text stringlengths 0 27.1M | meta dict |
|---|---|
#
import aei
import gdal
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib tk
# set the working directories
base = '/home/cba/Downloads/scale-conceptual/'
plots = base + 'plots/'
tch_file = base + 'marin_tch_byte.tif'
gd_file = base + 'marin_tch_mask.tif'
rnd_data = bas... | {
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import pandas as pd
import numpy as np
#https://iq-inc.com/importerror-attempted-relative-import/
import data.data_retrieve as dr
import features.data_preprocessing as pp
import models.train_model as tm
import models.train_model_FTR as tmFTR
#could make it object oriented so that different models can be different o... | {
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"""
Provides a subclass to DSNFITSexaminer for plotting data.
Possible symbols::
================ ===============================
character description
================ ===============================
``'o'`` circle marker
``'v'`` triangle_down marker
``'... | {
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import argparse
import os
import pandas as pd
import imagesize
import nltk
from nltk.corpus import wordnet as wn
import numpy as np
import re
from ..create.scads_classes import Node, Image, Clip
from ..create.create_scads import add_conceptnet
from ..create.add_datasets import add_dataset
from .wnids_to_concept import... | {
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import csv
import numpy as np
import cv2
def read_data(file_path,header=True,delimiter=','):
# The read-in data should be a N*W matrix,
# where N is the length of the time sequences,
# W is the number of sensors/data features
i = 0
with open(file_path, 'r') as file:
reader = csv.reader(file... | {
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\section{EDA Figures}
\begin{figure}[!h]
\begin{minipage}{.45\textwidth}
\caption{Date of birth frequency.}
\label{fig:birth_date_freq}
\centering
\scalebox{0.5}{\input{./img/birth_date_freq.pgf}}
% \includegraphics[width=10]{./img/birth_date_freq.pgf}
\end{minipage}
%\end{figure}
%\begin{figure}[!h... | {
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import random
import numpy as np
import geomstats.backend as gs
import geomstats.tests
from geomstats.geometry.poincare_half_space import PoincareHalfSpace
from tests.data_generation import _OpenSetTestData, _RiemannianMetricTestData
class PoincareHalfSpaceTestData(_OpenSetTestData):
dim_list = random.sample(ra... | {
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[STATEMENT]
lemma naturality_hom_induced:
assumes "continuous_map X Y f" "f ` S \<subseteq> T"
shows "hom_boundary q Y T \<circ> hom_induced q X S Y T f
= hom_induced (q - 1) (subtopology X S) {} (subtopology Y T) {} f \<circ> hom_boundary q X S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. hom_boundary... | {
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import datetime
import logging
import sys
from pytorch_lightning.core import datamodule
from torch.utils import data
#cd /home/dcast/adversarial_project ; /usr/bin/env /home/dcast/anaconda3/envs/deep_learning_torch/bin/python -- /home/dcast/adversarial_project/irt_to_nlp/train.py
# sys.path.append("/content/adversari... | {
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[STATEMENT]
lemma OrdP_linear_lemma:
assumes j: "atom j \<sharp> i"
shows "{ OrdP (Var i) } \<turnstile> All j (OrdP (Var j) IMP (Var i IN Var j OR Var i EQ Var j OR Var j IN Var i))"
(is "_ \<turnstile> ?scheme")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {OrdP (Var i)} \<turnstile> SyntaxN.All j (... | {
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# Problem: https://projecteuler.net/problem=367
# To run the code: `./problem367.sh`
"""
* Define notations as follows:
. let e(i) represent the element at position i of a permutation
. a permutation p of n elements is represented as follows
p = [e(1) e(2) e(3) ... e(n)]
... | {
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/-
Copyright (c) 2018 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl, Jens Wagemaker
-/
import algebra.big_operators.basic
import algebra.divisibility
import algebra.invertible
/-!
