sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5546062d66f01a2bf4dda48c5b9b23746370b046bd14a82ad1170ae68de5907e | Python | 1,079 | 30 | """Prompt for the router chain in the multi-retrieval qa chain."""
MULTI_RETRIEVAL_ROUTER_TEMPLATE = """\
Given a query to a question answering system select the system best suited \
for the input. You will be given the names of the available systems and a description \
of what questions the system is best suited for.... |
b62c8a5b591217bb3c61fb10a5f9074a0e291ea0d846a2279fb6182da82dca95 | Python | 1,080 | 35 | from setuptools import setup
def readme():
with open('README.md') as f:
return f.read()
setup(name='independent_vector_analysis',
version='0.3.6',
description='Implementation of IVA-G and IVA-L-SOS',
long_description=readme(),
long_description_content_type='text/markdown',
... |
4c0dddcdc585f00372ab0412146152f66513583830b2f1df7fce0ded446c8705 | Python | 1,081 | 32 | # third party packages
import numpy as np
def clipOutliers(data, method="IQR", axis=None, impute_nans=True):
if method == "z-score":
try:
from scipy.stats import zscore
except ModuleNotFoundError:
raise ModuleNotFoundError("The dependency 'SciPy' is required for this functio... |
52d6681d66a35e3e0bafa2e75fdb83d2035918ee4f631651ebc353df5167c02f | Python | 1,081 | 38 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/9/28 10:46
# @Author : zhangchao
# @File : loader.py
# @Software: PyCharm
# @Email : zhangchao5@genomics.cn
import numpy as np
import scipy.sparse as sp
from torch.utils.data import Dataset
class DNNDataset(Dataset):
def __init__(self, adata, ... |
b1e070ae167182ada780340ea727f3c825319a83f4e34f66cab564d26e1827da | Python | 1,081 | 40 | # %% imports
import argparse
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
DATASETS = ['chbp', 'lemon', 'tuab', 'camcan']
BENCHMARKS = ['dummy', 'filterbank-riemann', 'filterbank-source',
'handcrafted', 'shallow', 'deep']
parser = argparse.ArgumentParser(description='Comput... |
e8dc05596f091c6d3260a011991de1b4c397106e3006ff4cd44092330a4b7c35 | Python | 1,081 | 42 | import torch
from torch import nn
from genie.utils.encoding import sinusoidal_encoding
class SingleFeatureNet(nn.Module):
def __init__(self,
c_s,
n_timestep,
c_pos_emb,
c_timestep_emb
):
super(SingleFeatureNet, self).__init__()
self.c_s = c_s
self.n_timestep = n_timestep
self.c_pos_emb = c_pos_em... |
53172f776f1c19a2cfe88308ab466078e04a86150a94489eb38de4817d7bcd21 | Python | 1,082 | 29 | import pkgutil
from pathlib import Path
import pytest
from langchain_core.messages.block_translators import PROVIDER_TRANSLATORS
def test_all_providers_registered() -> None:
"""Test that all block translators implemented in langchain-core are registered.
If this test fails, it is likely that a block transl... |
c5f5e552d77aad166383b9ecb3ed43f99742a54664a3240fd2b477eb321b23b9 | Python | 1,082 | 26 | ###########################################################################################
# #
# Set up paths for the Object Detection Metrics #
# ... |
eaff4de8b65f6aa9e1d054331e7af08fc84b0cc452f1cc26aa496dc206c34e01 | Python | 1,082 | 44 | #!/usr/bin/python3 -B
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
def plot(rayx_times, rayui_times, beamlines):
"""
Plot the Beamline time; x, for rayx. ui, for RAY-UI
beamlines is also the chaning variable throughout the beamline
"""
assert(len(rayx_times) == len(rayui_times))
N = le... |
50d1a3b71c2612ba58bb711e638bacfe9e52a19fadca99ef4d0d58f0d9f2b633 | Python | 1,083 | 27 | """
Analyze participant demographics data.
"""
import pandas as pd
DEMOGRAPHICS_DATA_FILE = "data/demographic-data/ada-learn_23-02-17/ada-learn_23-02-17_demographics_data.csv"
DEMOGRAPHICS_SUMMARY_FILE = "data/demographic-data/ada-learn_23-02-17/ada-learn_23-02-17_demographics_summary.csv"
def main():
data = pd.... |
b43f304fce8cde21a92d7147c90692135b1904396748273892e51e977df9194c | Python | 1,084 | 43 | # For this you need matlab and the new requirements.txt
import argparse
import matlab
import matlab.engine
import scipy.io
from oset.ecg.peak_detection.peak_det_local_search import (
peak_det_local_search,
)
import unit_test as testing
mat = scipy.io.loadmat("../../../datasets/sample-data/SampleECG1.mat")["data"... |
c9e0aeef6546671b52079cb033d4ab0160131314a843a9abcca8d12656ac400e | Python | 1,084 | 37 | from langchain_core.prompt_values import ChatPromptValue, ChatPromptValueConcrete
from langchain_core.prompts.chat import (
AIMessagePromptTemplate,
BaseChatPromptTemplate,
BaseStringMessagePromptTemplate,
ChatMessagePromptTemplate,
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessageLike... |
333e1e2d2d0e6358c3893fdd2cc9fb375fa5bb0a6d1a866a0e8a57cddf3cd3bb | Python | 1,085 | 35 | """ Module initializer includes all sub-modules.
