text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import argparse
import numpy as np
import os
import pprint
import yaml
# HACK: Get logger to print to stdout
import sys
sys.ps1 = '>>> ' # Make it "interactive"
import tensorflow as tf
from multiprocessing import Queue
from lib.config import cfg_from_file, cfg_from_list, cfg
from lib.data_process import make_data_p... | {"hexsha": "9f64bdb6b6aa9101f9b48d231f028e2692647260", "size": 25278, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "kendemu/text2shape", "max_stars_repo_head_hexsha": "2f62ebc15587f171758c71e3daf0235d7380df41", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 72, ... |
[STATEMENT]
lemma infnorm_Max:
fixes x :: "'a::euclidean_space"
shows "infnorm x = Max ((\<lambda>i. \<bar>x \<bullet> i\<bar>) ` Basis)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. infnorm x = (MAX i\<in>Basis. \<bar>x \<bullet> i\<bar>)
[PROOF STEP]
by (simp add: infnorm_def infnorm_set_image cSup_eq_Max) | {"llama_tokens": 135, "file": null, "length": 1} |
#!/usr/bin/python
# -*- coding:utf-8 -*-
import cv2
import numpy as np
import matplotlib.pyplot as plt
#Numpy：(a+b)%255
#OpenCV：Math.min(a+b,255)
def add(img1,img2):
result = cv2.add(img1, img2)
cv2.imshow("add", result)
cv2.waitKey(0)
#融合
def addWeighted(img1, alpha, img2, beta, gamma):
result = c... | {"hexsha": "0d73c3a1ca7ad101ce9cad09ad7b5dcfaa103700", "size": 850, "ext": "py", "lang": "Python", "max_stars_repo_path": "AddSubtract.py", "max_stars_repo_name": "neohope/NeoDemosImageProcess", "max_stars_repo_head_hexsha": "ff08fb110464fef3433d74500792894408e26051", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
# nodenet/tester/fctest.py
# Description:
# "fctest.py" provide fullyconnected neuralnet testing.
# Copyright 2018 NOOXY. All Rights Reserved.
import numpy as np
import nodenet.neuralnets as nn
import nodenet.layers as layers
import nodenet.functions as f
import nodenet.trainingsessions as sessions
import nodenet.inte... | {"hexsha": "5a641caaa0acbb2c9c52cd189a77b400c3d26ca9", "size": 2732, "ext": "py", "lang": "Python", "max_stars_repo_path": "nodenet/python/examples/sin.py", "max_stars_repo_name": "NOOXY-research/NodeNet", "max_stars_repo_head_hexsha": "8bf7e0c2fd0e4fae4a51b2900014004728f3c935", "max_stars_repo_licenses": ["Apache-2.0"... |
\documentclass{mcmthesis}
\mcmsetup{CTeX = false,
tcn = 0000000, problem = A,
sheet = true, titleinsheet = true, keywordsinsheet = true, titlepage = false, abstract = false}
\usepackage{newtxtext}%\usepackage{palatino}
\usepackage{lipsum}
\usepackage{diagbox}
\usepackage{setspace}
\usepackage[nottoc]{to... | {"hexsha": "0f76102888f0c79fc01851de3daa4223ef130b03", "size": 2710, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "mcm-thesis.tex", "max_stars_repo_name": "lethal233/mcm-report", "max_stars_repo_head_hexsha": "3890a9834674aed243612453e59f7e9ddec18cf2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from winning.std_calibration import centered_std_density
from winning.lattice_calibration import dividend_implied_ability
import numpy as np
import matplotlib.pyplot as plt
from winning.lattice_plot import densitiesPlot
from winning.lattice import skew_normal_density
# Illustrates the basic calibration
# Exactly the s... | {"hexsha": "c8b6fc2ae7a3e900c76f4dc1cc466c67d1977d3d", "size": 1325, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples_basic/calibrate_horse_race_with_std_normal_and_plot.py", "max_stars_repo_name": "microprediction/winning", "max_stars_repo_head_hexsha": "f03b74fc87cfe4b317a3b0ac3e165bc5e49a39fc", "max_s... |
import gym
import time
import random
import numpy as np
from collections import deque
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.system('export CUDA_VISIBLE_DEVICES=""')
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
def get_session(gpu_fraction=0.7):
'''Assume that you have 6GB... | {"hexsha": "a7580661d0b1d4174129cc07cf31d3f4c5db1744", "size": 10967, "ext": "py", "lang": "Python", "max_stars_repo_path": "dqn.py", "max_stars_repo_name": "sotirisnik/dqn", "max_stars_repo_head_hexsha": "cdf0569ad0cdd554180d709785bdad93af60d671", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
function tSeries = rmBlurGrayTSeries(view,tSeries,iterlambda)
% rmBlurGrayTSeries - smooth raw time series across cortical surface
%
% tSeries = rmBlurGrayTSeries(view,tSeries,iterlambda);
%
% result^{i+1}[x] = c2[x] s^i[x] data missing
% = c1[x] (input[x] + lambda s^i[x]) otherwise
%
% c_1[x] =... | {"author": "vistalab", "repo": "vistasoft", "sha": "7f0102c696c091c858233340cc7e1ab02f064d4c", "save_path": "github-repos/MATLAB/vistalab-vistasoft", "path": "github-repos/MATLAB/vistalab-vistasoft/vistasoft-7f0102c696c091c858233340cc7e1ab02f064d4c/mrBOLD/Analysis/retinotopyModel/rmBlurGrayTSeries.m"} |
import json
import sys
from collections import defaultdict
from math import sqrt
import numpy as np
import theano.tensor as T
import utils
from rbm import CFRBM
from experiments import read_experiment
from utils import revert_expected_value, expand, iteration_str
from dataset import load_dataset
def run(name, da... | {"hexsha": "6431d14268fe263f504a2de4c7cc234067fe1a1b", "size": 6219, "ext": "py", "lang": "Python", "max_stars_repo_path": "cfrbm/item_based.py", "max_stars_repo_name": "kisai19980124/CFRBM", "max_stars_repo_head_hexsha": "a4c2ad0b40c4e958802d2c177e2b83f306e2d9fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib as mpl
import matplotlib.ticker as mtick
#from brokenaxes import brokenaxes
from matplotlib import gridspec
from ... | {"hexsha": "29fd2e154791227491147152455c892caac9bc73", "size": 2159, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/asplos/schedule_overhead/expected_results/draw.py", "max_stars_repo_name": "lzjzx1122/FaaSFlow", "max_stars_repo_head_hexsha": "c4a32a04797770c21fe6a0dcacd85ac27a3d29ec", "max_stars_repo_lice... |
import sys
import time
import numpy as np
import matplotlib.pyplot as plt
from multiprocessing import Pool
# if len(sys.argv) < 3:
# print('ERROR Args number. Needed: \n[1]In Path(with file.npy) -- prepros file \n[2]Out Path(with .json)')
# sys.exit()
#
#
# in_path = str(sys.argv[1])
# out_path = str(sys.argv[... | {"hexsha": "76d80fc79c7f4ce8abf29e93de4040e283128d07", "size": 11466, "ext": "py", "lang": "Python", "max_stars_repo_path": "proto/adabox_for_cuda_proto_improved.py", "max_stars_repo_name": "jnfran92/adaptive-boxes", "max_stars_repo_head_hexsha": "bcf03a91d48877b3a24125b74a233bda5bd8e044", "max_stars_repo_licenses": ["... |
import pickle
import os
import numpy as np
import argparse
from matplotlib import pyplot as plt
import matplotlib
import glob
import pandas as pd
from PIL import Image
from tqdm import tqdm
parser = argparse.ArgumentParser(description='read two annotations files')
parser.add_argument('--aff_wild2_pkl', type=str, defaul... | {"hexsha": "b26bfa86f8a98198fce77e102a94db1429b47cd6", "size": 3305, "ext": "py", "lang": "Python", "max_stars_repo_path": "create_annotation_file/AFEW-VA/create_annotation_files_Mixed_VA.py", "max_stars_repo_name": "wtomin/Multitask-Emotion-Recognition-with-Incomplete-Labels", "max_stars_repo_head_hexsha": "e6df7ffc9b... |
%% DEMO 18: Arbitrary axis of rotation
%
%
%
% Some modenr CT geometires are starting to be a bit more complex, one of
% the common things being arbitrary axis of rotation i.e. the detector and the
% source can move not in a circular path, but in a "spherical" path.
