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/*
* MyIMUAgent.cpp
*
* Created on: 23 dic 2015
* Author: andrea
*/
#include <agents/MyIMUAgent.h>
#include <iostream>
#include <boost/shared_ptr.hpp>
#include <boost/make_shared.hpp>
#include <boost/uuid/uuid_io.hpp>
#include <boost/log/trivial.hpp>
#include <boost/math/quaternion.hpp>
#include <events/MyI... | {"hexsha": "e46dfff5f4f1f3dec51132045f0074cc5b22fe11", "size": 2052, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/agents/MyIMUAgent.cpp", "max_stars_repo_name": "LinuxDroneLab/MyDrone", "max_stars_repo_head_hexsha": "33b8e9f15cebf79da0141e4d8aa5f4d57da73b3e", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
//
// Created by Martin Drewes on 13/05/2020.
//
#include <filesystem>
#include <fstream>
#include <iostream>
#include <sstream>
#include <regex>
#include <boost/algorithm/string.hpp>
#include "template.hpp"
#include "utils.hpp"
namespace Templates {
std::string Template::Render() {
return text;
... | {"hexsha": "06ebab4479230c4611b8c40f01c96ad9c17af9e3", "size": 2062, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/framework/template.cpp", "max_stars_repo_name": "martinskou/wolf", "max_stars_repo_head_hexsha": "7fa0b6a99ce2fa5086f665cac9e78806648c0517", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import torch
import torch.nn.functional as F
from torch import nn, optim
import numpy as np
from src.utils.generators.shapenet_generater import Generator
from src.utils.generators.mixed_len_generator import MixedGenerateData
from src.utils.generators.wake_sleep_gen import WakeSleepGen
from src.utils.generators.shapenet... | {"hexsha": "86fdae8717fc68d9a92d3182e767dd5a5f7e20a7", "size": 6448, "ext": "py", "lang": "Python", "max_stars_repo_path": "fid_model.py", "max_stars_repo_name": "HomerW/CSGNet", "max_stars_repo_head_hexsha": "4ecc7f3e836867118dba3d5f220ed5e74a536b93", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
#!/usr/local/bin/python
import pandas as pd
from datetime import timedelta
import numpy as np
import networkx as nx
from MarkovChain import *
import os
DATA_DIR = "~/Projects/markov_traffic/data/"
PLOTS_DIR = "~/Projects/markov_traffic/Plots_data/"
def read_trips_file(filename):
filename = DATA_DIR + filename
... | {"hexsha": "3c70dd0e0bc093e76b357331281adb4dbe8ff96b", "size": 5431, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python_old/plot_hubway_stations.py", "max_stars_repo_name": "chdhr-harshal/MCMonitor", "max_stars_repo_head_hexsha": "330fc1a8f8cf83620fd6b0e503707c91e97af16d", "max_stars_repo_licenses": ["MI... |
import numpy as np
import scipy.sparse as sp
import copy
import warnings
import pandas as pd
import sys
import math
from sklearn.metrics.pairwise import euclidean_distances,pairwise_distances_argmin_min
from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin
from sklearn.utils import check_random_state,... | {"hexsha": "43c2c3aa9242e54b562f4e32fdf6a50bc4597ad2", "size": 13066, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "IoTDATALab/EC-Clustering", "max_stars_repo_head_hexsha": "2a5dfaca0198728f5e80963e5ad07023363e80fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache
from sqlalchemy import delete
from sqlalchemy.orm import sessionmaker
import json
import numpy as np
import pandas as pd
from datetime import date,datetime,timedelta
import ast
import sqla_schema as sch
import ingest
import num... | {"hexsha": "ab196f4f9b616f1cefb75342d11f6fa12d73ebc5", "size": 2853, "ext": "py", "lang": "Python", "max_stars_repo_path": "pgaf/ingest_spikes.py", "max_stars_repo_name": "AllenNeuralDynamics/ephys-framework-tests", "max_stars_repo_head_hexsha": "ee940afeab54e5e25765a903a6b65f2e95be4c48", "max_stars_repo_licenses": ["M... |
# Carlos Morato, PhD.
# cwmorato@wpi.edu
# Deep Learning for Advanced Robot Perception
#
# MLP for Pima Indians Dataset serialize to YAML and HDF5
import os
import yaml
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_yaml... | {"hexsha": "33c507c8cd9d7361431d42bac4730ad42f81d0e5", "size": 2125, "ext": "py", "lang": "Python", "max_stars_repo_path": "week4/Save your models for later using serialization/serialize_yaml.py", "max_stars_repo_name": "JackHaoyingZhou/RBE595_DL_Discussion", "max_stars_repo_head_hexsha": "db16141b1cd18a03f182d418a2cf0... |
# -*- coding: utf-8 -*-
from wide_resnet import WideResNet
import numpy as np
import cv2
import dlib
depth = 16
width = 8
img_size = 64
# 人脸性别年龄预测模型
model = WideResNet(img_size, depth=depth, k=width)()
model.load_weights('weights.hdf5')
def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=1... | {"hexsha": "f86fb42b2c80519f95492c22ec72115e316709b3", "size": 2451, "ext": "py", "lang": "Python", "max_stars_repo_path": "CycleGAN/video_gender/age-gender-estimation/gender_age_detect_guide.py", "max_stars_repo_name": "RacleRay/-Have_Fun_Doing", "max_stars_repo_head_hexsha": "8ebb7fcabc6148571d38f2f51eac47952ce54424"... |
See my concert information under Bill Wagman. I have been involved with KDVS for nearly 15 years, alternating weeks on The Saturday Morning Folk Show, Saturdays from 9:00 to noon.
| {"hexsha": "af5f8a502baa878db7308dfae6a1ae790be3463a", "size": 181, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/WjWagman.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#coding=utf-8
from __future__ import print_function
import os,logging,math,time
import argparse
import mxnet as mx
from mxnet import gluon,nd
import numpy as np
from mxnet.gluon.data.vision import transforms
from mxnet.gluon.data import DataLoader
from mxnet.gluon.data import Dataset
import mxnet.autograd as autogra... | {"hexsha": "7c1352366a8155a4ac52014c833a7d4b5d910ace", "size": 10886, "ext": "py", "lang": "Python", "max_stars_repo_path": "mxnet/train_mixup_resnext101.py", "max_stars_repo_name": "nipeone/dl", "max_stars_repo_head_hexsha": "5dec328077accc18adac05ffd1ea27cd474b176c", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import string
import pandas as pd
import os
from scipy.stats import ttest_ind
from matplotlib.ticker import FormatStrFormatter
matplotlib.use("agg")
types = ["MoA validation", "Multiple cell types", "Unseen cell type", "shRNA fo... | {"hexsha": "f0154281c3b005d36195ae87cb4f5a10cc29b409", "size": 1446, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/multi_violin.py", "max_stars_repo_name": "umarov90/DeepFake", "max_stars_repo_head_hexsha": "e65c72f255817532e8a8a3afe2138ae270477601", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
Lemma silly_implication : (1 + 1) = 2 -> 0 * 3 = 0.
Proof. intros H. simpl. reflexivity. Qed.
Inductive and (P Q : Prop) : Prop :=
conj : P -> Q -> (and P Q).
Notation "P /\ Q" := (and P Q) : type_scope.
Theorem and_example : (0 = 0) /\ (4 = mult 2 2).
Proof.
apply conj.
reflexivity.
simpl.
reflexivity.
Qed.