:Version:
1.1.0
:Date:
29.03.2017
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2017 Jan Melchior
This file is part of the Python library PyDeep.
PyDe... |
cc705ebede944c8b374b87e0919c26f12930f938024e61975e4e95d76f59e042 | Python | 1,085 | 36 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.utils import (
map_ai_messages,
map_ai_messages_in_session,
merge_chat_runs,
merge_chat_runs_in_session,
)
# Create a way to dynamic... |
1e3082dcb4f97335f15a1ad25af0225ca98986e35be0288650642e5ab3d917d9 | Python | 1,086 | 35 | import torch
import numpy as np
from contextlib import nullcontext
FORWARD_ITERS = np.array([1, 2, 3, 4, 5])
FORWARD_ITERS_P = np.array([10, 6, 3, 2, 1], dtype=float)
FORWARD_ITERS_P /= np.sum(FORWARD_ITERS_P)
def loss_function(model, sample):
input1 = sample["input1"]
input2 = sample["input2"]
output1 =... |
4ff54c1476fc01b50956733d9495828c3061709ffd438a4cc617e62b3b8b3296 | Python | 1,086 | 35 |
from functools import lru_cache
from typing import Dict
import biomart
@lru_cache()
def get_ensembl_mappings() -> Dict[str, str]:
# Set up connection to server
server = biomart.BiomartServer('http://uswest.ensembl.org/biomart')
mart = server.datasets['mmusculus_gene_ensembl']
# List the types of da... |
ba6645485e32224fbff7fb412cfd837f67b8b977f7e92a23210a66e5ea60c3b2 | Python | 1,086 | 50 | '''
R.py - Intergrate python and R
===========================================================
:Tags: Python
Purpose
-------
This module file contains code to allow integration of python and
R:
- Redefined R class which enables saving R history.
Usage
-----
'''
from rpy2.robjects import R as RBase
class R_wi... |
e77845b84377f2ee0a89e3252a55ee78a1561fc483f4ab4bfd14fe7c4d4959f2 | Python | 1,086 | 36 | """**Docstores** are classes to store and load Documents.
The **Docstore** is a simplified version of the Document Loader.
"""
from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.docstore.arbitrary_fn import DocstoreFn
from lang... |
7fbb4f861e6ba0f9a27903a139a7168e7864f0357b0cf53cb561f6808328766b | Python | 1,087 | 38 | import numpy as np
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.utils import Params
from dliplib.utils.helper import select_hyper_best_parameters, load_standard_dataset
from dliplib.reconstructors.tv import TVReconstructor
from dliplib.utils.reports import save_results_table
# load ... |
9de4898605f1a6f6eba33fbaaa0d54c1d015e148751646d4fdd8080be6d25acc | Python | 1,088 | 41 | import numpy as np
import matplotlib.pyplot as plt
import functions_emt as f
from params_emt import *
import time
import pandas as pd
def main():
tm = 10000. # simulation time (dt=0.1 is set in the time_course_emt() function )
b = 1.
np.random.seed(100)
# load initial conditions for E, E/M, and ... |
caa23ea3ec9934ad474eb85f5fc48c290693c1b12a1302a1a474d9aa1db70bc5 | Python | 1,089 | 25 | from nowcasting.encoder_forecaster import *
from nowcasting.ops import *
from nowcasting.operators import *
class MovingMNISTFactory(EncoderForecasterBaseFactory):
def __init__(self,
batch_size,
in_seq_len,
out_seq_len):
super(MovingMNISTFactory, self).__... |
f8861ff311b2d894473df5a1e40c1b1887d3229af11bc35d00fececc281cac49 | Python | 1,090 | 47 | import numpy as np
import scipy.sparse as sp
from collections import Counter
####################################################
# This tool is to generate positive set with a thre-
# shold "pos_num".
# dataset pos_num
# acm 5
# dblp 1000
# aminer 15
# freebase 80
#
#
# Notice: The best pos_num of acm is ... |
01f163ab37b9c3683965339f2956cc08db10ad26ba8e6949978422073d966d11 | Python | 1,091 | 42 | """
Add ground-truth labels to deepsignal call_mods output.
"""
import sys
import pandas as pd
sys.path.append("..")