%
% In TIGRE this has been implemented by defining t... | {"author": "CERN", "repo": "TIGRE", "sha": "8df632662228d1b1c52afd95c90d0f7a9f8dc4b3", "save_path": "github-repos/MATLAB/CERN-TIGRE", "path": "github-repos/MATLAB/CERN-TIGRE/TIGRE-8df632662228d1b1c52afd95c90d0f7a9f8dc4b3/MATLAB/Demos/d18_ArbitraryAxisOfRotation.m"} |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
import matplotlib.patches as patches
#my imports
import cv2
from LucasKanade import LucasKanade
# write your script here, we recommend the above libraries for making your animation
frames = np.load('../data/carseq.npy')
H, W, T =... | {"hexsha": "c27102790b6d3f9e0bf89706e3e4141f4aace0ab", "size": 2221, "ext": "py", "lang": "Python", "max_stars_repo_path": "LK-Tracking/code/testCarSequenceWithTemplateCorrection.py", "max_stars_repo_name": "danenigma/Traditional-Computer-Vision", "max_stars_repo_head_hexsha": "04e66c8d6bdb3e5eb5d1ab05674e4a5ea3b9f823"... |
export TemperatureSensor, getTemperatureSensors, getTemperatureSensor, numChannels, getTemperatures, getTemperature
abstract type TemperatureSensor <: Device end
include("DummyTemperatureSensor.jl")
include("ArduinoTemperatureSensor.jl")
#include("FOTemp.jl")
Base.close(t::TemperatureSensor) = nothing
@mustimplemen... | {"hexsha": "6b8981017ec49c19852ace4d510b80e5137f0f84", "size": 658, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Devices/TemperatureSensor/TemperatureSensor.jl", "max_stars_repo_name": "Fypsilonn/MPIMeasurements.jl", "max_stars_repo_head_hexsha": "fbd615499f0baf9cf7816ab6a14e914dd72aadcf", "max_stars_repo_... |
# Import the used packages.
import numpy as np
import numpy.random as rd
# Define the static methods used in the Game class.
def move_column(column, max=3, length=4):
"""
Moves the elements of the column from left to right as in 2048.
Modifies the given array.
Parameters
----------
column : ... | {"hexsha": "eacad0804c0b466d0e3bdd351dab1030a8cc0e0d", "size": 2043, "ext": "py", "lang": "Python", "max_stars_repo_path": "2048/utils.py", "max_stars_repo_name": "gwatkinson/2048", "max_stars_repo_head_hexsha": "f18ca3a01754cb31e20067842c48fec51c79c4c8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
C
C $Id$
C
LOGICAL FUNCTION CSSWPTST (N1,N2,N3,N4,X,Y,Z)
INTEGER N1, N2, N3, N4
DOUBLE PRECISION X(*), Y(*), Z(*)
C
C***********************************************************
C
C From STRIPACK
C Robert J. Renka
C... | {"hexsha": "861a72ef5aaaf429b36619ffd2d316ef32bd202f", "size": 2797, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "contrib/cssgrid/Src/csswptst.f", "max_stars_repo_name": "xylar/cdat", "max_stars_repo_head_hexsha": "8a5080cb18febfde365efc96147e25f51494a2bf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
from jax import grad, jit, vmap, value_and_grad
import jax.numpy as jnp
import jax.scipy as jsp
import numpy as np
import jax
from jax import random
from functools import partial
from jax.example_libraries import optimizers
from scipy.optimize import nnls
jax.config.update("jax_platform_name", "cpu")
class UBVI:
... | {"hexsha": "bbf8774d3f3cbfc71dc527cfae4b28e83ffc3f71", "size": 2662, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ubvi.py", "max_stars_repo_name": "geometrikz/gvi", "max_stars_repo_head_hexsha": "0dee690a5b60f60ae64a3a80bc1482c955115360", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 26 16:02:16 2019
@author: justin
"""
import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim
import sys
sys.path.append('../')
import layers
def lrelu(x):
return tf.maximum(x*0.2,x)
def relu(x):
return tf.nn.relu(... | {"hexsha": "ecab11b5f4f476c09d4cc8ef54eedfc5508136e8", "size": 96336, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/models/archs/sent/sent/my_model_pool1.py", "max_stars_repo_name": "GuoShi28/TwoStageAlignment_for_BurstRestoration", "max_stars_repo_head_hexsha": "7abfdbfd6248fbbb1aeae359cf658c5a445c9f40"... |
import numpy as np
import torch
import argparse
import os
import gym
gym.logger.set_level(40)
import time
import json
import dmc2gym
import utils
from logger import Logger
from video import VideoRecorder
from dynode import DyNODESacAgent
def parse_args():
parser = argparse.ArgumentParser()
# environment
... | {"hexsha": "11a8107c53e888b45055402d5342d2b52f4df2b1", "size": 8217, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_dynode.py", "max_stars_repo_name": "vmartinezalvarez/DyNODE", "max_stars_repo_head_hexsha": "518d63972afea8ab31a9a8250be34b440c44f409", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
//
// Copyright Markus Rickert 2008
//
// 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 <algorithm>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/num... | {"hexsha": "eb356f7c2557ff40b06cc9bfe16532a14a8571fd", "size": 7270, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "PROX/3RDPARTY/BOOST_BINDINGS/boost_bindings/libs/numeric/bindings/blas/test/ublas_slice.cpp", "max_stars_repo_name": "diku-dk/PROX", "max_stars_repo_head_hexsha": "c6be72cc253ff75589a1cac28e4e91e788... |
import matplotlib.pyplot as plt
from envs.gridworld import AGENT_SIZES, RGB_COLORS
import numpy as np
import os
from envs.key_door import *
from envs.gridworld import *
from utils.gen_utils import *
from model import get_discrete_representation
from torchvision.utils import save_image
def visualize_representations(en... | {"hexsha": "031c37728561edd64ab058826f4e911a5ad0d343", "size": 15449, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/visualize.py", "max_stars_repo_name": "thanard/dorp", "max_stars_repo_head_hexsha": "3e635699018365696fb7d623cf1c519121fafa69", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import numpy as np
import sklearn as sk
def get_class_weights(total_counts, class_positive_counts, multiply, use_class_balancing, class_mode="multiclass"):
"""
Calculate class_weight used in training
Arguments:
total_counts - int
class_positive_counts - dict of int, ex: {"Effusion": 300, "Infiltr... | {"hexsha": "2e1e245304028ea4d62d85349991c46e41673412", "size": 2214, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/weights.py", "max_stars_repo_name": "taoyilee/Keras_MedicalImgAI", "max_stars_repo_head_hexsha": "02ecf1f52ee91e3050b2d30f602a3161ff0726cf", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import pylab as plt
from transitionControl import transitionControl
alpha=0.1
epsilon=0.1 # exploration policy
num_iters=1e2
gamma=0.95
allstates=np.zeros([6,9]) # Change the size of the maze.
num_step_taken=np.zeros(int(num_iters))
# action 0 is right,left is 1, up is 2,down is 3.
qvalues=np.zeros... | {"hexsha": "70c3d290910b91a67217196603bff1b079605ca2", "size": 1932, "ext": "py", "lang": "Python", "max_stars_repo_path": "maze.py", "max_stars_repo_name": "sritee/Q-Learning", "max_stars_repo_head_hexsha": "fec4da0fe36af83fd01b4e49945e2aefc639b4a8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
# Copyright 2022 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "c79826f114a08b0ca8367fcf6be3c80311733d9d", "size": 22555, "ext": "py", "lang": "Python", "max_stars_repo_path": "vmoe/moe.py", "max_stars_repo_name": "google-research/vmoe", "max_stars_repo_head_hexsha": "38a1e4f75c3eb268b974f8860497ec9f17b46504", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
"""Collection of functions to run simulation studies and plot results"""
from time import time
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D # noqa:F401
import src.functions_to_approximate as functions
import src.interpolate as interpolators
from src.auxiliary import get_... | {"hexsha": "5e856f79b586da59d5844fcc34cd03bdcb493f95", "size": 12021, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/execute.py", "max_stars_repo_name": "simonjheiler/function_approximation", "max_stars_repo_head_hexsha": "1b52177ba5d80a02227f3f8f45b5dc2b73bd16bb", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import numpy.random as npr
import flymc as ff
from util import nd, nd_bounds
import unittest
from model_setup import *
npr.seed(1)
class DerivativesTest(object):
PLACES = 4
NUM_TRIALS = 10
N = 30
D = 4
K = 3
def test_prior(self):
for model, th, z in self.random_setu... | {"hexsha": "59afd08ae180ce3a54a001db2aed133447c83a7b", "size": 3000, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_model_derivatives.py", "max_stars_repo_name": "HIPS/firefly-monte-carlo", "max_stars_repo_head_hexsha": "e72f95a73e9da59850307fb7a29d386a93236d56", "max_stars_repo_licenses": ["MIT"], "... |
!AceGenModule rmodel
!nstatv: 58
!ElasticType: NeoHookean
!IsoHardType: TwoIsoHard
!KinHardType: BC_D07mod
!DistHardType: rmodel
!
