sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5c070ac46c156d2363c54b94a734a6a53bda9d61db4053f89826c45f774ef8e1 | Python | 945 | 20 | import visualkeras
from utils.constants import *
from utils.regressor_tools import *
from utils.experiments_tools import *
import numpy as np
from PIL import ImageFont
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf", 32)
if __name__ == '__main__':
input_dataset = read_csv... |
67b1226e3be9428e12b07bd1def449c81da800616d78481b1e432eb3ae1e4e55 | Python | 946 | 28 | import torch
import clip
from PIL import Image
import requests
# Load the CLIP model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
# Prepare the image
image_url = "https://example.com/image.jpg" # Replace with your image URL
image_response... |
d286e4a47e0529fa6a387ec5a390923bd5a5084d800769fd9a9880bd8484b8e3 | Python | 946 | 36 | # -*- coding: utf-8 -*-
import matplotlib
matplotlib.use('Agg')
from mpi4py import MPI
from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore
from parameters import ParameterSet
from analysis_and_visualization import perform_analysis_and_visualization_stc
from model import SelfSustainedPushPull
from expe... |
77dc3cdea516d4f77f2f18de95e3f74726c368eae4a96d94b3b71c2219395a36 | Python | 947 | 30 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
UMA Nitro: a standalone UMA-small model for energy, force, and stress inference.
Select it with ``inference_settings="nitro"``. The model is b... |
2547ea5369f7b52287c966e3497ae9756fe3afe61b33102f0212e53dadbf471b | Python | 948 | 32 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class AudioDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "librosa"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "librosa"])
... |
717021fb15fdfb7e80a02d8783ce270e6047803807f3ed90b7419618c99d8f4c | Python | 948 | 34 | import os
from deepface.commons.logger import Logger
logger = Logger()
def initialize_folder() -> None:
"""
Initialize the folder for storing model weights.
Raises:
OSError: if the folder cannot be created.
"""
home = get_deepface_home()
deepface_home_path = os.path.join(home, ".deep... |
b40254a5574caa2735c0df6462aeaee517d30e333a74144cc2ecd46979d40105 | Python | 948 | 27 | # 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
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
c167f4e3d2aaa366f10aee89d49b58f657b2548df9c42c76f879fee7cf3ab8bf | Python | 950 | 27 | class NormalizedRewardFilter(object):
def __init__(self, tau=1e-3, scale=1, eps=1e-1):
self.tau = tau
self.scale = scale
self.average_reward = 0
self.average_reward_squared = 0
self.eps = eps
def __call__(self, reward):
self.average_reward *= 1 - self.tau
... |
0d3bbc93fe8224987e5335be2a2cdf03a057a08c734e24f02fbffac0ae0d08fa | Python | 951 | 29 | model = dict(
type='BaseEditModel',
data_preprocessor=dict(
type='EditDataPreprocessor',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
bgr_to_rgb=True,
pad_size_divisor=32,
gt_name='img'),
generator=dict(
type='UNetGenerator',
model=dict(
... |
fee42e47d8a662133807b31caef51b33e422dd0c1a0c72db671004bfa1932a21 | Python | 951 | 35 | import pytest
import torch
import pfrl
from pfrl.testing import torch_assert_allclose
@pytest.mark.parametrize("n", [1, 5])
class TestConjugateGradient:
@pytest.fixture(autouse=True)
def setUp(self, n):
self.n = n
def _test(self, device):
# A must be symmetric and positive-definite
... |
b4100017da96b2f77f6bb87ce2b3c54e2be0779b2f507fea107d62cf8d1c60eb | Python | 952 | 29 | import os
import signal
import sys
from pybind.controller.controller_instance import ControllerInstance
from pybind.controller.demo import utils
from projectmetis.python.logging.metis_logger import MetisLogger
if __name__ == "__main__":
MetisLogger.info('\n'.join(os.environ['PYTHONPATH'].split(':')))
MetisLog... |
7fa074cf9f46bd5c629832f339c31cfd6db1667f6cf374a4769c1d79b6c4cc3d | Python | 954 | 30 | import torch
from ..utils import common_functions as c_f
from ..losses.large_margin_softmax_loss import LargeMarginSoftmaxLoss
class CosFaceLoss(LargeMarginSoftmaxLoss):
"""
Implementation of https://arxiv.org/pdf/1801.07698.pdf
"""
def __init__(self, *args, margin=0.35, scale=64, **kwargs):
... |
8d78825a34f7150484174e33b5fc15e6e8317c798dda2fdaf0a7a3ad31230ebe | Python | 955 | 35 | import logging
from dataclasses import dataclass, field
from typing import Dict, Optional
def str_to_logger_enum(s):
if s == "notset":
return logging.NOTSET
if s == "debug":
return logging.DEBUG
if s == "info":
return logging.INFO
if s == "warning":
return logging.WARNI... |
b2b49824cabe8dec8362979b104594f9a22087a9837ae274ea11b012480f10ef | Python | 955 | 20 | '''
*******************************************
LISA: Landscape In-Silico deletion Analysis
*******************************************
LISA is a statistical test that finds the most influential Transcription Factors related to a set of genes. We leverage integrative modeling of a comprehensive dataset
of 100s of chr... |
eb4b6cbc400c9ceb73cd601f3f0864165bd1734b2ac041cd7eeac41a7c821293 | Python | 955 | 32 | # torch
import torch
import torch.nn.functional as F
import torch.nn as nn
# transformer for reggan
class Transformer_2D(nn.Module):
def __init__(self):
super(Transformer_2D, self).__init__()
# @staticmethod
def forward(self, src, flow):
b = flow.shape[0]
h = flow.shape[2]
... |
69556a29c56b475a691e6d4dc9a4748941729082a77578937e859c8d777f8b3d | Python | 956 | 40 | import numpy as np
import networkx as nx
import os.path
import matplotlib.pyplot as plt
from scipy import signal
import time
import math
import gc
gc.collect()
import warnings
warnings.filterwarnings('ignore')
import os
import pysindy as ps
os.system('cls' if os.name == 'nt' else 'clear')
plt.rc('font',si... |
2da3fee455410573ca56f4aa0e86fb0141465eb4f9b4ceece5e4cc2d056d73ba | Python | 957 | 34 | #!/usr/bin/env python3
"""This script checks whether header file inclusion for ray core C++ code is correct.
