text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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\section{Defining Engineering Design}
\newthought{Over the years}, there has been many attempts to define Engineering Design.
This has been a non-trivial task and is mainly due to the increasingly complex and multi-disciplinary nature of design.
An element that continue to increase over the years with the rate of te... | {"hexsha": "0b53bd608532bb87fdc5af943520cc7d7c03a305", "size": 1427, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "02_engineering_design/section.tex", "max_stars_repo_name": "JamesGopsill/DesignAndMakeCourseNotes", "max_stars_repo_head_hexsha": "ac52e37b77ad2200088677efef5356b8ecaac990", "max_stars_repo_licenses... |
function chanind = selectchannels(this, channels)
% Method for getting channel indices based on labels and/or types
% FORMAT res = selectchannels(this, label)
% this - MEEG object
% channels - string or cell array of labels that may also include
% 'all', or types ('EEG', 'MEG' etc.)
%
% res ... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/@meeg/selectchannels.m"} |
mutable struct DQMCStack{
GreensElType <: Number,
HoppingElType <: Number,
GreensMatType <: AbstractArray{GreensElType},
HoppingMatType <: AbstractArray{HoppingElType},
InteractionMatType <: AbstractArray
} <: AbstractDQMCStack
u_stack::Vector{GreensMatType}
d_stac... | {"hexsha": "55ea4ab9b810aedd0b31c726734e4ac29de7471c", "size": 24077, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/flavors/DQMC/stack.jl", "max_stars_repo_name": "crstnbr/MonteCarlo.jl", "max_stars_repo_head_hexsha": "1c3a678a9991ce9770222e658aee358d39f3693e", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import argparse, time, os
from networks import create_model, define_net
import numpy as np
import torch
import imageio
from data import common
import options.options as option
from utils import util
from solvers import SRSolver
from data import create_dataloader
from data import create_dataset
networks= {
"whic... | {"hexsha": "aed3e895c483e9593e2df1ee3ddbbce6f874647e", "size": 1642, "ext": "py", "lang": "Python", "max_stars_repo_path": "run.py", "max_stars_repo_name": "thekevinscott/SRFBN_CVPR19", "max_stars_repo_head_hexsha": "be4bd02e77c0f40419cc8d885febbec517ef9234", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
module Stat
import Gadfly
import StatsBase
import Contour
using Colors
using Compat
using Compose
using DataArrays
using DataStructures
using Distributions
using Hexagons
using Loess
using CoupledFields # It is registered in METADATA.jl
using IndirectArrays
import Gadfly: Scale, Coord, input_aesthetics, output_aesthe... | {"hexsha": "f176246a10d67b81a7338f72000ac74af8bd3f12", "size": 61450, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/statistics.jl", "max_stars_repo_name": "Mattriks/Gadfly.jl", "max_stars_repo_head_hexsha": "d31554bba68194793e1c6e5afbda57111433d1c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
//
// Created by Alex Beccaro on 18/01/18.
//
#define BOOST_TEST_DYN_LINK
#include <boost/test/unit_test.hpp>
#include "../../src/problems/1-50/18/problem18.hpp"
BOOST_AUTO_TEST_SUITE( Problem18 )
BOOST_AUTO_TEST_CASE( Solution ) {
auto res = problems::problem18::solve();
BOOST_CHECK_EQUAL(res, 1... | {"hexsha": "047b6c5abfed54160fc7cb2eea5f4f733b33421e", "size": 360, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/1-50/test_problem18.cpp", "max_stars_repo_name": "abeccaro/project-euler", "max_stars_repo_head_hexsha": "c3b124bb973dc3a1cf29e8c96c3e70c8816d5fa3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
Bitmap manipulation
Friedrich Schotte, Hun Sun Cho, Dec 2008 - 5 Sep 2009
"""
version = "1.2"
def grow_bitmap(mask,count=1):
"""Extents the area where the pixels have to value 1 by one pixel in each
direction, including diagnonal by the number of pixels given by the
parameter 'count'.
If count is ... | {"hexsha": "81e7ef85695e772590d68f6adf6a82d89edff6a0", "size": 2139, "ext": "py", "lang": "Python", "max_stars_repo_path": "grow_bitmap.py", "max_stars_repo_name": "bopopescu/Lauecollect", "max_stars_repo_head_hexsha": "60ae2b05ea8596ba0decf426e37aeaca0bc8b6be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# Empirical estimation of CDF and PDF
## Empirical CDF
struct ECDF{T <: AbstractVector{<:Real}}
sorted_values::T
end
function (ecdf::ECDF)(x::Real)
searchsortedlast(ecdf.sorted_values, x) / length(ecdf.sorted_values)
end
function (ecdf::ECDF)(v::RealVector)
ord = sortperm(v)
m = length(v)
r = s... | {"hexsha": "3e9c31ef58142b7bdff1b72693410007a6bc6b06", "size": 1326, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/empirical.jl", "max_stars_repo_name": "jgoldfar/StatsBase.jl", "max_stars_repo_head_hexsha": "f4567cb9f5a8bd00c146eadae781bdf1b467938a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
# rvt.blend quick TEST
import rvt.default
import rvt.vis
import rvt.blend
import numpy as np
# test blend combination archeological (VAT), general
#####
# manual blending, custom raster numpy arrays
# if you create_layer and don't input image or image_path then if vis_method is correct it automatically
# calculates... | {"hexsha": "fbe15b1833219f41cabaf91d0387af58dafa54b1", "size": 4291, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_blend.py", "max_stars_repo_name": "H4estu/RVT_py", "max_stars_repo_head_hexsha": "6dc408f495c455c2b5d88f552f22d4496d288fb2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12, ... |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 26 18:53:22 2020
@author: Lenovo
"""
"""
Horizontal analysis of Limit Order Book:
Given an initial order book status, the Horizontal Tests will simulate the first arrived order 10000 or even more times,
and verify the distribution of each type of order.
"""
im... | {"hexsha": "5063fae5b967016ce06b15efff5da14aff5e821b", "size": 15151, "ext": "py", "lang": "Python", "max_stars_repo_path": "ZIAgent/HorizontalAnalysis.py", "max_stars_repo_name": "HKUST-DB-Capstone2020/Market-Agent-Simulation", "max_stars_repo_head_hexsha": "227514a118d6ebcdc81f9948b1a21af71492ca40", "max_stars_repo_l... |
# Import packages
from datetime import date
from pathlib import Path
import numpy as np
import pandas as pd
# Import data
import_fp = Path("data/main/cornelia-raw.csv")
dataset = pd.read_csv(import_fp, encoding="utf-8", sep=";")
# Fix pseudo-NaN values
dataset.loc[:, "actor_first_name"] = (dataset.loc[:, "actor_firs... | {"hexsha": "1e1fd69073613b6a97b87b75d5e79a40d145cd17", "size": 3015, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data-cleaning/clean-main.py", "max_stars_repo_name": "ejgenc/data-analysis_cornelia", "max_stars_repo_head_hexsha": "e1c855aec786427ad18a28274895719fab7987ef", "max_stars_repo_licenses": ["MIT... |
import sys
import torch
import torch.nn as nn
from torchvision import transforms
sys.path.append("/opt/cocoapi/PythonAPI")
from data_loader_wrapper import DataLoaderWrapper
from model import EncoderCNN, DecoderRNN
import math
## TODO #1: Select appropriate values for the Python variables below.
batch_size = 64
vocab... | {"hexsha": "3e9eea5af9fa34bdc1976e261e58a39ee6266894", "size": 8584, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/2_Training.py", "max_stars_repo_name": "hogansung/udacity-computer-vision-nanodegree-program-project-2", "max_stars_repo_head_hexsha": "3c9cfb42532f5149003017acb9c950c0d2d7499b", "max_stars_re... |
"""
$(SIGNATURES)
Update flow variables with finite volume formulation
"""
function step!(fwL::X, w::X, prim::AV{X}, fwR::X, a, dx, RES, AVG) where {X<:FN} # scalar
#--- store W^n and calculate H^n,\tau^n ---#
w_old = deepcopy(w)
#--- update W^{n+1} ---#
w += (fwL - fwR) / dx
prim .= conserve_prim... | {"hexsha": "2ee637199f48f9c55982e9e77d09e10ee382506a", "size": 33527, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Solver/solver_step.jl", "max_stars_repo_name": "vavrines/KineticBase.jl", "max_stars_repo_head_hexsha": "d00cefe073346a3bab3b4d3577a95631e320dc9f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
""" gdsfactory loads a configuration from 3 files, high priority overwrites low priority:
1. A config.yml found in the current working directory (highest priority)
2. ~/.gdsfactory/config.yml specific for the machine
3. the default_config in pp/config.py (lowest priority)
`CONFIG` has all the paths that we do not car... | {"hexsha": "0ae30e1fd45f8b967371f421b2e0466964b97466", "size": 5359, "ext": "py", "lang": "Python", "max_stars_repo_path": "pp/config.py", "max_stars_repo_name": "smartalecH/gdsfactory", "max_stars_repo_head_hexsha": "66dfbf740704f1a6155f4812a1d9483ccf5c116c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
function elliptic_ea ( a )
!*****************************************************************************80
!
