sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
174754a57e791a657725aef033fac1e32762d102bc8493a80900755242c7dd38 | Python | 908 | 32 | from sys import argv
from msp import MSP
from msp_fly import fly_am
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fly_am(msp)
msp.architecture["propagate_sparsity_spec"] = dict(
type="OnlyAllowInfluenceOnSplicesites"
)
msp.architecture["final_processor_spec"].update(
dict(
bottleneck_spec=... |
5f88aad4de509d03aea821b65c658bf5da6b5bfb655fadb720e3b4acafadb7f3 | Python | 908 | 43 | """Add workflow_step_index_path column to workflow_invocation_message table
Revision ID: 312602e3191d
Revises: 75b461a2b24a
Create Date: 2025-12-27 12:00:00.000000
"""
import sqlalchemy as sa
from galaxy.model.migrations.util import (
add_column,
drop_column,
)
# revision identifiers, used by Alembic.
revis... |
ead64b6f455282a7e9a40a7c5beb34ecd946c6f3c8aa82f85af785b55b56e74d | Python | 908 | 31 | import numpy as np
import pytest
from pyriemann.utils.utils import check_weights
@pytest.mark.parametrize("n_matrices", [3, 4, 5])
def test_check_weights_none(n_matrices):
w = check_weights(None, n_matrices)
assert np.sum(w) == pytest.approx(1.0, abs=1e-10)
@pytest.mark.parametrize("n_matrices", [3, 4, 5])... |
0cfb8934bcd6a43414021224b132cbbcf85b84673af3018aa254bc56992cf3c9 | Python | 909 | 32 | """Utilities for testing spectral parameterization."""
from functools import wraps
from specparam.modutils.dependencies import safe_import
plt = safe_import('.pyplot', 'matplotlib')
###################################################################################################
##################################... |
7f90e44a14c24c82c567ac518588ec41a1e318dc787c28c9493d66d27c5a9798 | Python | 909 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
f0e3bd70067a4df19190ac127ea0fea6b9e797dd150812f25e1725f0b701ed69 | Python | 909 | 28 | import itertools
import numpy as np
from larvaworld.lib import util
from larvaworld.lib.plot import configure_subplot_grid
def test_subplot_grid_configuration():
vs = [None] + np.arange(20).tolist()
for idx in itertools.product(vs, vs, vs):
if None not in idx or 0 in idx:
continue
... |
41a9e4057cf07736a14e9d9ea23a729f8168053675a23be866ba9d9e01bc5880 | Python | 910 | 28 | import pandas as pd
from rdkit import Chem
def valid_smiles(smi):
mol = Chem.MolFromSmiles(smi)
if mol is None: # check validity
return False
try: # check valence, aromaticity, conjugation and hybridization
Chem.SanitizeMol(mol)
except:
return False
... |
88a120b95348de1d4bcfb6e3c697970f27c08e8cc5e9b9f6e42a497338f17dc4 | Python | 910 | 31 | import sqlite3
from .db import DB
class DistrictDeathInfo(DB):
def __init__(self):
super().__init__()
self.table_name = 'KL_district_death_info'
self.table_desc = 'District wise Death Cases through online and offline mode - post scrutiny status'
self.cols = self.getcolumns()
... |
3e21beed1b7781ece084379d7410668d921cbbe0fa02c9516c025003b8cdeb12 | Python | 911 | 30 | from typing import Union
import torch
from torch import Tensor
class RFFT2DTransform:
"""2D Fast Fourier Transform (RFFT2D) Transformation.
This transformation applies the 2D Fast Fourier Transform (RFFT2D)
to an image, converting it from the spatial domain to the frequency domain.
Input:
-... |
55aa1365d042d02d04cb1cc1c502771e5a205a7cca8ee83503046906b84b3ae7 | Python | 911 | 30 | """Set of random number generator functions: seeding, generator, hashing seeds."""