"""
import re
import sys
def check_ray_core_inclusion(fname: str):
"""Check whether ray core file has incorrect inclusion `src/ray/...`, which doesn't
build on windows env.
If invalid inclusion i... |
d4b21d21ec528c5bc9467697f71a3d046551331dc2898b77f83b0322a1c23092 | Python | 957 | 34 | import unittest
import torch
from rising.transforms.format import MapToSeq, RenameKeys, SeqToMap
class TestFormat(unittest.TestCase):
def setUp(self) -> None:
torch.manual_seed(0)
def test_map_to_seq(self):
trafo = MapToSeq(("data", "seg", "label"))
out = trafo(**{"data": 0, "seg": ... |
628c4113bd4a546a1034863075b9e48a19de97569d07f6d8c3df9aad4f60bc64 | Python | 959 | 28 | from typing import List, Optional
from ci.ray_ci.tester_container import TesterContainer
from ci.ray_ci.windows_container import WindowsContainer
class WindowsTesterContainer(TesterContainer, WindowsContainer):
def __init__(
self,
docker_tag: str,
shard_count: int = 1,
test_envs: ... |
e49d112887c64fa0fc8662ff59f3aab002f7d9f6b6d32ac5c2369e1cf789bb74 | Python | 959 | 30 | from typing import Any, Dict, List
from ipywidgets import GridspecLayout, VBox, widgets
from data_profiling.report.formatters import fmt_color
from data_profiling.report.presentation.core.table import Table
def get_table(items: List[Dict[str, Any]]) -> GridspecLayout:
table = GridspecLayout(len(items), 2)
f... |
60c72410c07e7cba3a611525fbee5e8c6a8c87767cb654087e09a3621634e318 | Python | 960 | 22 | import numpy as np
from siapy.entities import SpectralImage
from siapy.transformations.image import area_normalization
# Create a sample spectral image
rng = np.random.default_rng(seed=42)
image_array = rng.random((100, 100, 10))
image = SpectralImage.from_numpy(image_array)
# Apply area normalization to standardize... |
28b8b49a2ecff08884bdc781c24f455148848d2c87f1159131b09ee37c48c62f | Python | 961 | 26 | #*
# @file Different utility functions
# Copyright (c) Zhewei Yao, Amir Gholami
# All rights reserved.
# This file is part of PyHessian library.
#
# PyHessian is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, ei... |
2f82dbc638bda7cfde207107bb8183537fbaebd428c0d9ba2f720f951a53173e | Python | 962 | 30 | """Test for the duplicates functionality"""
import numpy as np
import pandas as pd
import pytest
from data_profiling.model.duplicates import get_duplicates
@pytest.fixture(scope="module")
def test_data():
np.random.seed(5)
df = pd.DataFrame(
np.random.randint(1, 100, (100, 5)),
c... |
13104f08b1c56dcf545fdc596168724eca07259b6b69d3f280798e365a80fac1 | Python | 963 | 26 | import tensorflow as tf
from projectmetis.python.models.model_def import ModelDef
class MnistModel(ModelDef):
def __init__(self, learning_rate=0.02):
super(MnistModel, self).__init__()
self.learning_rate = learning_rate
def get_model(self):
"""Prepare a simple dense model."""
... |
c8ae71e109a6e3b6065fe74173ac2d7a27aac6eb8ceab5f7699c2c11481d4eba | Python | 963 | 31 | #!/usr/bin/env python
"""Each sample contains multiple masks. This script combines theese masks into
one image, thus creating one mask for each sample.
