text stringlengths 0 27.1M | meta dict |
|---|---|
#!/usr/bin/env Rscript
f1="./sheet1.csv";
f2="./sheet2.csv";
f3="./sheet3.csv";
res1 = read.csv(f1, sep=',',header=TRUE);
res2 = read.csv(f2, sep=',',header=TRUE);
res3 = read.csv(f3, sep=',',header=TRUE);
img_xlab = "log2(fold_change)";
img_ylab = "-log10(p.Value)";
p1="./image1.pdf";
p2="./image2.pdf";
p3="./im... | {
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import numpy as np
import matplotlib.pyplot as plt
#import katfile
from matplotlib.backends.backend_pdf import PdfPages
import optparse
import katholog
import os
def radial_data(data,annulus_width=1,working_mask=None,x=None,y=None,rmax=None):
"""
r = radial_data(data,annulus_width,working_mask,x,y)
A ... | {
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import torch
from torch.nn import functional as F
from mmdet3d.ops import Voxelization
from mmdet.models import DETECTORS
from .. import builder
from .two_stage import TwoStage3DDetector
from .voxelnet import Fusion
import numpy as np
@DETECTORS.register_module()
class PartA2(TwoStage3DDetector):
r"""Part-A2 det... | {
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From Coqprime Require Import PocklingtonRefl.
Local Open Scope positive_scope.
Lemma primo76:
prime 201471189889->
prime 39488365236169.
Proof.
intro H.
apply (Pocklington_refl
(Ell_certif
39488365236169
196
((201471189889,1)::nil)
0
3584
8
64)
((Proof_certif ... | {
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"""
Export function for the JEOL field-emission gun electron probe microanalyser (EPMA)
using "probe for EPMA".
"""
import csv
import numpy as np
import pandas as pd
from pathlib import Path
def write_pos(
df, filepath=Path("./exportedpoints.pos"), encoding="cp1252", z=10.7, **kwargs
):
"""
Export an data... | {
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"""
Script to show how the COVID-19 cases in the US are changing over time.
Plot shows the new daily confirmed cases and is sorted by number of new cases.
To run: `python us_cases.py`
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from statsmodels.nonpar... | {
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import numpy as np
import sympy as sp
import vorpy.linalg
def test_scalar_cross_product_tensor_symbolically ():
scalar_cross_product_tensor = vorpy.linalg.scalar_cross_product_tensor(dtype=sp.Integer)
u = np.array(sp.var('u_0,u_1'))
v = np.array(sp.var('v_0,v_1'))
# The awkward `[()]` notation on the l... | {
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# _____ ______ _____
# / ____/ /\ | ____ | __ \
# | | / \ | |__ | |__) | Caer - Modern Computer Vision
# | | / /\ \ | __| | _ / Languages: Python, C, C++, Cuda
# | |___ / ____ \ | |____ | | \ \ http://github.com/jasmcaus/caer
# \_____\/_/ \_ \______ |_| \_\
# Lic... | {
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ITensors.itensor(A::ITensor) = A
# Insert missing diagonal blocks
function insert_diag_blocks!(T::Tensor)
for b in eachdiagblock(T)
blockT = blockview(T, b)
if isnothing(blockT)
# Block was not found in the list, insert it
insertblock!(T, b)
end
end
end
insert_diag_blocks!(T::ITensor) = in... | {
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[STATEMENT]
lemma PO_l2_inv8 [iff]: "reach l2 \<subseteq> l2_inv8"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. reach l2 \<subseteq> l2_inv8
[PROOF STEP]
by (rule_tac J="l2_inv1 \<inter> l2_inv3" in inv_rule_incr) (auto) | {
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using Documenter, SparseRegression
makedocs(
format = Documenter.HTML(),
sitename = "SparseRegression.jl",
authors = "Josh Day",
clean = true,
pages = [
"index.md",
"usage.md",
"algorithms.md"
]
)
deploydocs(
repo = "github.com/joshday/SparseRegression.jl.git",
)
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from data import common
from data.sr import dataclass
import numpy as np
from PIL import Image
_parent_class = dataclass.SRData
class Demo(_parent_class):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@staticmethod
def get_kwargs(cfg, train=False):
kwargs = _par... | {
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import streamlit as st
import pandas as pd
import numpy as np
import json
from matplotlib import pyplot as plt
from matplotlib.backends.backend_agg import RendererAgg
import logging
import sys
logging.basicConfig(level=logging.DEBUG)
_lock = RendererAgg.lock
sys.path.append('../src/')
sys.path.append('..')
sys.path.... | {
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"""
Test cases for the regi0.geographic.outliers.find_value_outliers function.