from utils.constants.functional import METHYL_BED_COLS_NA
from utils.constants.functional import DS_CALL_COLS
if __name__ == '__main__':
call_filename = sys.argv[1]
methyl_filename = sys.arg... |
31152fe76505e28ce59108a822f6a75255509bd7e2922644ac04fd2cf99b15f7 | Python | 1,091 | 28 | from torch import nn
import sys
sys.path.append("../../")
from TCN.tcn import TemporalConvNet
class TCN(nn.Module):
def __init__(self, input_size, output_size, num_channels, kernel_size=2, dropout=0.2, emb_dropout=0.2):
super(TCN, self).__init__()
self.encoder = nn.Embedding(output_size, input_siz... |
95c9b8bf330b0ba31dd5d55df91f110e6a0c14a555b47b291efdffc059693a37 | Python | 1,092 | 36 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
POWERBI_CHAT_SUFFIX,
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
# Create a way to dynamically ... |
2b50a34ca1d1cf2b2ecf05601b619cb70cc4aaa9447a49624f33f082c9b3aeb0 | Python | 1,093 | 34 | aa_list = list("QWERTYIPASDFGHKLCVNM")
aa_one_letter = set(aa_list)
aa_three_letters = ["GLN", "TRP", "GLU", "ARG", "THR",
"TYR", "ILE", "PRO", "ALA", "SER",
"ASP", "PHE", "GLY", "HIS", "LYS",
"LEU", "CYS", "VAL", "ASN", "MET"]
aa_one_to_three_dict = {
"... |
84febd5d9f85048c9acb71b4d069cf08d8cecbe4ca93f43b0ec4e6fe18e279ef | Python | 1,093 | 53 | #!/usr/bin/env python
"""
geometry (/trigonometry) functions for cilium analysis
"""
import numpy
def calculate_theta(disp):
x, y, z = disp
return numpy.arctan2(numpy.sqrt(x**2 + y**2), z)
def calculate_phi(disp):
x, y = disp[:2]
return numpy.arctan2(y, x)
def calculate_dist(disp):
return nump... |
22b34bbf4fa70d40b7628644d4ea4a128f9f15d8756ae0331341872134128ece | Python | 1,094 | 27 | model_names_eps30 = ['24x6_e1000_OGMW6BZD',
'24x8_e1000_PXTN6NGN',
'14x8_e1000_JPEMNEVL',
'24x8_e1000_MKE3W7TE',
'18x7_e5000_3ZDQ3YRK',
'24x5_e1000_JQIWIS62',
'18x8_e1000_KO2T5FHQ',
... |
788b2c7206a950cf60be712dc2dd0e1b3b0dedbb2bafbf17f2f58083cd2d798d | Python | 1,094 | 41 | """O365 tools."""
from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import (
O365CreateDraftMessage,
O365SearchEmails,
O365SearchEvents,
O365SendEvent,
O365SendMessage,
)
# Create ... |
505af8d2718876eac68857c7e72711c181f8007083b3742f32cf25157c4b1e39 | Python | 1,095 | 38 | # Copyright 2021 AlQuraishi Laboratory
#
# 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 i... |
d93cd88bc7555ed572d33c6c61620a400687f7c11ff56c52d7e53b6f94514681 | Python | 1,095 | 35 | """ Package providing rbm models and corresponding sampler, trainer and estimator.
:Version:
1.1.0
:Date:
04.04.2017
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2017 Jan Melchior
This file is part of the Py... |
0202012181a2d6f13d10423f258a0279afeacc55ca1b47892f68ebae3c8781d0 | Python | 1,098 | 36 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.chains.graph_qa.gremlin import (
GRAPHDB_SPARQL_FIX_TEMPLATE,
INTERMEDIATE_STEPS_KEY,
GremlinQAChain,
extract_gremlin,
)
# Create a way to dynami... |
3d443cdbe1e4e0006dd347bc9da6bcae879807e044814a0941e6bd099cc62464 | Python | 1,098 | 23 | from dliplib.utils.models.skip import Skip
from dliplib.utils.models.unet import UNet
from dliplib.utils.models.iradonmap import IRadonMap
def get_unet_model(in_ch=1, out_ch=1, scales=5, skip=4, channels=(32, 32, 64, 64, 128, 128), use_sigmoid=True, use_norm=True):
assert (1 <= scales <= 6)
skip_channels = [s... |
cdf41512bc413e5db9aca3bb3aa26debab1a5265312a758d82dba4bc55c5844e | Python | 1,099 | 39 | import sys;
import numpy as np
import joblib
from dt_utils import Timer
from data_utils import generate_recognition_data
from plastic import PlasticNet
d = 50 #length of input vector
R = int(sys.argv[1]) #repeat interval
P = .5 #probability of repeat
np.random.seed(12345) #for re... |
5917f59422571fc0b092d05143108d5cd451aba6c64038f00266aa87eada5f63 | Python | 1,100 | 32 | import scrublet as scr
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Arial'
plt.rc('font', size=14)
plt.rcParams['pdf.fonttype'] = 42
args = sys.argv
input_dir_prefix = args[1]
input_dir_suffix = a... |
7205aaa9a192b97ad05d90ebe7a3eaff0ab86fa8d36edd5ad7e3a5242f2ba7aa | Python | 1,100 | 45 | import numpy as np
import cv2
from collections import abc as container_abc
import io
from PIL import Image
import matplotlib.pyplot as plt
COLOR_POOL = [(255, 0, 0), (0, 255, 0), (0, 255, 255), (255, 255, 0)]
def plt_show(x, title=None):
import matplotlib.pyplot as plt
plt.imshow(x)
if title is not None:... |
72bf1990f865db85b012a444de2e3f5115dfeb64b37d3aeeb9963263da1aae38 | Python | 1,100 | 38 | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="comformer",
version="2024.06.05",
author="Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji",
author_email="keqiangyan@tamu.edu",
description="comformer",
... |
aaffee5905be6d0fcdc2e2ed604caf9cc0b2fcb17c8866dbc5a323315782b02b | Python | 1,100 | 45 | from collections.abc import Iterable
from itertools import chain, combinations
def _variable_or_iterable_to_set(x):
"""
Convert variable, set, or iterable x to a frozenset.
If x is None, returns the empty set. A string is one variable.