"""
from multiprocessing import Pool
from pathlib import Path
from contextlib import ExitStack
from tqdm import tqdm
from PIL import Image
def combine_masks(mask_roo... |
d0b10e71f6575c8c1c398d6bb7a49a975c24fc9cfc4e5a9c12c3ba2d25ba5fff | Python | 963 | 31 | from data_profiling.config import Settings
from data_profiling.report.presentation.frequency_table_utils import (
extreme_obs_table,
freq_table,
)
def render_common(config: Settings, summary: dict) -> dict:
n_extreme_obs = config.n_extreme_obs
n_freq_table_max = config.n_freq_table_max
template_v... |
31687b831d74dcf01170c6a9794be82e05902696185d7f2ae661e594246e6b1e | Python | 964 | 36 | # __serve_example_begin__
import time
from ray import serve
@serve.deployment
class Preprocessor:
def __call__(self, input_data: str) -> str:
# Simulate preprocessing work
time.sleep(0.05)
return f"preprocessed_{input_data}"
@serve.deployment
class Model:
def __call__(self, preproces... |
408789caa29be33d6d8f9fbf6433fc0a66c96b8fa941ac340b0d6816beb0c9e6 | Python | 964 | 30 | import torch
class Branched(torch.nn.Module):
"""Module that calls forward functions of child modules in parallel.
When the `forward` method of this module is called, all the
arguments are forwarded to each child module's `forward` method.
The returned values from the child modules are returned as a... |
78971ab55e3aefaee3eddc68961bb8370e101a0e5a3e773fa0c4e447ddc9ab3f | Python | 964 | 28 | from flyvis.utils.cache_utils import context_aware_cache, make_hashable
class Responses:
def __init__(self, names):
self.names = names
self.cache = {}
@context_aware_cache(context=lambda self: self.names)
def responses(self, *args):
return list(self.names)
def test_context_aware... |
097c42a90d15e5c764a8b4368be02bceac8b30f5189317c3c651d5a37fc3a233 | Python | 966 | 19 | '''
*******************************************
LISA: Landscape In-Silico deletion Analysis
*******************************************
LISA is a statistical test that finds the most influential Transcription Factors related to a set of genes. We leverage integrative modeling of a comprehensive dataset
of 100s of chr... |
63484980f4644759ea493ddb45b8323edf10f76de11d632f0824d2bf5fa71238 | Python | 966 | 36 | from typing import Tuple
import pandas as pd
from data_profiling.config import Settings
from data_profiling.model.summary_algorithms import describe_generic
@describe_generic.register
def pandas_describe_generic(
config: Settings, series: pd.Series, summary: dict
) -> Tuple[Settings, pd.Series, dict]:
"""De... |
058523f960e7106131e85a1f003c3646b1eaf1a89443f658afeb9ea57ae4b85c | Python | 967 | 41 | import logging
from datetime import datetime
from agent.classifiers.ffnn_classifier import ffnn
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
USE_GPU = False
if __name__ == "__main__":
starting_time = datetime.now()
ffnn(for_task_label="fc",
binary_classifier=True... |
7d63d9d1a72d81b610f19eab1cb2dcea57966c23ea4473c74dd26d775e88dc4d | Python | 967 | 35 | from dataclasses import dataclass, field
from typing import Any, Dict, Optional
import numpy as np
from .node import NIRNode
@dataclass(eq=False)
class SumPool2d(NIRNode):
"""Sum pooling layer in 2d."""
kernel_size: np.ndarray # (Height, Width)
stride: np.ndarray # (Height, width)
padding: np.nda... |
fd5e3b60e65ff9b51d4912840f00e355f859a39f840d3da881622ab6e2b6996d | Python | 967 | 43 | # -*- coding: utf-8 -*-
#
"""
std_melt.py
Standardize a MELT record.
"""
from .standardize import VCFStandardizer
@VCFStandardizer.register('melt')
class MeltStandardizer(VCFStandardizer):
def standardize_info(self, std_rec, raw_rec):
"""
Standardize MELT record.
1) Rename all SVTYPE fi... |
1cb6c52dbe2822d8dce71eca65817f727b51a26eb390cb9d22aa98d63d5c4a5c | Python | 968 | 35 | from typing import Any, Callable
from data_profiling.config import Style
from data_profiling.report.presentation.core.item_renderer import ItemRenderer
from data_profiling.report.presentation.core.renderable import Renderable
class Root(ItemRenderer):
"""
Wrapper for the report.
"""
def __init__(
... |
320d84041473246a5e9cebf6df2bf3db6dde5ed269a16789a8d5f5cf04fa3145 | Python | 968 | 27 | import numpy as np
from scipy import sparse
def IoU_2(GTMasks, Masks):
'''Calculate the recall, precision, and F1 score of segmented neurons by comparing with ground truth.
Inputs:
GTMasks (sparse.csr_matrix): Ground truth masks.
Masks (sparse.csr_matrix): Segmented masks.
