Daniel dannys4

## joker.py
import numpy as np
from dataclasses import dataclass
from functools import partial
from typing import Callable, Any

__any__ = ['logpdf', 'sample']


def joker_eye_logpdf(mean: np.ndarray, inv_std: np.ndarray, x: np.ndarray) -> np.ndarray:
    z = (x - mean) @ inv_std

## metal_cholesky.jl
using Metal
using LinearAlgebra
using LinearAlgebra: cholesky
using Metal: device, global_queue, PrivateStorage, SharedStorage
using Metal.MPS: MPSMatrix, MPSMatrixDecompositionStatus, MPSMatrixDecompositionCholesky, MPSMatrixCopy, MPSMatrixCopyDescriptor, MtlFloat, MPSCommandBuffer, encode!, commit!, commitAndContinue!, wait_completed
using Metal.MPS: checknonsingular, checkpositivedefinite

Metal.ObjectiveC.@autoreleasepool function LinearAlgebra.cholesky(A::MtlMatrix{T}, check::Bool = true, lower::Bool = true) where {T<:MtlFloat}
    M,N = size(A)
    @assert M == N

## script.js
function getHeader() {
  return `
<!doctype html><html>
<head>
    <!--[if gte mso 9]><xml><o:OfficeDocumentSettings><o:AllowPNG/><o:PixelsPerInch>96</o:PixelsPerInch></o:OfficeDocumentSettings></xml><![endif]-->
    <style>
        body {
            background-color: #fff
        }
        .gse_alrt_title {

## ExoticWeightQuadrature.jl
using SpecialFunctions, Statistics, LinearAlgebra, Random

abstract type AbstractIntegralWeight end

abstract type AbstractTruncatedGaussian <: AbstractIntegralWeight end

struct TruncatedGaussian <: AbstractTruncatedGaussian
    a::Float64
    b::Float64
end

## gausslaguerre_lobatto.jl
"""
This file is intended to create a Gauss quadrature rule with weight `exp(-x)` that includes point `x_1=0`.

Frankly, I have no idea if it works

=============

Copyright 2024, Daniel Sharp

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, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

## adaptiveMap.jl
using Distributions, SpecialFunctions, MParT, GLMakie

# Taken from StatsFuns
invsqrt2 = Float64(1 / sqrt(big(2)))
normcdf(z) = (erf(z * invsqrt2) + 1) / 2

function sample_banana(n_samples)
    z = randn(2, n_samples)
    [z[1, :]'; ((z[2, :]' .+ (z[1, :] .^ 2)') .- 1) / 1.75]
end

## gmm1d.py
import mpart as mt
import numpy as np
import pickle
from matplotlib import pyplot as plt

# Class to represent a GMM
# For some reason, there's no good way to evaluate the logpdf and gradlogpdf of a GMM in python
class GMM:
    # Initialize the GMM
    def __init__(self, weights, centers, vars):

## hermite_gk22.jl
function hermite_gk22( n ; normalize = true )

#*****************************************************************************80
#
## HERMITE_GK22_SET sets a Genz-Keister 22 Hermite rule.
#
#  Discussion:
#
#    The integral:
#

## startup.jl
# This belongs in JULIA_DEPOT_PATH/config
# You usually have to create this folder the first time
# type `julia --help` for more information on startup files

# Removes all files precompiled for a certain package, forcing it to recompile next time it is loaded
function removePrecompile(pkg::String)
    julia_path = DEPOT_PATH
    if DEPOT_PATH isa AbstractVector
        julia_path = DEPOT_PATH[findmin(length, DEPOT_PATH)[2]]
    end

## trainMap_ex.jl
# Test methods of TriangularMap object
using MParT, Distributions
using Random, Printf, LinearAlgebra

function Kolmogorov_Smirnov(x)
    dist = Normal()
    sorted_samps = sort(x[:])
    samps_cdf = cdf(dist, sorted_samps)
    N = length(sorted_samps)
    samps_ecdf = (1:N)/N
	import numpy as np
	from dataclasses import dataclass
	from functools import partial
	from typing import Callable, Any