Parameters
---------
x : None, str or Iterable[Hashable]... |
80c220c9387382756cebbc27f20bd48e6f5e82e8c6923ee6fbcd5deb291310f3 | Python | 1,101 | 33 | import torch
import torch.nn as nn
# 定义双向LSTM模型
class BiLSTM(nn.Module):
def __init__(self, input_dim, hidden_size, num_layers, output_dim):
super(BiLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
# 定义双向LSTM层
self.lstm = nn.LS... |
2c9f4dcc7cc376648a5045e387062d35890ea0bec4dd131682a6eda59aee3137 | Python | 1,103 | 55 | import random
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from rdkit import Chem
import io
import numpy as np
import threading
def nan_to_num(vec, num=0.0):
idx = np.isnan(vec)
vec[idx] = num
return vec
def _normalize(vec, axis=-1):
return nan_to_num(
... |
c3da88a944bb92ffa4d8263e8440011c1cbd12b5f049ce4255551ae6114233a3 | Python | 1,103 | 26 | import os
import re
def get_segment_index(segment_name: str) -> int:
return int(re.search("seg([0-9]+)", segment_name)[1])
def connec_wrapper_main(postproc_folder, fs=30000):
segment_folders = os.listdir(postproc_folder)
segment_folders = sorted(segment_folders, key=get_segment_index)
segment_folders ... |
f3a337719ec7bbdb267e58d184de35869d23220b65b9661c4d2ae460bc3daee7 | Python | 1,103 | 49 |
import sys
sys.path.append('../classes')
import class_stimuli
import numpy as np
import pickle
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
### MAIN SCRIPT
stimulus_tasks = ['varyposition', 'varysize', 'varyrotation']
stimulus_folder = '../stimuli/simple_seqs/'
## load classes
if Tru... |
3a604211191709969da3af40975db88d982971478e257b0337d87d86986c5302 | Python | 1,104 | 45 | """3D fluorescence deconvolution using the waveorder API."""
from waveorder.api import fluorescence
settings = fluorescence.Settings(
transfer_function=fluorescence.TransferFunctionSettings(
yx_pixel_size=0.1,
z_pixel_size=0.25,
wavelength_emission=0.532,
numerical_aperture_detecti... |
6a6b09f4a537760e38d8e9f75b27ce41670bd8b85ffe58f7bb0931c1e03d489c | Python | 1,104 | 45 | #!/usr/bin/env python3
from setuptools import setup, find_packages
version = "1.1.2"
with open("README.md", "r", encoding="UTF-8") as fh:
long_description = fh.read()
with open("LICENSE", encoding="UTF-8") as f:
license = f.read()
setup(
name="scaden",
version=version,
description="Cell type d... |
e1a4fe5f4c0bd8f0b651b058ad91a59017cda828db6b17465ef82554465e9af7 | Python | 1,106 | 30 | import torch
import torch.nn as nn
class TransformerModel(nn.Module):
def __init__(self, input_dim, nhead, num_encoder_layers, num_decoder_layers, hidden_dim, dropout, output_dim):
super(TransformerModel, self).__init__()
self.embedding = nn.Linear(input_dim, hidden_dim)
self.trans... |
c587c1b58344a6ea198ec73ad2ec682c3e53020a2ae33777e8f5af60b445e51f | Python | 1,107 | 36 | from pymaid.fetch.annotations import filter_by_query
import pandas as pd
import pytest
NAMES = pd.Series(["potato", "spade", "orange", "bears"])
@pytest.mark.parametrize("query", ["spade", "~spade", "annotation:spade"])
def test_filter_by_query_exact(query):
out = filter_by_query(NAMES, query)
assert list(o... |
1caf8a8cf7d07c1363d15fdab68d3f18decf16c0be5a2c6df15d90e75e457499 | Python | 1,108 | 16 | """Format instructions."""
JSON_FORMAT_INSTRUCTIONS = """STRICT OUTPUT FORMAT:
- Return only the JSON value that conforms to the schema. Do not include any additional text, explanations, headings, or separators.
- Do not wrap the JSON in Markdown or code fences (no ``` or ```json).
- Do not prepend or append any text ... |
72afe7b4822d7dcd59d46658168c18a59c35ec1ad4a29f46623a8a1c40118e98 | Python | 1,109 | 39 | import numpy as np
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.utils import Params
from dliplib.utils.helper import select_hyper_best_parameters
from dliplib.utils.helper import load_standard_dataset
from dliplib.reconstructors.tv import TVReconstructor
from dliplib.utils.reports imp... |
03ba4bb437fef083fe2a5da32be0eab58fc94cda5b0b67d811672ed7a99acb84 | Python | 1,110 | 35 | '''
producing random numbers as an example to test the codes
'''
import numpy as np
from tensorflow.keras.utils import Sequence
class BaseGenerator(Sequence):
def __init__(self, batch_size, CH):
self.batch_size = batch_size
self.CH = CH
self.__fullepoch_recordings_indices=[]
print('... |
1859d33bd76595146a9b0e2b27a1562297e09622593160db3f6466a90d114142 | Python | 1,110 | 37 | import pytest
from langchain_classic.base_memory import BaseMemory
from langchain_classic.chains.conversation.memory import (
ConversationBufferMemory,
ConversationBufferWindowMemory,
ConversationSummaryMemory,
)
from langchain_classic.memory import ReadOnlySharedMemory, SimpleMemory
from tests.unit_tests.... |
2f153a7d1f131e74ff553677024e1bd57eb6281f49dbdc03ec258cb3f6a70766 | Python | 1,111 | 42 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/10/7 16:09
# @Author : zhangchao
# @File : setup.py.py
# @Software: PyCharm
# @Email : zhangchao5@genomics.cn
import setuptools
from wheel.bdist_wheel import bdist_wheel
__version__ = "1.0.4"
class BDistWheel(bdist_wheel):
def get_tag(self):
... |
59e91a0c1b1eeb8e9c56f30aa391e43c32fca16da1b4b6ef7e0650c418f3360c | Python | 1,112 | 31 | # -- coding: utf-8 --
# @Time : 2022/12/1 18:38
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : load_part_expression.py
import sys
import pandas as pd
sys.path.append('..')