"""
import numpy as np
import pandas as pd
from regi0.geographic.outliers import find_value_outliers
def test_iqr(records):
result = find_value_outliers(
records, "scientificName", "minimumElevationInMeters", method="iqr"
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import math
import os
import textwrap
from functools import partial
from multiprocessing import Pool, Manager
from os.path import join
import matplotlib
matplotlib.use('Agg')
import numpy as np
import seaborn as sns
sns.set()
from tqdm import tqdm
import argparse
from config import MovieQAPath
from data.data_loader ... | {
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/**
* Copyright (c) 2018, University Osnabrück
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this li... | {
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# -*- coding: utf-8 -*-
import sys
import os
import numpy as np
import imgfileutils as imf
from aicsimageio import AICSImage, imread
import progressbar
import shutil
from apeer_ometiff_library import io, omexmlClass
import tifffile
import itertools as it
"""
def update5dstack(image5d, image2d,
dimst... | {
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[STATEMENT]
lemma doctor_optimal_match_unique:
assumes "doctor_optimal_match ds X"
assumes "doctor_optimal_match ds Y"
shows "X = Y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. X = Y
[PROOF STEP]
proof(rule iffD2[OF set_eq_iff, rule_format])
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>x. (x \<in... | {
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/-
Copyright (c) 2020 Simon Hudon. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Author(s): Simon Hudon
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.testing.slim_check.sampleable
import Mathlib.PostPort
universes l v u_1 u u_2
namespace Math... | {
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# Build a neural network for approximate Q learning
import gym
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
import keras.layers as L
def get_action(state, epsilon=0):
"""
sample actions with epsilon-greedy policy
recap: with p = epsilon pick ... | {
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import os
import shutil
import glob
import json
import argparse
import numpy as np
from transformations_np import TransformationSpherical, Transformation3D
from tqdm import tqdm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--src', required=True,
hel... | {
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// Luaponte library
// Copyright (c) 2012 Peter Colberg
// Luaponte is based on Luabind, a library, inspired by and similar to
// Boost.Python, that helps you create bindings between C++ and Lua,
// Copyright (c) 2003-2010 Daniel Wallin and Arvid Norberg.
// Use, modification and distribution is subject to the Boost... | {
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subroutine sub ()
print *, "output string ..."
end subroutine sub
end
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import cupy as cp
from model.utils.nms import non_maximum_suppression
from model.utils.loc2bbox_gpu import loc2bbox
from torch.utils.dlpack import to_dlpack
from torch.utils.dlpack import from_dlpack
class ProposalCreator:
def __init__(self,
parent_model,
nms_thresh=0.7,
... | {
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import logging
import yaml
from pathlib import Path
import astropy.units as u
from astropy.coordinates import SkyCoord, Angle
from regions import CircleSkyRegion
from gammapy.analysis import Analysis, AnalysisConfig
from gammapy.maps import MapAxis
from gammapy.modeling import Fit
from gammapy.data import DataStore
fro... | {
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"""need to refactor with common backend interface"""
from __future__ import annotations
import ast
import functools
import os
import typing
from typing_extensions import TypedDict
import ctc.config
from ctc import binary
from ctc import directory
from ctc import spec
from ctc import rpc
from ctc.toolbox import backe... | {
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#!/usr/bin/python
# DQN implementation of https://github.com/matthiasplappert/keras-rl for Keras
# was used with epsilon-greedy per-episode decay policy.
import numpy as np
import gym
from gym import wrappers
from tfinterface.utils import get_run
from tfinterface.reinforcement import DQN, ExpandedStateEnv
import rand... | {
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[STATEMENT]
lemma heap_upds_ok_upd:
"heap_upds_ok (\<Gamma>, Upd x # S) \<Longrightarrow> x \<notin> domA \<Gamma> \<and> x \<notin> upds S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. heap_upds_ok (\<Gamma>, Upd x # S) \<Longrightarrow> x \<notin> domA \<Gamma> \<and> x \<notin> upds S
[PROOF STEP]
by auto | {
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import numpy as np
from .sysEngVals import SysEngVals
__all__ = ['m5_flat_sed', 'm5_scale']
def m5_scale(expTime, nexp, airmass, FWHMeff, musky, darkSkyMag, Cm, dCm_infinity, kAtm,
tauCloud=0, baseExpTime=15):
""" Return m5 (scaled) value for all filters.
