a0f2240bac8ddb0cbb | Python | 2,465 | 71 | import pandas as pd
import numpy as np
import os, sys, shutil, importlib, glob
from tqdm.notebook import tqdm
import celloracle as co
from celloracle import motif_analysis as ma
from genomepy import Genome
import scanpy as sc
def download_refgenome(ref = "mm10") -> None:
genome_installation = ma.is_genome_installe... |
6a11e48bd6cc10ab73803ddfccf73c5283942d4dc15f1171626444b96a6f4957 | Python | 2,465 | 89 | """Persist validated job internal tool state and enforce tool request constraints.
Revision ID: 566b691307a5
Revises: 9930b68c85af
Create Date: 2025-09-30 04:48:19.727414
"""
from alembic import op
from sqlalchemy import (
Column,
Integer,
JSON,
String,
)
from sqlalchemy.dialects.postgresql import JS... |
7215a9bd704bd62d863df797c18581da3645f8db75dc3b823bd2b38d884a76f6 | Python | 2,465 | 83 | import os
import pytest
from nfp.frameworks import tf
from nfp.preprocessing.xtb_preprocessor import xTBSmilesPreprocessor
dir_path = os.path.dirname(os.path.realpath(__file__))
@pytest.fixture()
def get_2d_smiles_json():
train = ["C", "CO", "CC"]
train_json = [
os.path.join(dir_path, "xtb_inputs", ... |
cb224427a135316bd01ec4d9e645047dfd363e59910331eea0c1cb895ff54f88 | Python | 2,465 | 85 | from anndata import AnnData
from typing import Optional, Union
import numpy as np
from stlearn.em import run_pca, run_diffmap
from stlearn.pp import neighbors
from scipy.spatial.distance import cdist
def local_level(
adata: AnnData,
use_label: str = "louvain",
cluster: int = 9,
w: float = 0.5,
ret... |
03f8ee621dc236d324ad6abaf957faf6f9051feb02226d41c657d7bda8f1bd82 | Python | 2,466 | 80 | """Single authority for which benchmark operations TorchLens owns.
``perf_gate.py`` and ``perf_suite.py`` previously carried two independent
copies of the same 10-prefix classifier with no lockstep test (b2 R41 round
5, wave-born): after fixwave-4 the classifier decides missing-row blocking,
status-failure blocking, u... |
52e4b7559e579d5d6ee41b56f9029b16fd306af9654296e0b6c3887b95ef4e58 | Python | 2,466 | 53 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for visualization of dendrograms"""
import tempfile
import unittest
import numpy as np
from sknetwork.data.toy_graphs import karate_club, painters
from sknetwork.hierarchy import Paris
from sknetwork.visualization.dendrograms import visualize_dendrogram, svg_de... |
49f610d6bd16c5daf0a75785fd7f314bec7167a1fb66a1d1baea741a4af5b673 | Python | 2,467 | 80 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "choropleth"
_pat... |
f9c476c7b433a9c220bdc95dfaa51ce8ec6764246af558ecacf3b394a3fb4f64 | Python | 2,467 | 80 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "scattermap"
_pat... |
a910e78e99d8e6439236f7bb4871f22af9f2a7690929b909ecf31053cbddfdb1 | Python | 2,468 | 59 | from typing import Dict, Tuple
from pytorch_lightning import LightningModule
from torch import Tensor
from torch.nn import CrossEntropyLoss, Linear
from lightly.utils.benchmarking.topk import mean_topk_accuracy
class OnlineLinearClassifier(LightningModule):
def __init__(
self,
feature_dim: int =... |
c202c86c310e63f30d24ccc4c0ae843714b8d34a888bd99a9d9ec84451af8e9f | Python | 2,469 | 58 | """
This module allows us to run the simulation from the command line interface. To do so, you need to specify the
experimental constants file, as well as the part(s) of the simulation that you would like to run (controlled by flags:
parallelise_flag, train_flag, eval_flag, and analysis_flag). The flags control differe... |
76b5ec8fd21b74ce6bd7bb931d162e16cdcb7520376faaa27a1ab463a7102ecf | Python | 2,470 | 80 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Selected(_BaseTraceHierarchyType):
_parent_path_str = "choroplethmap"
_pa... |
8f6204e962f104556107d792e49dd039dd82a98672924ea3df343a6868734d75 | Python | 2,471 | 80 | import os.path
import shutil
import sys
import warnings
from pathlib import Path
def get_settings_xml(target_dir: str | Path):
"""
Creates a local copy of the settings ``xml`` file. This file can be used to configure the image processing and
feature extraction workflow.