# Associated, prime, and irreducible elements.
-/
varia... | {
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println("Fitting linear, time-invariant model")
n,λ = 150,2.48
M2 = RegularizedLTIModel(n,N1,λ)
lti = estfun(M2,p1,Q1)
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"""
Utiltiy helper functions of Machine Learning
"""
import numpy as np
def sigmoid(val: np.ndarray) -> np.ndarray:
"""Sigmoid function
.. math::
f(z) = \\frac{1}{1 + e^{-z}}
Args:
val (ndarray): input value
Returns:
np.ndarray: sigmoid value
"""
return 1 / (1 + np.... | {
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struct LSQOutput
β::Vector{Float64}
ζ::Vector{Float64}
Σβ::Matrix{Float64}
Σζ::Matrix{Float64}
Σβζ::Matrix{Float64}
χ²::Float64
dof::Int
βindex::Dict{Symbol, Union{Int, UnitRange{Int}}}
zindex::Dict{Symbol, Union{Int, UnitRange{Int}}}
end
_names_ordered(index) = map(x->x[1], sort(co... | {
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"""
This is an example of the application of DeepESN model for multivariate time-series prediction task
on Piano-midi.de (see http://www-etud.iro.umontreal.ca/~boulanni/icml2012) dataset.
The dataset is a polyphonic music task characterized by 88-dimensional sequences representing musical compositions.
Starting from p... | {
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'''
#Requirement: opencv-python, tkinter
# Usage instruction:
Choose point (double click left mouse button) in the order leftUpper, RightUpper, LeftLower, RightLower
Click "c" in the keyword for confirmation
This is for two-chamber crylic transfer box, the dimension is
two-chamber place preference
_________445mm__... | {
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"""
# WordSlots are the main objects of the grid, they are the word "spaces" to be filled
"""
import numpy as np
class WordSlot():
def __init__(self, identifiant, length, first_letter_position, direction, initial_letters):
self.identifiant = identifiant
self.length = length
sel... | {
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import argparse
import json
from pathlib import Path
import numpy as np
import torch
import yaml
from .binary_tree import BinaryTree
from .graph import Graph
from .random_walker import RandomWalker
from .skipgram import SkipGram
def run(args):
if args.config_file.absolute().exists():
with open(args.con... | {
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import matplotlib
matplotlib.use('pdf')
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import is_valid_dm
import numpy as np
import general_scripts as gs
matplotlib.rcParams['font.family'] ... | {
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!
! -- LAPACK95 interface driver routine (version 3.0) --
! UNI-C, Denmark; Univ. of Tennessee, USA; NAG Ltd., UK
! September, 2000
!
! .. USE STATEMENTS ..
USE LA_PRECISION, ONLY: WP => SP
USE LA_AUXMOD, ONLY: ERINFO, LSAME
! .. IMPLICIT STATEMENT ..
IMPLICIT NONE
! .. SCALAR ARGUMENTS ..... | {
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# This contains inherited classes from detectron2, which were defined wit h he detectron2 version on January 21, 2019.
# the changes are made to allow for logging validation set metrics during training and to allow for a custom data loader for tif imagery.
import copy
import logging
import numpy as np
import os
import ... | {
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import numpy as np
from ssr.ext.plyfile import PlyData, PlyElement
from ssr.utility.logging_extension import logger
from ssr.ssr_types.point import Point, Measurement
class Face:
# Do NOT confuse this class with a real triangle.
# A face saves ONLY VERTEX INDICES, NO 3D information.
def __init__(self, in... | {
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// Copyright (c) 2007-2017 Hartmut Kaiser
// Copyright (c) 2008-2009 Chirag Dekate, Anshul Tandon
// Copyright (c) 2012-2013 Thomas Heller
//
// 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 <hpx/runti... | {
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# coding: utf-8
# In[7]:
from __future__ import absolute_import
from __future__ import print_function
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import applications
from k... | {
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#!/usr/bin/env python
# coding: utf-8
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sb
sb.set(style="white")
sb.set(color_codes=True)
sb.set_context('paper')
os.chdir('/Users/pauline/Documents/Python')
df = pd.read_csv("Tab-Bathy.csv")
# define variables and ... | {
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module numz
!*************************************
integer, parameter:: b8 = selected_real_kind(14) ! basic real types
integer, parameter:: b4 = selected_real_kind(4)
! integer, parameter:: i8 = selected_int_kind(14)
integer, parameter:: out1 = 11 ! output file number for writes
integer, parameter:: ... | {
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"""
Set of functions for measuring the spectral properties of galaxies.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from ..plot import Plot
from astropy.stats import sigma_clip
from astropy.constants import c
from astropy.units import km, s, erg, ... | {