Parameters
----------
expTime :... | {
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from fastapi import FastAPI, WebSocket
import asyncio
import numpy as np
app = FastAPI()
@app.websocket("/pressureTaps")
# This is where the serial sensor data integrates to the Streamlit app using websockets
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
while True... | {
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@testset "Data" begin
@test artificialIn_SAR_image(2) == 2 * π * ones(2, 2)
@test artificial_S1_slope_signal(20, 0.0) == repeat([-π / 2], 20)
@test ismissing(artificial_S1_signal(-1.0))
@test ismissing(artificial_S1_signal(2.0))
@test artificial_S1_signal(2) == [-3 * π / 4, -3 * π / 4]
# for ... | {
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import csv
import numpy as np
import torch
import matplotlib.pyplot as plt
color_bar = ['black', 'red', 'blue', 'green', 'brown', 'yellow', 'cyan', 'magenta']
def TrainHistoryPlot(his, his_label, save_name, title, axis_name, save = True):
#history must be input as list[0]: iter or epoch
#and otehr of history... | {
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# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {
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[STATEMENT]
lemma len_space_left:
"left (space ts v c) \<le> right (ext v) \<longrightarrow> left (len v ts c) \<ge> left (space ts v c)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. left (space ts v c) \<le> right (ext v) \<longrightarrow> left (space ts v c) \<le> left (len v ts c)
[PROOF STEP]
proof
[PROOF S... | {
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function [dx,dy]= centered_gradient(input,dx,dy, nx,ny)
dx = zeros(1,round(nx*ny*+nx));
dy = zeros(1,round(nx*ny*+nx));
nx = round(nx);
ny = round(ny);
size(input);
for i = 1:ny-1
for j = 1: nx-1
k = round(i * nx + j);
if(nx+k < length(input))
dx(k) = 0.5*(input(k+1) - inpu... | {
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{-# OPTIONS --without-K --rewriting #-}
open import lib.Base
open import lib.PathFunctor
open import lib.PathGroupoid
open import lib.Equivalence
{- Structural lemmas about paths over paths
The lemmas here have the form
[↓-something-in] : introduction rule for the something
[↓-something-out] : elimination rule for... | {
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import networkx as nx
import numpy as np
import tensorflow as tf
from keras import Input
from keras import backend as K
from keras.models import Model
from matplotlib import pyplot as plt
from skimage import segmentation, color, filters
from skimage.color import rgb2gray, gray2rgb
from skimage.filters import sobel
from... | {
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"""Classification using random forest."""
import logging
import pickle
import numpy as np
from sklearn.ensemble import RandomForestClassifier
logger = logging.getLogger(__name__)
class RandomForest:
"""Train or classify using a RandomForest model."""
def __init__(self, num_features, model=None):
"""... | {
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import numpy as np
import pandas as pd
from ..base import sim_aux_rdash, MatchingResult
from ..sim_measures import sim_zero_one
def _score_min(rdash, aux, sim_scalar):
return sim_aux_rdash(rdash, aux, sim_scalar).min(axis=1)
def algorithm1a(rdash, aux,
sim_scalar=sim_zero_one,
a... | {
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SUBROUTINE POLY_OPNF ( nop, lunp, outfil, iret )
C************************************************************************
C* POLY_OPNF *
C* *
C* This subroutine opens a Common Alerting Protocol (CAP) file. The *
C* naming format: YYMMDDHH_cc_nn.xml, where cc is the center name, *
C* nn nth polygon. ... | {
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# Copyright 2018 The TensorFlow 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... | {
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {
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\chapter{Conclusion}
The aim of our research was to apply deep learning techniques to a WF environment to automatically extract fingerprints from a variable-length trace.
We do in fact show that this is possible by introducing a novel approach to perform the attack, together with two deep learning models, namely a \te... | {
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[STATEMENT]
lemma imethds_norec:
"\<lbrakk>iface G md = Some i; ws_prog G; table_of (imethods i) sig = Some mh\<rbrakk> \<Longrightarrow>
(md, mh) \<in> imethds G md sig"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>iface G md = Some i; ws_prog G; table_of (imethods i) sig = Some mh\<rbrakk> \<Longr... | {
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"""
Analytic functions for this test are defined in "analytic_filter.ipynb" in the development/ directory.