Parameters
----------
... |
9299a7e39e9bed047fa99ad1c73c260e927a1f92e4538350be479573e7d4c427 | Python | 2,471 | 70 | import nfp
import numpy as np
import pandas as pd
# Define how to featurize the input molecules
def atom_featurizer(atom):
""" Return an string representing the atom type
"""
return str(
(
atom.GetSymbol(),
atom.GetIsAromatic(),
nfp.get_ring_size(atom, max_size=6... |
03887ee2b1746fd58044224a2922c9546d6f2d8df10aca91661c0598e598aee1 | Python | 2,472 | 65 | import numpy as np
import random
import cv2
from collections import namedtuple, deque
import math
''' Replay Buffer
'''
N_CHANNEL, N_HIGH, N_WEIGHT = 50, 256, 256
class ReplayMemory():
def __init__(self, memory_size):
self.memory_counter = 0
self.memory_size = memory_size
self.state_memory... |
bc43e194c0769c1b9691f040a1289a5837ffae8617f485922262c4810abd4022 | Python | 2,472 | 85 | import torch.nn as nn
from modular_splicing.utils.construct import construct
from modular_splicing.models.modules.residual_unit_stack import ResidualStack
from modular_splicing.models.modules.fc_motif_feature_extractor import (
FCMotifFeatureExtractor,
get_fc_block,
)
class LearnedMotifModel(nn.Module):
... |
90e1433cf7621aa1be29c58d891c1b4bfcbf9e6c397d32984efba3ff1f3205dd | Python | 2,473 | 86 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in November 2019
@author: Nathan de Lara <nathan.delara@polytechnique.org>
"""
from typing import Union
import numpy as np
from scipy import sparse
from scipy.sparse.linalg import LinearOperator
def diagonal_pseudo_inverse(weights: np.ndarray) -> sparse.csr_... |
97e094610f63948267ce4190a65794fcdab9e127e8aec21fd41a69efabd53514 | Python | 2,473 | 77 | import numpy as np
import re
class Vote:
def __init__(self,pred_class,pred_seq,pred_score):
self.pred_class = pred_class
self.pred_seq = pred_seq
self.pred_score = pred_score
def __str__(self):
if self.pred_class == '<PC>':
excerpt = self.pred_seq[:20]+'...' if ... |
da2ba180a40c2bbadabca8d428cf6fa10c3a323f1ae855fe99ab6bce07e5c94a | Python | 2,474 | 100 | import numpy as np
import pandas as pd
import tqdm.auto as tqdm
from .standard_e2e_eval import evaluate_on_checkpoint
def evaluate_model_series(
model_series,
data_spec,
*,
split,
include_path=False,
data_path="dataset_test_0.h5",
):
"""
Evaluate a series of models, with the same data... |
f776ff80a60e294ecae03433e84000d59cfcb559596f137183bc3b1506048d75 | Python | 2,475 | 67 | import torch
from models.basic import DeepLearningBackbone
from models.utils import get_nonlinear
__all__ = ['OWMNet']
class OWMNet(DeepLearningBackbone):
def __init__(self, in_channels: int = 3, num_classes: int = 1000, nl='relu'):
super().__init__()
self.nl = get_nonlinear(nl)
self.m... |
2d1cccf6dee4f029f5db8fd9e5f2664bff79d684cccd47670439a80660326ce3 | Python | 2,476 | 81 | """Class to retreive and store ROI activations from murfi. See the
accompanying test_murfi_communicator.py for example usage.
"""
import socket
import re
import random
class MurfiActivationCommunicator:
def __init__(self, ip, port, num_trs, roi_names, fake=False):
self._ip = ip
self._port = port... |
3ab682eccdbec63779028cf7822871989fcf1501f266d6de1c03635303ea0746 | Python | 2,476 | 87 | import matplotlib.pyplot as plt
import networkx as nx
import seaborn as sns
class nxDraw:
@classmethod
def draw_spectral(cls, A, path=None, **kwargs):
G = nx.from_numpy_matrix(A)
nx.draw_spectral(G, **kwargs)
if path is not None:
plt.savefig(path)
@classmethod
def ... |
9b2dbc9402281676880455cf5f42b32f8cbe9e04fc8768c06c8e3a11e7f5714a | Python | 2,476 | 90 | # visualize AlphaFold pdb with AlphaFold pLDDT color.