!! ELLIPTIC_EA evaluates the complete elliptic integral E(A).
!
! Discussion:
!
! The value is computed using Carlson elliptic integrals:
!
! E(a) = RF ( 0, 1-sin^2(a), 1 ) - 1/3 sin^2(a) RD ( 0, 1-si... | {"hexsha": "b028667d4b1406f5438f25072ac07aec5faff95a", "size": 127307, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "utils/libraries/elliptic_integral.f90", "max_stars_repo_name": "Cirdans-Home/psfun", "max_stars_repo_head_hexsha": "1583d2715b0cadf6cd673b3f522b9699746cef3f", "max_stars_repo_licenses": ["BSD-... |
from config.config import models_dir
from core import utils
import re
import numpy as np
from functools import lru_cache
from core.representations import BagOfEntities
class Encoder:
"""Base class for making objects that encode one form of data into
another form, e.g., text to tokens or text to vectors.
"""
def... | {"hexsha": "fdea54bd4d465a9aaf0fe1e3c53395cc8328fd30", "size": 8254, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/encoders.py", "max_stars_repo_name": "vsvarunsharma10/pqai", "max_stars_repo_head_hexsha": "3ef1351fbc39671916517917de9074a62b092eef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
from micropsi_core.nodenet.stepoperators import Propagate, Calculate
import numpy as np
from micropsi_core.nodenet.theano_engine.theano_node import *
from micropsi_core.nodenet.theano_engine.theano_definitions import *
class TheanoPropagate(Propagate):
"""
theano implementation of the Propagate operator.... | {"hexsha": "56adbb5e461aa7b69283b8309d7c7e35d48394f2", "size": 2570, "ext": "py", "lang": "Python", "max_stars_repo_path": "micropsi_core/nodenet/theano_engine/theano_stepoperators.py", "max_stars_repo_name": "joschabach/micropsi2", "max_stars_repo_head_hexsha": "74a2642d20da9da1d64acc5e4c11aeabee192a27", "max_stars_re... |
import csv
import logging
import os
import cv2
import numpy as np
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# raw image properties
SUB_IMAGE_SIZE = (512, 512)
BEE_OBJECT_SIZES = {1: (20, 35), # bee class is labeled 1
2: (20, 20)} # butt class is ... | {"hexsha": "4f7b5679bebe6ec67085cfe20dd1b197a5979086", "size": 5099, "ext": "py", "lang": "Python", "max_stars_repo_path": "segmentation/bee_dataset.py", "max_stars_repo_name": "mlubega/DenseObjectDetection", "max_stars_repo_head_hexsha": "004ebf3d76bd66fcaa7f13ce3acafbf336927ed5", "max_stars_repo_licenses": ["MIT"], "... |
(** Generated by coq-of-ocaml *)
Require Import OCaml.OCaml.
Local Set Primitive Projections.
Local Open Scope string_scope.
Local Open Scope Z_scope.
Local Open Scope type_scope.
Import ListNotations.
Unset Positivity Checking.
Unset Guard Checking.
Inductive nat : Set :=
| O : nat
| S : nat -> nat.
Inductive natu... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal4_theorem0_39_lem/goal4conj72_coqof... |
\section{Background} \label{sec:background}
In this section, we describe the different systems and former research necessary to
a good understanding of this thesis.
First, we introduce Spark.
Second, we discuss possible \textsc{WCOJ} join algorithms and argue why we
choose to use Leapfrog Triejoin as basis for our thes... | {"hexsha": "86b7dc4a48bb4ed0441d98463eeb16edd15e27ef", "size": 81455, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "background.tex", "max_stars_repo_name": "PerFuchs/master-thesis", "max_stars_repo_head_hexsha": "85386c266fecf72348114bcbafeeb896a9e74601", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import logging
import cv2
import numpy as np
COLOR_RED = (0, 0, 255)
COLOR_GREEN = (0, 255, 0)
COLOR_BLUE = (255, 0, 0)
COLOR_BLACK = (0, 0, 0)
COLOR_DARK_GREEN = (34, 139, 34)
COLOR_YELLOW = (0, 255, 255)
def draw(image, pred_boxes_scores, gt_boxes, pred_landmarks, gt_landmarks):
pred_boxes = pred_boxes_scores... | {"hexsha": "9dadd592f674d9b58fcbb9d5fb225127291e5a04", "size": 1668, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/image_utils.py", "max_stars_repo_name": "piginzoo/Pytorch_Retinaface", "max_stars_repo_head_hexsha": "3bb028e078a36f5cf90f67dc1de313d2472ee464", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Author: Dirk Eilander (contact: dirk.eilancer@vu.nl)
# Created: Nov-2017
from .dd_ops import LDD, NextXY
import rasterio
import numpy as np
def read_dd_rasterio(fn, ddtype='ldd', **ddkwargs):
with rasterio.open(fn, 'r') as src:
if ddtype == 'ldd':
... | {"hexsha": "749340b496e9a723712d91fd08c885cd7b19a6ec", "size": 1578, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/1-prepare/cmftools/nb/nb_io.py", "max_stars_repo_name": "DirkEilander/compound_hotspots", "max_stars_repo_head_hexsha": "f9d7960633be80e8e24d2f2563df367cc3f060c6", "max_stars_repo_licenses": [... |
import sys
import os
import cv2
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])
import numpy as np
import mxnet as mx
input_path = sys.argv[1].rstrip(os.sep)
mod = mx.mod.Module.load('mnist_lenet', 35, context=mx.gpu(2))
mod.bind(
data_shapes=[('data', (1, 1, 28, 28))],
for_training=... | {"hexsha": "767a98e17e6a6605329357f883f3e594e25ed98b", "size": 801, "ext": "py", "lang": "Python", "max_stars_repo_path": "chap8/mxnet/recognize_digit.py", "max_stars_repo_name": "wang420349864/dlcv_for_beginners", "max_stars_repo_head_hexsha": "080c7d3bbb4a68e4fb79e33231ccc666ada16dcc", "max_stars_repo_licenses": ["BS... |
#
# Date created: 2022-03-14
# Author: aradclif
#
#
############################################################################################
function sizeblock(N::Int)
block = Expr(:block)
for d = 1:N
ex = Expr(:(=), Symbol(:D_, d), Expr(:call, :size, :A, d))
push!(block.args, ex)
end
... | {"hexsha": "d088025761b786a04f7965b5425efef71ba6be9a", "size": 14299, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lvfindmax_v1.jl", "max_stars_repo_name": "andrewjradcliffe/VectorizedArgMinMax.jl", "max_stars_repo_head_hexsha": "8c5789142b95e5cb348d3970c08341f3bd818e0d", "max_stars_repo_licenses": ["MIT"]... |
"""
A collection of IO related functions to support
1. reading scale factors
2. reading optimal scale factors
3. transforming scale factors
Author : Mike Stanley
Created : May 12, 2020
Modified : March 29, 2022
================================================================================
"""
from glob import gl... | {"hexsha": "f98d0988f4f989dd7d3f97f4565928d908dc2505", "size": 22245, "ext": "py", "lang": "Python", "max_stars_repo_path": "carbonfluxtools/io.py", "max_stars_repo_name": "mcstanle/carbonfluxtools", "max_stars_repo_head_hexsha": "9cb428a16ebb0b96e3cc08c3fdbac3e71751fbfc", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import torch