!Material parameters (16):
!G (Shear modulus)
!K (Bulk modulus)
!Y0 (Initial yield limit)
!k1 (Isotropic hardening constant 1)
!Ri... | {"hexsha": "1014591d5a00f246635105d6c6cb71feea1d29e3", "size": 953744, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "models/MM2021/src/rmodel.f90", "max_stars_repo_name": "kengwit/MaterialModels", "max_stars_repo_head_hexsha": "97de48a2f3ba8f2d605b892a6e0603c9b3feb42c", "max_stars_repo_licenses": ["MIT"], "m... |
!
! CalculiX - A 3-dimensional finite element program
! Copyright (C) 1998-2021 Guido Dhondt
!
! This program is free software; you can redistribute it and/or
! modify it under the terms of the GNU General Public License as
! published by the Free Software Foundation(version 2);
!
!
! ... | {"hexsha": "a300ac55698747c0f4e38b734bf2f1fb38803830", "size": 2483, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ccx_prool/CalculiX/ccx_2.19/src/extrapolateshell.f", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_rep... |
from numpy.core.fromnumeric import transpose
import torch
import torch.nn as nn
import torchvision.models
import torchvision.transforms as tranforms
import cv2
from PIL import Image
import numpy as np
from torchvision.transforms import transforms
import random
import os
gaussain_t = transforms.Compose([
transforms... | {"hexsha": "6694df0e8c5642386c9af46161e981de55a41e50", "size": 2282, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "HGLiu1998/nerf-pytorch", "max_stars_repo_head_hexsha": "3fa976040f1a4ec100dcf6e2ea4695a53f854f6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
module ThreadsXBenchmarks
import JSON
using ArgCheck: @argcheck
using BangBang
using InteractiveUtils: versioninfo
using Logging: current_logger
using ThreadsX
const setup_terminalloggers = """
let Logging =
Base.require(Base.PkgId(Base.UUID(0x56ddb016857b54e1b83ddb4d58db5568), "Logging")),
TerminalLogger... | {"hexsha": "2358e7175abab8b117ef7d365ac241c6596b32b4", "size": 3401, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ThreadsXBenchmarks.jl", "max_stars_repo_name": "tkf/ThreadsXBenchmarks.jl", "max_stars_repo_head_hexsha": "262560c2a39adf642c88112418398d9b736ce1ed", "max_stars_repo_licenses": ["MIT"], "max_st... |
import torch
import os
import random
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
import glob
# ==================================================================#
# == RafD
# ==================================================================#
class RafD(Dataset):
def __init__(se... | {"hexsha": "2d05303466b9f3df7ce0197060f1d71b2da8a36a", "size": 7945, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/RafD.py", "max_stars_repo_name": "BCV-Uniandes/SMIT", "max_stars_repo_head_hexsha": "c1084aa5040ac18a48db7679e050c4ce577b8535", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 41, ... |
"""
@author: hugonnet
get table of glacier number and areas for Table S2
"""
import os
import pandas as pd
import numpy as np
df=pd.read_csv('/home/atom/data/validation/Hugonnet_2020/dhdt_int_HR.csv')
list_sites = list(set(list(df.site)))
nb_gla = []
area_gla = []
for site in list_sites:
nb_gla.append(len(df[... | {"hexsha": "f50b8909e8be63ef06936f2eae5d107c477fe885", "size": 583, "ext": "py", "lang": "Python", "max_stars_repo_path": "dems/hr_dems/get_nb_area_site.py", "max_stars_repo_name": "subond/ww_tvol_study", "max_stars_repo_head_hexsha": "6fbcae251015a7cd49220abbb054914266b3b4a1", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma emeasure_count_space_density_singleton:
assumes "x \<in> A" "has_density M (count_space A) f"
shows "emeasure M {x} = f x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. emeasure M {x} = f x
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. emeasure M {x} = f x
[PROOF STEP]
... | {"llama_tokens": 1325, "file": "Density_Compiler_Density_Predicates", "length": 18} |
# --------------
# Importing header files
import numpy as np
# Path of the file has been stored in variable called 'path'
#New record
new_record = [[50, 9, 4, 1, 0, 0, 40, 0]]
#Code starts here
data = np.genfromtxt(path, delimiter=",", skip_header=1)
census = np.concatenate([data,new_record], axis=... | {"hexsha": "c45f5866801b1ac6fe7f873129b847f289a5f450", "size": 1581, "ext": "py", "lang": "Python", "max_stars_repo_path": "Getting-Started-with-NumPy/code.py", "max_stars_repo_name": "adrunkmechanicalengineer/ga-learner-dsb-repo", "max_stars_repo_head_hexsha": "b8243ad2965845ae00d1a18fe53a144ba47e991b", "max_stars_rep... |
import numpy as np
import tensorflow as tf
from yadlt.models.autoencoders import stacked_denoising_autoencoder
from yadlt.utils import datasets, utilities
# #################### #
# Flags definition #
# #################### #
flags = tf.app.flags
FLAGS = flags.FLAGS
# Global configuration
flags.DEFINE_string('da... | {"hexsha": "57fff753b213e7a9ab24dc77d2fc3f915d3f9692", "size": 8345, "ext": "py", "lang": "Python", "max_stars_repo_path": "cmd_line/autoencoders/run_stacked_autoencoder_supervised.py", "max_stars_repo_name": "akmeraki/deep-learning-", "max_stars_repo_head_hexsha": "ddeb1f2848da7b7bee166ad2152b4afc46bb2086", "max_stars... |
"""Contains example usage of the features in the OpticSim.jl package."""
module Examples
using ..OpticSim
using ..OpticSim.Vis
# using ..OpticSim.GlassCat use this if you want to type SCHOTT.N_BK7 rather than OpticSim.GlassCat.SCHOTT.N_BK7
using StaticArrays
using DataFrames
using Images
using Unitful
using Plots
using... | {"hexsha": "1972dc98567cd7591de5657f30538572c39612d8", "size": 27408, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Examples.jl", "max_stars_repo_name": "friggog/OpticSim.jl", "max_stars_repo_head_hexsha": "8c75d8519591f9311020fbd0a5f3ba4444570a61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import sys
import numpy as np
import optlang
import pytest
from cobra.util.solver import linear_reaction_coefficients
from numpy.testing._private.utils import assert_almost_equal
from optlang.util import solve_with_glpsol
from .load_test_model import build_test_model
@pytest.fixture
def tfa_model():
return buil... | {"hexsha": "c421adf73be333e6ae322e8133fbe59d871504c8", "size": 1268, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_unit/test_tmodel.py", "max_stars_repo_name": "biosustain/multitfa", "max_stars_repo_head_hexsha": "ab5a9bf6c19a94a221c9a137cccfcacdc2c1cb50", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import numpy as np
import networkx as nx
def distance_partition(g):
'''
input: NetworkX graph g. Assumption: g is a maximal connected subgraph of RH Graph.