from typing import Any, Optional, Tuple
import numpy as np
from gym import error
def np_random(seed: Optional[int] = None) -> Tuple[np.random.Generator, Any]:
"""Generates a random number generator from the seed and returns the G... |
019c10a02db3ad3c88888eda5d0e94d974c84595b528983156267aa585ad0861 | Python | 912 | 33 | # Generated by Django 3.0.4 on 2020-03-22 13:54
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0055_auto_20200321_1543'),
]
operations = [
migrations.AddField(
model_name='mushroomspot',
name='d... |
3c1fe427b70dbe10579a52fe2b3c8bbb685fea61bb6f539063be7d698a655b89 | Python | 912 | 35 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for base.py"""
import unittest
from sknetwork.base import Algorithm
class TestBase(unittest.TestCase):
def setUp(self):
class NewAlgo(Algorithm):
"""Docstring"""
def __init__(self, param: int, name: str):
sel... |
449795a25be5d33ee6b3fa5d23a3f62bf7ef796b33203f8a8a3b9a41168f0181 | Python | 912 | 29 | main_tissue_groups_v1 = {
"blood": ["Cells - EBV-transformed lymphocytes", "Whole Blood"],
"brain": [
"Brain - Amygdala",
"Brain - Anterior cingulate cortex (BA24)",
"Brain - Caudate (basal ganglia)",
"Brain - Cerebellar Hemisphere",
"Brain - Cerebellum",
"Brain -... |
ac340a78ab59f890b7d6bd5d2e26ee20192f1bbddbc8e992e7317c64bd21cd0b | Python | 912 | 23 | import os
import nibabel as nib
def list_scans(path = None):
return [os.path.join(path,scan) for scan in os.listdir(path) if scan.endswith(".nii")]
def normalize_HU(range, ct_type):
min_, max_ = range
for scan_id in list_scans(ct_type):
print(f"NORMALIZING {scan_id}")
scan_nii ... |
3d0090122e7a50f4645b4f0d397c90c8b614d2a8067d6c8dafa5d864ac79bdef | Python | 913 | 31 | import sqlite3
from .db import DB
class DistrictCaseInfo(DB):
def __init__(self):
super().__init__()
self.table_name = 'KL_district_case_info'
self.table_desc = 'District wise summary of Cumulative cases, Active cases, and New positive cases'
self.cols = self.getcolumns()
de... |
62986ed6c5874e217c0d8cd8a71ee86a54eb55a4fefc74a7d5e09c8553bdd4b9 | Python | 913 | 38 | import hashlib
import secrets
import bcrypt
PREFIX_KEY = "cpt_" # cpt stands for codecarbon project token
def generate_api_key() -> str:
# Generate a random API key
api_key = secrets.token_urlsafe(32)
prefixed_api_key = f"{PREFIX_KEY}{api_key}"
return prefixed_api_key
def get_api_key_hash(api_key... |
a46b16d4262db614acc98189ea941a91f74399fb3a701c3087198c127ff3ec67 | Python | 913 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.sparse_adaptation_params = lambda: ""
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(
type="Motifs",
motifs_spec=dict(
type="PSAMMotifModel",
... |
24d6ff3fa7cfac7153960883274daacab29cf27605d5116de6cd250e30221581 | Python | 914 | 36 | """Utility functions.
Don't import from here directly anymore, as these are only
here for backwards compatibility.
"""
from kombu.utils.objects import cached_property
from kombu.utils.uuid import uuid
from .functional import chunks, memoize, noop
from .imports import gen_task_name, import_from_cwd, instantiate
from .... |
6773fed3be88aa4a7f2222f024818d03feb3bc4210344bc772de055f9f9b7098 | Python | 914 | 31 | import os
import tempfile
import pandas as pd
def annotate_pfms(
filename,
motifs_db,
output_dir=None,
out_filename=None,
tomtom_exec_path='tomtom',
is_writing_tomtom_matrix: bool = True
):
if output_dir is None:
output_dir = os.getcwd()
_, tomtom_fname = tempfile.mkstemp()
out_filename = "motifs" i... |
71e21149f18139e2727278332e8284d1c9241c89e2948d59466c3faae4677c00 | Python | 914 | 23 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 56
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
79c7fc67c0c8cc201bb1ef8504d386ba61a9e644716f59e02296ac79ce48c544 | Python | 914 | 34 | from typing import List
import numpy as np
from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask
from hobj.learning_models.random_guesser import RandomGuesser
from hobj.types import ImageId
def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageId]:
images = []
... |
a1cf28f4bea8f45df869c476e5fd68d1ef10ded0248d644a09ddbfb91e0ae47f | Python | 914 | 26 | import unittest
import torch
from modular_splicing.models_for_testing.main_models import AM
class AMTest(unittest.TestCase):
def test_am(self):
model = AM.non_binarized_model(1, density_override=0.75).model
variable = torch.nn.Parameter(torch.rand(1, 100, 4).cuda())
result = model(varia... |
2a19245cb01613904e834bab3e6b53616d2594c73c632ed36470c949e1bcf4ad | Python | 915 | 28 | """
This module define a MSEStructure
"""
# pylint: disable=W0223
import torch
class MSEStructure(torch.nn.modules.loss._Loss): # pylint: disable=protected-access
"""
This class define a specific loss : MSE loss for each token in input
"""
def __init__(self):
super(MSEStructure, self).__init... |
64e2076da709f9226db99aeee06a00e43b92218c9e684e06e647d93dfe5c9f50 | Python | 915 | 28 | import os
import os.path
from pip._internal.utils.compat import get_path_uid
def check_path_owner(path):