"""
from hmf.density_field import filters
import numpy as np
from numpy import sin, cos, pi
import warnings
import pytest
# Need to do the following to catch repeated warnings.
warnings.simplefilter("always", Us... | {
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[STATEMENT]
lemma result_costD': assumes "result f_c = f \<and> cost f_c \<le> b"
"f_c = (a,c)"
shows "a = f" "c \<le> b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a = f &&& c \<le> b
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
result f_c = f \<and> cost f_c \<le> b
f_c = (a, c)
goal (... | {
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//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
@copyright 2016 J.T. Lapreste
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
... | {
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"""UNIT TESTS for the experimetn result maniupaltio
python -m unittest test_Result
"""
from commonLib.DBManager import DB
from stats import Result, Histogram, NumericStats, NumericList
import numpy as np
import os
import unittest
class TestResult(unittest.TestCase):
def setUp(self):
self._db ... | {
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import sys
import time
import numpy as np
import openml
from autogluon_benchmark.tasks import task_loader, task_utils
from openml.exceptions import OpenMLServerException
sys.path.append('../')
def get_dataset(task):
X, y, _, _ = task.get_dataset().get_data(task.target_name)
return X, y
if __name__ == '__m... | {
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"""
Generic helpers for LLVM code generation.
"""
from __future__ import print_function, division, absolute_import
import collections
from contextlib import contextmanager
import functools
from llvmlite import ir
from . import utils, config, types
bool_t = ir.IntType(1)
int8_t = ir.IntType(8)
int32_t = ir.IntType... | {
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/-
Stalk of rings on basis.
https://stacks.math.columbia.edu/tag/007L
(just says that the category of rings is a type of algebraic structure)
-/
import to_mathlib.opens
import topology.basic
import sheaves.stalk_on_basis
import sheaves.presheaf_of_rings_on_basis
universe u
open topological_space
names... | {
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#!/usr/bin/env python3
import os
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from optparse import OptionParser
from matplotlib.ticker import NullFormatter
# Usage: python3 genotypes_hwe_vs_maf.py -o output_file_prefix -e plink_hardy_file -f plink_afreq_file
... | {
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import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
def generate_colours(df, column, cmap_name):
# TODO: they get generated a little different than what pandas does automatically
labels = np.sort(df[column].unique())
cmap = plt.get_cmap(cmap_name)
colours = cmap(np.linspace(0,1... | {
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"""Power spectrum plotting functions.
Notes
-----
This file contains functions for plotting power spectra, that take in data directly.
"""
from inspect import isfunction
from itertools import repeat, cycle
import numpy as np
from scipy.stats import sem
from fooof.core.modutils import safe_import, check_dependency
f... | {
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""" Train a VAE model used to filter and enhance 3d points """
import json
from datetime import datetime
import matplotlib
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tqdm import tqdm
import cameras
import data_utils
import viz
from top_vae_... | {
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# -*- coding:utf-8 -*-
"""
-------------------------------------------------------------------------------
Project Name : ESEP
File Name : interpolate.py
Start Date : 2022-03-25 05:26... | {
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# coding: utf-8
"""Interatomic potential dataset for property of HDNNP. """
import numpy as np
from hdnnpy.dataset.property.property_dataset_base import PropertyDatasetBase
class InteratomicPotentialDataset(PropertyDatasetBase):
"""Interatomic potential dataset for property of HDNNP. """
PROPERTIES = ['ene... | {
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# Copyright (c) 2020 PHYTEC Messtechnik GmbH
# SPDX-License-Identifier: Apache-2.0
import os
import time
import cv2
import tflite_runtime.interpreter as tflite
import numpy as np
import json
import concurrent.futures
class Ai:
def __init__(self, model_path, embeddings_path, modeltype='quant'):
self.mode... | {
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[STATEMENT]
lemma Mertens_convergent: "convergent (\<lambda>n::nat. \<MM> n - ln n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. convergent (\<lambda>x. \<MM> (real x) - ln (real x))
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. convergent (\<lambda>x. \<MM> (real x) - ln (real x))
[PROOF... | {
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"ma... |
from __future__ import absolute_import
from __future__ import division
from tiny_faces.tiny_fd import TinyFacesDetector
import sys
import cv2
import os
import os.path as ops
import numpy as np
import json
import argparse
from manage_data.get_density_map import interpolate_scale
from manage_data.utils import mkdir_i... | {
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""" Module to fit the electronic charge density of closed shell molecules obtained
by self-consistent siesta calculations with Gaussian functionsi
HOW TO
-------
1) run get_data()
2) run get_atom_pos()
3) run add_core_density()
4) run fit_poly()
"""
import sys
import nu... | {
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(**
Here we define the signature for the coherent 2-groups.