import networks
from string import ascii_letters, punctuation, digits
alphabet = ascii_letters + punctuation + digits + " "
def to_ord(c, all_chars, alphabet):
if not (c in all_chars):
alphabet += c
all_chars[c] = all_chars["counter"]
all_chars["counter"] = ... | {"hexsha": "fe84096702c23ad222b834b8fa048071e86d7a42", "size": 1622, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference.py", "max_stars_repo_name": "imvladikon/string-embed", "max_stars_repo_head_hexsha": "49e5ab0ada37b497dac51974aff16eeac65627a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')
warnings.filterwarnings("ignore")
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
import networkx as nx
from sklearn.linear_model import LogisticRegression
from ge import LINE
from ge.classify import read_node_label, C... | {"hexsha": "74179540ca45752e69da5991b744580a68d2c8d7", "size": 1200, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/line_classification.py", "max_stars_repo_name": "237085795/GraphEmbedding_annotion", "max_stars_repo_head_hexsha": "973ee7dad5e65585407800720e4beb7137687a0e", "max_stars_repo_licenses": ["MIT... |
## ---------------------------------------------------------------------------- ##
# 08/03/2015 #
# #
# www.henesis.eu ... | {"hexsha": "b776e75457dfca6c2e5420e1ed117a69bb2f2d00", "size": 10951, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_pyerg.py", "max_stars_repo_name": "henesissrl/pyerg", "max_stars_repo_head_hexsha": "7793257a46fc083c387c4d30b8620173f0362abc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
# Common libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Restrict minor warnings
import warnings
warnings.filterwarnings('ignore')
# Import test and train data
df_train = pd.read_csv('../input/train.csv')
df_Test = pd.read_csv('../input/test.csv')
df_test = df_... | {"hexsha": "b8c6376e51944fb83146db60cd199788362ea62d", "size": 11541, "ext": "py", "lang": "Python", "max_stars_repo_path": "kaggle/forest-cover-type-prediction/script_38.py", "max_stars_repo_name": "josepablocam/janus-public", "max_stars_repo_head_hexsha": "4713092b27d02386bdb408213d8edc0dc5859eec", "max_stars_repo_li... |
from sys import argv
import numpy as np
import re
import itertools
import scipy.misc
import os
import array
from scipy.misc import imsave
class asmprocessor(object):
'''
Generate features from ASM files
'''
# helper methods
def asmToPng(self, asmFile, loc): # djaihfaig.asm
f = open(asmFile)... | {"hexsha": "16e3e335fbd9af42bd52a7721d9f6783a930f774", "size": 1879, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocess/AsmPreProcessing.py", "max_stars_repo_name": "ankit-vaghela30/Distributed-Malware-classification", "max_stars_repo_head_hexsha": "5479b5a9590c1ec436d937b287b7ffe08ff568b1", "max_sta... |
"""
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import pathlib
import sys
from collections import defaultdict
import numpy as np
import torch
from torch.utils.data import DataLoader
from ... | {"hexsha": "534b353c8b3d08ed38463e0dd11087335dcd4455", "size": 5699, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/unet/run_unet.py", "max_stars_repo_name": "vaibhavsaxena11/fastMRI", "max_stars_repo_head_hexsha": "9e1f1574ce25ee56e4c4e35c3b916119d4259ec5", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import sys
print(sys.version)
from mpi4py import MPI
comm = MPI.COMM_WORLD
num_procs = comm.Get_size()
rank = comm.Get_rank()
from sklearn.multiclass import OneVsRestClassifier
rank_i = rank%3
import gc
import pandas as pd
import numpy as np
import os
from sklearn.model_selection import GridSearchCV, cross_val_score, ... | {"hexsha": "4af6de3bd5a29c812e249ab17d97523b5c9c15fa", "size": 4810, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/2-cluster-ml-scripts/cv_svm_drivers.py", "max_stars_repo_name": "mcallaghan/regional-impacts-map", "max_stars_repo_head_hexsha": "10b95189255e5f626f94bc140ed16b7bcd7ca33e", "max_stars_rep... |
#!/usr/bin/env python
# -*- coding: utf-8; -*-
# Copyright (c) 2020, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.core.display import displ... | {"hexsha": "04518e986c99553cc048008e586d5a08ac2c2d66", "size": 27831, "ext": "py", "lang": "Python", "max_stars_repo_path": "ads/evaluations/evaluator.py", "max_stars_repo_name": "oracle/accelerated-data-science", "max_stars_repo_head_hexsha": "d594ed0c8c1365daf4cf9e860daebc760fa9a24b", "max_stars_repo_licenses": ["UPL... |
"""
Interpolation for BASTA: Across/between tracks
"""
import os
import sys
import copy
import h5py
import numpy as np
from tqdm import tqdm
from scipy import spatial
from scipy import interpolate
from basta import sobol_numbers
from basta import interpolation_helpers as ih
from basta import plot_interp as ip
# ====... | {"hexsha": "5de7b835ebe51944c4573c9759dfb24c36ab9d9d", "size": 23458, "ext": "py", "lang": "Python", "max_stars_repo_path": "basta/interpolation_across.py", "max_stars_repo_name": "BASTAcode/BASTA", "max_stars_repo_head_hexsha": "6de8b8b866787d6745c4e77378bb94e0bab97090", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
! { dg-do run }
!
! PR fortran/38669
! Loop bounds temporaries used before being defined for elemental subroutines
!
! Original testcase by Harald Anlauf <anlauf@gmx.de>
program gfcbu84_main
implicit none
integer :: jplev, k_lev
integer :: p(42)
real :: r(42)
integer, pointer :: q(:)
jplev = 4... | {"hexsha": "7c7875bbfcd3945e89e407a7bb144fd0c69a1ad8", "size": 852, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/CompileTests/Fortran_tests/gfortranTestSuite/gfortran.dg/elemental_subroutine_7.f90", "max_stars_repo_name": "maurizioabba/rose", "max_stars_repo_head_hexsha": "7597292cf14da292bdb9a4ef5730... |
import os
os.environ['THEANO_FLAGS'] = 'device=gpu'
import numpy as np
import theano as th
import theano.tensor as tt
rng = np.random
N = 400 # training sample size
feats = 784 # number of input variables
## generate a dataset: D = (input_values, target_class)
D = (rng.randn(N, feats), rng.randint(size=... | {"hexsha": "ee1eaaa08b44d93087aafe499a7e8a7947483838", "size": 1264, "ext": "py", "lang": "Python", "max_stars_repo_path": "logistic.regression.py", "max_stars_repo_name": "metapsycho/learning.theano", "max_stars_repo_head_hexsha": "3600083a6a9dbc76615aa1e650c38ff8f83653cf", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import brica
import numpy as np
import cv2
import math
from oculoenv.geom import Matrix4
class HP(object):
""" Hippocampal formation module.
Create allocentric panel image.