Th... | {"author": "DanielRrr", "repo": "Coq-Studies", "sha": "a7cd6bd7f61e91ca118a615e62dfe8fec50b70d3", "save_path": "github-repos/coq/DanielRrr-Coq-Studies", "path": "github-repos/coq/DanielRrr-Coq-Studies/Coq-Studies-a7cd6bd7f61e91ca118a615e62dfe8fec50b70d3/computational_logic9.v"} |
#=
Author: Shunsuke Hori
=#
"""
This holds the results for Harrison Kreps. In particular, it
accepts two matrices Qa and Qb and compares the single belief,
optimistic belief, and pessimistic belief prices
"""
struct PriceHolder{TF<:AbstractFloat}
qa::Matrix{TF}
qb::Matrix{TF}
qpess::Matrix{TF}
qopt:... | {"hexsha": "f71e49caecb2d5dc0c21d626b406566f98097cbc", "size": 3740, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "harrison_kreps/harrison_kreps_code.jl", "max_stars_repo_name": "chenwang/QuantEcon.lectures.code", "max_stars_repo_head_hexsha": "8832a74acd219a71cb0a99dc63c5e976598ac999", "max_stars_repo_licenses... |
I graduated from Davis and no longer live nearby to try out all the restaurants. Everything below is from my time in Davis, Sept 2003 August 2007. Appreciate the DavisWiki; I wish more cities would have their own with an active community.
I love to promote diversity and native languages so please feel free to ch... | {"hexsha": "39420f66d207ecd20ff13a9ffc84bf6760e258a7", "size": 17662, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/JoAnnaRich.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pandas as pd
import numpy as np
import copy
import math
import os
import sys
from tqdm import tqdm
from math import radians
import sklearn.metrics
from decouple import config
from config.construction_config import *
from heuristic.construction.insertion_generator import InsertionGenerator
from datetime import da... | {"hexsha": "a87eaef60b44cbff43f1daefc269175e17e91e63", "size": 9993, "ext": "py", "lang": "Python", "max_stars_repo_path": "heuristic/construction/construction.py", "max_stars_repo_name": "annalunde/master", "max_stars_repo_head_hexsha": "2552d43713e8ebca0b0e57bc5bebd1eaeeac1875", "max_stars_repo_licenses": ["MIT"], "m... |
program phaml_master
use phaml
implicit none
type(phaml_solution_type) :: soln
call phaml_create(soln,nproc=4)
call phaml_solve_pde(soln,print_grid_when=FINAL,print_grid_who=MASTER, &
print_error_when=FINAL,print_error_who=MASTER, &
print_errest_what=L2_ERREST, &
... | {"hexsha": "edb2001170592348eed14be90669d68f3ce6c946", "size": 405, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "testdir/test_termination/test07.f90", "max_stars_repo_name": "qsnake/phaml", "max_stars_repo_head_hexsha": "8925b4c32657bbd9f81cd5f8f9d6739151c66fec", "max_stars_repo_licenses": ["mpich2"], "max_... |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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 appli... | {"hexsha": "0a273e91dd5716b14cbd942d248fa51afcfa01d7", "size": 3334, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py", "max_stars_repo_name": "RangeKing/Paddle", "max_stars_repo_head_hexsha": "2d87300809ae75d76f5b0b457d8112cb88dc3e27", "max_star... |
//
// Copyright (c) 2019-2020 Kris Jusiak (kris at jusiak dot net)
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
#include <boost/ut.hpp>
#include <stdexcept>
namespace ut = boost::ut;
namespace cfg {
cl... | {"hexsha": "26f03ee206914117579db79af843c71b8cda1506", "size": 953, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "example/cfg/runner.cpp", "max_stars_repo_name": "ambushed/ut", "max_stars_repo_head_hexsha": "248df4dd091781b45b2cde7332774226d6a459b3", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 567... |
from os import listdir
from numpy import array
from keras.preprocessing.text import Tokenizer, one_hot
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model
from keras.models import load_model
from keras.utils import to_categorical
from keras.layers import Embedding, TimeDistributed, Rep... | {"hexsha": "acfc27c9ba7d6a34d4aa7bd82ec9887e791cedc4", "size": 2869, "ext": "py", "lang": "Python", "max_stars_repo_path": "local/HTML/HTML_write.py", "max_stars_repo_name": "landongw/pdf-to-code", "max_stars_repo_head_hexsha": "fa7612ec7b1364310d4686b731773cd4e201c7c2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import logging
import anndata as ad
import scipy.spatial
import scipy.sparse
import numpy as np
from sklearn.preprocessing import normalize
from sklearn.decomposition import TruncatedSVD
from sklearn.neighbors import NearestNeighbors
## VIASH START
# Anything within this block will be removed by `viash` and will be
... | {"hexsha": "f8bad3cffd19bccc2ca84f1754e8d8e3c711d7f9", "size": 4253, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/match_modality/methods/baseline_procrustes_knn/script.py", "max_stars_repo_name": "dburkhardt/neurips2021_multimodal_viash", "max_stars_repo_head_hexsha": "e3449af07749bac6faf32613f91fd149a232... |
#!/usr/bin/env python
import os
import sys
import numpy as np
from setuptools import setup, Extension
# Get the version number from ModelInterface.h
__version__ = None
with open("cthreeML/ModelInterface.h") as f:
for line in f:
if line.find("#define INTERFACE_VERSION")==0:
... | {"hexsha": "8af020fff44ae707440bda7c0d2e97e7b64b4f58", "size": 3672, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "giacomov/c_threeML", "max_stars_repo_head_hexsha": "1bce7ba11309ebf327720f3a614c578ed85d4726", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": nu... |
from dipsim import util
from dipsim import multiframe
import numpy as np
import matplotlib.pyplot as plt
import os; import time; start = time.time(); print('Running...')
# Main input parameters
row_labels = ['Geometry', r'$\sigma_{\Omega}$']
col_labels = ['Single-view (NA${}_\\textrm{ill}$=0, NA${}_\\textrm{det}$=1.1'... | {"hexsha": "97e4c7b867663f394f92d2b8a7068b993328a0d4", "size": 3453, "ext": "py", "lang": "Python", "max_stars_repo_path": "paper/figures/compare-microscopes.py", "max_stars_repo_name": "talonchandler/dipsim", "max_stars_repo_head_hexsha": "04904871924276fd1662ca15b7224166d271c0d8", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import cv2, PIL, os
from cv2 import aruco
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
#%matplotlib nbagg
workdir = "calibration-pics/"
aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)
board = aruco.CharucoBoard_create(7, 5... | {"hexsha": "405f7b5897df6e7117149df14e3802c79745a5da", "size": 564, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera-calibration/charuco-maker.py", "max_stars_repo_name": "SamyBarras/ocoda", "max_stars_repo_head_hexsha": "fc06fc5604500a0c31cb4ab8ddecb88e3fd282e5", "max_stars_repo_licenses": ["CC0-1.0"], "m... |
from __future__ import absolute_import, division, print_function, unicode_literals
import time, os, codecs, json
import numpy as np
from utils.tools import DatasetGenerator, ResultWriter, create_masks, generate_masks
from utils.CustomSchedule import CustomSchedule
from utils.EarlystopHelper import EarlystopHelper
fro... | {"hexsha": "bb445ee77c3516f9c7baf342bbddecb80817a5d4", "size": 24505, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "starkhxl/AMEX", "max_stars_repo_head_hexsha": "7c186ae4f1e7421eda5b37d7ae1d0f3bcb20e25c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_... |
// Copyright (c) 2014 Robert Ramey
//
// Distributed under the Boost Software License, Version 1.0. (See
// accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <iostream>
#include <cassert>
#include <typeinfo>
#include <boost/core/demangle.hpp>
#include "../include/safe_comp... | {"hexsha": "d132c2df942a1a4360e47736352fd99b9bff7bc8", "size": 4431, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_safe_compare.cpp", "max_stars_repo_name": "janisozaur/safe_numerics", "max_stars_repo_head_hexsha": "c494e9d6bddc47292b1bb1552469196b01fcdc55", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
from ..qt_compat import QtGui, QtCore
import numpy as np
import logging as log
import os
import matplotlib
from ..plugins import Plugin
from ..core import DataModel, LayerManager, LabelManager, Launcher
from .mpl_widgets import PerspectiveCanvas
from .base import SComboBox
class ConfidenceViewer(Plugin):
name... | {"hexsha": "06a9c5860e78ec4c47e725623b03bd56c976975a", "size": 3221, "ext": "py", "lang": "Python", "max_stars_repo_path": "survos/widgets/conficence_viewer.py", "max_stars_repo_name": "paskino/SuRVoS", "max_stars_repo_head_hexsha": "e01e784442e2e9f724826cdb70f3a50c034c6455", "max_stars_repo_licenses": ["Apache-2.0"], ... |
#include <istat/test.h>
#include <istat/istattime.h>
#include <istat/Mmap.h>
#include "../daemon/StatCounterFactory.h"
#include "../daemon/StatStore.h"
#include <boost/filesystem.hpp>
using namespace istat;
RetentionPolicy rp("10s:1d");
RetentionPolicy xrp("");
class FakeProtectedDiskMmap : public Mmap
{
public:
... | {"hexsha": "40cca1d0db1469e05bc0b886358173c7d21a7f79", "size": 4663, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_StatStore.cpp", "max_stars_repo_name": "yjpark/istatd", "max_stars_repo_head_hexsha": "859a67c4c633a9e96f3f0b990a94afa54aa20224", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import numpy as np
import pytest
from ntab import Table
#-------------------------------------------------------------------------------
def test_empty():
tab = Table()
assert tab.num_cols == 0
assert tab.num_rows == 0
tab.arrs["x"] = np.arange(10)
assert tab.num_cols == 1
assert tab.num_r... | {"hexsha": "4d55e25b53583a301d2f29003d03ac4f586e91b7", "size": 786, "ext": "py", "lang": "Python", "max_stars_repo_path": "ntab/test/test_arrs.py", "max_stars_repo_name": "alexhsamuel/ntab", "max_stars_repo_head_hexsha": "9039d0e10d0f1a86fb16a33c05c79dfb931b28ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
/-
Copyright (c) 2022 Andrew Yang. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Andrew Yang
-/
import morphisms.finite
import morphisms.finite_type
import for_mathlib.integral
import morphisms.universally_closed
import ring_theory.ring_hom.integral
import for_mathlib... | {"author": "erdOne", "repo": "lean-AG-morphisms", "sha": "bfb65e7d5c17f333abd7b1806717f12cd29427fd", "save_path": "github-repos/lean/erdOne-lean-AG-morphisms", "path": "github-repos/lean/erdOne-lean-AG-morphisms/lean-AG-morphisms-bfb65e7d5c17f333abd7b1806717f12cd29427fd/src/morphisms/integral.lean"} |
#!/usr/bin/env python
"""
Performs "standard" analysis on a SMLM movie given parameters.