Outputs:
... |
1d7da899778510ccd935a2fc10ae4145a1431cbbce3cb5ac016c6bd35dce766f | Python | 969 | 30 | import unittest
import numpy as np
import torch
from rising.ops import np_one_hot, torch_one_hot
class TestOneHot(unittest.TestCase):
def test_torch_one_hot(self):
target = torch.tensor([0, 1, 2])
target_onehot = torch_one_hot(target, 3)
expected_onehot = torch.tensor([[1, 0, 0], [0, 1, ... |
8c339354f5e8ebdbb70eaca207745cc3f24744bfa4c15fe21c139dadfc1a0504 | Python | 969 | 30 | import ray
from ray.util.dask import dataframe_optimize, ray_dask_get
import dask
import dask.dataframe as dd
import numpy as np
import pandas as pd
# Start Ray.
# Tip: If connecting to an existing cluster, use ray.init(address="auto").
ray.init()
# Set the Dask DataFrame optimizer to
# our custom optimization functi... |
03731b7a0e6f95a43967d915187bdc85a341c770f55fc632a84fe94fdb5f70d6 | Python | 970 | 24 | import os
import glob
import numpy as np
import nibabel as nib
import torch
import subprocess
import scipy.ndimage
from nibabel.orientations import axcodes2ornt, ornt_transform, inv_ornt_aff
def orientation(img):
orig_ornt = nib.io_orientation(img.affine)
targ_ornt = axcodes2ornt('RAS')
transform = ornt_tr... |
98d86009bfd434cba6ccfdbc8cb319aeba884d9779ae0ea5dbb0c336a463bdf6 | Python | 970 | 16 | # Design sweep for recovery of the slow-term magnitude b (Case 1, model M2)
import sys,json,os,time
import case1
from case1 import *
name=sys.argv[1]; R=int(sys.argv[2]); seed=int(sys.argv[3])
TS={"pub":[30.,90.,150.],"early":[5.,30.,90.,150.],"wide":[5.,150.,600.],"wide4":[5.,90.,300.,600.],"late":[30.,90.,150.,600.]}... |
3b061b293c2960a62c9c60969352edfdec7b6babcd319af83681d95027a5a9cc | Python | 972 | 36 | import numpy as np
import nir
nir_model = nir.NIRGraph(
nodes={
"in": nir.Input(input_type=np.array([1])), # dummy input, but needed for S2
"linear1": nir.Linear(
weight=np.array([[1.0]]),
),
"lif1": nir.LIF(
tau=np.array([0.01]),
r=np.array([1.... |
4bb2f2cbb8a6ca70516daab79508b06702f077395f30f6823456ae33394cdd6e | Python | 972 | 45 | """Paths that are useful throughout the project."""
from pathlib import Path
def get_project_root() -> Path:
"""Returns the path to the project root folder.
Returns:
The path to the project root folder.
"""
return Path(__file__).parent.parent.parent.parent
def get_config(file_name: str) -> ... |
f0f671d6a13440cc2d326d9867bef707b466b4a83393fc03f5b126b71d253c20 | Python | 972 | 43 | # __numpy_troubleshooting_start__
import ray
import numpy as np
from ray.dag import InputNode
@ray.remote
class NumPyActor:
def get_arr(self, _):
numpy_arr = np.ones((5, 1024))
return numpy_arr
actor = NumPyActor.remote()
with InputNode() as inp:
dag = actor.get_arr.bind(inp)
cgraph = dag.ex... |
55de296be7dd8ceda20da728f5679621c71c4a0a05f5e2e85ba82d4a002cb442 | Python | 974 | 34 | from abc import ABC, abstractmethod
from typing import TYPE_CHECKING
from ray.util.annotations import DeveloperAPI
if TYPE_CHECKING:
from ray.data._internal.execution.interfaces.execution_options import (
ExecutionResources,
)
@DeveloperAPI
class ClusterAutoscaler(ABC):
"""Abstract interface for... |
777f1a0bf3a6fac9387d4a8f5c33a25f810e7f29574b332117bfe035cea41791 | Python | 974 | 32 | import subprocess
import sys
import pytest
from siapy.core.configs import BASE_DIR
EXAMPLES_DIR = BASE_DIR / "docs" / "examples" / "src"
def get_example_files():
"""Get all Python files from examples directory."""
return sorted(EXAMPLES_DIR.glob("*.py"))
@pytest.mark.manual
@pytest.mark.parametrize("exam... |
cbdefe95538317bf597d5b1efa9959ea0da2e3dcaf73de03fea82620eb48cd9d | Python | 974 | 26 | import torch
from torch import nn
import torch.nn.functional as F
class ReconstructionLoss(nn.Module):
def __init__(self, lib_size=None, log_norm=False, **kwargs):
super().__init__()
self.reconstruction_loss = nn.MSELoss()
self.lib_size = lib_size
self.log_norm = log_norm
se... |
aafce69def2c2a37a12671abef9d97e166c3998e016905725f75aaad7641f12b | Python | 975 | 40 | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 16 23:04:00 2019
@author: ju-bar
Distribution functions
This code is part of the 'emilys' repository
https://github.com/ju-bar/emilys
published under the GNU General Publishing License, version 3
"""
import numpy as np
# %%
def bivnorm(x_tuple, x0, y0, a, b, c):
'''... |
b15c54588366762294d5befa1a767e3ded019f796ac64c95b5635e2f02bafa8e | Python | 975 | 37 | """Statistics of how spatial neighbours are arranged — ``ov.space.nb``.
The graph and what lives on it. Build the graph once with
:func:`~omicverse.space.spatial_neighbors`, then ask questions of it::
ov.space.spatial_neighbors(adata, n_neighs=6, coord_type='grid')
ov.space.nb.enrichment(adata, 'cell_type')
... |
3003a8b620eb5fbeecbe099053bba7cf20177ed124cdf004493d0ed6c4f1152c | Python | 976 | 26 | # -*- coding: utf-8 -*-
"""
scfea: single-cell Flux Estimation Analysis (vendored sub-package).