# It also highlight residues of interests as sphere.
import py3Dmol
import matplotlib.pyplot as plt
plddt_bands = ["#FF7D45", "#FFDB13", "#65CBF3", "#0053D6"]
styles = ["cartoon", "stick", "sphere"]
thresh = [
"Very low (pLDDT < 50)",
"Low (70 > pLDDT > 50... |
d9968e122ba38a0a144f9d10e34fda67c2bea234e9880409236b3f2c2520f448 | Python | 2,477 | 61 | from pyrosm.data_manager import get_osm_data
from pyrosm.frames import prepare_geodataframe
from pyrosm.utils import validate_custom_filter
import geopandas as gpd
import warnings
def get_boundary_data(node_coordinates, way_records, relations,
tags_as_columns, custom_filter,
... |
92b7e482ea2cc1e7cec12de47f417de1bade44e93d29fcd9dc3d208d3c659765 | Python | 2,478 | 70 | import os
from airPLS import airPLS
import nmrglue as ng
import numpy as np
from tqdm import tqdm
def read_bruker_h_base(nmr_path, bRaw=False, bMinMaxScale=False):
nmr_path = os.path.normpath(nmr_path)
if bRaw:
dic, fid = ng.fileio.bruker.read(f'{nmr_path}/1')
zero_fill_size = dic['acqus']['T... |
74f016e8a7c58a367270709dd072b3cd6daaf4ba3681233ea8d048e96f145235 | Python | 2,481 | 83 | """Multi-arm Bandit task."""
# TODO: add the actual papers.
import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
from neurogym.utils.logging import logger
class Bandit(TrialEnv):
"""Multi-arm bandit task.
On each trial, the agent is presented with multiple choices. Each
... |
efdd5f75da876667fe810f1ae0e3166cbb91ef1c7be6c670530db8e9d99ecdb3 | Python | 2,481 | 78 | """
BASIL GUI for Oxford ASL - Code to run oxford_asl script and display output
Copyright (C) 2020 University of Oxford
"""
import os
import wx
from wx.lib.pubsub import pub
from . import OptionComponent
from .cmdline import CmdRunner, Mkdir, FslCmd
class OxfordAslRunner(wx.Frame, OptionComponent):
"""
Run... |
d99920f4600174bee425a76bb7b2fcb4ce8b735dfe0ace521e566ab63761128c | Python | 2,482 | 78 | """Tests for LarvaworldParam.to_config / save_config / from_config."""
from __future__ import annotations
from pathlib import Path
import pytest
from larvaworld.lib import reg
from larvaworld.lib.reg.data_aux import LarvaworldParam
from larvaworld.lib.util import AttrDict
#: (key, expected `v` param class name) co... |
09537c36330cb17954394f0acf9f24254044124ae81ee2b37b8a85da0435d625 | Python | 2,483 | 65 | import math
import torch
import torch.nn as nn
import torch.distributions as D
class RepresentationLayer(torch.nn.Module):
'''
Implements a representation layer, that accumulates pytorch gradients.
Representations are vectors in nrep-dimensional real space. By default
they will be initialized as a ten... |
12c409600f4375392ece181efa4a3bb152d58234a9f25c529752c3adfc79b38b | Python | 2,483 | 71 | #!/usr/bin/env python
#=========================================================================
#
# Copyright NumFOCUS
#
# 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://... |
577832f9e088507a8333bf6bf835c9ea069349e1d5e1478107a4c77d1d2c54bb | Python | 2,483 | 65 | import os
import torch
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
def draw_chart(train_loss, train_r2_score, val_loss, val_r2_score, test_loss_list, test_r2_list, region, type_model="dis", output_prefix = 'images_lowpass'):
if not os.path.exists(outp... |
ff80da7ec884acbcb8b2ba28c2c88d69b263068a43f41b1bda1c8edf7f0f8fb1 | Python | 2,483 | 119 | """Tests for specparam.reports.strings."""