Hazen 1/18
"""
import numpy
import os
import storm_analysis.sa_library.sa_h5py as saH5Py
import storm_analysis.sa_utilities.fitz_c as fitzC
import storm_analysis.sa_utilities.hdf5_to_bin as hdf5ToBin
import storm_analysis.sa_util... | {"hexsha": "b70b54b0321664b9b555031d7a838172e1555687", "size": 8118, "ext": "py", "lang": "Python", "max_stars_repo_path": "storm_analysis/sa_utilities/std_analysis.py", "max_stars_repo_name": "oxfordni/storm-analysis", "max_stars_repo_head_hexsha": "835a5c17497c563c3632db561ae7e7c9144a8dd1", "max_stars_repo_licenses":... |
import matplotlib.pyplot as plt
import numpy as np
from skimage import data
from skimage.util import img_as_ubyte
from skimage.filters.rank import entropy
from skimage.morphology import disk
noise_mask = np.full((128, 128), 28, dtype=np.uint8)
noise_mask[32:-32, 32:-32] = 30
noise = (noise_mask * np.random.... | {"hexsha": "e2eb8ccfeff87492c866c1d13c01a7b7a934651b", "size": 1190, "ext": "py", "lang": "Python", "max_stars_repo_path": "Groups/Group_ID_37/Entropy feature/Entropy.py", "max_stars_repo_name": "aryapushpa/DataScience", "max_stars_repo_head_hexsha": "89ba01c18d3ed36942ffdf3e1f3c68fd08b05324", "max_stars_repo_licenses"... |
using DynamicHMCModels
ProjDir = @__DIR__
cd(ProjDir)
df = DataFrame(CSV.read(joinpath("..", "..", "data", "chimpanzees.csv"), delim=';'))
df[!, :pulled_left] = convert(Array{Int64}, df[:, :pulled_left])
df[!, :prosoc_left] = convert(Array{Int64}, df[:, :prosoc_left])
first(df, 5)
Base.@kwdef mutable struct Chimpanz... | {"hexsha": "fad58f438b5ec5375b18a90b593756cc424a834e", "size": 2463, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/10/m10.2d.jl", "max_stars_repo_name": "samusz/DynamicHMCModels.jl", "max_stars_repo_head_hexsha": "33fa8d2f84d0862f3b45c36aa349891b8b8dc5ea", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: Simplified BSD
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_allclose
import pytest
import mne
from mne.datasets import testing
fr... | {"hexsha": "8a097b3f3b1299636198d8f348ab36f6291ca7cf", "size": 9506, "ext": "py", "lang": "Python", "max_stars_repo_path": "mne/inverse_sparse/tests/test_mxne_inverse.py", "max_stars_repo_name": "vferat/mne-python", "max_stars_repo_head_hexsha": "54e07b3257ee44ae28c5253f47ef73909ef23bfd", "max_stars_repo_licenses": ["B... |
abstract type AbstractThunk <: AbstractTangent end
struct MutateThunkException <: Exception end
function Base.showerror(io::IO, e::MutateThunkException)
print(io, "Tried to mutate a thunk, this is not supported. `unthunk` it first.")
return nothing
end
Base.Broadcast.broadcastable(x::AbstractThunk) = broadca... | {"hexsha": "16384d69e246dff93b133098762b81ae19c6f0a4", "size": 8568, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tangent_types/thunks.jl", "max_stars_repo_name": "st--/ChainRulesCore.jl", "max_stars_repo_head_hexsha": "834901efde5698b7ae0ace1bd2a0ea7953e7fd1d", "max_stars_repo_licenses": ["MIT"], "max_sta... |
\documentclass[11pt]{article}
\usepackage{acl2014}
\usepackage{times}
\usepackage{url}
\usepackage{latexsym}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{array}
\usepackage{booktabs}
\usepackage{multirow}
\usepackage{multicol}% http://ctan.org/pkg/multicols
\usepackage{tabularx, booktabs}
\usepa... | {"hexsha": "19aad98c01ff03c6c72922bacd7b2d3a9f90641d", "size": 31743, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/D4/D4.tex", "max_stars_repo_name": "amkahn/question-answering", "max_stars_repo_head_hexsha": "bbdd82561a025569efd904b94153a1e6f233db6f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# This code is based on : https://www.tensorflow.org/guide/keras/functional#all_models_are_callable_just_like_layers
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import cv2
"""
In the code below, get_model function returns a convolution model.
model1 , mo... | {"hexsha": "72680eac89cd0c18ad4cc8a0e24224d255fa7e56", "size": 1548, "ext": "py", "lang": "Python", "max_stars_repo_path": "TensorflowFunctionalAPI/LayersAddExample.py", "max_stars_repo_name": "Atharva-Gundawar/Deep-Learning-Snippets", "max_stars_repo_head_hexsha": "5e351481e93e664d63cc0a3570add5b072cd621d", "max_stars... |
import os, sys
import gc
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, "utils"))
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from utils.kcnet_utils import Netpara, debugPrint... | {"hexsha": "c63ccc41a6ee5b372723de11d2a282538952d4f8", "size": 9995, "ext": "py", "lang": "Python", "max_stars_repo_path": "other_models/sph3d/SPH3D_modelnet.py", "max_stars_repo_name": "ZJUCAGD/GTS-CNN", "max_stars_repo_head_hexsha": "a329f314b795f0dea0f46db623ac955a47619e7d", "max_stars_repo_licenses": ["MIT"], "max_... |
module DoubleBLAS
# support for (fairly) efficient Linear Algebra with DoubleFloats
#FIXME: change to explicit lists because namespace pollution is epidemic
using DoubleFloats
using LinearAlgebra
using SIMD
using UnsafeArrays
using Base.Threads
# steal some internals
using LinearAlgebra: lapack_size, BlasInt, check... | {"hexsha": "aba8f42d1fd5e7a6ee3c7f75c11c2946a9be6cfd", "size": 4613, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DoubleBLAS.jl", "max_stars_repo_name": "RalphAS/DoubleBLAS", "max_stars_repo_head_hexsha": "db60c787fcb4e3d1153b8358a44d6d6e3b56f7c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: dataflow.py
# Author: Qian Ge <geqian1001@gmail.com>
import os
import scipy.misc
import numpy as np
from datetime import datetime
_RNG_SEED = None
def get_rng(obj=None):
"""
This function is copied from `tensorpack
<https://github.com/ppwwyyxx/tensor... | {"hexsha": "3f26943ac99b65c862d313e2a979320637bedc27", "size": 1232, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/dataflow.py", "max_stars_repo_name": "conan7882/variational-autoencoder", "max_stars_repo_head_hexsha": "4960f252784a7dd2fbe203d7dad65938b57ee9c2", "max_stars_repo_licenses": ["MIT"], "m... |
"""
"""
import abc
import logging
from typing import List,Tuple
from numpy import deprecate
import shapely.geometry as sg
from shapely import ops
from shapely.geometry import Point, LineString
class CadImporter(abc.ABC):
"""
Base abstract class. All cad importers should subclass this class.