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#include <turbodbc/field_translators/timestamp_translator.h>
#include <boost/variant/get.hpp>
#include <sql.h>
namespace turbodbc { namespace field_translators {
timestamp_translator::timestamp_translator() = default;
timestamp_translator::~timestamp_translator() = default;
field timestamp_translator::do_make_fi... | {
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Base.@kwdef struct FieldInfo
name::String
ltype::Union{DataType,String,Symbol}
rtype::Union{DataType,String,Symbol} = rtype
get_returns_reference::Bool = true
end
struct TypeInfo
name::String
fields::Vector{FieldInfo}
end
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"""
Kernel PCA object
"""
struct kPCA{T <: Real}
X::Matrix{Union{T, Missing}} # original data
κ::Kernel{T} # kernel function (from MLKernels)
μ::Vector{T} # row/col means of the kernel matrix
μ2::T # mean of the kernel matrix
λ::Vector{T} # eigenvalues in feature space
α::Matrix{T} # ... | {
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from keras.models import load_model
from load_face_dataset import load_dataset, IMAGE_SIZE, resize_image
import numpy as np
from fr_utils import img_to_encoding
from sklearn.model_selection import cross_val_score, ShuffleSplit, KFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.externals import... | {
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import os
import numpy as np
from pwtools import io
from pwtools.test import tools
from .testenv import testdir
rand = np.random.rand
def test_h5():
try:
import h5py
dct1 = \
{'/a': 'abcgs',
'/b/c/x1': 3,
'/b/c/x2': rand(2,3),
}
# writing a... | {
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"include": true,
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import time
import math
import numpy as np
from itertools import product
from collections import deque, defaultdict, namedtuple
#TODO #4 make 2nd part of the task
def load_data():
with open("AoC2020/aoc20-20-data.txt", "r") as f:
data = f.read()
print("Loaded lines:", len(data.splitlines()))
with... | {
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from kivy.config import Config
# Config.set('kivy', 'exit_on_escape', '0')
# Config.set('graphics', 'resizable', '0')
Config.set('graphics', 'width', '640')
Config.set('graphics', 'height', '480')
import os
import cv2
from detection import Face_Detector, Landmark_Detector
from faceswap_cam import face_swap
from kivy.... | {
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#include <boost/gil/extension/dynamic_image/algorithm.hpp>
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#!/usr/bin/env python
# Copyright 2014-2020 The PySCF Developers. 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
#
# U... | {
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import logging
from typing import Dict
from typing import Sequence
from typing import Tuple
import yaml
import argparse
import torch
import torch.nn.functional as F
from espnet2.tts.abs_tts import AbsTTS
from espnet.nets.pytorch_backend.gradtts.diffusion import Diffusion
from espnet2.torch_utils.device_funcs import f... | {
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# quantum solver
import numpy as np
from scipy.linalg import eigh
def solver(nk, nbasis, mu, hamiton_mat):
kpoints = np.linspace(0, np.pi, nk)
# build and solve eigenvalue problem
T = 0
# En = np.zeros((nk, nbasis))
density = np.zeros(nbasis, dtype=np.complex64)
for ki, k in enumerate(kpoints)... | {
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import oemof.solph as solph
from .component_electrolyzer import Electrolyzer
import pyomo.environ as po
class ElectrolyzerWasteHeat(Electrolyzer):
""" Electrolyzer agents with waste heat model are created through this subclass of the
Electrolyzer class """
def __init__(self, params):
# Split the... | {
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from efficientnet_pytorch import EfficientNet
import torch
from torch import nn
from torch import optim
from torch.utils.data import TensorDataset, DataLoader, Dataset
import tokenizers
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
import numpy as np
import pandas as pd
model = Efficie... | {
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[STATEMENT]
lemma sH_seq: "\<^bold>{P\<^bold>} X;Y \<^bold>{Q\<^bold>} = \<^bold>{P\<^bold>} X \<^bold>{\<lambda>s. \<forall>s'. (s, s') \<in> Y \<longrightarrow> Q s'\<^bold>}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<^bold>{P\<^bold>}X ; Y\<^bold>{Q\<^bold>} = \<^bold>{P\<^bold>}X\<^bold>{\<lambda>s. \<for... | {
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
def getting_and_knowing_your_data(chipo):
#Step 4. See the first 10 entries
print(chipo.head(10))
#Step 5. What is the number of observations in the dataset?
print(len(chipo.index))
#Ste... | {
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# Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
import random
import os
import numpy as np
from pythia.models.cnn_lstm import CNNLSTM
from pythia.common.registry import registry
from pythia.common.sample import Sample, SampleList
from pythia.utils.configuration import ConfigNode, Confi... | {
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% Ubah judul dan label berikut sesuai dengan yang diinginkan.