# If we don't have a way to check the effective uid of this process, then
# we'll just assume that we own the directory.
if not hasattr(os, "geteuid"):
return True
previous = None
whi... |
71b3d1dfa29f6ff5b73a70a4bbea0bc56028c76768a09b6b40165167e10b0a57 | Python | 915 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
7a16af91f9b2109cb6470d299a0973d43f189f4555d1991e843e893e01449b54 | Python | 915 | 44 | from .models_backbone import (
ElPiGraph_method,
PrinCurve_method,
SimplePPT_method,
backbone_scc,
construct_backbone,
map_gene_to_backbone,
map_points_to_backbone,
update_backbone,
)
from .models_individual import (
construct_cells,
construct_pc,
construct_surface,
voxel... |
51c2c3840c55fef86c796bad371244a6a63bb8dcbd0e609827ce2d52fee8a5bd | Python | 916 | 28 | import sqlite3
from .db import Database
from .TG_tables import TG_symptomatic, TG_comorbidities, TG_agedist, TG_caseinfo, \
TG_testinfo, TG_contact_testing, TG_district_cases
class TelanganaDB(Database):
def __init__(self, datadir):
super().__init__(datadir)
self.init_tables()
self.c... |
90d5a5cab475e0eaf65ad8a5fd3c21308302bf875b8900262d80ecb311dfdfe8 | Python | 916 | 22 | import numpy as np
import torch
import opt_einsum as oe
from torch.autograd.functional import jacobian
from src.MODEL import *
#============================calculate the energy===================================
class Property(torch.nn.Module):
def __init__(self,density,nnmod):
super(Property,self).__init_... |
cf7d250803466db4e6755c2dc679d5a82539605faeb87a6e569c62a9d088a1e0 | Python | 916 | 32 | #!/usr/bin/env python
import sys,re, collections
'''reduce the provided example file to only have a single positive and single negative example per receptor (at most)'''
#first identify best rmsd
bestval = dict()
bestlig = dict()
for line in open(sys.argv[1]):
vals = line.rstrip().split()
if len(vals) < 6:
... |
86213e8df7e1d9ac5a3a4b0241b437114760751f4dc8ed5c3be2c0b1eaac67f0 | Python | 917 | 40 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
am_e = dict(
type="PretrainedEclipMotifM... |
26ec076b571abfb0a342c680135a2ff26ff5e7f70a9b8c1b55b5266ffb6e2fdf | Python | 918 | 36 | # Copyright 2023 Blue Brain Project, EPFL.
# See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: Apache-2.0
class Argument:
"""Utility class for holding all arguments for node classes"""
def __init__(self):
# BaseNode
self.class_name = ""
self.nmodl_name = ""
... |
60e3a2b0b2a1db5362a028ee1c4a9df1862b8db7ea3c333afe623fe4e18d476c | Python | 918 | 32 | import sqlite3
from .db import DB
class DistrictInformation(DB):
def __init__(self):
super().__init__()
self.table_name = 'MH_district_caseinfo'
self.table_desc = 'Maharashtra daily district level case information'
self.cols = self.getcolumns()
def getcolumns(self):
... |
c955e378249522fd59cae8cdeb33997261356e22772d54edfedc4465181223e9 | Python | 918 | 31 | """
michael.reimann@epfl.ch
"""
from setuptools import setup, find_packages
setup(
name='projection_voxels',
version='0.10',
install_requires=['h5py', 'numpy', 'voxcell', 'simplejson'],
packages=find_packages(),
include_package_data=True,
author=['Michael Reimann'],
scripts=['bin/download... |
203a9fee5afcbdfe7fe312cc007e16f8f3eb8c78e80ef185688ddb7f7a771f0f | Python | 919 | 29 | import sqlite3
from .db import DB
class PatientMgmtTable(DB):
def __init__(self):
super().__init__()
self.table_name = 'DL_patient_mgmt'
self.table_desc = 'Patient management table containing hospital occupany information for Delhi'
self.cols = self.getcolumns()
def getcolum... |
e74e6a63b3ba26982161d88807f9283924a05ec0200fc012a8829f85c0f0701d | Python | 919 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
e7be33f01b6b83d815f92d66cea1218f066cc8230765b0c8883ad75b42e36012 | Python | 919 | 39 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import pandas as pd
import sys
import pdb
import csv
def get_cid():
chembl_fname = "Chembl_ids.txt"
with open(chembl_fname, 'r') as f:
data... |
fb7c2a04fbd333b8c375f4f2b483445f13367ae8098db09944b88bcbda6499a7 | Python | 919 | 37 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on May 2019
@author: Nathan de Lara <nathan.delara@polytechnique.org>
"""
import numpy as np
def top_k(scores: np.ndarray, k: int = 1, sort: bool = True):
"""Return the indices of the k elements of highest values.