"""
def __init__(self):
self.timing = brica.Timing(2, 1, 0)
# Allocentric panel map image
self.map_... | {"hexsha": "e45e981c4366368b072ad9463eb0986999333acd", "size": 8867, "ext": "py", "lang": "Python", "max_stars_repo_path": "application/functions/hp.py", "max_stars_repo_name": "miyosuda/oculomotor", "max_stars_repo_head_hexsha": "78e7ec61a808d058116c69bff1ea71ecf117c126", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
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=PHASES,print_grid_who=MASTER, &
max_eq=500, mg_cycles=5, &
reftype=H_ADAPTIVE)
call phaml_destroy(soln)
end program phaml_mas... | {"hexsha": "27e50d7114297424c7d3dcf32225c589469c942e", "size": 324, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "testdir/test_refinement/test02.f90", "max_stars_repo_name": "qsnake/phaml", "max_stars_repo_head_hexsha": "8925b4c32657bbd9f81cd5f8f9d6739151c66fec", "max_stars_repo_licenses": ["mpich2"], "max_s... |
"""
Copyright 2020 The OneFlow 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 applicable law or agr... | {"hexsha": "73a545bf284bada93a9c22257df929bd444101d3", "size": 7577, "ext": "py", "lang": "Python", "max_stars_repo_path": "oneflow/python/test/ops/test_binary_elementwise_ops.py", "max_stars_repo_name": "MaoXianXin/oneflow", "max_stars_repo_head_hexsha": "6caa52f3c5ba11a1d67f183bac4c1559b2a58ef5", "max_stars_repo_lice... |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
... | {"hexsha": "f69c000a36e679d888035f29675813f39879fb5b", "size": 1443, "ext": "py", "lang": "Python", "max_stars_repo_path": "relancer-exp/original_notebooks/sammy123_lower-back-pain-symptoms-dataset/logistic-regression.py", "max_stars_repo_name": "Chenguang-Zhu/relancer", "max_stars_repo_head_hexsha": "bf1a175b77b7da4cf... |
"""Utility functions"""
import os
import numpy as np
TRANSFER_COST = 2 * 60 # Default transfer time is 2 minutes
LARGE_NUMBER = 2147483647 # Earliest arrival time at start of algorithm
TRANSFER_TRIP = None
def mkdir_if_not_exists(name: str) -> None:
"""Create directory if not exists"""
if not os.path.exis... | {"hexsha": "e1a7cace872f3deebd5b268f671323710574589e", "size": 1287, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyraptor/util.py", "max_stars_repo_name": "yfredrix/pyraptor", "max_stars_repo_head_hexsha": "a00b1d5576cd4126611483409e293d283cb7917d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "... |
Require Import compcert.lib.Coqlib.
Require Import VST.msl.Coqlib2.
(* deliberately imported here *)
Require Import Coq.Wellfounded.Inclusion.
Require Import Coq.Wellfounded.Inverse_Image.
(* ssreflect *)
From mathcomp.ssreflect Require Import ssreflect ssrbool ssrnat ssrfun eqtype seq fintype finfun.
Set Implicit A... | {"author": "ildyria", "repo": "coq-verif-tweetnacl", "sha": "8181ab4406cefd03ab0bd53d4063eb1644a2673d", "save_path": "github-repos/coq/ildyria-coq-verif-tweetnacl", "path": "github-repos/coq/ildyria-coq-verif-tweetnacl/coq-verif-tweetnacl-8181ab4406cefd03ab0bd53d4063eb1644a2673d/packages/coq-vst/coq-vst.2.0/concurrency... |
using LineEdit
include("keymaps.jl")
| {"hexsha": "f19840451339360944e3a49a3bae6e749ee43501", "size": 38, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/LineEdit.jl-3f7aa1fa-0fbc-58b7-89cd-01d1190b12a7", "max_stars_repo_head_hexsha": "52685d387a516e41a733baba1102439190a79204", "max_star... |
using TensorIntegration
using Test
using LinearAlgebra
@testset "Expectation of uniform by simpson rule, 1D/2D/3D" begin
# add test for basic integrals
N = 5
# uniform expectation
a = 1.
b = 3.
pdf(x, a, b) = 1.0 ./ (b-a)
grid, weights = TensorIntegration.simpson(a, b, N)
Exp = dot... | {"hexsha": "d1be127699238c0037cfdeff642651821a0055a3", "size": 1830, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "chrished/TensorIntegration.jl", "max_stars_repo_head_hexsha": "c97a57557d46eed491f2a3acf104fcd02890471b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/bin/python3
# encoding: utf-8
import sys
import numpy as np
from time import time
'''
x
[0, 2] => idx start 0, end 3
[3, 5] => idx start 3, end 6
[6, 8] => idx start 6, end 9
((0 + (r_idx // 3 * 3)): (3 + (r_idx // 3 * 3)), (0 + (c_idx // 3 * 3)): (3 + (c_idx // 3 * 3)))
np.random.randint(1, 10)
'''
sys.setrecu... | {"hexsha": "c99c9b261d2068fe9c60d5d654b34c3a8117a520", "size": 3050, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_code/sudoku.py", "max_stars_repo_name": "xiangnan-fan/proj01", "max_stars_repo_head_hexsha": "856b1a444a526fa35e3fc1328669526429fd56af", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
""" FlowNet model written in TF2/Keras
https://arxiv.org/pdf/1504.06852.pdf
"""
from typing import Dict, Tuple, Optional, Union
from pathlib import Path
from copy import deepcopy
from datetime import datetime
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
import utils_io as ... | {"hexsha": "b57020279726feabd70a804e5df2d7a6a9ae30bf", "size": 19143, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model.py", "max_stars_repo_name": "andrewlstewart/FlowNet_v1_TF2", "max_stars_repo_head_hexsha": "eb21cfca227c21707db57e9e9a0cd359ab849cdb", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
"""
Chance pair distance timeseries
Created on Sat Oct 12 13:42:52 2019
@author: Gebruiker
"""
import numpy as np
import pandas as pd
def ComputeDistance(ID1,ID2,Data_Mediterrenean):
id1 = [] #select only the 1st ID from all Mediterrenean data
id2 = [] #select only the 2nd ID from all... | {"hexsha": "0c244070276a4ef7271e0e438beb65dfba603ee5", "size": 2861, "ext": "py", "lang": "Python", "max_stars_repo_path": "computedistance.py", "max_stars_repo_name": "reint-fischer/MAIOproject", "max_stars_repo_head_hexsha": "564fd60b4835657a5f9f9a58b4dc822d80895f8d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import sys
import unittest
import numpy as np
from embryovision.attentionbox import BoundingBoxCalculator, find_bounding_box
LABEL_SHAPE = (500, 500)
class TestConvenienceFunction(unittest.TestCase):
def test_does_the_same_as_calculator(self):
box_zona = (20, 20, 33, 33)
box_well = (10, 10, 70... | {"hexsha": "6af324ba733749ed13271362d1692a25be18e980", "size": 8940, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_attentionbox.py", "max_stars_repo_name": "briandleahy/embryovision", "max_stars_repo_head_hexsha": "83a271ff71dcdc699e1d83b977a0e366e0870ef4", "max_stars_repo_licenses": ["BSD-4-Clause-... |
[STATEMENT]
lemma real_polynomial_function_divide [intro]:
assumes "real_polynomial_function p" shows "real_polynomial_function (\<lambda>x. p x / c)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. real_polynomial_function (\<lambda>x. p x / c)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1.... | {"llama_tokens": 541, "file": null, "length": 8} |
function _chol!(A::StridedMatrix{<:BlasFloat}, ::Type{UpperTriangular})
C, info = LAPACK.potrf!('U', A)
return UpperTriangular(C), info
end
function _chol!(A::StridedMatrix{<:BlasFloat}, ::Type{LowerTriangular})
C, info = LAPACK.potrf!('L', A)
return LowerTriangular(C), info
end
| {"hexsha": "e60bead99f10e92ed0fba8cfcf7f19ffcaf60512", "size": 297, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/cholesky.jl", "max_stars_repo_name": "Red-Portal/IonLinearAlgebra.jl", "max_stars_repo_head_hexsha": "5073647c79abdc816630baa5cb8611fab8a0085a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Credits: Grigorii Sukhorukov, Macha Nikolski
import numpy as np
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
import ray
import os
import pathlib
import math
import random
from sklearn.utils import shuffle
import h5py
def reverse_com... | {"hexsha": "5d009255d377c8582b7308a51bb651cd40c8006d", "size": 15177, "ext": "py", "lang": "Python", "max_stars_repo_path": "virhunter/utils/preprocess.py", "max_stars_repo_name": "admincbib/virhunter", "max_stars_repo_head_hexsha": "cf7b9122eeaaee2947c0bf2504b9b57df6580261", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python
import numpy as np
import argparse
import sys,math
def MLSQ(x,y):
n = len(x)
sx = np.sum(x)
sy = np.sum(y)
sxx = np.dot(x,x)
sxy = np.dot(x,y)
syy = np.dot(y,y)
denom = (n*sxx-sx*sx)
b = (n*sxy - sx*sy)/denom
a = (sy-b*sx)/n
estim = n... | {"hexsha": "1556cd1ee0eccfde2d79228ae6a9533c1a8a7882", "size": 1582, "ext": "py", "lang": "Python", "max_stars_repo_path": "killcurves_GrowthDeathRate.py", "max_stars_repo_name": "lukasgeyrhofer/antibiotics", "max_stars_repo_head_hexsha": "3fc81fa4006e56a65a9596ba79ad4b1a0287f1d1", "max_stars_repo_licenses": ["CC0-1.0"... |
classdef ShapeCircle < ShapeGeneral
%ShapeCircle represents a circular geographical selection of events
%
% see also ShapeGeneral, ShapePolygon
properties (SetObservable = true, AbortSet=true)
Radius (1,1) double = 5 % active radius in units defined by the RefEllipsoid
end
... | {"author": "CelsoReyes", "repo": "zmap7", "sha": "3895fcb3ca3073608abe22ca71960eb082fd0d9a", "save_path": "github-repos/MATLAB/CelsoReyes-zmap7", "path": "github-repos/MATLAB/CelsoReyes-zmap7/zmap7-3895fcb3ca3073608abe22ca71960eb082fd0d9a/src/cgr_utils/selections/ShapeCircle.m"} |
import numpy
N=int(input())
A=list(map(int,input().split()))
print(*numpy.argsort(A)+1) | {"hexsha": "ff43bf9d28f8d6bcfa5e391f0a2397998d541711", "size": 87, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/abc142_c_03.py", "max_stars_repo_name": "KoyanagiHitoshi/AtCoder", "max_stars_repo_head_hexsha": "731892543769b5df15254e1f32b756190378d292", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python3
__author__ = "Niklas Kroeger"
__email__ = "niklas@kroeger.dev"
__status__ = "Development"
import numpy as np
import tables
class ImageStack(object):
"""
Image stack class that stores data in a pytables table (hdf5 file format)
"""
def __init__(self, filename, dummy_img=None, me... | {"hexsha": "b067b01e6da253da82711b0875eefa216c4d4266", "size": 8276, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyImageStack/pyImageStack.py", "max_stars_repo_name": "NiklasKroeger/pyImageStack", "max_stars_repo_head_hexsha": "84bdb951ca5d66241796c174fd12473b459041ef", "max_stars_repo_licenses": ["MIT"], "m... |
function h = supportFunction(polygon, varargin)
%SUPPORTFUNCTION Compute support function of a polygon.