\section{Design and Implementation}
\label{sec:designandimplementation}
% Ubah paragraf-paragraf pada bagian ini sesuai dengan yang diinginkan.
This research is explaining about the implementation of one of the branch of deep learning studies with the aim t... | {
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import numpy as np
import tensorflow as tf
from tqdm import tqdm
from flearn.models.client import Client
from flearn.utils.tf_utils import process_grad
class BaseFedarated(object):
def __init__(self, params, learner, dataset):
'''initialize local models, build comp. graph'''
# transfer... | {
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from typing import Union
from pathlib import Path
from glob import glob
from tqdm import tqdm
import json
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from utils.bert.utils.utils import BertTokenizer
def collate_fn(data):
document, section, sentence, contains_ds = data[0]
se... | {
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from flask import Flask,request, url_for, redirect, render_template, jsonify
from pycaret.regression import *
import pandas as pd
import pickle
import numpy as np
app = Flask(__name__)
model = load_model('insurance_14052020')
cols = ['age', 'sex', 'bmi', 'children', 'smoker', 'region']
@app.route('/')
def home():
... | {
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import random
import numpy as np
from rl.policies import Policy
class RandomPolicy(Policy):
def __init__(self,
action_mapper,
epsilon=0.1,
epsilon_decay_step=0.0001,
epsilon_decay_epoch=0.,
epsilon_min=0.001,
re... | {
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#!/usr/bin/env python
# A simple script to test the installed version of numpy by calling
# 'numpy.test()'. Key features:
# -- convenient command-line syntax
# -- sets exit status appropriately, useful for automated test environments
# It would be better to set this up as a module in the numpy namespace, so
# tha... | {
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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import tensorflow as tf
# import numpy as np
import time
import discogan
import sys
sys.path.insert(0, '../')
import image_utils as iu
from datasets import Pix2PixDataSet as DataSets
results = {
'sampl... | {
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//=============================================================================================================
/**
* @file mne_project_to_surface.cpp
* @author Jana Kiesel <jana.kiesel@tu-ilmenau.de>;
* Matti Hamalainen <msh@nmr.mgh.harvard.edu>
* @version 1.0
* @date August, 2016
*
* @section LI... | {
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#include <boost/python/class.hpp>
#include <boost/python/overloads.hpp>
#include <boost/python/manage_new_object.hpp>
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include "MultiChannel.h"
using namespace boost::python;
//
// MultiChannel class
//
void wrapMultiChannel()
{
#if PVA_API_VERSION >= ... | {
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SUBROUTINE ML5_0_HELAS_CALLS_AMPB_1(P,NHEL,H,IC)
C
C Modules
C
USE ML5_0_POLYNOMIAL_CONSTANTS
C
IMPLICIT NONE
C
C CONSTANTS
C
INTEGER NEXTERNAL
PARAMETER (NEXTERNAL=4)
INTEGER NCOMB
PARAMETER (NCOMB=16)
INTEGER NBORNAMPS
PARAME... | {
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Require Import Bool List.
Import ListNotations.
Require Import Undecidability.PCP.PCP.
Require Import Undecidability.PCP.Util.Facts.
Import PCPListNotation.
Require Import Undecidability.PCP.Util.PCP_facts.
Require Import Undecidability.Synthetic.Definitions.
Local Hint Rewrite <- app_assoc : list.
Local Hint Resolve... | {
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from tkinter import *
from PIL import ImageTk, Image
from tkinter import filedialog
import numpy as np
from tensorflow import keras
import tensorflow as tf
from tensorflow.python.keras.models import load_model
from tkinter import messagebox
longitud, altura = 200, 200
modelo = './modelo_project_modelo_20... | {
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import vnmrjpy as vj
import numpy as np
from scipy.ndimage.filters import gaussian_filter, median_filter
from vnmrjpy.core.utils import vprint
import copy
# for hiding zero-divide warnigns
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
"""
Generate fieldmap from a set of gradient echo imag... | {
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Quad Phone 2 on the Campus Payphones node
Phone Number: (530) 7599256
Location: Sits not exactly in the Quad, but on the same block. Lives in the indented area next to the Memorial Union computer lab.
Description: Its identical twin, Quad Phone 1, is to its right.
Charge: 50 cents for local.
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# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_transforms.fractal.ipynb (unless otherwise specified).