Parameters
----------
scores... |
2f51f9775aa9e40b5a7857f2a17a636c1780f375f11997c5d41e645dc090dc54 | Python | 920 | 26 | import pytest
import gym
from gym import spaces
from gym.wrappers import GrayScaleObservation
@pytest.mark.parametrize("env_id", ["CarRacing-v2"])
@pytest.mark.parametrize("keep_dim", [True, False])
def test_gray_scale_observation(env_id, keep_dim):
rgb_env = gym.make(env_id, disable_env_checker=True)
asser... |
3ece271642a661283f9c75116bf258f0da8cd0daec0cd0d4c37481f9e64d09f2 | Python | 920 | 31 | import os
from shutil import copyfile
def check_dir_exists(path):
if not os.path.exists(path):
os.mkdir(path)
def split_by_name(dir_path, real_save_path, fake_save_path):
# check_dir_exists(real_save_path)
# check_dir_exists(fake_save_path)
if not os.path.exists(real_save_path):
os.mk... |
bad36e2cdc578749a9e36701badf6445df122227fcb93635b855fc440984319d | Python | 920 | 35 | #!/usr/bin/env python3
import os
import sys
import re
import json
lengths = sys.argv[1]
map_all = sys.argv[2]
chunks = sys.argv[3]
threads = sys.argv[4]
n=0
def write_bed(bed):
bed.write(line.rstrip().split(' ')[1] + "\t0\t" + line.split(' ')[0] + "\n")
def split_bam(bed, bamchunk, bam_all, threads):
os.syste... |
c20ba326d5c534ec70aec0382a456bdc56b3767826b7be7e0fec81aa198e7e6c | Python | 920 | 30 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from ._marker import Marker
from ._selected import Selected
from ._textfont import Textfont
from ._unselected import Unselected
from . import hov... |
ce1627ea1353771ad64422cae159ae3c22708af1bbc9911276a39f3b657ee4c2 | Python | 920 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.sparse_adaptation_params = lambda: ""
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(
type="Motifs",
motifs_spec=dict(
type="PSAMMotifModel",
... |
50c0e5ce49a44bb096ba1acbfcaeb759fd6cda99dd945fa373bb3659c0e15ef6 | Python | 921 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.sparse_adaptation_params = lambda: ""
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(
type="Motifs",
motifs_spec=dict(
type="PSAMMotifModel",
... |
383a89768d6f95a16c55de1768c49f0964dd7b2a1630c20e2fe4ea96d49019d7 | Python | 922 | 37 | import os
PLOTLY_DIR = os.environ.get(
"PLOTLY_DIR", os.path.join(os.path.expanduser("~"), ".plotly")
)
TEST_FILE = os.path.join(PLOTLY_DIR, ".permission_test")
def _permissions():
try:
if not os.path.exists(PLOTLY_DIR):
try:
os.mkdir(PLOTLY_DIR)
except Excepti... |
776bcbd68da7386b688045f2f3b6cbcb62f2cb1d6dc416689d440ac86cf6af09 | Python | 922 | 30 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
"""Write text files"""
from typing import Iterable
from .._types import PathArg
from .._utils import ui
def txt(iterator: Iterable, fmt: str = '%s', delim: str = '\n', dest: PathArg = None):
"""Write any object that supports iteration to a text file
P... |
86e4fec63288b32bc9b2e128c653ec427eab9240f5505cd6d6932e4f56f1f413 | Python | 922 | 27 | #!/usr/bin/env python
"""
Extract importent information from the standard output file and put it in some standard format, like BED and tabular.
"""
import sys
bed = open(sys.argv[2], "w+")
tabular = open(sys.argv[3], "w+")
for line in open(sys.argv[1]):
# ORGANISM: gi|21226102|ref|NC_003901.1| Methanosarci... |
af384bc8950a21faf206c1c12bb302bfc57d2dd522a352a2476b6bb8202d4204 | Python | 922 | 30 | """
Provides functionality for persistence of data
"""
from codecarbon.output_methods.base_output import BaseOutput, OutputMethod # noqa: F401
# Output to BoAmps format
from codecarbon.output_methods.boamps import BoAmpsOutput # noqa: F401
# emissions data
from codecarbon.output_methods.emissions_data import ( # ... |
aa423939f6cf646f471638b8f03f0e301bbe1a972026b8fd1dd048cc27a72715 | Python | 923 | 41 | """Helper functions for Pillow visualization"""
from PIL import Image
import numpy as np
def make_mask_visible(mask):
"""Converts Pillow Image representing binary mask to helpful Pillow Image.
Args:
mask (PIL.Image): Pillow image of binary mask
Returns:
PIL.Image: Pillow image
"""
... |
bdec999c115ca92ada9f29279d213445893b10484301477401120fc04de0abe4 | Python | 923 | 35 | import torch
import torch.nn as nn
from .sparsity import SparseLayer
class DiscretizeMotifsIn(SparseLayer, nn.Module):
"""
Binarize the motifs after running the given sparse layer.