This sub-package is vendored from the scFEA project:
https://github.com/changwn/scFEA
commit 4c1fb76d52f07bafad84ce7686ad7c3acfcf0126
Reference:
Alghamdi N, Chang W, Dang P, Lu X, Wan C, Gampala S, Huang Z, Wan... |
78e3ccaff07fd4f9d14e865ad1688aa0ce99abfcad5ac64d790d6546aebf6ee0 | Python | 976 | 25 | import unittest
from neuron_morphology.validation import validate_reconstruction
class TestValidateReconstructionFunctions(unittest.TestCase):
""" Tests the functions in validate_reconstruction.py """
def test_parser_with_one_swc(self):
parser = validate_reconstruction.parse_arguments(['--swc', 'test... |
c558b7a33f8cb54397b0a45d531b86c531fb9bebd8c7910369ee94a97e8a3f57 | Python | 976 | 31 | from typing import Dict
import numpy as np
LEADS = [
'I', 'II', 'III', 'aVR', 'aVL', 'aVF',
'V1', 'V2', 'V3', 'V4', 'V5', 'V6',
]
def process_ecg(ecg: Dict[str, np.ndarray], ecg_samples: int = 4096) -> np.ndarray:
"""
Prepares an ECG for use in a tensorflow model
:param ecg: A dictionary mapping... |
be0b2b245cf99297f866ebeb23beb10b354f4981ef124043b8ea4e886cf6ec69 | Python | 977 | 26 | import tensorflow as tf
from projectmetis.python.models.model_def import ModelDef
class FashionMnistModel(ModelDef):
def __init__(self, learning_rate=0.02):
super(FashionMnistModel, self).__init__()
self.learning_rate = learning_rate
def get_model(self):
"""Prepare a simple dense mo... |
19435e2f24a7649b2bd260b7eb15f3c84af33a83b6e6a2cda667f1f478fc297e | Python | 978 | 25 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
294a92fc6d80361bd646451ddb2010ab9a956f973a5ff1adae5b24dbf61b8838 | Python | 978 | 55 | # Ultralytics YOLO 🚀, GPL-3.0 license
from collections import OrderedDict
import numpy as np
class TrackState:
New = 0
Tracked = 1
Lost = 2
Removed = 3
class BaseTrack:
_count = 0
track_id = 0
is_activated = False
state = TrackState.New
history = OrderedDict()
features =... |
5658904ad2340f9273459948c8f228801c3ab78068b4bdc1b4997193bf3f6d11 | Python | 978 | 31 | # rag_logger.py
import logging
import sys
from logging.handlers import RotatingFileHandler
import os
class RAGLogger:
def __init__(self, name: str, log_file: str = 'rag_system.log'):
self.logger = logging.getLogger(name)
self.logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(a... |
2bc57dc5f28e72dfb4ed55cec88859f32fea22e156c4a15318b81ea62e529fe8 | Python | 979 | 46 | import hamiltorch
import torch
import matplotlib.pyplot as plt
def log_pdf(x):
H = 0.5 * x[0]**2 + x[1]**2 + x[2]**2 + x[3]**2 + x[0]**2*x[2]**2
return -H
params_init = torch.zeros(4, requires_grad=True)
num_samples = 25000
step_size = .3
num_steps_per_sample = 5
sampler = hamiltorch.Sampler.HMC_NUTS
int... |
38cec24bf465e2f269c101312bab4a53596733781d93a844301f05065cb457c9 | Python | 979 | 33 | """Main module of data-profiling.
.. include:: ../../README.md
"""
# ignore numba warnings
import warnings # isort:skip # noqa
import importlib.util # isort:skip # noqa
from data_profiling.compare_reports import compare # isort:skip # noqa
from data_profiling.controller import pandas_decorator # isort:skip # noq... |
ee67147f0bd8eba433116b7247fd4e3da267c3f94a0f59e4b9f094f565d4149c | Python | 979 | 35 | #!/bin/python
#------------------- Description & Notes --------------------#
#------------------- Dependencies ---------------------------#
# Standard library imports
from pathlib import Path
# External imports
import pytest
# Internal imports
from synsor import io
from synsor import schema
#------------------- C... |
256836a85714b840242101c2d18be38398f2b9dc03291a31210594fa8ee4ec55 | Python | 980 | 36 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
from types import SimpleNamespace
from fairchem.core.launchers import ray_on_slurm_launch
def test_r... |
cd6259fd3305311bee27582f6dd4a207ba0cd04090a372705c6b7c9f96d8d886 | Python | 981 | 24 |
from functools import partial
from abberior import microscope
EXC_ID = 5
STED_ID = 6
if isinstance(EXC_ID, (list, tuple)):
ch1_p_ex = partial(microscope.set_power, laser_id=EXC_ID[0], channel_id=0)
ch1_p_sted = partial(microscope.set_power, laser_id=STED_ID, channel_id=0)
ch1_linestep = partial(microsco... |
d199b61f881dede7527ffcd5506eae3667201465d2e906a230af3d69fb69b540 | Python | 981 | 37 | import nibabel as nib
import numpy as np
import pandas as pd
lookup_df = pd.read_table(snakemake.input.lookup_tsv, index_col="index")
# get indices and names from lookup table
indices = lookup_df.index.to_list()
names = lookup_df.abbreviation.to_list()
hemis = ["L", "R"]
# collect output rows
rows = []
for in_img, ... |
ffda720f47846943aaa615a647280a2734323219bd8566a6b26f392d987c7aa6 | Python | 981 | 38 | """Deprecated plotting compatibility wrappers for ``omicverse.utils._venn``."""