from analysis_utils import ttest_plot_utils
from analysis_utils import homo_random_config as config
import os
import scanpy as... |
989ff39d52979969591c563e1fc375d15b29f5b6ab213f0fd721386f90bcbd9c | Python | 1,112 | 33 | import argparse
import trimesh
# Function to convert obj to stl
def convert_obj_to_stl(input_obj_path, output_stl_path):
# Load the obj file using trimesh
mesh_data = trimesh.load_mesh(input_obj_path)
# Check if the mesh is valid
if not mesh_data.is_watertight:
print("Warning: The mesh is not ... |
0b7dd196c5d3e6033cae301a1211ee57acc29ed7baa909602d0d3b25b8b43f40 | Python | 1,114 | 31 | """Scoring evaluators.
This module contains evaluators for scoring on a 1-10 the output of models,
be they LLMs, Chains, or otherwise. This can be based on a variety of
criteria and or a reference answer.
Example:
>>> from langchain_openai import ChatOpenAI
>>> from langchain_classic.evaluation.scoring import... |
3a66a5fc59f28eae5dd5e34a08f3291b770c92447d333b22179e92130ad2a094 | Python | 1,114 | 33 | """MutliOn Client API tools."""
from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.multion.close_session import MultionCloseSession
from langchain_community.tools.multion.create_session import MultionCreateSession
from... |
96dab2d72c17ee01a3c479fbd84b65917ecbb0f9c149dfc0fd56560081c3e482 | Python | 1,114 | 38 | import torch
from .solvers import FixedGridODESolver
from .rk_common import rk4_alt_step_func
class Euler(FixedGridODESolver):
order = 1
def __init__(self, eps=0., **kwargs):
super(Euler, self).__init__(**kwargs)
self.eps = torch.as_tensor(eps, dtype=self.dtype, device=self.device)
def _... |
0d52a1585f0d0c9c405d322d936a41f3df23753e574e0454648c0adb54c1607a | Python | 1,115 | 36 | import os
from _cif_to_substructure_dict import CifSubstructures
from openff.toolkit.utils import get_data_file_path
if not os.path.exists("aa-variants-v1.cif"):
import requests
r = requests.get("https://files.wwpdb.org/pub/pdb/data/monomers/aa-variants-v1.cif")
print(r.ok)
if r.ok:
with ope... |
2bf7082c2785b044c480a03b61a37389e769cdaa5a87cf1e642ff668ac59a7a2 | Python | 1,115 | 48 |
# plots trajectories of male and female fruit fly for one small period of courtship
import sys
sys.path.append('../classes')
import class_reconstruct
import numpy as np
import pickle
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
### MAIN SCRIPT
## load tracks
if True:
data_folder = '... |
3822263d73eae597b4f1bc77f5b5dc48b8cd006fab063ea13cc646daffe88311 | Python | 1,115 | 36 | from langchain_classic.output_parsers.regex import RegexParser
# NOTE: The almost same constant variables in ./test_combining_parser.py
DEF_EXPECTED_RESULT = {
"confidence": "A",
"explanation": "Paris is the capital of France according to Wikipedia.",
}
DEF_README = """```json
{
"answer": "Paris",
"so... |
a5bde2bd4a8809200a17d438c25af1fd6092f5321d3dee7de9f3d6ec372bfa3d | Python | 1,115 | 38 | # -*- coding: utf-8 -*-
"""
Created on Tue May 5 11:43:19 2020
@author: nishe
"""
import numpy as np
#%%
def ignore_shortevent(event, threshold=20):
'''
event: an array consists boolean value that indicates if an event happens
threshold (ms): if the duration of consecutive events(length of cons... |
fee4cde93d8fa98269309f4f604d1c06aef8e6ba814beede9cb43e63bd27008a | Python | 1,116 | 21 | import torch
# using x entropy loss. 3 year threshold, right inclusive (so if survive to 3 years, not progressors)
def sur_loss(preds, obss, hits, bins=torch.cuda.FloatTensor([[0, 24, 48, 108]])):
bin_centers = (bins[0, 1:] + bins[0, :-1])/2
survived_bins_censored = torch.ge(torch.mul(obss.view(-1, 1),1-hits.vi... |
08eb70dbb7f7abf5b0298cd6eebe29c184ce7679098155acac0dc2ef90a8e37d | Python | 1,117 | 39 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import (
BaseSQLDatabaseTool,
InfoSQLDatabaseTool,
ListSQLDatabaseTool,
QuerySQLCheckerTool,
QuerySQLDataBaseTool,
)
# Create a way... |
103b038605b3867a5d1c35cd288f930fd4d0e37d3b721f38ab84b6ed3701ddaf | Python | 1,117 | 30 | import logging
import os
import sys
import os.path as osp
import time
def setup_logger(name, save_dir, if_train):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s %(nam... |
a394b23d167f32214bd6b96027e030ef0b8e1d6f79068fd177b91aadc827acb4 | Python | 1,117 | 46 | """Unittests for langchain.agents.chat package."""
