Spencer Lyon sglyon

## eigen_xtensor_buf.cpp
#include <stdexcept>
#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"
#include "xtensor/xbuffer_adaptor.hpp"
#include "Eigen/Dense"

auto to_xarray(Eigen::VectorXd& in) {
  using buf = xt::xbuffer_adaptor<double>;
  using xbuf_arr = xt::xarray_container<buf, xt::layout_type::column_major, std::vector<std::size_t>>;

## extract_attachments.py
""" A preprocessor that extracts all of the attachments from the notebook file.
The extracted attachments are returned in the 'resources' dictionary.

Based on the ExtractOutputsProcessor in nbconvert... the license for
nbconvert is

# Licensing terms

This project is licensed under the terms of the Modified BSD License
(also known as New or Revised or 3-Clause BSD), as follows:

## Purchase.sol
// SPDX-License-Identifier: MIT

pragma solidity >=0.4.22;

  /**
   * @title Purchase
   * @dev Contract to facilitate purchase/sale of high value art
   */
contract Purchase {
    uint256 public price;

## package.json
{
  "name": "extradough_eater",
  "version": "1.0.3",
  "scripts": {
    "start": "expo start",
    "android": "expo start --android",
    "ios": "expo start --ios",
    "web": "expo start --web",
    "eject": "expo eject",
    "test": "jest",

## example.jl
using PlotlyJS

# set up data
na = 10
nw = 5
zz = rand(1:4, na, nw, nw)

text = similar(zz, String)
text[zz .== 1] = "stay, work"
text[zz .== 2] = "move, work"

## GrowthProject.toml
[deps]
BasisMatrices = "08854c51-b66b-5062-a90d-8e7ae4547a49"
DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"
Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
QuantEcon = "fcd29c91-0bd7-5a09-975d-7ac3f643a60c"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

## density.m
function [density, pts_PCn, di_min] = eval_density(data, points)
[n,d] = size(data);
n_points = size(points,1);

Datan = (data-ones(n,1)*mean(data))./(ones(n,1)*std(data));
[U,S,V] = svd(Datan,0);
PC = Datan*V;
PCn = PC./(ones(n,1)*std(PC));

Pointsn = (points-ones(n_points,1)*mean(data))./(ones(n_points,1)*std(data));

## wunderlist2todoist.py
import json
import os
import time
from collections import OrderedDict
from contextlib import contextmanager

import wunderpy2

import todoist

## dolo_framework.ipynb

      
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## get_irs_migration_data.go
package main

import (
	"fmt"
	"github.com/mitchellh/go-homedir"
	"io"
	"net/http"
	"os"
	"path"
	"sync"
	#include <stdexcept>
	#include "xtensor/xarray.hpp"
	#include "xtensor/xio.hpp"
	#include "xtensor/xbuffer_adaptor.hpp"
	#include "Eigen/Dense"

	auto to_xarray(Eigen::VectorXd& in) {
	using buf = xt::xbuffer_adaptor<double>;
	using xbuf_arr = xt::xarray_container<buf, xt::layout_type::column_major, std::vector<std::size_t>>;
	""" A preprocessor that extracts all of the attachments from the notebook file.
	The extracted attachments are returned in the 'resources' dictionary.

	Based on the ExtractOutputsProcessor in nbconvert... the license for
	nbconvert is

	# Licensing terms

	This project is licensed under the terms of the Modified BSD License
	(also known as New or Revised or 3-Clause BSD), as follows:
	// SPDX-License-Identifier: MIT

	pragma solidity >=0.4.22;

	/**
	* @title Purchase
	* @dev Contract to facilitate purchase/sale of high value art
	*/
	contract Purchase {
	uint256 public price;
	{
	"name": "extradough_eater",
	"version": "1.0.3",
	"scripts": {
	"start": "expo start",
	"android": "expo start --android",
	"ios": "expo start --ios",
	"web": "expo start --web",
	"eject": "expo eject",
	"test": "jest",
	using PlotlyJS

	# set up data
	na = 10
	nw = 5
	zz = rand(1:4, na, nw, nw)

	text = similar(zz, String)
	text[zz .== 1] = "stay, work"
	text[zz .== 2] = "move, work"
	[deps]
	BasisMatrices = "08854c51-b66b-5062-a90d-8e7ae4547a49"
	DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
	LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
	Optim = "429524aa-4258-5aef-a3af-852621145aeb"
	Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
	Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
	QuantEcon = "fcd29c91-0bd7-5a09-975d-7ac3f643a60c"
	Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
	Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
	function [density, pts_PCn, di_min] = eval_density(data, points)
	[n,d] = size(data);
	n_points = size(points,1);

	Datan = (data-ones(n,1)mean(data))./(ones(n,1)std(data));
	[U,S,V] = svd(Datan,0);
	PC = Datan*V;
	PCn = PC./(ones(n,1)*std(PC));

	Pointsn = (points-ones(n_points,1)mean(data))./(ones(n_points,1)std(data));
	import json
	import os
	import time
	from collections import OrderedDict
	from contextlib import contextmanager

	import wunderpy2

	import todoist
	package main

	import (
	"fmt"
	"github.com/mitchellh/go-homedir"
	"io"
	"net/http"
	"os"
	"path"
	"sync"