from __future__ import annotations
import importlib
import warnings
_REMOVAL_VERSION = "2.2"
def _warn(old_name: str, new_name: str) -> None:
warnings.warn(
f"`ov.utils.{old_name}` is deprecated and will be removed in omicv... |
e7278b81a3bd88a245e2728a3fa41229cfa566461dde22fa617cb9d2d84f9d51 | Python | 982 | 37 | # This file is part of the INTREPPPID programme.
# Copyright (c) 2023 Joseph Szymborski.
#
# This programme is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, version 3.
#
# This programme is distributed i... |
9fc2dfd2ad314a5bdb3adff341a5993a3f5cf27365c1afdb325bf5fbfef5a096 | Python | 983 | 21 | # -------------------------------------------------------------------------------------------------------------------------------------
# Following code curated for Bio-Diffusion (https://github.com/BioinfoMachineLearning/bio-diffusion):
# --------------------------------------------------------------------------------... |
a15b505ddb5c5d18af4e5c035d311b5b5222f864cc920ddd7ada74fae09370ad | Python | 985 | 30 | import torch.nn as nn
class Merge(nn.Module):
def __init__(self) -> None:
"""Module form for a merge operation.
In the context of events/spikes, events/spikes from two different sources/rasters will be
added.
"""
super().__init__()
def forward(self, data1, data2):
... |
7595887876f469df61b4026bc0124abd0f1c20d9ebde1c5fb6c4d8669736c67d | Python | 988 | 34 | import numpy as np
import networkx as nx
import os.path
import matplotlib.pyplot as plt
import time
import math
import gc
gc.collect()
import warnings
warnings.filterwarnings('ignore')
import os
os.system('cls' if os.name == 'nt' else 'clear')
plt.rc('font',size=15)
UseDoubleWell=1
RCType=0# Different R... |
9d60b5f2a9fde9f0b2c0de62cf77db3b64a64f46fa08ab8976bd1e902d04d56e | Python | 988 | 30 | import pytest
from DECIMER import predict_SMILES
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_imagetosmiles():
img_path = "tests/caffeine.png"
expected_result = "CN1C=NC2=C1C(=O)N(C)C(=O)N2C"
actual_result = predict_SMILES(img_path)
assert expected_result == actual_result
@pyt... |
c74c493a5ab5314a89cf5361a56e9c2cddc96f3a0b6675222e24179ab6047323 | Python | 988 | 43 | r"""
Visualization modules of SLAT
"""
from typing import Optional
import matplotlib.pyplot as plt
from matplotlib import colors
from .multi_dataset import *
def hist(data,
bins: Optional[int]=100,
cut: Optional[float]=0.9,
cut_height: Optional[int]=200
)->None:
r"""
Histogram... |
4ef51b1a67f4c337e5585d7bdbd63dd263d788c6dd0cb7ba6c9a01c284f7e739 | Python | 989 | 41 | # -*- coding: utf-8 -*-
"""
Created on Mon Jan 11 10:04:00 2021
@author: ju-bar
Correlation utilities
This code is part of the 'emilys' repository
https://github.com/ju-bar/emilys
published under the GNU General Publishing License, version 3
"""
import numpy as np
def corr_norm(a, b):
"""
Calculates the nor... |
9f8c7e41b4b579e461a86852cec7fd91eb5e3dee8260ebda7c9e6b1ea0e1665a | Python | 989 | 30 | """Supervisor that orchestrates research agents."""
from __future__ import annotations
from typing import Iterable, List, Sequence
from .agent import ResearchAgent, Finding
class Supervisor:
"""Breaks briefs into subtopics and spawns agents to handle each."""