from langchain_core.agents import AgentAction
from langchain_classic.agents.chat.output_parser import ChatOutputParser
output_parser = ChatOutputParser()
def get_action_and_input(text: str) -> tuple[str, str]:
output = output_parser.parse(text)
if isinstan... |
b15811780f5fbe118c7af0a429ccee3326d0d08bb914c4e7b0b56013fb04c4fb | Python | 1,117 | 38 | """Test configuration loading"""
import pytest
import yaml
from casanovo.config import Config
def test_default():
"""Test that loading the default works"""
config = Config()
assert config.random_seed == 454
assert config["random_seed"] == 454
assert config.accelerator == "auto"
assert config.... |
cbe6fe731dc20f68b25c8040c944657f51acbc69fde7d17d36093001ad34253d | Python | 1,117 | 36 | """LLM Chain for generating examples for question answering."""
from __future__ import annotations
from typing import Any
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseLLMOutputParser
from pydantic import Field
from typing_extensions import override
from ... |
337e99f07b2000d0939b29ac5f1cc99fd15e16308877f022ad811cdd41416229 | Python | 1,118 | 51 | from langchain_core.runnables.utils import (
Addable,
AddableDict,
AnyConfigurableField,
ConfigurableField,
ConfigurableFieldMultiOption,
ConfigurableFieldSingleOption,
ConfigurableFieldSpec,
GetLambdaSource,
Input,
IsFunctionArgDict,
IsLocalDict,
Output,
SupportsAdd,... |
465b9efa23c33da06103cbe887efe49472bdecfcc8c23de3d78fcfdbd3cda625 | Python | 1,118 | 41 | import os
import pathlib
import urllib.request
import pandas as pd
DEBUG = False
url_lemon = ('https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON'
'/EEG_MPILMBB_LEMON/EEG_Raw_BIDS_ID/')
lemon_info = pd.read_csv(
"./META_File_IDs_Age_Gender_Education_Drug_Smoke_SKID_LEMON.csv")
data_path =... |
79e187936abcf911bce85919b193227e58431c9f0d17f02cbbf5cce95f8440b7 | Python | 1,119 | 32 | # Copyright (c) MONAI Consortium
# 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 writing, so... |
9c58f62a43c8770f0175697ada5fa234e513ff421851d55bb0c67bd11fd3b4d2 | Python | 1,120 | 36 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.embeddings import (
HuggingFaceBgeEmbeddings,
HuggingFaceEmbeddings,
HuggingFaceInferenceAPIEmbeddings,
HuggingFaceInstructEmbeddings,
)
# Create... |
280329b20532ca5c54b0aacd996af8fb3e9b719c22e74c9cade9b1e216d1b839 | Python | 1,121 | 36 | import pandas as pd
import sys
print('\n> Processing GWAS locus file...')
gwas_df = pd.read_csv('gwas_locus_column.txt', index_col=None, header=None)
gwas_locus_gene_dict = {}
for row in gwas_df.iloc[:, 0]:
genes = row.split(';')
for g in genes:
gwas_locus_gene_dict[g] = gwas_locus_gene_dict.get(g, 0)... |
98cef4cfbd27920542f1390632cfd94d2f893a25814ca4da9af8b9a85bde635d | Python | 1,121 | 35 | """ Module initializer includes all sub-modules.
:Version:
1.1.0
:Date:
13.03.2017
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2017 Jan Melchior
This file is part of the Python library PyDeep.
PyDe... |
d33f8e131ddd861fbfd1c96578fa9c160ea519a7b1e13fa957f28d600bc3d7fb | Python | 1,121 | 41 | import logging
import os
import socket
import matplotlib.pyplot as plt
import numpy as np
if socket.gethostname() == 'hivemind':
import matplotlib
matplotlib.use('Agg')
def save_elbo_plot(
maxiter, log_interval: int, logf,
savedir: str = None, figure_name: str = None, figsize=(12, 6), figure... |
e53b6e55272133bcd03bf0cc8af786d09311e057a51e8af5e4322765356b9d20 | Python | 1,122 | 31 | import argparse
from pensa import load_selection, extract_coordinates
# -------------#
# --- MAIN --- #
# -------------#
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--sel_base", type=str, default='protein and ')
parser.add_argument("--sel_file", type=str, default='... |
6b7138614549013807e6fe53c69e376cff08a47a4e8491f91c6252ec04abdeb1 | Python | 1,123 | 42 | import torch
from .linear_operator import aslinearoperator, TorchLinearOperator
def cg(A, b, x0=None, max_iter=None, tol=1e-5):
if max_iter is None:
max_iter = 20 * b.numel()
if x0 is None:
x = torch.zeros_like(b)
r = b.clone()
else:
x = x0.clone()
r = b - A.mv(x)
... |
ad7037dceaa4bc732099aba0db2a070d37b8ec7bf12cdd62e6e301cc614b2abf | Python | 1,123 | 29 | from .attention import AttentionReadout
from .base import ClonedReadout, Readout
from .factorized import FullFactorized2d, FullSXF, SpatialXFeatureLinear
from .gaussian import (
DeterministicGaussian2d,
FullGaussian2d,
Gaussian2d,
Gaussian3d,
GeneralizedFullGaussianReadout2d,
RemappedGaussian2d,... |
db6a1ac71aef3849793efe6246c17380f830c86ead41bd0f78ff0cc81c986ae1 | Python | 1,123 | 32 | import numpy as np
import pandas as pd
import os
import anndata
import scanpy as sc
dataset_dir = "spatial_prompt/"
dt_dir = "benchmark/"
import time
#%% import data
libd_data_dir = dataset_dir+"benchmark/spatiallibd/st_data/spatialLIBD/"
libd_st_obj = sc.read_visium(libd_data_dir+"151673/")
libd_st_meta = pd.read_tab... |
41ba190bfefd634458b57b803d0626d064e01acfa715037bf78c80f8373826ae | Python | 1,124 | 30 | import fnmatch
import os,re,sys
if len(sys.argv)>2 :
d=sys.argv[1]
if not os.path.exists(d):
sys.exit(" ERROR : the directory you provided as data directory does not exist. Breaking up.")