%
% H = supportFunction(POLYGON, N)
% uses N points for suport function approximation
%
% H = supportFunction(POLYGON)
% assume 24 points for approximation
%
% H = supportFunction(POLYGON, V)
% where V i... | {"author": "mattools", "repo": "matGeom", "sha": "1fd2c937064be1ee1f4fd09fbfdf96145ebe5271", "save_path": "github-repos/MATLAB/mattools-matGeom", "path": "github-repos/MATLAB/mattools-matGeom/matGeom-1fd2c937064be1ee1f4fd09fbfdf96145ebe5271/matGeom/polygons2d/supportFunction.m"} |
import tensorflow as tf
from PIL import Image
import numpy as np
import cv2
def grad_cam_tf(model, im, cls_select, tf_sess, layer, alpha = 0.6, preproc_function = None, reverse_function = None):
image = im.copy()
if len(image) != 4:
# Make image batch-like
image = image[np.newaxis, :,:,:]
... | {"hexsha": "127ef9af9ee4f60e748e0286f53e113a59de8eae", "size": 2148, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_visualization.py", "max_stars_repo_name": "jimmy15923/Common_tools", "max_stars_repo_head_hexsha": "3e77dc1509ef8ac5173d41d792a170ba6ed98be0", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
// This file is part of libigl, a simple c++ geometry processing library.
//
// Copyright (C) 2017 Amir Vaxman <avaxman@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla Public License
// v. 2.0. If a copy of the MPL was not distributed with this file, You can
// obtain one at http://mozilla... | {"hexsha": "f352d22bfd58bd3759bc0e944447c8caf6624764", "size": 11060, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "thirdparty/simpleuv/thirdparty/libigl/include/igl/shapeup.cpp", "max_stars_repo_name": "MelvinG24/dust3d", "max_stars_repo_head_hexsha": "c4936fd900a9a48220ebb811dfeaea0effbae3ee", "max_stars_repo_... |
"""
Model to approximate cross products of node voltages
```
wdcr[(i,j)] <= wdc[i]*wdc[j]
```
"""
function constraint_voltage_dc(pm::_PM.AbstractWRMModel, n::Int)
wdc = _PM.var(pm, n, :wdc)
wdcr = _PM.var(pm, n, :wdcr)
for (i,j) in _PM.ids(pm, n, :buspairsdc)
JuMP.@constraint(pm.model, [ wdc[i]/sq... | {"hexsha": "3e9ae700a32e59d45d693c6e630d270ba0bdde17", "size": 1622, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/formdcgrid/wrm.jl", "max_stars_repo_name": "hakanergun/PowerModelsACDC.jl", "max_stars_repo_head_hexsha": "8ef219296223306a53e976005bad9ab788cb0171", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
[STATEMENT]
lemma less_multiset\<^sub>H\<^sub>O:
"M < N \<longleftrightarrow> M \<noteq> N \<and> (\<forall>y. count N y < count M y \<longrightarrow> (\<exists>x>y. count M x < count N x))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (M < N) = (M \<noteq> N \<and> (\<forall>y. count N y < count M y \<longright... | {"llama_tokens": 177, "file": null, "length": 1} |
# -*- coding: utf-8 -*-
"""
Functions to evaluate a trained model
Note: The file was more or less taken from Spotlight
"""
import numpy as np
import scipy.stats as st
FLOAT_MAX = np.finfo(np.float32).max
def mrr_score(model, test, train=None):
"""
Compute mean reciprocal rank (MRR) scores. One score
... | {"hexsha": "dfd0ea9434b28c0b8722f35f768e97ecbcdd017c", "size": 5436, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lrann/evaluations.py", "max_stars_repo_name": "FlorianWilhelm/lrann", "max_stars_repo_head_hexsha": "553ae98d48e76d0b827ba3fffa48e20c68dd475d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import sys
sys.path.append('..')