def __init__(self, vector_store, tools: Iterab... |
93d593dfca7273b2b778a9c3971f7daaf48dbfaa24056d0941f2fee033d23ac3 | Python | 990 | 48 | import jsonlines
import json
import sys
def read_gold_data(path):
fi = jsonlines.open(path, 'r')
labels = {line['index']: line['label'] for line in fi}
return labels
def evaluation_metrics(gold, predictions):
count = 0
for key, value in predictions.items():
try:
gold_label = ... |
4d7720c02539cf827d4ca402b9285fb3b0a898560e82a0eaa4eda71a26db0a3f | Python | 992 | 33 | import numpy as np
import pytest
from neuropower import BUM
def test_fpLL():
np.random.seed(seed=100)
testpeaks = np.vstack(
(np.random.uniform(0, 1, 10), np.random.uniform(0, 0.2, 10))
).flatten()
x = np.sum(BUM._fpLL([0.5, 0.5], testpeaks))
assert np.around(x, decimals=2) == 9.57
@pyt... |
c34fae20ed4da3d22082b0327883cc53efd70513901286762ccaf9cedab53e53 | Python | 992 | 31 | from ipywidgets import widgets
from data_profiling.report.presentation.core import Dropdown
class WidgetDropdown(Dropdown):
def render(self) -> widgets.VBox:
dropdown = widgets.Dropdown(
options=self.content["items"], description=self.content["name"]
)
titles = []
item... |
d7f5e6bcd4d83a85c6c2d9436776369a6329cb557e61c27e5a34c7ec9b18811d | Python | 992 | 33 | import sys
from parameters import ParameterSet
from mozaik.models import Model
from mozaik.connectors.modular import ModularSamplingProbabilisticConnector
from mozaik import load_component
from mozaik.space import VisualRegion
import mozaik
class ModelMapDependentModularConnectorFunction(Model):
required_paramete... |
ea5dfdbf643d526b9686159e8020c0ecdf827fbb91cd6ac026a69f112323857e | Python | 993 | 28 | import numpy as np
import os
import pickle
import matplotlib.pyplot as plt
from imp import reload
import defaultParams; reload(defaultParams); from defaultParams import *
import searchParams; reload(searchParams); from searchParams import *
from analysis import simdata
filename = 'sim_res_Be1.00_Bi-3.00'
dir_name = ... |
b1b4b66ade90c4bbc7037dcd624a7ad267735f202aedd0b2cb0f292f71869d09 | Python | 994 | 37 | # -*- coding: utf-8 -*-
"""
Project: CrowdingNumerosityGit
Creator: Miao
Create time: 2021-03-02 15:41
IDE: PyCharm
Introduction:
"""
import pandas as pd
def drop_df_nan_rows_according2cols(df: pd.DataFrame, cols: list) -> pd.DataFrame:
df = df.dropna(subset = cols)
return df
def drop_df_rows_according2_on... |
77ebbfa407458118ffdd57d1050b01d254274f28685cdbd9b7e25caf6540687e | Python | 995 | 24 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
print(find_packages('.'))
setup(
name="mdgru",
version="0.2.20181002",
description="Segmentation Suite for multi-dimensional gated recurrent units (MDGRU)",
long_description="Details can be found in the Readme f... |
d9e440aa7e4ba19b8c80b65becdfe64a18df9bb776e5e84076c7bb3936b5ea4f | Python | 995 | 35 | # __without_limit_start__
import ray
# Assume this Ray node has 16 CPUs and 16G memory.
ray.init()
@ray.remote
def process(file):
# Actual work is reading the file and process the data.
# Assume it needs to use 2G memory.
pass
NUM_FILES = 1000
result_refs = []
for i in range(NUM_FILES):
# By defaul... |
8296c1e145c4a92ffef0ee11f21af39f5232cd63a16bc5097cc236c39c472afa | Python | 996 | 34 | import ray
from ray.util.dask import ray_dask_get, enable_dask_on_ray, disable_dask_on_ray
import dask.array as da
import dask.dataframe as dd
import numpy as np
import pandas as pd
# Start Ray.
# Tip: If connecting to an existing cluster, use ray.init(address="auto").
ray.init()
d_arr = da.from_array(np.random.randi... |
d03c7b705decb4147f83581b89f5ea786ed7279e72f13c0777cd5b9c1ea43508 | Python | 996 | 22 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
6fe73547756425300e11715d150a0814315fa4b1dd92a4c5add808427f1b5679 | Python | 997 | 44 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""RPU simulator bindings."""
# This import is required in order to load the `torch` shared libraries, which
# the simulator shared library is linked against.
... |
62412e2823a22ceb422bbb2198004d78e5a92b41c59977d59b44a30280d6dc2c | Python | 998 | 26 | import nibabel as nib
import neuroboros as nb
import numpy as np
import os
if __name__ == "__main__":
for lr in "lr":
for ico in [32, 64, 128]:
nv = ico**2 * 10 + 2
den = str(round(nv / 1000)) + 'k'
fn = f"../tpl-onavg_hemi-{lr.upper()}_den-{den}_desc-cortex_dparc.label... |
14e7b68707ad8448b1c150ce9bd1837e7ed8ae331e291820e872282f7da6a7f2 | Python | 999 | 44 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import argparse
import pandas as pd
def identify_mosaics(metrics):
metrics = metrics.loc[metrics.Median_Separation.astype(
str) != 'coverage_failure'].copy()
metrics.Median_Separation = metrics.Median_Separation.astype(float)
q1 = metrics... |
3c5883a2095caf32386c82a050e786bb8ad75cfd2dbc62e9c26b238ff8805295 | Python | 1,000 | 29 | from pickle import dump
from lgca import get_lgca
import numpy as np
lx = 250
ly = round(lx * 1.1547) # distance in y direction is smaller than in x direction