__all__ = ['MandelBrotFractalTransform', 'JuliaFractalTransform']
# Cell
import numpy as np
from albumentations.core.transforms_interface import ImageOnlyTransform
from PIL import Image
# Cell
class MandelBrot... | {
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\section{Decomposition 8: UC22, UC23 (application upload and statistics)}
\subsection*{Selected architectural drivers}
The functional drivers are:
\begin{itemize}
\item \emph{UC22}: Upload an application
\item \emph{UC23}: Consult application statistics
\end{itemize}
% UC22:
% ... | {
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# -----------------------------------------------------------------------------
#
# -*- coding: utf-8 -*-
#
# phlox-libdc1394/dc1394/frame.py
#
# Copyright (C) 2016, by Matthias Yang Chen <matthias_cy@outlook.com>
# All rights reserved.
#
# phlox-libdc1394 is free software: you can redistribute it and/or modify it
# un... | {
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#' @title save_data
#' @description This function takes either a dataframe or all of the data you've filtered, and rolls it up into a csv and/
#' or a shapefile for continued analysis
#' @param df This is a dataframe that you want to save in some other format. If a spatial format is selected
#' (e.g. shapefile), it m... | {
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import sys
import numpy as np
import torch
from tqdm import trange
from atom_joggling.utils import ROOT, mean, parser
# Method specific options
# Mixup parameter of the Beta distribution
parser.add_argument("--alpha", default=0.75, type=float)
# weighting factor for the unlabeled loss term
parser.add_argument("--l... | {
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[STATEMENT]
lemma order_dense_order_ow_transfer[transfer_rule]:
assumes [transfer_rule]:
"bi_unique A" "right_total A" "bi_unique B" "right_total B"
shows
"(
rel_set A ===> (A ===> A ===> (=)) ===> (A ===> A ===> (=)) ===>
rel_set B ===> (B ===> B ===> (=)) ===> (B ===> B ===> (=)) ===>
... | {
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... |
import scanpy as sc
import muon as mu
import numpy as np
## VIASH START
par = {
'input': 'resources_test/pbmc_1k_protein_v3/pbmc_1k_protein_v3_filtered_feature_bc_matrix.h5mu',
'modality': ['rna'],
'output': 'output.h5mu',
'var_name_filter': 'filter_with_hvg',
'do_subset': False,
'flavor': 'seurat',
'n_t... | {
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#include <stdlib.h> /* srand, rand */
#include <iostream>
#include <fstream> // creating (.csv) files
#include <vector>
#include <cstring>
#include <string>
#include <functional>
#include <armadillo> // http://arma.sourceforge.net/docs.html
#include <thread> /* std::this_thread::sleep_for */
#include "EvolutionaryA... | {
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abstract type distribution end
module genericDistribution <: distribution
#default values
E(x)=0 #mean value #1st moment generating function
σ(x)=1 #variance #2nd moment generating function
function getPdf(x=smoothFunction)
"""
generalization of (any )
"""
return pdf(x)= smoothFunction
end
functio... | {
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from collections import OrderedDict
import copy
import cPickle
import gzip
import os
import urllib
import random
import stat
import subprocess
import sys
import time
import numpy
import theano
from theano import tensor as T
PREFIX = os.getenv(
'ATISDATA',
os.path.join(os.path.split(os.path.abspath(os.path.di... | {
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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... | {
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# influence functions for shapley values
def shapley_influence_function(Z, z_counts, W, v, psi, G, c_n, ics, measure):
"""
Compute influence function for the given predictiveness measure
@param Z the subsets of the power set with estimates
@param W the matrix of weights
@param v the estimated pred... | {
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import numpy as np
from pengle.transformer.base import FeatureOverwriter, timer
import copy
class ComplementMissingValue(FeatureOverwriter):
def __init__(self, columns, agg_func=np.mean):
super().__init__(columns)
self.agg_func = agg_func
def apply(self, train_dataset, test_dataset):
... | {
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import chainer
import chainer.functions as F
import chainer.links as L
import onnx
import onnx.helper as oh
from onnx import numpy_helper
from onnx import TensorProto
from onnx import ModelProto
from chainer_compiler.elichika.parser import core
from chainer_compiler.elichika.parser import graphs
from chainer_compiler... | {
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import argparse
import logging
import os
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import yaml
from torch import optim
from torch.utils.data import DataLoader, random_split
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from evaluation im... | {
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"""
============================
StrongTree Fit Example
============================
An example of fitting a StrongTree decision tree using :class:`trees.StrongTree.StrongTreeClassifier`
"""
from pickle import TRUE
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from trees.S... | {
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#include <server_lib/emergency_helper.h>
#if !defined(STACKTRACE_DISABLED)
#include <boost/version.hpp>
#include <boost/filesystem.hpp>
#include <iostream>
#include <sstream>
#include <fstream>
#if !defined(CUSTOM_STACKTRACE_IMPL) && BOOST_VERSION >= 106500
#include <boost/stacktrace.hpp>
namespace server_lib {
... | {
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"""
Algorithms that Involve Multiple DataFrames
===========================================
The pandas operations ``concat``, ``join``, and ``merge`` combine multiple
DataFrames. This module contains analogous algorithms in the parallel case.