"""
def __init__(self, sparse_layer):
super().__init__()
self.sparse_layer = sparse_layer
def forw... |
13b505359e1988dfab5e0d4a97aa61af26ac3d2ab5907bb762bdd754f7ab4520 | Python | 924 | 30 | import sys
import os
# setting path
sys.path.append('../TM2_segmentation')
import numpy as np
import pandas as pd
import nibabel as nib
from skimage.transform import resize, rescale
from scipy.ndimage import zoom
from settings import target_size_unet, scaling_factor
import matplotlib.pyplot as plt
from scripts.prep... |
60a577e0452419cdbcf2ad5f6654afbb8eb0ef8aef8b185bc4bf413511e98d02 | Python | 924 | 32 | import os
import sys
import importlib
import pandas as pd
import anndata as ad
from pyprojroot import here
proj_root: str = str(here())
sys.path.insert(0, os.path.join(proj_root, "package/python"))
from utils import set_file_logger
import cembav2env
importlib.reload(cembav2env)
# * configs
rsc_dir = os.path.join(proj... |
a952e6dfb1a6d434e5e51b9d8f6de46c1c24f91fdd39cdec00debc68136fbbfa | Python | 924 | 31 | """Utilities for debugging."""
import sys
import platform
###################################################################################################
###################################################################################################
def sys_info():
"""Print out system information."""
... |
3d8bfeef4ed64db7c491325bf85e8b637bd3af31b20c9902a00d4dcdc648c910 | Python | 925 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
4b0a7f878fb690bb3c0f687ff95a1717fb69678f01e2251614ffd6317c7e5033 | Python | 925 | 33 | from sys import argv
from msp import MSP
from msp_fly import fly_am
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fly_am(msp)
msp.architecture["propagate_sparsity_spec"] = dict(
type="OnlyAllowInfluenceOnSplicesites"
)
msp.architecture["final_processor_spec"].update(
dict(
bottleneck_spec=... |
c4c7c76efd432093bad37f98cd5fb1b07fc4bd8b1b25ad37aa46181142e0af40 | Python | 925 | 33 | """
Test crude AP detection
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0, PATH_SRC)
import matplotlib.pyplot as plt
import pyabf
import pyabf.tools.a... |
cbb57a6d534d4bb1e688e0c90cd973f7041a8e435e9d766fd412f56330b3fd98 | Python | 926 | 32 | """Simple conversion helpers."""
def int_to_rgba(v: int) -> list[float]:
"""Get rgba (0-1) e.g. (1, 0.5, 0, 1) from integer.
>>> print(int_to_rgba(0))
[0.0, 0.0, 0.0, 0.0]
>>> print([round(x, 3) for x in int_to_rgba(100100)])
[0.0, 0.004, 0.529, 0.016]
"""
return [x / 255 for x in v.to_byt... |
f05e21c6ff5ec88f9ebf94dfcbd4e5884df75b84015b5d88ea8079228b251595 | Python | 926 | 33 | from angorapy.tasks.world_building.base import PhysicalWorld
from angorapy.tasks.world_building.entities import Stage
from angorapy.tasks.envs.dexterity.mujoco_model.externals import Block
from angorapy.tasks.envs.dexterity.mujoco_model.robot import ShadowHand
class ShadowHandWithCubeWorld(PhysicalWorld):
def __... |
09006509efe340a45afe351c2cf3719f2cad78d3bef656acb2cae43bec8b639f | Python | 927 | 36 | """
Stand-alone module to provide information about whether optional deps exist.
"""
from importlib import import_module
import logging
import sys
logger = logging.getLogger(__name__)
_not_importable = set()
def get_module(name, should_load=True):
"""
Return module or None. Absolute import is required.
... |
46b8a6e84f2e9f09bca75afbf066a0845851ea8cb060c1d15c9ff4af0c2cd80d | Python | 927 | 35 | from .params import params
# Number of networks to include:
N_networks = 30
# Number of sequence templates to run:
N_templates = 1
# Number of sequence templates that save dynamics data:
N_templates_with_dynamics = 1
# Toggle STD - always an iterable of 0 and/or 1
STDs = (0,1)
# Toggle TA - always an iterable of 0... |
852850b22fd96bb53e1ad0bcae364bacbb2c0b67ab47fa12135c24a9d0ff0b78 | Python | 927 | 30 | from collections.abc import Callable
from dataclasses import dataclass
import numpy as np
import torch
from torch import nn
from neurogym.wrappers.components.modules.base import LayerProbeBase
@dataclass
class LinearLayerProbe(LayerProbeBase):
layer: nn.Linear
@staticmethod
def layer_types() -> set[typ... |
2a9c4c80a6bf1b91b92f800ca672870081942afcc3e3b6c6f7994599d347e83b | Python | 929 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
522e65e77a51de5a342dbc9413874778bdcc5568141c9e1ed60077c83ea31d2f | Python | 929 | 31 | """Shared config helpers for the models_architecture inspector data layers.
These helpers are reused by both the Model Inspector and the Module Inspector
data layers to avoid duplicating canonical defaults and copy semantics.
