Tamas K. Papp tpapp

## DynamicHMC_DiffEq.jl
#####
##### Self-contained example to dissect broken tests in
##### https://github.com/JuliaDiffEq/DiffEqBayes.jl/blob/e033307768892e2f7242ae0aab3e09ec4819c11b/test/dynamicHMC.jl
#####
##### NOTE: you need DynamicHMC#master and LogDensityProblems#0.9.x for this to work

####
#### part that just relies on the DiffEq ecosystem
####

## Manifest.toml
[[AltDistributions]]
deps = ["ArgCheck", "Distributions", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "Parameters", "Random", "SpecialFunctions", "StatsFuns"]
path = "/home/tamas/code/julia/AltDistributions"
uuid = "875e7ca2-ab9d-5828-93ae-a098eac53262"
version = "0.1.0"

[[ArgCheck]]
deps = ["Random", "Test"]
git-tree-sha1 = "9e3bff61bc46d3420d903b53ad07f85325fddc28"
uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197"

## code.jl
using OrdinaryDiffEq, DynamicHMC, TransformVariables, LogDensityProblems, MCMCDiagnostics,
    Parameters, Distributions, LinearAlgebra

struct BayesianODEProblem{F, U, S, T, P, N, D}
    f::F                        # ODE
    u::U                        # initial value
    timespan::S                 # timespan as a tuple
    timepoints::T               # evaluated at these points
    logprior::P                 # callable, return the log prior of parameters
    noise::N                    # a noise distribution for observations

## make-REQUIRE.jl
#!/usr/bin/env julia
######################################################################
# Overwrite REQUIRE using dependency information from Project.toml.
#
# Call from the root of the package repository.
#
# Has some basic sanity checks, but **use at your own risk**, `REQUIRE`
# will be overwritten.
#
# The purpose of this script is to appease attobot, until

## mycollect.jl
# define an iterator with unknown type and length
struct MyItr N::Int end
Base.IteratorSize(::Type{MyItr}) = Base.SizeUnknown()
Base.IteratorEltype(::Type{MyItr}) = Base.EltypeUnknown()
function Base.iterate(itr::MyItr, state = 1)
    if state > itr.N
        nothing
    else
        val = state == 200 ? missing : state
        (val, state + 1)

## diffeqhmc.jl
# DiffEq tools
using DiffEqBayes, OrdinaryDiffEq, ParameterizedFunctions, RecursiveArrayTools

# clone from github.com/tpapp
using DynamicHMC, MCMCDiagnostics, DiffWrappers, ContinuousTransformations

using Parameters, Distributions, Optim

f1 = @ode_def_nohes LotkaVolterraTest1 begin
  dx = a*x - b*x*y

## vectriu.jl
# see https://discourse.julialang.org/t/vector-of-upper-triangle/7764

function vec_triu(M::AbstractMatrix{T}) where T
    m, n = size(M)
    m == n || throw(error("not square"))
    l = n*(n+1) ÷ 2
    v = Vector{T}(l)
    k = 0
    for i in 1:n
        v[k + (1:i)] .= M[1:i, i]

## buffer.html
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN">
<!-- Created by htmlize-1.51 in inline-css mode. -->
<html>
  <head>
    <title>*scratch*</title>
  </head>
  <body style="color: #262626; background-color: #ded6c5;">
    <pre>
<span style="color: #008b45;">;; </span><span style="color: #008b45;">This buffer is for text that is not saved, and for Lisp evaluation.
</span><span style="color: #008b45;">;; </span><span style="color: #008b45;">To create a file, visit it with &lt;open&gt; and enter text in its buffer.

## read_UInt8_lines.jl
######################################################################
# context: reading UInt8 lines from a gzipped stream
# - the real dataset has about 5e10 lines, this is a self-contaned MWE
# - the real dataset lines are then processed, this MEW is just about optimizing reading
# - line length can be bounded (relevant for buffered reading)
######################################################################

using CodecZlib

"""

## log1p_error.jl
# consistent random numbers
srand(UInt32[0xfd909253, 0x7859c364, 0x7cd42419, 0x4c06a3b6])

"""
    err(x, [prec])

Return two values, which are the log2 relative errors for calculating
`log1p(x)`, using `Base.log1p` and `Base.Math.JuliaLibm.log1p`.