Imports CA... | {"hexsha": "7990d00b5b96a5fa0f511683343fb6e3a03d6a2b", "size": 3764, "ext": "py", "lang": "Python", "max_stars_repo_path": "cad_to_shapely/cadimporter.py", "max_stars_repo_name": "aegis1980/cad2shapely", "max_stars_repo_head_hexsha": "729e5ad1d987de09e3818ef15a691b0e0b2407de", "max_stars_repo_licenses": ["MIT"], "max_s... |
from __future__ import print_function, division
"""...
"""
import petsc4py.PETSc as petsc
from six.moves import range
import config as cfg
from mrpy.mr_utils import mesh
import numpy as np
import math
import importlib
def matrix_add(tree, matrix, row, value, level, index_x=0, index_y=0, index_z=0, add_to_col=0):
... | {"hexsha": "14c4b422d94e382780d6282a63dcc13169ea9d65", "size": 1495, "ext": "py", "lang": "Python", "max_stars_repo_path": "mrpy/spatial_operators/haar/2nd_order_ctr_finite_diff/matrix_aux.py", "max_stars_repo_name": "marc-nguessan/mrpy", "max_stars_repo_head_hexsha": "6fb0bce485234a45bb863f71bc2bdf0a22014de3", "max_st... |
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Layer, Dense, Embedding, TimeDistributed, Dropout
# from tensorflow_core.python.keras.layers import Layer, Dense, Embedding, TimeDistributed, Dropout
from tensorflow.python.keras.layers.dense_attention import BaseDenseAttention
def ... | {"hexsha": "f499d3433fb82161a00ea610a03ad4aebb15de88", "size": 6043, "ext": "py", "lang": "Python", "max_stars_repo_path": "layers/common.py", "max_stars_repo_name": "dlkt-review-and-empirical-evaluation/dlkt-review-and-empirical-evaluation", "max_stars_repo_head_hexsha": "d1540513056190ab0fbf547d22257dda2dfcd323", "ma... |
\section{Interviews}\label{InterviewAnalysis}
Five persons from different households participated in interviews, and the answers can be seen in \cref{Interview}. In this section similarities and differences are looked upon.
\subsection{General}
This subsection contain some of the general questions.
\textbf{Sex:}
Two ... | {"hexsha": "2ec92fd37461cee10523bc12a890dccd0327185d", "size": 3438, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Analysis/InterviewAnalysis.tex", "max_stars_repo_name": "amatt13/FoodPlanner-Report", "max_stars_repo_head_hexsha": "387a7c769cdda4913b81838bc8feffc9fbcafcc8", "max_stars_repo_licenses": ["Apache-2.... |
[STATEMENT]
lemma bounded_bilinear_matrix_matrix_mult[bounded_bilinear]:
"bounded_bilinear ((**)::
('a::{euclidean_space, real_normed_algebra_1}^'n^'m) \<Rightarrow>
('a::{euclidean_space, real_normed_algebra_1}^'p^'n) \<Rightarrow>
('a::{euclidean_space, real_normed_algebra_1}^'p^'m))"
[PROOF STATE]
pro... | {"llama_tokens": 389, "file": "Affine_Arithmetic_Floatarith_Expression", "length": 4} |
\section{UML Activities and Surface Languages}
\label{sec:grammars-and-metamodels:Preliminaries}
The surface language we present is a textual alternative for the activity diagrams of the \UML.
In this section, we give a brief description of Activities and explain what a surface language is.
We use the naming conventio... | {"hexsha": "9c13be665a73314d695b4179abef6cf59932b552", "size": 3192, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "grammars-and-metamodels/preliminaries.tex", "max_stars_repo_name": "ljpengelen/latex-phd-thesis", "max_stars_repo_head_hexsha": "8cabcf160a6f06e12b5ced92bb5cec06983e5bb7", "max_stars_repo_licenses":... |
"""
This class contains the code to encode/decode data using BB-ANS with a VAE
"""
from ans import ANSCoder
import numpy as np
import distributions
def BBANS_append(posterior_pop, likelihood_append, prior_append):
"""
Given functions to pop a posterior, append a likelihood and append the prior,
return a f... | {"hexsha": "13d8de72ae16461c7fda69a9d809b2d6a0a2ecea", "size": 3410, "ext": "py", "lang": "Python", "max_stars_repo_path": "BitsBack/bbans.py", "max_stars_repo_name": "deepaks4077/bits-back", "max_stars_repo_head_hexsha": "0d4355302eb4c5a18a229fa15a0a1caf8fe529d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#redirect Users/Cox
| {"hexsha": "e0cf4638ebca84b0e85e6d234ea7886136399252", "size": 20, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/KellyCox.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# Copyright (C) 2019 SAMSUNG SDS <Team.SAIDA@gmail.com>