"""
from __future__ import annotations
import copy
from typing import Any
__all... |
05cf79399d3c5e9989a10ff5332200009838310bb128116600edbbd228a531c7 | Python | 930 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 17
msp.architecture["motif_model_spec"]["motif_feature... |
0b141071fc9d2e3c5506907369ce79eefb3e6758760fb1dfcf7c711ce7085ce7 | Python | 930 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
0b1a314c59a69fa9847b0f7ae49c1904918d0a9d18df64ee80beb1d13320344a | Python | 930 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 17
msp.architecture["motif_model_spec"]["motif_feature... |
21e9e492588a93ab009366b535f63287e76834396615619189a25de5ef002e92 | Python | 930 | 36 | """Worker Event Heartbeat Bootstep."""
from celery import bootsteps
from celery.worker import heartbeat
from .events import Events
__all__ = ('Heart',)
class Heart(bootsteps.StartStopStep):
"""Bootstep sending event heartbeats.
This service sends a ``worker-heartbeat`` message every n seconds.
Note:
... |
29a8201c583adc28e8b92fcfe88bda0fd09317cab2b80700a290539368a52db8 | Python | 930 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 21
msp.architecture["motif_model_spec"]["motif_feature... |
4535ff0d05f3fd5d03fda0ef6aa1886aaed8e849fd74ff469013bb9a687a06ed | Python | 930 | 35 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
from setuptools import (
setup,
find_packages,
)
import versioneer
# Give setuptools a hint to complain if it's too old a version
# 30.3.0 allows us to put most metadata in setup.cfg
# Should match pyproject.toml
SETUP_REQUIRES = ['setuptools >= 30.3.0'... |
1b37914e36f6a6fde0f2f3701ca4b7125c5776c172fb4da52f19c213b713284b | Python | 931 | 25 | import os
import numpy as np
import shutil
from os.path import exists
path_annotations = '/mnt/datosnas/Users/cmejia/EMOTION-DETECTION/AFFECTNET-raw/train_set/annotations/'
path_images = '/mnt/datosnas/Users/cmejia/EMOTION-DETECTION/AFFECTNET-raw/train_set/images/'
path_target = '/mnt/datosnas/Users/cmejia/EMOTION-DET... |
f05a5e1beba0131ce99149c774c078c615f1473f3b39ace683e0f0d4dabba702 | Python | 931 | 49 | __author__ = 'quentin'
from ethoscope.tracking.roi_builders import ImgMaskROIBuilder
from ethoscope.tracking.cameras import MovieVirtualCamera
from ethoscope.tracking.monitor import Monitor
from ethoscope.tracking.trackers import AdaptiveBGModel
from ethoscope.tracking.interactors import SystemPlaySoundOnStop
import... |
f21a470b58f00ed932aabd3fb20e953b4c1530b8dad3b9b6dbb46ee2ffd1485d | Python | 931 | 31 | import argparse
import pandas as pd
from statsmodels.stats.multitest import fdrcorrection
parser = argparse.ArgumentParser()
parser.add_argument("--dapeaks", action = "store",
help = "da peaks csv file to be converted to bed")
parser.add_argument("--celltype", action = "store",
help = "cell type")
parser.add_argumen... |
4b27ce8bd819f256b24452fe2ebb6b6b1e4ff6a9c1ff8d94ced3bb097e87b638 | Python | 932 | 34 | from tensorboardX import SummaryWriter
from jax.experimental.host_callback import id_tap
import jax.numpy as jnp
from absl import logging
import os
summary_writer = None
last_step = 0
def init_writer():
global summary_writer
logging.info('summary_writer init')
if 'custom_config' in os.environ:
summary_writer... |
51632382f04f2715cd28fad890856bcb7c7fdfa857795ee7cf2cc55e254260b3 | Python | 932 | 34 | import os;
import sys;
my_path=os.path.dirname(os.path.realpath(__file__));
root=os.path.abspath(my_path+'/../..')
sys.path.insert(0,root);
from biswebpython.core.bis_objects import *
import biswebpython.core.bis_baseutils as bis_baseutils;
# change this
fname1= root +'/test/testdata/indiv/prep.nii.gz';
fname2= roo... |
803e19d970a736c6eacbeeb28ba9e3d2415269d219ac02ba1c0ed4f684db51ce | Python | 932 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
8ad714e4542c93cc45d67a194bcb034eb2534c25b96512521a5b6a78ea6edc13 | Python | 932 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 21
msp.architecture["motif_model_spec"]["motif_feature... |
a6904f49a4d2f8fbb9b40f836772eb5a53549768cf43b4cd06d86d0a0255fde6 | Python | 932 | 33 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 11
msp.architecture["motif_model_spec"]["motif_feature... |
269441b25d98864223295f2f08324e29a8eda838922fcfaa37f384dab30669a1 | Python | 933 | 65 | # limits maps to just the TFs which regualte the network
from sys import argv
from pprint import pprint as pp
map_file = open(argv[1])
reg_dic = {}
tf_dic = {}
line_count = 0
head_line = 0
for n, line in enumerate(map_file):
# if n > 5:
# break
# print(line)
if line_count == 0:
lin... |
f6df5d06e2d98e061a439c429c7073897e553093f366c56f692b781e062e6902 | Python | 933 | 38 | from dependency_injector import containers, providers
from carbonserver.api.infra.repositories.repository_projects import (
SqlAlchemyRepository as ProjectSqlRepository,
)
from carbonserver.api.services.project_service import ProjectService
class DatabaseMock:
def __init__(self, db_url):
self.db_url ... |
000dedf828115a009d8a4bede9aa9f007e01044fc35eb8175ef6feaa2aeae818 | Python | 934 | 39 | """Spatiotemporal modeling of spatial transcriptomics
"""
from .agg import box_qc_regions, imshow, qc_regions
from .align import (
optimization_animation,
overlay_slices_2d,
plot_deformation_grid,
slices_2d,
)
from .bbs import delaunay, polygon
from .colorlabel import color_label
from .contour import sp... |
248c49bb8f50605dffb96dff251abe245078fdcdbc2ea2756907e2ae6c091c30 | Python | 934 | 26 | """Guard against README indexes referencing files that no longer exist."""