else :
sys.exit(" ERROR : please provide the name of the data directory (where you have all your snapshots) and the name of th... |
09b5d868ff6ef030ce5a51fb26dbe8e25c7bbefba365cbbc7622ba250b440783 | Python | 1,125 | 31 | #!/bin/python3
# Author: Nicole Gay, Anna Scherbina
# Updated: 7 May 2020
# Script: truncate_narrowpeak_200bp_summit.py
# Purpose: For an input peak file (concatenated .narrowpeak.gz file), truncate each peak to 100bp on either side of the summit
import gzip
import argparse
def parse_args():
parser=argparse.A... |
c0eebd6eca09ea202711333b5589a6126772ad41a2e4734a4ee717afcc37717a | Python | 1,125 | 33 | import sys
sys.path.append('../source')
from matplotlib import pyplot as plt # for visualisation of imagedata
import networkx as nx
import tinaui as ui
import numpy as np
import os
# plt.ion()
t1 = ui.load_status(folder='../test/sampledata/ocn', load_imagedata=True)
ui.load_stack(t1, folder=os.path.join(t1.folder,... |
def60b5d4abe73c652ff57db19ccc6880b3f07926f84caec9c88fdb4df32d3e5 | Python | 1,125 | 39 | from typing import Any
import pytest
from langchain_core.messages import AIMessage
from langchain_core.outputs import ChatGeneration
@pytest.mark.parametrize(
("content", "expected"),
[
("foo", "foo"),
(["foo"], "foo"),
(["foo", "bar"], "foobar"),
([{"text": "foo", "type": "t... |
1c470be70e9fbc18058bf0d7a6d7ad693cab97ab9a4452054abdbde57a081dc5 | Python | 1,126 | 33 | class ScaleEdgeFeatures(object):
r"""docstring
"""
def __init__(self, method="norm"):
self.method = method
def __call__(self, data):
assert data.edge_attr is not None
if self.method == "clip":
# scale edge features to lie within [0,1]
e_mean = d... |
c570c6e5fc855bd6b155683c47eae632e9a6ec3e44c53ddcd6ea4af2e01e3411 | Python | 1,127 | 34 | import numpy as numx
import pydeep.misc.visualization as VIS
def load_Van_Hateren_Image(path):
import array
fin = open( path, 'rb' )
s = fin.read()
fin.close()
arr = array.array('H', s)
arr.byteswap()
return numx.array(arr, dtype='uint16').reshape(1024,1536)
def create_shift_trajectory... |
4611190d5e19dca89646a6c7c9ca46364985defb38e07ac8e757f8f6aab9eea3 | Python | 1,128 | 35 | from langchain_classic._api.module_import import create_importer
def test_import_from_non_deprecated_path() -> None:
"""Test importing all modules in langchain."""
module_lookup = {
"Document": "langchain_core.documents",
}
lookup = create_importer(__package__, module_lookup=module_lookup)
... |
10882ea9ac23c05fdfda416752044ed120ef93190ec7ca8d117fdbb68e0dbfb8 | Python | 1,129 | 19 | import sys, time, warnings; sys.path.insert(0,"."); warnings.filterwarnings("ignore")
import numpy as np
def log(m): print(m, flush=True)
from pydeepmapper.io import load_10x_populations, highly_variable_subset
from sklearn.model_selection import train_test_split
from tabfm import TabFMClassifier, tabfm_v1_0_0_pytorch ... |
0cae98e385a4c6e6b0852c87e4dfcfe53cfe576a73764f10ec1fc0ec108ecf44 | Python | 1,130 | 46 | """2D fluorescence deconvolution using the waveorder API."""
from waveorder.api import fluorescence
settings = fluorescence.Settings(
transfer_function=fluorescence.TransferFunctionSettings(
yx_pixel_size=0.1,
z_pixel_size=0.25,
z_focus_offset=0,
wavelength_emission=0.532,
... |
a259ec73fc86bd606919eb745eecbbb5c56fcf42338760d516acc22411b52018 | Python | 1,130 | 30 | import argparse
from pensa.preprocessing import load_selection, extract_coordinates
# -------------#
# --- MAIN --- #
# -------------#
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--sel_base", type=str, default='protein and ')
parser.add_argument("--sel_file", type=... |
e9f8d51ec3cbd571ad0d5441afa4f0aaf1bdae54547dcb2199679cb34c3fc620 | Python | 1,130 | 38 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
TelegramChatApiLoader,
TelegramChatFileLoader,
)
from langchain_community.document_loaders.telegram import (
concatenate_row... |
6ea77d5aad46749f67311da62662f00981a7e3f6958a7b1ea849b5b65a639c6f | Python | 1,131 | 52 | """
This example shows how to do basic micro-manager introspection using
pycro-manager.