from specparam.reports.strings import *
from specparam.reports.strings import _format, _no_model_str
###################################################################################################
##########################################################################... |
85d417125c34fb3884d183b4761825e23cdee6e9bdca6bd00099578f76e287bb | Python | 2,484 | 104 | import ctypes
import os
import warnings
from ctypes.util import find_library
# Check if the ray package is available
RAY_AVAILABLE = True
try:
import ray
except ImportError:
RAY_AVAILABLE = False
def limit_inner_threads(n_threads: int = 1):
# OpenBLAS-based multi-threading libraries
try_paths = [
... |
99c38a57853bf222d298ce41a0b6f3d8b3937f3df61adc63e3aacc12b8f73fa8 | Python | 2,484 | 67 | """Test cases for to_coco.py."""
import os
import unittest
from bdd100k.common.utils import load_bdd100k_config
from bdd100k.label.to_coco import (
bitmask2coco_ins_seg,
bitmask2coco_seg_track,
bitmasks_loader,
)
SHAPE = (720, 1280)
class TestBitmasks2COCO(unittest.TestCase):
"""Test Cases for the d... |
adf4e8e8c4697091d34550b9f98bbf04688d0727937252e78066f6ad89ce6c85 | Python | 2,485 | 75 | import torch
import torch.nn as nn
from torch.linalg import svd
class MMCRLoss(nn.Module):
"""Implementation of the loss function from MMCR [0] using Manifold Capacity.
All hyperparameters are set to the default values from the paper for ImageNet.
- [0]: Efficient Coding of Natural Images using Maximum M... |
4327827345fff4fc3acf0a157c96d9e9528b3411d7d2f875691e3b4fe37d7ba0 | Python | 2,486 | 73 | # coding=utf-8
# Copyright 2024 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... |
d24344fcbc3f7d9f99c6e7cab6a30e00b70ae04b9b760f3c8ff38a99cada47c1 | Python | 2,486 | 79 | """
Module for handling the conversion of a normal dataset of splicepoints
to a dataset of intron/exon annotations.
"""
import numpy as np
IS_INTRON = {(1, 2): False, (2, 1): True}
ANNOTATION_INTRON = -2
ANNOTATION_EXON = -1
def to_intron_exon_annotation(ys):
"""
Take the given annotation of outputs, and c... |
1d9741689f0ff0b049fb7fd3ca973c424bec868f60352cb5f8200d133f2b43e4 | Python | 2,487 | 56 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for activation"""
import unittest
from sknetwork.gnn.activation import *
class TestActivation(unittest.TestCase):
def test_get_activation(self):
self.assertTrue(isinstance(get_activation('Identity'), BaseActivation))
self.assertTrue(isinst... |
c5709d124b2db9da790c65c1c4424dd43d9f29f3f67183442e17f8918c16a246 | Python | 2,487 | 71 | """Which closed ways are areas: the OSM wiki's list of polygon features
(https://wiki.openstreetmap.org/wiki/Overpass_turbo/Polygon_Features), as OSMnx uses it.
"""
ALL = "all"
PASSLIST = "passlist"
BLOCKLIST = "blocklist"
# Per key: every value makes a closed way an area (ALL), only the listed values do
# (PASSLIST)... |
5bc6a48d86c7720e37343887d919f37b2417794ec7bac2fe0dccc358473a7b2d | Python | 2,488 | 82 | """add page history columns
Revision ID: b75f0f4dbcd4
Revises: b8d5e2f9a1c7
Create Date: 2025-01-06
"""
import sqlalchemy as sa
from galaxy.model.custom_types import TrimmedString
from galaxy.model.migrations.util import (
add_column,
create_foreign_key,
create_index,
drop_column,
drop_constrain... |
bbcd64b202349e400a5a88b6cec2b73af9eba9ae3ee2b7d494719a15273833bf | Python | 2,488 | 78 | import itertools
import tqdm.auto as tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from modular_splicing.dataset.h5_dataset import H5Dataset
def get_location_info(y, m, look_cl):
overall = []
splice_points = np.where(y)[0]
for site in np.where(m[look_cl:-look_cl])[0]:
... |
faaf900b3e9cd941499bdb808dca37bceb7d32d31f4fc710d776ec84d9635a6f | Python | 2,488 | 62 | # coding: utf-8
#new version of flip catching/correcting script that detects strand flips and corrects them back in RFmix output
#this version fixes the RFmix output, .msp file with LAI calls for 2-way admixed individuals
__author__ = 'egatkinson'
import argparse
import re
import numpy as np
def parse_args():
parse... |
3c88d5d635521bb7b4ba85ebaea4322a79a392841e10601e8310deb5dcc5e050 | Python | 2,489 | 67 | from typing import Tuple
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from pytorch_lightning import Callback
from pl_bolts.utils import _TORCHVISION_AVAILABLE
from pl_bolts.utils.warnings import warn_missing_pkg
if _TORCHVISION_AVAILABLE:
from torchvision.utils import make_grid
els... |
a8484fc7b0e1fd9aca5ec6f61a89c80e97f16acfd484ce2caaa50951668ff4e7 | Python | 2,489 | 77 | """Static Galaxy file sources - ftp and libraries."""