	__any__ = ['logpdf', 'sample']


	def joker_eye_logpdf(mean: np.ndarray, inv_std: np.ndarray, x: np.ndarray) -> np.ndarray:
	z = (x - mean) @ inv_std
	using Metal
	using LinearAlgebra
	using LinearAlgebra: cholesky
	using Metal: device, global_queue, PrivateStorage, SharedStorage
	using Metal.MPS: MPSMatrix, MPSMatrixDecompositionStatus, MPSMatrixDecompositionCholesky, MPSMatrixCopy, MPSMatrixCopyDescriptor, MtlFloat, MPSCommandBuffer, encode!, commit!, commitAndContinue!, wait_completed
	using Metal.MPS: checknonsingular, checkpositivedefinite

	Metal.ObjectiveC.@autoreleasepool function LinearAlgebra.cholesky(A::MtlMatrix{T}, check::Bool = true, lower::Bool = true) where {T<:MtlFloat}
	M,N = size(A)
	@assert M == N
	function getHeader() {
	return `
	<!doctype html><html>
	<head>
	<!--[if gte mso 9]><xml><o:OfficeDocumentSettings><o:AllowPNG/><o:PixelsPerInch>96</o:PixelsPerInch></o:OfficeDocumentSettings></xml><![endif]-->
	<style>
	body {
	background-color: #fff
	}
	.gse_alrt_title {
	using SpecialFunctions, Statistics, LinearAlgebra, Random

	abstract type AbstractIntegralWeight end

	abstract type AbstractTruncatedGaussian <: AbstractIntegralWeight end

	struct TruncatedGaussian <: AbstractTruncatedGaussian
	a::Float64
	b::Float64
	end
	"""
	This file is intended to create a Gauss quadrature rule with weight `exp(-x)` that includes point `x_1=0`.

	Frankly, I have no idea if it works

	=============

	Copyright 2024, Daniel Sharp

	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, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
	using Distributions, SpecialFunctions, MParT, GLMakie

	# Taken from StatsFuns
	invsqrt2 = Float64(1 / sqrt(big(2)))
	normcdf(z) = (erf(z * invsqrt2) + 1) / 2

	function sample_banana(n_samples)
	z = randn(2, n_samples)
	[z[1, :]'; ((z[2, :]' .+ (z[1, :] .^ 2)') .- 1) / 1.75]
	end
	import mpart as mt
	import numpy as np
	import pickle
	from matplotlib import pyplot as plt

	# Class to represent a GMM
	# For some reason, there's no good way to evaluate the logpdf and gradlogpdf of a GMM in python
	class GMM:
	# Initialize the GMM
	def __init__(self, weights, centers, vars):
	function hermite_gk22( n ; normalize = true )

	#*****************************************************************************80
	#
	## HERMITE_GK22_SET sets a Genz-Keister 22 Hermite rule.
	#
	# Discussion:
	#
	# The integral:
	#
	# This belongs in JULIA_DEPOT_PATH/config
	# You usually have to create this folder the first time
	# type `julia --help` for more information on startup files

	# Removes all files precompiled for a certain package, forcing it to recompile next time it is loaded
	function removePrecompile(pkg::String)
	julia_path = DEPOT_PATH
	if DEPOT_PATH isa AbstractVector
	julia_path = DEPOT_PATH[findmin(length, DEPOT_PATH)[2]]
	end
	# Test methods of TriangularMap object
	using MParT, Distributions
	using Random, Printf, LinearAlgebra

	function Kolmogorov_Smirnov(x)
	dist = Normal()
	sorted_samps = sort(x[:])
	samps_cdf = cdf(dist, sorted_samps)
	N = length(sorted_samps)
	samps_ecdf = (1:N)/N