#
# This code is distribued under the terms and conditions from the MIT License (MIT).
#
# Authors : Uk Jo, Iljoo Yoon, Hyunjae Lee, Daehun Jun
from __future__ import division
import numpy as np
import copy
from math import sqrt
class RandomProcess(object):
... | {"hexsha": "6271745fea275cd27c94a0fe28f421211845f5b2", "size": 5731, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/core/common/random.py", "max_stars_repo_name": "BupyeongHealer/SAMSUNG_SAIDALAB_RLCustom", "max_stars_repo_head_hexsha": "b6b1e8a473e134b548db5a1c235cf71ef83e36e2", "max_stars_repo_licenses... |
#!/usr/bin/env python
# coding=utf-8
# aeneas is a Python/C library and a set of tools
# to automagically synchronize audio and text (aka forced alignment)
#
# Copyright (C) 2012-2013, Alberto Pettarin (www.albertopettarin.it)
# Copyright (C) 2013-2015, ReadBeyond Srl (www.readbeyond.it)
# Copyright (C) 2015-2017, A... | {"hexsha": "0828564ddbf57e84e7d376685308b00e00ff8991", "size": 10691, "ext": "py", "lang": "Python", "max_stars_repo_path": "Aeneas/aeneas/aeneas/mfcc.py", "max_stars_repo_name": "yalhaizaey/Dreich", "max_stars_repo_head_hexsha": "9528856c3879d4c9d3ced453f223785a71188808", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import os
import re
import time
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
from nba_api.stats.endpoints import leaguestandings
from src.team_colors import team_colors
table_cols = ['Rk', 'Team', 'Record', 'PCT', 'GB', 'Home', 'Away', 'Div',
'PPG', 'Opp ... | {"hexsha": "7224a9fad0429125dc435d9ee49a82aa170806c7", "size": 2369, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/nba_data.py", "max_stars_repo_name": "Reece323/NBA-Dash", "max_stars_repo_head_hexsha": "1700ec0143a82a668264e5aad98878238211d751", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
\section{Main}
\begin{frame}{Main}
\end{frame}
| {"hexsha": "a87afb537b51329d1f327ebaa1c9563e07352083", "size": 48, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "content/main.tex", "max_stars_repo_name": "bencwbrown/beamer-template", "max_stars_repo_head_hexsha": "a709ea73793ca4ff5d5bd38cdc68ffe3253c6127", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
import copy
from typing import Union
import numpy as np
def temperature(
t: np.array,
fire_load_density_MJm2: float,
fire_hrr_density_MWm2: float,
room_length_m: float,
room_width_m: float,
fire_spread_rate_ms: float,
beam_location_heigh... | {"hexsha": "68a477822791bb1bdd82adb8255db21be7e27309", "size": 11140, "ext": "py", "lang": "Python", "max_stars_repo_path": "fsetools/lib/fse_travelling_fire.py", "max_stars_repo_name": "fsepy/fsetools", "max_stars_repo_head_hexsha": "6b6c647912551680109a84d8640b9cfbe7970970", "max_stars_repo_licenses": ["Apache-2.0"],... |
// Copyright 2011-2014 Renato Tegon Forti
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt
// or copy at http://www.boost.org/LICENSE_1_0.txt)
// For more information, see http://www.boost.org
#define BOOST_APPLICATION_FEATURE_NS_SELECT_BOOST
#include <iostream>... | {"hexsha": "2894985350155ed6c62fd33dd4b2307607378355", "size": 810, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/args_aspect_test.cpp", "max_stars_repo_name": "museghost/Boost.Application", "max_stars_repo_head_hexsha": "e3d16df35023ee90aea51631e4ffbb688341d61b", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
import LoggingSetup
import logging
import itertools
import numpy as np
import ast
import math
import random
from functools import partial
from io import BytesIO
import pathlib
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.patches import Circle, PathPatch, Arrow, FancyArrow
from ... | {"hexsha": "60942088093b0d0979184d6958552d26f466e16f", "size": 11756, "ext": "py", "lang": "Python", "max_stars_repo_path": "VisualWorld.py", "max_stars_repo_name": "lboloni/MREM", "max_stars_repo_head_hexsha": "f0d6354d1d3b625e71597a42e03f4e5d859a2ef2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
MODULE CONVERTWGS84
! ** CONVERT GEOGRAPHIC COORDINATES TO UTM AND REVERSE
! ** AUTHOR: DH CHUNG
! ** START : 2008
! ** UPDATE: 2016-06-16
USE GLOBAL,ONLY:RKD,PI,HEMI,UTMZ
IMPLICIT NONE
! ** HEMI = HEMISPHERE (1:NORTH/2SOUTH)
! ** UTMZ = ZONE NUMBER (1:60)
! ** GEOGRAPHY SYSTEM:
! ** 1.WGS84/NAD83 /2.GRS80 /3.WGS72... | {"hexsha": "58bb3566b57af07ed2f75cd5a7b1ef71361c9b02", "size": 3982, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "EFDC/convertwgs84.f90", "max_stars_repo_name": "dsi-llc/EFDCPlus", "max_stars_repo_head_hexsha": "27ece1cd0bb9e02a46d1ad20f343bc5d109acfb3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
'''
Created on April 16th, 2015
@author: bennettd
'''
import numpy
import pylab
import gpib_instrument
class AgilentE4407B(gpib_instrument.Gpib_Instrument):
'''
Agililent E4407B control class
'''
def __init__(self, pad, board_number = 0, name = '', sad = 0, timeout = 13, send_eoi = 1, eos_mode = 0):... | {"hexsha": "ebb513a4017d8621ba248e12530fab2ed86e8194", "size": 3266, "ext": "py", "lang": "Python", "max_stars_repo_path": "waferscreen/inst_control/inactive/agilent_e4407B.py", "max_stars_repo_name": "chw3k5/WaferScreen", "max_stars_repo_head_hexsha": "c0ca7fe939fe7cd0b722b7d6129b148c03a7505c", "max_stars_repo_license... |
using Test
using TestSetExtensions
using LinearAlgebra
using Qaintessent
##==----------------------------------------------------------------------------------------------------------------------
# adapted from https://github.com/FluxML/Zygote.jl/blob/master/test/gradcheck.jl
function ngradient(f, xs::AbstractArray... | {"hexsha": "184b00c42b66579605bafa3f80ea86363f88536d", "size": 8624, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_gradients.jl", "max_stars_repo_name": "oguzcankirmemis/Qaintessent.jl", "max_stars_repo_head_hexsha": "6261dc5d8a9a7ea7d406ea39cac950747583f414", "max_stars_repo_licenses": ["Apache-2.0"]... |
"""
Test script for weight_set.py.
"""
import unittest
import numpy as np
np.random.seed(1234)
from copy import deepcopy
from models.tools.weight_set import WeightSet
initializations = ['random', 'glorot_normal',
'glorot_uniform',
'he_normal', 'he_uniform',
'lec... | {"hexsha": "20be302b6c50ef60e491b6cbbcebcf05bd894a87", "size": 6377, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/models/tools/test_weight_set.py", "max_stars_repo_name": "TeamSerpentine/retro-baselines", "max_stars_repo_head_hexsha": "9b2c725604496aca9c382a53f456d31fdbcaa5b1", "max_stars_repo_licenses":... |
"""Problem 4.
Author: Lucas David -- <ld492@drexel.edu>
"""
import multiprocessing
from mpl_toolkits.mplot3d import Axes3D
from scipy.io import loadmat
from sklearn.cluster import KMeans
from sklearn.model_selection import GridSearchCV, train_test_split
from algorithms import RBFRegressor
Axes3D
N_JOBS = multipr... | {"hexsha": "5b1aa4df02e6906efddc3d0ed18187fcbb246ab0", "size": 1755, "ext": "py", "lang": "Python", "max_stars_repo_path": "tasks/assignment-1/p4.py", "max_stars_repo_name": "Comp-UFSCar/neural-networks-2", "max_stars_repo_head_hexsha": "e5e105c91bcd1d63b200f36b9e02dbcde54ae756", "max_stars_repo_licenses": ["MIT"], "ma... |
#include <iostream>
#include <queue>
#include <string>
#include <boost/random.hpp>
#include <boost/generator_iterator.hpp>
#include <glog/logging.h>
using boost::variate_generator;
using boost::mt19937;
using boost::exponential_distribution;
#define ONLY_EVENT_TYPE 0
#define NUMBER_EVENT_TYPES 2 //NEED TO MA... | {"hexsha": "8bae782e41899be60de07e4c5dfd58f34a8613df", "size": 6360, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/hw1.cpp", "max_stars_repo_name": "Kazz47/cs445", "max_stars_repo_head_hexsha": "eb991eda50e395a2f10ea943eabe7f74b30f38f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# ade:
# Asynchronous Differential Evolution.
#
# Copyright (C) 2018-19 by Edwin A. Suominen,
# http://edsuom.com/ade
#
# See edsuom.com for API documentation as well as information about
# Ed's background and other projects, software and otherwise.
#
# Licensed under th... | {"hexsha": "005e2eca5dbd9f7f21bb28f291e3422e8ee47b6c", "size": 44535, "ext": "py", "lang": "Python", "max_stars_repo_path": "ade/population.py", "max_stars_repo_name": "vishalbelsare/ade", "max_stars_repo_head_hexsha": "c2d16fe5544a130d509cb1e430b170a2b77e520b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
import time
from torch.utils.data import Dataset, DataLoader
import torchaudio
import torchvision.transforms as transforms
import... | {"hexsha": "406a8623f0f3cfdc374380da8a9f4ca91dbb636f", "size": 9033, "ext": "py", "lang": "Python", "max_stars_repo_path": "python_files/CNN_LSTM_Spectrogram_2/CNN_LSTM_Spectrogram_2.py", "max_stars_repo_name": "ChaojieZhang-cz/TS-Project-VoxCeleb", "max_stars_repo_head_hexsha": "c11941f0019d74e064726469cfa6ec9e5772c56... |
%----------------------------------------------------------------
%---------------------BASIC SETUP-------------------------------
%----------------------------------------------------------------
\documentclass[9pt]{article}
\usepackage[
top=1.4cm,
bottom=2.4cm,
left=1.5cm,
right=1.5cm,
headsep=10pt,
letterpape... | {"hexsha": "23e2362aec484b45cbb73258f070886fd1db91be", "size": 4240, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/structure.tex", "max_stars_repo_name": "sesjehen-vestha-kxall/identity-cv", "max_stars_repo_head_hexsha": "b741e4759a23a7b9c43aa334855862c7eea7e6e2", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
File: examples/model/output_interpolated_model.py
Author: Keith Tauscher
Date: 30 Jul 2019
Description: Shows a usage of the OutputInterpolatedModel class.