import re
from pathlib import Path
import pytest
REPO_ROOT = Path(__file__).parent.parent
# Markdown files whose relative links must all resolve.
CHECKED_FILES = [REPO_ROOT / "examples" / "README.md"]
LINK_RE = re.compile(r"\[[^\]]*\]\(([^)]... |
11d4620742eca9cd833b37847da52878c7bb7678a2b5881ccadd06331ac71e2c | Python | 935 | 34 | import pathlib
import tempfile
import zipfile
import numpy as np
from impose import formats
data_path = pathlib.Path(__file__).parent / "data"
def get_ometif():
td = pathlib.Path(tempfile.mkdtemp(prefix="impose_ometif_"))
with zipfile.ZipFile(data_path / "fmt_fl_ometif.zip") as arc:
arc.extractall... |
7e08b1bc2d51eec1f7af5de4b727fb3741402e8a845cbd5c05aae6369484a168 | Python | 935 | 23 | from distutils.dep_util import newer_group
# yes, this is was almost entirely copy-pasted from
# 'newer_pairwise()', this is just another convenience
# function.
def newer_pairwise_group(sources_groups, targets):
"""Walk both arguments in parallel, testing if each source group is newer
than its corresponding t... |
a04aae171a52f881e2fe5bf3a9f0cf5350617fe9e425bdd575df07a30f550cb9 | Python | 935 | 22 | from __future__ import annotations
import torch
from lightly.loss.dense_relational_utils import roi_resample_to_grid
class TestRoiResampleToGrid:
def test_output_shape(self) -> None:
feat = torch.randn(2, 8, 16, 16)
boxes = torch.tensor([[0.0, 0.0, 16.0, 16.0], [4.0, 4.0, 12.0, 12.0]])
o... |
b4e62b8ce4d5c8a142b433d650c052e1e88327c2fd187321932e9117e3cfa88e | Python | 935 | 33 | """Handles the TerminalOut Class"""
# License: GNU General Public License v3.0
import ipywidgets as widgets
from ipywidgets import Widget
class TerminalOutput(Widget):
"""Shows the algorithm output"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.out_put = widgets.Output(
... |
e04db9d79042f8007b6e5eb5ca61edbd1b1b4d0778f163564e20af248b011030 | Python | 935 | 30 | from comet_ml import Experiment # isort:skip
import tensorflow as tf
# Assumes Comet variable environment configuration.
# See here to get your API_KEY:
# https://www.comet.ml/user/settings/account#section-DEVELOPER_INFORMATION
# And here for setup information:
# https://www.comet.ml/docs/python-sdk/advanced/#experi... |
f9ba5fe88291248f480cd4d5a0534ecff2a647e586bf027fa748db37c72213dc | Python | 935 | 32 | from pathlib import Path
from typing import Dict, Tuple
JOINT_GROUP: Dict[str, Tuple[str, ...]] = {
"wrist": ("WRJ1", "WRJ0"),
"thumb": ("THJ4", "THJ3", "THJ2", "THJ1", "THJ0"),
"first": ("FFJ3", "FFJ2", "FFJ1", "FFJ0"),
"middle": ("MFJ3", "MFJ2", "MFJ1", "MFJ0"),
"ring": ("RFJ3", "RFJ2", "RFJ1", ... |
38655b04aaf34848bd1732308c9cdd51975b1e78f0bf18c7d18b209ef8cd3903 | Python | 936 | 33 | import os
import shutil
import tempfile
from unittest import TestCase
import numpy as np
from anndata import AnnData
from spateo.configuration import SKM
def create_random_adata(layers=None, shape=(3, 3), t=SKM.ADATA_AGG_TYPE):
adata = AnnData(X=np.random.random(shape))
if layers:
for layer in layer... |
28ec7f1e2d2bc1ffa50114560b04446b1388edd5ccbc076b90853bf9ceecc89c | Python | 937 | 38 | __author__ = "quentin"
import cv2
import numpy as np
class EthoscopeException(Exception):
def __init__(self, value, img=None):
"""
A custom exception. It can store an image
:param value: A value passed to the exception, generally a text message
:param img: an image
:type... |
bede8d07ba673c3f539bae8923362d939cd8e5ff9f7fa5b11af68ab9ea9e7e4a | Python | 937 | 31 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._domain import Domain
from ._hoverlabel import Hoverlabel
from ._insidetextfont import Insidetextfont
from ._legendgrouptitle import Legendgrouptitle
from ._marker import Marker
from ._textfont import Textfont
from ._title ... |
118ca08535db1d1af521afbc2a85e8cae988143ffaaba6d9c790c371a6be3834 | Python | 938 | 37 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
""""tests for search.py"""
import unittest
import numpy as np
from sknetwork.data import cyclic_digraph
from sknetwork.data.test_graphs import *
from sknetwork.path import get_dag
class TestSearch(unittest.TestCase):
def test(self):
adjacency = cyclic_dig... |
99b31f59e35f1c8012c48a42761886977b368d763dc96b069dd78f6b500899c3 | Python | 938 | 33 | import numpy as np
from .annotation import Annotation
class AlternateSites(Annotation):
"""
Represents a pair of alternate sites, that is,
a pair of sites where one should be included and the other should not.