import numpy as np
from common.trainer import Trainer
from common.optimizer import Adam
from common.layers import MatMul, SoftmaxWithLoss
from common.util import preprocess, convert_one_hot
def create_context_target(corpus,window_size=1):
target = corpus[window_size:-window_size]
... | {"hexsha": "98a45cb57dbad4d99e6624b11760d7ce2aa12040", "size": 2652, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch03/test3.py", "max_stars_repo_name": "gangigammo/deep-learning-2", "max_stars_repo_head_hexsha": "6bce355261d8ad5135c104fca32946aa13dc0ba4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#include <geometry.h>
#include <tiny_math_types.h>
#define BOOST_AUTO_TEST_MAIN
#include <boost/test/auto_unit_test.hpp>
#include <boost/test/unit_test_suite.hpp>
#include <boost/test/floating_point_comparison.hpp>
#include <boost/test/test_tools.hpp>
BOOST_AUTO_TEST_SUITE(geometry);
BOOST_AUTO_TEST_CASE(inside_sphe... | {"hexsha": "6d13c4569350ad77d7229d5e5f60386ee853eed8", "size": 1130, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "PROX/FOUNDATION/GEOMETRY/unit_tests/geometry_inside_sphere/geometry_inside_sphere.cpp", "max_stars_repo_name": "diku-dk/PROX", "max_stars_repo_head_hexsha": "c6be72cc253ff75589a1cac28e4e91e788376900... |
import networkx as nx
from matplotlib import pyplot as plt
import numpy as np
import pytest
import qleet
@pytest.mark.parametrize("ensemble_size", [3, 5])
def test_plot_histogram(ensemble_size):
graph = nx.gnm_random_graph(n=10, m=40)
qaoa = qleet.QAOACircuitMaxCut(graph, p=2)
circuit = qleet.CircuitDes... | {"hexsha": "4cd08401fe627ea12137c4a852399c03c19d4b9a", "size": 1159, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/analyzers/test_histograms.py", "max_stars_repo_name": "AnimeshSinha1309/qaoa-optimizer", "max_stars_repo_head_hexsha": "2a93a46bacc99f22f49e7b5121eb3aa9f12c0163", "max_stars_repo_licenses": ... |
#!/usr/bin/env python
# K_modularity using weighted edges
import matplotlib.pyplot as plt
import re, os, sys
import networkx as nx
from numpy import linalg as la
from networkx.generators.atlas import *
import numpy as np
import networkx as nx
import random, copy
import math
from scipy.sparse import csr_matrix
import ... | {"hexsha": "8924c6f1bbe406f74ae20e1dd1bf504629c445e9", "size": 4560, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithm/k-community_detection/quantum_kcommunity_detection_hybrid.py", "max_stars_repo_name": "lanl/Quantum_Graph_Algorithms", "max_stars_repo_head_hexsha": "3e8e63fe98866321b417b8d47cb52a9ce605... |
import tactic
import tactic.induction
noncomputable theory
open_locale classical
@[to_additive]
lemma finset_prod_congr_set
{α : Type*} [comm_monoid α] {β : Type*} [fintype β] (s : set β) (f : β → α) (g : s → α)
(w : ∀ (x : β) (h : x ∈ s), f x = g ⟨x, h⟩) (w' : ∀ (x : β), x ∉ s → f x = 1) :
finset.univ.prod f =... | {"author": "user7230724", "repo": "lean-projects", "sha": "ab9a83874775efd18f8c5b867e480bae4d596b31", "save_path": "github-repos/lean/user7230724-lean-projects", "path": "github-repos/lean/user7230724-lean-projects/lean-projects-ab9a83874775efd18f8c5b867e480bae4d596b31/src/other/prod_congr_set.lean"} |
using HDF5
using Merlin
include("parser.jl")
include("model.jl")
const wordembeds_file = ".data/glove.6B.100d.h5"
#traindoc = CoNLL.read(".data/wsj_00-18.conll")
#testdoc = CoNLL.read(".data/wsj_22-24.conll")
#info("# sentences of train doc: $(length(traindoc))")
#info("# sentences of test doc: $(length(testdoc))")
... | {"hexsha": "4febe31f173d93e9f8f48612c35b95dad55a22f2", "size": 544, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "depparsing/main.jl", "max_stars_repo_name": "hshindo/Merlin-Examples", "max_stars_repo_head_hexsha": "a12fd471d5271b99f6d9680d8c768661dca1ea31", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# Helper data for color parsing
include("names_data.jl")
# Color Parsing
# -------------
const col_pat_hex1 = r"(#|0x)([[:xdigit:]])([[:xdigit:]])([[:xdigit:]])"
const col_pat_hex2 = r"(#|0x)([[:xdigit:]]{2})([[:xdigit:]]{2})([[:xdigit:]]{2})"
const col_pat_rgb = r"rgb\((\d+%?),(\d+%?),(\d+%?)\)"
const col_pat_hsl ... | {"hexsha": "2234963cee123828ec61ea5bac4a8f48326e0c9a", "size": 5171, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "public/.julia/v0.5/Colors/src/parse.jl", "max_stars_repo_name": "Giarcr0b/MVO_Tool", "max_stars_repo_head_hexsha": "8f3348b8b56968febca8307acea3ebe1817fccae", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma fresh_pgwt_same_type:
assumes "finite S" "wf\<^sub>t\<^sub>r\<^sub>m t"
shows "\<Gamma> (fresh_pgwt S (\<Gamma> t)) = \<Gamma> t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Gamma> (fresh_pgwt S (\<Gamma> t)) = \<Gamma> t
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
... | {"llama_tokens": 7856, "file": "Stateful_Protocol_Composition_and_Typing_Typing_Result", "length": 43} |
using VXI11
using Base.Test | {"hexsha": "848f14c4169bebbff7b43c1ee228069244814766", "size": 27, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "Keno/VXI11.jl", "max_stars_repo_head_hexsha": "279808d3f0a99e5d09da028dd322692087f7bf27", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_sta... |
#!/usr/bin/env python3
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | {"hexsha": "b15cee1f520fd65cc9207384183079ad04c5c6c1", "size": 26574, "ext": "py", "lang": "Python", "max_stars_repo_path": "mediapy_test.py", "max_stars_repo_name": "hhoppe/mediapy", "max_stars_repo_head_hexsha": "8a31181da5eab219bde30b1033f8813b6dc3b396", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import unittest
import numpy as np
from polynomials_on_simplices.linalg.vector_space_projection import (
subspace_projection_map, vector_oblique_projection_2, vector_projection, vector_rejection)
class TestVectorProjection(unittest.TestCase):
def test_projection(self):
a = np.array([1.3, 1.5, 0])
... | {"hexsha": "92e56318d1000f06cd15751d94643b2873edcfe8", "size": 3213, "ext": "py", "lang": "Python", "max_stars_repo_path": "polynomials_on_simplices/linalg/test/vector_space_projection_test.py", "max_stars_repo_name": "FAndersson/polynomials_on_simplices", "max_stars_repo_head_hexsha": "f015a4772c817bfa99b0d6b726667a38... |
\input{header.tex}
%\setlength{\headsep}{-10pt}
\setlength{\parskip}{0.2em}
%\setlength{\textheight}{11 in}
\setlength{\skip\footins}{20pt}
% opening
\title{Plotting large datasets, Part 3: Advanced experiments}
\author{Colin Leach, March 2020}
\date{\vspace{-3ex}}
\hyphenpenalty=1000
\begin{document}
\maketitle
... | {"hexsha": "100f3c07efbc37d1e98c40bc031bbbf4d8595871", "size": 4256, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "animations/howto/advanced_experiments.tex", "max_stars_repo_name": "colinleach/400B_Leach", "max_stars_repo_head_hexsha": "656abe04237d7a8de2cf56e9bfe986c333c62739", "max_stars_repo_licenses": ["MIT... |
"""
This module provies a RHESSI `~sunpy.timeseries.TimeSeries` source.