There are two important cases:
1. We combine along a partitioned index
2... | {
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In order to give a better sense of the approach to reliability analysis and
optimization presented in \sref{chaos-reliability-analysis} and
\sref{chaos-optimization}, we consider a concrete application, meaning that we
specify the uncertain parameters and discuss the accompanying computations. This
application is also ... | {
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import os
import pytest
import musdb
import simplejson as json
import museval
import numpy as np
json_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'data/Music Delta - Rock.json',
)
@pytest.fixture()
def mus():
return musdb.DB(root_dir='data/MUS-STEMS-SAMPLE', is_wav=True)
def test... | {
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// Boost.Geometry (aka GGL, Generic Geometry Library)
// Unit Test
// Copyright (c) 2015, Oracle and/or its affiliates.
// Contributed and/or modified by Menelaos Karavelas, on behalf of Oracle
// Licensed under the Boost Software License version 1.0.
// http://www.boost.org/users/license.html
#ifndef BOOST_GEOMETR... | {
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#Djnago stuff
from .models import *
from django.shortcuts import render,redirect
from django.http import HttpResponse,JsonResponse
from django.core.serializers import serialize
import time
#helper functions
from .utils import traverse,convertGraph
#run binaries
import subprocess
from subprocess import PIPE, run
#netw... | {
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from datetime import datetime, timezone
import numpy as np
import pytest
import astropy.units as u
from stixcore.time import SCETime, SCETimeDelta, SCETimeRange
from stixcore.time.datetime import MAX_COARSE, MAX_FINE
def test_time_init():
t1 = SCETime(0, 0)
t2 = SCETime.from_float(0*u.s)
t3 = SCETime(t... | {
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import os
import sys
import time
import pickle
import random
import tarfile
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy import ndimage
from urllib.request import urlretrieve
from sklearn.linear_model import LogisticRegression
from utils import *
fro... | {
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import sys, os
sys.path.append(os.getcwd())
import entities, viewer
import numpy as np
import importlib
importlib.reload(entities)
importlib.reload(viewer)
cube = entities.Entity(node_color=(255, 255, 255), name="cube")
cube_nodes = [(x, y, z) for x in (-75, 75) for y in (-75, 75) for z in (-75, 75)]
cube.addNodes(np... | {
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import numpy as np
import matplotlib.pyplot as plt
from IPython.display import clear_output
import os
import contextlib
np.seterr(all='raise')
class NoSignChange(Exception):
def __init__(self, message='No sign change in specified bounds'):
# Call the base class constructor with the parameters it needs
... | {
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import abc
import os
import inspect
import requests
import pandas as pd
import numpy as np
import xarray as xr
from .adapters import Adapter
from .bounds import Bounds
from .constants import (RCMED_QUERY_URL, ESGF_NODES,
DEFAULT_INTAKE_CAT, DEFAULT_INTAKE_ESM_CAT)
from .ensemble impor... | {
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[STATEMENT]
lemma numsubst0_numbound0:
assumes "numbound0 t"
shows "numbound0 (numsubst0 t a)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. numbound0 (numsubst0 t a)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
numbound0 t
goal (1 subgoal):
1. numbound0 (numsubst0 t a)
[PROOF STEP]
proof ... | {
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function vertCongruenceAA(W)
err, i, todelete, vclasses = 10^-6, 1, [], []
verts = convert(Lar.Points, W')
kdtree = NearestNeighbors.KDTree(verts)
newverts = zeros(Int, size(verts,2))
for vi in 1:size(verts,2)
if !(vi in todelete)
nearvs = NearestNeighbors.inrange(kdtree, verts[:,vi], err)
push!(vclasses,n... | {
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const INTERNALNAMES = (:__model__, :__context__, :__varinfo__)
"""
isassumption(expr[, vn])
Return an expression that can be evaluated to check if `expr` is an assumption in the
model.