from galaxy.files.sources import PluginKind
from .posix import (
PosixFilesSource,
PosixTemplateConfiguration,
)
class UserFtpFilesSource(PosixFilesSource):
plugin_type = "gxftp"
plugin_kind = PluginKind.stock
def __init__(self, template_conf... |
537a7de94a2068ed54db968ccf03f3ffadfa2bce6b5094116543368409a4e6d7 | Python | 2,490 | 65 | '''Test tns.generate.section code'''
import json
import os
import numpy as np
from nose.tools import assert_dict_equal
from numpy.testing import assert_array_almost_equal, assert_equal
from tns.generate.algorithms.common import TMDStop
from tns.generate.algorithms.barcode import Barcode
_PATH = os.path.join(os.path.d... |
bb14573569e5a0dde43db6ec140432e17c87449bc03207a882b13bbed8870d32 | Python | 2,490 | 87 | # Copyright 2022 Deepfold 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 required by applicable law or agreed to in writin... |
bc54726bcfb707f0a80cb1218a59a3459799c41fe0da69151f752a298a727683 | Python | 2,490 | 77 | """
Reusable widgets for editing stored configurations.
Exposes the per-conftype editors, the preset managers and the field-level
widgets the portal apps build their forms from.
"""
from .arena_widget import build_area_widget
from .border_widget import build_border_widget
from .conftype_actions import ConftypeActions... |
32a8693ab9427ddbbe6fe0edcbbf207269d5f921fe373c3c50b502c662ba3457 | Python | 2,491 | 90 | from typing import (
Optional, # Special
)
from anndata import AnnData
from stlearn.pl._docs import doc_spatial_base_plot, doc_subcluster_plot
from stlearn.pl.classes import SubClusterPlot
from stlearn.utils import _AxesSubplot, _docs_params
@_docs_params(
spatial_base_plot=doc_spatial_base_plot, subcluste... |
3523e2f5f37ea94a85b859ab0954ef24222530bee1400561116fc1ce805421ad | Python | 2,491 | 88 | import json
import sys
import os
import traceback
import anarci
"""
This script checks whether the input JSON sequences contain antibodies.
"""
FAIL_MESSAGE = "MSA diversion check failed! The input is not supported currently."
def read_json(path):
"""
Read a JSON file and return its contents as a Python obje... |
42ff8bc33c9508c8da846f8694cf88610d1b7f0c5cfffb78f69be72b18302a61 | Python | 2,491 | 75 | """
SimplyFire is a customizable analysis software for electrophysiologists.
It is written in Python.
SimplyFire can be downloaded from TestPyPI as follows:
pip install -i https://test.pypi.org/simple/simplyfire
To run the software from command line, use script:
py -m simplyfire
The software is currently under pre... |
5630d5401932a60bb8bf105318f1760ea336ef5b6c351eacb7a9867eecfe4c1f | Python | 2,491 | 75 | from typing import Tuple
from lightly.transforms.multi_view_transform import MultiViewTransform
from lightly.transforms.torchvision_v2_compatibility import torchvision_transforms as T
class MultiCropTranform(MultiViewTransform):
"""Implements the multi-crop transformations. Used by Swav.
Input to this trans... |
52140e5f8936da0a7d9e7a18209c6b4c15f083476fc2a14ca71bac96f724bbef | Python | 2,492 | 97 | import multiprocessing as mp
# from multiprocessing import Process as Process
from peakME_functions_2022 import *
from map_build_functions import *
from sys import argv
import os
import pandas as pd
if __name__ == '__main__':
threads = mp.cpu_count()
cmf_dir = argv[1] # close match files
mir... |
d64015db668df2de2fe126d7b9f7292332ec0588eb41db671e0fbf5bd7e6e397 | Python | 2,492 | 47 | """C.3: калибровка метрик знака на рукописных политиках.