output: list of sets of nodes: partition
partition[0] = {soln_states}
partition[i] = {states | soln_dis... | {"hexsha": "97db4faa95777a045467384ad5efee3890c303cb", "size": 1193, "ext": "py", "lang": "Python", "max_stars_repo_path": "RushHourPy/numpy_distance_partition.py", "max_stars_repo_name": "crhaithcock/RushHour", "max_stars_repo_head_hexsha": "9c1854dd117e43ec38a6eacf74a8365a6e01f25c", "max_stars_repo_licenses": ["CC0-1... |
import s_discrete
open_locale classical
noncomputable def s_finset {s : ℕ} (s_ne_zero : s ≠ 0) : finset ℝ :=
begin
let numerators : finset ℕ := finset.range s,
let div_by_s_fn : ℕ ↪ ℝ :=
begin
let div_by_s_fn : ℕ → ℝ := λ numerator, ↑numerator / ↑s,
have div_by_s_fn_injective : function.injective ... | {"author": "JOSHCLUNE", "repo": "Keller_reduction", "sha": "dc392b3da352fc1ffcfbecb1d4717d05f5faed4a", "save_path": "github-repos/lean/JOSHCLUNE-Keller_reduction", "path": "github-repos/lean/JOSHCLUNE-Keller_reduction/Keller_reduction-dc392b3da352fc1ffcfbecb1d4717d05f5faed4a/src/no_clique_implies_keller.lean"} |
import os
import copy
import json
import logging
import pymongo
import numpy as np
from torch import set_grad_enabled
from torch import load
from torch import device as D
from pymongo import MongoClient
from collections import defaultdict
from flask import Flask, jsonify, request
from flask_cors import CORS
from poker... | {"hexsha": "d75b77a451554f6e09ffc5961753c9f599ec0c1d", "size": 17554, "ext": "py", "lang": "Python", "max_stars_repo_path": "poker/server.py", "max_stars_repo_name": "MorGriffiths/PokerAI", "max_stars_repo_head_hexsha": "a68400f4918f10dde82574ad19654243c9a65024", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
(* File: Profile_List_Monadic.thy
Copyright 2023 Karlsruhe Institute of Technology (KIT)
*)
\<^marker>\<open>creator "Valentin Springsklee, Karlsruhe Institute of Technology (KIT)"\<close>
section \<open>Refined Profile Evaluation\<close>
theory Profile_List_Monadic
imports "Verified_Voting_Rule_Const... | {"author": "SpringVaS", "repo": "RefinementOfVotingRules", "sha": "a01e44b062fb43e172dff81cffbf941856c977d8", "save_path": "github-repos/isabelle/SpringVaS-RefinementOfVotingRules", "path": "github-repos/isabelle/SpringVaS-RefinementOfVotingRules/RefinementOfVotingRules-a01e44b062fb43e172dff81cffbf941856c977d8/theories... |
# Copyright (c) 2021 J.A. Duffek
# Copyright (c) 2000 D.M. Spink
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any la... | {"hexsha": "c817f9e6b3763519c2e0b3cd9154a9d8582f8d37", "size": 2379, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IGA/nurbs_toolbox/nrbruled.jl", "max_stars_repo_name": "LazyScholar/MagMechFEM_Matlab2Julia", "max_stars_repo_head_hexsha": "367f39b8ddfcee7407bc922df8fecc9a80e1355a", "max_stars_repo_licenses"... |
MODULE bc_variables
implicit none
PRIVATE
PUBLIC :: n_neighbor, fdinfo_send, fdinfo_recv
PUBLIC :: www, sbuf, rbuf
PUBLIC :: TYPE_MAIN, zero, Md
include 'mpif.h'
integer :: n_neighbor(6)
integer,allocatable :: fdinfo_send(:,:,:),fdinfo_recv(:,:,:)
#ifdef _DRSDFT_
integer,parameter :: TYPE_MAIN=MP... | {"hexsha": "e4b7d8cb68b0a27bfba6145298a504e60894b5a1", "size": 687, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/bc_variables.f90", "max_stars_repo_name": "j-iwata/RSDFT", "max_stars_repo_head_hexsha": "2a961b2c8339a49de9bd09f55e7d6a45b6159d2e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
#
# COPYRIGHT:
# The Leginon software is Copyright 2003
# The Scripps Research Institute, La Jolla, CA
# For terms of the license agreement
# see http://ami.scripps.edu/software/leginon-license
#
import calibrator
import calibrationclient
import event
from leginon import leginondata
import node
import gui.... | {"hexsha": "0f3db2400c66e7de37287ddf9dc5f02ba0163950", "size": 8893, "ext": "py", "lang": "Python", "max_stars_repo_path": "leginon/magcalibrator.py", "max_stars_repo_name": "leschzinerlab/myami-3.2-freeHand", "max_stars_repo_head_hexsha": "974b8a48245222de0d9cfb0f433533487ecce60d", "max_stars_repo_licenses": ["MIT"], ... |
function mori = parents(mori)
% variants of an orientation relationship
%
% Syntax
%
% ori_parents = ori_child * inv(mori.parents)
%
% Input
% mori - child to parent @orientation relationship
% ori_child - child orientation
%
% Output
% ori_parents - all possible parent @orientation
%
% Example
% % parent sym... | {"author": "mtex-toolbox", "repo": "mtex", "sha": "f0ce46a720935e9ae8106ef919340534bca1adcb", "save_path": "github-repos/MATLAB/mtex-toolbox-mtex", "path": "github-repos/MATLAB/mtex-toolbox-mtex/mtex-f0ce46a720935e9ae8106ef919340534bca1adcb/geometry/@orientation/parents.m"} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `openclsim` package."""
import pytest
import simpy
import shapely.geometry
import logging
import datetime
import time
import numpy as np
import pandas as pd
from click.testing import CliRunner
from openclsim import core
from openclsim import model
from open... | {"hexsha": "0ef6b76917f96e21da8e24fddc7dcb95a40df6f3", "size": 6682, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_depth_limitation.py", "max_stars_repo_name": "DreamEmulator/OpenCLSim", "max_stars_repo_head_hexsha": "3e35f8d8c308d6f55b8cc3b695603d755b1ee3d4", "max_stars_repo_licenses": ["MIT"], "ma... |
import pandas
import numpy as np
from typing import List
def read_blosum(fn):
""" Read .txt encoded blosum matrix and return as dictionary of lists.
:param fn:
:return: Blosum embedding: keys are amino acids to embed and values are embedding as list
(similarity ot other amino acids).
"""
... | {"hexsha": "6a764feeb0b92483900469caa8d9c56ff956ea66", "size": 4040, "ext": "py", "lang": "Python", "max_stars_repo_path": "tcellmatch/utils/utils_aa_embedding.py", "max_stars_repo_name": "theislab/tcellmatch", "max_stars_repo_head_hexsha": "ddd344e44147f97f35d6a4e7c3c7677981fd177e", "max_stars_repo_licenses": ["BSD-3-... |
import unittest
import tensorflow.contrib.keras as keras
import numpy as np
from vixstructure.models import term_structure_to_spread_price, term_structure_to_spread_price_v2
from vixstructure.models import term_structure_to_single_spread_price
from vixstructure.models import mask_output
from vixstructure.data import ... | {"hexsha": "6b1cbcc8a1c0139dd58c60d7e250b8fee03a3228", "size": 3638, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_vixstructure/test_models.py", "max_stars_repo_name": "leyhline/vix-term-structure", "max_stars_repo_head_hexsha": "b00ad5025bc68280a21e58cecbf5944aee03da97", "max_stars_repo_licenses": ... |
import gdspy
import numpy as np
from ..core.entity import gen_name
from ..utils import Vector, parse_entry, val
TOLERANCE = 1e-9 # for arcs
print("gdspy_version : ", gdspy.__version__)
class GdsModeler:
gds_object_instances = {}
gds_cells = {}
dict_units = {"km": 1.0e3, "m": 1.0, "cm": 1.0e-2, "mm": 1.0... | {"hexsha": "d5e5e6d99fadc5c11d4a2fc801b3719c392305b0", "size": 14596, "ext": "py", "lang": "Python", "max_stars_repo_path": "HFSSdrawpyC12/interfaces/gds_modeler.py", "max_stars_repo_name": "c12qe/HFSSdrawpy", "max_stars_repo_head_hexsha": "ef0d7218fdfe0a9d868deb83f8b50907e99ebd37", "max_stars_repo_licenses": ["MIT"], ... |
SUBROUTINE POLATEV2(IPOPT,IGDTNUMI,IGDTMPLI,IGDTLENI, &
IGDTNUMO,IGDTMPLO,IGDTLENO, &
MI,MO,KM,IBI,LI,UI,VI, &
NO,RLAT,RLON,CROT,SROT,IBO,LO,UO,VO,IRET)
!$$$ SUBPROGRAM DOCUMENTATION BLOCK
!
! $Revision: 74685 $
!