A coherent 2-group has a unit, a multiplication, and an inverse operation.
The inverse laws are witnessed up to adjoint equivalence while associativity and unitality are witnessed as in a monoidal category.
For more details, see:
- https://ncatlab.org/nlab/sh... | {
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classdef PTKDensityInterpolation < PTKPlugin
% PTKDensityInterpolation. Plugin for interpolating density values to a
% different voxel size
%
% This is a plugin for the Pulmonary Toolkit. Plugins can be run using
% the gui, or through the interfaces provided by the Pulmonary Toolkit.
% ... | {
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import matplotlib.pyplot as plt
from scipy.io import wavfile
import os
from pydub import AudioSegment
# Calculate and plot spectrogram for a wav audio file
def graph_spectrogram(wav_file, sec=10):
rate, data = get_wav_info(wav_file)
nfft = 200 # Length of each window segment
fs = 8000 # Sampling frequencie... | {
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# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2019, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... | {
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[STATEMENT]
lemma tranclp_unfold [code]:
"tranclp r a b \<longleftrightarrow> (a, b) \<in> trancl {(x, y). r x y}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. r\<^sup>+\<^sup>+ a b = ((a, b) \<in> {(x, y). r x y}\<^sup>+)
[PROOF STEP]
by (simp add: trancl_def) | {
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//---------------------------------------------------------------------------//
//!
//! \file tstNormalDistribution.cpp
//! \author Alex Robinson
//! \brief Normal distribution unit tests.
//!
//---------------------------------------------------------------------------//
// Std Lib Includes
#include <iostream>
//... | {
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#!/usr/bin/env python
from pyquil.api import QVMConnection
from pyquil.quil import Program as P
from pyquil.gates import H
from functools import reduce
from numpy import average
from numpy import ceil
from numpy import log
from numpy import std
import argparse
class QDice:
def __init__(self):
self.qvm ... | {
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using Plots
include("src/FluidQueues.jl")
include("model_def.jl")
h(t) = (t>0.0) ? FluidQueues.hitting_times_cdf(model,t,1.0,2.0) : zeros(2,4)
tvec = 0.0:0.1:3
cdfs = zeros(length(tvec),4)
for (c,t) in enumerate(tvec)
h_mat = h(t)
cdfs[c,:] = h_mat[3:6]
end
plot(tvec,cdfs) | {
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import numpy as np
import logging
import sys
from fvcore.transforms.transform import (
HFlipTransform,
NoOpTransform,
VFlipTransform,
)
from PIL import Image
from detectron2.data import transforms as T
class ResizeShortestEdge(T.Augmentation):
"""
Scale the shorter edge to the given size, with a ... | {
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#include <vector>
#include <string>
#include <utility>
#include <map>
#include <boost/shared_ptr.hpp>
#ifndef HEADER_B
#define HEADER_B
#include "A.hpp"
enum testB {
BB, BBB
};
class Bklass : public A_second {
public:
// int i_; // from parent class
Bklass(int i): A_second(i) { };
B... | {
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import streamlit as st
import numpy as np
from PIL import Image
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.engine import DefaultPredictor
from detectron2.data import MetadataCatalog
# https://en.wikipedia.org/wiki/YUV#SDTV_w... | {
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! mimic NWChem tgt_sd_t_s1_1 kernel
! RL: do not redefine simd clause to be schedule(static, 1)
! RL: make the schedule clause usage be explicit
implicit integer (a-z)
l1 = 1; l2 = 1; l3 = 1; l4 = 1; l5 = 1; l6 = 1;
u1 = 24; u2 = 24; u3 = 24; u4 = 24; u5 = 24; u6 = 24;
call tgt_sd_t_s1_1(l1,l2,l3,l4,l5,l6, u1,... | {
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#
#
# Copyright (C) University of Melbourne 2012
#
#
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify... | {
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import torch
import numpy as np
from torch.autograd import Variable
import torch.nn.functional as F
def sampler(faces, verts, n, bypass=False,sigma = 0.0):
"""Sample n verts given tri-mesh.