The errors are calculated by compating to `BigFloat` calculations with the given
	#####
	##### Self-contained example to dissect broken tests in
	##### https://github.com/JuliaDiffEq/DiffEqBayes.jl/blob/e033307768892e2f7242ae0aab3e09ec4819c11b/test/dynamicHMC.jl
	#####
	##### NOTE: you need DynamicHMC#master and LogDensityProblems#0.9.x for this to work

	####
	#### part that just relies on the DiffEq ecosystem
	####
	[[AltDistributions]]
	deps = ["ArgCheck", "Distributions", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "Parameters", "Random", "SpecialFunctions", "StatsFuns"]
	path = "/home/tamas/code/julia/AltDistributions"
	uuid = "875e7ca2-ab9d-5828-93ae-a098eac53262"
	version = "0.1.0"

	[[ArgCheck]]
	deps = ["Random", "Test"]
	git-tree-sha1 = "9e3bff61bc46d3420d903b53ad07f85325fddc28"
	uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197"
	using OrdinaryDiffEq, DynamicHMC, TransformVariables, LogDensityProblems, MCMCDiagnostics,
	Parameters, Distributions, LinearAlgebra

	struct BayesianODEProblem{F, U, S, T, P, N, D}
	f::F # ODE
	u::U # initial value
	timespan::S # timespan as a tuple
	timepoints::T # evaluated at these points
	logprior::P # callable, return the log prior of parameters
	noise::N # a noise distribution for observations
	#!/usr/bin/env julia
	######################################################################
	# Overwrite REQUIRE using dependency information from Project.toml.
	#
	# Call from the root of the package repository.
	#
	# Has some basic sanity checks, but use at your own risk, `REQUIRE`
	# will be overwritten.
	#
	# The purpose of this script is to appease attobot, until
	# define an iterator with unknown type and length
	struct MyItr N::Int end
	Base.IteratorSize(::Type{MyItr}) = Base.SizeUnknown()
	Base.IteratorEltype(::Type{MyItr}) = Base.EltypeUnknown()
	function Base.iterate(itr::MyItr, state = 1)
	if state > itr.N
	nothing
	else
	val = state == 200 ? missing : state
	(val, state + 1)
	# DiffEq tools
	using DiffEqBayes, OrdinaryDiffEq, ParameterizedFunctions, RecursiveArrayTools

	# clone from github.com/tpapp
	using DynamicHMC, MCMCDiagnostics, DiffWrappers, ContinuousTransformations

	using Parameters, Distributions, Optim

	f1 = @ode_def_nohes LotkaVolterraTest1 begin
	dx = ax - bx*y
	# see https://discourse.julialang.org/t/vector-of-upper-triangle/7764

	function vec_triu(M::AbstractMatrix{T}) where T
	m, n = size(M)
	m == n \|\| throw(error("not square"))
	l = n*(n+1) ÷ 2
	v = Vector{T}(l)
	k = 0
	for i in 1:n
	v[k + (1:i)] .= M[1:i, i]
	<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN">
	<!-- Created by htmlize-1.51 in inline-css mode. -->
	<html>
	<head>
	<title>scratch</title>
	</head>
	<body style="color: #262626; background-color: #ded6c5;">
	<pre>
	<span style="color: #008b45;">;; </span><span style="color: #008b45;">This buffer is for text that is not saved, and for Lisp evaluation.
	</span><span style="color: #008b45;">;; </span><span style="color: #008b45;">To create a file, visit it with <open> and enter text in its buffer.
	######################################################################
	# context: reading UInt8 lines from a gzipped stream
	# - the real dataset has about 5e10 lines, this is a self-contaned MWE
	# - the real dataset lines are then processed, this MEW is just about optimizing reading
	# - line length can be bounded (relevant for buffered reading)
	######################################################################

	using CodecZlib

	"""
	# consistent random numbers
	srand(UInt32[0xfd909253, 0x7859c364, 0x7cd42419, 0x4c06a3b6])

	"""
	err(x, [prec])

	Return two values, which are the log2 relative errors for calculating
	`log1p(x)`, using `Base.log1p` and `Base.Math.JuliaLibm.log1p`.

	The errors are calculated by compating to `BigFloat` calculations with the given