! SUBPROGRAM: POLATEV2 INTERPOLATE VEC... | {"hexsha": "116f6fd032f6c352f0afdecd0e466d45bc58b554", "size": 18290, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "third_party_open/wgrib2/ip2lib_d/polatev2.f90", "max_stars_repo_name": "noaa-ocs-modeling/PaHM", "max_stars_repo_head_hexsha": "e5673c9c80a76a06534acb1542c3c16af2e3cd02", "max_stars_repo_licens... |
#!/usr/bin/env python3.0
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 22 14:38:25 2017
@author: wangronin & Bas van Stein
"""
import pdb
import subprocess, os, sys
from subprocess import STDOUT, check_output
import numpy as np
import time
import gputil as gp
from mipego import mipego
from mipego.Surrogate import ... | {"hexsha": "d4f6462792d7d90bfdad8ac63661dc8725c2deba", "size": 5022, "ext": "py", "lang": "Python", "max_stars_repo_path": "mbarrier_configurator_bi_mult.py", "max_stars_repo_name": "christiaanlamers/sms-mip-ego", "max_stars_repo_head_hexsha": "3601efcb9cfe069e8543d3c29a6102ebbeb9a78c", "max_stars_repo_licenses": ["MIT... |
function[varargout] = vmoment(varargin)
%VMOMENT Central moment over non-NaN elements along a specfied dimension.
%
% Y=VMOMENT(X,N,DIM) finds the Nth central moment of all non-NaN elements
% of X along dimension DIM.
%
% [Y,NUM]=VMOMENT(X... | {"author": "jonathanlilly", "repo": "jLab", "sha": "9f32f63e647209bc1cb81c8713deb954857f1919", "save_path": "github-repos/MATLAB/jonathanlilly-jLab", "path": "github-repos/MATLAB/jonathanlilly-jLab/jLab-9f32f63e647209bc1cb81c8713deb954857f1919/jVarfun/vmoment.m"} |
#!/usr/bin/env python3
'''
Created on Mon Mar 1 02:21:48 2021
@author: skhalil
The script writes the data collected by VNA (.txt) into .s2p format, and
plot the S paramters in frequency domain, and impedance in time domain.
Some manupulation of S parameter indices is done since the data is for 4-point
VNA measurem... | {"hexsha": "31185240577f1a2bef4584140c38aa4959e340a9", "size": 13487, "ext": "py", "lang": "Python", "max_stars_repo_path": "VNA/python/plotVNAFeatures.py", "max_stars_repo_name": "ku-cms/eLink_Instrumentation", "max_stars_repo_head_hexsha": "ce36df795028098b672b0a818f05a9ee8bb75edb", "max_stars_repo_licenses": ["CC0-1... |
import os
import time
import re
import codecs
import itertools
import numpy as np
import pandas as pd
import tensorflow as tf
import gensim
from gensim import utils
from twtokenize import tokenize
import util
from sklearn.model_selection import train_test_split
from ftfy import fix_text
class streamtwElec(object):
... | {"hexsha": "41318b0103d513493c10ede6dfee90332b56ae63", "size": 13569, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/electionprocessor.py", "max_stars_repo_name": "bluemonk482/tdlstm", "max_stars_repo_head_hexsha": "29032c3d116866e3d30b29fdf1a0af605c054d93", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
program decl_all
integer(KIND=8) a
integer :: b, c = -10
real(KIND=4) :: d = -99.34
logical :: e = .true.
real(4) :: arr(10,-1:100), f
integer(2), DIMENSION(1:10,-1:100, 9:10) :: g
end program decl_all
| {"hexsha": "faf8153d05aa4cacce8c7617db50fa406ff0b10f", "size": 204, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/decl/arrays/decl_kind_multi.f90", "max_stars_repo_name": "clementval/fc", "max_stars_repo_head_hexsha": "a5b444963c1b46e4eb34d938d992836d718010f7", "max_stars_repo_licenses": ["BSD-2-Clause"... |
import re
import pandas as pd
import numpy as np
from avaml.aggregatedata import PROBLEMS
from avaml.aggregatedata.download import CAUSES, REG_ENG_V4, REGOBS_CLASSES, _camel_to_snake, REGOBS_SCALARS
def coeff(series):
x = np.arange(series.shape[0])
y = series.values
return np.linalg.lstsq(np.vstack([x, ... | {"hexsha": "e1b133ab8d52041807d2a1951e1421233cd9cf98", "size": 4263, "ext": "py", "lang": "Python", "max_stars_repo_path": "avaml/aggregatedata/time_parameters.py", "max_stars_repo_name": "NVE/avalanche_ml", "max_stars_repo_head_hexsha": "64b2c13484bf603e2ff7b7ac3d989f4c8372ecde", "max_stars_repo_licenses": ["MIT"], "m... |
import sys
import os
import logging
import time
import numpy as np
sys.path.append('../../pyNeuIR/')
from pyNeuIR.utils.preprocess import pad_sequences, load_idfs, load_histograms
from pyNeuIR.utils.pairs_generator import PairsGenerator
from pyNeuIR.models.drmm import DRMM, HingeLoss
from pyNeuIR.configs.drmm_config ... | {"hexsha": "bf715ec4467521f01f2a71247b00867217379657", "size": 5127, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainers/train_drmm_idf.py", "max_stars_repo_name": "felipemoraes/pyNeuIR", "max_stars_repo_head_hexsha": "5256857387c8fe57d28167e42077ad1dcade1983", "max_stars_repo_licenses": ["MIT"], "max_stars... |
// Group E - Excel Visualization
//
// by Scott Sidoli
//
// 6-15-19
//
// Main.cpp
//
// In this work we use the excel visualization functionality to produce spreadsheet output for the
// four batches. It should be noted that some of the includes need to have the path adjusted to work
// other machines. We ... | {"hexsha": "12e6bbf14445639be03d278daae51fd528df2ed1", "size": 5197, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "GroupE/Level9/Level9/Level9Code/Level9Code/Projects/ExcelVisualisation/ExcelVisualisation/Main.cpp", "max_stars_repo_name": "scottsidoli/C-for-Financial-Engineering---Baruch-Pre-MFE-Certificate", "m... |
from sklearn.feature_selection import f_regression
import numpy as np
from sklearn import svm
from sklearn import linear_model
import svmcrossvalidate
from array import array
# Main
#f = open("testdata1.txt")
f = open("testdata.txt")
mylist = f.readlines()
testdata = []
for i in range(0, len(mylist), 1):
... | {"hexsha": "5cda13657ddea8bf2b69ce01f981787daa28537c", "size": 2917, "ext": "py", "lang": "Python", "max_stars_repo_path": "_posts/2017007-07-Feature selection in high dimensional data.py", "max_stars_repo_name": "asabade/asabade-asabade.github.io", "max_stars_repo_head_hexsha": "6828fbef05ccf09cd5b4c3e6d421078e6bebe40... |
import os
import cv2
import time
import random
import traceback
import subprocess
import numpy as np
import concurrent.futures
from PyQt5.QtCore import QObject, pyqtSignal
# QWidget无法在主线程之外被调用，因此构造一个QObject，使用自定义的信号来触发主线程的槽函数
# 具体可以看：https://stackoverflow.com/questions/2104779/qobject-qplaintextedit-multithreading-iss... | {"hexsha": "027643faf475a8038f26b795d3a022bf51e579af", "size": 30828, "ext": "py", "lang": "Python", "max_stars_repo_path": "core.py", "max_stars_repo_name": "oscarcx123/afk-arena-tools", "max_stars_repo_head_hexsha": "6cf4b0165a78049af7bcfffc3bc11e94255bf115", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
import numpy as np
import openmdao.api as om
class KappaComp(om.ExplicitComponent):
r""" Computes the term kappa in the drag equation:
.. math::
C_D = C_{D0} + \kappa C_{L\alpha} \alpha^2
"""
def initialize(self):
self.options.declare('num_nodes', types=int)
def setup(self):
... | {"hexsha": "7c8b0f957832f290668aeb5807c1560d619df0fb", "size": 1431, "ext": "py", "lang": "Python", "max_stars_repo_path": "dymos/examples/min_time_climb/aero/kappa_comp.py", "max_stars_repo_name": "kaushikponnapalli/dymos", "max_stars_repo_head_hexsha": "3fba91d0fc2c0e8460717b1bec80774676287739", "max_stars_repo_licen... |
From iris.algebra Require Import gmap agree auth.
From iris.proofmode Require Import tactics.
From cap_machine Require Export stdpp_extra region_invariants multiple_updates
region_invariants_revocation region_invariants_static sts.
Require Import stdpp.countable.