:Parameters:
faces : batch_size * face_num * 3
The faces of the mesh, start from 1.
verts : batch_size * vert_num *... | {
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#!/usr/bin/env python3
#
# BSD 3-Clause License
#
# This file is part of the Basalt project.
# https://gitlab.com/VladyslavUsenko/basalt.git
#
# Copyright (c) 2019-2021, Vladyslav Usenko and Nikolaus Demmel.
# All rights reserved.
#
import argparse
import json
import numpy as np
from scipy.spatial.transform import Ro... | {
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import cv2
import numpy as np
import copy
# 画像のトリミングとかできるならここでやる
def preprocessing(img, top=0, bottom=-1, left=0, right=-1):
h, w, c = img.shape
if bottom == -1:
bottom = h
if right == -1:
right = w
# 投入部分をトリミングする方針に変更
# return crop_img(img)
return [img[top:bottom, left:right, :]]
def crop_img(im... | {
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################################################################################
#
# Morphism types
#
################################################################################
morphism_type(::Type{T}, ::Type{S}) where {R, T <: AbsAlgAss{R}, S <: AbsAlgAss{R}} = AbsAlgAssMor{T, S, Generic.Mat{R}}
morphism_type... | {
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from PIL import ImageTk
import PIL.Image
import tkinter as tk
import tkinter.messagebox
from tkinter import *
from tkinter.ttk import *
from time import strftime
from datetime import datetime as dt
import datetime
import pandas as pd
import pytz
from timezonefinder import TimezoneFinder
from geopy.geocoders... | {
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from numpy import pi, exp
def _comp_point_coordinate(self):
"""Compute the point coordinates needed to plot the Slot.
Parameters
----------
self : SlotM18
A SlotM18 object
Returns
-------
point_dict: dict
A dict of the slot coordinates
"""
Rbo = self.get_Rbo()
... | {
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import numpy as np
from importlib import import_module
from TexasHoldem import Card, Hand, Deck, Player, Game
def test_game():
cash = 10
ai_name = 'AI.DefaultTexasHoldemAI'
n_players = 5
names = np.random.choice(Player.names, n_players, replace=False)
players = [Player(ai=import_module(ai_name).Te... | {
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'''
The trade algorithm for WealthSimple Assignment
Author: Jinhua Wang
License MIT
For simplicity purposes, this file assumes stock prices follows Brownian Motion,
and we only trade SP500.
'''
import numpy as np
class TradeData:
'''
Soure of the trade data
to simplify matters, assume that only SP500 is traded
... | {
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#
# Copyright 2017 Scott A Dixon
#
# 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 t... | {
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import numpy as np
import generators as ge
def question(n):
print(f'{n}. ' + ge.QHA[f'q{n}'])
def hint(n):
print(ge.QHA[f'h{n}'])
def answer(n):
print(ge.QHA[f'a{n}'])
def pick():
n = np.random.randint(1, 100)
question(n)
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mutable struct Input
graph::DiGraph
weights::Array{Float64,2}
ndds::Array{Int64,1}
max_cycle_length::Int64
max_chain_length::Int64
solver_instance::Any
time_param::String
time_factor::Float64
start_time::Float64
function Input(
graph::DiGraph,
weights::Array{Floa... | {
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import matplotlib.pyplot as plt
import numpy as np
#Plot e scatter
fig = plt.figure()
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(np.random.randn(50), color='red')
ax2 = fig.add_subplot(1, 2, 2)
ax2.scatter(np.arange(50), np.random.randn(50))
plt.show()
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#include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include <algorithm>
#include <iterator>
#include <string.h>
#include <boost/tokenizer.hpp>
#define DATA_FOLDERS_PATH_FORMAT "../location_001_dataset_%03d/"
#define FILE_FORMAT "location_001_ivdata_%03d.txt"
struct event
{
publ... | {
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#include <gtest/gtest.h>
#include "scheme/actor/Atom.hh"
#include "ligand_factory.hh"
#include "scheme/util/SimpleArray.hh"
#include <iterator> // std::back_inserter
#include <fstream>
#include <boost/foreach.hpp>
namespace scheme { namespace chemical { namespace test {
using std::cout;
using std::endl;
TEST(l... | {
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import networkx.algorithms.tree.tests.test_mst
import pytest
from graphscope.nx.utils.compat import import_as_graphscope_nx
from graphscope.nx.utils.compat import with_graphscope_nx_context
import_as_graphscope_nx(networkx.algorithms.tree.tests.test_mst,
decorators=pytest.mark.usefixtures("gra... | {
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from torch import nn
import numpy as np
import torch
from utils import (
add_device,
get_logger,
)
logger = get_logger()
def train(model, dataloader, input_key, target_key, optimizer, loss_func,
device=torch.device('cpu')):
train_loss = 0.0
for step, data in enumerate(dataloader):
... | {
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 19 15:23:20 2020
@author: grat05
"""
import sys
import os
sys.path.append(os.path.abspath(
os.path.join(os.path.dirname(__file__),
os.pardir, os.pardir, os.pardir)))
#from iNa_models import Koval_ina, OHaraRudy_INa
import atr... | {
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From Tweetnacl Require Import Libs.Export.