"""
import os
import numpy as np
import matplotlib.pyplot as pl
from pylinex import FixedModel, OutputInterpolatedModel,\
load_model_from_hdf5_file
file_n... | {"hexsha": "9488c3e5f71e01d0f605ee5641ecb45021382c9f", "size": 1842, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/model/output_interpolated_model.py", "max_stars_repo_name": "CU-NESS/pylinex", "max_stars_repo_head_hexsha": "b6f342595b6a154e129eb303782e5268088f34d5", "max_stars_repo_licenses": ["Apach... |
# Copyright (C) 2008 University of Maryland
# All rights reserved.
# See LICENSE.txt for details.
# Author: Christopher Metting
#Starting Date:6/5/2009
from numpy import size,array,shape,indices, searchsorted, linspace
from numpy import log, log10, abs, min, max, nonzero,isnan
from .zoom_colorbar import *
import sys,c... | {"hexsha": "008b018afe7ed003cde34f0b292c96caf3811ea7", "size": 6038, "ext": "py", "lang": "Python", "max_stars_repo_path": "osrefl/viewers/view.py", "max_stars_repo_name": "reflectometry/osrefl", "max_stars_repo_head_hexsha": "ddf55d542f2eab2a29fd6ffc862379820a06d5c7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
from math import ceil, sqrt
import numpy as np
from scipy.special import sph_harm, spherical_jn
def trunc_H3d(k, T):
l = np.arange(ceil(16 + k * T))
I = np.where(
np.abs(np.sqrt((2 * l + 1) / (4 * np.pi)) * spherical_jn(l, k * T)) > 1e-6
)
return I[0][-1]
def incident_field(k, z):
retur... | {"hexsha": "db85bf14143b0e1ca08ce94da119bfa609f1e0dd", "size": 944, "ext": "py", "lang": "Python", "max_stars_repo_path": "dev/dev_uinc_3d.py", "max_stars_repo_name": "zmoitier/accoster", "max_stars_repo_head_hexsha": "648b9edf7e73848eacb60af0885be4d30fdbbafc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# Copyright (c) OpenMMLab. All rights reserved.
from collections import defaultdict
import numpy as np
def _create_coco_gt_results(dataset):
from mmdet.core import bbox2result
from mmtrack.core import track2result
results = defaultdict(list)
for img_info in dataset.data_infos:
ann = dataset.... | {"hexsha": "4c39c14f9e926cc4bcbf2059e085101e859db9d1", "size": 877, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_data/test_datasets/utils.py", "max_stars_repo_name": "dzambrano/mmtracking", "max_stars_repo_head_hexsha": "ec7a2e36fbf99effed4602a4df929f495efe73c5", "max_stars_repo_licenses": ["Apache... |
# Takuya Ito
# 09/11/2018
# Modified by Michael Cole, June 2020
# Post-processing nuisance regression using Ciric et al. 2017 inspired best-practices
## OVERVIEW
# There are two main parts to this script/set of functions
# 1. "step1_createNuisanceRegressors"
# Generates a variety of nuisance regressors, such as mot... | {"hexsha": "914f832d7e7c4ab727f9c99d539b62dd0c3bd17c", "size": 33725, "ext": "py", "lang": "Python", "max_stars_repo_path": "glmScripts/vertexwise_postproc/nuisanceRegressionPipeline_VertexWise.py", "max_stars_repo_name": "McGintyLab/TaskFCActflow_release", "max_stars_repo_head_hexsha": "b277eb669cfb8ca48e98a3329ccd9c5... |
include("common/constructor_validations.jl")
include("common/device_constructor_utils.jl")
include("thermalgeneration_constructor.jl")
include("hydrogeneration_constructor.jl")
include("branch_constructor.jl")
include("renewablegeneration_constructor.jl")
include("load_constructor.jl")
include("storage_constructor.jl")... | {"hexsha": "286896aea7ddc201d118ba994d6cd79ade3e900e", "size": 321, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/devices/device_constructors/device_constructors.jl", "max_stars_repo_name": "UnofficialJuliaMirror/PowerSimulations.jl-e690365d-45e2-57bb-ac84-44ba829e73c4", "max_stars_repo_head_hexsha": "3f943... |
import torch
import numpy as np
import math
import operator
from global_random_seed import RANDOM_SEED
# make everything reproducible
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed_all(RANDOM_SEED)
# Alr... | {"hexsha": "db7d424f497a2a67c94bc684116c12227c72feb1", "size": 27673, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/attention_investigation.py", "max_stars_repo_name": "ivan-bilan/tac-self-attention", "max_stars_repo_head_hexsha": "8dd583ac960716bbf0c645c23f2c50bd36ca042a", "max_stars_repo_licenses": ["A... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright (c) 2019 by Inria
Authored by Mostafa Sadeghi (mostafa.sadeghi@inria.fr)
License agreement in LICENSE.txt
"""
import numpy as np
import torch
import torch.nn as nn
#%% The following implements the MCEM algorithm for audio-only VAE
class MCEM_algo:
def ... | {"hexsha": "628f49d5f7d1917639ba5db10588f0caec0e67cf", "size": 8877, "ext": "py", "lang": "Python", "max_stars_repo_path": "asteroid/masknn/MCEM_algo.py", "max_stars_repo_name": "flyingleafe/asteroid", "max_stars_repo_head_hexsha": "1c3c68ffc83f4b0bf7b00893083e4eff1f577b88", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
Code edited from:
https://pythonprogramming.net/training-deep-q-learning-dqn-reinforcement-learning-python-tutorial/?completed=/deep-q-learning-dqn-reinforcement-learning-python-tutorial/
Train the Agent in a simulated environment -> Much faster than training by playing on the emulator directly.
"""
import os
im... | {"hexsha": "7a20c8b548c5b183ba2c5783180db9acf530cedf", "size": 3403, "ext": "py", "lang": "Python", "max_stars_repo_path": "OutOfMyRoom/train_simulation.py", "max_stars_repo_name": "uncleman11/pokemonBot", "max_stars_repo_head_hexsha": "86975557ecef0ffe8a0f154f21ba2bf3fab69b8e", "max_stars_repo_licenses": ["Apache-2.0"... |
program number_guess
!! not every compiler inits arandom seed (Gfortran yes, flang no)
use, intrinsic:: iso_fortran_env, only: stdin=>input_unit, stdout=>output_unit
use numerical, only: isprime
implicit none
integer :: secret, guess, i
real :: r
character(20) :: msg, buf
call random_init(.false., .false.)
call ra... | {"hexsha": "e9ed88adb2b2cfdd2268f9c2f99e2c3b87cc8d19", "size": 1042, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "number_guess.f90", "max_stars_repo_name": "scivision/preschool-coding", "max_stars_repo_head_hexsha": "0cd63b0693b31b1381aaa484a3b8b3671b036270", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""Summarizes `astro_utils` test results."""