Import uPred.
Section heap.
Context {Σ:gFunctors... | {"author": "logsem", "repo": "cerise-stack", "sha": "f68111362730aff998798d63c7d6a0a7176eff44", "save_path": "github-repos/coq/logsem-cerise-stack", "path": "github-repos/coq/logsem-cerise-stack/cerise-stack-f68111362730aff998798d63c7d6a0a7176eff44/theories/region_invariants_uninitialized.v"} |
Require Import A1_Plan A2_Orientation A7_Tactics .
Require Import B7_Tactics .
Require Import C1_Distance C7_Tactics .
Require Import F5_Tactics .
Require Import G1_Angles G3_ParticularAngle .
Require Import H1_Triangles .
Require Import I2_Supplement I3_OpposedAngles .
Require Import K3_Tactics .
Require Import L1_Par... | {"author": "coq-contribs", "repo": "euclidean-geometry", "sha": "06838851a5924918d98e5a9c07ffa84021e13af7", "save_path": "github-repos/coq/coq-contribs-euclidean-geometry", "path": "github-repos/coq/coq-contribs-euclidean-geometry/euclidean-geometry-06838851a5924918d98e5a9c07ffa84021e13af7/L4_ParallelogrammAngles.v"} |
#
# Copyright (C) 2019 Luca Pasqualini
# University of Siena - Artificial Intelligence Laboratory - SAILab
#
# Inspired by the work of David Johnston (C) 2017: https://github.com/dj-on-github/sp800_22_tests
#
# NistRng is licensed under a BSD 3-Clause.
#
# You should have received a copy of the license along with this
... | {"hexsha": "8092041eb98a50f5a09dedd8177a1faca0837cd7", "size": 3849, "ext": "py", "lang": "Python", "max_stars_repo_path": "nistrng/sp800_22r1a/test_frequency_within_block.py", "max_stars_repo_name": "LAllen1996/NistRng", "max_stars_repo_head_hexsha": "3f485fa5d9e30786f474cc57b350389e756fc2cb", "max_stars_repo_licenses... |
import matplotlib.pyplot as plt
import numpy as np
import qcodes as qc
from qcodes import (
Measurement,
experiments,
initialise_database,
initialise_or_create_database_at,
load_by_guid,
load_by_run_spec,
load_experiment,
load_last_experiment,
load_or_create_experiment,
... | {"hexsha": "928a7866c3369c3dea9cfb922d991108e752911e", "size": 3245, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/external-frameworks/qcodes/demo.py", "max_stars_repo_name": "qua-platform/qua-libs", "max_stars_repo_head_hexsha": "805a3b1a69980b939b370b3ba09434bc26dc45ec", "max_stars_repo_licenses": [... |
#!/usr/bin/env python
# license removed for brevity
import rospy
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import math as math
from focus_control.msg import status
from nav_msgs.msg import Odometry
from std_msgs.msg import Float32
def desired_input():
rospy.init_node('desired_data... | {"hexsha": "997f6b6d91181cfbf416549bd0a0e22ad2380b4f", "size": 1303, "ext": "py", "lang": "Python", "max_stars_repo_path": "focus_controller_ws/src/focus_control/src/desired_input.py", "max_stars_repo_name": "rajnikant1010/EVAutomation", "max_stars_repo_head_hexsha": "a9e27e8916fd6dfc060dac1496a3b2f327ef6322", "max_sta... |
"""
dot
The dot tool returns the dot product of two arrays.
import numpy
A = numpy.array([ 1, 2 ])
B = numpy.array([ 3, 4 ])
print numpy.dot(A, B) #Output : 11
cross
The cross tool returns the cross product of two arrays.
import numpy
A = numpy.array([ 1, 2 ])
B = numpy.array([ 3, 4 ])
print numpy.cross(A... | {"hexsha": "13a8e5c6c2c5a3cbc986eed9455f2aa6f52228ab", "size": 1012, "ext": "py", "lang": "Python", "max_stars_repo_path": "Hackerrank_codes/Numpy/numpy_dot_cross_product.py", "max_stars_repo_name": "Vyshnavmt94/HackerRankTasks", "max_stars_repo_head_hexsha": "634c71ccf0bea7585498bcd7d63e34d0334b4678", "max_stars_repo_... |
import torch
import torch.nn as nn
from torchvision import transforms
from tqdm import tqdm
import os
import cv2 as cv
import numpy as np
import models
import models_v16
from config import device
# old_model = models.DIMModel()
# new_model = models_v16.DIMModel()
from config import device
from data_gen import data_... | {"hexsha": "efe3acdcd2e02e07161c7b46ab00a6b9f5ee3f5f", "size": 8129, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_model.py", "max_stars_repo_name": "vietnamican/Deep-Image-Matting-PyTorch", "max_stars_repo_head_hexsha": "3e5cbd5c1038e2bc864010b647522024a5ae4c8b", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import cv2
import torch
import torch.nn.functional as F
def to_homogeneous(points):
return np.concatenate([points, np.ones((points.shape[0], 1), dtype=points.dtype)], axis=-1)
def from_homogeneous(points):
return points[:, :-1] / points[:, -1:]
def compute_homography_error(H, H_gt, h, w... | {"hexsha": "bdcb7b3090577f6d49b4d58bcb92cda03c7cbb42", "size": 1175, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/service/utils.py", "max_stars_repo_name": "imelekhov/HNDesc", "max_stars_repo_head_hexsha": "47680371c0758518a66d5a12f29dc9ded6ae790d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
'''
File: HAPT_Dataset.py
Author: Federico Cruciani
Date: 03/10/2019
Version: 1.0
Description:
utility functions to load the
Human Activities and Postural Transitions (HAPT) dataset
'''
import numpy as np
import pandas as pd
from os.path import expanduser
from keras.utils import to_categorical
from multiprocessi... | {"hexsha": "ac304219a24df0676feb382070af801423957772", "size": 9042, "ext": "py", "lang": "Python", "max_stars_repo_path": "IMU/HAPT_Dataset.py", "max_stars_repo_name": "fcruciani/cnn_rf_har", "max_stars_repo_head_hexsha": "2a7da7ab6eafa749ebbeb7d7f880b6f4b0e9aef7", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: dataflow.py
# Author: Qian Ge <geqian1001@gmail.com>
import os
import imageio
import numpy as np
from datetime import datetime
import xml.etree.ElementTree as ET
import src.utils.utils as utils
def identity(inputs, *args):
return inputs
def l... | {"hexsha": "441201a1f28263f679fe8c0e382456588898f883", "size": 5656, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/dataflow.py", "max_stars_repo_name": "conan7882/tmp", "max_stars_repo_head_hexsha": "669161ff20770c5698f2e4f5acc3c366a4cd7151", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, ... |
/*
* This file is a part of HAXX
*
* Copyright (c) 2017 David Williams-Young
* All rights reserved.
*
* See LICENSE.txt
*/
#ifdef BOOST_TEST_MODULE
#undef BOOST_TEST_MODULE
#endif
#define BOOST_NO_MAIN
#include <boost/test/unit_test.hpp>
#include <boost/iterator/counting_iterator.hpp>
#include "h... | {"hexsha": "bc85759d0c74c1c84f68816f59802a5d21a84b92", "size": 1486, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "tests/haxx_ut.hpp", "max_stars_repo_name": "wavefunction91/HAXX", "max_stars_repo_head_hexsha": "e0c282b70ee009a2a01871979f505bcca89713ba", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# 从整张图中裁剪224*224，能裁多少裁多少
import numpy as np
import os
import cv2
import math
from utility import Method
import torch
import torch.nn as nn
import torch.nn.functional as f
import torchvision.transforms as transforms
from nets.models.unet_model import UNet
class ImageFusion(Method):
block_size = 5
pyramid_leve... | {"hexsha": "82c43dd66b0edf931fe62b254fde5c9584af38eb", "size": 25985, "ext": "py", "lang": "Python", "max_stars_repo_path": "second_experiment/crop_all_regions.py", "max_stars_repo_name": "Jacen-Chou/Paste-Video-Classification", "max_stars_repo_head_hexsha": "58af07a343bcd4f95f26a71d6ae9791552b2fe6e", "max_stars_repo_l... |
import numpy as np
# metodos para calcular porosidade
def PhiVel(vellog, velma, velfl): # calculo da porosidade usando o perfil de velocidade
phiv = (vellog - velma) / (velfl - velma)
phiv = np.clip(phiv, 0.0, 1.0)
return phiv
def PhiDens(denslog, densma, densfl): # calculo da porosidade usando o pe... | {"hexsha": "b8ae7bea1f9dc43c3e9199fc9a508a62f03262c7", "size": 715, "ext": "py", "lang": "Python", "max_stars_repo_path": "algo/rockphysics/Porosity.py", "max_stars_repo_name": "giruenf/GRIPy-3", "max_stars_repo_head_hexsha": "ea0f5175bbdd966ca2df956f4667a806d035e532", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
"""
FluPop(f::Union{AbstractString,IO}, sequencetype::Symbol, headerfields;
flulineage=missing, segment=missing, strainfilters = [!is_flu_outlier(flulineage), BioTools.hasdate], separator = '|')
Call `readfastastrains` to read `f`. Store the result in a `FluPop` object.