ly += 1 if ly % 2 == 1 else 0
dims = lx, ly
capacity = 50
tmax = 300
restchannels = 1
kappa_max = 4
kappa_std = 0.05 * kappa_max
params = [{'r_d': 0.2, 'theta'... |
142694b255c3b207ab112c27a4921d97eb672af4ae1e5a9e8bfe12461bb8e7cd | Python | 1,002 | 33 | import unittest
from neuron_morphology.feature_extractor.data import Data, get_morphology
from neuron_morphology.morphology_builder import MorphologyBuilder
class TestData(unittest.TestCase):
def setUp(self):
self.morphology = (
MorphologyBuilder()
.root()
... |
57094a51688eb55dc1be4934ff55e2e5a9650b977a65e26422430bb4c88d3a6b | Python | 1,003 | 29 | import glob
import logging
import numpy as np
import os
import random
import torch
import torch.utils.data
import deep_sdf.workspace as ws
def get_instance_filenames(data_source, split):
txtfiles = []
for dataset in split:
for class_name in split[dataset]:
for instance_name i... |
67b3b93c2e46fb236b17c233a9792e71825c1762b51b9181f5aecbe58dc3008e | Python | 1,003 | 22 | from setuptools import setup
import io
setup(name='deltasplice',
description='DeltaSplice: A neural network model to predict splice site usage and splicing-altering mutations',
long_description=io.open('README.md', encoding='utf-8').read(),
long_description_content_type='text/markdown',
versio... |
7271548fcfa465726ad30d3b887c8db7592d6923c7a783ed3a042d7eb8282c75 | Python | 1,003 | 42 | # -*- coding: utf-8 -*-
"""
@author: wnchang
"""
import torch
def pearsonr(x, y):
"""
Mimics `scipy.stats.pearsonr`
Arguments
---------
x : 1D torch.Tensor
y : 1D torch.Tensor
Returns
-------
r_val : float
pearsonr correlation coefficient between x and y
Scipy d... |
0b245d965ede80934c3cd28f75f91c444fc47797a0ea3d6a793f01450b5609de | Python | 1,005 | 30 | # Copyright 2021 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
3456d3b10606da28efa32b7bf5f44930f9b9b7b6352b6786ba85d14c88f86d88 | Python | 1,006 | 25 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
f996c98259cbf2c6d7c13b25236d585615ed27e535024befb577e816e11b5973 | Python | 1,006 | 38 | from typing import List
import pandas as pd
from data_profiling.config import Settings
from data_profiling.model.sample import Sample, get_sample
@get_sample.register(Settings, pd.DataFrame)
def pandas_get_sample(config: Settings, df: pd.DataFrame) -> List[Sample]:
"""Obtains a sample from head and tail of the ... |
0024beeed266ee4a3cf4a1d34bcd78ea90b4618d1398a13ea5a9e6f3902217df | Python | 1,007 | 29 | from typing import List
from lqit.edit.models.base_models import BaseGenerator
from lqit.edit.structures import BatchPixelData
from lqit.registry import MODELS
from lqit.utils import ConfigType, OptConfigType, OptMultiConfig
@MODELS.register_module()
class UNetGenerator(BaseGenerator):
def __init__(self,
... |
354d0cb710f3e6ced1cbfd606eb3fc887f4e3435207c8ad57bda80478bc68ed1 | Python | 1,007 | 23 | import argparse
import pickle
import lib.dataset_config_parser.dataset_config_parser as dcp
from lib.data_utils.matched_cells_struct import OrderedMatchedCellsStruct
if __name__ == '__main__':
parser = argparse.ArgumentParser(
'Print out the available WN cell ids available for use in reconstruction (i.e.... |
b6ec95dafaa83af468f77bec3a538b0faba4ce449ed0bf0c4f1f6d4c16395f17 | Python | 1,007 | 36 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import pytest
from ase import Atoms
from fairchem.core.datasets._utils import rename_data_object_keys
... |
3d10d7aa31141f86d05a9726033f7c95ff529a0f16ba895e8eaaf1f63cd146cf | Python | 1,009 | 31 | import sys
sys.path.insert(1, '..')
# 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
# -- Project information -----------------------------------------------------
... |
25889d7781f27b035d402c1ad4bb31ccf6f219a98e31b03c9e927d8f6b41b6e2 | Python | 1,010 | 34 | import io
import logging
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
pass
import ray._private.utils
import ray.cloudpickle as pickle
import ray.exceptions
from ray._private import ray_constants
from ray.util import inspect_serializability
logger = logging.getLogger(__name__)
ALLOW_OUT_OF_BAND_OBJECT... |
37681151cb0bfee021852aceba0b53e8996ee6ae16afe11da41eb3e9c3d8ddd2 | Python | 1,011 | 36 | import pytest
from flyvis.network import dynamics
from flyvis.network.dynamics import NetworkDynamics, activation_fns
from flyvis.utils.class_utils import forward_subclass
@pytest.fixture(
scope="module",
params=[
"NetworkDynamics",
*[sc.__name__ for sc in dynamics.NetworkDynamics.__subclasse... |
70e8c64ba4cd5b791316c352bc382af9f12b2b0bc5d0cb117bdf2fc0ffc108c5 | Python | 1,011 | 18 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
9bf46689589f903248d4d9623d9566d333c64db6581241952e3c3e0c727d5b3b | Python | 1,012 | 18 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
a7988246632ee2f1545df8af8df67a8fb6ef72c49cb78bea54d6e62b5aff6ad0 | Python | 1,014 | 18 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
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