"""
import datetime
import itertools
from collections import OrderedDict
import matplotlib.dates
import matplotlib.pyplot as plt
import numpy as np
from pandas import DataFrame
import astropy.units as u
from astropy.time import TimeDelta
import ... | {"hexsha": "8a189ea1e1100f4687b4079c137ffeb89fb85ad3", "size": 9401, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunpy/timeseries/sources/rhessi.py", "max_stars_repo_name": "RhnSharma/sunpy", "max_stars_repo_head_hexsha": "03700193d287156ca1922eb27c4c2ad50040e53f", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
import json
import keras
import numpy as np
import tensorflow as tf
import keras.backend as K
from .data.vocab import TextEncoder
from musket_text.bert.modeling import BertConfig
from .model import create_transformer
def load_openai_transformer(path: str = './openai/model/', use_attn_mask: bool = True,
... | {"hexsha": "6a3fa9fd33ea79b3b1574c7e5f9050eb796bdd3f", "size": 8580, "ext": "py", "lang": "Python", "max_stars_repo_path": "musket_text/bert/load.py", "max_stars_repo_name": "petrochenko-pavel-a/musket_text", "max_stars_repo_head_hexsha": "9571b9d554ed66496c911222d319e42242351eb6", "max_stars_repo_licenses": ["MIT"], "... |
import pickle
import numpy as np
import librosa
import pandas as pd
from raw_audio_segmented_save_data import split_data
path = '/scratch/speech/raw_audio_dataset/raw_audio_segmented_full.pkl'
file = open(path, 'rb')
data = pickle.load(file)
thresh = 32000
sr_standard = 16000
input_new = []
#seq_length_new = []
for ... | {"hexsha": "15ffffa6106eaafccc94879aa0e1611201167cf4", "size": 1521, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocessing/equalize_segment_lengths.py", "max_stars_repo_name": "dem123456789/Speech-Emotion-Recognition-with-Dual-Sequence-LSTM-Architecture", "max_stars_repo_head_hexsha": "a072cb940201bb... |
"""Модуль для определения режима потока в кольцевом пространстве"""
import uniflocpy.uTools.uconst as uc
import math
import scipy.optimize as sp
class flow_pattern_annulus_Caetano(object):
"""Класс для определения режимов потока в кольцевом пространстве
по Caetano (1992)"""
def __init__(self):
sel... | {"hexsha": "a84885d82c83ffbfb64e49f7c2c36c00f8702d96", "size": 6536, "ext": "py", "lang": "Python", "max_stars_repo_path": "uniflocpy/uMultiphaseFlow/flow_pattern_annulus_Caetano.py", "max_stars_repo_name": "Shabonasar/unifloc", "max_stars_repo_head_hexsha": "1f12d6b4110a9ff0e10817560ad99d55c9133954", "max_stars_repo_l... |
from gan.output import Output, OutputType, Normalization
from pcaputilities import chunk_and_convert_to_training, convertToFeatures, sequences_sample, chunk_and_convert_ps_and_durations, extract_dictionaries_from_activities, convert_to_durations, signatureExtractionAll, all_greedy_activity_conversion, chunk_and_convert... | {"hexsha": "9a123200168c6f72c3ff11b890c2cba71340e953", "size": 9007, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_prep.py", "max_stars_repo_name": "pumperknickle/DoppelGANger", "max_stars_repo_head_hexsha": "bb92853f6d3a4d100caab7d5030c94d5064a7e66", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"], "ma... |
import numpy as np
from pymoo.model.mutation import Mutation
from pymoo.operators.repair.to_bound import set_to_bounds_if_outside_by_problem
class PolynomialMutation(Mutation):
def __init__(self, eta, prob=None):
super().__init__()
self.eta = float(eta)
if prob is not None:
s... | {"hexsha": "e6cb2c448234bb74d1b1f24b1b88cd616c157b62", "size": 1908, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymoo/operators/mutation/polynomial_mutation.py", "max_stars_repo_name": "Alaya-in-Matrix/pymoo", "max_stars_repo_head_hexsha": "02d6e7085f5fe88dbd56b2a9f5173abe20c54caf", "max_stars_repo_licenses... |
from flask import Flask, render_template, request, flash, redirect, Response, send_from_directory
import os,time
import torch
import cv2
import numpy as np
import glob
import pathlib
UPLOAD_FOLDER = './components'
DATASET_FOLDER = './components/Test'
ALLOWED_IMAGES = {'jpg', 'jpeg'}
ALLOWED_LABELS = {'txt'}
app = Flas... | {"hexsha": "3f9342e3eb91fc40464c8efb54e30fb9a6b39136", "size": 10611, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "Dodalpaga/YOLO-Object-Detection-Template", "max_stars_repo_head_hexsha": "cd93d9a9f571976e6fc47cf86c9bc7145a0654c1", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# coding: utf-8
# # The Shockley-Queisser limit
#
# By Steven J. Byrnes ([https://sjbyrnes.com/](https://sjbyrnes.com/)). This document lives at [https://github.com/sbyrnes321/SolarCellEfficiencyLimits](https://github.com/sbyrnes321/SolarCellEfficiencyLimits). Please email me any feedback: steve... | {"hexsha": "5e3ba66a20ef0a70dbd60cdb6bba98c5d0cfff3e", "size": 26673, "ext": "py", "lang": "Python", "max_stars_repo_path": "sq.py", "max_stars_repo_name": "NREL/PVwindow", "max_stars_repo_head_hexsha": "df7091c9d1ebd280aca53c50015e3b1ee7a3183e", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "ma... |
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/common/transforms.h>
#include <iostream>
#include <fstream>
#include <string>
int main(int argc, char* argv[])
{
if(argc !... | {"hexsha": "be400482b1b99a5c435d1eba605549f3b9baf4a5", "size": 2526, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "data/run_dataset_visualizer.cpp", "max_stars_repo_name": "StarRealMan/StarNet", "max_stars_repo_head_hexsha": "5fd36b4a545a494eb4dc6d309469696b5d2f8abb", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
"""
# Light field images: input_Cam000-080.png
# All viewpoints = 9x9(81)
# -- LF viewpoint ordering --
# 00 01 02 03 04 05 06 07 08
# 09 10 11 12 13 14 15 16 17
# 18 19 20 21 22 23 24 25 26
# 27 28 29 30 31 32 33 34 35
# 36 37 38 39 40 41 42 43 44
# 45 46 47 48 49 50 51 52 53
# 54 55 56 57 58... | {"hexsha": "0839d6e887afb113940e4f47eba9d051b1e37d9a", "size": 7085, "ext": "py", "lang": "Python", "max_stars_repo_path": "cEPINET_test.py", "max_stars_repo_name": "marmus12/CornerView", "max_stars_repo_head_hexsha": "f76cd1cb4c402c59bafbf66b5e038c2d1ab9610b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, ... |
\documentclass{article}
\usepackage{alltt}
\newcommand{\RewriteGen}{{\sf RewriteGen}}
\newcommand{\KW}[2]{\newcommand{#1}[1]{{\bf #2}\ }}
\newcommand{\END}{{\bf end}}
\newcommand{\OF}{{\bf of}}
\KW{\FUN}{fun} \KW{\VAL}{val}
\KW{\CASE}{case} \KW{\LET}{let}
\KW{\INCLUDE}{include} \KW{\SIGNATURE}{s... | {"hexsha": "6e5c38101bba048c97750ec893907824f7c98f10", "size": 9756, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "build/lib/sml/mlrisc-lib/Tools/Doc/rewrite-gen.tex", "max_stars_repo_name": "Bxc8214/mlton-test", "max_stars_repo_head_hexsha": "153db2d029f5191b26d68361922be34eabf4cac9", "max_stars_repo_licenses":... |
"""
Tools for nonparametric statistics, mainly density estimation and regression.
For an overview of this module, see docs/source/nonparametric.rst
"""
from statsmodels.tools._testing import PytestTester
test = PytestTester()
| {"hexsha": "bdd0fc3a075b4a0f46ac85251bf1990ca5cc8695", "size": 229, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/statsmodels/nonparametric/__init__.py", "max_stars_repo_name": "EkremBayar/bayar", "max_stars_repo_head_hexsha": "aad1a32044da671d0b4f11908416044753360b39", "max_stars_repo_l... |
import logging
import argparse
import ast
from collections import OrderedDict
import torch
import os
import scipy
from datetime import datetime
import time
import math
import random
import numpy as np
import sys
import cv2
irange = range
def mylogger(logpath='./param.log'):
logger = logging.getLogger('mylogger')
... | {"hexsha": "a12a49d4eec43e7302a7258e334726d9304e57aa", "size": 16402, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/myutils.py", "max_stars_repo_name": "zhouxiaowei1120/practice", "max_stars_repo_head_hexsha": "95dd7ffa65f34a867578bea2f80404677cc5f5e5", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
function [Population,W,B] = WeightUpdate(Population,W,Archive,Z,T,Global)
% Weight Update
%------------------------------- Copyright --------------------------------
% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for
% research purposes. All publications which use this platform or any code
% in the p... | {"author": "BIMK", "repo": "PlatEMO", "sha": "c5b5b7c37a9bb42689a5ac2a0d638d9c4f5693d5", "save_path": "github-repos/MATLAB/BIMK-PlatEMO", "path": "github-repos/MATLAB/BIMK-PlatEMO/PlatEMO-c5b5b7c37a9bb42689a5ac2a0d638d9c4f5693d5/PlatEMO/Algorithms/Multi-objective optimization/AdaW/WeightUpdate.m"} |
import yaml
import operator
import random
import os
import math
import numpy
import sys
from collections import OrderedDict
from utils import *
import itertools
import subprocess
import pandas
from pyDOE import *
import warnings
import pickle
import core_count
import json
# Returns an ordered dictionary based on the or... | {"hexsha": "88147a2a5e0d14f9d64bcf5e1e2692dbfcadab2c", "size": 13833, "ext": "py", "lang": "Python", "max_stars_repo_path": "tuning/misc/rule_based1.py", "max_stars_repo_name": "MBtech/stormbenchmark", "max_stars_repo_head_hexsha": "16bd8971011ff4ac34b5d457cecb55f5dfc76106", "max_stars_repo_licenses": ["Apache-2.0"], "... |
/-
Copyright (c) 2021 OpenAI. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kunhao Zheng, Stanislas Polu, David Renshaw, OpenAI GPT-f
-/
import mathzoo.imports.miniF2F
open_locale nat rat real big_operators topological_space
theorem mathd_algebra_142
(m b : ℝ)
(... | {"author": "leanprover-community", "repo": "mathzoo", "sha": "87e9b492daeb929838706942aaa2437621b34a0e", "save_path": "github-repos/lean/leanprover-community-mathzoo", "path": "github-repos/lean/leanprover-community-mathzoo/mathzoo-87e9b492daeb929838706942aaa2437621b34a0e/src/mathzoo/olympiads/mathd/algebra/p142.lean"} |
#!/usr/bin/python
# -*- coding:utf-8 -*-
# @author : east
# @time : 2020/7/28 14:39
# @file : basic_func.py
# @project : SpectralMethod
# @software : PyCharm
import numpy as np
# RHS Equation
# -------------
def g(k, t):
return np.exp((k**4 - k**2) * t)
def spec_rhs(t0, vt, kx):
# print("tspan:... | {"hexsha": "e9ea63f46d0b4caedcaee91f608fba6d0a855e00", "size": 603, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/KS/basic_func.py", "max_stars_repo_name": "EastMagica/SpectralMethod", "max_stars_repo_head_hexsha": "fbed7fa236c26cfe5cc77d65e4309fd33dca3e3b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
-- Vect.idr
--
-- Vector type to demonstrate dependent types
||| Vect data type: A List with defined length
data Vect : Nat -> Type -> Type where
||| Empty vector
Nil : Vect Z a
||| Prepend a new element to vector
(::) : (x : a) -> (xs : Vect k a) -> Vect (S k) a
%name Vect xs, ys, zs
||| appends two vecto... | {"hexsha": "016cc43efeae5a7e7a94543443f9b6741cccd4ac", "size": 591, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Idris/TDD/Chapter_4/Vect.idr", "max_stars_repo_name": "kkirstein/proglang-playground", "max_stars_repo_head_hexsha": "d00be09ba2bb2351c6f5287cc4d93fcaf21f75fd", "max_stars_repo_licenses": ["MIT"], ... |
!