"""
function FluPop(f::Union{AbstractString,... | {"hexsha": "277448b0df12d2ce8f18c2d3d8306138360659b6", "size": 2573, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IO.jl", "max_stars_repo_name": "PierreBarrat/FluPredictibility", "max_stars_repo_head_hexsha": "2325bc3eba688e9d882622704bf4a9713c36fbc4", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from rdkit import Chem
from mordred import Calculator, descriptors
from Bio.PDB import PDBParser, Dice
from snakemake.shell import shell
import pandas as pd
import numpy as np
import joblib as jl
import warnings
def reset_indices(chain):
for i, residue in enumerate(chain.get_residues(), start=1):
res_id... | {"hexsha": "19377ecd615a215c895d80d57b1706ea0f159f96", "size": 2142, "ext": "py", "lang": "Python", "max_stars_repo_path": "nodes/encodings/qsar/scripts/compute_qsar.py", "max_stars_repo_name": "spaenigs/peptidereactor", "max_stars_repo_head_hexsha": "17efcb993505934f5b9c2d63f5cc040bb244dde9", "max_stars_repo_licenses"... |
# Simulating Molecules using VQE
In this tutorial, we introduce the Variational Quantum Eigensolver (VQE), motivate its use, explain the necessary theory, and demonstrate its implementation in finding the ground state energy of molecules.
## Contents
1. [Introduction](#introduction)
2. [The Variational Method of Quan... | {"hexsha": "54eb8862a81ec6c53d236483a3f546cfd7a85d4e", "size": 62856, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "ch-applications/vqe-molecules.ipynb", "max_stars_repo_name": "ThePrez/qiskit-textbook", "max_stars_repo_head_hexsha": "f76197ae05ed17157e994adce884dd9f6cf62b18", "max_stars_repo_lice... |
"""
@author: Guan'an Wang
@contact: guan.wang0706@gmail.com
"""
import numpy as np
__all__ = ['accuracy']
def accuracy4tensor(output, target, topk=[1]):
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -... | {"hexsha": "cfafe89bb9aaabd30920cd304934abb31915ddf9", "size": 959, "ext": "py", "lang": "Python", "max_stars_repo_path": "lightreid/evaluations/classification.py", "max_stars_repo_name": "nataliamiccini/light-reid", "max_stars_repo_head_hexsha": "1bd823849e4a40c9a17497bb7d7ec6097635f4d6", "max_stars_repo_licenses": ["... |
Some things Im interested in:
University Airport
Cal Aggie Flying Farmers
Friends of the University Airport
VCOA Volvo Show and Swap Meet VCOAs Annual Davis Volvo Show and Swap Meet
Yolo Masonic Lodge
when is the next Yolo swap meet? Daubert
20130926 18:13:16 nbsp Hey, quick question is the name of the group Frien... | {"hexsha": "bbff54785b2f36933fa5f921d7f8acb0b8c7c328", "size": 426, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/AndrewHartman.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env julia
using Dierckx
using Test
using Random: seed!
# Answers 'ans' are from scipy.interpolate,
# generated with genanswers.py script.
# -----------------------------------------------------------------------------
# Spline1D
x = [1., 2., 3.]
y = [0., 2., 4.]
spl = Spline1D(x, y; k=1, s=length(x))
yi ... | {"hexsha": "667e8d6e153f3deab60ef803bdd001579ee455c2", "size": 8941, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "fguevaravas/Dierckx.jl", "max_stars_repo_head_hexsha": "c8d50c1b0d7f9c79bd80370a910491089727e420", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
#!/usr/bin/env python
# A basic tool that downsamples a radar scan by taking one point for each n
import math
import rospy
import tf
import numpy as np
from sensor_msgs.msg import LaserScan
from geometry_msgs.msg import Point
from visualization_msgs.msg import Marker
from geometry_msgs.msg import Twist
class Coll_av... | {"hexsha": "e49ccfe8171c212ba029c02860bd3ebf2f2fa065", "size": 3572, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/coll_avoidance_bug.py", "max_stars_repo_name": "sejavichan/rva_basic_tools", "max_stars_repo_head_hexsha": "138d5df854e5869718cd9577cbc9d47f832bfdd2", "max_stars_repo_licenses": ["MIT"], "... |
using RealNeuralNetworks
using RealNeuralNetworks.Neurons
using RealNeuralNetworks.Neurons.Segments
using Test
const SWC_BIN_PATH = joinpath(@__DIR__, "../asset/77625.swc.bin")
@testset "test Segments" begin
# construct a segment
neuron = Neurons.load_swc_bin( SWC_BIN_PATH )
# get a random segment
pri... | {"hexsha": "9304bde9a194b666b4958e6f6ed50033c1eb1e58", "size": 978, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Segments.jl", "max_stars_repo_name": "UnofficialJuliaMirror/RealNeuralNetworks.jl-4491297b-8966-5840-8cb9-b189d60f3398", "max_stars_repo_head_hexsha": "e6f19dd0515e2105de0ff7c26997d7de64fb3153"... |
# This file holds the definition of the functions pertaining to the
# PersonnelDatabase type.
# The functions of the PersonnelDatabase require the HistoryEntry, History, and
# Personnel types.
requiredTypes = [ "personnel", "personnelDatabase" ]
for reqType in requiredTypes
if !isdefined( Symbol( uppercase( s... | {"hexsha": "e11e0d96f2ab2eec0a9c16548a05aefe4466809c", "size": 16875, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Functions/personnelDatabase.jl", "max_stars_repo_name": "Omazaria/ManpowerPlanning.jl", "max_stars_repo_head_hexsha": "21929fb519942f21afde5300edcda2b00d530dcf", "max_stars_repo_licenses": ["M... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from sklearn.metrics import roc_auc_score,roc_curve,auc,classification_report
from sklearn.svm import SVC,SVR,LinearSVR
from sklearn.linear_model import SGDRegressor
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from sklearn.feature_extr... | {"hexsha": "70b9329fee5fe77c7e5f6a59f5a73edfd9c057b5", "size": 1539, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_swt.py", "max_stars_repo_name": "clarinsi/kas-term", "max_stars_repo_head_hexsha": "5081bb1bab8c89076ec03d980ce14797c8e58d3c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import numpy as np
from dash.dependencies import Input, Output
from plotly import graph_objs as go
from plotly.graph_objs import *
from datetime import datetime as dt
app = dash.Dash(
__name__, meta_tags=[{"nam... | {"hexsha": "38d233c80dae4676935862fd0b4baf1d252c22bd", "size": 17927, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/dash-uber-rides-demo/app.py", "max_stars_repo_name": "ohaanika/dash-sample-apps", "max_stars_repo_head_hexsha": "8a2cfc54548cf729f95a5d0242c058b45dbf4205", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #
# MAIN #
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #
def Larsen_wake(x, r, v_inflow, D_r, C_T, I_a, z_hub):
... | {"hexsha": "0d43f870fc40c669fcae0a19d72b7b60d55ad00f", "size": 2844, "ext": "py", "lang": "Python", "max_stars_repo_path": "floris/utils/models/wakes/Larsen.py", "max_stars_repo_name": "forestriveral/floris", "max_stars_repo_head_hexsha": "02c31e121283ad6ccae987cfa3aa1bf1e4b43014", "max_stars_repo_licenses": ["Apache-2... |
using LaTeXTabulars, Test, LaTeXStrings
# for testing
using LaTeXTabulars: latex_cell
"Normalize whitespace, for more convenient testing."
squash_whitespace(string) = strip(replace(string, r"[ \n\t]+" => " "))
@test squash_whitespace(" something \n with line breaks \n and stuff \n") ==
"something with line ... | {"hexsha": "59a8c98bd76d3d031f1a65193c19946a57ba6cc8", "size": 2953, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaTagBot/LaTeXTabulars.jl", "max_stars_repo_head_hexsha": "d17c10b8d7938f191e437ba59a975df822afa8f7", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
import numpy as np
#import numbers
#import re
import numba
from numba.core import types,cgutils # utils, typing, errors, extending, sigutils
from numba import (
njit,
generated_jit
)
from numba.core.errors import TypingError
from numba.extending import (
overload,
overload_attri... | {"hexsha": "0260312053ad51707f9a4c87a35ee2ab53510606", "size": 53333, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/fractalshades/numpy_utils/numba_xr.py", "max_stars_repo_name": "GBillotey/Fractal-shades", "max_stars_repo_head_hexsha": "99c690cb1114ab7edcbfd9836af585fed2b133e8", "max_stars_repo_licenses":... |
(* (c) Copyright Microsoft Corporation and Inria. All rights reserved. *)
Require Import ssreflect ssrfun ssrbool eqtype ssrnat seq choice fintype.