Let `expr` be `:(x[1])`. It is an assumption in the following cases:
1. `x` is not among the input data to the model,
2.... | {
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import os, sys
lib_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(lib_path)
import tensorflow as tf
from ddpg_actor import DDPG_Actor
from ddpg_critic import DDPG_Critic
class Model(object):
def __init__(self,
state_dim,
action_dim,
optimizer=... | {
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[STATEMENT]
lemma f_Exec_Stream_Acc_Output_drop: "
f_Exec_Comp_Stream_Acc_Output k output_fun trans_fun xs c \<up> n =
f_Exec_Comp_Stream_Acc_Output k output_fun trans_fun (xs \<up> n) (
f_Exec_Comp trans_fun (xs \<down> n \<odot>\<^sub>f k) c)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. f_Exec_Comp_Stre... | {
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import sys
import networkx as nx
import matplotlib
import matplotlib.pyplot as plt
import pygraphviz
from networkx.drawing.nx_agraph import graphviz_layout
graph = nx.Graph()
def dtoCOM(a):
global graph
return nx.dijkstra_path_length(graph, a, "COM")
for orbit in sys.stdin:
A, B = [i.strip() for i in o... | {
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import math
from typing import Iterable, Union
import numpy as np
from shor.errors import CircuitError
from shor.layers import _Layer
from shor.utils.collections import flatten
QbitOrIterable = Union[int, Iterable]
class _Gate(_Layer):
"""Abstract base quantum gate class
# Properties
input_length = va... | {
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import argparse
import logging
import json
from pprint import pprint
import numpy as np
import os
from astropy.coordinates import EarthLocation, SkyCoord
from astropy.time import Time
from astropy import units as u
from astropy.coordinates import AltAz
from datetime import datetime
import ctbend.ctbendbase.CTBend as... | {
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[STATEMENT]
lemma dj_cp:
fixes pi1::"'x prm"
and pi2::"'y prm"
and x ::"'a"
assumes cp: "cp TYPE ('a) TYPE('x) TYPE('y)"
and dj: "disjoint TYPE('y) TYPE('x)"
shows "pi1\<bullet>(pi2\<bullet>x) = (pi2)\<bullet>(pi1\<bullet>x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. pi1 \<bullet> pi2 \<bul... | {
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"""
To run tests:
pytest test_game.py
"""
import numpy as np
import curling.constants
import log_handler
from curling import board as board_utils
from curling import constants as c
from curling import game
from curling import simulation
from curling import utils
log_handler.flush_on_error()
def test_simulation_set... | {
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# Imports here
import matplotlib.pyplot as plt
import numpy as np
import time
import json
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
from collections import OrderedDict
import PIL
from PIL import Image
import argparse
... | {
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import os
from os.path import basename
os.environ['CUDA_VISIBLE_DEVICES'] = ''
from keras.models import load_model
import matplotlib.pyplot as plt
from skimage import io
import numpy as np
from skimage.color import gray2rgb, label2rgb
from skimage.io import imsave
from datetime import datetime
from functions import y... | {
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C
C $Id: pakrsp.f,v 1.5 2008-07-27 00:17:10 haley Exp $
C
C Copyright (C) 2000
C University Corporation for Atmospheric Research
C All Rights Reserved
C
C The use of this Software is governed by a License Agreement.
C
REAL FUNCTION PAKRSP (ANG)
C
C Function to convert DMS pac... | {
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rng = StableRNG(614)
# convert a Binary vector into vector of +1 or -1 values
# (for testing only):
pm1(y) = Int8(2) .* (Int8.(MLJBase.int(y))) .- Int8(3)
const MARGIN_LOSSES = MLJBase.MARGIN_LOSSES
const DISTANCE_LOSSES = MLJBase.DISTANCE_LOSSES
# using `WeightedSum` instead of `WeightedMean`; see
# https://github.... | {
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import numpy as np
import pandas as pd
data = pd.read_csv('input.txt', sep="\n", header=None)
# Part 1
def calculate_fuel_required(mass):
return int(mass / 3) - 2 # divide by 3, round down, subtract 2
fuel = data.apply(calculate_fuel_required, axis=1)
fuel_required_sum = fuel.values.sum()
print("Fuel required ... | {
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