! Special_Funcitons
!
! Module containing procedures for providing/evaluating various special
! functions.
!
!
! CREATION HISTORY:
! Written by: Paul van Delst, 28-Nov-2001
! paul.vandelst@nooa.gov
!
MODULE Special_Functions
! -----------------
! Environment setup
! ----------... | {"hexsha": "feb228ed6196c479280663ae8494ee95596609c5", "size": 14611, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/Utility/Math_Utility/Special_Functions.f90", "max_stars_repo_name": "hsbadr/crtm", "max_stars_repo_head_hexsha": "bfeb9955637f361fc69fa0b7af0e8d92d40718b1", "max_stars_repo_licenses": ["CC0... |
#include <iostream>
#include <armadillo>
using namespace std;
using namespace arma;
int main()
{
// Constructor
arma::mat x,y;
x << 0.1778 << 0.1203 << -0.2264 << endr
<< 0.0957 << 0.2403 << -0.3400 << endr
<< 0.1397 << 0.1925 << -0.3336 << endr
<< 0.2256 << 0.3144 << -0.8695 << endr;
y << 1 <... | {"hexsha": "ba168f0b6926a37fa8e7f3f4238aefa434d5151d", "size": 2517, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "soft_margin_loss/test.cpp", "max_stars_repo_name": "iamshnoo/mlpack-testing", "max_stars_repo_head_hexsha": "43f9fde18afc7f1e6d54c0a2bd59709c103eed55", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
#!/usr/bin/env python
# coding: utf-8
# In[38]:
import os
import time
import csv
import numpy as np
import torch
import torch.nn.parallel
import torch.optim
import models
import utils
from PIL import Image
import matplotlib.pyplot as plt
# In[2]:
checkpoint = torch.load('./mobilenet-nnconv5dw-skipadd-pruned.pth... | {"hexsha": "ea97d3c3b2211833ce020361db1602f9cab7509f", "size": 1479, "ext": "py", "lang": "Python", "max_stars_repo_path": "fastdepth.py", "max_stars_repo_name": "tolleybot/fast-depth", "max_stars_repo_head_hexsha": "f5488d8bcfbfc2f50186fb200224f06509c4ef23", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import Thermodynamics
import StatsBase
import PrettyTables
import OrderedCollections
const TD = Thermodynamics
const TP = TD.Parameters
using JET
using Test
import UnPack
import BenchmarkTools
import CLIMAParameters
const CP = CLIMAParameters
const FT = Float64
toml_dict = CP.create_toml_dict(FT; dict_type = "alias")... | {"hexsha": "f4caadc78e7b8e7bbe112af30929fceebea2a44d", "size": 5610, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "perf/common_micro_bm.jl", "max_stars_repo_name": "climate-machine/MoistThermodynamics.jl", "max_stars_repo_head_hexsha": "a7cadd68d5241d6059eafc82133da2358a1a4ec9", "max_stars_repo_licenses": ["Apa... |
# -*- coding: utf-8 -*-
"""customer_Churn_prediction_using_ANN.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/114T0FexoqqbqdxPn9f-FqSy7F0ly6CMY
"""
from google.colab import drive
drive.mount('/content/gdrive/')
# Importing useful Libraries
imp... | {"hexsha": "e52d661c507de0bad4b8749b44cbe0526c3519fe", "size": 13786, "ext": "py", "lang": "Python", "max_stars_repo_path": "customer_churn_prediction_using_ann.py", "max_stars_repo_name": "prabhat-123/Customer-Churn-Prediction-Deployment-In-Flask", "max_stars_repo_head_hexsha": "5d596f0d5d4b3bf52687ceb14e1053783334ec1... |
import sys
import unittest
import array
import pickle
import operator
import platform
import numpy
from deap import creator
from deap import base
from deap import gp
from deap import tools
def func():
return "True"
class Pickling(unittest.TestCase):
def setUp(self):
creator.create("FitnessMax", ba... | {"hexsha": "ed8c327d10123bc18f6ef3045086d9d29c053cd7", "size": 6760, "ext": "py", "lang": "Python", "max_stars_repo_path": "env/lib/python3.9/site-packages/deap/tests/test_pickle.py", "max_stars_repo_name": "wphoong/flappy_doge", "max_stars_repo_head_hexsha": "c778f0e4820c1ed46e50a56f989d57df4f386736", "max_stars_repo_... |
orth(S::AbstractArray{<:Real}) = Matrix(qr(S).Q)
"""
S_update_a
Random set of orthonormal sample directions.
See: Equation (6.1.a).
"""
function S_update_a(Sₖ, Yₖ, pₖ)
return orth(randn(eltype(Sₖ), size(Sₖ)...))
end
"""
S_update_b
Random set of sample directions orthogonal to the previous sample spa... | {"hexsha": "9982837cfa03ed88ca6c637a977023f86a914a78", "size": 5035, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/driver.jl", "max_stars_repo_name": "danphenderson/BlockOptim.jl", "max_stars_repo_head_hexsha": "c53672e67e8aba4daea2f1b8c7d2effd042d63c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
r"""
Contains methods to calculate certain quantities used in thermal states, open systems, and quantum thermodynamics.
TODO update docstring examples and write some tests after writing some tutorials
.. currentmodule:: quanguru.QuantumToolbox.thermodynamics
Functions
---------
.. autosummary... | {"hexsha": "f3973b698e44ef5d2b7eb329fbc74defb1ee87d0", "size": 3458, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/quanguru/QuantumToolbox/thermodynamics.py", "max_stars_repo_name": "Qfabiolous/QuanGuru", "max_stars_repo_head_hexsha": "285ca44ae857cc61337f73ea2eb600f485a09e32", "max_stars_repo_licenses": [... |
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import requests
import streamlit as st
from azure.storage.blob import BlobServiceClient
from dotenv import load_dotenv
from pandas_profiling import ProfileReport
fr... | {"hexsha": "be1c60aaaa16a91a7810dedbe47619ae36afb6f8", "size": 5422, "ext": "py", "lang": "Python", "max_stars_repo_path": "ui/app.py", "max_stars_repo_name": "lordlinus/parallel-file-processing-serverless", "max_stars_repo_head_hexsha": "751830c3edfafd935e14fdc2ffbbe28dcb704170", "max_stars_repo_licenses": ["MIT"], "m... |
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