Require Import finfun path matrix.
Require Import bigop ssralg poly polydiv ssrnum zmodp div ssrint.
Require Import polyorder polyrcf interval polyXY.
Require Import qe_rcf... | {"author": "math-comp", "repo": "mathcomp-history-before-github", "sha": "19ef9415e2b509a2327f9ef704268ce8570b607c", "save_path": "github-repos/coq/math-comp-mathcomp-history-before-github", "path": "github-repos/coq/math-comp-mathcomp-history-before-github/mathcomp-history-before-github-19ef9415e2b509a2327f9ef704268ce... |
import cv2
import os
import numpy as np
import faceRecognition as fr
import xlrd
#This module takes images stored in diskand performs face recognition
test_img=cv2.imread('TestImages/a.jpg')#test_img path
faces_detected,gray_img=fr.faceDetection(test_img)
print("faces_detected:",faces_detected)
#Comment belows lin... | {"hexsha": "da832066f308cbc93327b8978d9a42955bb79fdb", "size": 1702, "ext": "py", "lang": "Python", "max_stars_repo_path": "tester.py", "max_stars_repo_name": "tarungarg2207/Advance-Security-System", "max_stars_repo_head_hexsha": "fb7f624855ee880e26079b4f09f91d187cc4c79b", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import tensorflow as tf
import numpy as np
import deepirl.utils.vizualization as v
import time
w = 512
h = 512
batch_size = 1024
noise_dim = 32
layers = 10
num_hidden = 64
stddev = 1.0
use_color = False
position_scale = 1.2
activation = tf.nn.tanh
def get_pos(x: int, y: int, w: int, h: int):
x = position_scale *... | {"hexsha": "06735e8c49b41b54aaf8e2e5dfd4ef202f543f1d", "size": 2115, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepirl/playground/drawer.py", "max_stars_repo_name": "bshishov/DeepIRL", "max_stars_repo_head_hexsha": "5f02499b364a960bd54d0741282cccf1582cd5d0", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 18 14:27:27 2019
@author: avelinojaver
"""
import math
import random
from pathlib import Path
import numpy as np
import cv2
from torch.utils.data import Dataset
#%%
class MergeFlow(Dataset):
def __init__(self,
ch1_dir = None,
... | {"hexsha": "867b82f89cc56feff947a3848617e36a15e3d1b7", "size": 8643, "ext": "py", "lang": "Python", "max_stars_repo_path": "cell_localization/flow/_old/flow_merged.py", "max_stars_repo_name": "ver228/cell_localization", "max_stars_repo_head_hexsha": "9739afc7a54f730056945c0a6380896747235099", "max_stars_repo_licenses":... |
"""Define the types of Game of Life (GoL) initial conditions"""
import numpy as np
def loaf():
"""Create the canvas with the initial values of the loaf"""
canvas = np.zeros(shape=(6, 6), dtype=int, order='F')
# make the loaf
canvas[1][2] = 1
canvas[1][3] = 1
canvas[2][4] = 1
canvas[3][4] ... | {"hexsha": "ae55c1828f7dc0edaa6ca5521de4d5ea9970bdaf", "size": 1467, "ext": "py", "lang": "Python", "max_stars_repo_path": "PSet1/P4/classes/types.py", "max_stars_repo_name": "alpha-leo/ComputationalPhysics-Fall2020", "max_stars_repo_head_hexsha": "737769d4a046b4ecea885cafeaf26e26075f7320", "max_stars_repo_licenses": [... |
"""Dynamic Object Models
AgentDoubleInt2D : Double Integrator Model in 2D
state: x,y,xdot,ydot
AgentSE2 : SE2 Model
state x,y,theta
Agent2DFixedPath : Model with a pre-defined path
Agent_InfoPlanner : Model from the InfoPlanner repository
SE2Dynamics : update dynamics function with a c... | {"hexsha": "f977292d970601aa72d787228869263388aa850c", "size": 7534, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ray/rllib/RL/envs/target_tracking/agent_models.py", "max_stars_repo_name": "christopher-hsu/ray", "max_stars_repo_head_hexsha": "abe84b596253411607a91b3a44c135f5e9ac6ac7", "max_stars_repo_l... |
import sys
sys.path.append('..')
import numpy as np
from truefalsepython import fast_sample
random_arr = np.random.random(100)
random_probs = random_arr/random_arr.sum()
%timeit np.random.choice(random_arr, 1, p = random_probs)
# 72.9 µs ± 7.18 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit f... | {"hexsha": "b92efe78fe34edb3c12ee7ec35ce5026ad701d15", "size": 443, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/fast_sample.py", "max_stars_repo_name": "PasaOpasen/true-false-python", "max_stars_repo_head_hexsha": "a26ddd7c61471dd4d60d8ece00c6f03750bb7dca", "max_stars_repo_licenses": ["MIT"], "max_star... |
from flask import Flask, request, render_template, Markup, url_for
import cPickle as pickle
import json
import socket
import requests
import pandas as pd
import numpy as np
import pymongo as mdb
import operator
app = Flask(__name__)
# helper function
@app.context_processor
def add_vars_to_context():
return dict(s... | {"hexsha": "8586bc1f09339ea5bad817a622ca9a0ee12bcd50", "size": 6265, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/app.py", "max_stars_repo_name": "rob-dalton/amazon-product-recommender", "max_stars_repo_head_hexsha": "eca9b376423fdac9fdecb6dee551673d54a80aba", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# coding: utf-8
import os
import csv
import time
import random
import cPickle
import pandas as pd
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import pylab
import matplotlib.pyplot as plt
#from IPython.display import Image as IPImage
from sklearn import preprocessing
#normalized_data, norm = sklearn... | {"hexsha": "0ca04abb34f74e7b480b6e2abccfe3519995fc7d", "size": 30247, "ext": "py", "lang": "Python", "max_stars_repo_path": "GRCNN.py", "max_stars_repo_name": "subin2/Grouped-Convolutional-Neural-Networks", "max_stars_repo_head_hexsha": "4cca3e795e13eded1593d7c2dd9355338901c839", "max_stars_repo_licenses": ["MIT"], "ma... |
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from astropy.io import ascii
from uncertainties import ufloat
import uncertainties.unumpy as unp
Z=[29,29,29,30,32,35,37,38,40,41,79,79]
Z=np.asarray(Z)
E=[8.048,8.028,8.905,9.65,11.1031,13.4737,15.1997,16.1046,17.9976,18.9856,13.73... | {"hexsha": "3b65fef7d568b3407514b5696cfa131b76670b21", "size": 3782, "ext": "py", "lang": "Python", "max_stars_repo_path": "V602/plot.py", "max_stars_repo_name": "nsalewski/laboratory", "max_stars_repo_head_hexsha": "e30d187a3f5227d5e228b0132c3de4d426d85ffb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
# Copyright 2019 GreenWaves Technologies, SAS
# 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... | {"hexsha": "142b0fe12a02767598e9f7556afa33040dbcae17", "size": 2180, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/nntool/execution/kernels/utils.py", "max_stars_repo_name": "danieldennett/gap_sdk", "max_stars_repo_head_hexsha": "5667c899025a3a152dbf91e5c18e5b3e422d4ea6", "max_stars_repo_licenses": ["Apa... |
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