import os
import numpy as np
import pandas as pd
_DATA_DIR_PATH = r'C:\Users\Harold\Desktop\NFC\Data\USNO Tables'
# _DATA_DIR_PATH = '/Users/Harold/Desktop/NFC/Data/USNO Tables'
_CSV_FILE_NAME = 'Rise Set Data.csv'
def _main():
csv_file_path = os.path.join(_D... | {"hexsha": "3653a58c0e0b4216235f0b1413d6cc18feccf369", "size": 1219, "ext": "py", "lang": "Python", "max_stars_repo_path": "vesper/ephem/tests/scripts/summarize_astro_utils_test_results.py", "max_stars_repo_name": "RichardLitt/Vesper", "max_stars_repo_head_hexsha": "5360844f42a06942e7684121c650b08cf8616285", "max_stars... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) BaseDetection, Inc. and its affiliates. All Rights Reserved
import json
import os
import numpy as np
PERSON_CLASSES = ['background', 'person']
class Image(object):
def __init__(self, mode):
self.ID = None
self._width = None
... | {"hexsha": "4c4910924502fe76c5d854f5c8327931905772a6", "size": 17803, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvpods/evaluation/crowdhumantools.py", "max_stars_repo_name": "reinforcementdriving/cvpods", "max_stars_repo_head_hexsha": "32d98b74745020be035a0e20337ad934201615c4", "max_stars_repo_licenses": [... |
import unittest
from collections import Counter
from math import sqrt
import scipy.stats
from multinomial import binomial, int_sqrt, sample_binomial, sample_binomial_p, sample_multinomial_p
def get_tuples(length, total):
"""
Computes all possible multinomial draws, the support of the multinomial distribution
... | {"hexsha": "6ab894d96e8da7718b8e322ece06dc2bd6c9fa93", "size": 4030, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/tests.py", "max_stars_repo_name": "murbard/multinomial", "max_stars_repo_head_hexsha": "a3afddb51158fa6dd5218ecd286d1756daaecc4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import itertools
import numpy as np
import torch
import detectron2.lib.ops as ops
def make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/... | {"hexsha": "16ae70280365285e9eeb307a908b86d588ff06fa", "size": 8286, "ext": "py", "lang": "Python", "max_stars_repo_path": "detectron2/lib/utils/net.py", "max_stars_repo_name": "BUPT-PRIV/detectron2", "max_stars_repo_head_hexsha": "3163664cd5f43d50ea1966f410dc82410b9ccbf4", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
using SHA
using Random
block_string = "test5"
block_string = randstring(10)
#println(bytes2hex(sha256("test")))
max_nonce = 2 ^ 32 # 4 billion
max_nonce = 2 ^ 16
is_hash_found = 0
for nonce in 0:max_nonce
hash = bytes2hex(sha2_256(string(nonce) * block_string))
#println(hash)
if startswith(h... | {"hexsha": "b9e05e1ecb05a8af5256102ae78c06e525649d45", "size": 499, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/proof_of_work.jl", "max_stars_repo_name": "marchunter/litcoin", "max_stars_repo_head_hexsha": "b8dca6afc507915df8b0a07c06f7235a351d4a62", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
class Policy():
def __init__(self,num_observations,num_actions,lr,num_dirs,num_dirs_best,noise):
self.theta = np.zeros((num_actions,num_observations))
self.learning_rate = lr
self.num_directions = num_dirs
self.num_best_directions = num_dirs_best
self.nois... | {"hexsha": "e1e8187f11ac24458dc5a37c4b76c0a9518821d6", "size": 1030, "ext": "py", "lang": "Python", "max_stars_repo_path": "ARS/policy.py", "max_stars_repo_name": "7enTropy7/BipedalWalker", "max_stars_repo_head_hexsha": "be699026fd556ad242896412c34af1401582ba50", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6... |
import os
import time
from collections import deque
import pickle
from baselines.ddpg_custom.ddpg import DDPG
import baselines.common.tf_util as U
from baselines import logger
import numpy as np
import tensorflow as tf
from mpi4py import MPI
def print_n_txt(_f,_chars,_addNewLine=True,_DO_PRINT=True,_DO_SAVE=True):
... | {"hexsha": "7a02171533dd31591a50b9db3c0d5e131ad4cd79", "size": 10565, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/ddpg_custom/training.py", "max_stars_repo_name": "kyungjaelee/customized_open_ai_baselines", "max_stars_repo_head_hexsha": "f10dd63d00efa3653377272662581c493da60417", "max_stars_repo_li... |
\section{Control Plane on Power Line} | {"hexsha": "64a9cb21c91b4a08308877b89bfda2b662a8016d", "size": 37, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/paper/sections/control.tex", "max_stars_repo_name": "HKUST-SING/p4mr", "max_stars_repo_head_hexsha": "82f0916d9a9ab8036123742061d5b21779277800", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
/*
Part of the Fluid Corpus Manipulation Project (http://www.flucoma.org/)
Copyright 2017-2019 University of Huddersfield.
Licensed under the BSD-3 License.
See license.md file in the project root for full license information.
This project has received funding from the European Research Council (ERC)
under the European... | {"hexsha": "ce6af5b955d6f2fbdd109af77b17b557422f34a0", "size": 2004, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/algorithms/util/SpectralEmbedding.hpp", "max_stars_repo_name": "elgiano/flucoma-core", "max_stars_repo_head_hexsha": "d34a04e7a68f24eaf09b24df57020d45664061fc", "max_stars_repo_licenses": ["... |
#################################################################################
# The Institute for the Design of Advanced Energy Systems Integrated Platform
# Framework (IDAES IP) was produced under the DOE Institute for the
# Design of Advanced Energy Systems (IDAES), and is copyright (c) 2018-2021
# by the softwar... | {"hexsha": "714e1d177c62313e6366c5fedf865ab1f82a1e11", "size": 10847, "ext": "py", "lang": "Python", "max_stars_repo_path": "idaes/generic_models/properties/core/eos/eos_base.py", "max_stars_repo_name": "dangunter/idaes-pse", "max_stars_repo_head_hexsha": "8f63b4ad8000af8a3eb0316a5f61c32e206925d0", "max_stars_repo_lice... |
[STATEMENT]
lemma Let_is_action:
"(relation_of A;;
(R(true \<turnstile> (\<lambda> (A, A'). tr A' = tr A \<and> \<not>wait A' \<and> more A' = (decrease v (more A)))))) \<in> {p. is_CSP_process p}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (action.relation_of A ;; R (true \<turnstile> \<lambda>(A, A'). tr... | {"llama_tokens": 658, "file": "Circus_Denotational_Semantics", "length": 6} |
\documentclass{sig-alternate-05-2015}
% \usepackage{subfigure}
\usepackage{subfig}
\usepackage{balance}
\usepackage{multirow}
\usepackage{color}
\usepackage{chngpage}
\usepackage{url}
\usepackage{amsmath}
\usepackage{caption}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{hyperref}
\newtheorem{theorem... | {"hexsha": "e17c76bdad00d7530c2522855eed37810a7ac137", "size": 4328, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/head-full.tex", "max_stars_repo_name": "lzhbrian/gpr", "max_stars_repo_head_hexsha": "912c530fec02e4fe1a4d49b96e6fc3a25b2bdf3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_st... |
# !/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, unicode_literals
# -------------------------------------------#
# author: sean lee #
# email: xmlee97@gmail.com #
#--------------------------------------------#
"""MIT License
Copyright ... | {"hexsha": "b5545600ac784bff913827191a6083d735515b84", "size": 5133, "ext": "py", "lang": "Python", "max_stars_repo_path": "xmnlp/summary/textrank.py", "max_stars_repo_name": "cukuangjiangjun/Sebastian0606", "max_stars_repo_head_hexsha": "d7bb38ae23f22f95d555b2505411473440bde298", "max_stars_repo_licenses": ["MIT"], "m... |
*> \brief \b STRSM
*
* =========== DOCUMENTATION ===========
*
* Online html documentation available at
* http://www.netlib.org/lapack/explore-html/
*
* Definition:
* ===========
*
* SUBROUTINE STRSM(SIDE,UPLO,TRANSA,DIAG,M,N,ALPHA,A,LDA,B,LDB)
*
* .. Scalar Arguments ..