Без калибровки метрики результаты этапа 3 интерпретировать нельзя. Три
политики с заранее известным ответом (signs=True):
* mark_always — метить всегда, содержание — шум: MI(состояние; содержание)
должна быть ~0 (знак есть, смысла нет);
* mark_when_hungr... |
067b9bfec708a92e683a32b904f13efa7d0ae5ecbfb34308b2c80bd0ab4589ba | Python | 2,493 | 73 | #######################################################################################################
##################### Preprocessing for multi-task models #############################################
#######################################################################################################
import... |
1f6702ef71a48d84cf048adec19be66ec06fabd13c6528fdf7217250768fba08 | Python | 2,493 | 51 | import torch.nn as nn
from models.encoders import VGG11Encoder, AlexNetEncoder
from models.resnets import resnet50
class LPLNet(nn.Module):
def __init__(self, encoder='vgg', pretrained=False, train_end_to_end=False, projector_mlp=False, projection_size=256, mlp_hidden_size=2048, base_image_size=32, no_biases=Fal... |
a838804b2886c85595c5f6cb80df3635c0bbaac98dba7a4629a15a7bace7cb95 | Python | 2,493 | 68 | import os
import sys
from robustness import datasets
from robustness.attacker import AttackerModel
from robustness.model_utils import make_and_restore_model
import torch
torch.backends.cudnn.benchmark = True
from model_analysis_folders.all_model_info import IMAGENET_PATH, MODEL_BASE_PATH
def build_net(ds_kwargs={}, re... |
146974811a5dc67b721cfd7eddda8e8bfbef8b168329238d6ba71b7d8a91307a | Python | 2,494 | 80 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "choroplethmap"
_... |
2565bc77c0b1b43024764b2f4539dd110a22eb449d44f4ad5ab55b3ee4c72207 | Python | 2,494 | 73 | """ Utilities for using ggplot2 witn ipython / jupyter. """
from rpy2 import robjects
from rpy2.robjects.lib import ggplot2, grdevices
from IPython import get_ipython # type: ignore
from IPython.core.display import Image # type: ignore
class GGPlot(ggplot2.GGPlot):
def png(self, width=700, height=500):
... |
40d524ac9fc1a9a6edc6290f8f9d19c7fabbe5163fa4d4c75dda2f7a35ee8940 | Python | 2,495 | 63 | #!/usr/bin/env python
#
# Copyright 2018 Google LLC. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of... |
85e0a334380f7b198f7650281bbe85bd8203383c7345922c342605de465eb637 | Python | 2,495 | 67 | import os
import pytorch_lightning as pl
from rdkit import Chem # safe import before ccdc imports
from bioconfpred.data.dataset import PyGDataset
from bioconfpred.data.split import (RandomSplit,
ScaffoldSplit)
from bioconfpred.model import (SchNetModel,
... |
87ea19b4301a7e745471fd7d7b37412f25ec875a7f3b70a0f2ce780c45d9823e | Python | 2,495 | 73 | import numpy as np
from scipy import sparse
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing._data import _handle_zeros_in_scale
from sklearn.utils.sparsefuncs import min_max_axis
class MinMaxScaler(TransformerMixin, BaseEstimator):
"""
If I wanted to use min-max scaling on ... |
f3fb07a7274bd28a278be6e0baf2740cb3ea298bf795e342229d9838e7f9e1e7 | Python | 2,495 | 64 | #!/usr/bin/env python
# Created on: July 8, 2013
__author__ = "Sven Kreiss, Kyle Cranmer"
__version__ = "0.1"
import effectiveModel
import optparse
parser = optparse.OptionParser(version=__version__)
parser.add_option("-i", "--input", dest="input", default="inputs/atlas_prodModes_hists_and_response_functions.root:... |
371dcf26f9618395070199998db3d0267249f03644f1846778d12081cb0e9114 | Python | 2,496 | 60 | """01. Single object tracking with pre-trained SiamRPN models
=============================================================
Object tracking is a long standing and useful computer vision task. Unlike image object detection,
it predicts the position of the object in the next frame according to its position
in the curren... |
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