"""
@classmethod
def patterns(self):
return ["AA", "DD", "AAA", "DDD"]
... |
cf4d2b7203f931a58e26231da1467517b35d9477880f35162a4d58d3a4e28f39 | Python | 938 | 48 | """add export association table
Revision ID: 59e024ceaca1
Revises: e0e3bb173ee6
Create Date: 2022-10-12 18:02:34.659770
"""
from sqlalchemy import (
Column,
DateTime,
Integer,
)
from galaxy.model.custom_types import (
JSONType,
TrimmedString,
UUIDType,
)
from galaxy.model.migrations.util imp... |
2f4bd84db8df5f2d330555f15add1c4db80e6872fa324030757429829d7d2faa | Python | 939 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
3aace398adefd4445c3a4189b80f83b40ab9b43ad571c71d29b6220b48a52a9c | Python | 939 | 37 | """
Test a file that Bear had trouble opening
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0, PATH_SRC)
import matplotlib.pyplot as plt
import pyabf
im... |
532d40bad87baa4705c289cf8e45b61e661a5bfaa318e7fbf5b63c0a43bcac9a | Python | 939 | 27 | from ..parametric import _compute_hommel_value, _true_positive_fraction
from nilearn.glm.thresholding import _compute_hommel_value as nl_compute_hommel_value
from nilearn.glm.thresholding import _true_positive_fraction as nl_true_positive_fraction
from numpy.testing import assert_allclose
from scipy.stats import norm
... |
59dea0aac4d9e3201eedaf27cd51c00126bf89a2ac408549631376024c9556dc | Python | 939 | 31 | import sqlite3
from .db import DB
class SurveillanceInfo(DB):
def __init__(self):
super().__init__()
self.table_name = 'KL_surveillance_info'
self.table_desc = 'District wise distribution of persons under surveillance (quarantine and isolation)'
self.cols = self.getcolumns()
... |
63fdb45f3a0e7226ed7f756737caaa4ca27e2a5298e176f2d8b6d0642b396165 | Python | 939 | 32 | #!/usr/bin/env python
# code is same as ~/tools/stats/wiggle_to_simple.py
"""
Read a wiggle track and print out a series of lines containing
"chrom position score". Ignores track lines, handles bed, variableStep
and fixedStep wiggle lines.
"""
import sys
import bx.wiggle
from galaxy.util.ucsc import (
UCSCLimitE... |
69d2228e91e38b5f3b26f3128fcb1f9658501337500be6c3a11a1f5f30ba1604 | Python | 939 | 19 | import os
import sys
import numpy as np
"""
this is the only script you actually should call, it will submit a job
this job makes the features, and then does the regression.
only change the home_dir variable, everything else should run!
"""
homedir = '/cbica/projects/abcdfnets/scripts/keller_ridge/pennlinc-regressio... |
6ed67e90fa2248503181ba44ec0788157cfa8a5048dcdd1e1916c1915138cbc6 | Python | 939 | 1 | import numpy as np
import pandas as pd
import time
import random
# [1] data preprocessing #
df = pd.read_csv('./data/GSE76312_Single_cell_HSCs_expression_RPKM_table.csv', index_col=0) # dimension = (genes x cells)
#df1 = df.iloc[0:3,:]
print("starting preprocessing:")
n1 = df.shape[0] # row genes
n2 = df.shape[1] # ... |
364d62b15149fe1adea83a1acb636100c8d10d1d463d068313c260097f53901e | Python | 940 | 33 | """Tests for specparam.modes.jacobians."""
from specparam.modes.jacobians import *
###################################################################################################
###################################################################################################
def test_jacobian_gauss():
xs... |
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