* REAL ALPHA
* ... | {"hexsha": "aa805f6b6c4b06d81eca64985c0bf62a0c94d8a7", "size": 13679, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lapack-netlib/BLAS/SRC/strsm.f", "max_stars_repo_name": "drhpc/OpenBLAS", "max_stars_repo_head_hexsha": "9721b57ecfd194f1a4aaa08d715735cd9e8ad8b6", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "2b39cdde44ece0a2c40f586b6baf8f44c74d6b7d", "size": 8464, "ext": "py", "lang": "Python", "max_stars_repo_path": "aux_tasks/auxiliary_mc/multi_mc_replay_buffer.py", "max_stars_repo_name": "dumpmemory/google-research", "max_stars_repo_head_hexsha": "bc87d010ab9086b6e92c3f075410fa6e1f27251b", "max_stars_repo_li... |
import mysql.connector
from asyncore import read
from PyQt5 import uic
from PyQt5 import QtWidgets
from numpy import save
from reportlab.pdfgen import canvas
c = 0
# Conectando com o banco de dados
con = mysql.connector.connect(
host='localhost', database='cadastro_estoque', user='andre2', password='anova123')
... | {"hexsha": "af9f9905b7162b507306b46c8df041976601f3ff", "size": 7611, "ext": "py", "lang": "Python", "max_stars_repo_path": "cadastro.py", "max_stars_repo_name": "Andreambu23/Cadastro-de-Produtos-main", "max_stars_repo_head_hexsha": "34ee6d6bf1016defae6ad62fb1301bc7bc203bd9", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
import sys
import os
def harmonicProps(_direction, _frequency):
if _direction == 'vertical':
if _frequency > 1 and _frequency < 2.6:
if _frequency >= 1.7 and _frequency <= 2.1:
return 1, 280, '1'... | {"hexsha": "577e0ed68a5bbd175ffa4e1d3fc9505da02ac0a8", "size": 9497, "ext": "py", "lang": "Python", "max_stars_repo_path": "engine/main.py", "max_stars_repo_name": "Rfaelv/Dinpass", "max_stars_repo_head_hexsha": "d2191e9a243b3620c715205b3e499f56abf98ddb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import os
import numpy as np
from PIL import Image
from pprint import pprint
from torchvision import datasets, transforms
import shutil
BASE_DIR = "./mnist_data/"
IMG_DIR = "imgs/"
def download_mnist_image_files(data_num, save_dir=BASE_DIR):
transform = transforms.Compose([transforms.ToTensor()])
... | {"hexsha": "6a896200126fd1d215296eb076c3ee7cb2a27023", "size": 1158, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/download_mnist_data.py", "max_stars_repo_name": "sari-rev00/pytorch_image_clissifier", "max_stars_repo_head_hexsha": "08698b1023e08cdde561d492074e7ee8c41be8ac", "max_stars_repo_licenses": ["... |
// Copyright (c) 2017-2019 The Blocknet developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <rpc/server.h>
#include <xbridge/util/logger.h>
#include <xbridge/util/settings.h>
#include <xbridge/util/xbridgeerror.... | {"hexsha": "6d31a761de6552b8f302eb64521b0cd5505aac37", "size": 169380, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/xbridge/rpcxbridge.cpp", "max_stars_repo_name": "shrnkld/blocknet", "max_stars_repo_head_hexsha": "f85bdf3eeebb1ed8c2321ebd928232d4885b30b6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
*deck l2opxv
subroutine l2opxv( lmn, v, itl, itu, lmnv, lenscr, scr, eval,
& leig )
c
c compute the matrix vector product, (L^2) * v, in an unnormalized
c cartesian basis, and determine if v(*) is an eigenvector of the
c total angular momentum operator.
c
c input:
c lmn = (l + m + n) where l, m, and n a... | {"hexsha": "1ffa37bf753839b839d2c0a9c78aa15fb918b9e0", "size": 6885, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "source/argosecp/argos1e/l2opxv.f", "max_stars_repo_name": "MOLFDIR/MOLFDIR", "max_stars_repo_head_hexsha": "e71c7ecf77ee018bbdeaa004dda04369ce02be89", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
"""
Main file to run for each experiment with the correct config.yml file as the argument.
"""
import argparse
import copy
import json
import logging
from pathlib import Path
import numpy as np
import torch
import yaml
from aif360.algorithms.postprocessing import (CalibratedEqOddsPostprocessing,
... | {"hexsha": "68affcde54ae506cc86aa72f9550a6404a3d4f49", "size": 10497, "ext": "py", "lang": "Python", "max_stars_repo_path": "intraproc_tabular.py", "max_stars_repo_name": "abacusai/intraprocessing_debiasing", "max_stars_repo_head_hexsha": "b4f0c35e299022b1e71e26686220e90440687100", "max_stars_repo_licenses": ["Apache-2... |
# MIT license
# Copyright (c) Microsoft Corporation. All rights reserved.
# See LICENSE in the project root for full license information.
module NotebooksUtils
# import Pluto
import PlutoUI
# import Markdown
import Format
import Makie
import Makie.AbstractPlotting
import Makie.AbstractPlotting.MakieLayout
import ... | {"hexsha": "942f3187a810d94eba68c0eeef4e8073492317a6", "size": 13961, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NotebooksUtils/NotebooksUtils.jl", "max_stars_repo_name": "KristofferC/OpticSim.jl", "max_stars_repo_head_hexsha": "3dbe82a51fb3c7d2896f19318d3e4756e54fb6d8", "max_stars_repo_licenses": ["MIT"... |
# coding=utf-8
"""
This is the python implementation of the online learning method using Iterative Parameter Mixture.
This implementation is now supporting:
- Perceptron
- PA-I, PA-II
- CW
- AROW
- SCW-I, SCW-II
"""
import numpy as np
import scipy.sparse as sp
from joblib import Parallel, delayed
... | {"hexsha": "4f336aa71ed8fcae38d3eb50706be74a78ce4eb9", "size": 7859, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/updater.py", "max_stars_repo_name": "AkihikoWatanabe/online_learning_libs", "max_stars_repo_head_hexsha": "e23d644728657914a3d2f0e13068ee2a2869815c", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Tamer Abousoud
Main Robot Controls
---
Functions robot can perform:
- Move in a straight path
- Turn to a given angle
- Take pictures/video
- Return GPS coordinates
- Return sensor data (e.g. distance sensors, accelerometer, gyro)
"""
import os
import sys
import time
import math
import datetime as dt
import numpy... | {"hexsha": "dbda1262a5798c5109bacbe7168cd0b20d456b49", "size": 11875, "ext": "py", "lang": "Python", "max_stars_repo_path": "Webots_Object_Finding/controllers/simulation/robotControl.py", "max_stars_repo_name": "tsoud/robotics", "max_stars_repo_head_hexsha": "ca3626fd3fce67afb65fcb97f6df4b1112033a7b", "max_stars_repo_l... |
import numpy as np
class BackendOperations(object):
"""A class for centralizing backend operations
This class will be growing systematically. This is probably not the best
solution but can be worked out later.
Parameters
----------
backend : object
A backend object: numpy, tensorflo... | {"hexsha": "f862270fe5d5c4679df9a8a2e06d6dd7d679103d", "size": 2124, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml4chem/backends/operations.py", "max_stars_repo_name": "muammar/mlchem", "max_stars_repo_head_hexsha": "365487c23ea3386657e178e56ab31adfe8d5d073", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"]... |
from manimlib import *
import copy
import networkx as nx
from .algo_vgroup import *
from .algo_node import *
import queue
class DataNode(object):
def __init__(self, id, k, v, raw):
self.id = id
self.k = k
self.v = v
self.raw = raw
class AlgoRBTreeNode(object):
def __init__(self... | {"hexsha": "943c686523141e285e7866c7304060517d0e52dc", "size": 17794, "ext": "py", "lang": "Python", "max_stars_repo_path": "animations/src/algo_rbtree.py", "max_stars_repo_name": "mckm2000/algorithm-stone", "max_stars_repo_head_hexsha": "23bad1c093093e311d7fe7cc57c6877b26a711c7", "max_stars_repo_licenses": ["MIT"], "m... |
import sys
import os
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, os.path.join(ROOT_DIR, "src"))
import util
import torch
import numpy as np
from model import make_model
from render import NeRFRenderer
import torchvision.transforms as T
import tqdm
import imageio
impor... | {"hexsha": "d5025445e42c34d9e16ef50ac906b6bb39482c8a", "size": 5814, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval/render_ct.py", "max_stars_repo_name": "abrilcf/pixel-nerf", "max_stars_repo_head_hexsha": "9a6a8ab6c39ec01d52df3bf4c03830f7162cc679", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
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