"""
from pycromanager import Core
# Connect to MM.
core = Core()
# Current configuration.
def current_config():
devices = core.get_loaded_devices()
for i in range(devices.size()):
devstr = devices.get(i)
... |
d3984577acae3ae80dccd36c0def67cb7ec894e09f93eb263f75baf0122e2854 | Python | 1,132 | 46 | import hexalattice.hexalattice as hex
from voxcell import VoxelData
from scipy.spatial import KDTree
import flatmap_util as fmutil
import numpy as np
import math
import sys
flatmap_nrrd = sys.argv[1]
output_nrrd = sys.argv[2]
hex_diam = 0.07
if len(sys.argv) > 3:
hex_diam = float(sys.argv[3])
# rule of thumb
hex... |
ead6e64a3f9abd19d76ecb3d25bb699820edce49684664d49f54226fa95b05f1 | Python | 1,133 | 35 | from pycromanager import ZMQRemoteMMCoreJ, JavaBackendAcquisition, multi_d_acquisition_events, start_headless
import time
mm_app_path = 'C:/Program Files/Micro-Manager-2.0'
config_file = mm_app_path + "/MMConfig_demo.cfg"
# start_headless(mm_app_path, config_file, debug=True)
# mmc = Core()
# mmc.set_property('Z', 'U... |
37259ad24f5301a4da96ebb441a2978ceb222f2bd9200e40b56b2d7480d5a971 | Python | 1,134 | 35 | from .ori_QHNet_wo_bias import QHNet as QHNet_wo_bias
from .ori_QHNet_with_bias import QHNet as QHNet_w_bias
__all__ = ['QHNet_w_bias', 'get_model', 'QHNet_wo_bias']
# version: wo bias and with bias model are used to load the model for the paper reproduction
# QHNet is the clean version, and we use QHNet to build th... |
7d2e20feea7b59a1933037959cbbf8ceda1ea013e2e568a87a07da3cfaafd3d4 | Python | 1,135 | 29 | from langchain_core.documents import Document
from langchain_core.language_models import FakeListLLM
from langchain_core.prompts import PromptTemplate
from langchain_classic.chains import create_history_aware_retriever
from tests.unit_tests.retrievers.parrot_retriever import FakeParrotRetriever
def test_create() -> ... |
931bd9e675c7e9a6189503b08463519649b078ae80396429d333f45fa9ff6281 | Python | 1,135 | 36 | import os
import cv2
from glob import glob,iglob
from tqdm import tqdm
from joblib import Parallel,delayed
from engine import utils
from constants import COVID_KAGGLE_DIR,COVID_XRAY_SZ
from constants import BASE_DATA_DIR
def resize_covid_kaggle_images():
dst_folder = utils.get_prepared_path('%scovid_kaggle_... |
f0bf4b94850ca2b7ee752af46430807c675e1812aa011444e5dbd344e17efcdd | Python | 1,135 | 30 | import numpy as np
import pandas as pd
def augmente_data(raw_df, x_cols, y_cols, n_augmented=0,
y_std_cols=None, x_std=0, y_std=0,
id_col='sID', da_col='DA#', random_state=None):
if y_std_cols is not None:
y_std_df = raw_df[std_cols]
else:
y_std_df = np.ones... |
397f8bf34a04754af8c9ff83172b7227c426a8342acddb5d7bb3598624643206 | Python | 1,136 | 42 | import sys
import numpy as np
import torch
import torch.nn as nn
import joblib
from dt_utils import Timer
from data_utils import generate_recognition_data
from plastic import PlasticNet
from neural_net_utils import random_weight_init
d = 50 #length of input vector
R = int(sys.argv[1]) #repe... |
6084cc72f056cdfb006ae0c25e91bcbfcbec23f50d834e42c01df4a80fcfbfcb | Python | 1,136 | 29 | import argparse
import pandas as pd
from preparation.dataset_functions import create_stratified_splits_master_file, replace_dict
def main():
# Set up the argument parser
parser = argparse.ArgumentParser(description="Generate master split file for BCN_20K dataset.")
parser.add_argument("--input_csv", type=s... |
6bdcd69447dcd439aa8b573bc2d737fafc78c48b6babd81c224f255160d15740 | Python | 1,137 | 27 | # This module is used to obtain the absolute position information of the entire protein sequence and pockets
def get_pro_seq(path):
aa_codes = {
'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E',
'PHE': 'F', 'GLY': 'G', 'HIS': 'H', 'LYS': 'K',
'ILE': 'I', 'LEU': 'L', 'MET': 'M', 'ASN': 'N'... |
63a251b2a967fd1ebe425c05871cdaeebb7fd1b0f1b2496baf8bdb3db8f7e5ca | Python | 1,138 | 40 | from functools import partial
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from utils_for_tests import compare_vars
from neuralpredictors.layers.cores import TransferLearningCore
def test_detached_mode():
model = TransferLearningCore(
input_channels=1,
tl_mode... |
d05bd41e9dccead8823dc7efac017be00fabd2be1f7ea41b8a54ebbe4e19c1f3 | Python | 1,138 | 44 | # File to deal read and write the .mask extensions
import zlib
import numpy as np
from concurrent.futures import ThreadPoolExecutor, wait
_executor_pool = ThreadPoolExecutor(max_workers=16)
def read_mask_file(filepath, out):
"""Load mask file to numpy array
Parameters
----------
filepath : str
ou... |
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