Open Scope Z.
Definition set_xor (i:Z) := Z.lnot (i - 1).
Lemma set_xor_0 : set_xor 0 = 0.
Proof. reflexivity. Qed.
Lemma set_xor_1 : set_xor 1 = -1.
Proof. reflexivity. Qed.
Lemma land_0 : forall i, Z.land 0 i = 0.
Proof. intro. go. Qed.
Lemma land_minus_1 : forall i, Z... | {
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#= ######################################################
model structs
###################################################### =#
mutable struct ElementRaw{T1<:Integer, T2<:AbstractFloat}
node1::T1
node2::T1
com::Point2D{T2}
nvec::Point2D{T2}
area::T2
end
struct Element{T1<:Integer, T2<:AbstractF... | {
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(*<*)
(*
* Copyright 2015, NICTA
*
* This software may be distributed and modified according to the terms of
* the BSD 2-Clause license. Note that NO WARRANTY is provided.
* See "LICENSE_BSD2.txt" for details.
*
* @TAG(NICTA_BSD)
*)
theory Noninterference
imports
Global_Invariants_Lemmas
Local_Invariants_L... | {
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import numpy as np
import torch
from collections import defaultdict
#ops = ['~', '^', '+', '*', '<', '>']
ops = ['~', '+', '>']
symb2nary = {
'0' : 0,
'1' : 0,
'~' : 1,
'+' : 2,
'>' : 3
}
class NAryBooleanExpLang(object):
def __init__(self, n = 3, p = 0.5):
assert n <= 3
self.n = n
self.... | {
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import numpy as np
if __name__ =="__main__":
xx = np.array([[0.1, 0.2, 0.3, 0.4],
[0.5, 0.6, 0.7, 0.8],
[0.8, 0.6, 0.3, 0.4],
[0.8, 0.6, 0.7, 0.8],
[0.8, 0.8, 0.8, 0.4],
[0.1, 0.6, 0.7, 0.8],
[0.1, 0.2, 0.... | {
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# Fixed point classification in 2D
using DrWatson
@quickactivate "NonlinearDynamicsTextbook"
include(srcdir("style.jl"))
using DynamicalSystems, PyPlot, Random
alleigs = []
fig, axs = subplots(2,3; figsize = (figx, 2figy))
ax = gca()
xgrid = -5:0.05:5
ygrid = xgrid
ux = zeros(length(xgrid), length(ygrid))
uy = copy(ux... | {
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"""
to do...
"""
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
import argparse
import gym
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```julia
using DifferentialEquations
using Plots
```
Consider the simple reaction:
\begin{align}
A &\longleftrightarrow B\\
B &\longleftrightarrow C\\
\end{align}
Both are elementary steps that occur in the liquid phase, and we will consider it in a few different solvent environments.
```julia
gammaA(XA, X... | {
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# -*- coding: utf-8 -*-
"""
Automatic Colour Conversion Graph
=================================
Defines the automatic colour conversion graph objects:
- :func:`colour.describe_conversion_path`
- :func:`colour.convert`
"""
import inspect
import numpy as np
import textwrap
from collections import namedtuple
from c... | {
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